60
Mid Sweden University The Department of Information Technology and Media (ITM) Author: Shashank Tadakamadla E-mail address: [email protected] Study programme: Master of Science in Electrical Engineering, 20 points Examiner: Prof. Bengt Oelmann, [email protected] Tutor: Prof. Bengt Oelmann, [email protected] Date: 2006-10-09 M.Sc. Thesis report within Electrical Engineering D, Electronics Design Programme, 20 points Indoor Local Positioning System For ZigBee, Based On RSSI Shashank Tadakamadla

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Mid Sweden University The Department of Information Technology and Media (ITM)

Author Shashank Tadakamadla E-mail address shta0500studentmiunse Study programme Master of Science in Electrical Engineering 20 points Examiner Prof Bengt Oelmann BengtOelmannmiunse Tutor Prof Bengt Oelmann BengtOelmannmiunse Date 2006-10-09

MSc Thesis report within Electrical Engineering D Electronics Design

Programme 20 points

Indoor Local Positioning System For ZigBee Based On RSSI

Shashank Tadakamadla

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

Abstract10102006

ii

Abstract

This thesis report describes a model for monitoring the presence and movements of vehicles and humans in an indoor environment In IEEE 802154 the Received Signal Strength Indicator is used to determine the quality of the communication from one node to another By tagging vehicleshumans with a ZigBee node and deploy a number of nodes at fixed position in the room the received signal strength indicator can be used to determine the position of tagged object This system operates by recording and processing signal strength information at multiple base stations positioned to provide information in the area of interest It combines Euclidean distance technique with signal strength matrix obtained during offline measurement to determine the location of user The experimental results presented in this report demonstrate the ability of this system to estimate userrsquos location with a high degree of accuracy

Keywords ZigBee based user location and tracking RSSI Euclidean distance technique SENTIO

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

Acknowledgements10102006

iii

Acknowledgements

This work would not have been possible without the support of my friends and mentors Specifically I would like to thank my advisor Bengt Oelmann who has given me all the support and guidance I needed as a Master student I am very grateful to have had trust in my ability and I have often benefited from his insight and advice I would also like to thank Fredrik Linnarsson Niklas Lepisto and Cheng Peng from whom I have had benefit of learning from I also owe a great deal of thanks for their time and advice which is reflected in this thesis

Finally I would like to acknowledge my friends and parents who have supported me through these efforts

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

Table of Contents10102006

iv

Table of Contents Abstract ii Acknowledgements iii Table of Contents iv List of Tables vi List of Figures vii Terminology Notation viii 1 INTRODUCTION1 11 Introduction to the study 1 12 Background2 13 Objectives 3 14 Research Questions4 15 Scope 4 16 Methodology5 17 Outline 6

2 LITERATURE REVIEW8 21 Common components of indoor positioning system 8 22 Classification of Indoor positioning systems9 23 Indoor local positioning using ZigBee based on RSSI11 24 Protocols and Industry standards for WSN14

3 RECEIVED SIGNAL STRENGTH (RSS)26 31 Parameters that affect RSS 26 32 Related work analysis28 33 Properties of RSSI29

4 OBJECT TRACKING 30 41 Introduction 30 42 Overview of hardware product 30 43 Experimental Testbed32 44 Obtaining data from the sensor network 33 45 Object tracking application Testing and Evaluation33

5 RESULTS 38 51 Measurement setup 38 52 Number of samples test 39 53 0ne dimensional test 40 54 Two dimensional test 41 55 Causes of error in location determination41

6 DISCUSSIONS44 61 Summary and Conclusion 44 62 Future work 44

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

Table of Contents10102006

v

APPENDIX 1 45 APPENDIX 2 46 APPENDIX 3 47 REFERENCES 49

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

List of Tables10102006

vi

List of Tables Table 1 Comparison of Indoor Positioning Systems [7] 3 Table 2 Performance comparison of Indoor Positioning Systems [7] 3 Table 3 Frequency bands and data rates for IEEE 802154 based on the IEEE 802154 standard [18] 16 Table 4 Format based on the IEEE 802154 standard [18] 17 Table 5 General MAC frame format IEEE 802154 standard [18] 17 Table 6 Beacon frame format [18] 18 Table 7 Data frame format [18] 18 Table 8 Acknowledgement frame format [18] 18 Table 9 Command frame format [18] 18 Table 10 Classification of factors the affect RSS [7] 27 Table 11 Accuracy of positioning system 37 Table 12 One dimensional position estimation results 41

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

List of Figures10102006

vii

List of Figures Figure 1 Schema of thesis work 5 Figure 2 Block diagram representing hardware module 8 Figure 3 Taxonomy of positioning system [7] 10 Figure 4 Star and peer‐to‐peer topologies in LR‐WPAN [18] 15 Figure 5 WPAN architecture [18] 16 Figure 6 Example of super frame structure [18] 19 Figure 7 Communication from a device to a PAN coordinator in (a) a beacon‐enabled network and (b) a non‐beacon enabled network [18] 20 Figure 8 Communication from a PAN coordinator to a device in (a) a beacon‐enabled network and (b) a non‐beacon enabled network [18] 21 Figure 9 Overview of the coverage for different wireless communication standards [17] 21 Figure 10 IEEE 802154ZigBee stack [20] 22 Figure 11 IEEE 802154ZigBee stack [17] 23 Figure 12 ZigBee secure frame in MAC layer [17] 24 Figure 13 ZigBee secure frame in network layer [17] 24 Figure 14 ZigBee secure frame in application layer [17] 25 Figure 15 SENTIO Platform [8] 31 Figure 16 Layout of Masterʹs Room 32 Figure 17 Flow chart representing the algorithm of fixed sensors 35 Figure 18 Flow chart representing the algorithm of main sensor 37 Figure 19 Test bed for one dimensional test in Masters Room 38 Figure 20 Histogram plot of RSSI values at 1m and 4m 39 Figure 21 Histogram plot of RSSI values at 6m and 8m 39 Figure 22 Distance vs RSSI 40 Figure 23 Error Rate vs Number of samples 42 Figure 24 RSS distribution at 214 pm 42 Figure 25 RSS distribution at 100pm and 1100 am 43

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

Terminology Notation10102006

viii

Terminology Notation

GPS ‐ Global Positioning System

RSSI ‐ Received Signal Strength Indication

MS ‐ Mobile Station

RF ‐ Radio Frequency

WSN ‐ Wireless Sensor Network

AP ‐ Access Point

RSS ‐ Received Signal Strength

PC ‐ Personal Computer

WLAN ‐ Wireless local Area Network

SNR ‐ Signal to Noise Ratio

RFD ‐ Reduced‐Function Device

WPAN ‐ Wireless Personal Area Network

FFD ‐ Full Functional Device

MAC ‐ Medium Access Control

LR‐WPAN ‐ Low Rate WPAN

PAN ‐ Personal Area Network

OSI ‐ Open Systems Interconnection

LLC ‐ Logical Link Control

SSCS ‐ Service Specific Convergence Sub layer

PHY ‐ Physical Layer

PPDU ‐ PHY Protocol Data Unit

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

Terminology Notation10102006

ix

ED ‐ Energy Detection

CCA ‐ Clear Channel Assessment

LQI ‐ Link Quality Indication

ISM ‐ Industrial Scientific and Medical

DSSS ‐ Direct‐Sequence Spread Spectrum technique

LSB ‐ Least Significant Bit

SHR ‐ Synchronization Header

SFD ‐ Start of Frame Delimiter

PHR ‐ PHY Header

MPDU ‐ MAC Protocol Data Units

GTS ‐ Guaranteed Time Slot

MHR ‐ MAC Header

MFC ‐ MAC Footer

FCS ‐ Frame Check Sequence

CAP ‐ Contention Access Period

CFP ‐ Contention Free Period

ACL ‐ Access Control List

AES ‐ Advance Encryption Standard

MIC ‐ Message Integrity Code

CBC ‐ Cipher Block Chaining

ZDO ‐ ZigBee Device Objects

OS ‐ Operating System

LOS ‐ Line of Sight

NLOS ‐ Non Line of Sight

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

Terminology Notation10102006

x

FPGA ‐ Field Programmable Gate Array

BS ‐ Base Station

AOA ‐ Angle of Arrival

TDOA ‐ Time difference of arrival

TOA ‐ Time of Arrival

TOF ‐ Time of flight

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

1

1 INTRODUCTION 11 Introduction to the study Advancement in wireless sensing technologies has encouraged in developing a model for indoor object‐tracking application using low power IEEE 802154 compliant radios Stimulus used in this model is RSS This application is not only limited to object tracking in an indoor environment Concierge services enable users to become aware of nearest supporting facilities For example smart home applications such as multimedia appliances that forward multimedia stream to the nearest video screen can be achieved with a home positioning system [3] In the field of robotics a robot can navigate by itself using the assistance of indoor positioning system [2] Granularity of location information is most important in location determination applications For example to locate a book in a library requires coarse grained granularity whereas locating a user requires coarse grained granularity Position estimation refers to a process used to obtain location information of a MS with respect to a set of reference positions within a pre‐defined space A system deployed to determine or estimate the location of an entity is called a position location system or positioning system A wireless indoor positioning system refers to a wireless network infrastructure that provides indoor location information to any requesting end user One of the key technical elements allowing the realisation of this vision will be the knowledge of the accurate position of the personobjects Already today accurate location mechanisms such as GPS are commercially available Many more will become part of our daily life with the introduction of third generation mobile phones But none of these techniques will allow the meter or sub‐meter level of accuracy within buildings which is necessary for smart houses Hence this report introduces an object tracking application in an indoor environment using IEEE 802154 compliant radios which gives meter level accuracy First this chapter presents the background of indoor positioning systems identifies the challenges of such systems and briefly describes the scope of indoor positioning systems based on location fingerprints Next the objective of study the overview of approaches and the contributions are presented Finally the scope and organization of thesis work is outlined

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

2

12 Background The success of outdoor positioning and applications based on the GPS provides an incentive to the research and development of indoor positioning systems It has been developed over several decades and it relies on constellation of satellites Ranges to several satellites are used in multi‐lateration procedure to infer the position of the receiver This localization scheme requires hardware that is both expensive and consumes significant power As a result indoor positioning systems require alternative means to detect the MS location without relying on the direct signal from GPS satellites Infrared RF and ultra sound signals are major technologies used for indoor positioning systems Unlike outdoor areas the indoor environment imposes different challenges on location discovery due to the dense multi‐path effect and building material dependent propagation effect Thus an in‐depth understanding of indoor radio propagation for positioning is crucial for efficient design and deployment The existence of radio connectivity and the attenuation of radio signal with distance are attractive properties that could potentially be exploited to estimate the positions of small wireless devices featuring low‐power radios RSSI a standard feature in most radios has attracted a lot of attention in the recent literature for obvious reasons RSSI eliminates the need for additional hardware in small wireless devices and exhibits favourable properties with respect to power consumption size and cost As mentioned previously RF‐based location tracking has been widely studied [5 6] Given a model of radio signal propagation in a building or other environment RSSI can be used to estimate the distance from a transmitter to a receiver and thereby estimate the position of a mobile node However this approach requires detailed models of RF propagation and does not account for variations in receiver sensitivity and orientation Table 1 below briefly summarizes performance of existing indoor positioning system based on location fingerprints and WLANrsquos All the systems mentioned below in table 1 follow IEEE 80211 protocol standards The major performance metrics studied by all systems are positioning accuracy which is a form of error measurement The accuracy of the location information is usually reported as an error distance between the estimated location and the actual mobile location However the report of accuracy should include the confidence interval or percentage of successful location detection which is called the precision The system parameters of existing systems are summarized in table 1 and the best reported performance of these systems are listed in table 2

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

3

System Spacing(m) Positions SamplesPos APs Orientation Environment

RADAR [6] Non

uniform 70 80 3 4 Hall-way Saha et al [9] 3 19 1200 3 NA 1-floor

Roos et al [10] 2 155 40 10 NA 1-floor Battiti et at [11] NA 257 NA 6 NA 1-floor Ladd et al [2] 3 11 1307 5 2 Hall-way

Prasithsangaree et al [12] 153 60 40

2 to 7 4 1-floor

Youssef et al[13] 15 110 300 4 NA Hall-way Xiang et al [14] NA 100 300 5 4 1-floor

Table 1 Comparison of Indoor Positioning Systems [7]

System Algorithm Type Accuracy and Precision RADAR [6] Nearest Neighbour within 7 feet 38

Saha et al [9] Nearest neighbour amp Neural Network no specified accuracy

90 Roos et al [10] Bayesian best within 828 feet 90

Battiti et at [11] Neural Network amp Weighted k-Nearest

Neighbour all within 16-17 feet 90 Ladd et al [2] Bayesian within 5 feet 77

Prasithsangaree et al [12] Weighted k-Nearest Neighbour

25 feet at 75 amp 40 fet at 95

Youssef et al [13] Bayesian within 7 feet more than

90 Xiang et al [14] Bayesian with RSS distribution model within 6 feet 90 Table 2 Performance comparison of Indoor Positioning Systems [7]

Based on the study of results obtained in various positioning systems as in table 2 we have developed a model for an indoor positioning system using SENTIO [8] platform based on RSSI

13 Objectives In the last two or three years a number of theoretical practical andor simulation studies were done on the topic of object‐tracking While these studies are useful they are too general and provide little guidance for the actual deployment of sensor networks for real life location‐tracking of an object This thesis focuses on developing an object‐tracking application and prescribes sensor network configurations that work well with our algorithms I implemented my software using SENTIO [8] platform for sensor networking The major issue addressed in this project are the accuracy and precision in tracking an object in a real‐world application and efficient ways to algorithmically analyze the collected raw data from the specific wireless sensor product

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

4

Although the focus is to develop a real‐world application using WSN it also provides great opportunity to explore the new area of wireless communication overall

14 Research Questions My primary target is to find a method to determine the position of an object inside a building using ZigBee protocol standards based on RSSI The study addresses the following questions What is WSN What requirements do WSN need What are their main ideas and concepts What are the associated technologies What are the standards that sensor networks use

What are the existing hardware implementations that use this particular technology How can we build our new applications using WSN What do sensor network systems require for general or specific applications Are they applicable And what is the appropriate design for sensor network implementations in order to combine them with other existing systems At this point we describe and evaluate the specific object‐tracking application to be built

15 Scope The study area of the thesis is an overview of the WSN technology study of SENTIO platform and implementation of an object tracking application Thus the study is divided into two parts Part one is an overview of WSN it includes study of SENTIO platform and IEEE 802154 protocol standards The second part describes the implementation of the object tracking algorithm The object tracking application receives and uses the raw values returned by the sensor network system to produce clear and meaningful outputs Schema of thesis work is as shown in figure 1

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

5

Figure 1 Schema of thesis work

16 Methodology The ldquoIndoor Local Positioning System for ZigBee based on RSSI ldquois based on ldquolocation fingerprintingrdquo technique Location fingerprinting refers to a technique that exploits the relation between any measurable physical stimulus and a specific location This approach uses empirical measurements of RSSI to estimate location By recording a database of radio ldquosignaturesrdquo along with their known locations a mobile node can estimate its position by acquiring a signature and comparing it to the known signatures in the database All of these systems require the database of signatures which should be collected manually prior to the system installation and rely on central server (or the userrsquos mobile node) to perform location calculation For example in RADAR [6] technology the RSSI is the stimulus This type of positioning system does not require specialized hardware other than the common wireless network interfaces with received signal strength measurement capability thus it is simple to deploy compared to other techniques Unlike outdoor counterpart systems which can use AOA and TDOA techniques effectively indoor positioning systems encounter the problem of NLOS and the dense multi path effect that render these two techniques ineffective or complex for practical implementation It is also difficult for a MS to always see three or more access points or base stations in indoor environment which is essential for triangulation by AOA and TDOA Location fingerprinting can also be implemented as software based positioning system which can reduce

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

6

complexity and cost Generally the deployment of fingerprinting based positioning systems can be divided into two phases First in the off‐line phase the locations where the fingerprints are required is pre‐determined and then the fingerprints are collected by performing a site‐survey of the RSS from multiple APs The distance between two closest positions is called grid spacing and usually reported in meters or feet However some points may be omitted due to inaccessibility The RSS is measured at each predetermined location to create a database of RSS patterns of the indoor environment The database of RSS patterns is called a radio map The vector of RSS values at a point on the grid is called the location fingerprint of that point Second in the on‐line phase a MS will report a sample measured vector of RSS from different APs to a base station The base station uses a positioning algorithm to estimate the location of the MS The most common algorithm used to estimate the location is Euclidean algorithm It computes the Euclidean distance between the measured RSS vector and each fingerprint in the database and then the coordinates associated with the fingerprint that provides the smallest Euclidean distance is returned as the estimate of the position It is clear from the above discussion that the designer should not start out to do the site survey without proper system design objectives and guideline If the performance is not sufficient collecting more fingerprints in between previous location fingerprints may help It is a problem of choice as to how many access points are required for the system and what the minimum distance between physical positions on the grid should be in order to provide a good position resolution and best system performance The system parameters and factors that improve the accuracy and the precision performance are still not clear

17 Outline Chapter 2 provides literature review of ldquoIndoor Local Positioning System for ZigBee based on RSSIrdquo it introduces the basic components required for a positioning system general classification of indoor positioning system Finally it explains ldquoLocation Fingerprintrdquo technology used in this thesis work to determine the position of an object in a real‐world application It briefly explains the protocols and industry standards for wireless sensor networks Chapter 3 describes some properties of RSS which is a stimulus to determine

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

7

the position of an object RSS relation with energy and on what parameters is the RSS value dependent Chapter 4 describes the model of an indoor local positioning system and its probability of returning the correct location It also explains its performance evaluation and its error distance distribution Chapter 5 describes results obtained from the model of positioning system Chapter 6 includes conclusions from the experimental analysis and future work

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

8

2 LITERATURE REVIEW This chapter reviews the literature on wireless indoor positioning system as a means of providing an intellectual background for the thesis work First in section 21 the common components of indoor positioning system are described Then in section 22 discusses different means to classify indoor positioning systems In section 23 reviews of all relevant literature on location fingerprint technique Finally in section 24 protocols and industry standards for wireless sensor networks are explained

21 Common components of indoor positioning system Basically a wireless local positioning system consists of at least two separate hardware components a measurable unit that usually carries the major part of the system intelligence and a signal transmitterreceiver The transmitter in the simplest case is just a beacon Figure 2 represents basic hardware model of a positioning system

Figure 2 Block diagram representing hardware module

First the location sensing devices detect the signals transmitted by or received at known reference points using radio sensing technologies The sensing technique is based on signal strength level that converts the sensed signal into RSS Given a set of reference points the relative position of the MS can be derived from the signal characteristics of RSS The signal characteristics of RSS are unique for a location Then the microcontroller processes the position algorithm and estimates the position of object and displays it on PC

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

9

22 Classification of Indoor positioning systems Indoor positioning system can be categorized based on the stimulus used in particular application and measurement principle

221 Classification based on Stimulus Based on the stimulus used in an application WSN inherit certain characteristics and limitations on sensorrsquos signal The propagation delay diffraction reflection and scattering are the basic signal characteristics which affect all signal types The effective range available bandwidth regulatory constraints interference power constraints safety and cost of technology limitations The wireless signals commonly used for indoor positioning systems are infrared radio frequency and ultrasound Brief descriptions of the three major technologies are as follows

Infrared Infrared signal is an electromagnetic signal of a wavelength longer than that of visible light but shorter than that of radio waves Infrared is just below the visible spectrum of light in frequency and is radiated strongly by hot bodies Many objects such as people vehicle engines and aircraft generate and retain heat and as such are especially visible in the infrared wavelengths of light compared to objects in the background It cannot pass through walls or obstructions therefore it has a rather limited range of indoor environments Thus it requires a more sophisticated circuitry than ultrasound signals Indoor lighting interferes with this type of signal and causes problems in accurate sensing It generally has a range of around 5 m The infrared devices are usually small in size compared to ultrasound devices

Radio frequency The RF signal can penetrate through most of the building material therefore it has an excellent range in indoor environments The propagation speed is also high approximately 3108 ms There are unlicensed frequencies available freely for use This type of signal has the longest range compared to infrared and ultrasound Since the propagation waves are of higher frequency (24GHz) interference with other frequency components is less

Ultrasound Although ultrasound operates at low frequency bands (typical 40 kHz) compared to the other two signaling technologies it possesses a good precision for location sensing at a slow propagation speed of sound (343 ms) The advantages of ultrasound devices are their simplicity and that they are inexpensive However ultrasound does not penetrate walls

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

10

but reflects off most of the indoor obstructions It has a short range around 3 m to 10 m but has a 1 cm resolution of distance measurement The operating temperature influences the performance of ultrasound

222 Classification based on measurement principle Wireless positioning systems can be categorized by measurement techniques which are used to derive the position of mobile stations Mainly three different measurement principles are used today AOA RSS and propagation‐time based systems that can further be divided into two different sub‐classes TOA and TDOA Figure 3 shows taxonomy based on the technology and the technique classifications

Figure 3 Taxonomy of positioning system [7]

In AOA systems the position is calculated via goniometry With the use of directional antennas or antenna arrays the angle or bearing relative to points located at known positions is measured The intersection of several measured directions pointers the yields the position value The accuracy of this approach is limited by the possible directivity of the measuring aperture by shadowing and by multi‐path reflections arriving from misleading directions In TOA and TDOA techniques rely on the precision of timing between the signal transmitter and the receiver in order to use the propagation delay or TOF to calculate the distance between transmitter and receiver Therefore a precise synchronization is also very important in such systems By combining at least three distances from three reference positions triangulation can be used to estimate the mobile stationʹs location This type of technique will

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

11

require a high accuracy clock in the communication system TDOA is more practical [1] Examples of location‐sensing systems that use time of flight are GPS the Active Bats [15] and the Cricket [16] Besides these time delay‐based techniques the distance between transmitter and receiver can also be determined from signal strength attenuation and direct distance measurement (such as dead reckoning) Fingerprinting or Location Pattern Matching this technique generally requires only measurement of RSS or other non‐geometric features at several locations to form a database of location fingerprints To estimate the mobile location the system needs to first measure the received signal strength at particular locations and then search for the pattern or fingerprint with the closest match in the database This technique does not require the mobile station to see at least three base stations or access points in order to determine the location The disadvantage of this technique is that it is very time‐consuming to perform an exhaustive data collection for a wide area network such as in outdoor positioning systems

23 Indoor local positioning using ZigBee based on RSSI This section reviews relevant RF‐based indoor positioning systems which can be used to locate stationary objects and track mobile users The impressive growth of IEEE 802154 wireless PANs (WPAN) in recent years suggests an interesting future for the location fingerprinting technique The system is quite flexible because system designers can select whether to have a centralized positioning server or let the mobile to determine its own position However the fingerprinting technique requires a training phase (off‐line phase) to collect location fingerprints for all positions in the operating area before the actual deployment (on‐line phase) [7] The following discussion is divided according to the positioning system components First the common form of location fingerprinting and its relationship with physical locations are explained Finally location estimation algorithm is reviewed

231 Location Fingerprint A location fingerprint based on RF characteristics such as RSS is the basis for representing a unique location It is created under the assumption that each position or location inside a building has unique RF signature [4] Generally a fingerprint F is labelled with a location information L The location fingerprints and their labels (eg location information) are maintained in

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

12

database and used during the on‐line phase to estimate the location The label and fingerprint are usually denoted as a tuple of (L F) Battiti et al [11] point out that the location information L for indoor location can be recorded in two forms as either a tuple of coordinates or an indicator variable The tuple of real coordinates can vary from one dimension to five dimensions which include the three dimension space and two orientation variables expressed in spherical coordinates For instance a location information of a two‐dimension system with an orientation could be expressed as a triplet L = (x y d) | x y є R 2 d є North East South West The RSS was found to be more location‐dependent than the SNR because the noise component is rather random in nature However the RSS itself fluctuates over time for each access point and location Each RSS element can be considered as a random variable therefore it can be captured by recording its descriptive statistics parameters approximating its distribution or maintaining the whole measurement dataset These approaches of RSS representation result in different procedures for location estimation algorithm Regardless of the approach the location fingerprint is usually denoted as an array or vector of signal strength (random variables) received at any position in the location‐based service area The size of the vector is determined by the number of access points that can be heard To create a basis fingerprint such as in a number of samples of vectors of signal strength are collected over a window of time for each position This basis is called a prototype Then the average RSS of each access point is calculated and recorded as an element in the location fingerprint For an area that can receive signals from N access points the location fingerprint can be expressed as a vector of average RSS elements βi

F = (β1 β2hellip βN)T Besides the basis location fingerprint the samples of location fingerprint measured during the on‐line phase are also important for the system that tracks the mobile object The number of samples should be selected appropriately to represent the location fingerprint for the mobile application A step in statistical analysis method called pre‐processing is another important issue needed to be considered because it can impact the estimation of dependency between location fingerprint and the location information easier The pre‐processing refers to a step that cleans the raw data (in this case the training set) before any further operations or analysis The cleaning may consist of encoding dimensionality reduction (reduce unnecessary elements) feature extractionselection clustering and outlier elimination Roos et al [10] point out that the pre‐processing enables faster location estimation and

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

13

reduce the noise from the training data The process that creates the basis of location fingerprint discussed above could be considered as a part of pre‐processing

232 Location Estimation Algorithm Location estimation algorithms or positioning algorithms are procedures that exploit dependency between location information and location fingerprint basis in order to determine a position or location from samples of RSS signals Position algorithms can be classified into deterministic and probabilistic types based on the approaches that model the relationship between location fingerprints and location information The deterministic type of algorithms are those that based in the nearest neighbour classifiers and neural network classifiers The probabilistic type of algorithms is those that are based on the Bayesian inference and statistical learning theory such as support vector machines (SVR) The algorithm used in this project is discussed briefly

Euclidean distance method The Euclidean distance method is a deterministic approach of position estimation because they require only a set of constant location fingerprints which includes mean vectors In order to determine the location a form of discriminate function is used to classify a sample of RSS fingerprint into a position The mean or average RSS vector is a centre of a mass which represents each class of location fingerprint Suppose a set of l location fingerprints is denoted by F1 F2hellip Fl exist and each fingerprint has a one‐to‐one mapping to a set of positions L1 L2hellip Ll A sample of an RSS fingerprint measured during an on‐line phase is denoted as S which can be another mean or average RSS vector of small window of RSS samples Assuming that an indoor positioning system only considers the average RSS from N access points as a location fingerprint the sample of RSS vector is S = (s1 s2 helliphellip sN)T and each location fingerprint i in the database can be expressed as Fi = (β1 β2helliphellip βN)T The simplest closeness metric is a distance measurement in signal space denoted as the Dist () function [6] Thus the simple procedure of nearest neighbour algorithm is expressed as picking the fingerprint j that has the shortest signal distance

Dist(S Fj) le Dist(S Fk) for all k ne j A generalized weighted distance Lp summarized by [12] can be used to calculate different forms of distance in signal space as

Lp = 1N pN

i

pPiSiWi 1

1)||1(sum

=

minus

where N is the number of access points wi is a weighting factor (wi le 1) and p is the norm parameter starting from 1 The weighting factor wi is a bias

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

14

parameter that can demote or promote an important RSS component in the fingerprint

24 Protocols and Industry standards for WSN Although many applications for WSN are proposed the communication protocols supporting them remain mainly unexplored and diverged This is because the area of interest is new each implementation is application specific and the components of WSNrsquos have a lot of constraints Wireless sensor networks use the wireless medium to communicate However the traditional communication protocols that support wireless communications especially ad‐hoc mesh networks may not be well suited for them WSN communication is an active research area and many algorithms and protocols have already been proposed This section introduces two proposed standards the IEEE 802154 and the ZigBee The IEEE 802154 covers the physical layer and the MAC layer of low‐rate WPAN The ZigBee is ldquoan emerging standard that is based on the IEEE 802154 and adds network construction (star networks peer‐to‐peermesh networks and cluster‐tree networks) application services and morerdquo

241 IEEE 802154 Holger and Willig (2005) [17] mention home automation home networking and home security as possible applications for IEEE 802154 They add that ldquomost of these applications require only low‐to‐medium bitrates (up to some few hundreds of kbps) moderate average delays without too stringent delay guarantees and for certain nodes it is highly desirable to reduce the energy consumption to a minimumrdquo The IEEE 802154 standard (2003) defines the device types that can be used in a LR‐WPAN A device can be a FFD or a RFD The RFD can be used in simple applications in which they do not need to transmit large amounts of data and they have to communicate only with a specific FFD The FFD can work as a PAN coordinator as a coordinator or as a simple device It can communicate with either another FFD or a RFD In keeping with the application requirements the LR‐WPAN operates in a star or peer‐to‐peer topology (figure 4) In the star topology the RFD communicates with a single controller the PAN coordinator The PAN coordinator can perform the same function as the RFD but it is also responsible for controlling the PAN ldquoit initiates terminates or routes communication around the networkrdquo [18] The peer‐to‐peer topology

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

15

supports ad‐hoc mesh multi‐hop networking Any device in the peer‐to‐peer topology can communicate with any other device within its communication range however this topology also has a PAN coordinator All the devices in a LR‐WPAN have a unique 64‐bit address This or a short address allocated by the PAN coordinator can be used inside a PAN Additionally each PAN has a unique identifier The combination of the PAN identifier and the sort addresses allows communication across different PANs (IEEE 802154 Standard 2003)

Figure 4 Star and peer‐to‐peer topologies in LR‐WPAN [18]

The LR‐WPAN based on the OSI seven‐layer model has the layered architecture presented in figure 5 The application and the network layer are the upper layers in the LR‐WPAN architecture but are outside the scope of the IEEE 802154 standard Only the physical layer ldquowhich contains the RF transceiver along with its low‐level control mechanismrdquo and the MAC layer ldquothat provides access to the physical channelrdquo are included in the standard and will be introduced in the following sections Finally the Type I 8022 LLC and the SSCS are intermediate sub layers supporting communication with the above layers (IEEE 802154 Standard 2003)

Physical Layer The 802154 standard specifies two different services that the PHY provides The PHY data service controls the radio and thus the transmission and reception of the PPDUs In addition the management service performs ED in the channel The management service also performs CCA before sending the messages and provides LQI for the received packets

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2 LITERATURE REVIEW10102006

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Figure 5 WPAN architecture [18]

Three different bands are defined by the standard The 868‐8686 MHz for Europe the 902‐928 MHz for North America and the 2400‐24835 MHz worldwide All of them belong in the ISM radio bands and they are using DSSS Each of the bands supports different a data rate and it uses a different modulation technique chip rate and number of channels Moreover if the system does not use the 2450 MHz frequency it operates in both the 802 MHz and 902 MHz frequencies Table 3 summarizes the supported bands and data rates (IEEE 802154 standard 2003)

PHY (MHz)

Frequency Band (MHz) Modulation

Bit Rate Number of Channels

868-8686 BPSK 20 1 868 915 902-928 BPSK 40 10

2450 2400-24835 O-QPSK 230 16 Table 3 Frequency bands and data rates for IEEE 802154 based on the IEEE

802154 standard [18] The physical layer uses PPDU packets to communicate Table 4 demonstrates its structure The LSB is always transmitted and received first The SHR contains the preamble and the SFD fields which helps receiver

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

17

synchronization The 8‐bytes preamble contains only zero and is used for synchronization The SFD contains a specific sequence of one and zeros and specifies the beginning of the frame The PHR contains the payload length Packets with a length of 9 or more bytes are MPDU as the next section further explains Packets with length 5 are used for MPDU acknowledgements The payload part of the PPDU encloses the MAC layer packet Finally the PPDU size can be up to 127 bytes [18]

4 bytes 1 byte 1 byte Variable

Preamble SFD Frame length

(7 bits) Reserved

(1 bit) Payload (PSU)

SHR PHR PHY Payload Table 4 Format based on the IEEE 802154 standard [18]

MAC Layer The MAC layer is the interface between the SSCS and the PHY layer Similar to the PHY layer the MAC layer supports two services The MAC data service is responsible for the transmission and reception of the MPDUs through the PHY data service The MAC management service if the device is a coordinator manages the network beacons It is also responsible for PAN association and disassociation frame validation and acknowledgment providing ldquoa reliable link between two peer MAC entitiesrdquo In addition it uses the CSMA‐CA for channel access and handles and maintains the GTS mechanism Finally it supports device security [18] The IEEE 802154 standard defines four different frame types the beacon data acknowledgment and MAC command frame All frame types are based on the general MAC frame format (table 5) The frame control field describes and specifies the above different frame types Every MAC frame comprises a MHR which consists of a frame control sequence number and the information field It also contains the MAC payload different frame types have different MAC payload fields The acknowledgment type does not have a payload Finally each frame includes a MFR which contains a FCS The data in the MPDU follows the same order as the PPDU the least significant bits are left in the frame and are transited first Octets2 1 02 028 02 028 Variable 2

Destination PAN Identifier

Destination Address

Source PAN

Identifier Source Address Frame

Control Sequence Number Addressing Field

Frame Payload FCS

MHR MAC

Payload MFRTable 5 General MAC frame format IEEE 802154 standard [18]

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Table 6 7 8 and 9 present the four different MAC frame types The beacon frame is transmitted periodically by the PAN coordinator It provides information about the network management through the super frame and GTS fields which are analyzed later in the section It also synchronizes the network devices and indicates the proper communication period for them The data frame payload encapsulates data from the higher layers When a device receives a packet it is not obliged to response with an acknowledgement packet Finally the command frame identifier and command payload fields of the command frame are used for communication between the network devices The command identifier specifies actions like association disassociation and data GTS or beacon request Octets2 1 410 2 Variable Variable Variable 2

Frame Control

Sequence Number

Addressing Fields

Super frame Specification

GTS fields

Pending Addressing

Fields Beacon Payload FCS

MHR MAC Payload MFRTable 6 Beacon frame format [18]

Octets 2 1 Variable 2

Frame Control

Sequence Number

Addressing Field

Data Payload FCS

MHR MAC

Payload MFR Table 7 Data frame format [18]

Octets 2 1 2

Frame Control

Sequence Number FCS

MHR MFR Table 8 Acknowledgement frame format [18]

Octets2 1 1 Variable 2

Frame Control

Sequence Number

Addressing Fields

Command Frame Identifier

Command Payload FCS

MHR MAC Payload MFR Table 9 Command frame format [18]

In the LR‐WPAN every PAN has its own coordinator The PAN coordinator manages the communication in the local area it has two options to use or not use the super frame structure The super frame (figure 6) uses network beacons If the coordinator does not want to use a super frame structure it suspends the beacon transmission However the beacon is important for device association and disassociation If the coordinator wishes to maintain close communication control in the PAN and to support low‐latency devices it usually uses the super frame A super frame determines a specific time

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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period beacons bound it The beacon is transmitted in the first of the sixteen equal time slots that the super frame has It is used to ldquosynchronize the attached devices to identify the PAN and to describe the structure of the super framerdquo The super frame can have active and inactive periods All the communications have to be finished inside the super frame period In the inactive periods the devices can switch to the sleep mode but they have to be ready for the next beacon The active period is further divided into a CAP and a CFP During the CAP any device can communicate competing with the other devices in the PAN using slotted CSMA‐CA The CFP contains GTSs and follows the CAP A CFP may maintain up to seven GTSs and each GTS can reserve more than one time slot However the CAP should always be sufficient to allow new devices t join the PAN The GTSs are allocated to specific devices All the transactions must be completed inside the assigned time period (CAP GTS and CFP) (IEEE 802154 standard 2003)

Figure 6 Example of super frame structure [18]

In accordance with the 802154 standard (2003) three different types of data‐transfer exist In addition the types differ if the coordinator uses or does not beacons Data transfer from a device to the PAN coordinator is the first type (figure 7) For a ldquonon‐beacon‐enable networkrdquo it first senses the medium by using ldquoun‐slotted CSMA‐CArdquo and then a simple transmit to the data frame In a ldquobeacon‐enabled networkrdquo the sender waits for the beacon when it finds it ldquothe device synchronizes to the super fame structurerdquo In the specified time frame the sender again senses the medium by using ldquoslotted CSMA‐CArdquo and transmits the data to the coordinator In either case the coordinator has the option to acknowledge or not acknowledge the data reception after that the transaction is completed Data transfer from the coordinator is the next type described in the standard (figure 8) In a beacon‐enabled network the coordinator indicates a pending message through the beacon The messagersquos target device receives the beacon and if the message is pending it responses with a MAC command request

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message using slotted CSMA‐CA the coordinator may or may not acknowledge the command message and through slotted CSMA‐CA sends the pending message The device acknowledges the received message After that the coordinator removes the message from the beaconrsquos pending list and completes the transaction In a non‐beacon enable network the device periodically sends a MAC command‐request frame to the coordinator The coordinator acknowledges the data request Then by using un‐slotted CSMA‐CA if it has a pending message it transmits it If it has not it respond with a data frame with a ldquozero‐length payloadrdquo To complete the transaction the device acknowledges the data reception

Figure 7 Communication from a device to a PAN coordinator in (a) a beacon‐enabled network and (b) a non‐beacon enabled network [18]

Peer‐to‐peer is the last type of data transfer In this situation the devices are free to communicate with any other device within their communication range In a peer‐to‐peer PAN the devices can ldquoeither receive constantly or synchronize with each otherrdquo If they are receiving constantly to transmit data they use un‐slotted CSAM‐CA In the second case synchronization must be achieved first (IEEE 802154 standard [IEEE 2003]) The IEEE 802154 (2003) standard establishes MAC and PHY standards for low‐cost low‐power and high‐density node deployments In addition to the above PHY and MAC characteristics IEEE 802154 provides a security baseline including ldquothe ability to maintain an ACL and use symmetric cryptographyrdquo for data encryption The algorithm that is used for encryption is the AES However the higher level layers decide when security is need The upper layers are in general responsible for device authentication and key management The next section introduces the ZigBee standard which

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

21

encapsulates the IEEE 802154 and provides additional standardization for the higher levels

Figure 8 Communication from a PAN coordinator to a device in (a) a beacon‐enabled network and (b) a non‐beacon enabled network [18]

242 ZigBee Heily (2004) defines ZigBee as ldquoa rapidly growing worldwide non‐profit industry consortiumrdquo whose mission is ldquoto define a reliable cost‐effective low‐power wirelessly networked monitoring and control product based on an open global standardrdquo Figure 9 illustrates the areas of interest for different wireless communication standards

Figure 9 Overview of the coverage for different wireless communication

standards [17]

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ZigBee a new standard which became publicly available in June 2005 is based on the IEEE 802154 standard It expands the IEEE 802154 by adding the framework for ldquothe network security and applicationrdquo (Craig 2005) Figure 10 presents the IEEE 802154ZigBee stack and the areas of responsibilities

Figure 10 IEEE 802154ZigBee stack [20]

Craig (2005) mentions three networking topologies that the standard covers the star mesh and cluster tree (figure 11) The ZigBee standard works on top of the IEEE 802154 addressing schema by using the standard 64‐bit and the short 16‐bit addressing Kinney (2005) summarizes the ZigBee network layer responsibilities the successful establishment of a new the network and successful new device configuration addressing assignment network synchronization frames security and message routing ZigBee further distinguishes the concept of the physical devices (RFD FFD) by using the notion of ldquological devicesrdquo ldquoZigBee Coordinatorrdquo is the first type of logical devices It is responsible for initializing maintaining and managing the network Under the coordinator in the network hierarchy is the ldquoZigBee routerrdquo which is responsible for controlling the message routing between the nodes Finally the ldquoZigBee End Devicerdquo acts as the end point of the network

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

23

structure The tasks that an end device can perform are specified in the ldquoApplication Profilesrdquo (Craig 2005)

Figure 11 IEEE 802154ZigBee stack [17]

The ZigBee specifications (2005) summarize the security services provided by ZigBee ldquokey establishment key transport frame protection and device managementrdquo ZigBee builds its security mechanism using symmetric key cryptography The security services also depend on the associated layer thus as the figure 10 shows the security mechanism covers the network and the application layer In addition if a MAC frame needs security protection the MAC layer is able to secure it Moreover the notion of end‐to‐end security is supported the source and destination devices have access and use the same share key In the MAC layer the 802154 AES mechanism provides the proper security The mechanism protects ldquothe confidentiality integrity and authenticity of the MAC framesrdquo (Kinney 2005) An auxiliary header field in front of the MAC payload indicates if the frame is encrypted or not The MAC framesrsquo integrity is supported by calculating and using a MIC at the end of the MAC payload In addition to the AES nonce is used to provide MAC confidentiality and authenticity Figure 12 illustrates a MAC frame with security For different security aspects the MAC layer uses different mode of the AES for the encryption it uses the AES in Counter (CTR) mode and for the integrity the

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

24

CBC‐MAC Finally the combination (CCM) of the above two modes is available providing both encryption and integrity (Kinney 2005)

Figure 12 ZigBee secure frame in MAC layer [17]

In the network layer the CCM (a modified MAC layer CCM mode) is used for encryption Because the network layer uses only the CCM mode a single key is used for all different security options The network layer security message format is similar to the MAC frame it is presented in the figure 13 Finally although the network layer is responsible for securing its layer messages the above layers specify the keys and the CCM option for each frame (Kinney 2005)

Figure 13 ZigBee secure frame in network layer [17]

Security in the application layer works similar to the network and MAC layers It uses the ldquolink keyrdquo or the ldquonetwork keyrdquo to secure the message and then encapsulate it inside a set of fields similar to the network format (figure 14) Other security responsibilities that the application layer has are to provide the ZDO and the applications with device management services key establishment and key transport (ZigBee specifications 2005)

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

25

Figure 14 ZigBee secure frame in application layer [17]

The ZigBee application layer contains the manufacturer‐defined application objects the ZDO and the application sub‐layer In addition to the security responsibilities the application sub‐layer binds devices based on their duties and needs maintains the binding tables and forwards messages between them The application sub‐layer also discovers the neighbour devices for a given device The ZDO is responsible for determining the devicersquos duty in the network for communicating using binding requests and for supporting security as was mentioned above The sub‐layer that implements the actual application is the manufacturer‐defined application object (Kinney 2005) The ZigBee specifications are a great step toward the wireless sensors networks standardization They cover all the OSI layers from the physical to the application providing guidance to the developers The specifications are the product of the ZigBee alliances a consortium of companies and academic institutes Thus they not only produce the specifications but also are the first use them The next section goes beyond the layer architecture and standardization and introduces the OS field another crucial element of WSNs

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

26

3 RECEIVED SIGNAL STRENGTH (RSS) Majority of the existing methods leverage the existence of IEEE 80211 base stations with powerful radio transmit powers of approximately 100mW per base station Such radios are in a different class from the low power IEEE 802154 compliant radios that typically transmit at low power levels ranging from 52mW to 29mW [19] The wide availability of larger number of IEEE 802154 radios in the research community has revived the interest for signal strength based localization in sensor network Despite of rapidly increasing popularity of IEEE 802154 radios and signal strength localization there is a lack of detailed characterization of the fundamental factors contributing to large signal strength variation This chapter investigates some of the properties of RSS observed during testing and analysis of research work collected during the period of thesis The analysis of RSS values is needed to understand the underlying features of location dependent RSS patterns and location fingerprints An understanding of the properties of the RSS values for location fingerprinting can assist in improving the design of positioning algorithms and in deployment of indoor positioning systems

31 Parameters that affect RSS Different factors that affect the indoor RSS are the userrsquos presenceabsence hisher orientation time of the day building type and material distance from transmitter and type of radio These factors are by no means exhaustive Other possible factors are antenna orientation directionality and type Table 10 classify the parameters which affect RSS [7]

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

27

Effect on Factors Options

1 Proximity of user Userrsquos presence or absence

2 Orientation of user and terminal

North East South West

Data Collection

3 Make of Radio 4 Time of measurement Time of day amp days of

week 5 Period of measurement

Second minute hour

7 Interference Co‐channel adjacent co‐channel

Statistics

8 Building environment Small offices or large hall

Table 10 Classification of factors the affect RSS [7]

Transmitter variability Different transmitters behave differently even when they are configured exactly in the same way In practice this means that when a transmitter is configured to send packets at a power level of d dBm then the transmitter will send these packets at a power level that is very close to d dBm but not necessarily exactly equal to d dBm This can alter the received signal strength indication and thus it can lead to inaccurate distance estimation

Receiver variability The sensitivity of the receivers across different radio chips is different In practice this means that the RSSI value recorded at different receivers can be different even when all the other parameters that affect the received signal strength are kept constant

Antenna orientation Each antenna has its own radiation pattern that is not uniform In practice this means that the RSSI value recorded at the receiver for a given pair of communicating nodes and for a given distance between them varies as the pair wise antenna orientations of the transmitter and the receiver are changed

Multi‐path fading and shadowing in the RF channel In indoor environments the transmitted signals get reflected after hitting on the walls andor on other objects in the room such as furniture Both the original signal and the reflected signal reach the receiver almost at the same time since they both travel at the speed of light As a result of this the receiver is not able to

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

28

distinguish the two signals and it measures the received signal strength for both of them

32 Related work analysis A number of indoor location tracking systems have been proposed in the literature based on RF signals ultrasound infrared and some combinations of these As mentioned previously RF based positioning system are widely studied as shown in Table 2 Given a model of radio signal propagation in a building or other environment RSS can be used to estimate the distance between the transmitter and receiver Most of the algorithms (Table 2) have been evaluated in practice using 80211b compliant wireless radios and seem to produce a location error of 15ft to 20ft for an indoor network area of approximately 225ft x 144ft According to the results of this study all the localization techniques produce approximately the same location error over a range of environments Ecolocation [22] Mote Track [5] are two of the few localization algorithms that have been evaluated on a real sensor network that uses a low power wireless radio Ecolocation reports a location error of 10ft for a very small outdoor network deployment area (26ft x 49ft) Mote Track reports a location error of approximately 13ft for an indoor network deployment area of 18751ft2 In general the RSSI based localization algorithms can be divided into two broad categories the map based and the distance (or area) prediction based algorithms In the case of the map based algorithms the deployment area is divided into a large set of sample points that cover the whole area Most techniques collect one or more signal strength measurements from all visible beacon nodes and for each sample point Each sample point is then mapped to either a signal strength vector or a signal strength probability distribution This process generates an RSSI fingerprint map which is unique with respect to the beacon locations and the deployment area This fingerprint map is stored to a central base station A node that is not aware of its location collects one or more signal strength measurements from all the visible beacon nodes and creates its own RSSI fingerprint that is sent to the central base station The base station computes nodersquos location as the sample pointrsquos location whose RSSI fingerprint distance from the unknown nodersquos RSSI fingerprint is the minimum among all the sample points Despite the fact that the map based RSSI localization methods produce relatively small location errors they are not attractive mainly because they require an excessive amount of profiling data and because they are not fully distributed The detailed profiling of a building is a major holdback because it is complex time consuming and depends on beacon locations Therefore for different buildings or even for the

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

29

same building and different beacon locations different RSSI maps have to be obtained The second category of RSSI based algorithms are the distance (or area) prediction algorithms which focus on minimizing the amount of profiling data In distance prediction based methods raw RSSI data are directly mapped to distances through a signal propagation model In some cases distances are not explicitly measured but they are taken into consideration through assumptions on the underlying distributions In the case of outdoor deployments where there are no obstacles a single signal propagation model is all the profiling data required In the case of indoor deployments the same applies but now the signal propagation model might be different across different buildings with different furniture and wall arrangements Given an underlying signal propagation model raw RSSI data can be mapped to specific distances These distances can be either used directly in order to perform localization or assigned a probability and used in a learning basedprobabilistic localization algorithm Obviously the accuracy of the distance prediction based methods depends heavily on the accuracy of the signal propagation model used for translating RSSI values to distances

33 Properties of RSSI Changes in signal due to propagation indoors are difficult to predict because of dense environment and propagation effects such as reflection diffraction and scattering The multi‐path fading effect which is the result of either constructive or destructive combination of multiple signal copies at the receiver causes the received signal to fluctuate around a mean value at a particular location The received signal is usually modelled by the combined effects of large‐scale fading and small‐scale fading The large‐scale fading component describes the signal attenuation as the signal travels over a distance and is absorbed by material such as walls and floors along the way to the receiver This component predicts the mean of the RSS and usually has a lognormal distribution On the other hand the small scale fading component explains the dramatic fluctuation of the signal due to multi‐path fading On the other hand the small scale‐fading component explains the dramatic fluctuation of the signal due to multi‐path fading If there is no LOS component the small‐scale‐fading is often modelled with a Rayleigh distribution Such scenarios are commonly called NLOS If there is a LOS component the small‐scale‐fading is typically modelled by a Rician distribution Note that when the RSS is measured the measurement averages out the small‐scale‐fading effects

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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4 OBJECT TRACKING 41 Introduction This chapter presents the object‐tracking application the main objective of this thesis work The application is an algorithmic‐software implementation written in Embedded C which is capable of receiving data messages and generating object position estimation data The implementation of the object‐tracking application makes use of the SENTIO platform and uses the background knowledge introduced in the preceding chapters The motivation for the object‐tracking application is to determine the position of an object in indoor environments (Eg Home) The application acts as an interface which collects the raw data that the sensors generate (numerical values) It makes the proper comparisons and algorithmic processes to identify and track the object while it is static inside the area covered by the wireless sensor network Finally with the detection and tracking data it alerts the user or another system The following sections provide an overview of the object‐tracking application

42 Overview of hardware product Before describing the object tracking application it is important to know the characteristics of hardware product The SENTIO platform (figure 15) is built in modular fashion and has the following key features [8]

Flexibility This platform is very flexible in such a way that it is made easy to add or change the hardware for each sensor task depending on the requirement in the WSN For a specific platform this can be hard or sometimes even impossible to accomplish

Sensor resources Resources in a sensor module includes the ability of usage of controllerrsquos resources as much as possible such as ADC‐channels interrupts and digital Input‐Outputrsquos Local voltage regulation should also be possible since many sensors require higher supply voltages than digital Integrated Circuits do Further support of a shut‐down mechanism to power down the sensor modules when not needed

Computation and memory The processor module based on microcontroller should support many devices in a microcontroller family and support different power supply voltages enabling full range of clock frequencies All power‐down modes of the devices should be

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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31

supported The platform should support different processor modules including FPGArsquos and very simple microcontrollers

Communication The focus should be on enabling an IEEE 802154 compliant communication interface With or without ZigBee stack this will provide a flexible platform for exploring different network topologies Support for power‐down of the entire communication module should be supported

Cost and form factor High flexibility is in conflict with low cost and small size The primary objective should be flexibility but the size should be considered in order to enable sensor device integration in systems where space is limited

Figure 15 SENTIO Platform [8]

The communication subsystem of the SENTIO sensor node was designed so that the correct operation of the radio chip is ensured The radio chip is powered by the on‐board voltage regulator and thus fluctuations in the battery voltage level do not affect the operation of the communication subsystem This platform uses Chipcon CC2420 IEEE 802154 radio transceiver operates in the 24GHz ISM band and includes a digital direct sequence spread spectrum (DSSS) modem providing a spreading gain of 9dB and an effective data rate of 250Kbps It was specifically designed for low power wireless applications and supports 8 discrete power levels 0dBm minus1dBm minus3dBm minus5dBm minus7dBm minus10dBm minus15dBm and minus25dBm at which its power consumption varies from 29mW to 52mW [21] A built‐in received signal strength indicator gives an 8‐bit digital value RSSIVAL The RSSIVAL is always averaged over 8 symbol periods (128μs) and a status bit indicates when the RSSIVAL is valid (meaning that the receiver was enabled for at least 8 symbol periods) The

Antenna

Communication Module

Processing Module

Sensing Module

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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32

power P at the RF pins can be obtained directly from RSSIVAL using the following equation

P = RSSIVAL + RSSIOFFSET [dBm] where the RSSIOFFSET is found empirically from the front‐end gain and it is approximately equal to minus45dBm In the next sections when we refer to the RSSI value we refer to the RSSIVAL and not the power P unless otherwise stated

43 Experimental Testbed The experimental test bed for object tracking application is as shown in figure 16 Experiment was conducted in Masterrsquos room at Mid Sweden University A DELL laptop is connected to SENTIO platform and software were primarily used to collect samples of RSS from APrsquos The software monitors IEEE 802154 radio frequency channels which operate in the 24GHz band

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

1030

BC - 1030

AP 3 AP 1

AP 2

AP 4

BS

HEWLETT

PACKARD

Figure 16 Layout of Masterʹs Room

A three dimensional scalable testbed is used for indoor sensor network deployments The testbed illustrated in figure 16 is a 3‐D structure measuring 16m (L) x 12m (W) x 3m (H) and it is designed to host a large number of static nodes as shown in figure 16 Arrow marks in the above figure represent the location where fingerprints were taken The centrepiece of our infrastructure

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

33

is the SENTIO sensor node [8] an open‐source general purpose sensing platform designed with AT Mega 128 microprocessor and the IEEE 802154 compliant CC2420 radio from Chipcon [19]

44 Obtaining data from the sensor network The next important step in the development of the object tracking application is to obtain the data from the sensor network and send it to the base station From there the data is transmitted through a serial port to the computer that runs the object tracking application When the data arrives at the serial port the application must capture it This paragraph gives a brief explanation of how and what kind data is transmitted by the static object fixed sensors and how this data is being used to estimate the position of the object First the static object transmits Beacon packet continuously which is received by all the fixed APrsquos The beacon packet format is as shown in table 6 Then the fixed APrsquos calculates the 8‐bit value of RSSI from the transmitted beacon packet and then sends the resultant RSSI value as a form of data packet (table 7) to the BS which calculates the position and sends back to the object

45 Object tracking application Testing and Evaluation The purpose of this section is to discuss and evaluate the object tracking application Finally we are ready with the experimental testbed as shown in figure 16 As shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors placed at the corners of the masterrsquos room BS is placed at the centre of the room connected to a PC All the nodes are power connected before an actual measurement takes place At first the object is placed at random position inside the room and transmits beacon packets continuously Then these packets are received by the fixed APrsquos which calculate the RSSI from the packet and then averages it until it receives 50 beacon packets and then transmits the data to the BS (figure 17) Then the BS waits until it receives the data from all the APrsquos Once it receives from all the APrsquos it calculates the position of the object (figure 18) The application was developed to demonstrate a real world use of a WSN in object tracking In evaluating the final position obtained with respect to the actual position we conclude that the object‐tracking was successful Although

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

34

it is a research prototype object tracking application is a simple stable stand‐alone system capable of demonstrating an important application of WSN It takes raw data from the WSN aggregates it evaluates it and based on the evaluation tracks the object The accuracy of estimating the position is less than 5m Table 11 below shows a log of readings taken during test

Start

Intialize micro‐controller and transeciever in receieve mode

If received_packet=

Beacon

Average RSSI values

Increase the counter

If countergt1000050

A

No

Yes

B

B

Yes

No

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

35

A

Reset counter

Set transceiever in transmit mode

Transmit averaged RSSI to Main sensor and set it back to receive mode

10000 samples ‐ Offline

50 samples ‐ Online

Figure 17 Flow chart representing the algorithm of fixed sensors

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

36

Start

Intialize micro‐controller and transeciever in receieve mode

Received RSSI values from all fixed Sensors

Open radio map

Calculate Euclidean distance between obtained RSSI values from fixed sensors and fingerprints

in radio map

No

Yes

Bubble sort for least error in signal strength values

A

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

37

A

Get the location of the object from the radiousing the least error value

Display location of object on PC

End

Figure 18 Flow chart representing the algorithm of main sensor

Table 11 Accuracy of positioning system

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

38

5 RESULTS This chapter presents how the system parameters such as number of samples mean or median etchellipare finalized All the tests are performed in masterrsquos room at Mid Sweden University This chapter is divided into three subsections First in Section 51 it reviews results obtained from samples test Section 52 discusses one dimensional test results and finally section 53 discusses results of two dimensional tests

51 Measurement setup A Dell laptop was connected to a SENTIO platform and is loaded with software (Appendix 2) This setup was used to determine number of samples required during object tracking mean or median position estimation algorithm and one dimensional test The setup is as follows

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

103

0

BC - 1030

AP 1AP 2

BS

HEWLETT

PACKARD

Figure 19 Test bed for one dimensional test in Masters Room

Two SENTIO nodes (AP1 and AP2 as shown above) were separated by a distance of 10 meters and these sensors are known as fixed sensors which are fixed to the ceiling such that there antenna is faced towards the ground They are linear is position A BS was connected to Dell laptop and it was placed in such a way that its antenna was faced towards the ceiling Another SENTIO

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

39

node was used as an object and its orientation was same as BS Figure 19 represents the complete setup for object tracking application Mobile node was moved between these fixed ones at regular intervals of one meter and RSSI values were logged at each interval

52 Number of samples test This test was performed to determine number of samples required for accurate positioning and either mean of the obtained RSSI values or median of them Figure 19 represents the setup for number of samples test Once the setup is ready the measurements are taken as follows

1 An object was tagged with SENTIO node and was placed stationary linear to the fixed sensors

2 10000 samples (RSSI) were taken at regular intervals of 1 meter up to 10 meters

3 The sample values were logged at every interval 4 Using these logged samples I have plotted graphs such as distribution

of RSS values (figure 20 21) meanmedian at intervals of 1 meter using MATLAB

5 The mean and median for various numbers of samples is as shown in figure 22

Figure 20 Histogram plot of RSSI values at 1m and 4m

Figure 21 Histogram plot of RSSI values at 6m and 8m

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

40

Figure 22 Distance vs RSSI

From the above graphs following conclusions were made 1 From the histogram plots we can observe that the distribution of RSSI

values follow Gaussians distribution law 2 As we can see from figure 22 that median curve has drastic changes

when compared to mean curve Hence mean is considered while estimating the position

3 From the observations we conclude that the number of samples does not matter since mean curve tends to be same for various numbers of samples

4 Hence 10000 samples are considered in order to get a proper location fingerprint during off‐line phase and during online phase 50 samples are enough to estimate the position of object

53 0ne dimensional test Measurement setup for one dimensional test is as shown in figure 19 This test was conducted to check the performance of algorithm and how accurate it was At first ie during offline phase fingerprints at each interval averaging 10000 samples were taken During online phase Euclidean algorithm was used estimate the position averaging 50 samples of RSSI From the results we

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

41

can realize that the error was less than 2 meters Table 12 shows the result of location estimation in one dimensional case

Table 12 One dimensional position estimation results

54 Two dimensional test This test is the final stage of my thesis work Experimental testbed for this stage is as shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors fixed at the corners of the room and a BS at the centre of the room There are 18 pre‐determined locations where the fingerprints (RSSI) were taken The fingerprints are as shown in Appendix 1 All the readings were taken in presence of userrsquos The algorithm for position estimation is shown in Appendix 2 Position estimation test is performed as follows

Tagged object is placed at random inside the masterrsquos room All the sensors including the base station and object are switched on Using the algorithm (Appendix 2) the BS estimates the position of the object and displays it on the PC

This test was performed using various numbers of samples to test which has a better accuracy Figure 23 represents the error rate using various numbers of samples From figure 23 we can conclude that 50 numbers of samples is enough to estimate the position of object with an accuracy of 3m

55 Causes of error in location determination The randomness of RSS is the major cause of error in any indoor

positioning system that uses the RSS for location inference If there is no randomness in the RSS and its location fingerprint every indoor positioning systems of this type will have no location detection error For example I have recorded RSS at one particular location during different times of the day From figures 24 and 25 you can observe that RSS values are different at different times of day

The performance of indoor positioning systems depends greatly on the separation of location fingerprints A location fingerprint

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

42

corresponding to a location can be identified correctly if it is difficult to classify it as another fingerprint by a pattern classifier

Path loss is one of the basic signal propagation properties of the RSS that best describes attenuation of transmit signal power over a distance between an access point and a mobile device

Presence of Userrsquos or userrsquos orientation affects RSS

Figure 23 Error Rate vs Number of samples

Figure 24 RSS distribution at 214 pm

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

43

Figure 25 RSS distribution at 100pm and 1100 am

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

6 DISCUSSIONS10102006

44

6 DISCUSSIONS 61 Summary and Conclusion While empirical results and performance studies of positioning systems based on location fingerprint have been presented in this report analytical model that can be used as a framework for efficiently designing the positioning systems are not available This thesis design work uses general principles of position estimation such as position estimation of sensors grid spacing etc This design work focuses on accuracy and precision The concept of location fingerprint technique based on RSS us explained in detail in chapter 2 It is found that RSS is random It is possible to approximate the RSS as being normally distributed While the RSS random process is non‐stationary the mean is more or less constant and could be used as the fingerprint of the location Model designed for position estimation is as shown in chapter 4 Accuracy and precision are the two major performance matrices in this study Uniform grid spacing has been used From the experimental analysis I have obtained an accuracy of 3 meters and 35 precision The sensitivity analysis among the system parameters and the performance matrices were done using the model as shown in chapter 4 For instance our study found that the minimum distance between two adjacent positions also affects the positioning performance This is due to the randomness of RSS patterns induced by the ever changing indoor environment

62 Future work The research in this thesis provides a ground work for the future study of efficient design of indoor positioning system The RSSI based position estimation still has limitations First the RSSI value is time dependent Thus the future study should address this issue Second the movement of object is not considered Third orientations of sensors affect the performance which should be taken care off Fourth to improve the performance of the system we need to find a solution to deal with those incomplete or missing data during the off‐line and the on‐line phase Fifth position estimation on multi‐floor and three‐dimensional coordinates is not available

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 110102006

45

APPENDIX 1

Location Fingerprints of Masters Room Time 1315 Observation Time 20 minutes Number of Samples 10000

People Presence SNo Co-ordinates AP1 (0x0001) AP2 (0x0002) AP3 (0x0004) AP4 (0x0008)

1 11 -24 -16 -27 -33 2 13 -26 -8 -34 -31 3 15 -43 -9 -39 -25 4 32 -21 -11 -27 -33 5 61 -6 -23 -32 -28 6 64 -20 -31 -25 -34 7 35 -23 -35 -29 -38 8 37 -29 -35 -22 -27 9 39 -38 -29 -21 -27 10 67 -27 -23 -20 -23 11 611 -26 -29 -23 -30 12 613 -35 -32 -2 -31 13 513 -34 -31 -20 -26 14 11 -39 -22 -29 -17 15 212 -37 -42 -28 -31 16 -212 -38 -33 -29 -6 17 -21 -42 -29 -33 -19 18 -38 -40 -23 -35 -22

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 210102006

46

APPENDIX 2

Begin

Open radio map and raw data files for write

Intialize radio map in memory

Check whether the

Packet is data or Beacon packet If data packet

Save the data to register

Check if data is

Received from all

sensors

Estimate the position of static

Display the position on PC

End Yes

No

A

A

Yes

No

Flow chart representing the main algorithm for position estimation

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

47

APPENDIX 3

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

48

Distribution of RSSI

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

49

REFERENCES [1] T S Rappaport J H Reed and B D Woerner ldquoPosition location using wireless communications on highways of the futureʺ IEEE Commun Mag vol 34 no 10 pp33‐41 Oct 1996 [2] A M Ladd K E Bekris G Marceau A Rudys L E Kavraki and D S Wallach rdquoRobotics‐based location sensing using wireless ethernetʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ02) 2002 pp 227‐238 [3] I Ramani R Bharadwaja and P V Rangan ldquoLocation tracking for media appliances in wireless home networksʺ in Proc International Conference on Multimedia and Expo (ICMEʹ03) July 2003 pp 769‐772 [4] K Pahlavan X Li and J P Makela ldquoIndoor geolocation science and technologyʺ IEEE Commun Mag vol 40 no 2 pp 112‐118 Feb 2002 [5] Konard Lorincz and Matt Welsh ldquoMote Track A Robust Decentralized Approach to RF‐Based Location Trackingrdquo [6] Paramvir Bahl and Venkata N Padmanabham ldquoRADAR An In‐Building RF‐based User Location and Tracking Systemrdquo [7] Kamol Kaemarungsi ldquoDesign of Indoor positioning system based on location fingerprint techiniquerdquo University of Pittsburgh 2005 [8] Fredrik Linnarsson Cheng Peng and Bengt Oelmann ldquoSENTIO A Hardware Platform for Rapid Prototyping of Wireless Sensor Networksrdquo [9] S Saha K Chaudhuri D Sanghi and P Bhagwat ldquoLocation determination of a mobile device using IEEE 80211b access point signalsʺ in Proc IEEE Wireless Communications and Networking Conference (WCNCʹ03) New Orleans LA Mar 2003 pp 1987‐1992 [10] T Roos P Myllymaki H Tirri P Misikangas and J Sievanen ldquoA probabilistic approach to WLAN user location estimationʺ International Journal of Wireless Information Networks vol 9 no 3 pp 155‐164 July 2002 [11] R Battiti M Brunato and A Villani ldquoStatistical learning theory for location fingerprinting in wireless LANrsquosʺ Technical Report Oct 2002 [Online] Available httprtmscienceunitnitraquobattitiarchive86pdf [12] P Prasithsangaree P Krishnamurthy and P K Chrysanthis ldquoAn indoor position location with wireless LANsʺ in Proc IEEE International Symposium on Personal Indoor and Mobile Radio Communications (PIMRCʹ02) Lisbon Portugal Sept 2002 [13] M A Youssef A Agrawala and A U Shankar ldquoWLAN location determination via clustering and probability distributionsʺ in Proc IEEE

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

50

International Conference on Pervasive Computing and Communications (PerComʹ03) Dallas‐Fort Worth TX Mar 23‐26 2003 pp 23‐26 [14] Z Xiang S Song J Chen H Wang J Huang and X Gao ldquoA wireless LAN‐based indoor positioning technologyʺ IBM Journal of Research and Development vol 48 no 56 pp 617‐626 SeptNov 2004 [15] R Want A Hopper V Falcao and J Gibbons ldquoThe active badge location systemʺACM Transactions on Information Systems vol 40 no 1 pp 91‐102 Jan 1992 [16] N B Priyantha A Chakraborty and H Balakrishnan ldquoThe cricket location‐support systemʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ00) Boston MA Aug 2000 pp 32‐43 [17] Holger K Willig A (2005) Protocols and Architectures for Wireless Sensor Networks London [18] IEEE (2003) Part 154 Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low‐Rate Wireless Personal Area Networks (LR‐WPANs) New York [19] Chipcon CC2420 802154 compliant radio httpwwwchipconcom [20] ZigBee Specification v10 ZigBee Alliance [21] Dimitrios Lymberopoulos Quentin Lindsey and Andreas Savvides ldquoAn Empirical Analysis of Radio Signal Strength Variability in IEEE 802154 Networks using Monopole Antennasrdquo [22] Kiran Yedavalli Bhaskar Krishnamachari Sharmila Ravula and Bhaskar Srinivasan ldquoEcolocation A sequence based technique for RF localization in wireless sensor networksrdquo Robert Bosch Research and Technology Centre Palo Alto CA

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

Abstract10102006

ii

Abstract

This thesis report describes a model for monitoring the presence and movements of vehicles and humans in an indoor environment In IEEE 802154 the Received Signal Strength Indicator is used to determine the quality of the communication from one node to another By tagging vehicleshumans with a ZigBee node and deploy a number of nodes at fixed position in the room the received signal strength indicator can be used to determine the position of tagged object This system operates by recording and processing signal strength information at multiple base stations positioned to provide information in the area of interest It combines Euclidean distance technique with signal strength matrix obtained during offline measurement to determine the location of user The experimental results presented in this report demonstrate the ability of this system to estimate userrsquos location with a high degree of accuracy

Keywords ZigBee based user location and tracking RSSI Euclidean distance technique SENTIO

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

Acknowledgements10102006

iii

Acknowledgements

This work would not have been possible without the support of my friends and mentors Specifically I would like to thank my advisor Bengt Oelmann who has given me all the support and guidance I needed as a Master student I am very grateful to have had trust in my ability and I have often benefited from his insight and advice I would also like to thank Fredrik Linnarsson Niklas Lepisto and Cheng Peng from whom I have had benefit of learning from I also owe a great deal of thanks for their time and advice which is reflected in this thesis

Finally I would like to acknowledge my friends and parents who have supported me through these efforts

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

Table of Contents10102006

iv

Table of Contents Abstract ii Acknowledgements iii Table of Contents iv List of Tables vi List of Figures vii Terminology Notation viii 1 INTRODUCTION1 11 Introduction to the study 1 12 Background2 13 Objectives 3 14 Research Questions4 15 Scope 4 16 Methodology5 17 Outline 6

2 LITERATURE REVIEW8 21 Common components of indoor positioning system 8 22 Classification of Indoor positioning systems9 23 Indoor local positioning using ZigBee based on RSSI11 24 Protocols and Industry standards for WSN14

3 RECEIVED SIGNAL STRENGTH (RSS)26 31 Parameters that affect RSS 26 32 Related work analysis28 33 Properties of RSSI29

4 OBJECT TRACKING 30 41 Introduction 30 42 Overview of hardware product 30 43 Experimental Testbed32 44 Obtaining data from the sensor network 33 45 Object tracking application Testing and Evaluation33

5 RESULTS 38 51 Measurement setup 38 52 Number of samples test 39 53 0ne dimensional test 40 54 Two dimensional test 41 55 Causes of error in location determination41

6 DISCUSSIONS44 61 Summary and Conclusion 44 62 Future work 44

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

Table of Contents10102006

v

APPENDIX 1 45 APPENDIX 2 46 APPENDIX 3 47 REFERENCES 49

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

List of Tables10102006

vi

List of Tables Table 1 Comparison of Indoor Positioning Systems [7] 3 Table 2 Performance comparison of Indoor Positioning Systems [7] 3 Table 3 Frequency bands and data rates for IEEE 802154 based on the IEEE 802154 standard [18] 16 Table 4 Format based on the IEEE 802154 standard [18] 17 Table 5 General MAC frame format IEEE 802154 standard [18] 17 Table 6 Beacon frame format [18] 18 Table 7 Data frame format [18] 18 Table 8 Acknowledgement frame format [18] 18 Table 9 Command frame format [18] 18 Table 10 Classification of factors the affect RSS [7] 27 Table 11 Accuracy of positioning system 37 Table 12 One dimensional position estimation results 41

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

List of Figures10102006

vii

List of Figures Figure 1 Schema of thesis work 5 Figure 2 Block diagram representing hardware module 8 Figure 3 Taxonomy of positioning system [7] 10 Figure 4 Star and peer‐to‐peer topologies in LR‐WPAN [18] 15 Figure 5 WPAN architecture [18] 16 Figure 6 Example of super frame structure [18] 19 Figure 7 Communication from a device to a PAN coordinator in (a) a beacon‐enabled network and (b) a non‐beacon enabled network [18] 20 Figure 8 Communication from a PAN coordinator to a device in (a) a beacon‐enabled network and (b) a non‐beacon enabled network [18] 21 Figure 9 Overview of the coverage for different wireless communication standards [17] 21 Figure 10 IEEE 802154ZigBee stack [20] 22 Figure 11 IEEE 802154ZigBee stack [17] 23 Figure 12 ZigBee secure frame in MAC layer [17] 24 Figure 13 ZigBee secure frame in network layer [17] 24 Figure 14 ZigBee secure frame in application layer [17] 25 Figure 15 SENTIO Platform [8] 31 Figure 16 Layout of Masterʹs Room 32 Figure 17 Flow chart representing the algorithm of fixed sensors 35 Figure 18 Flow chart representing the algorithm of main sensor 37 Figure 19 Test bed for one dimensional test in Masters Room 38 Figure 20 Histogram plot of RSSI values at 1m and 4m 39 Figure 21 Histogram plot of RSSI values at 6m and 8m 39 Figure 22 Distance vs RSSI 40 Figure 23 Error Rate vs Number of samples 42 Figure 24 RSS distribution at 214 pm 42 Figure 25 RSS distribution at 100pm and 1100 am 43

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

Terminology Notation10102006

viii

Terminology Notation

GPS ‐ Global Positioning System

RSSI ‐ Received Signal Strength Indication

MS ‐ Mobile Station

RF ‐ Radio Frequency

WSN ‐ Wireless Sensor Network

AP ‐ Access Point

RSS ‐ Received Signal Strength

PC ‐ Personal Computer

WLAN ‐ Wireless local Area Network

SNR ‐ Signal to Noise Ratio

RFD ‐ Reduced‐Function Device

WPAN ‐ Wireless Personal Area Network

FFD ‐ Full Functional Device

MAC ‐ Medium Access Control

LR‐WPAN ‐ Low Rate WPAN

PAN ‐ Personal Area Network

OSI ‐ Open Systems Interconnection

LLC ‐ Logical Link Control

SSCS ‐ Service Specific Convergence Sub layer

PHY ‐ Physical Layer

PPDU ‐ PHY Protocol Data Unit

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

Terminology Notation10102006

ix

ED ‐ Energy Detection

CCA ‐ Clear Channel Assessment

LQI ‐ Link Quality Indication

ISM ‐ Industrial Scientific and Medical

DSSS ‐ Direct‐Sequence Spread Spectrum technique

LSB ‐ Least Significant Bit

SHR ‐ Synchronization Header

SFD ‐ Start of Frame Delimiter

PHR ‐ PHY Header

MPDU ‐ MAC Protocol Data Units

GTS ‐ Guaranteed Time Slot

MHR ‐ MAC Header

MFC ‐ MAC Footer

FCS ‐ Frame Check Sequence

CAP ‐ Contention Access Period

CFP ‐ Contention Free Period

ACL ‐ Access Control List

AES ‐ Advance Encryption Standard

MIC ‐ Message Integrity Code

CBC ‐ Cipher Block Chaining

ZDO ‐ ZigBee Device Objects

OS ‐ Operating System

LOS ‐ Line of Sight

NLOS ‐ Non Line of Sight

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

Terminology Notation10102006

x

FPGA ‐ Field Programmable Gate Array

BS ‐ Base Station

AOA ‐ Angle of Arrival

TDOA ‐ Time difference of arrival

TOA ‐ Time of Arrival

TOF ‐ Time of flight

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

1

1 INTRODUCTION 11 Introduction to the study Advancement in wireless sensing technologies has encouraged in developing a model for indoor object‐tracking application using low power IEEE 802154 compliant radios Stimulus used in this model is RSS This application is not only limited to object tracking in an indoor environment Concierge services enable users to become aware of nearest supporting facilities For example smart home applications such as multimedia appliances that forward multimedia stream to the nearest video screen can be achieved with a home positioning system [3] In the field of robotics a robot can navigate by itself using the assistance of indoor positioning system [2] Granularity of location information is most important in location determination applications For example to locate a book in a library requires coarse grained granularity whereas locating a user requires coarse grained granularity Position estimation refers to a process used to obtain location information of a MS with respect to a set of reference positions within a pre‐defined space A system deployed to determine or estimate the location of an entity is called a position location system or positioning system A wireless indoor positioning system refers to a wireless network infrastructure that provides indoor location information to any requesting end user One of the key technical elements allowing the realisation of this vision will be the knowledge of the accurate position of the personobjects Already today accurate location mechanisms such as GPS are commercially available Many more will become part of our daily life with the introduction of third generation mobile phones But none of these techniques will allow the meter or sub‐meter level of accuracy within buildings which is necessary for smart houses Hence this report introduces an object tracking application in an indoor environment using IEEE 802154 compliant radios which gives meter level accuracy First this chapter presents the background of indoor positioning systems identifies the challenges of such systems and briefly describes the scope of indoor positioning systems based on location fingerprints Next the objective of study the overview of approaches and the contributions are presented Finally the scope and organization of thesis work is outlined

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

2

12 Background The success of outdoor positioning and applications based on the GPS provides an incentive to the research and development of indoor positioning systems It has been developed over several decades and it relies on constellation of satellites Ranges to several satellites are used in multi‐lateration procedure to infer the position of the receiver This localization scheme requires hardware that is both expensive and consumes significant power As a result indoor positioning systems require alternative means to detect the MS location without relying on the direct signal from GPS satellites Infrared RF and ultra sound signals are major technologies used for indoor positioning systems Unlike outdoor areas the indoor environment imposes different challenges on location discovery due to the dense multi‐path effect and building material dependent propagation effect Thus an in‐depth understanding of indoor radio propagation for positioning is crucial for efficient design and deployment The existence of radio connectivity and the attenuation of radio signal with distance are attractive properties that could potentially be exploited to estimate the positions of small wireless devices featuring low‐power radios RSSI a standard feature in most radios has attracted a lot of attention in the recent literature for obvious reasons RSSI eliminates the need for additional hardware in small wireless devices and exhibits favourable properties with respect to power consumption size and cost As mentioned previously RF‐based location tracking has been widely studied [5 6] Given a model of radio signal propagation in a building or other environment RSSI can be used to estimate the distance from a transmitter to a receiver and thereby estimate the position of a mobile node However this approach requires detailed models of RF propagation and does not account for variations in receiver sensitivity and orientation Table 1 below briefly summarizes performance of existing indoor positioning system based on location fingerprints and WLANrsquos All the systems mentioned below in table 1 follow IEEE 80211 protocol standards The major performance metrics studied by all systems are positioning accuracy which is a form of error measurement The accuracy of the location information is usually reported as an error distance between the estimated location and the actual mobile location However the report of accuracy should include the confidence interval or percentage of successful location detection which is called the precision The system parameters of existing systems are summarized in table 1 and the best reported performance of these systems are listed in table 2

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

3

System Spacing(m) Positions SamplesPos APs Orientation Environment

RADAR [6] Non

uniform 70 80 3 4 Hall-way Saha et al [9] 3 19 1200 3 NA 1-floor

Roos et al [10] 2 155 40 10 NA 1-floor Battiti et at [11] NA 257 NA 6 NA 1-floor Ladd et al [2] 3 11 1307 5 2 Hall-way

Prasithsangaree et al [12] 153 60 40

2 to 7 4 1-floor

Youssef et al[13] 15 110 300 4 NA Hall-way Xiang et al [14] NA 100 300 5 4 1-floor

Table 1 Comparison of Indoor Positioning Systems [7]

System Algorithm Type Accuracy and Precision RADAR [6] Nearest Neighbour within 7 feet 38

Saha et al [9] Nearest neighbour amp Neural Network no specified accuracy

90 Roos et al [10] Bayesian best within 828 feet 90

Battiti et at [11] Neural Network amp Weighted k-Nearest

Neighbour all within 16-17 feet 90 Ladd et al [2] Bayesian within 5 feet 77

Prasithsangaree et al [12] Weighted k-Nearest Neighbour

25 feet at 75 amp 40 fet at 95

Youssef et al [13] Bayesian within 7 feet more than

90 Xiang et al [14] Bayesian with RSS distribution model within 6 feet 90 Table 2 Performance comparison of Indoor Positioning Systems [7]

Based on the study of results obtained in various positioning systems as in table 2 we have developed a model for an indoor positioning system using SENTIO [8] platform based on RSSI

13 Objectives In the last two or three years a number of theoretical practical andor simulation studies were done on the topic of object‐tracking While these studies are useful they are too general and provide little guidance for the actual deployment of sensor networks for real life location‐tracking of an object This thesis focuses on developing an object‐tracking application and prescribes sensor network configurations that work well with our algorithms I implemented my software using SENTIO [8] platform for sensor networking The major issue addressed in this project are the accuracy and precision in tracking an object in a real‐world application and efficient ways to algorithmically analyze the collected raw data from the specific wireless sensor product

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

4

Although the focus is to develop a real‐world application using WSN it also provides great opportunity to explore the new area of wireless communication overall

14 Research Questions My primary target is to find a method to determine the position of an object inside a building using ZigBee protocol standards based on RSSI The study addresses the following questions What is WSN What requirements do WSN need What are their main ideas and concepts What are the associated technologies What are the standards that sensor networks use

What are the existing hardware implementations that use this particular technology How can we build our new applications using WSN What do sensor network systems require for general or specific applications Are they applicable And what is the appropriate design for sensor network implementations in order to combine them with other existing systems At this point we describe and evaluate the specific object‐tracking application to be built

15 Scope The study area of the thesis is an overview of the WSN technology study of SENTIO platform and implementation of an object tracking application Thus the study is divided into two parts Part one is an overview of WSN it includes study of SENTIO platform and IEEE 802154 protocol standards The second part describes the implementation of the object tracking algorithm The object tracking application receives and uses the raw values returned by the sensor network system to produce clear and meaningful outputs Schema of thesis work is as shown in figure 1

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

5

Figure 1 Schema of thesis work

16 Methodology The ldquoIndoor Local Positioning System for ZigBee based on RSSI ldquois based on ldquolocation fingerprintingrdquo technique Location fingerprinting refers to a technique that exploits the relation between any measurable physical stimulus and a specific location This approach uses empirical measurements of RSSI to estimate location By recording a database of radio ldquosignaturesrdquo along with their known locations a mobile node can estimate its position by acquiring a signature and comparing it to the known signatures in the database All of these systems require the database of signatures which should be collected manually prior to the system installation and rely on central server (or the userrsquos mobile node) to perform location calculation For example in RADAR [6] technology the RSSI is the stimulus This type of positioning system does not require specialized hardware other than the common wireless network interfaces with received signal strength measurement capability thus it is simple to deploy compared to other techniques Unlike outdoor counterpart systems which can use AOA and TDOA techniques effectively indoor positioning systems encounter the problem of NLOS and the dense multi path effect that render these two techniques ineffective or complex for practical implementation It is also difficult for a MS to always see three or more access points or base stations in indoor environment which is essential for triangulation by AOA and TDOA Location fingerprinting can also be implemented as software based positioning system which can reduce

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

6

complexity and cost Generally the deployment of fingerprinting based positioning systems can be divided into two phases First in the off‐line phase the locations where the fingerprints are required is pre‐determined and then the fingerprints are collected by performing a site‐survey of the RSS from multiple APs The distance between two closest positions is called grid spacing and usually reported in meters or feet However some points may be omitted due to inaccessibility The RSS is measured at each predetermined location to create a database of RSS patterns of the indoor environment The database of RSS patterns is called a radio map The vector of RSS values at a point on the grid is called the location fingerprint of that point Second in the on‐line phase a MS will report a sample measured vector of RSS from different APs to a base station The base station uses a positioning algorithm to estimate the location of the MS The most common algorithm used to estimate the location is Euclidean algorithm It computes the Euclidean distance between the measured RSS vector and each fingerprint in the database and then the coordinates associated with the fingerprint that provides the smallest Euclidean distance is returned as the estimate of the position It is clear from the above discussion that the designer should not start out to do the site survey without proper system design objectives and guideline If the performance is not sufficient collecting more fingerprints in between previous location fingerprints may help It is a problem of choice as to how many access points are required for the system and what the minimum distance between physical positions on the grid should be in order to provide a good position resolution and best system performance The system parameters and factors that improve the accuracy and the precision performance are still not clear

17 Outline Chapter 2 provides literature review of ldquoIndoor Local Positioning System for ZigBee based on RSSIrdquo it introduces the basic components required for a positioning system general classification of indoor positioning system Finally it explains ldquoLocation Fingerprintrdquo technology used in this thesis work to determine the position of an object in a real‐world application It briefly explains the protocols and industry standards for wireless sensor networks Chapter 3 describes some properties of RSS which is a stimulus to determine

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

7

the position of an object RSS relation with energy and on what parameters is the RSS value dependent Chapter 4 describes the model of an indoor local positioning system and its probability of returning the correct location It also explains its performance evaluation and its error distance distribution Chapter 5 describes results obtained from the model of positioning system Chapter 6 includes conclusions from the experimental analysis and future work

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

8

2 LITERATURE REVIEW This chapter reviews the literature on wireless indoor positioning system as a means of providing an intellectual background for the thesis work First in section 21 the common components of indoor positioning system are described Then in section 22 discusses different means to classify indoor positioning systems In section 23 reviews of all relevant literature on location fingerprint technique Finally in section 24 protocols and industry standards for wireless sensor networks are explained

21 Common components of indoor positioning system Basically a wireless local positioning system consists of at least two separate hardware components a measurable unit that usually carries the major part of the system intelligence and a signal transmitterreceiver The transmitter in the simplest case is just a beacon Figure 2 represents basic hardware model of a positioning system

Figure 2 Block diagram representing hardware module

First the location sensing devices detect the signals transmitted by or received at known reference points using radio sensing technologies The sensing technique is based on signal strength level that converts the sensed signal into RSS Given a set of reference points the relative position of the MS can be derived from the signal characteristics of RSS The signal characteristics of RSS are unique for a location Then the microcontroller processes the position algorithm and estimates the position of object and displays it on PC

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

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22 Classification of Indoor positioning systems Indoor positioning system can be categorized based on the stimulus used in particular application and measurement principle

221 Classification based on Stimulus Based on the stimulus used in an application WSN inherit certain characteristics and limitations on sensorrsquos signal The propagation delay diffraction reflection and scattering are the basic signal characteristics which affect all signal types The effective range available bandwidth regulatory constraints interference power constraints safety and cost of technology limitations The wireless signals commonly used for indoor positioning systems are infrared radio frequency and ultrasound Brief descriptions of the three major technologies are as follows

Infrared Infrared signal is an electromagnetic signal of a wavelength longer than that of visible light but shorter than that of radio waves Infrared is just below the visible spectrum of light in frequency and is radiated strongly by hot bodies Many objects such as people vehicle engines and aircraft generate and retain heat and as such are especially visible in the infrared wavelengths of light compared to objects in the background It cannot pass through walls or obstructions therefore it has a rather limited range of indoor environments Thus it requires a more sophisticated circuitry than ultrasound signals Indoor lighting interferes with this type of signal and causes problems in accurate sensing It generally has a range of around 5 m The infrared devices are usually small in size compared to ultrasound devices

Radio frequency The RF signal can penetrate through most of the building material therefore it has an excellent range in indoor environments The propagation speed is also high approximately 3108 ms There are unlicensed frequencies available freely for use This type of signal has the longest range compared to infrared and ultrasound Since the propagation waves are of higher frequency (24GHz) interference with other frequency components is less

Ultrasound Although ultrasound operates at low frequency bands (typical 40 kHz) compared to the other two signaling technologies it possesses a good precision for location sensing at a slow propagation speed of sound (343 ms) The advantages of ultrasound devices are their simplicity and that they are inexpensive However ultrasound does not penetrate walls

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

10

but reflects off most of the indoor obstructions It has a short range around 3 m to 10 m but has a 1 cm resolution of distance measurement The operating temperature influences the performance of ultrasound

222 Classification based on measurement principle Wireless positioning systems can be categorized by measurement techniques which are used to derive the position of mobile stations Mainly three different measurement principles are used today AOA RSS and propagation‐time based systems that can further be divided into two different sub‐classes TOA and TDOA Figure 3 shows taxonomy based on the technology and the technique classifications

Figure 3 Taxonomy of positioning system [7]

In AOA systems the position is calculated via goniometry With the use of directional antennas or antenna arrays the angle or bearing relative to points located at known positions is measured The intersection of several measured directions pointers the yields the position value The accuracy of this approach is limited by the possible directivity of the measuring aperture by shadowing and by multi‐path reflections arriving from misleading directions In TOA and TDOA techniques rely on the precision of timing between the signal transmitter and the receiver in order to use the propagation delay or TOF to calculate the distance between transmitter and receiver Therefore a precise synchronization is also very important in such systems By combining at least three distances from three reference positions triangulation can be used to estimate the mobile stationʹs location This type of technique will

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

11

require a high accuracy clock in the communication system TDOA is more practical [1] Examples of location‐sensing systems that use time of flight are GPS the Active Bats [15] and the Cricket [16] Besides these time delay‐based techniques the distance between transmitter and receiver can also be determined from signal strength attenuation and direct distance measurement (such as dead reckoning) Fingerprinting or Location Pattern Matching this technique generally requires only measurement of RSS or other non‐geometric features at several locations to form a database of location fingerprints To estimate the mobile location the system needs to first measure the received signal strength at particular locations and then search for the pattern or fingerprint with the closest match in the database This technique does not require the mobile station to see at least three base stations or access points in order to determine the location The disadvantage of this technique is that it is very time‐consuming to perform an exhaustive data collection for a wide area network such as in outdoor positioning systems

23 Indoor local positioning using ZigBee based on RSSI This section reviews relevant RF‐based indoor positioning systems which can be used to locate stationary objects and track mobile users The impressive growth of IEEE 802154 wireless PANs (WPAN) in recent years suggests an interesting future for the location fingerprinting technique The system is quite flexible because system designers can select whether to have a centralized positioning server or let the mobile to determine its own position However the fingerprinting technique requires a training phase (off‐line phase) to collect location fingerprints for all positions in the operating area before the actual deployment (on‐line phase) [7] The following discussion is divided according to the positioning system components First the common form of location fingerprinting and its relationship with physical locations are explained Finally location estimation algorithm is reviewed

231 Location Fingerprint A location fingerprint based on RF characteristics such as RSS is the basis for representing a unique location It is created under the assumption that each position or location inside a building has unique RF signature [4] Generally a fingerprint F is labelled with a location information L The location fingerprints and their labels (eg location information) are maintained in

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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database and used during the on‐line phase to estimate the location The label and fingerprint are usually denoted as a tuple of (L F) Battiti et al [11] point out that the location information L for indoor location can be recorded in two forms as either a tuple of coordinates or an indicator variable The tuple of real coordinates can vary from one dimension to five dimensions which include the three dimension space and two orientation variables expressed in spherical coordinates For instance a location information of a two‐dimension system with an orientation could be expressed as a triplet L = (x y d) | x y є R 2 d є North East South West The RSS was found to be more location‐dependent than the SNR because the noise component is rather random in nature However the RSS itself fluctuates over time for each access point and location Each RSS element can be considered as a random variable therefore it can be captured by recording its descriptive statistics parameters approximating its distribution or maintaining the whole measurement dataset These approaches of RSS representation result in different procedures for location estimation algorithm Regardless of the approach the location fingerprint is usually denoted as an array or vector of signal strength (random variables) received at any position in the location‐based service area The size of the vector is determined by the number of access points that can be heard To create a basis fingerprint such as in a number of samples of vectors of signal strength are collected over a window of time for each position This basis is called a prototype Then the average RSS of each access point is calculated and recorded as an element in the location fingerprint For an area that can receive signals from N access points the location fingerprint can be expressed as a vector of average RSS elements βi

F = (β1 β2hellip βN)T Besides the basis location fingerprint the samples of location fingerprint measured during the on‐line phase are also important for the system that tracks the mobile object The number of samples should be selected appropriately to represent the location fingerprint for the mobile application A step in statistical analysis method called pre‐processing is another important issue needed to be considered because it can impact the estimation of dependency between location fingerprint and the location information easier The pre‐processing refers to a step that cleans the raw data (in this case the training set) before any further operations or analysis The cleaning may consist of encoding dimensionality reduction (reduce unnecessary elements) feature extractionselection clustering and outlier elimination Roos et al [10] point out that the pre‐processing enables faster location estimation and

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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reduce the noise from the training data The process that creates the basis of location fingerprint discussed above could be considered as a part of pre‐processing

232 Location Estimation Algorithm Location estimation algorithms or positioning algorithms are procedures that exploit dependency between location information and location fingerprint basis in order to determine a position or location from samples of RSS signals Position algorithms can be classified into deterministic and probabilistic types based on the approaches that model the relationship between location fingerprints and location information The deterministic type of algorithms are those that based in the nearest neighbour classifiers and neural network classifiers The probabilistic type of algorithms is those that are based on the Bayesian inference and statistical learning theory such as support vector machines (SVR) The algorithm used in this project is discussed briefly

Euclidean distance method The Euclidean distance method is a deterministic approach of position estimation because they require only a set of constant location fingerprints which includes mean vectors In order to determine the location a form of discriminate function is used to classify a sample of RSS fingerprint into a position The mean or average RSS vector is a centre of a mass which represents each class of location fingerprint Suppose a set of l location fingerprints is denoted by F1 F2hellip Fl exist and each fingerprint has a one‐to‐one mapping to a set of positions L1 L2hellip Ll A sample of an RSS fingerprint measured during an on‐line phase is denoted as S which can be another mean or average RSS vector of small window of RSS samples Assuming that an indoor positioning system only considers the average RSS from N access points as a location fingerprint the sample of RSS vector is S = (s1 s2 helliphellip sN)T and each location fingerprint i in the database can be expressed as Fi = (β1 β2helliphellip βN)T The simplest closeness metric is a distance measurement in signal space denoted as the Dist () function [6] Thus the simple procedure of nearest neighbour algorithm is expressed as picking the fingerprint j that has the shortest signal distance

Dist(S Fj) le Dist(S Fk) for all k ne j A generalized weighted distance Lp summarized by [12] can be used to calculate different forms of distance in signal space as

Lp = 1N pN

i

pPiSiWi 1

1)||1(sum

=

minus

where N is the number of access points wi is a weighting factor (wi le 1) and p is the norm parameter starting from 1 The weighting factor wi is a bias

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

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parameter that can demote or promote an important RSS component in the fingerprint

24 Protocols and Industry standards for WSN Although many applications for WSN are proposed the communication protocols supporting them remain mainly unexplored and diverged This is because the area of interest is new each implementation is application specific and the components of WSNrsquos have a lot of constraints Wireless sensor networks use the wireless medium to communicate However the traditional communication protocols that support wireless communications especially ad‐hoc mesh networks may not be well suited for them WSN communication is an active research area and many algorithms and protocols have already been proposed This section introduces two proposed standards the IEEE 802154 and the ZigBee The IEEE 802154 covers the physical layer and the MAC layer of low‐rate WPAN The ZigBee is ldquoan emerging standard that is based on the IEEE 802154 and adds network construction (star networks peer‐to‐peermesh networks and cluster‐tree networks) application services and morerdquo

241 IEEE 802154 Holger and Willig (2005) [17] mention home automation home networking and home security as possible applications for IEEE 802154 They add that ldquomost of these applications require only low‐to‐medium bitrates (up to some few hundreds of kbps) moderate average delays without too stringent delay guarantees and for certain nodes it is highly desirable to reduce the energy consumption to a minimumrdquo The IEEE 802154 standard (2003) defines the device types that can be used in a LR‐WPAN A device can be a FFD or a RFD The RFD can be used in simple applications in which they do not need to transmit large amounts of data and they have to communicate only with a specific FFD The FFD can work as a PAN coordinator as a coordinator or as a simple device It can communicate with either another FFD or a RFD In keeping with the application requirements the LR‐WPAN operates in a star or peer‐to‐peer topology (figure 4) In the star topology the RFD communicates with a single controller the PAN coordinator The PAN coordinator can perform the same function as the RFD but it is also responsible for controlling the PAN ldquoit initiates terminates or routes communication around the networkrdquo [18] The peer‐to‐peer topology

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supports ad‐hoc mesh multi‐hop networking Any device in the peer‐to‐peer topology can communicate with any other device within its communication range however this topology also has a PAN coordinator All the devices in a LR‐WPAN have a unique 64‐bit address This or a short address allocated by the PAN coordinator can be used inside a PAN Additionally each PAN has a unique identifier The combination of the PAN identifier and the sort addresses allows communication across different PANs (IEEE 802154 Standard 2003)

Figure 4 Star and peer‐to‐peer topologies in LR‐WPAN [18]

The LR‐WPAN based on the OSI seven‐layer model has the layered architecture presented in figure 5 The application and the network layer are the upper layers in the LR‐WPAN architecture but are outside the scope of the IEEE 802154 standard Only the physical layer ldquowhich contains the RF transceiver along with its low‐level control mechanismrdquo and the MAC layer ldquothat provides access to the physical channelrdquo are included in the standard and will be introduced in the following sections Finally the Type I 8022 LLC and the SSCS are intermediate sub layers supporting communication with the above layers (IEEE 802154 Standard 2003)

Physical Layer The 802154 standard specifies two different services that the PHY provides The PHY data service controls the radio and thus the transmission and reception of the PPDUs In addition the management service performs ED in the channel The management service also performs CCA before sending the messages and provides LQI for the received packets

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Figure 5 WPAN architecture [18]

Three different bands are defined by the standard The 868‐8686 MHz for Europe the 902‐928 MHz for North America and the 2400‐24835 MHz worldwide All of them belong in the ISM radio bands and they are using DSSS Each of the bands supports different a data rate and it uses a different modulation technique chip rate and number of channels Moreover if the system does not use the 2450 MHz frequency it operates in both the 802 MHz and 902 MHz frequencies Table 3 summarizes the supported bands and data rates (IEEE 802154 standard 2003)

PHY (MHz)

Frequency Band (MHz) Modulation

Bit Rate Number of Channels

868-8686 BPSK 20 1 868 915 902-928 BPSK 40 10

2450 2400-24835 O-QPSK 230 16 Table 3 Frequency bands and data rates for IEEE 802154 based on the IEEE

802154 standard [18] The physical layer uses PPDU packets to communicate Table 4 demonstrates its structure The LSB is always transmitted and received first The SHR contains the preamble and the SFD fields which helps receiver

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2 LITERATURE REVIEW10102006

17

synchronization The 8‐bytes preamble contains only zero and is used for synchronization The SFD contains a specific sequence of one and zeros and specifies the beginning of the frame The PHR contains the payload length Packets with a length of 9 or more bytes are MPDU as the next section further explains Packets with length 5 are used for MPDU acknowledgements The payload part of the PPDU encloses the MAC layer packet Finally the PPDU size can be up to 127 bytes [18]

4 bytes 1 byte 1 byte Variable

Preamble SFD Frame length

(7 bits) Reserved

(1 bit) Payload (PSU)

SHR PHR PHY Payload Table 4 Format based on the IEEE 802154 standard [18]

MAC Layer The MAC layer is the interface between the SSCS and the PHY layer Similar to the PHY layer the MAC layer supports two services The MAC data service is responsible for the transmission and reception of the MPDUs through the PHY data service The MAC management service if the device is a coordinator manages the network beacons It is also responsible for PAN association and disassociation frame validation and acknowledgment providing ldquoa reliable link between two peer MAC entitiesrdquo In addition it uses the CSMA‐CA for channel access and handles and maintains the GTS mechanism Finally it supports device security [18] The IEEE 802154 standard defines four different frame types the beacon data acknowledgment and MAC command frame All frame types are based on the general MAC frame format (table 5) The frame control field describes and specifies the above different frame types Every MAC frame comprises a MHR which consists of a frame control sequence number and the information field It also contains the MAC payload different frame types have different MAC payload fields The acknowledgment type does not have a payload Finally each frame includes a MFR which contains a FCS The data in the MPDU follows the same order as the PPDU the least significant bits are left in the frame and are transited first Octets2 1 02 028 02 028 Variable 2

Destination PAN Identifier

Destination Address

Source PAN

Identifier Source Address Frame

Control Sequence Number Addressing Field

Frame Payload FCS

MHR MAC

Payload MFRTable 5 General MAC frame format IEEE 802154 standard [18]

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Table 6 7 8 and 9 present the four different MAC frame types The beacon frame is transmitted periodically by the PAN coordinator It provides information about the network management through the super frame and GTS fields which are analyzed later in the section It also synchronizes the network devices and indicates the proper communication period for them The data frame payload encapsulates data from the higher layers When a device receives a packet it is not obliged to response with an acknowledgement packet Finally the command frame identifier and command payload fields of the command frame are used for communication between the network devices The command identifier specifies actions like association disassociation and data GTS or beacon request Octets2 1 410 2 Variable Variable Variable 2

Frame Control

Sequence Number

Addressing Fields

Super frame Specification

GTS fields

Pending Addressing

Fields Beacon Payload FCS

MHR MAC Payload MFRTable 6 Beacon frame format [18]

Octets 2 1 Variable 2

Frame Control

Sequence Number

Addressing Field

Data Payload FCS

MHR MAC

Payload MFR Table 7 Data frame format [18]

Octets 2 1 2

Frame Control

Sequence Number FCS

MHR MFR Table 8 Acknowledgement frame format [18]

Octets2 1 1 Variable 2

Frame Control

Sequence Number

Addressing Fields

Command Frame Identifier

Command Payload FCS

MHR MAC Payload MFR Table 9 Command frame format [18]

In the LR‐WPAN every PAN has its own coordinator The PAN coordinator manages the communication in the local area it has two options to use or not use the super frame structure The super frame (figure 6) uses network beacons If the coordinator does not want to use a super frame structure it suspends the beacon transmission However the beacon is important for device association and disassociation If the coordinator wishes to maintain close communication control in the PAN and to support low‐latency devices it usually uses the super frame A super frame determines a specific time

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19

period beacons bound it The beacon is transmitted in the first of the sixteen equal time slots that the super frame has It is used to ldquosynchronize the attached devices to identify the PAN and to describe the structure of the super framerdquo The super frame can have active and inactive periods All the communications have to be finished inside the super frame period In the inactive periods the devices can switch to the sleep mode but they have to be ready for the next beacon The active period is further divided into a CAP and a CFP During the CAP any device can communicate competing with the other devices in the PAN using slotted CSMA‐CA The CFP contains GTSs and follows the CAP A CFP may maintain up to seven GTSs and each GTS can reserve more than one time slot However the CAP should always be sufficient to allow new devices t join the PAN The GTSs are allocated to specific devices All the transactions must be completed inside the assigned time period (CAP GTS and CFP) (IEEE 802154 standard 2003)

Figure 6 Example of super frame structure [18]

In accordance with the 802154 standard (2003) three different types of data‐transfer exist In addition the types differ if the coordinator uses or does not beacons Data transfer from a device to the PAN coordinator is the first type (figure 7) For a ldquonon‐beacon‐enable networkrdquo it first senses the medium by using ldquoun‐slotted CSMA‐CArdquo and then a simple transmit to the data frame In a ldquobeacon‐enabled networkrdquo the sender waits for the beacon when it finds it ldquothe device synchronizes to the super fame structurerdquo In the specified time frame the sender again senses the medium by using ldquoslotted CSMA‐CArdquo and transmits the data to the coordinator In either case the coordinator has the option to acknowledge or not acknowledge the data reception after that the transaction is completed Data transfer from the coordinator is the next type described in the standard (figure 8) In a beacon‐enabled network the coordinator indicates a pending message through the beacon The messagersquos target device receives the beacon and if the message is pending it responses with a MAC command request

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message using slotted CSMA‐CA the coordinator may or may not acknowledge the command message and through slotted CSMA‐CA sends the pending message The device acknowledges the received message After that the coordinator removes the message from the beaconrsquos pending list and completes the transaction In a non‐beacon enable network the device periodically sends a MAC command‐request frame to the coordinator The coordinator acknowledges the data request Then by using un‐slotted CSMA‐CA if it has a pending message it transmits it If it has not it respond with a data frame with a ldquozero‐length payloadrdquo To complete the transaction the device acknowledges the data reception

Figure 7 Communication from a device to a PAN coordinator in (a) a beacon‐enabled network and (b) a non‐beacon enabled network [18]

Peer‐to‐peer is the last type of data transfer In this situation the devices are free to communicate with any other device within their communication range In a peer‐to‐peer PAN the devices can ldquoeither receive constantly or synchronize with each otherrdquo If they are receiving constantly to transmit data they use un‐slotted CSAM‐CA In the second case synchronization must be achieved first (IEEE 802154 standard [IEEE 2003]) The IEEE 802154 (2003) standard establishes MAC and PHY standards for low‐cost low‐power and high‐density node deployments In addition to the above PHY and MAC characteristics IEEE 802154 provides a security baseline including ldquothe ability to maintain an ACL and use symmetric cryptographyrdquo for data encryption The algorithm that is used for encryption is the AES However the higher level layers decide when security is need The upper layers are in general responsible for device authentication and key management The next section introduces the ZigBee standard which

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encapsulates the IEEE 802154 and provides additional standardization for the higher levels

Figure 8 Communication from a PAN coordinator to a device in (a) a beacon‐enabled network and (b) a non‐beacon enabled network [18]

242 ZigBee Heily (2004) defines ZigBee as ldquoa rapidly growing worldwide non‐profit industry consortiumrdquo whose mission is ldquoto define a reliable cost‐effective low‐power wirelessly networked monitoring and control product based on an open global standardrdquo Figure 9 illustrates the areas of interest for different wireless communication standards

Figure 9 Overview of the coverage for different wireless communication

standards [17]

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ZigBee a new standard which became publicly available in June 2005 is based on the IEEE 802154 standard It expands the IEEE 802154 by adding the framework for ldquothe network security and applicationrdquo (Craig 2005) Figure 10 presents the IEEE 802154ZigBee stack and the areas of responsibilities

Figure 10 IEEE 802154ZigBee stack [20]

Craig (2005) mentions three networking topologies that the standard covers the star mesh and cluster tree (figure 11) The ZigBee standard works on top of the IEEE 802154 addressing schema by using the standard 64‐bit and the short 16‐bit addressing Kinney (2005) summarizes the ZigBee network layer responsibilities the successful establishment of a new the network and successful new device configuration addressing assignment network synchronization frames security and message routing ZigBee further distinguishes the concept of the physical devices (RFD FFD) by using the notion of ldquological devicesrdquo ldquoZigBee Coordinatorrdquo is the first type of logical devices It is responsible for initializing maintaining and managing the network Under the coordinator in the network hierarchy is the ldquoZigBee routerrdquo which is responsible for controlling the message routing between the nodes Finally the ldquoZigBee End Devicerdquo acts as the end point of the network

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23

structure The tasks that an end device can perform are specified in the ldquoApplication Profilesrdquo (Craig 2005)

Figure 11 IEEE 802154ZigBee stack [17]

The ZigBee specifications (2005) summarize the security services provided by ZigBee ldquokey establishment key transport frame protection and device managementrdquo ZigBee builds its security mechanism using symmetric key cryptography The security services also depend on the associated layer thus as the figure 10 shows the security mechanism covers the network and the application layer In addition if a MAC frame needs security protection the MAC layer is able to secure it Moreover the notion of end‐to‐end security is supported the source and destination devices have access and use the same share key In the MAC layer the 802154 AES mechanism provides the proper security The mechanism protects ldquothe confidentiality integrity and authenticity of the MAC framesrdquo (Kinney 2005) An auxiliary header field in front of the MAC payload indicates if the frame is encrypted or not The MAC framesrsquo integrity is supported by calculating and using a MIC at the end of the MAC payload In addition to the AES nonce is used to provide MAC confidentiality and authenticity Figure 12 illustrates a MAC frame with security For different security aspects the MAC layer uses different mode of the AES for the encryption it uses the AES in Counter (CTR) mode and for the integrity the

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

24

CBC‐MAC Finally the combination (CCM) of the above two modes is available providing both encryption and integrity (Kinney 2005)

Figure 12 ZigBee secure frame in MAC layer [17]

In the network layer the CCM (a modified MAC layer CCM mode) is used for encryption Because the network layer uses only the CCM mode a single key is used for all different security options The network layer security message format is similar to the MAC frame it is presented in the figure 13 Finally although the network layer is responsible for securing its layer messages the above layers specify the keys and the CCM option for each frame (Kinney 2005)

Figure 13 ZigBee secure frame in network layer [17]

Security in the application layer works similar to the network and MAC layers It uses the ldquolink keyrdquo or the ldquonetwork keyrdquo to secure the message and then encapsulate it inside a set of fields similar to the network format (figure 14) Other security responsibilities that the application layer has are to provide the ZDO and the applications with device management services key establishment and key transport (ZigBee specifications 2005)

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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25

Figure 14 ZigBee secure frame in application layer [17]

The ZigBee application layer contains the manufacturer‐defined application objects the ZDO and the application sub‐layer In addition to the security responsibilities the application sub‐layer binds devices based on their duties and needs maintains the binding tables and forwards messages between them The application sub‐layer also discovers the neighbour devices for a given device The ZDO is responsible for determining the devicersquos duty in the network for communicating using binding requests and for supporting security as was mentioned above The sub‐layer that implements the actual application is the manufacturer‐defined application object (Kinney 2005) The ZigBee specifications are a great step toward the wireless sensors networks standardization They cover all the OSI layers from the physical to the application providing guidance to the developers The specifications are the product of the ZigBee alliances a consortium of companies and academic institutes Thus they not only produce the specifications but also are the first use them The next section goes beyond the layer architecture and standardization and introduces the OS field another crucial element of WSNs

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

26

3 RECEIVED SIGNAL STRENGTH (RSS) Majority of the existing methods leverage the existence of IEEE 80211 base stations with powerful radio transmit powers of approximately 100mW per base station Such radios are in a different class from the low power IEEE 802154 compliant radios that typically transmit at low power levels ranging from 52mW to 29mW [19] The wide availability of larger number of IEEE 802154 radios in the research community has revived the interest for signal strength based localization in sensor network Despite of rapidly increasing popularity of IEEE 802154 radios and signal strength localization there is a lack of detailed characterization of the fundamental factors contributing to large signal strength variation This chapter investigates some of the properties of RSS observed during testing and analysis of research work collected during the period of thesis The analysis of RSS values is needed to understand the underlying features of location dependent RSS patterns and location fingerprints An understanding of the properties of the RSS values for location fingerprinting can assist in improving the design of positioning algorithms and in deployment of indoor positioning systems

31 Parameters that affect RSS Different factors that affect the indoor RSS are the userrsquos presenceabsence hisher orientation time of the day building type and material distance from transmitter and type of radio These factors are by no means exhaustive Other possible factors are antenna orientation directionality and type Table 10 classify the parameters which affect RSS [7]

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

27

Effect on Factors Options

1 Proximity of user Userrsquos presence or absence

2 Orientation of user and terminal

North East South West

Data Collection

3 Make of Radio 4 Time of measurement Time of day amp days of

week 5 Period of measurement

Second minute hour

7 Interference Co‐channel adjacent co‐channel

Statistics

8 Building environment Small offices or large hall

Table 10 Classification of factors the affect RSS [7]

Transmitter variability Different transmitters behave differently even when they are configured exactly in the same way In practice this means that when a transmitter is configured to send packets at a power level of d dBm then the transmitter will send these packets at a power level that is very close to d dBm but not necessarily exactly equal to d dBm This can alter the received signal strength indication and thus it can lead to inaccurate distance estimation

Receiver variability The sensitivity of the receivers across different radio chips is different In practice this means that the RSSI value recorded at different receivers can be different even when all the other parameters that affect the received signal strength are kept constant

Antenna orientation Each antenna has its own radiation pattern that is not uniform In practice this means that the RSSI value recorded at the receiver for a given pair of communicating nodes and for a given distance between them varies as the pair wise antenna orientations of the transmitter and the receiver are changed

Multi‐path fading and shadowing in the RF channel In indoor environments the transmitted signals get reflected after hitting on the walls andor on other objects in the room such as furniture Both the original signal and the reflected signal reach the receiver almost at the same time since they both travel at the speed of light As a result of this the receiver is not able to

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

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28

distinguish the two signals and it measures the received signal strength for both of them

32 Related work analysis A number of indoor location tracking systems have been proposed in the literature based on RF signals ultrasound infrared and some combinations of these As mentioned previously RF based positioning system are widely studied as shown in Table 2 Given a model of radio signal propagation in a building or other environment RSS can be used to estimate the distance between the transmitter and receiver Most of the algorithms (Table 2) have been evaluated in practice using 80211b compliant wireless radios and seem to produce a location error of 15ft to 20ft for an indoor network area of approximately 225ft x 144ft According to the results of this study all the localization techniques produce approximately the same location error over a range of environments Ecolocation [22] Mote Track [5] are two of the few localization algorithms that have been evaluated on a real sensor network that uses a low power wireless radio Ecolocation reports a location error of 10ft for a very small outdoor network deployment area (26ft x 49ft) Mote Track reports a location error of approximately 13ft for an indoor network deployment area of 18751ft2 In general the RSSI based localization algorithms can be divided into two broad categories the map based and the distance (or area) prediction based algorithms In the case of the map based algorithms the deployment area is divided into a large set of sample points that cover the whole area Most techniques collect one or more signal strength measurements from all visible beacon nodes and for each sample point Each sample point is then mapped to either a signal strength vector or a signal strength probability distribution This process generates an RSSI fingerprint map which is unique with respect to the beacon locations and the deployment area This fingerprint map is stored to a central base station A node that is not aware of its location collects one or more signal strength measurements from all the visible beacon nodes and creates its own RSSI fingerprint that is sent to the central base station The base station computes nodersquos location as the sample pointrsquos location whose RSSI fingerprint distance from the unknown nodersquos RSSI fingerprint is the minimum among all the sample points Despite the fact that the map based RSSI localization methods produce relatively small location errors they are not attractive mainly because they require an excessive amount of profiling data and because they are not fully distributed The detailed profiling of a building is a major holdback because it is complex time consuming and depends on beacon locations Therefore for different buildings or even for the

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

29

same building and different beacon locations different RSSI maps have to be obtained The second category of RSSI based algorithms are the distance (or area) prediction algorithms which focus on minimizing the amount of profiling data In distance prediction based methods raw RSSI data are directly mapped to distances through a signal propagation model In some cases distances are not explicitly measured but they are taken into consideration through assumptions on the underlying distributions In the case of outdoor deployments where there are no obstacles a single signal propagation model is all the profiling data required In the case of indoor deployments the same applies but now the signal propagation model might be different across different buildings with different furniture and wall arrangements Given an underlying signal propagation model raw RSSI data can be mapped to specific distances These distances can be either used directly in order to perform localization or assigned a probability and used in a learning basedprobabilistic localization algorithm Obviously the accuracy of the distance prediction based methods depends heavily on the accuracy of the signal propagation model used for translating RSSI values to distances

33 Properties of RSSI Changes in signal due to propagation indoors are difficult to predict because of dense environment and propagation effects such as reflection diffraction and scattering The multi‐path fading effect which is the result of either constructive or destructive combination of multiple signal copies at the receiver causes the received signal to fluctuate around a mean value at a particular location The received signal is usually modelled by the combined effects of large‐scale fading and small‐scale fading The large‐scale fading component describes the signal attenuation as the signal travels over a distance and is absorbed by material such as walls and floors along the way to the receiver This component predicts the mean of the RSS and usually has a lognormal distribution On the other hand the small scale fading component explains the dramatic fluctuation of the signal due to multi‐path fading On the other hand the small scale‐fading component explains the dramatic fluctuation of the signal due to multi‐path fading If there is no LOS component the small‐scale‐fading is often modelled with a Rayleigh distribution Such scenarios are commonly called NLOS If there is a LOS component the small‐scale‐fading is typically modelled by a Rician distribution Note that when the RSS is measured the measurement averages out the small‐scale‐fading effects

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

30

4 OBJECT TRACKING 41 Introduction This chapter presents the object‐tracking application the main objective of this thesis work The application is an algorithmic‐software implementation written in Embedded C which is capable of receiving data messages and generating object position estimation data The implementation of the object‐tracking application makes use of the SENTIO platform and uses the background knowledge introduced in the preceding chapters The motivation for the object‐tracking application is to determine the position of an object in indoor environments (Eg Home) The application acts as an interface which collects the raw data that the sensors generate (numerical values) It makes the proper comparisons and algorithmic processes to identify and track the object while it is static inside the area covered by the wireless sensor network Finally with the detection and tracking data it alerts the user or another system The following sections provide an overview of the object‐tracking application

42 Overview of hardware product Before describing the object tracking application it is important to know the characteristics of hardware product The SENTIO platform (figure 15) is built in modular fashion and has the following key features [8]

Flexibility This platform is very flexible in such a way that it is made easy to add or change the hardware for each sensor task depending on the requirement in the WSN For a specific platform this can be hard or sometimes even impossible to accomplish

Sensor resources Resources in a sensor module includes the ability of usage of controllerrsquos resources as much as possible such as ADC‐channels interrupts and digital Input‐Outputrsquos Local voltage regulation should also be possible since many sensors require higher supply voltages than digital Integrated Circuits do Further support of a shut‐down mechanism to power down the sensor modules when not needed

Computation and memory The processor module based on microcontroller should support many devices in a microcontroller family and support different power supply voltages enabling full range of clock frequencies All power‐down modes of the devices should be

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

31

supported The platform should support different processor modules including FPGArsquos and very simple microcontrollers

Communication The focus should be on enabling an IEEE 802154 compliant communication interface With or without ZigBee stack this will provide a flexible platform for exploring different network topologies Support for power‐down of the entire communication module should be supported

Cost and form factor High flexibility is in conflict with low cost and small size The primary objective should be flexibility but the size should be considered in order to enable sensor device integration in systems where space is limited

Figure 15 SENTIO Platform [8]

The communication subsystem of the SENTIO sensor node was designed so that the correct operation of the radio chip is ensured The radio chip is powered by the on‐board voltage regulator and thus fluctuations in the battery voltage level do not affect the operation of the communication subsystem This platform uses Chipcon CC2420 IEEE 802154 radio transceiver operates in the 24GHz ISM band and includes a digital direct sequence spread spectrum (DSSS) modem providing a spreading gain of 9dB and an effective data rate of 250Kbps It was specifically designed for low power wireless applications and supports 8 discrete power levels 0dBm minus1dBm minus3dBm minus5dBm minus7dBm minus10dBm minus15dBm and minus25dBm at which its power consumption varies from 29mW to 52mW [21] A built‐in received signal strength indicator gives an 8‐bit digital value RSSIVAL The RSSIVAL is always averaged over 8 symbol periods (128μs) and a status bit indicates when the RSSIVAL is valid (meaning that the receiver was enabled for at least 8 symbol periods) The

Antenna

Communication Module

Processing Module

Sensing Module

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

32

power P at the RF pins can be obtained directly from RSSIVAL using the following equation

P = RSSIVAL + RSSIOFFSET [dBm] where the RSSIOFFSET is found empirically from the front‐end gain and it is approximately equal to minus45dBm In the next sections when we refer to the RSSI value we refer to the RSSIVAL and not the power P unless otherwise stated

43 Experimental Testbed The experimental test bed for object tracking application is as shown in figure 16 Experiment was conducted in Masterrsquos room at Mid Sweden University A DELL laptop is connected to SENTIO platform and software were primarily used to collect samples of RSS from APrsquos The software monitors IEEE 802154 radio frequency channels which operate in the 24GHz band

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

1030

BC - 1030

AP 3 AP 1

AP 2

AP 4

BS

HEWLETT

PACKARD

Figure 16 Layout of Masterʹs Room

A three dimensional scalable testbed is used for indoor sensor network deployments The testbed illustrated in figure 16 is a 3‐D structure measuring 16m (L) x 12m (W) x 3m (H) and it is designed to host a large number of static nodes as shown in figure 16 Arrow marks in the above figure represent the location where fingerprints were taken The centrepiece of our infrastructure

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

33

is the SENTIO sensor node [8] an open‐source general purpose sensing platform designed with AT Mega 128 microprocessor and the IEEE 802154 compliant CC2420 radio from Chipcon [19]

44 Obtaining data from the sensor network The next important step in the development of the object tracking application is to obtain the data from the sensor network and send it to the base station From there the data is transmitted through a serial port to the computer that runs the object tracking application When the data arrives at the serial port the application must capture it This paragraph gives a brief explanation of how and what kind data is transmitted by the static object fixed sensors and how this data is being used to estimate the position of the object First the static object transmits Beacon packet continuously which is received by all the fixed APrsquos The beacon packet format is as shown in table 6 Then the fixed APrsquos calculates the 8‐bit value of RSSI from the transmitted beacon packet and then sends the resultant RSSI value as a form of data packet (table 7) to the BS which calculates the position and sends back to the object

45 Object tracking application Testing and Evaluation The purpose of this section is to discuss and evaluate the object tracking application Finally we are ready with the experimental testbed as shown in figure 16 As shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors placed at the corners of the masterrsquos room BS is placed at the centre of the room connected to a PC All the nodes are power connected before an actual measurement takes place At first the object is placed at random position inside the room and transmits beacon packets continuously Then these packets are received by the fixed APrsquos which calculate the RSSI from the packet and then averages it until it receives 50 beacon packets and then transmits the data to the BS (figure 17) Then the BS waits until it receives the data from all the APrsquos Once it receives from all the APrsquos it calculates the position of the object (figure 18) The application was developed to demonstrate a real world use of a WSN in object tracking In evaluating the final position obtained with respect to the actual position we conclude that the object‐tracking was successful Although

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

34

it is a research prototype object tracking application is a simple stable stand‐alone system capable of demonstrating an important application of WSN It takes raw data from the WSN aggregates it evaluates it and based on the evaluation tracks the object The accuracy of estimating the position is less than 5m Table 11 below shows a log of readings taken during test

Start

Intialize micro‐controller and transeciever in receieve mode

If received_packet=

Beacon

Average RSSI values

Increase the counter

If countergt1000050

A

No

Yes

B

B

Yes

No

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

35

A

Reset counter

Set transceiever in transmit mode

Transmit averaged RSSI to Main sensor and set it back to receive mode

10000 samples ‐ Offline

50 samples ‐ Online

Figure 17 Flow chart representing the algorithm of fixed sensors

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

36

Start

Intialize micro‐controller and transeciever in receieve mode

Received RSSI values from all fixed Sensors

Open radio map

Calculate Euclidean distance between obtained RSSI values from fixed sensors and fingerprints

in radio map

No

Yes

Bubble sort for least error in signal strength values

A

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

37

A

Get the location of the object from the radiousing the least error value

Display location of object on PC

End

Figure 18 Flow chart representing the algorithm of main sensor

Table 11 Accuracy of positioning system

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

38

5 RESULTS This chapter presents how the system parameters such as number of samples mean or median etchellipare finalized All the tests are performed in masterrsquos room at Mid Sweden University This chapter is divided into three subsections First in Section 51 it reviews results obtained from samples test Section 52 discusses one dimensional test results and finally section 53 discusses results of two dimensional tests

51 Measurement setup A Dell laptop was connected to a SENTIO platform and is loaded with software (Appendix 2) This setup was used to determine number of samples required during object tracking mean or median position estimation algorithm and one dimensional test The setup is as follows

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

103

0

BC - 1030

AP 1AP 2

BS

HEWLETT

PACKARD

Figure 19 Test bed for one dimensional test in Masters Room

Two SENTIO nodes (AP1 and AP2 as shown above) were separated by a distance of 10 meters and these sensors are known as fixed sensors which are fixed to the ceiling such that there antenna is faced towards the ground They are linear is position A BS was connected to Dell laptop and it was placed in such a way that its antenna was faced towards the ceiling Another SENTIO

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

39

node was used as an object and its orientation was same as BS Figure 19 represents the complete setup for object tracking application Mobile node was moved between these fixed ones at regular intervals of one meter and RSSI values were logged at each interval

52 Number of samples test This test was performed to determine number of samples required for accurate positioning and either mean of the obtained RSSI values or median of them Figure 19 represents the setup for number of samples test Once the setup is ready the measurements are taken as follows

1 An object was tagged with SENTIO node and was placed stationary linear to the fixed sensors

2 10000 samples (RSSI) were taken at regular intervals of 1 meter up to 10 meters

3 The sample values were logged at every interval 4 Using these logged samples I have plotted graphs such as distribution

of RSS values (figure 20 21) meanmedian at intervals of 1 meter using MATLAB

5 The mean and median for various numbers of samples is as shown in figure 22

Figure 20 Histogram plot of RSSI values at 1m and 4m

Figure 21 Histogram plot of RSSI values at 6m and 8m

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

40

Figure 22 Distance vs RSSI

From the above graphs following conclusions were made 1 From the histogram plots we can observe that the distribution of RSSI

values follow Gaussians distribution law 2 As we can see from figure 22 that median curve has drastic changes

when compared to mean curve Hence mean is considered while estimating the position

3 From the observations we conclude that the number of samples does not matter since mean curve tends to be same for various numbers of samples

4 Hence 10000 samples are considered in order to get a proper location fingerprint during off‐line phase and during online phase 50 samples are enough to estimate the position of object

53 0ne dimensional test Measurement setup for one dimensional test is as shown in figure 19 This test was conducted to check the performance of algorithm and how accurate it was At first ie during offline phase fingerprints at each interval averaging 10000 samples were taken During online phase Euclidean algorithm was used estimate the position averaging 50 samples of RSSI From the results we

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

41

can realize that the error was less than 2 meters Table 12 shows the result of location estimation in one dimensional case

Table 12 One dimensional position estimation results

54 Two dimensional test This test is the final stage of my thesis work Experimental testbed for this stage is as shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors fixed at the corners of the room and a BS at the centre of the room There are 18 pre‐determined locations where the fingerprints (RSSI) were taken The fingerprints are as shown in Appendix 1 All the readings were taken in presence of userrsquos The algorithm for position estimation is shown in Appendix 2 Position estimation test is performed as follows

Tagged object is placed at random inside the masterrsquos room All the sensors including the base station and object are switched on Using the algorithm (Appendix 2) the BS estimates the position of the object and displays it on the PC

This test was performed using various numbers of samples to test which has a better accuracy Figure 23 represents the error rate using various numbers of samples From figure 23 we can conclude that 50 numbers of samples is enough to estimate the position of object with an accuracy of 3m

55 Causes of error in location determination The randomness of RSS is the major cause of error in any indoor

positioning system that uses the RSS for location inference If there is no randomness in the RSS and its location fingerprint every indoor positioning systems of this type will have no location detection error For example I have recorded RSS at one particular location during different times of the day From figures 24 and 25 you can observe that RSS values are different at different times of day

The performance of indoor positioning systems depends greatly on the separation of location fingerprints A location fingerprint

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

42

corresponding to a location can be identified correctly if it is difficult to classify it as another fingerprint by a pattern classifier

Path loss is one of the basic signal propagation properties of the RSS that best describes attenuation of transmit signal power over a distance between an access point and a mobile device

Presence of Userrsquos or userrsquos orientation affects RSS

Figure 23 Error Rate vs Number of samples

Figure 24 RSS distribution at 214 pm

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

43

Figure 25 RSS distribution at 100pm and 1100 am

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

6 DISCUSSIONS10102006

44

6 DISCUSSIONS 61 Summary and Conclusion While empirical results and performance studies of positioning systems based on location fingerprint have been presented in this report analytical model that can be used as a framework for efficiently designing the positioning systems are not available This thesis design work uses general principles of position estimation such as position estimation of sensors grid spacing etc This design work focuses on accuracy and precision The concept of location fingerprint technique based on RSS us explained in detail in chapter 2 It is found that RSS is random It is possible to approximate the RSS as being normally distributed While the RSS random process is non‐stationary the mean is more or less constant and could be used as the fingerprint of the location Model designed for position estimation is as shown in chapter 4 Accuracy and precision are the two major performance matrices in this study Uniform grid spacing has been used From the experimental analysis I have obtained an accuracy of 3 meters and 35 precision The sensitivity analysis among the system parameters and the performance matrices were done using the model as shown in chapter 4 For instance our study found that the minimum distance between two adjacent positions also affects the positioning performance This is due to the randomness of RSS patterns induced by the ever changing indoor environment

62 Future work The research in this thesis provides a ground work for the future study of efficient design of indoor positioning system The RSSI based position estimation still has limitations First the RSSI value is time dependent Thus the future study should address this issue Second the movement of object is not considered Third orientations of sensors affect the performance which should be taken care off Fourth to improve the performance of the system we need to find a solution to deal with those incomplete or missing data during the off‐line and the on‐line phase Fifth position estimation on multi‐floor and three‐dimensional coordinates is not available

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 110102006

45

APPENDIX 1

Location Fingerprints of Masters Room Time 1315 Observation Time 20 minutes Number of Samples 10000

People Presence SNo Co-ordinates AP1 (0x0001) AP2 (0x0002) AP3 (0x0004) AP4 (0x0008)

1 11 -24 -16 -27 -33 2 13 -26 -8 -34 -31 3 15 -43 -9 -39 -25 4 32 -21 -11 -27 -33 5 61 -6 -23 -32 -28 6 64 -20 -31 -25 -34 7 35 -23 -35 -29 -38 8 37 -29 -35 -22 -27 9 39 -38 -29 -21 -27 10 67 -27 -23 -20 -23 11 611 -26 -29 -23 -30 12 613 -35 -32 -2 -31 13 513 -34 -31 -20 -26 14 11 -39 -22 -29 -17 15 212 -37 -42 -28 -31 16 -212 -38 -33 -29 -6 17 -21 -42 -29 -33 -19 18 -38 -40 -23 -35 -22

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 210102006

46

APPENDIX 2

Begin

Open radio map and raw data files for write

Intialize radio map in memory

Check whether the

Packet is data or Beacon packet If data packet

Save the data to register

Check if data is

Received from all

sensors

Estimate the position of static

Display the position on PC

End Yes

No

A

A

Yes

No

Flow chart representing the main algorithm for position estimation

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

47

APPENDIX 3

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

48

Distribution of RSSI

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

49

REFERENCES [1] T S Rappaport J H Reed and B D Woerner ldquoPosition location using wireless communications on highways of the futureʺ IEEE Commun Mag vol 34 no 10 pp33‐41 Oct 1996 [2] A M Ladd K E Bekris G Marceau A Rudys L E Kavraki and D S Wallach rdquoRobotics‐based location sensing using wireless ethernetʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ02) 2002 pp 227‐238 [3] I Ramani R Bharadwaja and P V Rangan ldquoLocation tracking for media appliances in wireless home networksʺ in Proc International Conference on Multimedia and Expo (ICMEʹ03) July 2003 pp 769‐772 [4] K Pahlavan X Li and J P Makela ldquoIndoor geolocation science and technologyʺ IEEE Commun Mag vol 40 no 2 pp 112‐118 Feb 2002 [5] Konard Lorincz and Matt Welsh ldquoMote Track A Robust Decentralized Approach to RF‐Based Location Trackingrdquo [6] Paramvir Bahl and Venkata N Padmanabham ldquoRADAR An In‐Building RF‐based User Location and Tracking Systemrdquo [7] Kamol Kaemarungsi ldquoDesign of Indoor positioning system based on location fingerprint techiniquerdquo University of Pittsburgh 2005 [8] Fredrik Linnarsson Cheng Peng and Bengt Oelmann ldquoSENTIO A Hardware Platform for Rapid Prototyping of Wireless Sensor Networksrdquo [9] S Saha K Chaudhuri D Sanghi and P Bhagwat ldquoLocation determination of a mobile device using IEEE 80211b access point signalsʺ in Proc IEEE Wireless Communications and Networking Conference (WCNCʹ03) New Orleans LA Mar 2003 pp 1987‐1992 [10] T Roos P Myllymaki H Tirri P Misikangas and J Sievanen ldquoA probabilistic approach to WLAN user location estimationʺ International Journal of Wireless Information Networks vol 9 no 3 pp 155‐164 July 2002 [11] R Battiti M Brunato and A Villani ldquoStatistical learning theory for location fingerprinting in wireless LANrsquosʺ Technical Report Oct 2002 [Online] Available httprtmscienceunitnitraquobattitiarchive86pdf [12] P Prasithsangaree P Krishnamurthy and P K Chrysanthis ldquoAn indoor position location with wireless LANsʺ in Proc IEEE International Symposium on Personal Indoor and Mobile Radio Communications (PIMRCʹ02) Lisbon Portugal Sept 2002 [13] M A Youssef A Agrawala and A U Shankar ldquoWLAN location determination via clustering and probability distributionsʺ in Proc IEEE

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

50

International Conference on Pervasive Computing and Communications (PerComʹ03) Dallas‐Fort Worth TX Mar 23‐26 2003 pp 23‐26 [14] Z Xiang S Song J Chen H Wang J Huang and X Gao ldquoA wireless LAN‐based indoor positioning technologyʺ IBM Journal of Research and Development vol 48 no 56 pp 617‐626 SeptNov 2004 [15] R Want A Hopper V Falcao and J Gibbons ldquoThe active badge location systemʺACM Transactions on Information Systems vol 40 no 1 pp 91‐102 Jan 1992 [16] N B Priyantha A Chakraborty and H Balakrishnan ldquoThe cricket location‐support systemʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ00) Boston MA Aug 2000 pp 32‐43 [17] Holger K Willig A (2005) Protocols and Architectures for Wireless Sensor Networks London [18] IEEE (2003) Part 154 Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low‐Rate Wireless Personal Area Networks (LR‐WPANs) New York [19] Chipcon CC2420 802154 compliant radio httpwwwchipconcom [20] ZigBee Specification v10 ZigBee Alliance [21] Dimitrios Lymberopoulos Quentin Lindsey and Andreas Savvides ldquoAn Empirical Analysis of Radio Signal Strength Variability in IEEE 802154 Networks using Monopole Antennasrdquo [22] Kiran Yedavalli Bhaskar Krishnamachari Sharmila Ravula and Bhaskar Srinivasan ldquoEcolocation A sequence based technique for RF localization in wireless sensor networksrdquo Robert Bosch Research and Technology Centre Palo Alto CA

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

Acknowledgements10102006

iii

Acknowledgements

This work would not have been possible without the support of my friends and mentors Specifically I would like to thank my advisor Bengt Oelmann who has given me all the support and guidance I needed as a Master student I am very grateful to have had trust in my ability and I have often benefited from his insight and advice I would also like to thank Fredrik Linnarsson Niklas Lepisto and Cheng Peng from whom I have had benefit of learning from I also owe a great deal of thanks for their time and advice which is reflected in this thesis

Finally I would like to acknowledge my friends and parents who have supported me through these efforts

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

Table of Contents10102006

iv

Table of Contents Abstract ii Acknowledgements iii Table of Contents iv List of Tables vi List of Figures vii Terminology Notation viii 1 INTRODUCTION1 11 Introduction to the study 1 12 Background2 13 Objectives 3 14 Research Questions4 15 Scope 4 16 Methodology5 17 Outline 6

2 LITERATURE REVIEW8 21 Common components of indoor positioning system 8 22 Classification of Indoor positioning systems9 23 Indoor local positioning using ZigBee based on RSSI11 24 Protocols and Industry standards for WSN14

3 RECEIVED SIGNAL STRENGTH (RSS)26 31 Parameters that affect RSS 26 32 Related work analysis28 33 Properties of RSSI29

4 OBJECT TRACKING 30 41 Introduction 30 42 Overview of hardware product 30 43 Experimental Testbed32 44 Obtaining data from the sensor network 33 45 Object tracking application Testing and Evaluation33

5 RESULTS 38 51 Measurement setup 38 52 Number of samples test 39 53 0ne dimensional test 40 54 Two dimensional test 41 55 Causes of error in location determination41

6 DISCUSSIONS44 61 Summary and Conclusion 44 62 Future work 44

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

Table of Contents10102006

v

APPENDIX 1 45 APPENDIX 2 46 APPENDIX 3 47 REFERENCES 49

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

List of Tables10102006

vi

List of Tables Table 1 Comparison of Indoor Positioning Systems [7] 3 Table 2 Performance comparison of Indoor Positioning Systems [7] 3 Table 3 Frequency bands and data rates for IEEE 802154 based on the IEEE 802154 standard [18] 16 Table 4 Format based on the IEEE 802154 standard [18] 17 Table 5 General MAC frame format IEEE 802154 standard [18] 17 Table 6 Beacon frame format [18] 18 Table 7 Data frame format [18] 18 Table 8 Acknowledgement frame format [18] 18 Table 9 Command frame format [18] 18 Table 10 Classification of factors the affect RSS [7] 27 Table 11 Accuracy of positioning system 37 Table 12 One dimensional position estimation results 41

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

List of Figures10102006

vii

List of Figures Figure 1 Schema of thesis work 5 Figure 2 Block diagram representing hardware module 8 Figure 3 Taxonomy of positioning system [7] 10 Figure 4 Star and peer‐to‐peer topologies in LR‐WPAN [18] 15 Figure 5 WPAN architecture [18] 16 Figure 6 Example of super frame structure [18] 19 Figure 7 Communication from a device to a PAN coordinator in (a) a beacon‐enabled network and (b) a non‐beacon enabled network [18] 20 Figure 8 Communication from a PAN coordinator to a device in (a) a beacon‐enabled network and (b) a non‐beacon enabled network [18] 21 Figure 9 Overview of the coverage for different wireless communication standards [17] 21 Figure 10 IEEE 802154ZigBee stack [20] 22 Figure 11 IEEE 802154ZigBee stack [17] 23 Figure 12 ZigBee secure frame in MAC layer [17] 24 Figure 13 ZigBee secure frame in network layer [17] 24 Figure 14 ZigBee secure frame in application layer [17] 25 Figure 15 SENTIO Platform [8] 31 Figure 16 Layout of Masterʹs Room 32 Figure 17 Flow chart representing the algorithm of fixed sensors 35 Figure 18 Flow chart representing the algorithm of main sensor 37 Figure 19 Test bed for one dimensional test in Masters Room 38 Figure 20 Histogram plot of RSSI values at 1m and 4m 39 Figure 21 Histogram plot of RSSI values at 6m and 8m 39 Figure 22 Distance vs RSSI 40 Figure 23 Error Rate vs Number of samples 42 Figure 24 RSS distribution at 214 pm 42 Figure 25 RSS distribution at 100pm and 1100 am 43

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

Terminology Notation10102006

viii

Terminology Notation

GPS ‐ Global Positioning System

RSSI ‐ Received Signal Strength Indication

MS ‐ Mobile Station

RF ‐ Radio Frequency

WSN ‐ Wireless Sensor Network

AP ‐ Access Point

RSS ‐ Received Signal Strength

PC ‐ Personal Computer

WLAN ‐ Wireless local Area Network

SNR ‐ Signal to Noise Ratio

RFD ‐ Reduced‐Function Device

WPAN ‐ Wireless Personal Area Network

FFD ‐ Full Functional Device

MAC ‐ Medium Access Control

LR‐WPAN ‐ Low Rate WPAN

PAN ‐ Personal Area Network

OSI ‐ Open Systems Interconnection

LLC ‐ Logical Link Control

SSCS ‐ Service Specific Convergence Sub layer

PHY ‐ Physical Layer

PPDU ‐ PHY Protocol Data Unit

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

Terminology Notation10102006

ix

ED ‐ Energy Detection

CCA ‐ Clear Channel Assessment

LQI ‐ Link Quality Indication

ISM ‐ Industrial Scientific and Medical

DSSS ‐ Direct‐Sequence Spread Spectrum technique

LSB ‐ Least Significant Bit

SHR ‐ Synchronization Header

SFD ‐ Start of Frame Delimiter

PHR ‐ PHY Header

MPDU ‐ MAC Protocol Data Units

GTS ‐ Guaranteed Time Slot

MHR ‐ MAC Header

MFC ‐ MAC Footer

FCS ‐ Frame Check Sequence

CAP ‐ Contention Access Period

CFP ‐ Contention Free Period

ACL ‐ Access Control List

AES ‐ Advance Encryption Standard

MIC ‐ Message Integrity Code

CBC ‐ Cipher Block Chaining

ZDO ‐ ZigBee Device Objects

OS ‐ Operating System

LOS ‐ Line of Sight

NLOS ‐ Non Line of Sight

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

Terminology Notation10102006

x

FPGA ‐ Field Programmable Gate Array

BS ‐ Base Station

AOA ‐ Angle of Arrival

TDOA ‐ Time difference of arrival

TOA ‐ Time of Arrival

TOF ‐ Time of flight

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

1

1 INTRODUCTION 11 Introduction to the study Advancement in wireless sensing technologies has encouraged in developing a model for indoor object‐tracking application using low power IEEE 802154 compliant radios Stimulus used in this model is RSS This application is not only limited to object tracking in an indoor environment Concierge services enable users to become aware of nearest supporting facilities For example smart home applications such as multimedia appliances that forward multimedia stream to the nearest video screen can be achieved with a home positioning system [3] In the field of robotics a robot can navigate by itself using the assistance of indoor positioning system [2] Granularity of location information is most important in location determination applications For example to locate a book in a library requires coarse grained granularity whereas locating a user requires coarse grained granularity Position estimation refers to a process used to obtain location information of a MS with respect to a set of reference positions within a pre‐defined space A system deployed to determine or estimate the location of an entity is called a position location system or positioning system A wireless indoor positioning system refers to a wireless network infrastructure that provides indoor location information to any requesting end user One of the key technical elements allowing the realisation of this vision will be the knowledge of the accurate position of the personobjects Already today accurate location mechanisms such as GPS are commercially available Many more will become part of our daily life with the introduction of third generation mobile phones But none of these techniques will allow the meter or sub‐meter level of accuracy within buildings which is necessary for smart houses Hence this report introduces an object tracking application in an indoor environment using IEEE 802154 compliant radios which gives meter level accuracy First this chapter presents the background of indoor positioning systems identifies the challenges of such systems and briefly describes the scope of indoor positioning systems based on location fingerprints Next the objective of study the overview of approaches and the contributions are presented Finally the scope and organization of thesis work is outlined

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

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12 Background The success of outdoor positioning and applications based on the GPS provides an incentive to the research and development of indoor positioning systems It has been developed over several decades and it relies on constellation of satellites Ranges to several satellites are used in multi‐lateration procedure to infer the position of the receiver This localization scheme requires hardware that is both expensive and consumes significant power As a result indoor positioning systems require alternative means to detect the MS location without relying on the direct signal from GPS satellites Infrared RF and ultra sound signals are major technologies used for indoor positioning systems Unlike outdoor areas the indoor environment imposes different challenges on location discovery due to the dense multi‐path effect and building material dependent propagation effect Thus an in‐depth understanding of indoor radio propagation for positioning is crucial for efficient design and deployment The existence of radio connectivity and the attenuation of radio signal with distance are attractive properties that could potentially be exploited to estimate the positions of small wireless devices featuring low‐power radios RSSI a standard feature in most radios has attracted a lot of attention in the recent literature for obvious reasons RSSI eliminates the need for additional hardware in small wireless devices and exhibits favourable properties with respect to power consumption size and cost As mentioned previously RF‐based location tracking has been widely studied [5 6] Given a model of radio signal propagation in a building or other environment RSSI can be used to estimate the distance from a transmitter to a receiver and thereby estimate the position of a mobile node However this approach requires detailed models of RF propagation and does not account for variations in receiver sensitivity and orientation Table 1 below briefly summarizes performance of existing indoor positioning system based on location fingerprints and WLANrsquos All the systems mentioned below in table 1 follow IEEE 80211 protocol standards The major performance metrics studied by all systems are positioning accuracy which is a form of error measurement The accuracy of the location information is usually reported as an error distance between the estimated location and the actual mobile location However the report of accuracy should include the confidence interval or percentage of successful location detection which is called the precision The system parameters of existing systems are summarized in table 1 and the best reported performance of these systems are listed in table 2

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

3

System Spacing(m) Positions SamplesPos APs Orientation Environment

RADAR [6] Non

uniform 70 80 3 4 Hall-way Saha et al [9] 3 19 1200 3 NA 1-floor

Roos et al [10] 2 155 40 10 NA 1-floor Battiti et at [11] NA 257 NA 6 NA 1-floor Ladd et al [2] 3 11 1307 5 2 Hall-way

Prasithsangaree et al [12] 153 60 40

2 to 7 4 1-floor

Youssef et al[13] 15 110 300 4 NA Hall-way Xiang et al [14] NA 100 300 5 4 1-floor

Table 1 Comparison of Indoor Positioning Systems [7]

System Algorithm Type Accuracy and Precision RADAR [6] Nearest Neighbour within 7 feet 38

Saha et al [9] Nearest neighbour amp Neural Network no specified accuracy

90 Roos et al [10] Bayesian best within 828 feet 90

Battiti et at [11] Neural Network amp Weighted k-Nearest

Neighbour all within 16-17 feet 90 Ladd et al [2] Bayesian within 5 feet 77

Prasithsangaree et al [12] Weighted k-Nearest Neighbour

25 feet at 75 amp 40 fet at 95

Youssef et al [13] Bayesian within 7 feet more than

90 Xiang et al [14] Bayesian with RSS distribution model within 6 feet 90 Table 2 Performance comparison of Indoor Positioning Systems [7]

Based on the study of results obtained in various positioning systems as in table 2 we have developed a model for an indoor positioning system using SENTIO [8] platform based on RSSI

13 Objectives In the last two or three years a number of theoretical practical andor simulation studies were done on the topic of object‐tracking While these studies are useful they are too general and provide little guidance for the actual deployment of sensor networks for real life location‐tracking of an object This thesis focuses on developing an object‐tracking application and prescribes sensor network configurations that work well with our algorithms I implemented my software using SENTIO [8] platform for sensor networking The major issue addressed in this project are the accuracy and precision in tracking an object in a real‐world application and efficient ways to algorithmically analyze the collected raw data from the specific wireless sensor product

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

4

Although the focus is to develop a real‐world application using WSN it also provides great opportunity to explore the new area of wireless communication overall

14 Research Questions My primary target is to find a method to determine the position of an object inside a building using ZigBee protocol standards based on RSSI The study addresses the following questions What is WSN What requirements do WSN need What are their main ideas and concepts What are the associated technologies What are the standards that sensor networks use

What are the existing hardware implementations that use this particular technology How can we build our new applications using WSN What do sensor network systems require for general or specific applications Are they applicable And what is the appropriate design for sensor network implementations in order to combine them with other existing systems At this point we describe and evaluate the specific object‐tracking application to be built

15 Scope The study area of the thesis is an overview of the WSN technology study of SENTIO platform and implementation of an object tracking application Thus the study is divided into two parts Part one is an overview of WSN it includes study of SENTIO platform and IEEE 802154 protocol standards The second part describes the implementation of the object tracking algorithm The object tracking application receives and uses the raw values returned by the sensor network system to produce clear and meaningful outputs Schema of thesis work is as shown in figure 1

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

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Figure 1 Schema of thesis work

16 Methodology The ldquoIndoor Local Positioning System for ZigBee based on RSSI ldquois based on ldquolocation fingerprintingrdquo technique Location fingerprinting refers to a technique that exploits the relation between any measurable physical stimulus and a specific location This approach uses empirical measurements of RSSI to estimate location By recording a database of radio ldquosignaturesrdquo along with their known locations a mobile node can estimate its position by acquiring a signature and comparing it to the known signatures in the database All of these systems require the database of signatures which should be collected manually prior to the system installation and rely on central server (or the userrsquos mobile node) to perform location calculation For example in RADAR [6] technology the RSSI is the stimulus This type of positioning system does not require specialized hardware other than the common wireless network interfaces with received signal strength measurement capability thus it is simple to deploy compared to other techniques Unlike outdoor counterpart systems which can use AOA and TDOA techniques effectively indoor positioning systems encounter the problem of NLOS and the dense multi path effect that render these two techniques ineffective or complex for practical implementation It is also difficult for a MS to always see three or more access points or base stations in indoor environment which is essential for triangulation by AOA and TDOA Location fingerprinting can also be implemented as software based positioning system which can reduce

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

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complexity and cost Generally the deployment of fingerprinting based positioning systems can be divided into two phases First in the off‐line phase the locations where the fingerprints are required is pre‐determined and then the fingerprints are collected by performing a site‐survey of the RSS from multiple APs The distance between two closest positions is called grid spacing and usually reported in meters or feet However some points may be omitted due to inaccessibility The RSS is measured at each predetermined location to create a database of RSS patterns of the indoor environment The database of RSS patterns is called a radio map The vector of RSS values at a point on the grid is called the location fingerprint of that point Second in the on‐line phase a MS will report a sample measured vector of RSS from different APs to a base station The base station uses a positioning algorithm to estimate the location of the MS The most common algorithm used to estimate the location is Euclidean algorithm It computes the Euclidean distance between the measured RSS vector and each fingerprint in the database and then the coordinates associated with the fingerprint that provides the smallest Euclidean distance is returned as the estimate of the position It is clear from the above discussion that the designer should not start out to do the site survey without proper system design objectives and guideline If the performance is not sufficient collecting more fingerprints in between previous location fingerprints may help It is a problem of choice as to how many access points are required for the system and what the minimum distance between physical positions on the grid should be in order to provide a good position resolution and best system performance The system parameters and factors that improve the accuracy and the precision performance are still not clear

17 Outline Chapter 2 provides literature review of ldquoIndoor Local Positioning System for ZigBee based on RSSIrdquo it introduces the basic components required for a positioning system general classification of indoor positioning system Finally it explains ldquoLocation Fingerprintrdquo technology used in this thesis work to determine the position of an object in a real‐world application It briefly explains the protocols and industry standards for wireless sensor networks Chapter 3 describes some properties of RSS which is a stimulus to determine

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

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the position of an object RSS relation with energy and on what parameters is the RSS value dependent Chapter 4 describes the model of an indoor local positioning system and its probability of returning the correct location It also explains its performance evaluation and its error distance distribution Chapter 5 describes results obtained from the model of positioning system Chapter 6 includes conclusions from the experimental analysis and future work

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

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2 LITERATURE REVIEW This chapter reviews the literature on wireless indoor positioning system as a means of providing an intellectual background for the thesis work First in section 21 the common components of indoor positioning system are described Then in section 22 discusses different means to classify indoor positioning systems In section 23 reviews of all relevant literature on location fingerprint technique Finally in section 24 protocols and industry standards for wireless sensor networks are explained

21 Common components of indoor positioning system Basically a wireless local positioning system consists of at least two separate hardware components a measurable unit that usually carries the major part of the system intelligence and a signal transmitterreceiver The transmitter in the simplest case is just a beacon Figure 2 represents basic hardware model of a positioning system

Figure 2 Block diagram representing hardware module

First the location sensing devices detect the signals transmitted by or received at known reference points using radio sensing technologies The sensing technique is based on signal strength level that converts the sensed signal into RSS Given a set of reference points the relative position of the MS can be derived from the signal characteristics of RSS The signal characteristics of RSS are unique for a location Then the microcontroller processes the position algorithm and estimates the position of object and displays it on PC

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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22 Classification of Indoor positioning systems Indoor positioning system can be categorized based on the stimulus used in particular application and measurement principle

221 Classification based on Stimulus Based on the stimulus used in an application WSN inherit certain characteristics and limitations on sensorrsquos signal The propagation delay diffraction reflection and scattering are the basic signal characteristics which affect all signal types The effective range available bandwidth regulatory constraints interference power constraints safety and cost of technology limitations The wireless signals commonly used for indoor positioning systems are infrared radio frequency and ultrasound Brief descriptions of the three major technologies are as follows

Infrared Infrared signal is an electromagnetic signal of a wavelength longer than that of visible light but shorter than that of radio waves Infrared is just below the visible spectrum of light in frequency and is radiated strongly by hot bodies Many objects such as people vehicle engines and aircraft generate and retain heat and as such are especially visible in the infrared wavelengths of light compared to objects in the background It cannot pass through walls or obstructions therefore it has a rather limited range of indoor environments Thus it requires a more sophisticated circuitry than ultrasound signals Indoor lighting interferes with this type of signal and causes problems in accurate sensing It generally has a range of around 5 m The infrared devices are usually small in size compared to ultrasound devices

Radio frequency The RF signal can penetrate through most of the building material therefore it has an excellent range in indoor environments The propagation speed is also high approximately 3108 ms There are unlicensed frequencies available freely for use This type of signal has the longest range compared to infrared and ultrasound Since the propagation waves are of higher frequency (24GHz) interference with other frequency components is less

Ultrasound Although ultrasound operates at low frequency bands (typical 40 kHz) compared to the other two signaling technologies it possesses a good precision for location sensing at a slow propagation speed of sound (343 ms) The advantages of ultrasound devices are their simplicity and that they are inexpensive However ultrasound does not penetrate walls

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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but reflects off most of the indoor obstructions It has a short range around 3 m to 10 m but has a 1 cm resolution of distance measurement The operating temperature influences the performance of ultrasound

222 Classification based on measurement principle Wireless positioning systems can be categorized by measurement techniques which are used to derive the position of mobile stations Mainly three different measurement principles are used today AOA RSS and propagation‐time based systems that can further be divided into two different sub‐classes TOA and TDOA Figure 3 shows taxonomy based on the technology and the technique classifications

Figure 3 Taxonomy of positioning system [7]

In AOA systems the position is calculated via goniometry With the use of directional antennas or antenna arrays the angle or bearing relative to points located at known positions is measured The intersection of several measured directions pointers the yields the position value The accuracy of this approach is limited by the possible directivity of the measuring aperture by shadowing and by multi‐path reflections arriving from misleading directions In TOA and TDOA techniques rely on the precision of timing between the signal transmitter and the receiver in order to use the propagation delay or TOF to calculate the distance between transmitter and receiver Therefore a precise synchronization is also very important in such systems By combining at least three distances from three reference positions triangulation can be used to estimate the mobile stationʹs location This type of technique will

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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11

require a high accuracy clock in the communication system TDOA is more practical [1] Examples of location‐sensing systems that use time of flight are GPS the Active Bats [15] and the Cricket [16] Besides these time delay‐based techniques the distance between transmitter and receiver can also be determined from signal strength attenuation and direct distance measurement (such as dead reckoning) Fingerprinting or Location Pattern Matching this technique generally requires only measurement of RSS or other non‐geometric features at several locations to form a database of location fingerprints To estimate the mobile location the system needs to first measure the received signal strength at particular locations and then search for the pattern or fingerprint with the closest match in the database This technique does not require the mobile station to see at least three base stations or access points in order to determine the location The disadvantage of this technique is that it is very time‐consuming to perform an exhaustive data collection for a wide area network such as in outdoor positioning systems

23 Indoor local positioning using ZigBee based on RSSI This section reviews relevant RF‐based indoor positioning systems which can be used to locate stationary objects and track mobile users The impressive growth of IEEE 802154 wireless PANs (WPAN) in recent years suggests an interesting future for the location fingerprinting technique The system is quite flexible because system designers can select whether to have a centralized positioning server or let the mobile to determine its own position However the fingerprinting technique requires a training phase (off‐line phase) to collect location fingerprints for all positions in the operating area before the actual deployment (on‐line phase) [7] The following discussion is divided according to the positioning system components First the common form of location fingerprinting and its relationship with physical locations are explained Finally location estimation algorithm is reviewed

231 Location Fingerprint A location fingerprint based on RF characteristics such as RSS is the basis for representing a unique location It is created under the assumption that each position or location inside a building has unique RF signature [4] Generally a fingerprint F is labelled with a location information L The location fingerprints and their labels (eg location information) are maintained in

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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database and used during the on‐line phase to estimate the location The label and fingerprint are usually denoted as a tuple of (L F) Battiti et al [11] point out that the location information L for indoor location can be recorded in two forms as either a tuple of coordinates or an indicator variable The tuple of real coordinates can vary from one dimension to five dimensions which include the three dimension space and two orientation variables expressed in spherical coordinates For instance a location information of a two‐dimension system with an orientation could be expressed as a triplet L = (x y d) | x y є R 2 d є North East South West The RSS was found to be more location‐dependent than the SNR because the noise component is rather random in nature However the RSS itself fluctuates over time for each access point and location Each RSS element can be considered as a random variable therefore it can be captured by recording its descriptive statistics parameters approximating its distribution or maintaining the whole measurement dataset These approaches of RSS representation result in different procedures for location estimation algorithm Regardless of the approach the location fingerprint is usually denoted as an array or vector of signal strength (random variables) received at any position in the location‐based service area The size of the vector is determined by the number of access points that can be heard To create a basis fingerprint such as in a number of samples of vectors of signal strength are collected over a window of time for each position This basis is called a prototype Then the average RSS of each access point is calculated and recorded as an element in the location fingerprint For an area that can receive signals from N access points the location fingerprint can be expressed as a vector of average RSS elements βi

F = (β1 β2hellip βN)T Besides the basis location fingerprint the samples of location fingerprint measured during the on‐line phase are also important for the system that tracks the mobile object The number of samples should be selected appropriately to represent the location fingerprint for the mobile application A step in statistical analysis method called pre‐processing is another important issue needed to be considered because it can impact the estimation of dependency between location fingerprint and the location information easier The pre‐processing refers to a step that cleans the raw data (in this case the training set) before any further operations or analysis The cleaning may consist of encoding dimensionality reduction (reduce unnecessary elements) feature extractionselection clustering and outlier elimination Roos et al [10] point out that the pre‐processing enables faster location estimation and

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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reduce the noise from the training data The process that creates the basis of location fingerprint discussed above could be considered as a part of pre‐processing

232 Location Estimation Algorithm Location estimation algorithms or positioning algorithms are procedures that exploit dependency between location information and location fingerprint basis in order to determine a position or location from samples of RSS signals Position algorithms can be classified into deterministic and probabilistic types based on the approaches that model the relationship between location fingerprints and location information The deterministic type of algorithms are those that based in the nearest neighbour classifiers and neural network classifiers The probabilistic type of algorithms is those that are based on the Bayesian inference and statistical learning theory such as support vector machines (SVR) The algorithm used in this project is discussed briefly

Euclidean distance method The Euclidean distance method is a deterministic approach of position estimation because they require only a set of constant location fingerprints which includes mean vectors In order to determine the location a form of discriminate function is used to classify a sample of RSS fingerprint into a position The mean or average RSS vector is a centre of a mass which represents each class of location fingerprint Suppose a set of l location fingerprints is denoted by F1 F2hellip Fl exist and each fingerprint has a one‐to‐one mapping to a set of positions L1 L2hellip Ll A sample of an RSS fingerprint measured during an on‐line phase is denoted as S which can be another mean or average RSS vector of small window of RSS samples Assuming that an indoor positioning system only considers the average RSS from N access points as a location fingerprint the sample of RSS vector is S = (s1 s2 helliphellip sN)T and each location fingerprint i in the database can be expressed as Fi = (β1 β2helliphellip βN)T The simplest closeness metric is a distance measurement in signal space denoted as the Dist () function [6] Thus the simple procedure of nearest neighbour algorithm is expressed as picking the fingerprint j that has the shortest signal distance

Dist(S Fj) le Dist(S Fk) for all k ne j A generalized weighted distance Lp summarized by [12] can be used to calculate different forms of distance in signal space as

Lp = 1N pN

i

pPiSiWi 1

1)||1(sum

=

minus

where N is the number of access points wi is a weighting factor (wi le 1) and p is the norm parameter starting from 1 The weighting factor wi is a bias

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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parameter that can demote or promote an important RSS component in the fingerprint

24 Protocols and Industry standards for WSN Although many applications for WSN are proposed the communication protocols supporting them remain mainly unexplored and diverged This is because the area of interest is new each implementation is application specific and the components of WSNrsquos have a lot of constraints Wireless sensor networks use the wireless medium to communicate However the traditional communication protocols that support wireless communications especially ad‐hoc mesh networks may not be well suited for them WSN communication is an active research area and many algorithms and protocols have already been proposed This section introduces two proposed standards the IEEE 802154 and the ZigBee The IEEE 802154 covers the physical layer and the MAC layer of low‐rate WPAN The ZigBee is ldquoan emerging standard that is based on the IEEE 802154 and adds network construction (star networks peer‐to‐peermesh networks and cluster‐tree networks) application services and morerdquo

241 IEEE 802154 Holger and Willig (2005) [17] mention home automation home networking and home security as possible applications for IEEE 802154 They add that ldquomost of these applications require only low‐to‐medium bitrates (up to some few hundreds of kbps) moderate average delays without too stringent delay guarantees and for certain nodes it is highly desirable to reduce the energy consumption to a minimumrdquo The IEEE 802154 standard (2003) defines the device types that can be used in a LR‐WPAN A device can be a FFD or a RFD The RFD can be used in simple applications in which they do not need to transmit large amounts of data and they have to communicate only with a specific FFD The FFD can work as a PAN coordinator as a coordinator or as a simple device It can communicate with either another FFD or a RFD In keeping with the application requirements the LR‐WPAN operates in a star or peer‐to‐peer topology (figure 4) In the star topology the RFD communicates with a single controller the PAN coordinator The PAN coordinator can perform the same function as the RFD but it is also responsible for controlling the PAN ldquoit initiates terminates or routes communication around the networkrdquo [18] The peer‐to‐peer topology

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supports ad‐hoc mesh multi‐hop networking Any device in the peer‐to‐peer topology can communicate with any other device within its communication range however this topology also has a PAN coordinator All the devices in a LR‐WPAN have a unique 64‐bit address This or a short address allocated by the PAN coordinator can be used inside a PAN Additionally each PAN has a unique identifier The combination of the PAN identifier and the sort addresses allows communication across different PANs (IEEE 802154 Standard 2003)

Figure 4 Star and peer‐to‐peer topologies in LR‐WPAN [18]

The LR‐WPAN based on the OSI seven‐layer model has the layered architecture presented in figure 5 The application and the network layer are the upper layers in the LR‐WPAN architecture but are outside the scope of the IEEE 802154 standard Only the physical layer ldquowhich contains the RF transceiver along with its low‐level control mechanismrdquo and the MAC layer ldquothat provides access to the physical channelrdquo are included in the standard and will be introduced in the following sections Finally the Type I 8022 LLC and the SSCS are intermediate sub layers supporting communication with the above layers (IEEE 802154 Standard 2003)

Physical Layer The 802154 standard specifies two different services that the PHY provides The PHY data service controls the radio and thus the transmission and reception of the PPDUs In addition the management service performs ED in the channel The management service also performs CCA before sending the messages and provides LQI for the received packets

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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Figure 5 WPAN architecture [18]

Three different bands are defined by the standard The 868‐8686 MHz for Europe the 902‐928 MHz for North America and the 2400‐24835 MHz worldwide All of them belong in the ISM radio bands and they are using DSSS Each of the bands supports different a data rate and it uses a different modulation technique chip rate and number of channels Moreover if the system does not use the 2450 MHz frequency it operates in both the 802 MHz and 902 MHz frequencies Table 3 summarizes the supported bands and data rates (IEEE 802154 standard 2003)

PHY (MHz)

Frequency Band (MHz) Modulation

Bit Rate Number of Channels

868-8686 BPSK 20 1 868 915 902-928 BPSK 40 10

2450 2400-24835 O-QPSK 230 16 Table 3 Frequency bands and data rates for IEEE 802154 based on the IEEE

802154 standard [18] The physical layer uses PPDU packets to communicate Table 4 demonstrates its structure The LSB is always transmitted and received first The SHR contains the preamble and the SFD fields which helps receiver

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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synchronization The 8‐bytes preamble contains only zero and is used for synchronization The SFD contains a specific sequence of one and zeros and specifies the beginning of the frame The PHR contains the payload length Packets with a length of 9 or more bytes are MPDU as the next section further explains Packets with length 5 are used for MPDU acknowledgements The payload part of the PPDU encloses the MAC layer packet Finally the PPDU size can be up to 127 bytes [18]

4 bytes 1 byte 1 byte Variable

Preamble SFD Frame length

(7 bits) Reserved

(1 bit) Payload (PSU)

SHR PHR PHY Payload Table 4 Format based on the IEEE 802154 standard [18]

MAC Layer The MAC layer is the interface between the SSCS and the PHY layer Similar to the PHY layer the MAC layer supports two services The MAC data service is responsible for the transmission and reception of the MPDUs through the PHY data service The MAC management service if the device is a coordinator manages the network beacons It is also responsible for PAN association and disassociation frame validation and acknowledgment providing ldquoa reliable link between two peer MAC entitiesrdquo In addition it uses the CSMA‐CA for channel access and handles and maintains the GTS mechanism Finally it supports device security [18] The IEEE 802154 standard defines four different frame types the beacon data acknowledgment and MAC command frame All frame types are based on the general MAC frame format (table 5) The frame control field describes and specifies the above different frame types Every MAC frame comprises a MHR which consists of a frame control sequence number and the information field It also contains the MAC payload different frame types have different MAC payload fields The acknowledgment type does not have a payload Finally each frame includes a MFR which contains a FCS The data in the MPDU follows the same order as the PPDU the least significant bits are left in the frame and are transited first Octets2 1 02 028 02 028 Variable 2

Destination PAN Identifier

Destination Address

Source PAN

Identifier Source Address Frame

Control Sequence Number Addressing Field

Frame Payload FCS

MHR MAC

Payload MFRTable 5 General MAC frame format IEEE 802154 standard [18]

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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Table 6 7 8 and 9 present the four different MAC frame types The beacon frame is transmitted periodically by the PAN coordinator It provides information about the network management through the super frame and GTS fields which are analyzed later in the section It also synchronizes the network devices and indicates the proper communication period for them The data frame payload encapsulates data from the higher layers When a device receives a packet it is not obliged to response with an acknowledgement packet Finally the command frame identifier and command payload fields of the command frame are used for communication between the network devices The command identifier specifies actions like association disassociation and data GTS or beacon request Octets2 1 410 2 Variable Variable Variable 2

Frame Control

Sequence Number

Addressing Fields

Super frame Specification

GTS fields

Pending Addressing

Fields Beacon Payload FCS

MHR MAC Payload MFRTable 6 Beacon frame format [18]

Octets 2 1 Variable 2

Frame Control

Sequence Number

Addressing Field

Data Payload FCS

MHR MAC

Payload MFR Table 7 Data frame format [18]

Octets 2 1 2

Frame Control

Sequence Number FCS

MHR MFR Table 8 Acknowledgement frame format [18]

Octets2 1 1 Variable 2

Frame Control

Sequence Number

Addressing Fields

Command Frame Identifier

Command Payload FCS

MHR MAC Payload MFR Table 9 Command frame format [18]

In the LR‐WPAN every PAN has its own coordinator The PAN coordinator manages the communication in the local area it has two options to use or not use the super frame structure The super frame (figure 6) uses network beacons If the coordinator does not want to use a super frame structure it suspends the beacon transmission However the beacon is important for device association and disassociation If the coordinator wishes to maintain close communication control in the PAN and to support low‐latency devices it usually uses the super frame A super frame determines a specific time

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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period beacons bound it The beacon is transmitted in the first of the sixteen equal time slots that the super frame has It is used to ldquosynchronize the attached devices to identify the PAN and to describe the structure of the super framerdquo The super frame can have active and inactive periods All the communications have to be finished inside the super frame period In the inactive periods the devices can switch to the sleep mode but they have to be ready for the next beacon The active period is further divided into a CAP and a CFP During the CAP any device can communicate competing with the other devices in the PAN using slotted CSMA‐CA The CFP contains GTSs and follows the CAP A CFP may maintain up to seven GTSs and each GTS can reserve more than one time slot However the CAP should always be sufficient to allow new devices t join the PAN The GTSs are allocated to specific devices All the transactions must be completed inside the assigned time period (CAP GTS and CFP) (IEEE 802154 standard 2003)

Figure 6 Example of super frame structure [18]

In accordance with the 802154 standard (2003) three different types of data‐transfer exist In addition the types differ if the coordinator uses or does not beacons Data transfer from a device to the PAN coordinator is the first type (figure 7) For a ldquonon‐beacon‐enable networkrdquo it first senses the medium by using ldquoun‐slotted CSMA‐CArdquo and then a simple transmit to the data frame In a ldquobeacon‐enabled networkrdquo the sender waits for the beacon when it finds it ldquothe device synchronizes to the super fame structurerdquo In the specified time frame the sender again senses the medium by using ldquoslotted CSMA‐CArdquo and transmits the data to the coordinator In either case the coordinator has the option to acknowledge or not acknowledge the data reception after that the transaction is completed Data transfer from the coordinator is the next type described in the standard (figure 8) In a beacon‐enabled network the coordinator indicates a pending message through the beacon The messagersquos target device receives the beacon and if the message is pending it responses with a MAC command request

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

20

message using slotted CSMA‐CA the coordinator may or may not acknowledge the command message and through slotted CSMA‐CA sends the pending message The device acknowledges the received message After that the coordinator removes the message from the beaconrsquos pending list and completes the transaction In a non‐beacon enable network the device periodically sends a MAC command‐request frame to the coordinator The coordinator acknowledges the data request Then by using un‐slotted CSMA‐CA if it has a pending message it transmits it If it has not it respond with a data frame with a ldquozero‐length payloadrdquo To complete the transaction the device acknowledges the data reception

Figure 7 Communication from a device to a PAN coordinator in (a) a beacon‐enabled network and (b) a non‐beacon enabled network [18]

Peer‐to‐peer is the last type of data transfer In this situation the devices are free to communicate with any other device within their communication range In a peer‐to‐peer PAN the devices can ldquoeither receive constantly or synchronize with each otherrdquo If they are receiving constantly to transmit data they use un‐slotted CSAM‐CA In the second case synchronization must be achieved first (IEEE 802154 standard [IEEE 2003]) The IEEE 802154 (2003) standard establishes MAC and PHY standards for low‐cost low‐power and high‐density node deployments In addition to the above PHY and MAC characteristics IEEE 802154 provides a security baseline including ldquothe ability to maintain an ACL and use symmetric cryptographyrdquo for data encryption The algorithm that is used for encryption is the AES However the higher level layers decide when security is need The upper layers are in general responsible for device authentication and key management The next section introduces the ZigBee standard which

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

21

encapsulates the IEEE 802154 and provides additional standardization for the higher levels

Figure 8 Communication from a PAN coordinator to a device in (a) a beacon‐enabled network and (b) a non‐beacon enabled network [18]

242 ZigBee Heily (2004) defines ZigBee as ldquoa rapidly growing worldwide non‐profit industry consortiumrdquo whose mission is ldquoto define a reliable cost‐effective low‐power wirelessly networked monitoring and control product based on an open global standardrdquo Figure 9 illustrates the areas of interest for different wireless communication standards

Figure 9 Overview of the coverage for different wireless communication

standards [17]

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ZigBee a new standard which became publicly available in June 2005 is based on the IEEE 802154 standard It expands the IEEE 802154 by adding the framework for ldquothe network security and applicationrdquo (Craig 2005) Figure 10 presents the IEEE 802154ZigBee stack and the areas of responsibilities

Figure 10 IEEE 802154ZigBee stack [20]

Craig (2005) mentions three networking topologies that the standard covers the star mesh and cluster tree (figure 11) The ZigBee standard works on top of the IEEE 802154 addressing schema by using the standard 64‐bit and the short 16‐bit addressing Kinney (2005) summarizes the ZigBee network layer responsibilities the successful establishment of a new the network and successful new device configuration addressing assignment network synchronization frames security and message routing ZigBee further distinguishes the concept of the physical devices (RFD FFD) by using the notion of ldquological devicesrdquo ldquoZigBee Coordinatorrdquo is the first type of logical devices It is responsible for initializing maintaining and managing the network Under the coordinator in the network hierarchy is the ldquoZigBee routerrdquo which is responsible for controlling the message routing between the nodes Finally the ldquoZigBee End Devicerdquo acts as the end point of the network

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2 LITERATURE REVIEW10102006

23

structure The tasks that an end device can perform are specified in the ldquoApplication Profilesrdquo (Craig 2005)

Figure 11 IEEE 802154ZigBee stack [17]

The ZigBee specifications (2005) summarize the security services provided by ZigBee ldquokey establishment key transport frame protection and device managementrdquo ZigBee builds its security mechanism using symmetric key cryptography The security services also depend on the associated layer thus as the figure 10 shows the security mechanism covers the network and the application layer In addition if a MAC frame needs security protection the MAC layer is able to secure it Moreover the notion of end‐to‐end security is supported the source and destination devices have access and use the same share key In the MAC layer the 802154 AES mechanism provides the proper security The mechanism protects ldquothe confidentiality integrity and authenticity of the MAC framesrdquo (Kinney 2005) An auxiliary header field in front of the MAC payload indicates if the frame is encrypted or not The MAC framesrsquo integrity is supported by calculating and using a MIC at the end of the MAC payload In addition to the AES nonce is used to provide MAC confidentiality and authenticity Figure 12 illustrates a MAC frame with security For different security aspects the MAC layer uses different mode of the AES for the encryption it uses the AES in Counter (CTR) mode and for the integrity the

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

24

CBC‐MAC Finally the combination (CCM) of the above two modes is available providing both encryption and integrity (Kinney 2005)

Figure 12 ZigBee secure frame in MAC layer [17]

In the network layer the CCM (a modified MAC layer CCM mode) is used for encryption Because the network layer uses only the CCM mode a single key is used for all different security options The network layer security message format is similar to the MAC frame it is presented in the figure 13 Finally although the network layer is responsible for securing its layer messages the above layers specify the keys and the CCM option for each frame (Kinney 2005)

Figure 13 ZigBee secure frame in network layer [17]

Security in the application layer works similar to the network and MAC layers It uses the ldquolink keyrdquo or the ldquonetwork keyrdquo to secure the message and then encapsulate it inside a set of fields similar to the network format (figure 14) Other security responsibilities that the application layer has are to provide the ZDO and the applications with device management services key establishment and key transport (ZigBee specifications 2005)

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2 LITERATURE REVIEW10102006

25

Figure 14 ZigBee secure frame in application layer [17]

The ZigBee application layer contains the manufacturer‐defined application objects the ZDO and the application sub‐layer In addition to the security responsibilities the application sub‐layer binds devices based on their duties and needs maintains the binding tables and forwards messages between them The application sub‐layer also discovers the neighbour devices for a given device The ZDO is responsible for determining the devicersquos duty in the network for communicating using binding requests and for supporting security as was mentioned above The sub‐layer that implements the actual application is the manufacturer‐defined application object (Kinney 2005) The ZigBee specifications are a great step toward the wireless sensors networks standardization They cover all the OSI layers from the physical to the application providing guidance to the developers The specifications are the product of the ZigBee alliances a consortium of companies and academic institutes Thus they not only produce the specifications but also are the first use them The next section goes beyond the layer architecture and standardization and introduces the OS field another crucial element of WSNs

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

26

3 RECEIVED SIGNAL STRENGTH (RSS) Majority of the existing methods leverage the existence of IEEE 80211 base stations with powerful radio transmit powers of approximately 100mW per base station Such radios are in a different class from the low power IEEE 802154 compliant radios that typically transmit at low power levels ranging from 52mW to 29mW [19] The wide availability of larger number of IEEE 802154 radios in the research community has revived the interest for signal strength based localization in sensor network Despite of rapidly increasing popularity of IEEE 802154 radios and signal strength localization there is a lack of detailed characterization of the fundamental factors contributing to large signal strength variation This chapter investigates some of the properties of RSS observed during testing and analysis of research work collected during the period of thesis The analysis of RSS values is needed to understand the underlying features of location dependent RSS patterns and location fingerprints An understanding of the properties of the RSS values for location fingerprinting can assist in improving the design of positioning algorithms and in deployment of indoor positioning systems

31 Parameters that affect RSS Different factors that affect the indoor RSS are the userrsquos presenceabsence hisher orientation time of the day building type and material distance from transmitter and type of radio These factors are by no means exhaustive Other possible factors are antenna orientation directionality and type Table 10 classify the parameters which affect RSS [7]

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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10102006

27

Effect on Factors Options

1 Proximity of user Userrsquos presence or absence

2 Orientation of user and terminal

North East South West

Data Collection

3 Make of Radio 4 Time of measurement Time of day amp days of

week 5 Period of measurement

Second minute hour

7 Interference Co‐channel adjacent co‐channel

Statistics

8 Building environment Small offices or large hall

Table 10 Classification of factors the affect RSS [7]

Transmitter variability Different transmitters behave differently even when they are configured exactly in the same way In practice this means that when a transmitter is configured to send packets at a power level of d dBm then the transmitter will send these packets at a power level that is very close to d dBm but not necessarily exactly equal to d dBm This can alter the received signal strength indication and thus it can lead to inaccurate distance estimation

Receiver variability The sensitivity of the receivers across different radio chips is different In practice this means that the RSSI value recorded at different receivers can be different even when all the other parameters that affect the received signal strength are kept constant

Antenna orientation Each antenna has its own radiation pattern that is not uniform In practice this means that the RSSI value recorded at the receiver for a given pair of communicating nodes and for a given distance between them varies as the pair wise antenna orientations of the transmitter and the receiver are changed

Multi‐path fading and shadowing in the RF channel In indoor environments the transmitted signals get reflected after hitting on the walls andor on other objects in the room such as furniture Both the original signal and the reflected signal reach the receiver almost at the same time since they both travel at the speed of light As a result of this the receiver is not able to

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

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28

distinguish the two signals and it measures the received signal strength for both of them

32 Related work analysis A number of indoor location tracking systems have been proposed in the literature based on RF signals ultrasound infrared and some combinations of these As mentioned previously RF based positioning system are widely studied as shown in Table 2 Given a model of radio signal propagation in a building or other environment RSS can be used to estimate the distance between the transmitter and receiver Most of the algorithms (Table 2) have been evaluated in practice using 80211b compliant wireless radios and seem to produce a location error of 15ft to 20ft for an indoor network area of approximately 225ft x 144ft According to the results of this study all the localization techniques produce approximately the same location error over a range of environments Ecolocation [22] Mote Track [5] are two of the few localization algorithms that have been evaluated on a real sensor network that uses a low power wireless radio Ecolocation reports a location error of 10ft for a very small outdoor network deployment area (26ft x 49ft) Mote Track reports a location error of approximately 13ft for an indoor network deployment area of 18751ft2 In general the RSSI based localization algorithms can be divided into two broad categories the map based and the distance (or area) prediction based algorithms In the case of the map based algorithms the deployment area is divided into a large set of sample points that cover the whole area Most techniques collect one or more signal strength measurements from all visible beacon nodes and for each sample point Each sample point is then mapped to either a signal strength vector or a signal strength probability distribution This process generates an RSSI fingerprint map which is unique with respect to the beacon locations and the deployment area This fingerprint map is stored to a central base station A node that is not aware of its location collects one or more signal strength measurements from all the visible beacon nodes and creates its own RSSI fingerprint that is sent to the central base station The base station computes nodersquos location as the sample pointrsquos location whose RSSI fingerprint distance from the unknown nodersquos RSSI fingerprint is the minimum among all the sample points Despite the fact that the map based RSSI localization methods produce relatively small location errors they are not attractive mainly because they require an excessive amount of profiling data and because they are not fully distributed The detailed profiling of a building is a major holdback because it is complex time consuming and depends on beacon locations Therefore for different buildings or even for the

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

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same building and different beacon locations different RSSI maps have to be obtained The second category of RSSI based algorithms are the distance (or area) prediction algorithms which focus on minimizing the amount of profiling data In distance prediction based methods raw RSSI data are directly mapped to distances through a signal propagation model In some cases distances are not explicitly measured but they are taken into consideration through assumptions on the underlying distributions In the case of outdoor deployments where there are no obstacles a single signal propagation model is all the profiling data required In the case of indoor deployments the same applies but now the signal propagation model might be different across different buildings with different furniture and wall arrangements Given an underlying signal propagation model raw RSSI data can be mapped to specific distances These distances can be either used directly in order to perform localization or assigned a probability and used in a learning basedprobabilistic localization algorithm Obviously the accuracy of the distance prediction based methods depends heavily on the accuracy of the signal propagation model used for translating RSSI values to distances

33 Properties of RSSI Changes in signal due to propagation indoors are difficult to predict because of dense environment and propagation effects such as reflection diffraction and scattering The multi‐path fading effect which is the result of either constructive or destructive combination of multiple signal copies at the receiver causes the received signal to fluctuate around a mean value at a particular location The received signal is usually modelled by the combined effects of large‐scale fading and small‐scale fading The large‐scale fading component describes the signal attenuation as the signal travels over a distance and is absorbed by material such as walls and floors along the way to the receiver This component predicts the mean of the RSS and usually has a lognormal distribution On the other hand the small scale fading component explains the dramatic fluctuation of the signal due to multi‐path fading On the other hand the small scale‐fading component explains the dramatic fluctuation of the signal due to multi‐path fading If there is no LOS component the small‐scale‐fading is often modelled with a Rayleigh distribution Such scenarios are commonly called NLOS If there is a LOS component the small‐scale‐fading is typically modelled by a Rician distribution Note that when the RSS is measured the measurement averages out the small‐scale‐fading effects

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4 OBJECT TRACKING 41 Introduction This chapter presents the object‐tracking application the main objective of this thesis work The application is an algorithmic‐software implementation written in Embedded C which is capable of receiving data messages and generating object position estimation data The implementation of the object‐tracking application makes use of the SENTIO platform and uses the background knowledge introduced in the preceding chapters The motivation for the object‐tracking application is to determine the position of an object in indoor environments (Eg Home) The application acts as an interface which collects the raw data that the sensors generate (numerical values) It makes the proper comparisons and algorithmic processes to identify and track the object while it is static inside the area covered by the wireless sensor network Finally with the detection and tracking data it alerts the user or another system The following sections provide an overview of the object‐tracking application

42 Overview of hardware product Before describing the object tracking application it is important to know the characteristics of hardware product The SENTIO platform (figure 15) is built in modular fashion and has the following key features [8]

Flexibility This platform is very flexible in such a way that it is made easy to add or change the hardware for each sensor task depending on the requirement in the WSN For a specific platform this can be hard or sometimes even impossible to accomplish

Sensor resources Resources in a sensor module includes the ability of usage of controllerrsquos resources as much as possible such as ADC‐channels interrupts and digital Input‐Outputrsquos Local voltage regulation should also be possible since many sensors require higher supply voltages than digital Integrated Circuits do Further support of a shut‐down mechanism to power down the sensor modules when not needed

Computation and memory The processor module based on microcontroller should support many devices in a microcontroller family and support different power supply voltages enabling full range of clock frequencies All power‐down modes of the devices should be

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supported The platform should support different processor modules including FPGArsquos and very simple microcontrollers

Communication The focus should be on enabling an IEEE 802154 compliant communication interface With or without ZigBee stack this will provide a flexible platform for exploring different network topologies Support for power‐down of the entire communication module should be supported

Cost and form factor High flexibility is in conflict with low cost and small size The primary objective should be flexibility but the size should be considered in order to enable sensor device integration in systems where space is limited

Figure 15 SENTIO Platform [8]

The communication subsystem of the SENTIO sensor node was designed so that the correct operation of the radio chip is ensured The radio chip is powered by the on‐board voltage regulator and thus fluctuations in the battery voltage level do not affect the operation of the communication subsystem This platform uses Chipcon CC2420 IEEE 802154 radio transceiver operates in the 24GHz ISM band and includes a digital direct sequence spread spectrum (DSSS) modem providing a spreading gain of 9dB and an effective data rate of 250Kbps It was specifically designed for low power wireless applications and supports 8 discrete power levels 0dBm minus1dBm minus3dBm minus5dBm minus7dBm minus10dBm minus15dBm and minus25dBm at which its power consumption varies from 29mW to 52mW [21] A built‐in received signal strength indicator gives an 8‐bit digital value RSSIVAL The RSSIVAL is always averaged over 8 symbol periods (128μs) and a status bit indicates when the RSSIVAL is valid (meaning that the receiver was enabled for at least 8 symbol periods) The

Antenna

Communication Module

Processing Module

Sensing Module

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power P at the RF pins can be obtained directly from RSSIVAL using the following equation

P = RSSIVAL + RSSIOFFSET [dBm] where the RSSIOFFSET is found empirically from the front‐end gain and it is approximately equal to minus45dBm In the next sections when we refer to the RSSI value we refer to the RSSIVAL and not the power P unless otherwise stated

43 Experimental Testbed The experimental test bed for object tracking application is as shown in figure 16 Experiment was conducted in Masterrsquos room at Mid Sweden University A DELL laptop is connected to SENTIO platform and software were primarily used to collect samples of RSS from APrsquos The software monitors IEEE 802154 radio frequency channels which operate in the 24GHz band

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

1030

BC - 1030

AP 3 AP 1

AP 2

AP 4

BS

HEWLETT

PACKARD

Figure 16 Layout of Masterʹs Room

A three dimensional scalable testbed is used for indoor sensor network deployments The testbed illustrated in figure 16 is a 3‐D structure measuring 16m (L) x 12m (W) x 3m (H) and it is designed to host a large number of static nodes as shown in figure 16 Arrow marks in the above figure represent the location where fingerprints were taken The centrepiece of our infrastructure

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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is the SENTIO sensor node [8] an open‐source general purpose sensing platform designed with AT Mega 128 microprocessor and the IEEE 802154 compliant CC2420 radio from Chipcon [19]

44 Obtaining data from the sensor network The next important step in the development of the object tracking application is to obtain the data from the sensor network and send it to the base station From there the data is transmitted through a serial port to the computer that runs the object tracking application When the data arrives at the serial port the application must capture it This paragraph gives a brief explanation of how and what kind data is transmitted by the static object fixed sensors and how this data is being used to estimate the position of the object First the static object transmits Beacon packet continuously which is received by all the fixed APrsquos The beacon packet format is as shown in table 6 Then the fixed APrsquos calculates the 8‐bit value of RSSI from the transmitted beacon packet and then sends the resultant RSSI value as a form of data packet (table 7) to the BS which calculates the position and sends back to the object

45 Object tracking application Testing and Evaluation The purpose of this section is to discuss and evaluate the object tracking application Finally we are ready with the experimental testbed as shown in figure 16 As shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors placed at the corners of the masterrsquos room BS is placed at the centre of the room connected to a PC All the nodes are power connected before an actual measurement takes place At first the object is placed at random position inside the room and transmits beacon packets continuously Then these packets are received by the fixed APrsquos which calculate the RSSI from the packet and then averages it until it receives 50 beacon packets and then transmits the data to the BS (figure 17) Then the BS waits until it receives the data from all the APrsquos Once it receives from all the APrsquos it calculates the position of the object (figure 18) The application was developed to demonstrate a real world use of a WSN in object tracking In evaluating the final position obtained with respect to the actual position we conclude that the object‐tracking was successful Although

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

34

it is a research prototype object tracking application is a simple stable stand‐alone system capable of demonstrating an important application of WSN It takes raw data from the WSN aggregates it evaluates it and based on the evaluation tracks the object The accuracy of estimating the position is less than 5m Table 11 below shows a log of readings taken during test

Start

Intialize micro‐controller and transeciever in receieve mode

If received_packet=

Beacon

Average RSSI values

Increase the counter

If countergt1000050

A

No

Yes

B

B

Yes

No

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

35

A

Reset counter

Set transceiever in transmit mode

Transmit averaged RSSI to Main sensor and set it back to receive mode

10000 samples ‐ Offline

50 samples ‐ Online

Figure 17 Flow chart representing the algorithm of fixed sensors

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

36

Start

Intialize micro‐controller and transeciever in receieve mode

Received RSSI values from all fixed Sensors

Open radio map

Calculate Euclidean distance between obtained RSSI values from fixed sensors and fingerprints

in radio map

No

Yes

Bubble sort for least error in signal strength values

A

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

37

A

Get the location of the object from the radiousing the least error value

Display location of object on PC

End

Figure 18 Flow chart representing the algorithm of main sensor

Table 11 Accuracy of positioning system

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

38

5 RESULTS This chapter presents how the system parameters such as number of samples mean or median etchellipare finalized All the tests are performed in masterrsquos room at Mid Sweden University This chapter is divided into three subsections First in Section 51 it reviews results obtained from samples test Section 52 discusses one dimensional test results and finally section 53 discusses results of two dimensional tests

51 Measurement setup A Dell laptop was connected to a SENTIO platform and is loaded with software (Appendix 2) This setup was used to determine number of samples required during object tracking mean or median position estimation algorithm and one dimensional test The setup is as follows

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

103

0

BC - 1030

AP 1AP 2

BS

HEWLETT

PACKARD

Figure 19 Test bed for one dimensional test in Masters Room

Two SENTIO nodes (AP1 and AP2 as shown above) were separated by a distance of 10 meters and these sensors are known as fixed sensors which are fixed to the ceiling such that there antenna is faced towards the ground They are linear is position A BS was connected to Dell laptop and it was placed in such a way that its antenna was faced towards the ceiling Another SENTIO

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

39

node was used as an object and its orientation was same as BS Figure 19 represents the complete setup for object tracking application Mobile node was moved between these fixed ones at regular intervals of one meter and RSSI values were logged at each interval

52 Number of samples test This test was performed to determine number of samples required for accurate positioning and either mean of the obtained RSSI values or median of them Figure 19 represents the setup for number of samples test Once the setup is ready the measurements are taken as follows

1 An object was tagged with SENTIO node and was placed stationary linear to the fixed sensors

2 10000 samples (RSSI) were taken at regular intervals of 1 meter up to 10 meters

3 The sample values were logged at every interval 4 Using these logged samples I have plotted graphs such as distribution

of RSS values (figure 20 21) meanmedian at intervals of 1 meter using MATLAB

5 The mean and median for various numbers of samples is as shown in figure 22

Figure 20 Histogram plot of RSSI values at 1m and 4m

Figure 21 Histogram plot of RSSI values at 6m and 8m

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

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Figure 22 Distance vs RSSI

From the above graphs following conclusions were made 1 From the histogram plots we can observe that the distribution of RSSI

values follow Gaussians distribution law 2 As we can see from figure 22 that median curve has drastic changes

when compared to mean curve Hence mean is considered while estimating the position

3 From the observations we conclude that the number of samples does not matter since mean curve tends to be same for various numbers of samples

4 Hence 10000 samples are considered in order to get a proper location fingerprint during off‐line phase and during online phase 50 samples are enough to estimate the position of object

53 0ne dimensional test Measurement setup for one dimensional test is as shown in figure 19 This test was conducted to check the performance of algorithm and how accurate it was At first ie during offline phase fingerprints at each interval averaging 10000 samples were taken During online phase Euclidean algorithm was used estimate the position averaging 50 samples of RSSI From the results we

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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can realize that the error was less than 2 meters Table 12 shows the result of location estimation in one dimensional case

Table 12 One dimensional position estimation results

54 Two dimensional test This test is the final stage of my thesis work Experimental testbed for this stage is as shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors fixed at the corners of the room and a BS at the centre of the room There are 18 pre‐determined locations where the fingerprints (RSSI) were taken The fingerprints are as shown in Appendix 1 All the readings were taken in presence of userrsquos The algorithm for position estimation is shown in Appendix 2 Position estimation test is performed as follows

Tagged object is placed at random inside the masterrsquos room All the sensors including the base station and object are switched on Using the algorithm (Appendix 2) the BS estimates the position of the object and displays it on the PC

This test was performed using various numbers of samples to test which has a better accuracy Figure 23 represents the error rate using various numbers of samples From figure 23 we can conclude that 50 numbers of samples is enough to estimate the position of object with an accuracy of 3m

55 Causes of error in location determination The randomness of RSS is the major cause of error in any indoor

positioning system that uses the RSS for location inference If there is no randomness in the RSS and its location fingerprint every indoor positioning systems of this type will have no location detection error For example I have recorded RSS at one particular location during different times of the day From figures 24 and 25 you can observe that RSS values are different at different times of day

The performance of indoor positioning systems depends greatly on the separation of location fingerprints A location fingerprint

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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corresponding to a location can be identified correctly if it is difficult to classify it as another fingerprint by a pattern classifier

Path loss is one of the basic signal propagation properties of the RSS that best describes attenuation of transmit signal power over a distance between an access point and a mobile device

Presence of Userrsquos or userrsquos orientation affects RSS

Figure 23 Error Rate vs Number of samples

Figure 24 RSS distribution at 214 pm

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

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Figure 25 RSS distribution at 100pm and 1100 am

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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44

6 DISCUSSIONS 61 Summary and Conclusion While empirical results and performance studies of positioning systems based on location fingerprint have been presented in this report analytical model that can be used as a framework for efficiently designing the positioning systems are not available This thesis design work uses general principles of position estimation such as position estimation of sensors grid spacing etc This design work focuses on accuracy and precision The concept of location fingerprint technique based on RSS us explained in detail in chapter 2 It is found that RSS is random It is possible to approximate the RSS as being normally distributed While the RSS random process is non‐stationary the mean is more or less constant and could be used as the fingerprint of the location Model designed for position estimation is as shown in chapter 4 Accuracy and precision are the two major performance matrices in this study Uniform grid spacing has been used From the experimental analysis I have obtained an accuracy of 3 meters and 35 precision The sensitivity analysis among the system parameters and the performance matrices were done using the model as shown in chapter 4 For instance our study found that the minimum distance between two adjacent positions also affects the positioning performance This is due to the randomness of RSS patterns induced by the ever changing indoor environment

62 Future work The research in this thesis provides a ground work for the future study of efficient design of indoor positioning system The RSSI based position estimation still has limitations First the RSSI value is time dependent Thus the future study should address this issue Second the movement of object is not considered Third orientations of sensors affect the performance which should be taken care off Fourth to improve the performance of the system we need to find a solution to deal with those incomplete or missing data during the off‐line and the on‐line phase Fifth position estimation on multi‐floor and three‐dimensional coordinates is not available

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 110102006

45

APPENDIX 1

Location Fingerprints of Masters Room Time 1315 Observation Time 20 minutes Number of Samples 10000

People Presence SNo Co-ordinates AP1 (0x0001) AP2 (0x0002) AP3 (0x0004) AP4 (0x0008)

1 11 -24 -16 -27 -33 2 13 -26 -8 -34 -31 3 15 -43 -9 -39 -25 4 32 -21 -11 -27 -33 5 61 -6 -23 -32 -28 6 64 -20 -31 -25 -34 7 35 -23 -35 -29 -38 8 37 -29 -35 -22 -27 9 39 -38 -29 -21 -27 10 67 -27 -23 -20 -23 11 611 -26 -29 -23 -30 12 613 -35 -32 -2 -31 13 513 -34 -31 -20 -26 14 11 -39 -22 -29 -17 15 212 -37 -42 -28 -31 16 -212 -38 -33 -29 -6 17 -21 -42 -29 -33 -19 18 -38 -40 -23 -35 -22

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 210102006

46

APPENDIX 2

Begin

Open radio map and raw data files for write

Intialize radio map in memory

Check whether the

Packet is data or Beacon packet If data packet

Save the data to register

Check if data is

Received from all

sensors

Estimate the position of static

Display the position on PC

End Yes

No

A

A

Yes

No

Flow chart representing the main algorithm for position estimation

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

47

APPENDIX 3

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

48

Distribution of RSSI

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

49

REFERENCES [1] T S Rappaport J H Reed and B D Woerner ldquoPosition location using wireless communications on highways of the futureʺ IEEE Commun Mag vol 34 no 10 pp33‐41 Oct 1996 [2] A M Ladd K E Bekris G Marceau A Rudys L E Kavraki and D S Wallach rdquoRobotics‐based location sensing using wireless ethernetʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ02) 2002 pp 227‐238 [3] I Ramani R Bharadwaja and P V Rangan ldquoLocation tracking for media appliances in wireless home networksʺ in Proc International Conference on Multimedia and Expo (ICMEʹ03) July 2003 pp 769‐772 [4] K Pahlavan X Li and J P Makela ldquoIndoor geolocation science and technologyʺ IEEE Commun Mag vol 40 no 2 pp 112‐118 Feb 2002 [5] Konard Lorincz and Matt Welsh ldquoMote Track A Robust Decentralized Approach to RF‐Based Location Trackingrdquo [6] Paramvir Bahl and Venkata N Padmanabham ldquoRADAR An In‐Building RF‐based User Location and Tracking Systemrdquo [7] Kamol Kaemarungsi ldquoDesign of Indoor positioning system based on location fingerprint techiniquerdquo University of Pittsburgh 2005 [8] Fredrik Linnarsson Cheng Peng and Bengt Oelmann ldquoSENTIO A Hardware Platform for Rapid Prototyping of Wireless Sensor Networksrdquo [9] S Saha K Chaudhuri D Sanghi and P Bhagwat ldquoLocation determination of a mobile device using IEEE 80211b access point signalsʺ in Proc IEEE Wireless Communications and Networking Conference (WCNCʹ03) New Orleans LA Mar 2003 pp 1987‐1992 [10] T Roos P Myllymaki H Tirri P Misikangas and J Sievanen ldquoA probabilistic approach to WLAN user location estimationʺ International Journal of Wireless Information Networks vol 9 no 3 pp 155‐164 July 2002 [11] R Battiti M Brunato and A Villani ldquoStatistical learning theory for location fingerprinting in wireless LANrsquosʺ Technical Report Oct 2002 [Online] Available httprtmscienceunitnitraquobattitiarchive86pdf [12] P Prasithsangaree P Krishnamurthy and P K Chrysanthis ldquoAn indoor position location with wireless LANsʺ in Proc IEEE International Symposium on Personal Indoor and Mobile Radio Communications (PIMRCʹ02) Lisbon Portugal Sept 2002 [13] M A Youssef A Agrawala and A U Shankar ldquoWLAN location determination via clustering and probability distributionsʺ in Proc IEEE

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

50

International Conference on Pervasive Computing and Communications (PerComʹ03) Dallas‐Fort Worth TX Mar 23‐26 2003 pp 23‐26 [14] Z Xiang S Song J Chen H Wang J Huang and X Gao ldquoA wireless LAN‐based indoor positioning technologyʺ IBM Journal of Research and Development vol 48 no 56 pp 617‐626 SeptNov 2004 [15] R Want A Hopper V Falcao and J Gibbons ldquoThe active badge location systemʺACM Transactions on Information Systems vol 40 no 1 pp 91‐102 Jan 1992 [16] N B Priyantha A Chakraborty and H Balakrishnan ldquoThe cricket location‐support systemʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ00) Boston MA Aug 2000 pp 32‐43 [17] Holger K Willig A (2005) Protocols and Architectures for Wireless Sensor Networks London [18] IEEE (2003) Part 154 Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low‐Rate Wireless Personal Area Networks (LR‐WPANs) New York [19] Chipcon CC2420 802154 compliant radio httpwwwchipconcom [20] ZigBee Specification v10 ZigBee Alliance [21] Dimitrios Lymberopoulos Quentin Lindsey and Andreas Savvides ldquoAn Empirical Analysis of Radio Signal Strength Variability in IEEE 802154 Networks using Monopole Antennasrdquo [22] Kiran Yedavalli Bhaskar Krishnamachari Sharmila Ravula and Bhaskar Srinivasan ldquoEcolocation A sequence based technique for RF localization in wireless sensor networksrdquo Robert Bosch Research and Technology Centre Palo Alto CA

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

Table of Contents10102006

iv

Table of Contents Abstract ii Acknowledgements iii Table of Contents iv List of Tables vi List of Figures vii Terminology Notation viii 1 INTRODUCTION1 11 Introduction to the study 1 12 Background2 13 Objectives 3 14 Research Questions4 15 Scope 4 16 Methodology5 17 Outline 6

2 LITERATURE REVIEW8 21 Common components of indoor positioning system 8 22 Classification of Indoor positioning systems9 23 Indoor local positioning using ZigBee based on RSSI11 24 Protocols and Industry standards for WSN14

3 RECEIVED SIGNAL STRENGTH (RSS)26 31 Parameters that affect RSS 26 32 Related work analysis28 33 Properties of RSSI29

4 OBJECT TRACKING 30 41 Introduction 30 42 Overview of hardware product 30 43 Experimental Testbed32 44 Obtaining data from the sensor network 33 45 Object tracking application Testing and Evaluation33

5 RESULTS 38 51 Measurement setup 38 52 Number of samples test 39 53 0ne dimensional test 40 54 Two dimensional test 41 55 Causes of error in location determination41

6 DISCUSSIONS44 61 Summary and Conclusion 44 62 Future work 44

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

Table of Contents10102006

v

APPENDIX 1 45 APPENDIX 2 46 APPENDIX 3 47 REFERENCES 49

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

List of Tables10102006

vi

List of Tables Table 1 Comparison of Indoor Positioning Systems [7] 3 Table 2 Performance comparison of Indoor Positioning Systems [7] 3 Table 3 Frequency bands and data rates for IEEE 802154 based on the IEEE 802154 standard [18] 16 Table 4 Format based on the IEEE 802154 standard [18] 17 Table 5 General MAC frame format IEEE 802154 standard [18] 17 Table 6 Beacon frame format [18] 18 Table 7 Data frame format [18] 18 Table 8 Acknowledgement frame format [18] 18 Table 9 Command frame format [18] 18 Table 10 Classification of factors the affect RSS [7] 27 Table 11 Accuracy of positioning system 37 Table 12 One dimensional position estimation results 41

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

List of Figures10102006

vii

List of Figures Figure 1 Schema of thesis work 5 Figure 2 Block diagram representing hardware module 8 Figure 3 Taxonomy of positioning system [7] 10 Figure 4 Star and peer‐to‐peer topologies in LR‐WPAN [18] 15 Figure 5 WPAN architecture [18] 16 Figure 6 Example of super frame structure [18] 19 Figure 7 Communication from a device to a PAN coordinator in (a) a beacon‐enabled network and (b) a non‐beacon enabled network [18] 20 Figure 8 Communication from a PAN coordinator to a device in (a) a beacon‐enabled network and (b) a non‐beacon enabled network [18] 21 Figure 9 Overview of the coverage for different wireless communication standards [17] 21 Figure 10 IEEE 802154ZigBee stack [20] 22 Figure 11 IEEE 802154ZigBee stack [17] 23 Figure 12 ZigBee secure frame in MAC layer [17] 24 Figure 13 ZigBee secure frame in network layer [17] 24 Figure 14 ZigBee secure frame in application layer [17] 25 Figure 15 SENTIO Platform [8] 31 Figure 16 Layout of Masterʹs Room 32 Figure 17 Flow chart representing the algorithm of fixed sensors 35 Figure 18 Flow chart representing the algorithm of main sensor 37 Figure 19 Test bed for one dimensional test in Masters Room 38 Figure 20 Histogram plot of RSSI values at 1m and 4m 39 Figure 21 Histogram plot of RSSI values at 6m and 8m 39 Figure 22 Distance vs RSSI 40 Figure 23 Error Rate vs Number of samples 42 Figure 24 RSS distribution at 214 pm 42 Figure 25 RSS distribution at 100pm and 1100 am 43

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

Terminology Notation10102006

viii

Terminology Notation

GPS ‐ Global Positioning System

RSSI ‐ Received Signal Strength Indication

MS ‐ Mobile Station

RF ‐ Radio Frequency

WSN ‐ Wireless Sensor Network

AP ‐ Access Point

RSS ‐ Received Signal Strength

PC ‐ Personal Computer

WLAN ‐ Wireless local Area Network

SNR ‐ Signal to Noise Ratio

RFD ‐ Reduced‐Function Device

WPAN ‐ Wireless Personal Area Network

FFD ‐ Full Functional Device

MAC ‐ Medium Access Control

LR‐WPAN ‐ Low Rate WPAN

PAN ‐ Personal Area Network

OSI ‐ Open Systems Interconnection

LLC ‐ Logical Link Control

SSCS ‐ Service Specific Convergence Sub layer

PHY ‐ Physical Layer

PPDU ‐ PHY Protocol Data Unit

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

Terminology Notation10102006

ix

ED ‐ Energy Detection

CCA ‐ Clear Channel Assessment

LQI ‐ Link Quality Indication

ISM ‐ Industrial Scientific and Medical

DSSS ‐ Direct‐Sequence Spread Spectrum technique

LSB ‐ Least Significant Bit

SHR ‐ Synchronization Header

SFD ‐ Start of Frame Delimiter

PHR ‐ PHY Header

MPDU ‐ MAC Protocol Data Units

GTS ‐ Guaranteed Time Slot

MHR ‐ MAC Header

MFC ‐ MAC Footer

FCS ‐ Frame Check Sequence

CAP ‐ Contention Access Period

CFP ‐ Contention Free Period

ACL ‐ Access Control List

AES ‐ Advance Encryption Standard

MIC ‐ Message Integrity Code

CBC ‐ Cipher Block Chaining

ZDO ‐ ZigBee Device Objects

OS ‐ Operating System

LOS ‐ Line of Sight

NLOS ‐ Non Line of Sight

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

Terminology Notation10102006

x

FPGA ‐ Field Programmable Gate Array

BS ‐ Base Station

AOA ‐ Angle of Arrival

TDOA ‐ Time difference of arrival

TOA ‐ Time of Arrival

TOF ‐ Time of flight

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

1

1 INTRODUCTION 11 Introduction to the study Advancement in wireless sensing technologies has encouraged in developing a model for indoor object‐tracking application using low power IEEE 802154 compliant radios Stimulus used in this model is RSS This application is not only limited to object tracking in an indoor environment Concierge services enable users to become aware of nearest supporting facilities For example smart home applications such as multimedia appliances that forward multimedia stream to the nearest video screen can be achieved with a home positioning system [3] In the field of robotics a robot can navigate by itself using the assistance of indoor positioning system [2] Granularity of location information is most important in location determination applications For example to locate a book in a library requires coarse grained granularity whereas locating a user requires coarse grained granularity Position estimation refers to a process used to obtain location information of a MS with respect to a set of reference positions within a pre‐defined space A system deployed to determine or estimate the location of an entity is called a position location system or positioning system A wireless indoor positioning system refers to a wireless network infrastructure that provides indoor location information to any requesting end user One of the key technical elements allowing the realisation of this vision will be the knowledge of the accurate position of the personobjects Already today accurate location mechanisms such as GPS are commercially available Many more will become part of our daily life with the introduction of third generation mobile phones But none of these techniques will allow the meter or sub‐meter level of accuracy within buildings which is necessary for smart houses Hence this report introduces an object tracking application in an indoor environment using IEEE 802154 compliant radios which gives meter level accuracy First this chapter presents the background of indoor positioning systems identifies the challenges of such systems and briefly describes the scope of indoor positioning systems based on location fingerprints Next the objective of study the overview of approaches and the contributions are presented Finally the scope and organization of thesis work is outlined

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

2

12 Background The success of outdoor positioning and applications based on the GPS provides an incentive to the research and development of indoor positioning systems It has been developed over several decades and it relies on constellation of satellites Ranges to several satellites are used in multi‐lateration procedure to infer the position of the receiver This localization scheme requires hardware that is both expensive and consumes significant power As a result indoor positioning systems require alternative means to detect the MS location without relying on the direct signal from GPS satellites Infrared RF and ultra sound signals are major technologies used for indoor positioning systems Unlike outdoor areas the indoor environment imposes different challenges on location discovery due to the dense multi‐path effect and building material dependent propagation effect Thus an in‐depth understanding of indoor radio propagation for positioning is crucial for efficient design and deployment The existence of radio connectivity and the attenuation of radio signal with distance are attractive properties that could potentially be exploited to estimate the positions of small wireless devices featuring low‐power radios RSSI a standard feature in most radios has attracted a lot of attention in the recent literature for obvious reasons RSSI eliminates the need for additional hardware in small wireless devices and exhibits favourable properties with respect to power consumption size and cost As mentioned previously RF‐based location tracking has been widely studied [5 6] Given a model of radio signal propagation in a building or other environment RSSI can be used to estimate the distance from a transmitter to a receiver and thereby estimate the position of a mobile node However this approach requires detailed models of RF propagation and does not account for variations in receiver sensitivity and orientation Table 1 below briefly summarizes performance of existing indoor positioning system based on location fingerprints and WLANrsquos All the systems mentioned below in table 1 follow IEEE 80211 protocol standards The major performance metrics studied by all systems are positioning accuracy which is a form of error measurement The accuracy of the location information is usually reported as an error distance between the estimated location and the actual mobile location However the report of accuracy should include the confidence interval or percentage of successful location detection which is called the precision The system parameters of existing systems are summarized in table 1 and the best reported performance of these systems are listed in table 2

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

3

System Spacing(m) Positions SamplesPos APs Orientation Environment

RADAR [6] Non

uniform 70 80 3 4 Hall-way Saha et al [9] 3 19 1200 3 NA 1-floor

Roos et al [10] 2 155 40 10 NA 1-floor Battiti et at [11] NA 257 NA 6 NA 1-floor Ladd et al [2] 3 11 1307 5 2 Hall-way

Prasithsangaree et al [12] 153 60 40

2 to 7 4 1-floor

Youssef et al[13] 15 110 300 4 NA Hall-way Xiang et al [14] NA 100 300 5 4 1-floor

Table 1 Comparison of Indoor Positioning Systems [7]

System Algorithm Type Accuracy and Precision RADAR [6] Nearest Neighbour within 7 feet 38

Saha et al [9] Nearest neighbour amp Neural Network no specified accuracy

90 Roos et al [10] Bayesian best within 828 feet 90

Battiti et at [11] Neural Network amp Weighted k-Nearest

Neighbour all within 16-17 feet 90 Ladd et al [2] Bayesian within 5 feet 77

Prasithsangaree et al [12] Weighted k-Nearest Neighbour

25 feet at 75 amp 40 fet at 95

Youssef et al [13] Bayesian within 7 feet more than

90 Xiang et al [14] Bayesian with RSS distribution model within 6 feet 90 Table 2 Performance comparison of Indoor Positioning Systems [7]

Based on the study of results obtained in various positioning systems as in table 2 we have developed a model for an indoor positioning system using SENTIO [8] platform based on RSSI

13 Objectives In the last two or three years a number of theoretical practical andor simulation studies were done on the topic of object‐tracking While these studies are useful they are too general and provide little guidance for the actual deployment of sensor networks for real life location‐tracking of an object This thesis focuses on developing an object‐tracking application and prescribes sensor network configurations that work well with our algorithms I implemented my software using SENTIO [8] platform for sensor networking The major issue addressed in this project are the accuracy and precision in tracking an object in a real‐world application and efficient ways to algorithmically analyze the collected raw data from the specific wireless sensor product

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

4

Although the focus is to develop a real‐world application using WSN it also provides great opportunity to explore the new area of wireless communication overall

14 Research Questions My primary target is to find a method to determine the position of an object inside a building using ZigBee protocol standards based on RSSI The study addresses the following questions What is WSN What requirements do WSN need What are their main ideas and concepts What are the associated technologies What are the standards that sensor networks use

What are the existing hardware implementations that use this particular technology How can we build our new applications using WSN What do sensor network systems require for general or specific applications Are they applicable And what is the appropriate design for sensor network implementations in order to combine them with other existing systems At this point we describe and evaluate the specific object‐tracking application to be built

15 Scope The study area of the thesis is an overview of the WSN technology study of SENTIO platform and implementation of an object tracking application Thus the study is divided into two parts Part one is an overview of WSN it includes study of SENTIO platform and IEEE 802154 protocol standards The second part describes the implementation of the object tracking algorithm The object tracking application receives and uses the raw values returned by the sensor network system to produce clear and meaningful outputs Schema of thesis work is as shown in figure 1

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

5

Figure 1 Schema of thesis work

16 Methodology The ldquoIndoor Local Positioning System for ZigBee based on RSSI ldquois based on ldquolocation fingerprintingrdquo technique Location fingerprinting refers to a technique that exploits the relation between any measurable physical stimulus and a specific location This approach uses empirical measurements of RSSI to estimate location By recording a database of radio ldquosignaturesrdquo along with their known locations a mobile node can estimate its position by acquiring a signature and comparing it to the known signatures in the database All of these systems require the database of signatures which should be collected manually prior to the system installation and rely on central server (or the userrsquos mobile node) to perform location calculation For example in RADAR [6] technology the RSSI is the stimulus This type of positioning system does not require specialized hardware other than the common wireless network interfaces with received signal strength measurement capability thus it is simple to deploy compared to other techniques Unlike outdoor counterpart systems which can use AOA and TDOA techniques effectively indoor positioning systems encounter the problem of NLOS and the dense multi path effect that render these two techniques ineffective or complex for practical implementation It is also difficult for a MS to always see three or more access points or base stations in indoor environment which is essential for triangulation by AOA and TDOA Location fingerprinting can also be implemented as software based positioning system which can reduce

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

6

complexity and cost Generally the deployment of fingerprinting based positioning systems can be divided into two phases First in the off‐line phase the locations where the fingerprints are required is pre‐determined and then the fingerprints are collected by performing a site‐survey of the RSS from multiple APs The distance between two closest positions is called grid spacing and usually reported in meters or feet However some points may be omitted due to inaccessibility The RSS is measured at each predetermined location to create a database of RSS patterns of the indoor environment The database of RSS patterns is called a radio map The vector of RSS values at a point on the grid is called the location fingerprint of that point Second in the on‐line phase a MS will report a sample measured vector of RSS from different APs to a base station The base station uses a positioning algorithm to estimate the location of the MS The most common algorithm used to estimate the location is Euclidean algorithm It computes the Euclidean distance between the measured RSS vector and each fingerprint in the database and then the coordinates associated with the fingerprint that provides the smallest Euclidean distance is returned as the estimate of the position It is clear from the above discussion that the designer should not start out to do the site survey without proper system design objectives and guideline If the performance is not sufficient collecting more fingerprints in between previous location fingerprints may help It is a problem of choice as to how many access points are required for the system and what the minimum distance between physical positions on the grid should be in order to provide a good position resolution and best system performance The system parameters and factors that improve the accuracy and the precision performance are still not clear

17 Outline Chapter 2 provides literature review of ldquoIndoor Local Positioning System for ZigBee based on RSSIrdquo it introduces the basic components required for a positioning system general classification of indoor positioning system Finally it explains ldquoLocation Fingerprintrdquo technology used in this thesis work to determine the position of an object in a real‐world application It briefly explains the protocols and industry standards for wireless sensor networks Chapter 3 describes some properties of RSS which is a stimulus to determine

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

7

the position of an object RSS relation with energy and on what parameters is the RSS value dependent Chapter 4 describes the model of an indoor local positioning system and its probability of returning the correct location It also explains its performance evaluation and its error distance distribution Chapter 5 describes results obtained from the model of positioning system Chapter 6 includes conclusions from the experimental analysis and future work

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

8

2 LITERATURE REVIEW This chapter reviews the literature on wireless indoor positioning system as a means of providing an intellectual background for the thesis work First in section 21 the common components of indoor positioning system are described Then in section 22 discusses different means to classify indoor positioning systems In section 23 reviews of all relevant literature on location fingerprint technique Finally in section 24 protocols and industry standards for wireless sensor networks are explained

21 Common components of indoor positioning system Basically a wireless local positioning system consists of at least two separate hardware components a measurable unit that usually carries the major part of the system intelligence and a signal transmitterreceiver The transmitter in the simplest case is just a beacon Figure 2 represents basic hardware model of a positioning system

Figure 2 Block diagram representing hardware module

First the location sensing devices detect the signals transmitted by or received at known reference points using radio sensing technologies The sensing technique is based on signal strength level that converts the sensed signal into RSS Given a set of reference points the relative position of the MS can be derived from the signal characteristics of RSS The signal characteristics of RSS are unique for a location Then the microcontroller processes the position algorithm and estimates the position of object and displays it on PC

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

9

22 Classification of Indoor positioning systems Indoor positioning system can be categorized based on the stimulus used in particular application and measurement principle

221 Classification based on Stimulus Based on the stimulus used in an application WSN inherit certain characteristics and limitations on sensorrsquos signal The propagation delay diffraction reflection and scattering are the basic signal characteristics which affect all signal types The effective range available bandwidth regulatory constraints interference power constraints safety and cost of technology limitations The wireless signals commonly used for indoor positioning systems are infrared radio frequency and ultrasound Brief descriptions of the three major technologies are as follows

Infrared Infrared signal is an electromagnetic signal of a wavelength longer than that of visible light but shorter than that of radio waves Infrared is just below the visible spectrum of light in frequency and is radiated strongly by hot bodies Many objects such as people vehicle engines and aircraft generate and retain heat and as such are especially visible in the infrared wavelengths of light compared to objects in the background It cannot pass through walls or obstructions therefore it has a rather limited range of indoor environments Thus it requires a more sophisticated circuitry than ultrasound signals Indoor lighting interferes with this type of signal and causes problems in accurate sensing It generally has a range of around 5 m The infrared devices are usually small in size compared to ultrasound devices

Radio frequency The RF signal can penetrate through most of the building material therefore it has an excellent range in indoor environments The propagation speed is also high approximately 3108 ms There are unlicensed frequencies available freely for use This type of signal has the longest range compared to infrared and ultrasound Since the propagation waves are of higher frequency (24GHz) interference with other frequency components is less

Ultrasound Although ultrasound operates at low frequency bands (typical 40 kHz) compared to the other two signaling technologies it possesses a good precision for location sensing at a slow propagation speed of sound (343 ms) The advantages of ultrasound devices are their simplicity and that they are inexpensive However ultrasound does not penetrate walls

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

10

but reflects off most of the indoor obstructions It has a short range around 3 m to 10 m but has a 1 cm resolution of distance measurement The operating temperature influences the performance of ultrasound

222 Classification based on measurement principle Wireless positioning systems can be categorized by measurement techniques which are used to derive the position of mobile stations Mainly three different measurement principles are used today AOA RSS and propagation‐time based systems that can further be divided into two different sub‐classes TOA and TDOA Figure 3 shows taxonomy based on the technology and the technique classifications

Figure 3 Taxonomy of positioning system [7]

In AOA systems the position is calculated via goniometry With the use of directional antennas or antenna arrays the angle or bearing relative to points located at known positions is measured The intersection of several measured directions pointers the yields the position value The accuracy of this approach is limited by the possible directivity of the measuring aperture by shadowing and by multi‐path reflections arriving from misleading directions In TOA and TDOA techniques rely on the precision of timing between the signal transmitter and the receiver in order to use the propagation delay or TOF to calculate the distance between transmitter and receiver Therefore a precise synchronization is also very important in such systems By combining at least three distances from three reference positions triangulation can be used to estimate the mobile stationʹs location This type of technique will

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

11

require a high accuracy clock in the communication system TDOA is more practical [1] Examples of location‐sensing systems that use time of flight are GPS the Active Bats [15] and the Cricket [16] Besides these time delay‐based techniques the distance between transmitter and receiver can also be determined from signal strength attenuation and direct distance measurement (such as dead reckoning) Fingerprinting or Location Pattern Matching this technique generally requires only measurement of RSS or other non‐geometric features at several locations to form a database of location fingerprints To estimate the mobile location the system needs to first measure the received signal strength at particular locations and then search for the pattern or fingerprint with the closest match in the database This technique does not require the mobile station to see at least three base stations or access points in order to determine the location The disadvantage of this technique is that it is very time‐consuming to perform an exhaustive data collection for a wide area network such as in outdoor positioning systems

23 Indoor local positioning using ZigBee based on RSSI This section reviews relevant RF‐based indoor positioning systems which can be used to locate stationary objects and track mobile users The impressive growth of IEEE 802154 wireless PANs (WPAN) in recent years suggests an interesting future for the location fingerprinting technique The system is quite flexible because system designers can select whether to have a centralized positioning server or let the mobile to determine its own position However the fingerprinting technique requires a training phase (off‐line phase) to collect location fingerprints for all positions in the operating area before the actual deployment (on‐line phase) [7] The following discussion is divided according to the positioning system components First the common form of location fingerprinting and its relationship with physical locations are explained Finally location estimation algorithm is reviewed

231 Location Fingerprint A location fingerprint based on RF characteristics such as RSS is the basis for representing a unique location It is created under the assumption that each position or location inside a building has unique RF signature [4] Generally a fingerprint F is labelled with a location information L The location fingerprints and their labels (eg location information) are maintained in

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

12

database and used during the on‐line phase to estimate the location The label and fingerprint are usually denoted as a tuple of (L F) Battiti et al [11] point out that the location information L for indoor location can be recorded in two forms as either a tuple of coordinates or an indicator variable The tuple of real coordinates can vary from one dimension to five dimensions which include the three dimension space and two orientation variables expressed in spherical coordinates For instance a location information of a two‐dimension system with an orientation could be expressed as a triplet L = (x y d) | x y є R 2 d є North East South West The RSS was found to be more location‐dependent than the SNR because the noise component is rather random in nature However the RSS itself fluctuates over time for each access point and location Each RSS element can be considered as a random variable therefore it can be captured by recording its descriptive statistics parameters approximating its distribution or maintaining the whole measurement dataset These approaches of RSS representation result in different procedures for location estimation algorithm Regardless of the approach the location fingerprint is usually denoted as an array or vector of signal strength (random variables) received at any position in the location‐based service area The size of the vector is determined by the number of access points that can be heard To create a basis fingerprint such as in a number of samples of vectors of signal strength are collected over a window of time for each position This basis is called a prototype Then the average RSS of each access point is calculated and recorded as an element in the location fingerprint For an area that can receive signals from N access points the location fingerprint can be expressed as a vector of average RSS elements βi

F = (β1 β2hellip βN)T Besides the basis location fingerprint the samples of location fingerprint measured during the on‐line phase are also important for the system that tracks the mobile object The number of samples should be selected appropriately to represent the location fingerprint for the mobile application A step in statistical analysis method called pre‐processing is another important issue needed to be considered because it can impact the estimation of dependency between location fingerprint and the location information easier The pre‐processing refers to a step that cleans the raw data (in this case the training set) before any further operations or analysis The cleaning may consist of encoding dimensionality reduction (reduce unnecessary elements) feature extractionselection clustering and outlier elimination Roos et al [10] point out that the pre‐processing enables faster location estimation and

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

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reduce the noise from the training data The process that creates the basis of location fingerprint discussed above could be considered as a part of pre‐processing

232 Location Estimation Algorithm Location estimation algorithms or positioning algorithms are procedures that exploit dependency between location information and location fingerprint basis in order to determine a position or location from samples of RSS signals Position algorithms can be classified into deterministic and probabilistic types based on the approaches that model the relationship between location fingerprints and location information The deterministic type of algorithms are those that based in the nearest neighbour classifiers and neural network classifiers The probabilistic type of algorithms is those that are based on the Bayesian inference and statistical learning theory such as support vector machines (SVR) The algorithm used in this project is discussed briefly

Euclidean distance method The Euclidean distance method is a deterministic approach of position estimation because they require only a set of constant location fingerprints which includes mean vectors In order to determine the location a form of discriminate function is used to classify a sample of RSS fingerprint into a position The mean or average RSS vector is a centre of a mass which represents each class of location fingerprint Suppose a set of l location fingerprints is denoted by F1 F2hellip Fl exist and each fingerprint has a one‐to‐one mapping to a set of positions L1 L2hellip Ll A sample of an RSS fingerprint measured during an on‐line phase is denoted as S which can be another mean or average RSS vector of small window of RSS samples Assuming that an indoor positioning system only considers the average RSS from N access points as a location fingerprint the sample of RSS vector is S = (s1 s2 helliphellip sN)T and each location fingerprint i in the database can be expressed as Fi = (β1 β2helliphellip βN)T The simplest closeness metric is a distance measurement in signal space denoted as the Dist () function [6] Thus the simple procedure of nearest neighbour algorithm is expressed as picking the fingerprint j that has the shortest signal distance

Dist(S Fj) le Dist(S Fk) for all k ne j A generalized weighted distance Lp summarized by [12] can be used to calculate different forms of distance in signal space as

Lp = 1N pN

i

pPiSiWi 1

1)||1(sum

=

minus

where N is the number of access points wi is a weighting factor (wi le 1) and p is the norm parameter starting from 1 The weighting factor wi is a bias

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

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parameter that can demote or promote an important RSS component in the fingerprint

24 Protocols and Industry standards for WSN Although many applications for WSN are proposed the communication protocols supporting them remain mainly unexplored and diverged This is because the area of interest is new each implementation is application specific and the components of WSNrsquos have a lot of constraints Wireless sensor networks use the wireless medium to communicate However the traditional communication protocols that support wireless communications especially ad‐hoc mesh networks may not be well suited for them WSN communication is an active research area and many algorithms and protocols have already been proposed This section introduces two proposed standards the IEEE 802154 and the ZigBee The IEEE 802154 covers the physical layer and the MAC layer of low‐rate WPAN The ZigBee is ldquoan emerging standard that is based on the IEEE 802154 and adds network construction (star networks peer‐to‐peermesh networks and cluster‐tree networks) application services and morerdquo

241 IEEE 802154 Holger and Willig (2005) [17] mention home automation home networking and home security as possible applications for IEEE 802154 They add that ldquomost of these applications require only low‐to‐medium bitrates (up to some few hundreds of kbps) moderate average delays without too stringent delay guarantees and for certain nodes it is highly desirable to reduce the energy consumption to a minimumrdquo The IEEE 802154 standard (2003) defines the device types that can be used in a LR‐WPAN A device can be a FFD or a RFD The RFD can be used in simple applications in which they do not need to transmit large amounts of data and they have to communicate only with a specific FFD The FFD can work as a PAN coordinator as a coordinator or as a simple device It can communicate with either another FFD or a RFD In keeping with the application requirements the LR‐WPAN operates in a star or peer‐to‐peer topology (figure 4) In the star topology the RFD communicates with a single controller the PAN coordinator The PAN coordinator can perform the same function as the RFD but it is also responsible for controlling the PAN ldquoit initiates terminates or routes communication around the networkrdquo [18] The peer‐to‐peer topology

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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supports ad‐hoc mesh multi‐hop networking Any device in the peer‐to‐peer topology can communicate with any other device within its communication range however this topology also has a PAN coordinator All the devices in a LR‐WPAN have a unique 64‐bit address This or a short address allocated by the PAN coordinator can be used inside a PAN Additionally each PAN has a unique identifier The combination of the PAN identifier and the sort addresses allows communication across different PANs (IEEE 802154 Standard 2003)

Figure 4 Star and peer‐to‐peer topologies in LR‐WPAN [18]

The LR‐WPAN based on the OSI seven‐layer model has the layered architecture presented in figure 5 The application and the network layer are the upper layers in the LR‐WPAN architecture but are outside the scope of the IEEE 802154 standard Only the physical layer ldquowhich contains the RF transceiver along with its low‐level control mechanismrdquo and the MAC layer ldquothat provides access to the physical channelrdquo are included in the standard and will be introduced in the following sections Finally the Type I 8022 LLC and the SSCS are intermediate sub layers supporting communication with the above layers (IEEE 802154 Standard 2003)

Physical Layer The 802154 standard specifies two different services that the PHY provides The PHY data service controls the radio and thus the transmission and reception of the PPDUs In addition the management service performs ED in the channel The management service also performs CCA before sending the messages and provides LQI for the received packets

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2 LITERATURE REVIEW10102006

16

Figure 5 WPAN architecture [18]

Three different bands are defined by the standard The 868‐8686 MHz for Europe the 902‐928 MHz for North America and the 2400‐24835 MHz worldwide All of them belong in the ISM radio bands and they are using DSSS Each of the bands supports different a data rate and it uses a different modulation technique chip rate and number of channels Moreover if the system does not use the 2450 MHz frequency it operates in both the 802 MHz and 902 MHz frequencies Table 3 summarizes the supported bands and data rates (IEEE 802154 standard 2003)

PHY (MHz)

Frequency Band (MHz) Modulation

Bit Rate Number of Channels

868-8686 BPSK 20 1 868 915 902-928 BPSK 40 10

2450 2400-24835 O-QPSK 230 16 Table 3 Frequency bands and data rates for IEEE 802154 based on the IEEE

802154 standard [18] The physical layer uses PPDU packets to communicate Table 4 demonstrates its structure The LSB is always transmitted and received first The SHR contains the preamble and the SFD fields which helps receiver

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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17

synchronization The 8‐bytes preamble contains only zero and is used for synchronization The SFD contains a specific sequence of one and zeros and specifies the beginning of the frame The PHR contains the payload length Packets with a length of 9 or more bytes are MPDU as the next section further explains Packets with length 5 are used for MPDU acknowledgements The payload part of the PPDU encloses the MAC layer packet Finally the PPDU size can be up to 127 bytes [18]

4 bytes 1 byte 1 byte Variable

Preamble SFD Frame length

(7 bits) Reserved

(1 bit) Payload (PSU)

SHR PHR PHY Payload Table 4 Format based on the IEEE 802154 standard [18]

MAC Layer The MAC layer is the interface between the SSCS and the PHY layer Similar to the PHY layer the MAC layer supports two services The MAC data service is responsible for the transmission and reception of the MPDUs through the PHY data service The MAC management service if the device is a coordinator manages the network beacons It is also responsible for PAN association and disassociation frame validation and acknowledgment providing ldquoa reliable link between two peer MAC entitiesrdquo In addition it uses the CSMA‐CA for channel access and handles and maintains the GTS mechanism Finally it supports device security [18] The IEEE 802154 standard defines four different frame types the beacon data acknowledgment and MAC command frame All frame types are based on the general MAC frame format (table 5) The frame control field describes and specifies the above different frame types Every MAC frame comprises a MHR which consists of a frame control sequence number and the information field It also contains the MAC payload different frame types have different MAC payload fields The acknowledgment type does not have a payload Finally each frame includes a MFR which contains a FCS The data in the MPDU follows the same order as the PPDU the least significant bits are left in the frame and are transited first Octets2 1 02 028 02 028 Variable 2

Destination PAN Identifier

Destination Address

Source PAN

Identifier Source Address Frame

Control Sequence Number Addressing Field

Frame Payload FCS

MHR MAC

Payload MFRTable 5 General MAC frame format IEEE 802154 standard [18]

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Table 6 7 8 and 9 present the four different MAC frame types The beacon frame is transmitted periodically by the PAN coordinator It provides information about the network management through the super frame and GTS fields which are analyzed later in the section It also synchronizes the network devices and indicates the proper communication period for them The data frame payload encapsulates data from the higher layers When a device receives a packet it is not obliged to response with an acknowledgement packet Finally the command frame identifier and command payload fields of the command frame are used for communication between the network devices The command identifier specifies actions like association disassociation and data GTS or beacon request Octets2 1 410 2 Variable Variable Variable 2

Frame Control

Sequence Number

Addressing Fields

Super frame Specification

GTS fields

Pending Addressing

Fields Beacon Payload FCS

MHR MAC Payload MFRTable 6 Beacon frame format [18]

Octets 2 1 Variable 2

Frame Control

Sequence Number

Addressing Field

Data Payload FCS

MHR MAC

Payload MFR Table 7 Data frame format [18]

Octets 2 1 2

Frame Control

Sequence Number FCS

MHR MFR Table 8 Acknowledgement frame format [18]

Octets2 1 1 Variable 2

Frame Control

Sequence Number

Addressing Fields

Command Frame Identifier

Command Payload FCS

MHR MAC Payload MFR Table 9 Command frame format [18]

In the LR‐WPAN every PAN has its own coordinator The PAN coordinator manages the communication in the local area it has two options to use or not use the super frame structure The super frame (figure 6) uses network beacons If the coordinator does not want to use a super frame structure it suspends the beacon transmission However the beacon is important for device association and disassociation If the coordinator wishes to maintain close communication control in the PAN and to support low‐latency devices it usually uses the super frame A super frame determines a specific time

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

19

period beacons bound it The beacon is transmitted in the first of the sixteen equal time slots that the super frame has It is used to ldquosynchronize the attached devices to identify the PAN and to describe the structure of the super framerdquo The super frame can have active and inactive periods All the communications have to be finished inside the super frame period In the inactive periods the devices can switch to the sleep mode but they have to be ready for the next beacon The active period is further divided into a CAP and a CFP During the CAP any device can communicate competing with the other devices in the PAN using slotted CSMA‐CA The CFP contains GTSs and follows the CAP A CFP may maintain up to seven GTSs and each GTS can reserve more than one time slot However the CAP should always be sufficient to allow new devices t join the PAN The GTSs are allocated to specific devices All the transactions must be completed inside the assigned time period (CAP GTS and CFP) (IEEE 802154 standard 2003)

Figure 6 Example of super frame structure [18]

In accordance with the 802154 standard (2003) three different types of data‐transfer exist In addition the types differ if the coordinator uses or does not beacons Data transfer from a device to the PAN coordinator is the first type (figure 7) For a ldquonon‐beacon‐enable networkrdquo it first senses the medium by using ldquoun‐slotted CSMA‐CArdquo and then a simple transmit to the data frame In a ldquobeacon‐enabled networkrdquo the sender waits for the beacon when it finds it ldquothe device synchronizes to the super fame structurerdquo In the specified time frame the sender again senses the medium by using ldquoslotted CSMA‐CArdquo and transmits the data to the coordinator In either case the coordinator has the option to acknowledge or not acknowledge the data reception after that the transaction is completed Data transfer from the coordinator is the next type described in the standard (figure 8) In a beacon‐enabled network the coordinator indicates a pending message through the beacon The messagersquos target device receives the beacon and if the message is pending it responses with a MAC command request

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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20

message using slotted CSMA‐CA the coordinator may or may not acknowledge the command message and through slotted CSMA‐CA sends the pending message The device acknowledges the received message After that the coordinator removes the message from the beaconrsquos pending list and completes the transaction In a non‐beacon enable network the device periodically sends a MAC command‐request frame to the coordinator The coordinator acknowledges the data request Then by using un‐slotted CSMA‐CA if it has a pending message it transmits it If it has not it respond with a data frame with a ldquozero‐length payloadrdquo To complete the transaction the device acknowledges the data reception

Figure 7 Communication from a device to a PAN coordinator in (a) a beacon‐enabled network and (b) a non‐beacon enabled network [18]

Peer‐to‐peer is the last type of data transfer In this situation the devices are free to communicate with any other device within their communication range In a peer‐to‐peer PAN the devices can ldquoeither receive constantly or synchronize with each otherrdquo If they are receiving constantly to transmit data they use un‐slotted CSAM‐CA In the second case synchronization must be achieved first (IEEE 802154 standard [IEEE 2003]) The IEEE 802154 (2003) standard establishes MAC and PHY standards for low‐cost low‐power and high‐density node deployments In addition to the above PHY and MAC characteristics IEEE 802154 provides a security baseline including ldquothe ability to maintain an ACL and use symmetric cryptographyrdquo for data encryption The algorithm that is used for encryption is the AES However the higher level layers decide when security is need The upper layers are in general responsible for device authentication and key management The next section introduces the ZigBee standard which

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

21

encapsulates the IEEE 802154 and provides additional standardization for the higher levels

Figure 8 Communication from a PAN coordinator to a device in (a) a beacon‐enabled network and (b) a non‐beacon enabled network [18]

242 ZigBee Heily (2004) defines ZigBee as ldquoa rapidly growing worldwide non‐profit industry consortiumrdquo whose mission is ldquoto define a reliable cost‐effective low‐power wirelessly networked monitoring and control product based on an open global standardrdquo Figure 9 illustrates the areas of interest for different wireless communication standards

Figure 9 Overview of the coverage for different wireless communication

standards [17]

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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22

ZigBee a new standard which became publicly available in June 2005 is based on the IEEE 802154 standard It expands the IEEE 802154 by adding the framework for ldquothe network security and applicationrdquo (Craig 2005) Figure 10 presents the IEEE 802154ZigBee stack and the areas of responsibilities

Figure 10 IEEE 802154ZigBee stack [20]

Craig (2005) mentions three networking topologies that the standard covers the star mesh and cluster tree (figure 11) The ZigBee standard works on top of the IEEE 802154 addressing schema by using the standard 64‐bit and the short 16‐bit addressing Kinney (2005) summarizes the ZigBee network layer responsibilities the successful establishment of a new the network and successful new device configuration addressing assignment network synchronization frames security and message routing ZigBee further distinguishes the concept of the physical devices (RFD FFD) by using the notion of ldquological devicesrdquo ldquoZigBee Coordinatorrdquo is the first type of logical devices It is responsible for initializing maintaining and managing the network Under the coordinator in the network hierarchy is the ldquoZigBee routerrdquo which is responsible for controlling the message routing between the nodes Finally the ldquoZigBee End Devicerdquo acts as the end point of the network

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2 LITERATURE REVIEW10102006

23

structure The tasks that an end device can perform are specified in the ldquoApplication Profilesrdquo (Craig 2005)

Figure 11 IEEE 802154ZigBee stack [17]

The ZigBee specifications (2005) summarize the security services provided by ZigBee ldquokey establishment key transport frame protection and device managementrdquo ZigBee builds its security mechanism using symmetric key cryptography The security services also depend on the associated layer thus as the figure 10 shows the security mechanism covers the network and the application layer In addition if a MAC frame needs security protection the MAC layer is able to secure it Moreover the notion of end‐to‐end security is supported the source and destination devices have access and use the same share key In the MAC layer the 802154 AES mechanism provides the proper security The mechanism protects ldquothe confidentiality integrity and authenticity of the MAC framesrdquo (Kinney 2005) An auxiliary header field in front of the MAC payload indicates if the frame is encrypted or not The MAC framesrsquo integrity is supported by calculating and using a MIC at the end of the MAC payload In addition to the AES nonce is used to provide MAC confidentiality and authenticity Figure 12 illustrates a MAC frame with security For different security aspects the MAC layer uses different mode of the AES for the encryption it uses the AES in Counter (CTR) mode and for the integrity the

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

24

CBC‐MAC Finally the combination (CCM) of the above two modes is available providing both encryption and integrity (Kinney 2005)

Figure 12 ZigBee secure frame in MAC layer [17]

In the network layer the CCM (a modified MAC layer CCM mode) is used for encryption Because the network layer uses only the CCM mode a single key is used for all different security options The network layer security message format is similar to the MAC frame it is presented in the figure 13 Finally although the network layer is responsible for securing its layer messages the above layers specify the keys and the CCM option for each frame (Kinney 2005)

Figure 13 ZigBee secure frame in network layer [17]

Security in the application layer works similar to the network and MAC layers It uses the ldquolink keyrdquo or the ldquonetwork keyrdquo to secure the message and then encapsulate it inside a set of fields similar to the network format (figure 14) Other security responsibilities that the application layer has are to provide the ZDO and the applications with device management services key establishment and key transport (ZigBee specifications 2005)

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

25

Figure 14 ZigBee secure frame in application layer [17]

The ZigBee application layer contains the manufacturer‐defined application objects the ZDO and the application sub‐layer In addition to the security responsibilities the application sub‐layer binds devices based on their duties and needs maintains the binding tables and forwards messages between them The application sub‐layer also discovers the neighbour devices for a given device The ZDO is responsible for determining the devicersquos duty in the network for communicating using binding requests and for supporting security as was mentioned above The sub‐layer that implements the actual application is the manufacturer‐defined application object (Kinney 2005) The ZigBee specifications are a great step toward the wireless sensors networks standardization They cover all the OSI layers from the physical to the application providing guidance to the developers The specifications are the product of the ZigBee alliances a consortium of companies and academic institutes Thus they not only produce the specifications but also are the first use them The next section goes beyond the layer architecture and standardization and introduces the OS field another crucial element of WSNs

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

26

3 RECEIVED SIGNAL STRENGTH (RSS) Majority of the existing methods leverage the existence of IEEE 80211 base stations with powerful radio transmit powers of approximately 100mW per base station Such radios are in a different class from the low power IEEE 802154 compliant radios that typically transmit at low power levels ranging from 52mW to 29mW [19] The wide availability of larger number of IEEE 802154 radios in the research community has revived the interest for signal strength based localization in sensor network Despite of rapidly increasing popularity of IEEE 802154 radios and signal strength localization there is a lack of detailed characterization of the fundamental factors contributing to large signal strength variation This chapter investigates some of the properties of RSS observed during testing and analysis of research work collected during the period of thesis The analysis of RSS values is needed to understand the underlying features of location dependent RSS patterns and location fingerprints An understanding of the properties of the RSS values for location fingerprinting can assist in improving the design of positioning algorithms and in deployment of indoor positioning systems

31 Parameters that affect RSS Different factors that affect the indoor RSS are the userrsquos presenceabsence hisher orientation time of the day building type and material distance from transmitter and type of radio These factors are by no means exhaustive Other possible factors are antenna orientation directionality and type Table 10 classify the parameters which affect RSS [7]

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

27

Effect on Factors Options

1 Proximity of user Userrsquos presence or absence

2 Orientation of user and terminal

North East South West

Data Collection

3 Make of Radio 4 Time of measurement Time of day amp days of

week 5 Period of measurement

Second minute hour

7 Interference Co‐channel adjacent co‐channel

Statistics

8 Building environment Small offices or large hall

Table 10 Classification of factors the affect RSS [7]

Transmitter variability Different transmitters behave differently even when they are configured exactly in the same way In practice this means that when a transmitter is configured to send packets at a power level of d dBm then the transmitter will send these packets at a power level that is very close to d dBm but not necessarily exactly equal to d dBm This can alter the received signal strength indication and thus it can lead to inaccurate distance estimation

Receiver variability The sensitivity of the receivers across different radio chips is different In practice this means that the RSSI value recorded at different receivers can be different even when all the other parameters that affect the received signal strength are kept constant

Antenna orientation Each antenna has its own radiation pattern that is not uniform In practice this means that the RSSI value recorded at the receiver for a given pair of communicating nodes and for a given distance between them varies as the pair wise antenna orientations of the transmitter and the receiver are changed

Multi‐path fading and shadowing in the RF channel In indoor environments the transmitted signals get reflected after hitting on the walls andor on other objects in the room such as furniture Both the original signal and the reflected signal reach the receiver almost at the same time since they both travel at the speed of light As a result of this the receiver is not able to

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

28

distinguish the two signals and it measures the received signal strength for both of them

32 Related work analysis A number of indoor location tracking systems have been proposed in the literature based on RF signals ultrasound infrared and some combinations of these As mentioned previously RF based positioning system are widely studied as shown in Table 2 Given a model of radio signal propagation in a building or other environment RSS can be used to estimate the distance between the transmitter and receiver Most of the algorithms (Table 2) have been evaluated in practice using 80211b compliant wireless radios and seem to produce a location error of 15ft to 20ft for an indoor network area of approximately 225ft x 144ft According to the results of this study all the localization techniques produce approximately the same location error over a range of environments Ecolocation [22] Mote Track [5] are two of the few localization algorithms that have been evaluated on a real sensor network that uses a low power wireless radio Ecolocation reports a location error of 10ft for a very small outdoor network deployment area (26ft x 49ft) Mote Track reports a location error of approximately 13ft for an indoor network deployment area of 18751ft2 In general the RSSI based localization algorithms can be divided into two broad categories the map based and the distance (or area) prediction based algorithms In the case of the map based algorithms the deployment area is divided into a large set of sample points that cover the whole area Most techniques collect one or more signal strength measurements from all visible beacon nodes and for each sample point Each sample point is then mapped to either a signal strength vector or a signal strength probability distribution This process generates an RSSI fingerprint map which is unique with respect to the beacon locations and the deployment area This fingerprint map is stored to a central base station A node that is not aware of its location collects one or more signal strength measurements from all the visible beacon nodes and creates its own RSSI fingerprint that is sent to the central base station The base station computes nodersquos location as the sample pointrsquos location whose RSSI fingerprint distance from the unknown nodersquos RSSI fingerprint is the minimum among all the sample points Despite the fact that the map based RSSI localization methods produce relatively small location errors they are not attractive mainly because they require an excessive amount of profiling data and because they are not fully distributed The detailed profiling of a building is a major holdback because it is complex time consuming and depends on beacon locations Therefore for different buildings or even for the

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

29

same building and different beacon locations different RSSI maps have to be obtained The second category of RSSI based algorithms are the distance (or area) prediction algorithms which focus on minimizing the amount of profiling data In distance prediction based methods raw RSSI data are directly mapped to distances through a signal propagation model In some cases distances are not explicitly measured but they are taken into consideration through assumptions on the underlying distributions In the case of outdoor deployments where there are no obstacles a single signal propagation model is all the profiling data required In the case of indoor deployments the same applies but now the signal propagation model might be different across different buildings with different furniture and wall arrangements Given an underlying signal propagation model raw RSSI data can be mapped to specific distances These distances can be either used directly in order to perform localization or assigned a probability and used in a learning basedprobabilistic localization algorithm Obviously the accuracy of the distance prediction based methods depends heavily on the accuracy of the signal propagation model used for translating RSSI values to distances

33 Properties of RSSI Changes in signal due to propagation indoors are difficult to predict because of dense environment and propagation effects such as reflection diffraction and scattering The multi‐path fading effect which is the result of either constructive or destructive combination of multiple signal copies at the receiver causes the received signal to fluctuate around a mean value at a particular location The received signal is usually modelled by the combined effects of large‐scale fading and small‐scale fading The large‐scale fading component describes the signal attenuation as the signal travels over a distance and is absorbed by material such as walls and floors along the way to the receiver This component predicts the mean of the RSS and usually has a lognormal distribution On the other hand the small scale fading component explains the dramatic fluctuation of the signal due to multi‐path fading On the other hand the small scale‐fading component explains the dramatic fluctuation of the signal due to multi‐path fading If there is no LOS component the small‐scale‐fading is often modelled with a Rayleigh distribution Such scenarios are commonly called NLOS If there is a LOS component the small‐scale‐fading is typically modelled by a Rician distribution Note that when the RSS is measured the measurement averages out the small‐scale‐fading effects

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

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4 OBJECT TRACKING 41 Introduction This chapter presents the object‐tracking application the main objective of this thesis work The application is an algorithmic‐software implementation written in Embedded C which is capable of receiving data messages and generating object position estimation data The implementation of the object‐tracking application makes use of the SENTIO platform and uses the background knowledge introduced in the preceding chapters The motivation for the object‐tracking application is to determine the position of an object in indoor environments (Eg Home) The application acts as an interface which collects the raw data that the sensors generate (numerical values) It makes the proper comparisons and algorithmic processes to identify and track the object while it is static inside the area covered by the wireless sensor network Finally with the detection and tracking data it alerts the user or another system The following sections provide an overview of the object‐tracking application

42 Overview of hardware product Before describing the object tracking application it is important to know the characteristics of hardware product The SENTIO platform (figure 15) is built in modular fashion and has the following key features [8]

Flexibility This platform is very flexible in such a way that it is made easy to add or change the hardware for each sensor task depending on the requirement in the WSN For a specific platform this can be hard or sometimes even impossible to accomplish

Sensor resources Resources in a sensor module includes the ability of usage of controllerrsquos resources as much as possible such as ADC‐channels interrupts and digital Input‐Outputrsquos Local voltage regulation should also be possible since many sensors require higher supply voltages than digital Integrated Circuits do Further support of a shut‐down mechanism to power down the sensor modules when not needed

Computation and memory The processor module based on microcontroller should support many devices in a microcontroller family and support different power supply voltages enabling full range of clock frequencies All power‐down modes of the devices should be

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

31

supported The platform should support different processor modules including FPGArsquos and very simple microcontrollers

Communication The focus should be on enabling an IEEE 802154 compliant communication interface With or without ZigBee stack this will provide a flexible platform for exploring different network topologies Support for power‐down of the entire communication module should be supported

Cost and form factor High flexibility is in conflict with low cost and small size The primary objective should be flexibility but the size should be considered in order to enable sensor device integration in systems where space is limited

Figure 15 SENTIO Platform [8]

The communication subsystem of the SENTIO sensor node was designed so that the correct operation of the radio chip is ensured The radio chip is powered by the on‐board voltage regulator and thus fluctuations in the battery voltage level do not affect the operation of the communication subsystem This platform uses Chipcon CC2420 IEEE 802154 radio transceiver operates in the 24GHz ISM band and includes a digital direct sequence spread spectrum (DSSS) modem providing a spreading gain of 9dB and an effective data rate of 250Kbps It was specifically designed for low power wireless applications and supports 8 discrete power levels 0dBm minus1dBm minus3dBm minus5dBm minus7dBm minus10dBm minus15dBm and minus25dBm at which its power consumption varies from 29mW to 52mW [21] A built‐in received signal strength indicator gives an 8‐bit digital value RSSIVAL The RSSIVAL is always averaged over 8 symbol periods (128μs) and a status bit indicates when the RSSIVAL is valid (meaning that the receiver was enabled for at least 8 symbol periods) The

Antenna

Communication Module

Processing Module

Sensing Module

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

32

power P at the RF pins can be obtained directly from RSSIVAL using the following equation

P = RSSIVAL + RSSIOFFSET [dBm] where the RSSIOFFSET is found empirically from the front‐end gain and it is approximately equal to minus45dBm In the next sections when we refer to the RSSI value we refer to the RSSIVAL and not the power P unless otherwise stated

43 Experimental Testbed The experimental test bed for object tracking application is as shown in figure 16 Experiment was conducted in Masterrsquos room at Mid Sweden University A DELL laptop is connected to SENTIO platform and software were primarily used to collect samples of RSS from APrsquos The software monitors IEEE 802154 radio frequency channels which operate in the 24GHz band

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

1030

BC - 1030

AP 3 AP 1

AP 2

AP 4

BS

HEWLETT

PACKARD

Figure 16 Layout of Masterʹs Room

A three dimensional scalable testbed is used for indoor sensor network deployments The testbed illustrated in figure 16 is a 3‐D structure measuring 16m (L) x 12m (W) x 3m (H) and it is designed to host a large number of static nodes as shown in figure 16 Arrow marks in the above figure represent the location where fingerprints were taken The centrepiece of our infrastructure

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

33

is the SENTIO sensor node [8] an open‐source general purpose sensing platform designed with AT Mega 128 microprocessor and the IEEE 802154 compliant CC2420 radio from Chipcon [19]

44 Obtaining data from the sensor network The next important step in the development of the object tracking application is to obtain the data from the sensor network and send it to the base station From there the data is transmitted through a serial port to the computer that runs the object tracking application When the data arrives at the serial port the application must capture it This paragraph gives a brief explanation of how and what kind data is transmitted by the static object fixed sensors and how this data is being used to estimate the position of the object First the static object transmits Beacon packet continuously which is received by all the fixed APrsquos The beacon packet format is as shown in table 6 Then the fixed APrsquos calculates the 8‐bit value of RSSI from the transmitted beacon packet and then sends the resultant RSSI value as a form of data packet (table 7) to the BS which calculates the position and sends back to the object

45 Object tracking application Testing and Evaluation The purpose of this section is to discuss and evaluate the object tracking application Finally we are ready with the experimental testbed as shown in figure 16 As shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors placed at the corners of the masterrsquos room BS is placed at the centre of the room connected to a PC All the nodes are power connected before an actual measurement takes place At first the object is placed at random position inside the room and transmits beacon packets continuously Then these packets are received by the fixed APrsquos which calculate the RSSI from the packet and then averages it until it receives 50 beacon packets and then transmits the data to the BS (figure 17) Then the BS waits until it receives the data from all the APrsquos Once it receives from all the APrsquos it calculates the position of the object (figure 18) The application was developed to demonstrate a real world use of a WSN in object tracking In evaluating the final position obtained with respect to the actual position we conclude that the object‐tracking was successful Although

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

34

it is a research prototype object tracking application is a simple stable stand‐alone system capable of demonstrating an important application of WSN It takes raw data from the WSN aggregates it evaluates it and based on the evaluation tracks the object The accuracy of estimating the position is less than 5m Table 11 below shows a log of readings taken during test

Start

Intialize micro‐controller and transeciever in receieve mode

If received_packet=

Beacon

Average RSSI values

Increase the counter

If countergt1000050

A

No

Yes

B

B

Yes

No

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

35

A

Reset counter

Set transceiever in transmit mode

Transmit averaged RSSI to Main sensor and set it back to receive mode

10000 samples ‐ Offline

50 samples ‐ Online

Figure 17 Flow chart representing the algorithm of fixed sensors

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

36

Start

Intialize micro‐controller and transeciever in receieve mode

Received RSSI values from all fixed Sensors

Open radio map

Calculate Euclidean distance between obtained RSSI values from fixed sensors and fingerprints

in radio map

No

Yes

Bubble sort for least error in signal strength values

A

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

37

A

Get the location of the object from the radiousing the least error value

Display location of object on PC

End

Figure 18 Flow chart representing the algorithm of main sensor

Table 11 Accuracy of positioning system

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

38

5 RESULTS This chapter presents how the system parameters such as number of samples mean or median etchellipare finalized All the tests are performed in masterrsquos room at Mid Sweden University This chapter is divided into three subsections First in Section 51 it reviews results obtained from samples test Section 52 discusses one dimensional test results and finally section 53 discusses results of two dimensional tests

51 Measurement setup A Dell laptop was connected to a SENTIO platform and is loaded with software (Appendix 2) This setup was used to determine number of samples required during object tracking mean or median position estimation algorithm and one dimensional test The setup is as follows

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

103

0

BC - 1030

AP 1AP 2

BS

HEWLETT

PACKARD

Figure 19 Test bed for one dimensional test in Masters Room

Two SENTIO nodes (AP1 and AP2 as shown above) were separated by a distance of 10 meters and these sensors are known as fixed sensors which are fixed to the ceiling such that there antenna is faced towards the ground They are linear is position A BS was connected to Dell laptop and it was placed in such a way that its antenna was faced towards the ceiling Another SENTIO

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

39

node was used as an object and its orientation was same as BS Figure 19 represents the complete setup for object tracking application Mobile node was moved between these fixed ones at regular intervals of one meter and RSSI values were logged at each interval

52 Number of samples test This test was performed to determine number of samples required for accurate positioning and either mean of the obtained RSSI values or median of them Figure 19 represents the setup for number of samples test Once the setup is ready the measurements are taken as follows

1 An object was tagged with SENTIO node and was placed stationary linear to the fixed sensors

2 10000 samples (RSSI) were taken at regular intervals of 1 meter up to 10 meters

3 The sample values were logged at every interval 4 Using these logged samples I have plotted graphs such as distribution

of RSS values (figure 20 21) meanmedian at intervals of 1 meter using MATLAB

5 The mean and median for various numbers of samples is as shown in figure 22

Figure 20 Histogram plot of RSSI values at 1m and 4m

Figure 21 Histogram plot of RSSI values at 6m and 8m

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

40

Figure 22 Distance vs RSSI

From the above graphs following conclusions were made 1 From the histogram plots we can observe that the distribution of RSSI

values follow Gaussians distribution law 2 As we can see from figure 22 that median curve has drastic changes

when compared to mean curve Hence mean is considered while estimating the position

3 From the observations we conclude that the number of samples does not matter since mean curve tends to be same for various numbers of samples

4 Hence 10000 samples are considered in order to get a proper location fingerprint during off‐line phase and during online phase 50 samples are enough to estimate the position of object

53 0ne dimensional test Measurement setup for one dimensional test is as shown in figure 19 This test was conducted to check the performance of algorithm and how accurate it was At first ie during offline phase fingerprints at each interval averaging 10000 samples were taken During online phase Euclidean algorithm was used estimate the position averaging 50 samples of RSSI From the results we

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

41

can realize that the error was less than 2 meters Table 12 shows the result of location estimation in one dimensional case

Table 12 One dimensional position estimation results

54 Two dimensional test This test is the final stage of my thesis work Experimental testbed for this stage is as shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors fixed at the corners of the room and a BS at the centre of the room There are 18 pre‐determined locations where the fingerprints (RSSI) were taken The fingerprints are as shown in Appendix 1 All the readings were taken in presence of userrsquos The algorithm for position estimation is shown in Appendix 2 Position estimation test is performed as follows

Tagged object is placed at random inside the masterrsquos room All the sensors including the base station and object are switched on Using the algorithm (Appendix 2) the BS estimates the position of the object and displays it on the PC

This test was performed using various numbers of samples to test which has a better accuracy Figure 23 represents the error rate using various numbers of samples From figure 23 we can conclude that 50 numbers of samples is enough to estimate the position of object with an accuracy of 3m

55 Causes of error in location determination The randomness of RSS is the major cause of error in any indoor

positioning system that uses the RSS for location inference If there is no randomness in the RSS and its location fingerprint every indoor positioning systems of this type will have no location detection error For example I have recorded RSS at one particular location during different times of the day From figures 24 and 25 you can observe that RSS values are different at different times of day

The performance of indoor positioning systems depends greatly on the separation of location fingerprints A location fingerprint

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

42

corresponding to a location can be identified correctly if it is difficult to classify it as another fingerprint by a pattern classifier

Path loss is one of the basic signal propagation properties of the RSS that best describes attenuation of transmit signal power over a distance between an access point and a mobile device

Presence of Userrsquos or userrsquos orientation affects RSS

Figure 23 Error Rate vs Number of samples

Figure 24 RSS distribution at 214 pm

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

43

Figure 25 RSS distribution at 100pm and 1100 am

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

6 DISCUSSIONS10102006

44

6 DISCUSSIONS 61 Summary and Conclusion While empirical results and performance studies of positioning systems based on location fingerprint have been presented in this report analytical model that can be used as a framework for efficiently designing the positioning systems are not available This thesis design work uses general principles of position estimation such as position estimation of sensors grid spacing etc This design work focuses on accuracy and precision The concept of location fingerprint technique based on RSS us explained in detail in chapter 2 It is found that RSS is random It is possible to approximate the RSS as being normally distributed While the RSS random process is non‐stationary the mean is more or less constant and could be used as the fingerprint of the location Model designed for position estimation is as shown in chapter 4 Accuracy and precision are the two major performance matrices in this study Uniform grid spacing has been used From the experimental analysis I have obtained an accuracy of 3 meters and 35 precision The sensitivity analysis among the system parameters and the performance matrices were done using the model as shown in chapter 4 For instance our study found that the minimum distance between two adjacent positions also affects the positioning performance This is due to the randomness of RSS patterns induced by the ever changing indoor environment

62 Future work The research in this thesis provides a ground work for the future study of efficient design of indoor positioning system The RSSI based position estimation still has limitations First the RSSI value is time dependent Thus the future study should address this issue Second the movement of object is not considered Third orientations of sensors affect the performance which should be taken care off Fourth to improve the performance of the system we need to find a solution to deal with those incomplete or missing data during the off‐line and the on‐line phase Fifth position estimation on multi‐floor and three‐dimensional coordinates is not available

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 110102006

45

APPENDIX 1

Location Fingerprints of Masters Room Time 1315 Observation Time 20 minutes Number of Samples 10000

People Presence SNo Co-ordinates AP1 (0x0001) AP2 (0x0002) AP3 (0x0004) AP4 (0x0008)

1 11 -24 -16 -27 -33 2 13 -26 -8 -34 -31 3 15 -43 -9 -39 -25 4 32 -21 -11 -27 -33 5 61 -6 -23 -32 -28 6 64 -20 -31 -25 -34 7 35 -23 -35 -29 -38 8 37 -29 -35 -22 -27 9 39 -38 -29 -21 -27 10 67 -27 -23 -20 -23 11 611 -26 -29 -23 -30 12 613 -35 -32 -2 -31 13 513 -34 -31 -20 -26 14 11 -39 -22 -29 -17 15 212 -37 -42 -28 -31 16 -212 -38 -33 -29 -6 17 -21 -42 -29 -33 -19 18 -38 -40 -23 -35 -22

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 210102006

46

APPENDIX 2

Begin

Open radio map and raw data files for write

Intialize radio map in memory

Check whether the

Packet is data or Beacon packet If data packet

Save the data to register

Check if data is

Received from all

sensors

Estimate the position of static

Display the position on PC

End Yes

No

A

A

Yes

No

Flow chart representing the main algorithm for position estimation

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

47

APPENDIX 3

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

48

Distribution of RSSI

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

49

REFERENCES [1] T S Rappaport J H Reed and B D Woerner ldquoPosition location using wireless communications on highways of the futureʺ IEEE Commun Mag vol 34 no 10 pp33‐41 Oct 1996 [2] A M Ladd K E Bekris G Marceau A Rudys L E Kavraki and D S Wallach rdquoRobotics‐based location sensing using wireless ethernetʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ02) 2002 pp 227‐238 [3] I Ramani R Bharadwaja and P V Rangan ldquoLocation tracking for media appliances in wireless home networksʺ in Proc International Conference on Multimedia and Expo (ICMEʹ03) July 2003 pp 769‐772 [4] K Pahlavan X Li and J P Makela ldquoIndoor geolocation science and technologyʺ IEEE Commun Mag vol 40 no 2 pp 112‐118 Feb 2002 [5] Konard Lorincz and Matt Welsh ldquoMote Track A Robust Decentralized Approach to RF‐Based Location Trackingrdquo [6] Paramvir Bahl and Venkata N Padmanabham ldquoRADAR An In‐Building RF‐based User Location and Tracking Systemrdquo [7] Kamol Kaemarungsi ldquoDesign of Indoor positioning system based on location fingerprint techiniquerdquo University of Pittsburgh 2005 [8] Fredrik Linnarsson Cheng Peng and Bengt Oelmann ldquoSENTIO A Hardware Platform for Rapid Prototyping of Wireless Sensor Networksrdquo [9] S Saha K Chaudhuri D Sanghi and P Bhagwat ldquoLocation determination of a mobile device using IEEE 80211b access point signalsʺ in Proc IEEE Wireless Communications and Networking Conference (WCNCʹ03) New Orleans LA Mar 2003 pp 1987‐1992 [10] T Roos P Myllymaki H Tirri P Misikangas and J Sievanen ldquoA probabilistic approach to WLAN user location estimationʺ International Journal of Wireless Information Networks vol 9 no 3 pp 155‐164 July 2002 [11] R Battiti M Brunato and A Villani ldquoStatistical learning theory for location fingerprinting in wireless LANrsquosʺ Technical Report Oct 2002 [Online] Available httprtmscienceunitnitraquobattitiarchive86pdf [12] P Prasithsangaree P Krishnamurthy and P K Chrysanthis ldquoAn indoor position location with wireless LANsʺ in Proc IEEE International Symposium on Personal Indoor and Mobile Radio Communications (PIMRCʹ02) Lisbon Portugal Sept 2002 [13] M A Youssef A Agrawala and A U Shankar ldquoWLAN location determination via clustering and probability distributionsʺ in Proc IEEE

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

50

International Conference on Pervasive Computing and Communications (PerComʹ03) Dallas‐Fort Worth TX Mar 23‐26 2003 pp 23‐26 [14] Z Xiang S Song J Chen H Wang J Huang and X Gao ldquoA wireless LAN‐based indoor positioning technologyʺ IBM Journal of Research and Development vol 48 no 56 pp 617‐626 SeptNov 2004 [15] R Want A Hopper V Falcao and J Gibbons ldquoThe active badge location systemʺACM Transactions on Information Systems vol 40 no 1 pp 91‐102 Jan 1992 [16] N B Priyantha A Chakraborty and H Balakrishnan ldquoThe cricket location‐support systemʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ00) Boston MA Aug 2000 pp 32‐43 [17] Holger K Willig A (2005) Protocols and Architectures for Wireless Sensor Networks London [18] IEEE (2003) Part 154 Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low‐Rate Wireless Personal Area Networks (LR‐WPANs) New York [19] Chipcon CC2420 802154 compliant radio httpwwwchipconcom [20] ZigBee Specification v10 ZigBee Alliance [21] Dimitrios Lymberopoulos Quentin Lindsey and Andreas Savvides ldquoAn Empirical Analysis of Radio Signal Strength Variability in IEEE 802154 Networks using Monopole Antennasrdquo [22] Kiran Yedavalli Bhaskar Krishnamachari Sharmila Ravula and Bhaskar Srinivasan ldquoEcolocation A sequence based technique for RF localization in wireless sensor networksrdquo Robert Bosch Research and Technology Centre Palo Alto CA

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

Table of Contents10102006

v

APPENDIX 1 45 APPENDIX 2 46 APPENDIX 3 47 REFERENCES 49

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

List of Tables10102006

vi

List of Tables Table 1 Comparison of Indoor Positioning Systems [7] 3 Table 2 Performance comparison of Indoor Positioning Systems [7] 3 Table 3 Frequency bands and data rates for IEEE 802154 based on the IEEE 802154 standard [18] 16 Table 4 Format based on the IEEE 802154 standard [18] 17 Table 5 General MAC frame format IEEE 802154 standard [18] 17 Table 6 Beacon frame format [18] 18 Table 7 Data frame format [18] 18 Table 8 Acknowledgement frame format [18] 18 Table 9 Command frame format [18] 18 Table 10 Classification of factors the affect RSS [7] 27 Table 11 Accuracy of positioning system 37 Table 12 One dimensional position estimation results 41

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

List of Figures10102006

vii

List of Figures Figure 1 Schema of thesis work 5 Figure 2 Block diagram representing hardware module 8 Figure 3 Taxonomy of positioning system [7] 10 Figure 4 Star and peer‐to‐peer topologies in LR‐WPAN [18] 15 Figure 5 WPAN architecture [18] 16 Figure 6 Example of super frame structure [18] 19 Figure 7 Communication from a device to a PAN coordinator in (a) a beacon‐enabled network and (b) a non‐beacon enabled network [18] 20 Figure 8 Communication from a PAN coordinator to a device in (a) a beacon‐enabled network and (b) a non‐beacon enabled network [18] 21 Figure 9 Overview of the coverage for different wireless communication standards [17] 21 Figure 10 IEEE 802154ZigBee stack [20] 22 Figure 11 IEEE 802154ZigBee stack [17] 23 Figure 12 ZigBee secure frame in MAC layer [17] 24 Figure 13 ZigBee secure frame in network layer [17] 24 Figure 14 ZigBee secure frame in application layer [17] 25 Figure 15 SENTIO Platform [8] 31 Figure 16 Layout of Masterʹs Room 32 Figure 17 Flow chart representing the algorithm of fixed sensors 35 Figure 18 Flow chart representing the algorithm of main sensor 37 Figure 19 Test bed for one dimensional test in Masters Room 38 Figure 20 Histogram plot of RSSI values at 1m and 4m 39 Figure 21 Histogram plot of RSSI values at 6m and 8m 39 Figure 22 Distance vs RSSI 40 Figure 23 Error Rate vs Number of samples 42 Figure 24 RSS distribution at 214 pm 42 Figure 25 RSS distribution at 100pm and 1100 am 43

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

Terminology Notation10102006

viii

Terminology Notation

GPS ‐ Global Positioning System

RSSI ‐ Received Signal Strength Indication

MS ‐ Mobile Station

RF ‐ Radio Frequency

WSN ‐ Wireless Sensor Network

AP ‐ Access Point

RSS ‐ Received Signal Strength

PC ‐ Personal Computer

WLAN ‐ Wireless local Area Network

SNR ‐ Signal to Noise Ratio

RFD ‐ Reduced‐Function Device

WPAN ‐ Wireless Personal Area Network

FFD ‐ Full Functional Device

MAC ‐ Medium Access Control

LR‐WPAN ‐ Low Rate WPAN

PAN ‐ Personal Area Network

OSI ‐ Open Systems Interconnection

LLC ‐ Logical Link Control

SSCS ‐ Service Specific Convergence Sub layer

PHY ‐ Physical Layer

PPDU ‐ PHY Protocol Data Unit

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

Terminology Notation10102006

ix

ED ‐ Energy Detection

CCA ‐ Clear Channel Assessment

LQI ‐ Link Quality Indication

ISM ‐ Industrial Scientific and Medical

DSSS ‐ Direct‐Sequence Spread Spectrum technique

LSB ‐ Least Significant Bit

SHR ‐ Synchronization Header

SFD ‐ Start of Frame Delimiter

PHR ‐ PHY Header

MPDU ‐ MAC Protocol Data Units

GTS ‐ Guaranteed Time Slot

MHR ‐ MAC Header

MFC ‐ MAC Footer

FCS ‐ Frame Check Sequence

CAP ‐ Contention Access Period

CFP ‐ Contention Free Period

ACL ‐ Access Control List

AES ‐ Advance Encryption Standard

MIC ‐ Message Integrity Code

CBC ‐ Cipher Block Chaining

ZDO ‐ ZigBee Device Objects

OS ‐ Operating System

LOS ‐ Line of Sight

NLOS ‐ Non Line of Sight

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

Terminology Notation10102006

x

FPGA ‐ Field Programmable Gate Array

BS ‐ Base Station

AOA ‐ Angle of Arrival

TDOA ‐ Time difference of arrival

TOA ‐ Time of Arrival

TOF ‐ Time of flight

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

1

1 INTRODUCTION 11 Introduction to the study Advancement in wireless sensing technologies has encouraged in developing a model for indoor object‐tracking application using low power IEEE 802154 compliant radios Stimulus used in this model is RSS This application is not only limited to object tracking in an indoor environment Concierge services enable users to become aware of nearest supporting facilities For example smart home applications such as multimedia appliances that forward multimedia stream to the nearest video screen can be achieved with a home positioning system [3] In the field of robotics a robot can navigate by itself using the assistance of indoor positioning system [2] Granularity of location information is most important in location determination applications For example to locate a book in a library requires coarse grained granularity whereas locating a user requires coarse grained granularity Position estimation refers to a process used to obtain location information of a MS with respect to a set of reference positions within a pre‐defined space A system deployed to determine or estimate the location of an entity is called a position location system or positioning system A wireless indoor positioning system refers to a wireless network infrastructure that provides indoor location information to any requesting end user One of the key technical elements allowing the realisation of this vision will be the knowledge of the accurate position of the personobjects Already today accurate location mechanisms such as GPS are commercially available Many more will become part of our daily life with the introduction of third generation mobile phones But none of these techniques will allow the meter or sub‐meter level of accuracy within buildings which is necessary for smart houses Hence this report introduces an object tracking application in an indoor environment using IEEE 802154 compliant radios which gives meter level accuracy First this chapter presents the background of indoor positioning systems identifies the challenges of such systems and briefly describes the scope of indoor positioning systems based on location fingerprints Next the objective of study the overview of approaches and the contributions are presented Finally the scope and organization of thesis work is outlined

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

2

12 Background The success of outdoor positioning and applications based on the GPS provides an incentive to the research and development of indoor positioning systems It has been developed over several decades and it relies on constellation of satellites Ranges to several satellites are used in multi‐lateration procedure to infer the position of the receiver This localization scheme requires hardware that is both expensive and consumes significant power As a result indoor positioning systems require alternative means to detect the MS location without relying on the direct signal from GPS satellites Infrared RF and ultra sound signals are major technologies used for indoor positioning systems Unlike outdoor areas the indoor environment imposes different challenges on location discovery due to the dense multi‐path effect and building material dependent propagation effect Thus an in‐depth understanding of indoor radio propagation for positioning is crucial for efficient design and deployment The existence of radio connectivity and the attenuation of radio signal with distance are attractive properties that could potentially be exploited to estimate the positions of small wireless devices featuring low‐power radios RSSI a standard feature in most radios has attracted a lot of attention in the recent literature for obvious reasons RSSI eliminates the need for additional hardware in small wireless devices and exhibits favourable properties with respect to power consumption size and cost As mentioned previously RF‐based location tracking has been widely studied [5 6] Given a model of radio signal propagation in a building or other environment RSSI can be used to estimate the distance from a transmitter to a receiver and thereby estimate the position of a mobile node However this approach requires detailed models of RF propagation and does not account for variations in receiver sensitivity and orientation Table 1 below briefly summarizes performance of existing indoor positioning system based on location fingerprints and WLANrsquos All the systems mentioned below in table 1 follow IEEE 80211 protocol standards The major performance metrics studied by all systems are positioning accuracy which is a form of error measurement The accuracy of the location information is usually reported as an error distance between the estimated location and the actual mobile location However the report of accuracy should include the confidence interval or percentage of successful location detection which is called the precision The system parameters of existing systems are summarized in table 1 and the best reported performance of these systems are listed in table 2

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

3

System Spacing(m) Positions SamplesPos APs Orientation Environment

RADAR [6] Non

uniform 70 80 3 4 Hall-way Saha et al [9] 3 19 1200 3 NA 1-floor

Roos et al [10] 2 155 40 10 NA 1-floor Battiti et at [11] NA 257 NA 6 NA 1-floor Ladd et al [2] 3 11 1307 5 2 Hall-way

Prasithsangaree et al [12] 153 60 40

2 to 7 4 1-floor

Youssef et al[13] 15 110 300 4 NA Hall-way Xiang et al [14] NA 100 300 5 4 1-floor

Table 1 Comparison of Indoor Positioning Systems [7]

System Algorithm Type Accuracy and Precision RADAR [6] Nearest Neighbour within 7 feet 38

Saha et al [9] Nearest neighbour amp Neural Network no specified accuracy

90 Roos et al [10] Bayesian best within 828 feet 90

Battiti et at [11] Neural Network amp Weighted k-Nearest

Neighbour all within 16-17 feet 90 Ladd et al [2] Bayesian within 5 feet 77

Prasithsangaree et al [12] Weighted k-Nearest Neighbour

25 feet at 75 amp 40 fet at 95

Youssef et al [13] Bayesian within 7 feet more than

90 Xiang et al [14] Bayesian with RSS distribution model within 6 feet 90 Table 2 Performance comparison of Indoor Positioning Systems [7]

Based on the study of results obtained in various positioning systems as in table 2 we have developed a model for an indoor positioning system using SENTIO [8] platform based on RSSI

13 Objectives In the last two or three years a number of theoretical practical andor simulation studies were done on the topic of object‐tracking While these studies are useful they are too general and provide little guidance for the actual deployment of sensor networks for real life location‐tracking of an object This thesis focuses on developing an object‐tracking application and prescribes sensor network configurations that work well with our algorithms I implemented my software using SENTIO [8] platform for sensor networking The major issue addressed in this project are the accuracy and precision in tracking an object in a real‐world application and efficient ways to algorithmically analyze the collected raw data from the specific wireless sensor product

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

4

Although the focus is to develop a real‐world application using WSN it also provides great opportunity to explore the new area of wireless communication overall

14 Research Questions My primary target is to find a method to determine the position of an object inside a building using ZigBee protocol standards based on RSSI The study addresses the following questions What is WSN What requirements do WSN need What are their main ideas and concepts What are the associated technologies What are the standards that sensor networks use

What are the existing hardware implementations that use this particular technology How can we build our new applications using WSN What do sensor network systems require for general or specific applications Are they applicable And what is the appropriate design for sensor network implementations in order to combine them with other existing systems At this point we describe and evaluate the specific object‐tracking application to be built

15 Scope The study area of the thesis is an overview of the WSN technology study of SENTIO platform and implementation of an object tracking application Thus the study is divided into two parts Part one is an overview of WSN it includes study of SENTIO platform and IEEE 802154 protocol standards The second part describes the implementation of the object tracking algorithm The object tracking application receives and uses the raw values returned by the sensor network system to produce clear and meaningful outputs Schema of thesis work is as shown in figure 1

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

5

Figure 1 Schema of thesis work

16 Methodology The ldquoIndoor Local Positioning System for ZigBee based on RSSI ldquois based on ldquolocation fingerprintingrdquo technique Location fingerprinting refers to a technique that exploits the relation between any measurable physical stimulus and a specific location This approach uses empirical measurements of RSSI to estimate location By recording a database of radio ldquosignaturesrdquo along with their known locations a mobile node can estimate its position by acquiring a signature and comparing it to the known signatures in the database All of these systems require the database of signatures which should be collected manually prior to the system installation and rely on central server (or the userrsquos mobile node) to perform location calculation For example in RADAR [6] technology the RSSI is the stimulus This type of positioning system does not require specialized hardware other than the common wireless network interfaces with received signal strength measurement capability thus it is simple to deploy compared to other techniques Unlike outdoor counterpart systems which can use AOA and TDOA techniques effectively indoor positioning systems encounter the problem of NLOS and the dense multi path effect that render these two techniques ineffective or complex for practical implementation It is also difficult for a MS to always see three or more access points or base stations in indoor environment which is essential for triangulation by AOA and TDOA Location fingerprinting can also be implemented as software based positioning system which can reduce

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complexity and cost Generally the deployment of fingerprinting based positioning systems can be divided into two phases First in the off‐line phase the locations where the fingerprints are required is pre‐determined and then the fingerprints are collected by performing a site‐survey of the RSS from multiple APs The distance between two closest positions is called grid spacing and usually reported in meters or feet However some points may be omitted due to inaccessibility The RSS is measured at each predetermined location to create a database of RSS patterns of the indoor environment The database of RSS patterns is called a radio map The vector of RSS values at a point on the grid is called the location fingerprint of that point Second in the on‐line phase a MS will report a sample measured vector of RSS from different APs to a base station The base station uses a positioning algorithm to estimate the location of the MS The most common algorithm used to estimate the location is Euclidean algorithm It computes the Euclidean distance between the measured RSS vector and each fingerprint in the database and then the coordinates associated with the fingerprint that provides the smallest Euclidean distance is returned as the estimate of the position It is clear from the above discussion that the designer should not start out to do the site survey without proper system design objectives and guideline If the performance is not sufficient collecting more fingerprints in between previous location fingerprints may help It is a problem of choice as to how many access points are required for the system and what the minimum distance between physical positions on the grid should be in order to provide a good position resolution and best system performance The system parameters and factors that improve the accuracy and the precision performance are still not clear

17 Outline Chapter 2 provides literature review of ldquoIndoor Local Positioning System for ZigBee based on RSSIrdquo it introduces the basic components required for a positioning system general classification of indoor positioning system Finally it explains ldquoLocation Fingerprintrdquo technology used in this thesis work to determine the position of an object in a real‐world application It briefly explains the protocols and industry standards for wireless sensor networks Chapter 3 describes some properties of RSS which is a stimulus to determine

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the position of an object RSS relation with energy and on what parameters is the RSS value dependent Chapter 4 describes the model of an indoor local positioning system and its probability of returning the correct location It also explains its performance evaluation and its error distance distribution Chapter 5 describes results obtained from the model of positioning system Chapter 6 includes conclusions from the experimental analysis and future work

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2 LITERATURE REVIEW This chapter reviews the literature on wireless indoor positioning system as a means of providing an intellectual background for the thesis work First in section 21 the common components of indoor positioning system are described Then in section 22 discusses different means to classify indoor positioning systems In section 23 reviews of all relevant literature on location fingerprint technique Finally in section 24 protocols and industry standards for wireless sensor networks are explained

21 Common components of indoor positioning system Basically a wireless local positioning system consists of at least two separate hardware components a measurable unit that usually carries the major part of the system intelligence and a signal transmitterreceiver The transmitter in the simplest case is just a beacon Figure 2 represents basic hardware model of a positioning system

Figure 2 Block diagram representing hardware module

First the location sensing devices detect the signals transmitted by or received at known reference points using radio sensing technologies The sensing technique is based on signal strength level that converts the sensed signal into RSS Given a set of reference points the relative position of the MS can be derived from the signal characteristics of RSS The signal characteristics of RSS are unique for a location Then the microcontroller processes the position algorithm and estimates the position of object and displays it on PC

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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22 Classification of Indoor positioning systems Indoor positioning system can be categorized based on the stimulus used in particular application and measurement principle

221 Classification based on Stimulus Based on the stimulus used in an application WSN inherit certain characteristics and limitations on sensorrsquos signal The propagation delay diffraction reflection and scattering are the basic signal characteristics which affect all signal types The effective range available bandwidth regulatory constraints interference power constraints safety and cost of technology limitations The wireless signals commonly used for indoor positioning systems are infrared radio frequency and ultrasound Brief descriptions of the three major technologies are as follows

Infrared Infrared signal is an electromagnetic signal of a wavelength longer than that of visible light but shorter than that of radio waves Infrared is just below the visible spectrum of light in frequency and is radiated strongly by hot bodies Many objects such as people vehicle engines and aircraft generate and retain heat and as such are especially visible in the infrared wavelengths of light compared to objects in the background It cannot pass through walls or obstructions therefore it has a rather limited range of indoor environments Thus it requires a more sophisticated circuitry than ultrasound signals Indoor lighting interferes with this type of signal and causes problems in accurate sensing It generally has a range of around 5 m The infrared devices are usually small in size compared to ultrasound devices

Radio frequency The RF signal can penetrate through most of the building material therefore it has an excellent range in indoor environments The propagation speed is also high approximately 3108 ms There are unlicensed frequencies available freely for use This type of signal has the longest range compared to infrared and ultrasound Since the propagation waves are of higher frequency (24GHz) interference with other frequency components is less

Ultrasound Although ultrasound operates at low frequency bands (typical 40 kHz) compared to the other two signaling technologies it possesses a good precision for location sensing at a slow propagation speed of sound (343 ms) The advantages of ultrasound devices are their simplicity and that they are inexpensive However ultrasound does not penetrate walls

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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10

but reflects off most of the indoor obstructions It has a short range around 3 m to 10 m but has a 1 cm resolution of distance measurement The operating temperature influences the performance of ultrasound

222 Classification based on measurement principle Wireless positioning systems can be categorized by measurement techniques which are used to derive the position of mobile stations Mainly three different measurement principles are used today AOA RSS and propagation‐time based systems that can further be divided into two different sub‐classes TOA and TDOA Figure 3 shows taxonomy based on the technology and the technique classifications

Figure 3 Taxonomy of positioning system [7]

In AOA systems the position is calculated via goniometry With the use of directional antennas or antenna arrays the angle or bearing relative to points located at known positions is measured The intersection of several measured directions pointers the yields the position value The accuracy of this approach is limited by the possible directivity of the measuring aperture by shadowing and by multi‐path reflections arriving from misleading directions In TOA and TDOA techniques rely on the precision of timing between the signal transmitter and the receiver in order to use the propagation delay or TOF to calculate the distance between transmitter and receiver Therefore a precise synchronization is also very important in such systems By combining at least three distances from three reference positions triangulation can be used to estimate the mobile stationʹs location This type of technique will

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11

require a high accuracy clock in the communication system TDOA is more practical [1] Examples of location‐sensing systems that use time of flight are GPS the Active Bats [15] and the Cricket [16] Besides these time delay‐based techniques the distance between transmitter and receiver can also be determined from signal strength attenuation and direct distance measurement (such as dead reckoning) Fingerprinting or Location Pattern Matching this technique generally requires only measurement of RSS or other non‐geometric features at several locations to form a database of location fingerprints To estimate the mobile location the system needs to first measure the received signal strength at particular locations and then search for the pattern or fingerprint with the closest match in the database This technique does not require the mobile station to see at least three base stations or access points in order to determine the location The disadvantage of this technique is that it is very time‐consuming to perform an exhaustive data collection for a wide area network such as in outdoor positioning systems

23 Indoor local positioning using ZigBee based on RSSI This section reviews relevant RF‐based indoor positioning systems which can be used to locate stationary objects and track mobile users The impressive growth of IEEE 802154 wireless PANs (WPAN) in recent years suggests an interesting future for the location fingerprinting technique The system is quite flexible because system designers can select whether to have a centralized positioning server or let the mobile to determine its own position However the fingerprinting technique requires a training phase (off‐line phase) to collect location fingerprints for all positions in the operating area before the actual deployment (on‐line phase) [7] The following discussion is divided according to the positioning system components First the common form of location fingerprinting and its relationship with physical locations are explained Finally location estimation algorithm is reviewed

231 Location Fingerprint A location fingerprint based on RF characteristics such as RSS is the basis for representing a unique location It is created under the assumption that each position or location inside a building has unique RF signature [4] Generally a fingerprint F is labelled with a location information L The location fingerprints and their labels (eg location information) are maintained in

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database and used during the on‐line phase to estimate the location The label and fingerprint are usually denoted as a tuple of (L F) Battiti et al [11] point out that the location information L for indoor location can be recorded in two forms as either a tuple of coordinates or an indicator variable The tuple of real coordinates can vary from one dimension to five dimensions which include the three dimension space and two orientation variables expressed in spherical coordinates For instance a location information of a two‐dimension system with an orientation could be expressed as a triplet L = (x y d) | x y є R 2 d є North East South West The RSS was found to be more location‐dependent than the SNR because the noise component is rather random in nature However the RSS itself fluctuates over time for each access point and location Each RSS element can be considered as a random variable therefore it can be captured by recording its descriptive statistics parameters approximating its distribution or maintaining the whole measurement dataset These approaches of RSS representation result in different procedures for location estimation algorithm Regardless of the approach the location fingerprint is usually denoted as an array or vector of signal strength (random variables) received at any position in the location‐based service area The size of the vector is determined by the number of access points that can be heard To create a basis fingerprint such as in a number of samples of vectors of signal strength are collected over a window of time for each position This basis is called a prototype Then the average RSS of each access point is calculated and recorded as an element in the location fingerprint For an area that can receive signals from N access points the location fingerprint can be expressed as a vector of average RSS elements βi

F = (β1 β2hellip βN)T Besides the basis location fingerprint the samples of location fingerprint measured during the on‐line phase are also important for the system that tracks the mobile object The number of samples should be selected appropriately to represent the location fingerprint for the mobile application A step in statistical analysis method called pre‐processing is another important issue needed to be considered because it can impact the estimation of dependency between location fingerprint and the location information easier The pre‐processing refers to a step that cleans the raw data (in this case the training set) before any further operations or analysis The cleaning may consist of encoding dimensionality reduction (reduce unnecessary elements) feature extractionselection clustering and outlier elimination Roos et al [10] point out that the pre‐processing enables faster location estimation and

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reduce the noise from the training data The process that creates the basis of location fingerprint discussed above could be considered as a part of pre‐processing

232 Location Estimation Algorithm Location estimation algorithms or positioning algorithms are procedures that exploit dependency between location information and location fingerprint basis in order to determine a position or location from samples of RSS signals Position algorithms can be classified into deterministic and probabilistic types based on the approaches that model the relationship between location fingerprints and location information The deterministic type of algorithms are those that based in the nearest neighbour classifiers and neural network classifiers The probabilistic type of algorithms is those that are based on the Bayesian inference and statistical learning theory such as support vector machines (SVR) The algorithm used in this project is discussed briefly

Euclidean distance method The Euclidean distance method is a deterministic approach of position estimation because they require only a set of constant location fingerprints which includes mean vectors In order to determine the location a form of discriminate function is used to classify a sample of RSS fingerprint into a position The mean or average RSS vector is a centre of a mass which represents each class of location fingerprint Suppose a set of l location fingerprints is denoted by F1 F2hellip Fl exist and each fingerprint has a one‐to‐one mapping to a set of positions L1 L2hellip Ll A sample of an RSS fingerprint measured during an on‐line phase is denoted as S which can be another mean or average RSS vector of small window of RSS samples Assuming that an indoor positioning system only considers the average RSS from N access points as a location fingerprint the sample of RSS vector is S = (s1 s2 helliphellip sN)T and each location fingerprint i in the database can be expressed as Fi = (β1 β2helliphellip βN)T The simplest closeness metric is a distance measurement in signal space denoted as the Dist () function [6] Thus the simple procedure of nearest neighbour algorithm is expressed as picking the fingerprint j that has the shortest signal distance

Dist(S Fj) le Dist(S Fk) for all k ne j A generalized weighted distance Lp summarized by [12] can be used to calculate different forms of distance in signal space as

Lp = 1N pN

i

pPiSiWi 1

1)||1(sum

=

minus

where N is the number of access points wi is a weighting factor (wi le 1) and p is the norm parameter starting from 1 The weighting factor wi is a bias

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14

parameter that can demote or promote an important RSS component in the fingerprint

24 Protocols and Industry standards for WSN Although many applications for WSN are proposed the communication protocols supporting them remain mainly unexplored and diverged This is because the area of interest is new each implementation is application specific and the components of WSNrsquos have a lot of constraints Wireless sensor networks use the wireless medium to communicate However the traditional communication protocols that support wireless communications especially ad‐hoc mesh networks may not be well suited for them WSN communication is an active research area and many algorithms and protocols have already been proposed This section introduces two proposed standards the IEEE 802154 and the ZigBee The IEEE 802154 covers the physical layer and the MAC layer of low‐rate WPAN The ZigBee is ldquoan emerging standard that is based on the IEEE 802154 and adds network construction (star networks peer‐to‐peermesh networks and cluster‐tree networks) application services and morerdquo

241 IEEE 802154 Holger and Willig (2005) [17] mention home automation home networking and home security as possible applications for IEEE 802154 They add that ldquomost of these applications require only low‐to‐medium bitrates (up to some few hundreds of kbps) moderate average delays without too stringent delay guarantees and for certain nodes it is highly desirable to reduce the energy consumption to a minimumrdquo The IEEE 802154 standard (2003) defines the device types that can be used in a LR‐WPAN A device can be a FFD or a RFD The RFD can be used in simple applications in which they do not need to transmit large amounts of data and they have to communicate only with a specific FFD The FFD can work as a PAN coordinator as a coordinator or as a simple device It can communicate with either another FFD or a RFD In keeping with the application requirements the LR‐WPAN operates in a star or peer‐to‐peer topology (figure 4) In the star topology the RFD communicates with a single controller the PAN coordinator The PAN coordinator can perform the same function as the RFD but it is also responsible for controlling the PAN ldquoit initiates terminates or routes communication around the networkrdquo [18] The peer‐to‐peer topology

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supports ad‐hoc mesh multi‐hop networking Any device in the peer‐to‐peer topology can communicate with any other device within its communication range however this topology also has a PAN coordinator All the devices in a LR‐WPAN have a unique 64‐bit address This or a short address allocated by the PAN coordinator can be used inside a PAN Additionally each PAN has a unique identifier The combination of the PAN identifier and the sort addresses allows communication across different PANs (IEEE 802154 Standard 2003)

Figure 4 Star and peer‐to‐peer topologies in LR‐WPAN [18]

The LR‐WPAN based on the OSI seven‐layer model has the layered architecture presented in figure 5 The application and the network layer are the upper layers in the LR‐WPAN architecture but are outside the scope of the IEEE 802154 standard Only the physical layer ldquowhich contains the RF transceiver along with its low‐level control mechanismrdquo and the MAC layer ldquothat provides access to the physical channelrdquo are included in the standard and will be introduced in the following sections Finally the Type I 8022 LLC and the SSCS are intermediate sub layers supporting communication with the above layers (IEEE 802154 Standard 2003)

Physical Layer The 802154 standard specifies two different services that the PHY provides The PHY data service controls the radio and thus the transmission and reception of the PPDUs In addition the management service performs ED in the channel The management service also performs CCA before sending the messages and provides LQI for the received packets

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Figure 5 WPAN architecture [18]

Three different bands are defined by the standard The 868‐8686 MHz for Europe the 902‐928 MHz for North America and the 2400‐24835 MHz worldwide All of them belong in the ISM radio bands and they are using DSSS Each of the bands supports different a data rate and it uses a different modulation technique chip rate and number of channels Moreover if the system does not use the 2450 MHz frequency it operates in both the 802 MHz and 902 MHz frequencies Table 3 summarizes the supported bands and data rates (IEEE 802154 standard 2003)

PHY (MHz)

Frequency Band (MHz) Modulation

Bit Rate Number of Channels

868-8686 BPSK 20 1 868 915 902-928 BPSK 40 10

2450 2400-24835 O-QPSK 230 16 Table 3 Frequency bands and data rates for IEEE 802154 based on the IEEE

802154 standard [18] The physical layer uses PPDU packets to communicate Table 4 demonstrates its structure The LSB is always transmitted and received first The SHR contains the preamble and the SFD fields which helps receiver

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synchronization The 8‐bytes preamble contains only zero and is used for synchronization The SFD contains a specific sequence of one and zeros and specifies the beginning of the frame The PHR contains the payload length Packets with a length of 9 or more bytes are MPDU as the next section further explains Packets with length 5 are used for MPDU acknowledgements The payload part of the PPDU encloses the MAC layer packet Finally the PPDU size can be up to 127 bytes [18]

4 bytes 1 byte 1 byte Variable

Preamble SFD Frame length

(7 bits) Reserved

(1 bit) Payload (PSU)

SHR PHR PHY Payload Table 4 Format based on the IEEE 802154 standard [18]

MAC Layer The MAC layer is the interface between the SSCS and the PHY layer Similar to the PHY layer the MAC layer supports two services The MAC data service is responsible for the transmission and reception of the MPDUs through the PHY data service The MAC management service if the device is a coordinator manages the network beacons It is also responsible for PAN association and disassociation frame validation and acknowledgment providing ldquoa reliable link between two peer MAC entitiesrdquo In addition it uses the CSMA‐CA for channel access and handles and maintains the GTS mechanism Finally it supports device security [18] The IEEE 802154 standard defines four different frame types the beacon data acknowledgment and MAC command frame All frame types are based on the general MAC frame format (table 5) The frame control field describes and specifies the above different frame types Every MAC frame comprises a MHR which consists of a frame control sequence number and the information field It also contains the MAC payload different frame types have different MAC payload fields The acknowledgment type does not have a payload Finally each frame includes a MFR which contains a FCS The data in the MPDU follows the same order as the PPDU the least significant bits are left in the frame and are transited first Octets2 1 02 028 02 028 Variable 2

Destination PAN Identifier

Destination Address

Source PAN

Identifier Source Address Frame

Control Sequence Number Addressing Field

Frame Payload FCS

MHR MAC

Payload MFRTable 5 General MAC frame format IEEE 802154 standard [18]

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Table 6 7 8 and 9 present the four different MAC frame types The beacon frame is transmitted periodically by the PAN coordinator It provides information about the network management through the super frame and GTS fields which are analyzed later in the section It also synchronizes the network devices and indicates the proper communication period for them The data frame payload encapsulates data from the higher layers When a device receives a packet it is not obliged to response with an acknowledgement packet Finally the command frame identifier and command payload fields of the command frame are used for communication between the network devices The command identifier specifies actions like association disassociation and data GTS or beacon request Octets2 1 410 2 Variable Variable Variable 2

Frame Control

Sequence Number

Addressing Fields

Super frame Specification

GTS fields

Pending Addressing

Fields Beacon Payload FCS

MHR MAC Payload MFRTable 6 Beacon frame format [18]

Octets 2 1 Variable 2

Frame Control

Sequence Number

Addressing Field

Data Payload FCS

MHR MAC

Payload MFR Table 7 Data frame format [18]

Octets 2 1 2

Frame Control

Sequence Number FCS

MHR MFR Table 8 Acknowledgement frame format [18]

Octets2 1 1 Variable 2

Frame Control

Sequence Number

Addressing Fields

Command Frame Identifier

Command Payload FCS

MHR MAC Payload MFR Table 9 Command frame format [18]

In the LR‐WPAN every PAN has its own coordinator The PAN coordinator manages the communication in the local area it has two options to use or not use the super frame structure The super frame (figure 6) uses network beacons If the coordinator does not want to use a super frame structure it suspends the beacon transmission However the beacon is important for device association and disassociation If the coordinator wishes to maintain close communication control in the PAN and to support low‐latency devices it usually uses the super frame A super frame determines a specific time

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period beacons bound it The beacon is transmitted in the first of the sixteen equal time slots that the super frame has It is used to ldquosynchronize the attached devices to identify the PAN and to describe the structure of the super framerdquo The super frame can have active and inactive periods All the communications have to be finished inside the super frame period In the inactive periods the devices can switch to the sleep mode but they have to be ready for the next beacon The active period is further divided into a CAP and a CFP During the CAP any device can communicate competing with the other devices in the PAN using slotted CSMA‐CA The CFP contains GTSs and follows the CAP A CFP may maintain up to seven GTSs and each GTS can reserve more than one time slot However the CAP should always be sufficient to allow new devices t join the PAN The GTSs are allocated to specific devices All the transactions must be completed inside the assigned time period (CAP GTS and CFP) (IEEE 802154 standard 2003)

Figure 6 Example of super frame structure [18]

In accordance with the 802154 standard (2003) three different types of data‐transfer exist In addition the types differ if the coordinator uses or does not beacons Data transfer from a device to the PAN coordinator is the first type (figure 7) For a ldquonon‐beacon‐enable networkrdquo it first senses the medium by using ldquoun‐slotted CSMA‐CArdquo and then a simple transmit to the data frame In a ldquobeacon‐enabled networkrdquo the sender waits for the beacon when it finds it ldquothe device synchronizes to the super fame structurerdquo In the specified time frame the sender again senses the medium by using ldquoslotted CSMA‐CArdquo and transmits the data to the coordinator In either case the coordinator has the option to acknowledge or not acknowledge the data reception after that the transaction is completed Data transfer from the coordinator is the next type described in the standard (figure 8) In a beacon‐enabled network the coordinator indicates a pending message through the beacon The messagersquos target device receives the beacon and if the message is pending it responses with a MAC command request

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message using slotted CSMA‐CA the coordinator may or may not acknowledge the command message and through slotted CSMA‐CA sends the pending message The device acknowledges the received message After that the coordinator removes the message from the beaconrsquos pending list and completes the transaction In a non‐beacon enable network the device periodically sends a MAC command‐request frame to the coordinator The coordinator acknowledges the data request Then by using un‐slotted CSMA‐CA if it has a pending message it transmits it If it has not it respond with a data frame with a ldquozero‐length payloadrdquo To complete the transaction the device acknowledges the data reception

Figure 7 Communication from a device to a PAN coordinator in (a) a beacon‐enabled network and (b) a non‐beacon enabled network [18]

Peer‐to‐peer is the last type of data transfer In this situation the devices are free to communicate with any other device within their communication range In a peer‐to‐peer PAN the devices can ldquoeither receive constantly or synchronize with each otherrdquo If they are receiving constantly to transmit data they use un‐slotted CSAM‐CA In the second case synchronization must be achieved first (IEEE 802154 standard [IEEE 2003]) The IEEE 802154 (2003) standard establishes MAC and PHY standards for low‐cost low‐power and high‐density node deployments In addition to the above PHY and MAC characteristics IEEE 802154 provides a security baseline including ldquothe ability to maintain an ACL and use symmetric cryptographyrdquo for data encryption The algorithm that is used for encryption is the AES However the higher level layers decide when security is need The upper layers are in general responsible for device authentication and key management The next section introduces the ZigBee standard which

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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21

encapsulates the IEEE 802154 and provides additional standardization for the higher levels

Figure 8 Communication from a PAN coordinator to a device in (a) a beacon‐enabled network and (b) a non‐beacon enabled network [18]

242 ZigBee Heily (2004) defines ZigBee as ldquoa rapidly growing worldwide non‐profit industry consortiumrdquo whose mission is ldquoto define a reliable cost‐effective low‐power wirelessly networked monitoring and control product based on an open global standardrdquo Figure 9 illustrates the areas of interest for different wireless communication standards

Figure 9 Overview of the coverage for different wireless communication

standards [17]

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ZigBee a new standard which became publicly available in June 2005 is based on the IEEE 802154 standard It expands the IEEE 802154 by adding the framework for ldquothe network security and applicationrdquo (Craig 2005) Figure 10 presents the IEEE 802154ZigBee stack and the areas of responsibilities

Figure 10 IEEE 802154ZigBee stack [20]

Craig (2005) mentions three networking topologies that the standard covers the star mesh and cluster tree (figure 11) The ZigBee standard works on top of the IEEE 802154 addressing schema by using the standard 64‐bit and the short 16‐bit addressing Kinney (2005) summarizes the ZigBee network layer responsibilities the successful establishment of a new the network and successful new device configuration addressing assignment network synchronization frames security and message routing ZigBee further distinguishes the concept of the physical devices (RFD FFD) by using the notion of ldquological devicesrdquo ldquoZigBee Coordinatorrdquo is the first type of logical devices It is responsible for initializing maintaining and managing the network Under the coordinator in the network hierarchy is the ldquoZigBee routerrdquo which is responsible for controlling the message routing between the nodes Finally the ldquoZigBee End Devicerdquo acts as the end point of the network

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structure The tasks that an end device can perform are specified in the ldquoApplication Profilesrdquo (Craig 2005)

Figure 11 IEEE 802154ZigBee stack [17]

The ZigBee specifications (2005) summarize the security services provided by ZigBee ldquokey establishment key transport frame protection and device managementrdquo ZigBee builds its security mechanism using symmetric key cryptography The security services also depend on the associated layer thus as the figure 10 shows the security mechanism covers the network and the application layer In addition if a MAC frame needs security protection the MAC layer is able to secure it Moreover the notion of end‐to‐end security is supported the source and destination devices have access and use the same share key In the MAC layer the 802154 AES mechanism provides the proper security The mechanism protects ldquothe confidentiality integrity and authenticity of the MAC framesrdquo (Kinney 2005) An auxiliary header field in front of the MAC payload indicates if the frame is encrypted or not The MAC framesrsquo integrity is supported by calculating and using a MIC at the end of the MAC payload In addition to the AES nonce is used to provide MAC confidentiality and authenticity Figure 12 illustrates a MAC frame with security For different security aspects the MAC layer uses different mode of the AES for the encryption it uses the AES in Counter (CTR) mode and for the integrity the

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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24

CBC‐MAC Finally the combination (CCM) of the above two modes is available providing both encryption and integrity (Kinney 2005)

Figure 12 ZigBee secure frame in MAC layer [17]

In the network layer the CCM (a modified MAC layer CCM mode) is used for encryption Because the network layer uses only the CCM mode a single key is used for all different security options The network layer security message format is similar to the MAC frame it is presented in the figure 13 Finally although the network layer is responsible for securing its layer messages the above layers specify the keys and the CCM option for each frame (Kinney 2005)

Figure 13 ZigBee secure frame in network layer [17]

Security in the application layer works similar to the network and MAC layers It uses the ldquolink keyrdquo or the ldquonetwork keyrdquo to secure the message and then encapsulate it inside a set of fields similar to the network format (figure 14) Other security responsibilities that the application layer has are to provide the ZDO and the applications with device management services key establishment and key transport (ZigBee specifications 2005)

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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Figure 14 ZigBee secure frame in application layer [17]

The ZigBee application layer contains the manufacturer‐defined application objects the ZDO and the application sub‐layer In addition to the security responsibilities the application sub‐layer binds devices based on their duties and needs maintains the binding tables and forwards messages between them The application sub‐layer also discovers the neighbour devices for a given device The ZDO is responsible for determining the devicersquos duty in the network for communicating using binding requests and for supporting security as was mentioned above The sub‐layer that implements the actual application is the manufacturer‐defined application object (Kinney 2005) The ZigBee specifications are a great step toward the wireless sensors networks standardization They cover all the OSI layers from the physical to the application providing guidance to the developers The specifications are the product of the ZigBee alliances a consortium of companies and academic institutes Thus they not only produce the specifications but also are the first use them The next section goes beyond the layer architecture and standardization and introduces the OS field another crucial element of WSNs

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3 RECEIVED SIGNAL STRENGTH (RSS) Majority of the existing methods leverage the existence of IEEE 80211 base stations with powerful radio transmit powers of approximately 100mW per base station Such radios are in a different class from the low power IEEE 802154 compliant radios that typically transmit at low power levels ranging from 52mW to 29mW [19] The wide availability of larger number of IEEE 802154 radios in the research community has revived the interest for signal strength based localization in sensor network Despite of rapidly increasing popularity of IEEE 802154 radios and signal strength localization there is a lack of detailed characterization of the fundamental factors contributing to large signal strength variation This chapter investigates some of the properties of RSS observed during testing and analysis of research work collected during the period of thesis The analysis of RSS values is needed to understand the underlying features of location dependent RSS patterns and location fingerprints An understanding of the properties of the RSS values for location fingerprinting can assist in improving the design of positioning algorithms and in deployment of indoor positioning systems

31 Parameters that affect RSS Different factors that affect the indoor RSS are the userrsquos presenceabsence hisher orientation time of the day building type and material distance from transmitter and type of radio These factors are by no means exhaustive Other possible factors are antenna orientation directionality and type Table 10 classify the parameters which affect RSS [7]

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

27

Effect on Factors Options

1 Proximity of user Userrsquos presence or absence

2 Orientation of user and terminal

North East South West

Data Collection

3 Make of Radio 4 Time of measurement Time of day amp days of

week 5 Period of measurement

Second minute hour

7 Interference Co‐channel adjacent co‐channel

Statistics

8 Building environment Small offices or large hall

Table 10 Classification of factors the affect RSS [7]

Transmitter variability Different transmitters behave differently even when they are configured exactly in the same way In practice this means that when a transmitter is configured to send packets at a power level of d dBm then the transmitter will send these packets at a power level that is very close to d dBm but not necessarily exactly equal to d dBm This can alter the received signal strength indication and thus it can lead to inaccurate distance estimation

Receiver variability The sensitivity of the receivers across different radio chips is different In practice this means that the RSSI value recorded at different receivers can be different even when all the other parameters that affect the received signal strength are kept constant

Antenna orientation Each antenna has its own radiation pattern that is not uniform In practice this means that the RSSI value recorded at the receiver for a given pair of communicating nodes and for a given distance between them varies as the pair wise antenna orientations of the transmitter and the receiver are changed

Multi‐path fading and shadowing in the RF channel In indoor environments the transmitted signals get reflected after hitting on the walls andor on other objects in the room such as furniture Both the original signal and the reflected signal reach the receiver almost at the same time since they both travel at the speed of light As a result of this the receiver is not able to

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

28

distinguish the two signals and it measures the received signal strength for both of them

32 Related work analysis A number of indoor location tracking systems have been proposed in the literature based on RF signals ultrasound infrared and some combinations of these As mentioned previously RF based positioning system are widely studied as shown in Table 2 Given a model of radio signal propagation in a building or other environment RSS can be used to estimate the distance between the transmitter and receiver Most of the algorithms (Table 2) have been evaluated in practice using 80211b compliant wireless radios and seem to produce a location error of 15ft to 20ft for an indoor network area of approximately 225ft x 144ft According to the results of this study all the localization techniques produce approximately the same location error over a range of environments Ecolocation [22] Mote Track [5] are two of the few localization algorithms that have been evaluated on a real sensor network that uses a low power wireless radio Ecolocation reports a location error of 10ft for a very small outdoor network deployment area (26ft x 49ft) Mote Track reports a location error of approximately 13ft for an indoor network deployment area of 18751ft2 In general the RSSI based localization algorithms can be divided into two broad categories the map based and the distance (or area) prediction based algorithms In the case of the map based algorithms the deployment area is divided into a large set of sample points that cover the whole area Most techniques collect one or more signal strength measurements from all visible beacon nodes and for each sample point Each sample point is then mapped to either a signal strength vector or a signal strength probability distribution This process generates an RSSI fingerprint map which is unique with respect to the beacon locations and the deployment area This fingerprint map is stored to a central base station A node that is not aware of its location collects one or more signal strength measurements from all the visible beacon nodes and creates its own RSSI fingerprint that is sent to the central base station The base station computes nodersquos location as the sample pointrsquos location whose RSSI fingerprint distance from the unknown nodersquos RSSI fingerprint is the minimum among all the sample points Despite the fact that the map based RSSI localization methods produce relatively small location errors they are not attractive mainly because they require an excessive amount of profiling data and because they are not fully distributed The detailed profiling of a building is a major holdback because it is complex time consuming and depends on beacon locations Therefore for different buildings or even for the

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

29

same building and different beacon locations different RSSI maps have to be obtained The second category of RSSI based algorithms are the distance (or area) prediction algorithms which focus on minimizing the amount of profiling data In distance prediction based methods raw RSSI data are directly mapped to distances through a signal propagation model In some cases distances are not explicitly measured but they are taken into consideration through assumptions on the underlying distributions In the case of outdoor deployments where there are no obstacles a single signal propagation model is all the profiling data required In the case of indoor deployments the same applies but now the signal propagation model might be different across different buildings with different furniture and wall arrangements Given an underlying signal propagation model raw RSSI data can be mapped to specific distances These distances can be either used directly in order to perform localization or assigned a probability and used in a learning basedprobabilistic localization algorithm Obviously the accuracy of the distance prediction based methods depends heavily on the accuracy of the signal propagation model used for translating RSSI values to distances

33 Properties of RSSI Changes in signal due to propagation indoors are difficult to predict because of dense environment and propagation effects such as reflection diffraction and scattering The multi‐path fading effect which is the result of either constructive or destructive combination of multiple signal copies at the receiver causes the received signal to fluctuate around a mean value at a particular location The received signal is usually modelled by the combined effects of large‐scale fading and small‐scale fading The large‐scale fading component describes the signal attenuation as the signal travels over a distance and is absorbed by material such as walls and floors along the way to the receiver This component predicts the mean of the RSS and usually has a lognormal distribution On the other hand the small scale fading component explains the dramatic fluctuation of the signal due to multi‐path fading On the other hand the small scale‐fading component explains the dramatic fluctuation of the signal due to multi‐path fading If there is no LOS component the small‐scale‐fading is often modelled with a Rayleigh distribution Such scenarios are commonly called NLOS If there is a LOS component the small‐scale‐fading is typically modelled by a Rician distribution Note that when the RSS is measured the measurement averages out the small‐scale‐fading effects

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

30

4 OBJECT TRACKING 41 Introduction This chapter presents the object‐tracking application the main objective of this thesis work The application is an algorithmic‐software implementation written in Embedded C which is capable of receiving data messages and generating object position estimation data The implementation of the object‐tracking application makes use of the SENTIO platform and uses the background knowledge introduced in the preceding chapters The motivation for the object‐tracking application is to determine the position of an object in indoor environments (Eg Home) The application acts as an interface which collects the raw data that the sensors generate (numerical values) It makes the proper comparisons and algorithmic processes to identify and track the object while it is static inside the area covered by the wireless sensor network Finally with the detection and tracking data it alerts the user or another system The following sections provide an overview of the object‐tracking application

42 Overview of hardware product Before describing the object tracking application it is important to know the characteristics of hardware product The SENTIO platform (figure 15) is built in modular fashion and has the following key features [8]

Flexibility This platform is very flexible in such a way that it is made easy to add or change the hardware for each sensor task depending on the requirement in the WSN For a specific platform this can be hard or sometimes even impossible to accomplish

Sensor resources Resources in a sensor module includes the ability of usage of controllerrsquos resources as much as possible such as ADC‐channels interrupts and digital Input‐Outputrsquos Local voltage regulation should also be possible since many sensors require higher supply voltages than digital Integrated Circuits do Further support of a shut‐down mechanism to power down the sensor modules when not needed

Computation and memory The processor module based on microcontroller should support many devices in a microcontroller family and support different power supply voltages enabling full range of clock frequencies All power‐down modes of the devices should be

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

31

supported The platform should support different processor modules including FPGArsquos and very simple microcontrollers

Communication The focus should be on enabling an IEEE 802154 compliant communication interface With or without ZigBee stack this will provide a flexible platform for exploring different network topologies Support for power‐down of the entire communication module should be supported

Cost and form factor High flexibility is in conflict with low cost and small size The primary objective should be flexibility but the size should be considered in order to enable sensor device integration in systems where space is limited

Figure 15 SENTIO Platform [8]

The communication subsystem of the SENTIO sensor node was designed so that the correct operation of the radio chip is ensured The radio chip is powered by the on‐board voltage regulator and thus fluctuations in the battery voltage level do not affect the operation of the communication subsystem This platform uses Chipcon CC2420 IEEE 802154 radio transceiver operates in the 24GHz ISM band and includes a digital direct sequence spread spectrum (DSSS) modem providing a spreading gain of 9dB and an effective data rate of 250Kbps It was specifically designed for low power wireless applications and supports 8 discrete power levels 0dBm minus1dBm minus3dBm minus5dBm minus7dBm minus10dBm minus15dBm and minus25dBm at which its power consumption varies from 29mW to 52mW [21] A built‐in received signal strength indicator gives an 8‐bit digital value RSSIVAL The RSSIVAL is always averaged over 8 symbol periods (128μs) and a status bit indicates when the RSSIVAL is valid (meaning that the receiver was enabled for at least 8 symbol periods) The

Antenna

Communication Module

Processing Module

Sensing Module

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

32

power P at the RF pins can be obtained directly from RSSIVAL using the following equation

P = RSSIVAL + RSSIOFFSET [dBm] where the RSSIOFFSET is found empirically from the front‐end gain and it is approximately equal to minus45dBm In the next sections when we refer to the RSSI value we refer to the RSSIVAL and not the power P unless otherwise stated

43 Experimental Testbed The experimental test bed for object tracking application is as shown in figure 16 Experiment was conducted in Masterrsquos room at Mid Sweden University A DELL laptop is connected to SENTIO platform and software were primarily used to collect samples of RSS from APrsquos The software monitors IEEE 802154 radio frequency channels which operate in the 24GHz band

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

1030

BC - 1030

AP 3 AP 1

AP 2

AP 4

BS

HEWLETT

PACKARD

Figure 16 Layout of Masterʹs Room

A three dimensional scalable testbed is used for indoor sensor network deployments The testbed illustrated in figure 16 is a 3‐D structure measuring 16m (L) x 12m (W) x 3m (H) and it is designed to host a large number of static nodes as shown in figure 16 Arrow marks in the above figure represent the location where fingerprints were taken The centrepiece of our infrastructure

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

33

is the SENTIO sensor node [8] an open‐source general purpose sensing platform designed with AT Mega 128 microprocessor and the IEEE 802154 compliant CC2420 radio from Chipcon [19]

44 Obtaining data from the sensor network The next important step in the development of the object tracking application is to obtain the data from the sensor network and send it to the base station From there the data is transmitted through a serial port to the computer that runs the object tracking application When the data arrives at the serial port the application must capture it This paragraph gives a brief explanation of how and what kind data is transmitted by the static object fixed sensors and how this data is being used to estimate the position of the object First the static object transmits Beacon packet continuously which is received by all the fixed APrsquos The beacon packet format is as shown in table 6 Then the fixed APrsquos calculates the 8‐bit value of RSSI from the transmitted beacon packet and then sends the resultant RSSI value as a form of data packet (table 7) to the BS which calculates the position and sends back to the object

45 Object tracking application Testing and Evaluation The purpose of this section is to discuss and evaluate the object tracking application Finally we are ready with the experimental testbed as shown in figure 16 As shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors placed at the corners of the masterrsquos room BS is placed at the centre of the room connected to a PC All the nodes are power connected before an actual measurement takes place At first the object is placed at random position inside the room and transmits beacon packets continuously Then these packets are received by the fixed APrsquos which calculate the RSSI from the packet and then averages it until it receives 50 beacon packets and then transmits the data to the BS (figure 17) Then the BS waits until it receives the data from all the APrsquos Once it receives from all the APrsquos it calculates the position of the object (figure 18) The application was developed to demonstrate a real world use of a WSN in object tracking In evaluating the final position obtained with respect to the actual position we conclude that the object‐tracking was successful Although

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

34

it is a research prototype object tracking application is a simple stable stand‐alone system capable of demonstrating an important application of WSN It takes raw data from the WSN aggregates it evaluates it and based on the evaluation tracks the object The accuracy of estimating the position is less than 5m Table 11 below shows a log of readings taken during test

Start

Intialize micro‐controller and transeciever in receieve mode

If received_packet=

Beacon

Average RSSI values

Increase the counter

If countergt1000050

A

No

Yes

B

B

Yes

No

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

35

A

Reset counter

Set transceiever in transmit mode

Transmit averaged RSSI to Main sensor and set it back to receive mode

10000 samples ‐ Offline

50 samples ‐ Online

Figure 17 Flow chart representing the algorithm of fixed sensors

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

36

Start

Intialize micro‐controller and transeciever in receieve mode

Received RSSI values from all fixed Sensors

Open radio map

Calculate Euclidean distance between obtained RSSI values from fixed sensors and fingerprints

in radio map

No

Yes

Bubble sort for least error in signal strength values

A

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

37

A

Get the location of the object from the radiousing the least error value

Display location of object on PC

End

Figure 18 Flow chart representing the algorithm of main sensor

Table 11 Accuracy of positioning system

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

38

5 RESULTS This chapter presents how the system parameters such as number of samples mean or median etchellipare finalized All the tests are performed in masterrsquos room at Mid Sweden University This chapter is divided into three subsections First in Section 51 it reviews results obtained from samples test Section 52 discusses one dimensional test results and finally section 53 discusses results of two dimensional tests

51 Measurement setup A Dell laptop was connected to a SENTIO platform and is loaded with software (Appendix 2) This setup was used to determine number of samples required during object tracking mean or median position estimation algorithm and one dimensional test The setup is as follows

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

103

0

BC - 1030

AP 1AP 2

BS

HEWLETT

PACKARD

Figure 19 Test bed for one dimensional test in Masters Room

Two SENTIO nodes (AP1 and AP2 as shown above) were separated by a distance of 10 meters and these sensors are known as fixed sensors which are fixed to the ceiling such that there antenna is faced towards the ground They are linear is position A BS was connected to Dell laptop and it was placed in such a way that its antenna was faced towards the ceiling Another SENTIO

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

39

node was used as an object and its orientation was same as BS Figure 19 represents the complete setup for object tracking application Mobile node was moved between these fixed ones at regular intervals of one meter and RSSI values were logged at each interval

52 Number of samples test This test was performed to determine number of samples required for accurate positioning and either mean of the obtained RSSI values or median of them Figure 19 represents the setup for number of samples test Once the setup is ready the measurements are taken as follows

1 An object was tagged with SENTIO node and was placed stationary linear to the fixed sensors

2 10000 samples (RSSI) were taken at regular intervals of 1 meter up to 10 meters

3 The sample values were logged at every interval 4 Using these logged samples I have plotted graphs such as distribution

of RSS values (figure 20 21) meanmedian at intervals of 1 meter using MATLAB

5 The mean and median for various numbers of samples is as shown in figure 22

Figure 20 Histogram plot of RSSI values at 1m and 4m

Figure 21 Histogram plot of RSSI values at 6m and 8m

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

40

Figure 22 Distance vs RSSI

From the above graphs following conclusions were made 1 From the histogram plots we can observe that the distribution of RSSI

values follow Gaussians distribution law 2 As we can see from figure 22 that median curve has drastic changes

when compared to mean curve Hence mean is considered while estimating the position

3 From the observations we conclude that the number of samples does not matter since mean curve tends to be same for various numbers of samples

4 Hence 10000 samples are considered in order to get a proper location fingerprint during off‐line phase and during online phase 50 samples are enough to estimate the position of object

53 0ne dimensional test Measurement setup for one dimensional test is as shown in figure 19 This test was conducted to check the performance of algorithm and how accurate it was At first ie during offline phase fingerprints at each interval averaging 10000 samples were taken During online phase Euclidean algorithm was used estimate the position averaging 50 samples of RSSI From the results we

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

41

can realize that the error was less than 2 meters Table 12 shows the result of location estimation in one dimensional case

Table 12 One dimensional position estimation results

54 Two dimensional test This test is the final stage of my thesis work Experimental testbed for this stage is as shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors fixed at the corners of the room and a BS at the centre of the room There are 18 pre‐determined locations where the fingerprints (RSSI) were taken The fingerprints are as shown in Appendix 1 All the readings were taken in presence of userrsquos The algorithm for position estimation is shown in Appendix 2 Position estimation test is performed as follows

Tagged object is placed at random inside the masterrsquos room All the sensors including the base station and object are switched on Using the algorithm (Appendix 2) the BS estimates the position of the object and displays it on the PC

This test was performed using various numbers of samples to test which has a better accuracy Figure 23 represents the error rate using various numbers of samples From figure 23 we can conclude that 50 numbers of samples is enough to estimate the position of object with an accuracy of 3m

55 Causes of error in location determination The randomness of RSS is the major cause of error in any indoor

positioning system that uses the RSS for location inference If there is no randomness in the RSS and its location fingerprint every indoor positioning systems of this type will have no location detection error For example I have recorded RSS at one particular location during different times of the day From figures 24 and 25 you can observe that RSS values are different at different times of day

The performance of indoor positioning systems depends greatly on the separation of location fingerprints A location fingerprint

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

42

corresponding to a location can be identified correctly if it is difficult to classify it as another fingerprint by a pattern classifier

Path loss is one of the basic signal propagation properties of the RSS that best describes attenuation of transmit signal power over a distance between an access point and a mobile device

Presence of Userrsquos or userrsquos orientation affects RSS

Figure 23 Error Rate vs Number of samples

Figure 24 RSS distribution at 214 pm

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

43

Figure 25 RSS distribution at 100pm and 1100 am

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

6 DISCUSSIONS10102006

44

6 DISCUSSIONS 61 Summary and Conclusion While empirical results and performance studies of positioning systems based on location fingerprint have been presented in this report analytical model that can be used as a framework for efficiently designing the positioning systems are not available This thesis design work uses general principles of position estimation such as position estimation of sensors grid spacing etc This design work focuses on accuracy and precision The concept of location fingerprint technique based on RSS us explained in detail in chapter 2 It is found that RSS is random It is possible to approximate the RSS as being normally distributed While the RSS random process is non‐stationary the mean is more or less constant and could be used as the fingerprint of the location Model designed for position estimation is as shown in chapter 4 Accuracy and precision are the two major performance matrices in this study Uniform grid spacing has been used From the experimental analysis I have obtained an accuracy of 3 meters and 35 precision The sensitivity analysis among the system parameters and the performance matrices were done using the model as shown in chapter 4 For instance our study found that the minimum distance between two adjacent positions also affects the positioning performance This is due to the randomness of RSS patterns induced by the ever changing indoor environment

62 Future work The research in this thesis provides a ground work for the future study of efficient design of indoor positioning system The RSSI based position estimation still has limitations First the RSSI value is time dependent Thus the future study should address this issue Second the movement of object is not considered Third orientations of sensors affect the performance which should be taken care off Fourth to improve the performance of the system we need to find a solution to deal with those incomplete or missing data during the off‐line and the on‐line phase Fifth position estimation on multi‐floor and three‐dimensional coordinates is not available

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 110102006

45

APPENDIX 1

Location Fingerprints of Masters Room Time 1315 Observation Time 20 minutes Number of Samples 10000

People Presence SNo Co-ordinates AP1 (0x0001) AP2 (0x0002) AP3 (0x0004) AP4 (0x0008)

1 11 -24 -16 -27 -33 2 13 -26 -8 -34 -31 3 15 -43 -9 -39 -25 4 32 -21 -11 -27 -33 5 61 -6 -23 -32 -28 6 64 -20 -31 -25 -34 7 35 -23 -35 -29 -38 8 37 -29 -35 -22 -27 9 39 -38 -29 -21 -27 10 67 -27 -23 -20 -23 11 611 -26 -29 -23 -30 12 613 -35 -32 -2 -31 13 513 -34 -31 -20 -26 14 11 -39 -22 -29 -17 15 212 -37 -42 -28 -31 16 -212 -38 -33 -29 -6 17 -21 -42 -29 -33 -19 18 -38 -40 -23 -35 -22

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 210102006

46

APPENDIX 2

Begin

Open radio map and raw data files for write

Intialize radio map in memory

Check whether the

Packet is data or Beacon packet If data packet

Save the data to register

Check if data is

Received from all

sensors

Estimate the position of static

Display the position on PC

End Yes

No

A

A

Yes

No

Flow chart representing the main algorithm for position estimation

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

47

APPENDIX 3

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

48

Distribution of RSSI

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

49

REFERENCES [1] T S Rappaport J H Reed and B D Woerner ldquoPosition location using wireless communications on highways of the futureʺ IEEE Commun Mag vol 34 no 10 pp33‐41 Oct 1996 [2] A M Ladd K E Bekris G Marceau A Rudys L E Kavraki and D S Wallach rdquoRobotics‐based location sensing using wireless ethernetʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ02) 2002 pp 227‐238 [3] I Ramani R Bharadwaja and P V Rangan ldquoLocation tracking for media appliances in wireless home networksʺ in Proc International Conference on Multimedia and Expo (ICMEʹ03) July 2003 pp 769‐772 [4] K Pahlavan X Li and J P Makela ldquoIndoor geolocation science and technologyʺ IEEE Commun Mag vol 40 no 2 pp 112‐118 Feb 2002 [5] Konard Lorincz and Matt Welsh ldquoMote Track A Robust Decentralized Approach to RF‐Based Location Trackingrdquo [6] Paramvir Bahl and Venkata N Padmanabham ldquoRADAR An In‐Building RF‐based User Location and Tracking Systemrdquo [7] Kamol Kaemarungsi ldquoDesign of Indoor positioning system based on location fingerprint techiniquerdquo University of Pittsburgh 2005 [8] Fredrik Linnarsson Cheng Peng and Bengt Oelmann ldquoSENTIO A Hardware Platform for Rapid Prototyping of Wireless Sensor Networksrdquo [9] S Saha K Chaudhuri D Sanghi and P Bhagwat ldquoLocation determination of a mobile device using IEEE 80211b access point signalsʺ in Proc IEEE Wireless Communications and Networking Conference (WCNCʹ03) New Orleans LA Mar 2003 pp 1987‐1992 [10] T Roos P Myllymaki H Tirri P Misikangas and J Sievanen ldquoA probabilistic approach to WLAN user location estimationʺ International Journal of Wireless Information Networks vol 9 no 3 pp 155‐164 July 2002 [11] R Battiti M Brunato and A Villani ldquoStatistical learning theory for location fingerprinting in wireless LANrsquosʺ Technical Report Oct 2002 [Online] Available httprtmscienceunitnitraquobattitiarchive86pdf [12] P Prasithsangaree P Krishnamurthy and P K Chrysanthis ldquoAn indoor position location with wireless LANsʺ in Proc IEEE International Symposium on Personal Indoor and Mobile Radio Communications (PIMRCʹ02) Lisbon Portugal Sept 2002 [13] M A Youssef A Agrawala and A U Shankar ldquoWLAN location determination via clustering and probability distributionsʺ in Proc IEEE

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

50

International Conference on Pervasive Computing and Communications (PerComʹ03) Dallas‐Fort Worth TX Mar 23‐26 2003 pp 23‐26 [14] Z Xiang S Song J Chen H Wang J Huang and X Gao ldquoA wireless LAN‐based indoor positioning technologyʺ IBM Journal of Research and Development vol 48 no 56 pp 617‐626 SeptNov 2004 [15] R Want A Hopper V Falcao and J Gibbons ldquoThe active badge location systemʺACM Transactions on Information Systems vol 40 no 1 pp 91‐102 Jan 1992 [16] N B Priyantha A Chakraborty and H Balakrishnan ldquoThe cricket location‐support systemʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ00) Boston MA Aug 2000 pp 32‐43 [17] Holger K Willig A (2005) Protocols and Architectures for Wireless Sensor Networks London [18] IEEE (2003) Part 154 Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low‐Rate Wireless Personal Area Networks (LR‐WPANs) New York [19] Chipcon CC2420 802154 compliant radio httpwwwchipconcom [20] ZigBee Specification v10 ZigBee Alliance [21] Dimitrios Lymberopoulos Quentin Lindsey and Andreas Savvides ldquoAn Empirical Analysis of Radio Signal Strength Variability in IEEE 802154 Networks using Monopole Antennasrdquo [22] Kiran Yedavalli Bhaskar Krishnamachari Sharmila Ravula and Bhaskar Srinivasan ldquoEcolocation A sequence based technique for RF localization in wireless sensor networksrdquo Robert Bosch Research and Technology Centre Palo Alto CA

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

List of Tables10102006

vi

List of Tables Table 1 Comparison of Indoor Positioning Systems [7] 3 Table 2 Performance comparison of Indoor Positioning Systems [7] 3 Table 3 Frequency bands and data rates for IEEE 802154 based on the IEEE 802154 standard [18] 16 Table 4 Format based on the IEEE 802154 standard [18] 17 Table 5 General MAC frame format IEEE 802154 standard [18] 17 Table 6 Beacon frame format [18] 18 Table 7 Data frame format [18] 18 Table 8 Acknowledgement frame format [18] 18 Table 9 Command frame format [18] 18 Table 10 Classification of factors the affect RSS [7] 27 Table 11 Accuracy of positioning system 37 Table 12 One dimensional position estimation results 41

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

List of Figures10102006

vii

List of Figures Figure 1 Schema of thesis work 5 Figure 2 Block diagram representing hardware module 8 Figure 3 Taxonomy of positioning system [7] 10 Figure 4 Star and peer‐to‐peer topologies in LR‐WPAN [18] 15 Figure 5 WPAN architecture [18] 16 Figure 6 Example of super frame structure [18] 19 Figure 7 Communication from a device to a PAN coordinator in (a) a beacon‐enabled network and (b) a non‐beacon enabled network [18] 20 Figure 8 Communication from a PAN coordinator to a device in (a) a beacon‐enabled network and (b) a non‐beacon enabled network [18] 21 Figure 9 Overview of the coverage for different wireless communication standards [17] 21 Figure 10 IEEE 802154ZigBee stack [20] 22 Figure 11 IEEE 802154ZigBee stack [17] 23 Figure 12 ZigBee secure frame in MAC layer [17] 24 Figure 13 ZigBee secure frame in network layer [17] 24 Figure 14 ZigBee secure frame in application layer [17] 25 Figure 15 SENTIO Platform [8] 31 Figure 16 Layout of Masterʹs Room 32 Figure 17 Flow chart representing the algorithm of fixed sensors 35 Figure 18 Flow chart representing the algorithm of main sensor 37 Figure 19 Test bed for one dimensional test in Masters Room 38 Figure 20 Histogram plot of RSSI values at 1m and 4m 39 Figure 21 Histogram plot of RSSI values at 6m and 8m 39 Figure 22 Distance vs RSSI 40 Figure 23 Error Rate vs Number of samples 42 Figure 24 RSS distribution at 214 pm 42 Figure 25 RSS distribution at 100pm and 1100 am 43

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

Terminology Notation10102006

viii

Terminology Notation

GPS ‐ Global Positioning System

RSSI ‐ Received Signal Strength Indication

MS ‐ Mobile Station

RF ‐ Radio Frequency

WSN ‐ Wireless Sensor Network

AP ‐ Access Point

RSS ‐ Received Signal Strength

PC ‐ Personal Computer

WLAN ‐ Wireless local Area Network

SNR ‐ Signal to Noise Ratio

RFD ‐ Reduced‐Function Device

WPAN ‐ Wireless Personal Area Network

FFD ‐ Full Functional Device

MAC ‐ Medium Access Control

LR‐WPAN ‐ Low Rate WPAN

PAN ‐ Personal Area Network

OSI ‐ Open Systems Interconnection

LLC ‐ Logical Link Control

SSCS ‐ Service Specific Convergence Sub layer

PHY ‐ Physical Layer

PPDU ‐ PHY Protocol Data Unit

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

Terminology Notation10102006

ix

ED ‐ Energy Detection

CCA ‐ Clear Channel Assessment

LQI ‐ Link Quality Indication

ISM ‐ Industrial Scientific and Medical

DSSS ‐ Direct‐Sequence Spread Spectrum technique

LSB ‐ Least Significant Bit

SHR ‐ Synchronization Header

SFD ‐ Start of Frame Delimiter

PHR ‐ PHY Header

MPDU ‐ MAC Protocol Data Units

GTS ‐ Guaranteed Time Slot

MHR ‐ MAC Header

MFC ‐ MAC Footer

FCS ‐ Frame Check Sequence

CAP ‐ Contention Access Period

CFP ‐ Contention Free Period

ACL ‐ Access Control List

AES ‐ Advance Encryption Standard

MIC ‐ Message Integrity Code

CBC ‐ Cipher Block Chaining

ZDO ‐ ZigBee Device Objects

OS ‐ Operating System

LOS ‐ Line of Sight

NLOS ‐ Non Line of Sight

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

Terminology Notation10102006

x

FPGA ‐ Field Programmable Gate Array

BS ‐ Base Station

AOA ‐ Angle of Arrival

TDOA ‐ Time difference of arrival

TOA ‐ Time of Arrival

TOF ‐ Time of flight

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

1

1 INTRODUCTION 11 Introduction to the study Advancement in wireless sensing technologies has encouraged in developing a model for indoor object‐tracking application using low power IEEE 802154 compliant radios Stimulus used in this model is RSS This application is not only limited to object tracking in an indoor environment Concierge services enable users to become aware of nearest supporting facilities For example smart home applications such as multimedia appliances that forward multimedia stream to the nearest video screen can be achieved with a home positioning system [3] In the field of robotics a robot can navigate by itself using the assistance of indoor positioning system [2] Granularity of location information is most important in location determination applications For example to locate a book in a library requires coarse grained granularity whereas locating a user requires coarse grained granularity Position estimation refers to a process used to obtain location information of a MS with respect to a set of reference positions within a pre‐defined space A system deployed to determine or estimate the location of an entity is called a position location system or positioning system A wireless indoor positioning system refers to a wireless network infrastructure that provides indoor location information to any requesting end user One of the key technical elements allowing the realisation of this vision will be the knowledge of the accurate position of the personobjects Already today accurate location mechanisms such as GPS are commercially available Many more will become part of our daily life with the introduction of third generation mobile phones But none of these techniques will allow the meter or sub‐meter level of accuracy within buildings which is necessary for smart houses Hence this report introduces an object tracking application in an indoor environment using IEEE 802154 compliant radios which gives meter level accuracy First this chapter presents the background of indoor positioning systems identifies the challenges of such systems and briefly describes the scope of indoor positioning systems based on location fingerprints Next the objective of study the overview of approaches and the contributions are presented Finally the scope and organization of thesis work is outlined

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

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12 Background The success of outdoor positioning and applications based on the GPS provides an incentive to the research and development of indoor positioning systems It has been developed over several decades and it relies on constellation of satellites Ranges to several satellites are used in multi‐lateration procedure to infer the position of the receiver This localization scheme requires hardware that is both expensive and consumes significant power As a result indoor positioning systems require alternative means to detect the MS location without relying on the direct signal from GPS satellites Infrared RF and ultra sound signals are major technologies used for indoor positioning systems Unlike outdoor areas the indoor environment imposes different challenges on location discovery due to the dense multi‐path effect and building material dependent propagation effect Thus an in‐depth understanding of indoor radio propagation for positioning is crucial for efficient design and deployment The existence of radio connectivity and the attenuation of radio signal with distance are attractive properties that could potentially be exploited to estimate the positions of small wireless devices featuring low‐power radios RSSI a standard feature in most radios has attracted a lot of attention in the recent literature for obvious reasons RSSI eliminates the need for additional hardware in small wireless devices and exhibits favourable properties with respect to power consumption size and cost As mentioned previously RF‐based location tracking has been widely studied [5 6] Given a model of radio signal propagation in a building or other environment RSSI can be used to estimate the distance from a transmitter to a receiver and thereby estimate the position of a mobile node However this approach requires detailed models of RF propagation and does not account for variations in receiver sensitivity and orientation Table 1 below briefly summarizes performance of existing indoor positioning system based on location fingerprints and WLANrsquos All the systems mentioned below in table 1 follow IEEE 80211 protocol standards The major performance metrics studied by all systems are positioning accuracy which is a form of error measurement The accuracy of the location information is usually reported as an error distance between the estimated location and the actual mobile location However the report of accuracy should include the confidence interval or percentage of successful location detection which is called the precision The system parameters of existing systems are summarized in table 1 and the best reported performance of these systems are listed in table 2

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

3

System Spacing(m) Positions SamplesPos APs Orientation Environment

RADAR [6] Non

uniform 70 80 3 4 Hall-way Saha et al [9] 3 19 1200 3 NA 1-floor

Roos et al [10] 2 155 40 10 NA 1-floor Battiti et at [11] NA 257 NA 6 NA 1-floor Ladd et al [2] 3 11 1307 5 2 Hall-way

Prasithsangaree et al [12] 153 60 40

2 to 7 4 1-floor

Youssef et al[13] 15 110 300 4 NA Hall-way Xiang et al [14] NA 100 300 5 4 1-floor

Table 1 Comparison of Indoor Positioning Systems [7]

System Algorithm Type Accuracy and Precision RADAR [6] Nearest Neighbour within 7 feet 38

Saha et al [9] Nearest neighbour amp Neural Network no specified accuracy

90 Roos et al [10] Bayesian best within 828 feet 90

Battiti et at [11] Neural Network amp Weighted k-Nearest

Neighbour all within 16-17 feet 90 Ladd et al [2] Bayesian within 5 feet 77

Prasithsangaree et al [12] Weighted k-Nearest Neighbour

25 feet at 75 amp 40 fet at 95

Youssef et al [13] Bayesian within 7 feet more than

90 Xiang et al [14] Bayesian with RSS distribution model within 6 feet 90 Table 2 Performance comparison of Indoor Positioning Systems [7]

Based on the study of results obtained in various positioning systems as in table 2 we have developed a model for an indoor positioning system using SENTIO [8] platform based on RSSI

13 Objectives In the last two or three years a number of theoretical practical andor simulation studies were done on the topic of object‐tracking While these studies are useful they are too general and provide little guidance for the actual deployment of sensor networks for real life location‐tracking of an object This thesis focuses on developing an object‐tracking application and prescribes sensor network configurations that work well with our algorithms I implemented my software using SENTIO [8] platform for sensor networking The major issue addressed in this project are the accuracy and precision in tracking an object in a real‐world application and efficient ways to algorithmically analyze the collected raw data from the specific wireless sensor product

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

4

Although the focus is to develop a real‐world application using WSN it also provides great opportunity to explore the new area of wireless communication overall

14 Research Questions My primary target is to find a method to determine the position of an object inside a building using ZigBee protocol standards based on RSSI The study addresses the following questions What is WSN What requirements do WSN need What are their main ideas and concepts What are the associated technologies What are the standards that sensor networks use

What are the existing hardware implementations that use this particular technology How can we build our new applications using WSN What do sensor network systems require for general or specific applications Are they applicable And what is the appropriate design for sensor network implementations in order to combine them with other existing systems At this point we describe and evaluate the specific object‐tracking application to be built

15 Scope The study area of the thesis is an overview of the WSN technology study of SENTIO platform and implementation of an object tracking application Thus the study is divided into two parts Part one is an overview of WSN it includes study of SENTIO platform and IEEE 802154 protocol standards The second part describes the implementation of the object tracking algorithm The object tracking application receives and uses the raw values returned by the sensor network system to produce clear and meaningful outputs Schema of thesis work is as shown in figure 1

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

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Figure 1 Schema of thesis work

16 Methodology The ldquoIndoor Local Positioning System for ZigBee based on RSSI ldquois based on ldquolocation fingerprintingrdquo technique Location fingerprinting refers to a technique that exploits the relation between any measurable physical stimulus and a specific location This approach uses empirical measurements of RSSI to estimate location By recording a database of radio ldquosignaturesrdquo along with their known locations a mobile node can estimate its position by acquiring a signature and comparing it to the known signatures in the database All of these systems require the database of signatures which should be collected manually prior to the system installation and rely on central server (or the userrsquos mobile node) to perform location calculation For example in RADAR [6] technology the RSSI is the stimulus This type of positioning system does not require specialized hardware other than the common wireless network interfaces with received signal strength measurement capability thus it is simple to deploy compared to other techniques Unlike outdoor counterpart systems which can use AOA and TDOA techniques effectively indoor positioning systems encounter the problem of NLOS and the dense multi path effect that render these two techniques ineffective or complex for practical implementation It is also difficult for a MS to always see three or more access points or base stations in indoor environment which is essential for triangulation by AOA and TDOA Location fingerprinting can also be implemented as software based positioning system which can reduce

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

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complexity and cost Generally the deployment of fingerprinting based positioning systems can be divided into two phases First in the off‐line phase the locations where the fingerprints are required is pre‐determined and then the fingerprints are collected by performing a site‐survey of the RSS from multiple APs The distance between two closest positions is called grid spacing and usually reported in meters or feet However some points may be omitted due to inaccessibility The RSS is measured at each predetermined location to create a database of RSS patterns of the indoor environment The database of RSS patterns is called a radio map The vector of RSS values at a point on the grid is called the location fingerprint of that point Second in the on‐line phase a MS will report a sample measured vector of RSS from different APs to a base station The base station uses a positioning algorithm to estimate the location of the MS The most common algorithm used to estimate the location is Euclidean algorithm It computes the Euclidean distance between the measured RSS vector and each fingerprint in the database and then the coordinates associated with the fingerprint that provides the smallest Euclidean distance is returned as the estimate of the position It is clear from the above discussion that the designer should not start out to do the site survey without proper system design objectives and guideline If the performance is not sufficient collecting more fingerprints in between previous location fingerprints may help It is a problem of choice as to how many access points are required for the system and what the minimum distance between physical positions on the grid should be in order to provide a good position resolution and best system performance The system parameters and factors that improve the accuracy and the precision performance are still not clear

17 Outline Chapter 2 provides literature review of ldquoIndoor Local Positioning System for ZigBee based on RSSIrdquo it introduces the basic components required for a positioning system general classification of indoor positioning system Finally it explains ldquoLocation Fingerprintrdquo technology used in this thesis work to determine the position of an object in a real‐world application It briefly explains the protocols and industry standards for wireless sensor networks Chapter 3 describes some properties of RSS which is a stimulus to determine

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

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the position of an object RSS relation with energy and on what parameters is the RSS value dependent Chapter 4 describes the model of an indoor local positioning system and its probability of returning the correct location It also explains its performance evaluation and its error distance distribution Chapter 5 describes results obtained from the model of positioning system Chapter 6 includes conclusions from the experimental analysis and future work

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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2 LITERATURE REVIEW This chapter reviews the literature on wireless indoor positioning system as a means of providing an intellectual background for the thesis work First in section 21 the common components of indoor positioning system are described Then in section 22 discusses different means to classify indoor positioning systems In section 23 reviews of all relevant literature on location fingerprint technique Finally in section 24 protocols and industry standards for wireless sensor networks are explained

21 Common components of indoor positioning system Basically a wireless local positioning system consists of at least two separate hardware components a measurable unit that usually carries the major part of the system intelligence and a signal transmitterreceiver The transmitter in the simplest case is just a beacon Figure 2 represents basic hardware model of a positioning system

Figure 2 Block diagram representing hardware module

First the location sensing devices detect the signals transmitted by or received at known reference points using radio sensing technologies The sensing technique is based on signal strength level that converts the sensed signal into RSS Given a set of reference points the relative position of the MS can be derived from the signal characteristics of RSS The signal characteristics of RSS are unique for a location Then the microcontroller processes the position algorithm and estimates the position of object and displays it on PC

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22 Classification of Indoor positioning systems Indoor positioning system can be categorized based on the stimulus used in particular application and measurement principle

221 Classification based on Stimulus Based on the stimulus used in an application WSN inherit certain characteristics and limitations on sensorrsquos signal The propagation delay diffraction reflection and scattering are the basic signal characteristics which affect all signal types The effective range available bandwidth regulatory constraints interference power constraints safety and cost of technology limitations The wireless signals commonly used for indoor positioning systems are infrared radio frequency and ultrasound Brief descriptions of the three major technologies are as follows

Infrared Infrared signal is an electromagnetic signal of a wavelength longer than that of visible light but shorter than that of radio waves Infrared is just below the visible spectrum of light in frequency and is radiated strongly by hot bodies Many objects such as people vehicle engines and aircraft generate and retain heat and as such are especially visible in the infrared wavelengths of light compared to objects in the background It cannot pass through walls or obstructions therefore it has a rather limited range of indoor environments Thus it requires a more sophisticated circuitry than ultrasound signals Indoor lighting interferes with this type of signal and causes problems in accurate sensing It generally has a range of around 5 m The infrared devices are usually small in size compared to ultrasound devices

Radio frequency The RF signal can penetrate through most of the building material therefore it has an excellent range in indoor environments The propagation speed is also high approximately 3108 ms There are unlicensed frequencies available freely for use This type of signal has the longest range compared to infrared and ultrasound Since the propagation waves are of higher frequency (24GHz) interference with other frequency components is less

Ultrasound Although ultrasound operates at low frequency bands (typical 40 kHz) compared to the other two signaling technologies it possesses a good precision for location sensing at a slow propagation speed of sound (343 ms) The advantages of ultrasound devices are their simplicity and that they are inexpensive However ultrasound does not penetrate walls

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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but reflects off most of the indoor obstructions It has a short range around 3 m to 10 m but has a 1 cm resolution of distance measurement The operating temperature influences the performance of ultrasound

222 Classification based on measurement principle Wireless positioning systems can be categorized by measurement techniques which are used to derive the position of mobile stations Mainly three different measurement principles are used today AOA RSS and propagation‐time based systems that can further be divided into two different sub‐classes TOA and TDOA Figure 3 shows taxonomy based on the technology and the technique classifications

Figure 3 Taxonomy of positioning system [7]

In AOA systems the position is calculated via goniometry With the use of directional antennas or antenna arrays the angle or bearing relative to points located at known positions is measured The intersection of several measured directions pointers the yields the position value The accuracy of this approach is limited by the possible directivity of the measuring aperture by shadowing and by multi‐path reflections arriving from misleading directions In TOA and TDOA techniques rely on the precision of timing between the signal transmitter and the receiver in order to use the propagation delay or TOF to calculate the distance between transmitter and receiver Therefore a precise synchronization is also very important in such systems By combining at least three distances from three reference positions triangulation can be used to estimate the mobile stationʹs location This type of technique will

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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require a high accuracy clock in the communication system TDOA is more practical [1] Examples of location‐sensing systems that use time of flight are GPS the Active Bats [15] and the Cricket [16] Besides these time delay‐based techniques the distance between transmitter and receiver can also be determined from signal strength attenuation and direct distance measurement (such as dead reckoning) Fingerprinting or Location Pattern Matching this technique generally requires only measurement of RSS or other non‐geometric features at several locations to form a database of location fingerprints To estimate the mobile location the system needs to first measure the received signal strength at particular locations and then search for the pattern or fingerprint with the closest match in the database This technique does not require the mobile station to see at least three base stations or access points in order to determine the location The disadvantage of this technique is that it is very time‐consuming to perform an exhaustive data collection for a wide area network such as in outdoor positioning systems

23 Indoor local positioning using ZigBee based on RSSI This section reviews relevant RF‐based indoor positioning systems which can be used to locate stationary objects and track mobile users The impressive growth of IEEE 802154 wireless PANs (WPAN) in recent years suggests an interesting future for the location fingerprinting technique The system is quite flexible because system designers can select whether to have a centralized positioning server or let the mobile to determine its own position However the fingerprinting technique requires a training phase (off‐line phase) to collect location fingerprints for all positions in the operating area before the actual deployment (on‐line phase) [7] The following discussion is divided according to the positioning system components First the common form of location fingerprinting and its relationship with physical locations are explained Finally location estimation algorithm is reviewed

231 Location Fingerprint A location fingerprint based on RF characteristics such as RSS is the basis for representing a unique location It is created under the assumption that each position or location inside a building has unique RF signature [4] Generally a fingerprint F is labelled with a location information L The location fingerprints and their labels (eg location information) are maintained in

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database and used during the on‐line phase to estimate the location The label and fingerprint are usually denoted as a tuple of (L F) Battiti et al [11] point out that the location information L for indoor location can be recorded in two forms as either a tuple of coordinates or an indicator variable The tuple of real coordinates can vary from one dimension to five dimensions which include the three dimension space and two orientation variables expressed in spherical coordinates For instance a location information of a two‐dimension system with an orientation could be expressed as a triplet L = (x y d) | x y є R 2 d є North East South West The RSS was found to be more location‐dependent than the SNR because the noise component is rather random in nature However the RSS itself fluctuates over time for each access point and location Each RSS element can be considered as a random variable therefore it can be captured by recording its descriptive statistics parameters approximating its distribution or maintaining the whole measurement dataset These approaches of RSS representation result in different procedures for location estimation algorithm Regardless of the approach the location fingerprint is usually denoted as an array or vector of signal strength (random variables) received at any position in the location‐based service area The size of the vector is determined by the number of access points that can be heard To create a basis fingerprint such as in a number of samples of vectors of signal strength are collected over a window of time for each position This basis is called a prototype Then the average RSS of each access point is calculated and recorded as an element in the location fingerprint For an area that can receive signals from N access points the location fingerprint can be expressed as a vector of average RSS elements βi

F = (β1 β2hellip βN)T Besides the basis location fingerprint the samples of location fingerprint measured during the on‐line phase are also important for the system that tracks the mobile object The number of samples should be selected appropriately to represent the location fingerprint for the mobile application A step in statistical analysis method called pre‐processing is another important issue needed to be considered because it can impact the estimation of dependency between location fingerprint and the location information easier The pre‐processing refers to a step that cleans the raw data (in this case the training set) before any further operations or analysis The cleaning may consist of encoding dimensionality reduction (reduce unnecessary elements) feature extractionselection clustering and outlier elimination Roos et al [10] point out that the pre‐processing enables faster location estimation and

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reduce the noise from the training data The process that creates the basis of location fingerprint discussed above could be considered as a part of pre‐processing

232 Location Estimation Algorithm Location estimation algorithms or positioning algorithms are procedures that exploit dependency between location information and location fingerprint basis in order to determine a position or location from samples of RSS signals Position algorithms can be classified into deterministic and probabilistic types based on the approaches that model the relationship between location fingerprints and location information The deterministic type of algorithms are those that based in the nearest neighbour classifiers and neural network classifiers The probabilistic type of algorithms is those that are based on the Bayesian inference and statistical learning theory such as support vector machines (SVR) The algorithm used in this project is discussed briefly

Euclidean distance method The Euclidean distance method is a deterministic approach of position estimation because they require only a set of constant location fingerprints which includes mean vectors In order to determine the location a form of discriminate function is used to classify a sample of RSS fingerprint into a position The mean or average RSS vector is a centre of a mass which represents each class of location fingerprint Suppose a set of l location fingerprints is denoted by F1 F2hellip Fl exist and each fingerprint has a one‐to‐one mapping to a set of positions L1 L2hellip Ll A sample of an RSS fingerprint measured during an on‐line phase is denoted as S which can be another mean or average RSS vector of small window of RSS samples Assuming that an indoor positioning system only considers the average RSS from N access points as a location fingerprint the sample of RSS vector is S = (s1 s2 helliphellip sN)T and each location fingerprint i in the database can be expressed as Fi = (β1 β2helliphellip βN)T The simplest closeness metric is a distance measurement in signal space denoted as the Dist () function [6] Thus the simple procedure of nearest neighbour algorithm is expressed as picking the fingerprint j that has the shortest signal distance

Dist(S Fj) le Dist(S Fk) for all k ne j A generalized weighted distance Lp summarized by [12] can be used to calculate different forms of distance in signal space as

Lp = 1N pN

i

pPiSiWi 1

1)||1(sum

=

minus

where N is the number of access points wi is a weighting factor (wi le 1) and p is the norm parameter starting from 1 The weighting factor wi is a bias

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parameter that can demote or promote an important RSS component in the fingerprint

24 Protocols and Industry standards for WSN Although many applications for WSN are proposed the communication protocols supporting them remain mainly unexplored and diverged This is because the area of interest is new each implementation is application specific and the components of WSNrsquos have a lot of constraints Wireless sensor networks use the wireless medium to communicate However the traditional communication protocols that support wireless communications especially ad‐hoc mesh networks may not be well suited for them WSN communication is an active research area and many algorithms and protocols have already been proposed This section introduces two proposed standards the IEEE 802154 and the ZigBee The IEEE 802154 covers the physical layer and the MAC layer of low‐rate WPAN The ZigBee is ldquoan emerging standard that is based on the IEEE 802154 and adds network construction (star networks peer‐to‐peermesh networks and cluster‐tree networks) application services and morerdquo

241 IEEE 802154 Holger and Willig (2005) [17] mention home automation home networking and home security as possible applications for IEEE 802154 They add that ldquomost of these applications require only low‐to‐medium bitrates (up to some few hundreds of kbps) moderate average delays without too stringent delay guarantees and for certain nodes it is highly desirable to reduce the energy consumption to a minimumrdquo The IEEE 802154 standard (2003) defines the device types that can be used in a LR‐WPAN A device can be a FFD or a RFD The RFD can be used in simple applications in which they do not need to transmit large amounts of data and they have to communicate only with a specific FFD The FFD can work as a PAN coordinator as a coordinator or as a simple device It can communicate with either another FFD or a RFD In keeping with the application requirements the LR‐WPAN operates in a star or peer‐to‐peer topology (figure 4) In the star topology the RFD communicates with a single controller the PAN coordinator The PAN coordinator can perform the same function as the RFD but it is also responsible for controlling the PAN ldquoit initiates terminates or routes communication around the networkrdquo [18] The peer‐to‐peer topology

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supports ad‐hoc mesh multi‐hop networking Any device in the peer‐to‐peer topology can communicate with any other device within its communication range however this topology also has a PAN coordinator All the devices in a LR‐WPAN have a unique 64‐bit address This or a short address allocated by the PAN coordinator can be used inside a PAN Additionally each PAN has a unique identifier The combination of the PAN identifier and the sort addresses allows communication across different PANs (IEEE 802154 Standard 2003)

Figure 4 Star and peer‐to‐peer topologies in LR‐WPAN [18]

The LR‐WPAN based on the OSI seven‐layer model has the layered architecture presented in figure 5 The application and the network layer are the upper layers in the LR‐WPAN architecture but are outside the scope of the IEEE 802154 standard Only the physical layer ldquowhich contains the RF transceiver along with its low‐level control mechanismrdquo and the MAC layer ldquothat provides access to the physical channelrdquo are included in the standard and will be introduced in the following sections Finally the Type I 8022 LLC and the SSCS are intermediate sub layers supporting communication with the above layers (IEEE 802154 Standard 2003)

Physical Layer The 802154 standard specifies two different services that the PHY provides The PHY data service controls the radio and thus the transmission and reception of the PPDUs In addition the management service performs ED in the channel The management service also performs CCA before sending the messages and provides LQI for the received packets

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Figure 5 WPAN architecture [18]

Three different bands are defined by the standard The 868‐8686 MHz for Europe the 902‐928 MHz for North America and the 2400‐24835 MHz worldwide All of them belong in the ISM radio bands and they are using DSSS Each of the bands supports different a data rate and it uses a different modulation technique chip rate and number of channels Moreover if the system does not use the 2450 MHz frequency it operates in both the 802 MHz and 902 MHz frequencies Table 3 summarizes the supported bands and data rates (IEEE 802154 standard 2003)

PHY (MHz)

Frequency Band (MHz) Modulation

Bit Rate Number of Channels

868-8686 BPSK 20 1 868 915 902-928 BPSK 40 10

2450 2400-24835 O-QPSK 230 16 Table 3 Frequency bands and data rates for IEEE 802154 based on the IEEE

802154 standard [18] The physical layer uses PPDU packets to communicate Table 4 demonstrates its structure The LSB is always transmitted and received first The SHR contains the preamble and the SFD fields which helps receiver

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synchronization The 8‐bytes preamble contains only zero and is used for synchronization The SFD contains a specific sequence of one and zeros and specifies the beginning of the frame The PHR contains the payload length Packets with a length of 9 or more bytes are MPDU as the next section further explains Packets with length 5 are used for MPDU acknowledgements The payload part of the PPDU encloses the MAC layer packet Finally the PPDU size can be up to 127 bytes [18]

4 bytes 1 byte 1 byte Variable

Preamble SFD Frame length

(7 bits) Reserved

(1 bit) Payload (PSU)

SHR PHR PHY Payload Table 4 Format based on the IEEE 802154 standard [18]

MAC Layer The MAC layer is the interface between the SSCS and the PHY layer Similar to the PHY layer the MAC layer supports two services The MAC data service is responsible for the transmission and reception of the MPDUs through the PHY data service The MAC management service if the device is a coordinator manages the network beacons It is also responsible for PAN association and disassociation frame validation and acknowledgment providing ldquoa reliable link between two peer MAC entitiesrdquo In addition it uses the CSMA‐CA for channel access and handles and maintains the GTS mechanism Finally it supports device security [18] The IEEE 802154 standard defines four different frame types the beacon data acknowledgment and MAC command frame All frame types are based on the general MAC frame format (table 5) The frame control field describes and specifies the above different frame types Every MAC frame comprises a MHR which consists of a frame control sequence number and the information field It also contains the MAC payload different frame types have different MAC payload fields The acknowledgment type does not have a payload Finally each frame includes a MFR which contains a FCS The data in the MPDU follows the same order as the PPDU the least significant bits are left in the frame and are transited first Octets2 1 02 028 02 028 Variable 2

Destination PAN Identifier

Destination Address

Source PAN

Identifier Source Address Frame

Control Sequence Number Addressing Field

Frame Payload FCS

MHR MAC

Payload MFRTable 5 General MAC frame format IEEE 802154 standard [18]

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Table 6 7 8 and 9 present the four different MAC frame types The beacon frame is transmitted periodically by the PAN coordinator It provides information about the network management through the super frame and GTS fields which are analyzed later in the section It also synchronizes the network devices and indicates the proper communication period for them The data frame payload encapsulates data from the higher layers When a device receives a packet it is not obliged to response with an acknowledgement packet Finally the command frame identifier and command payload fields of the command frame are used for communication between the network devices The command identifier specifies actions like association disassociation and data GTS or beacon request Octets2 1 410 2 Variable Variable Variable 2

Frame Control

Sequence Number

Addressing Fields

Super frame Specification

GTS fields

Pending Addressing

Fields Beacon Payload FCS

MHR MAC Payload MFRTable 6 Beacon frame format [18]

Octets 2 1 Variable 2

Frame Control

Sequence Number

Addressing Field

Data Payload FCS

MHR MAC

Payload MFR Table 7 Data frame format [18]

Octets 2 1 2

Frame Control

Sequence Number FCS

MHR MFR Table 8 Acknowledgement frame format [18]

Octets2 1 1 Variable 2

Frame Control

Sequence Number

Addressing Fields

Command Frame Identifier

Command Payload FCS

MHR MAC Payload MFR Table 9 Command frame format [18]

In the LR‐WPAN every PAN has its own coordinator The PAN coordinator manages the communication in the local area it has two options to use or not use the super frame structure The super frame (figure 6) uses network beacons If the coordinator does not want to use a super frame structure it suspends the beacon transmission However the beacon is important for device association and disassociation If the coordinator wishes to maintain close communication control in the PAN and to support low‐latency devices it usually uses the super frame A super frame determines a specific time

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period beacons bound it The beacon is transmitted in the first of the sixteen equal time slots that the super frame has It is used to ldquosynchronize the attached devices to identify the PAN and to describe the structure of the super framerdquo The super frame can have active and inactive periods All the communications have to be finished inside the super frame period In the inactive periods the devices can switch to the sleep mode but they have to be ready for the next beacon The active period is further divided into a CAP and a CFP During the CAP any device can communicate competing with the other devices in the PAN using slotted CSMA‐CA The CFP contains GTSs and follows the CAP A CFP may maintain up to seven GTSs and each GTS can reserve more than one time slot However the CAP should always be sufficient to allow new devices t join the PAN The GTSs are allocated to specific devices All the transactions must be completed inside the assigned time period (CAP GTS and CFP) (IEEE 802154 standard 2003)

Figure 6 Example of super frame structure [18]

In accordance with the 802154 standard (2003) three different types of data‐transfer exist In addition the types differ if the coordinator uses or does not beacons Data transfer from a device to the PAN coordinator is the first type (figure 7) For a ldquonon‐beacon‐enable networkrdquo it first senses the medium by using ldquoun‐slotted CSMA‐CArdquo and then a simple transmit to the data frame In a ldquobeacon‐enabled networkrdquo the sender waits for the beacon when it finds it ldquothe device synchronizes to the super fame structurerdquo In the specified time frame the sender again senses the medium by using ldquoslotted CSMA‐CArdquo and transmits the data to the coordinator In either case the coordinator has the option to acknowledge or not acknowledge the data reception after that the transaction is completed Data transfer from the coordinator is the next type described in the standard (figure 8) In a beacon‐enabled network the coordinator indicates a pending message through the beacon The messagersquos target device receives the beacon and if the message is pending it responses with a MAC command request

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

20

message using slotted CSMA‐CA the coordinator may or may not acknowledge the command message and through slotted CSMA‐CA sends the pending message The device acknowledges the received message After that the coordinator removes the message from the beaconrsquos pending list and completes the transaction In a non‐beacon enable network the device periodically sends a MAC command‐request frame to the coordinator The coordinator acknowledges the data request Then by using un‐slotted CSMA‐CA if it has a pending message it transmits it If it has not it respond with a data frame with a ldquozero‐length payloadrdquo To complete the transaction the device acknowledges the data reception

Figure 7 Communication from a device to a PAN coordinator in (a) a beacon‐enabled network and (b) a non‐beacon enabled network [18]

Peer‐to‐peer is the last type of data transfer In this situation the devices are free to communicate with any other device within their communication range In a peer‐to‐peer PAN the devices can ldquoeither receive constantly or synchronize with each otherrdquo If they are receiving constantly to transmit data they use un‐slotted CSAM‐CA In the second case synchronization must be achieved first (IEEE 802154 standard [IEEE 2003]) The IEEE 802154 (2003) standard establishes MAC and PHY standards for low‐cost low‐power and high‐density node deployments In addition to the above PHY and MAC characteristics IEEE 802154 provides a security baseline including ldquothe ability to maintain an ACL and use symmetric cryptographyrdquo for data encryption The algorithm that is used for encryption is the AES However the higher level layers decide when security is need The upper layers are in general responsible for device authentication and key management The next section introduces the ZigBee standard which

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2 LITERATURE REVIEW10102006

21

encapsulates the IEEE 802154 and provides additional standardization for the higher levels

Figure 8 Communication from a PAN coordinator to a device in (a) a beacon‐enabled network and (b) a non‐beacon enabled network [18]

242 ZigBee Heily (2004) defines ZigBee as ldquoa rapidly growing worldwide non‐profit industry consortiumrdquo whose mission is ldquoto define a reliable cost‐effective low‐power wirelessly networked monitoring and control product based on an open global standardrdquo Figure 9 illustrates the areas of interest for different wireless communication standards

Figure 9 Overview of the coverage for different wireless communication

standards [17]

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

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ZigBee a new standard which became publicly available in June 2005 is based on the IEEE 802154 standard It expands the IEEE 802154 by adding the framework for ldquothe network security and applicationrdquo (Craig 2005) Figure 10 presents the IEEE 802154ZigBee stack and the areas of responsibilities

Figure 10 IEEE 802154ZigBee stack [20]

Craig (2005) mentions three networking topologies that the standard covers the star mesh and cluster tree (figure 11) The ZigBee standard works on top of the IEEE 802154 addressing schema by using the standard 64‐bit and the short 16‐bit addressing Kinney (2005) summarizes the ZigBee network layer responsibilities the successful establishment of a new the network and successful new device configuration addressing assignment network synchronization frames security and message routing ZigBee further distinguishes the concept of the physical devices (RFD FFD) by using the notion of ldquological devicesrdquo ldquoZigBee Coordinatorrdquo is the first type of logical devices It is responsible for initializing maintaining and managing the network Under the coordinator in the network hierarchy is the ldquoZigBee routerrdquo which is responsible for controlling the message routing between the nodes Finally the ldquoZigBee End Devicerdquo acts as the end point of the network

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

23

structure The tasks that an end device can perform are specified in the ldquoApplication Profilesrdquo (Craig 2005)

Figure 11 IEEE 802154ZigBee stack [17]

The ZigBee specifications (2005) summarize the security services provided by ZigBee ldquokey establishment key transport frame protection and device managementrdquo ZigBee builds its security mechanism using symmetric key cryptography The security services also depend on the associated layer thus as the figure 10 shows the security mechanism covers the network and the application layer In addition if a MAC frame needs security protection the MAC layer is able to secure it Moreover the notion of end‐to‐end security is supported the source and destination devices have access and use the same share key In the MAC layer the 802154 AES mechanism provides the proper security The mechanism protects ldquothe confidentiality integrity and authenticity of the MAC framesrdquo (Kinney 2005) An auxiliary header field in front of the MAC payload indicates if the frame is encrypted or not The MAC framesrsquo integrity is supported by calculating and using a MIC at the end of the MAC payload In addition to the AES nonce is used to provide MAC confidentiality and authenticity Figure 12 illustrates a MAC frame with security For different security aspects the MAC layer uses different mode of the AES for the encryption it uses the AES in Counter (CTR) mode and for the integrity the

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

24

CBC‐MAC Finally the combination (CCM) of the above two modes is available providing both encryption and integrity (Kinney 2005)

Figure 12 ZigBee secure frame in MAC layer [17]

In the network layer the CCM (a modified MAC layer CCM mode) is used for encryption Because the network layer uses only the CCM mode a single key is used for all different security options The network layer security message format is similar to the MAC frame it is presented in the figure 13 Finally although the network layer is responsible for securing its layer messages the above layers specify the keys and the CCM option for each frame (Kinney 2005)

Figure 13 ZigBee secure frame in network layer [17]

Security in the application layer works similar to the network and MAC layers It uses the ldquolink keyrdquo or the ldquonetwork keyrdquo to secure the message and then encapsulate it inside a set of fields similar to the network format (figure 14) Other security responsibilities that the application layer has are to provide the ZDO and the applications with device management services key establishment and key transport (ZigBee specifications 2005)

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

25

Figure 14 ZigBee secure frame in application layer [17]

The ZigBee application layer contains the manufacturer‐defined application objects the ZDO and the application sub‐layer In addition to the security responsibilities the application sub‐layer binds devices based on their duties and needs maintains the binding tables and forwards messages between them The application sub‐layer also discovers the neighbour devices for a given device The ZDO is responsible for determining the devicersquos duty in the network for communicating using binding requests and for supporting security as was mentioned above The sub‐layer that implements the actual application is the manufacturer‐defined application object (Kinney 2005) The ZigBee specifications are a great step toward the wireless sensors networks standardization They cover all the OSI layers from the physical to the application providing guidance to the developers The specifications are the product of the ZigBee alliances a consortium of companies and academic institutes Thus they not only produce the specifications but also are the first use them The next section goes beyond the layer architecture and standardization and introduces the OS field another crucial element of WSNs

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

26

3 RECEIVED SIGNAL STRENGTH (RSS) Majority of the existing methods leverage the existence of IEEE 80211 base stations with powerful radio transmit powers of approximately 100mW per base station Such radios are in a different class from the low power IEEE 802154 compliant radios that typically transmit at low power levels ranging from 52mW to 29mW [19] The wide availability of larger number of IEEE 802154 radios in the research community has revived the interest for signal strength based localization in sensor network Despite of rapidly increasing popularity of IEEE 802154 radios and signal strength localization there is a lack of detailed characterization of the fundamental factors contributing to large signal strength variation This chapter investigates some of the properties of RSS observed during testing and analysis of research work collected during the period of thesis The analysis of RSS values is needed to understand the underlying features of location dependent RSS patterns and location fingerprints An understanding of the properties of the RSS values for location fingerprinting can assist in improving the design of positioning algorithms and in deployment of indoor positioning systems

31 Parameters that affect RSS Different factors that affect the indoor RSS are the userrsquos presenceabsence hisher orientation time of the day building type and material distance from transmitter and type of radio These factors are by no means exhaustive Other possible factors are antenna orientation directionality and type Table 10 classify the parameters which affect RSS [7]

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

27

Effect on Factors Options

1 Proximity of user Userrsquos presence or absence

2 Orientation of user and terminal

North East South West

Data Collection

3 Make of Radio 4 Time of measurement Time of day amp days of

week 5 Period of measurement

Second minute hour

7 Interference Co‐channel adjacent co‐channel

Statistics

8 Building environment Small offices or large hall

Table 10 Classification of factors the affect RSS [7]

Transmitter variability Different transmitters behave differently even when they are configured exactly in the same way In practice this means that when a transmitter is configured to send packets at a power level of d dBm then the transmitter will send these packets at a power level that is very close to d dBm but not necessarily exactly equal to d dBm This can alter the received signal strength indication and thus it can lead to inaccurate distance estimation

Receiver variability The sensitivity of the receivers across different radio chips is different In practice this means that the RSSI value recorded at different receivers can be different even when all the other parameters that affect the received signal strength are kept constant

Antenna orientation Each antenna has its own radiation pattern that is not uniform In practice this means that the RSSI value recorded at the receiver for a given pair of communicating nodes and for a given distance between them varies as the pair wise antenna orientations of the transmitter and the receiver are changed

Multi‐path fading and shadowing in the RF channel In indoor environments the transmitted signals get reflected after hitting on the walls andor on other objects in the room such as furniture Both the original signal and the reflected signal reach the receiver almost at the same time since they both travel at the speed of light As a result of this the receiver is not able to

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

28

distinguish the two signals and it measures the received signal strength for both of them

32 Related work analysis A number of indoor location tracking systems have been proposed in the literature based on RF signals ultrasound infrared and some combinations of these As mentioned previously RF based positioning system are widely studied as shown in Table 2 Given a model of radio signal propagation in a building or other environment RSS can be used to estimate the distance between the transmitter and receiver Most of the algorithms (Table 2) have been evaluated in practice using 80211b compliant wireless radios and seem to produce a location error of 15ft to 20ft for an indoor network area of approximately 225ft x 144ft According to the results of this study all the localization techniques produce approximately the same location error over a range of environments Ecolocation [22] Mote Track [5] are two of the few localization algorithms that have been evaluated on a real sensor network that uses a low power wireless radio Ecolocation reports a location error of 10ft for a very small outdoor network deployment area (26ft x 49ft) Mote Track reports a location error of approximately 13ft for an indoor network deployment area of 18751ft2 In general the RSSI based localization algorithms can be divided into two broad categories the map based and the distance (or area) prediction based algorithms In the case of the map based algorithms the deployment area is divided into a large set of sample points that cover the whole area Most techniques collect one or more signal strength measurements from all visible beacon nodes and for each sample point Each sample point is then mapped to either a signal strength vector or a signal strength probability distribution This process generates an RSSI fingerprint map which is unique with respect to the beacon locations and the deployment area This fingerprint map is stored to a central base station A node that is not aware of its location collects one or more signal strength measurements from all the visible beacon nodes and creates its own RSSI fingerprint that is sent to the central base station The base station computes nodersquos location as the sample pointrsquos location whose RSSI fingerprint distance from the unknown nodersquos RSSI fingerprint is the minimum among all the sample points Despite the fact that the map based RSSI localization methods produce relatively small location errors they are not attractive mainly because they require an excessive amount of profiling data and because they are not fully distributed The detailed profiling of a building is a major holdback because it is complex time consuming and depends on beacon locations Therefore for different buildings or even for the

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

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29

same building and different beacon locations different RSSI maps have to be obtained The second category of RSSI based algorithms are the distance (or area) prediction algorithms which focus on minimizing the amount of profiling data In distance prediction based methods raw RSSI data are directly mapped to distances through a signal propagation model In some cases distances are not explicitly measured but they are taken into consideration through assumptions on the underlying distributions In the case of outdoor deployments where there are no obstacles a single signal propagation model is all the profiling data required In the case of indoor deployments the same applies but now the signal propagation model might be different across different buildings with different furniture and wall arrangements Given an underlying signal propagation model raw RSSI data can be mapped to specific distances These distances can be either used directly in order to perform localization or assigned a probability and used in a learning basedprobabilistic localization algorithm Obviously the accuracy of the distance prediction based methods depends heavily on the accuracy of the signal propagation model used for translating RSSI values to distances

33 Properties of RSSI Changes in signal due to propagation indoors are difficult to predict because of dense environment and propagation effects such as reflection diffraction and scattering The multi‐path fading effect which is the result of either constructive or destructive combination of multiple signal copies at the receiver causes the received signal to fluctuate around a mean value at a particular location The received signal is usually modelled by the combined effects of large‐scale fading and small‐scale fading The large‐scale fading component describes the signal attenuation as the signal travels over a distance and is absorbed by material such as walls and floors along the way to the receiver This component predicts the mean of the RSS and usually has a lognormal distribution On the other hand the small scale fading component explains the dramatic fluctuation of the signal due to multi‐path fading On the other hand the small scale‐fading component explains the dramatic fluctuation of the signal due to multi‐path fading If there is no LOS component the small‐scale‐fading is often modelled with a Rayleigh distribution Such scenarios are commonly called NLOS If there is a LOS component the small‐scale‐fading is typically modelled by a Rician distribution Note that when the RSS is measured the measurement averages out the small‐scale‐fading effects

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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4 OBJECT TRACKING 41 Introduction This chapter presents the object‐tracking application the main objective of this thesis work The application is an algorithmic‐software implementation written in Embedded C which is capable of receiving data messages and generating object position estimation data The implementation of the object‐tracking application makes use of the SENTIO platform and uses the background knowledge introduced in the preceding chapters The motivation for the object‐tracking application is to determine the position of an object in indoor environments (Eg Home) The application acts as an interface which collects the raw data that the sensors generate (numerical values) It makes the proper comparisons and algorithmic processes to identify and track the object while it is static inside the area covered by the wireless sensor network Finally with the detection and tracking data it alerts the user or another system The following sections provide an overview of the object‐tracking application

42 Overview of hardware product Before describing the object tracking application it is important to know the characteristics of hardware product The SENTIO platform (figure 15) is built in modular fashion and has the following key features [8]

Flexibility This platform is very flexible in such a way that it is made easy to add or change the hardware for each sensor task depending on the requirement in the WSN For a specific platform this can be hard or sometimes even impossible to accomplish

Sensor resources Resources in a sensor module includes the ability of usage of controllerrsquos resources as much as possible such as ADC‐channels interrupts and digital Input‐Outputrsquos Local voltage regulation should also be possible since many sensors require higher supply voltages than digital Integrated Circuits do Further support of a shut‐down mechanism to power down the sensor modules when not needed

Computation and memory The processor module based on microcontroller should support many devices in a microcontroller family and support different power supply voltages enabling full range of clock frequencies All power‐down modes of the devices should be

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supported The platform should support different processor modules including FPGArsquos and very simple microcontrollers

Communication The focus should be on enabling an IEEE 802154 compliant communication interface With or without ZigBee stack this will provide a flexible platform for exploring different network topologies Support for power‐down of the entire communication module should be supported

Cost and form factor High flexibility is in conflict with low cost and small size The primary objective should be flexibility but the size should be considered in order to enable sensor device integration in systems where space is limited

Figure 15 SENTIO Platform [8]

The communication subsystem of the SENTIO sensor node was designed so that the correct operation of the radio chip is ensured The radio chip is powered by the on‐board voltage regulator and thus fluctuations in the battery voltage level do not affect the operation of the communication subsystem This platform uses Chipcon CC2420 IEEE 802154 radio transceiver operates in the 24GHz ISM band and includes a digital direct sequence spread spectrum (DSSS) modem providing a spreading gain of 9dB and an effective data rate of 250Kbps It was specifically designed for low power wireless applications and supports 8 discrete power levels 0dBm minus1dBm minus3dBm minus5dBm minus7dBm minus10dBm minus15dBm and minus25dBm at which its power consumption varies from 29mW to 52mW [21] A built‐in received signal strength indicator gives an 8‐bit digital value RSSIVAL The RSSIVAL is always averaged over 8 symbol periods (128μs) and a status bit indicates when the RSSIVAL is valid (meaning that the receiver was enabled for at least 8 symbol periods) The

Antenna

Communication Module

Processing Module

Sensing Module

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32

power P at the RF pins can be obtained directly from RSSIVAL using the following equation

P = RSSIVAL + RSSIOFFSET [dBm] where the RSSIOFFSET is found empirically from the front‐end gain and it is approximately equal to minus45dBm In the next sections when we refer to the RSSI value we refer to the RSSIVAL and not the power P unless otherwise stated

43 Experimental Testbed The experimental test bed for object tracking application is as shown in figure 16 Experiment was conducted in Masterrsquos room at Mid Sweden University A DELL laptop is connected to SENTIO platform and software were primarily used to collect samples of RSS from APrsquos The software monitors IEEE 802154 radio frequency channels which operate in the 24GHz band

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

1030

BC - 1030

AP 3 AP 1

AP 2

AP 4

BS

HEWLETT

PACKARD

Figure 16 Layout of Masterʹs Room

A three dimensional scalable testbed is used for indoor sensor network deployments The testbed illustrated in figure 16 is a 3‐D structure measuring 16m (L) x 12m (W) x 3m (H) and it is designed to host a large number of static nodes as shown in figure 16 Arrow marks in the above figure represent the location where fingerprints were taken The centrepiece of our infrastructure

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

33

is the SENTIO sensor node [8] an open‐source general purpose sensing platform designed with AT Mega 128 microprocessor and the IEEE 802154 compliant CC2420 radio from Chipcon [19]

44 Obtaining data from the sensor network The next important step in the development of the object tracking application is to obtain the data from the sensor network and send it to the base station From there the data is transmitted through a serial port to the computer that runs the object tracking application When the data arrives at the serial port the application must capture it This paragraph gives a brief explanation of how and what kind data is transmitted by the static object fixed sensors and how this data is being used to estimate the position of the object First the static object transmits Beacon packet continuously which is received by all the fixed APrsquos The beacon packet format is as shown in table 6 Then the fixed APrsquos calculates the 8‐bit value of RSSI from the transmitted beacon packet and then sends the resultant RSSI value as a form of data packet (table 7) to the BS which calculates the position and sends back to the object

45 Object tracking application Testing and Evaluation The purpose of this section is to discuss and evaluate the object tracking application Finally we are ready with the experimental testbed as shown in figure 16 As shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors placed at the corners of the masterrsquos room BS is placed at the centre of the room connected to a PC All the nodes are power connected before an actual measurement takes place At first the object is placed at random position inside the room and transmits beacon packets continuously Then these packets are received by the fixed APrsquos which calculate the RSSI from the packet and then averages it until it receives 50 beacon packets and then transmits the data to the BS (figure 17) Then the BS waits until it receives the data from all the APrsquos Once it receives from all the APrsquos it calculates the position of the object (figure 18) The application was developed to demonstrate a real world use of a WSN in object tracking In evaluating the final position obtained with respect to the actual position we conclude that the object‐tracking was successful Although

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

34

it is a research prototype object tracking application is a simple stable stand‐alone system capable of demonstrating an important application of WSN It takes raw data from the WSN aggregates it evaluates it and based on the evaluation tracks the object The accuracy of estimating the position is less than 5m Table 11 below shows a log of readings taken during test

Start

Intialize micro‐controller and transeciever in receieve mode

If received_packet=

Beacon

Average RSSI values

Increase the counter

If countergt1000050

A

No

Yes

B

B

Yes

No

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

35

A

Reset counter

Set transceiever in transmit mode

Transmit averaged RSSI to Main sensor and set it back to receive mode

10000 samples ‐ Offline

50 samples ‐ Online

Figure 17 Flow chart representing the algorithm of fixed sensors

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

36

Start

Intialize micro‐controller and transeciever in receieve mode

Received RSSI values from all fixed Sensors

Open radio map

Calculate Euclidean distance between obtained RSSI values from fixed sensors and fingerprints

in radio map

No

Yes

Bubble sort for least error in signal strength values

A

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

37

A

Get the location of the object from the radiousing the least error value

Display location of object on PC

End

Figure 18 Flow chart representing the algorithm of main sensor

Table 11 Accuracy of positioning system

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

38

5 RESULTS This chapter presents how the system parameters such as number of samples mean or median etchellipare finalized All the tests are performed in masterrsquos room at Mid Sweden University This chapter is divided into three subsections First in Section 51 it reviews results obtained from samples test Section 52 discusses one dimensional test results and finally section 53 discusses results of two dimensional tests

51 Measurement setup A Dell laptop was connected to a SENTIO platform and is loaded with software (Appendix 2) This setup was used to determine number of samples required during object tracking mean or median position estimation algorithm and one dimensional test The setup is as follows

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

103

0

BC - 1030

AP 1AP 2

BS

HEWLETT

PACKARD

Figure 19 Test bed for one dimensional test in Masters Room

Two SENTIO nodes (AP1 and AP2 as shown above) were separated by a distance of 10 meters and these sensors are known as fixed sensors which are fixed to the ceiling such that there antenna is faced towards the ground They are linear is position A BS was connected to Dell laptop and it was placed in such a way that its antenna was faced towards the ceiling Another SENTIO

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

39

node was used as an object and its orientation was same as BS Figure 19 represents the complete setup for object tracking application Mobile node was moved between these fixed ones at regular intervals of one meter and RSSI values were logged at each interval

52 Number of samples test This test was performed to determine number of samples required for accurate positioning and either mean of the obtained RSSI values or median of them Figure 19 represents the setup for number of samples test Once the setup is ready the measurements are taken as follows

1 An object was tagged with SENTIO node and was placed stationary linear to the fixed sensors

2 10000 samples (RSSI) were taken at regular intervals of 1 meter up to 10 meters

3 The sample values were logged at every interval 4 Using these logged samples I have plotted graphs such as distribution

of RSS values (figure 20 21) meanmedian at intervals of 1 meter using MATLAB

5 The mean and median for various numbers of samples is as shown in figure 22

Figure 20 Histogram plot of RSSI values at 1m and 4m

Figure 21 Histogram plot of RSSI values at 6m and 8m

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

40

Figure 22 Distance vs RSSI

From the above graphs following conclusions were made 1 From the histogram plots we can observe that the distribution of RSSI

values follow Gaussians distribution law 2 As we can see from figure 22 that median curve has drastic changes

when compared to mean curve Hence mean is considered while estimating the position

3 From the observations we conclude that the number of samples does not matter since mean curve tends to be same for various numbers of samples

4 Hence 10000 samples are considered in order to get a proper location fingerprint during off‐line phase and during online phase 50 samples are enough to estimate the position of object

53 0ne dimensional test Measurement setup for one dimensional test is as shown in figure 19 This test was conducted to check the performance of algorithm and how accurate it was At first ie during offline phase fingerprints at each interval averaging 10000 samples were taken During online phase Euclidean algorithm was used estimate the position averaging 50 samples of RSSI From the results we

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

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can realize that the error was less than 2 meters Table 12 shows the result of location estimation in one dimensional case

Table 12 One dimensional position estimation results

54 Two dimensional test This test is the final stage of my thesis work Experimental testbed for this stage is as shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors fixed at the corners of the room and a BS at the centre of the room There are 18 pre‐determined locations where the fingerprints (RSSI) were taken The fingerprints are as shown in Appendix 1 All the readings were taken in presence of userrsquos The algorithm for position estimation is shown in Appendix 2 Position estimation test is performed as follows

Tagged object is placed at random inside the masterrsquos room All the sensors including the base station and object are switched on Using the algorithm (Appendix 2) the BS estimates the position of the object and displays it on the PC

This test was performed using various numbers of samples to test which has a better accuracy Figure 23 represents the error rate using various numbers of samples From figure 23 we can conclude that 50 numbers of samples is enough to estimate the position of object with an accuracy of 3m

55 Causes of error in location determination The randomness of RSS is the major cause of error in any indoor

positioning system that uses the RSS for location inference If there is no randomness in the RSS and its location fingerprint every indoor positioning systems of this type will have no location detection error For example I have recorded RSS at one particular location during different times of the day From figures 24 and 25 you can observe that RSS values are different at different times of day

The performance of indoor positioning systems depends greatly on the separation of location fingerprints A location fingerprint

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

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corresponding to a location can be identified correctly if it is difficult to classify it as another fingerprint by a pattern classifier

Path loss is one of the basic signal propagation properties of the RSS that best describes attenuation of transmit signal power over a distance between an access point and a mobile device

Presence of Userrsquos or userrsquos orientation affects RSS

Figure 23 Error Rate vs Number of samples

Figure 24 RSS distribution at 214 pm

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

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Figure 25 RSS distribution at 100pm and 1100 am

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

6 DISCUSSIONS10102006

44

6 DISCUSSIONS 61 Summary and Conclusion While empirical results and performance studies of positioning systems based on location fingerprint have been presented in this report analytical model that can be used as a framework for efficiently designing the positioning systems are not available This thesis design work uses general principles of position estimation such as position estimation of sensors grid spacing etc This design work focuses on accuracy and precision The concept of location fingerprint technique based on RSS us explained in detail in chapter 2 It is found that RSS is random It is possible to approximate the RSS as being normally distributed While the RSS random process is non‐stationary the mean is more or less constant and could be used as the fingerprint of the location Model designed for position estimation is as shown in chapter 4 Accuracy and precision are the two major performance matrices in this study Uniform grid spacing has been used From the experimental analysis I have obtained an accuracy of 3 meters and 35 precision The sensitivity analysis among the system parameters and the performance matrices were done using the model as shown in chapter 4 For instance our study found that the minimum distance between two adjacent positions also affects the positioning performance This is due to the randomness of RSS patterns induced by the ever changing indoor environment

62 Future work The research in this thesis provides a ground work for the future study of efficient design of indoor positioning system The RSSI based position estimation still has limitations First the RSSI value is time dependent Thus the future study should address this issue Second the movement of object is not considered Third orientations of sensors affect the performance which should be taken care off Fourth to improve the performance of the system we need to find a solution to deal with those incomplete or missing data during the off‐line and the on‐line phase Fifth position estimation on multi‐floor and three‐dimensional coordinates is not available

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 110102006

45

APPENDIX 1

Location Fingerprints of Masters Room Time 1315 Observation Time 20 minutes Number of Samples 10000

People Presence SNo Co-ordinates AP1 (0x0001) AP2 (0x0002) AP3 (0x0004) AP4 (0x0008)

1 11 -24 -16 -27 -33 2 13 -26 -8 -34 -31 3 15 -43 -9 -39 -25 4 32 -21 -11 -27 -33 5 61 -6 -23 -32 -28 6 64 -20 -31 -25 -34 7 35 -23 -35 -29 -38 8 37 -29 -35 -22 -27 9 39 -38 -29 -21 -27 10 67 -27 -23 -20 -23 11 611 -26 -29 -23 -30 12 613 -35 -32 -2 -31 13 513 -34 -31 -20 -26 14 11 -39 -22 -29 -17 15 212 -37 -42 -28 -31 16 -212 -38 -33 -29 -6 17 -21 -42 -29 -33 -19 18 -38 -40 -23 -35 -22

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 210102006

46

APPENDIX 2

Begin

Open radio map and raw data files for write

Intialize radio map in memory

Check whether the

Packet is data or Beacon packet If data packet

Save the data to register

Check if data is

Received from all

sensors

Estimate the position of static

Display the position on PC

End Yes

No

A

A

Yes

No

Flow chart representing the main algorithm for position estimation

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

47

APPENDIX 3

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

48

Distribution of RSSI

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

49

REFERENCES [1] T S Rappaport J H Reed and B D Woerner ldquoPosition location using wireless communications on highways of the futureʺ IEEE Commun Mag vol 34 no 10 pp33‐41 Oct 1996 [2] A M Ladd K E Bekris G Marceau A Rudys L E Kavraki and D S Wallach rdquoRobotics‐based location sensing using wireless ethernetʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ02) 2002 pp 227‐238 [3] I Ramani R Bharadwaja and P V Rangan ldquoLocation tracking for media appliances in wireless home networksʺ in Proc International Conference on Multimedia and Expo (ICMEʹ03) July 2003 pp 769‐772 [4] K Pahlavan X Li and J P Makela ldquoIndoor geolocation science and technologyʺ IEEE Commun Mag vol 40 no 2 pp 112‐118 Feb 2002 [5] Konard Lorincz and Matt Welsh ldquoMote Track A Robust Decentralized Approach to RF‐Based Location Trackingrdquo [6] Paramvir Bahl and Venkata N Padmanabham ldquoRADAR An In‐Building RF‐based User Location and Tracking Systemrdquo [7] Kamol Kaemarungsi ldquoDesign of Indoor positioning system based on location fingerprint techiniquerdquo University of Pittsburgh 2005 [8] Fredrik Linnarsson Cheng Peng and Bengt Oelmann ldquoSENTIO A Hardware Platform for Rapid Prototyping of Wireless Sensor Networksrdquo [9] S Saha K Chaudhuri D Sanghi and P Bhagwat ldquoLocation determination of a mobile device using IEEE 80211b access point signalsʺ in Proc IEEE Wireless Communications and Networking Conference (WCNCʹ03) New Orleans LA Mar 2003 pp 1987‐1992 [10] T Roos P Myllymaki H Tirri P Misikangas and J Sievanen ldquoA probabilistic approach to WLAN user location estimationʺ International Journal of Wireless Information Networks vol 9 no 3 pp 155‐164 July 2002 [11] R Battiti M Brunato and A Villani ldquoStatistical learning theory for location fingerprinting in wireless LANrsquosʺ Technical Report Oct 2002 [Online] Available httprtmscienceunitnitraquobattitiarchive86pdf [12] P Prasithsangaree P Krishnamurthy and P K Chrysanthis ldquoAn indoor position location with wireless LANsʺ in Proc IEEE International Symposium on Personal Indoor and Mobile Radio Communications (PIMRCʹ02) Lisbon Portugal Sept 2002 [13] M A Youssef A Agrawala and A U Shankar ldquoWLAN location determination via clustering and probability distributionsʺ in Proc IEEE

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

50

International Conference on Pervasive Computing and Communications (PerComʹ03) Dallas‐Fort Worth TX Mar 23‐26 2003 pp 23‐26 [14] Z Xiang S Song J Chen H Wang J Huang and X Gao ldquoA wireless LAN‐based indoor positioning technologyʺ IBM Journal of Research and Development vol 48 no 56 pp 617‐626 SeptNov 2004 [15] R Want A Hopper V Falcao and J Gibbons ldquoThe active badge location systemʺACM Transactions on Information Systems vol 40 no 1 pp 91‐102 Jan 1992 [16] N B Priyantha A Chakraborty and H Balakrishnan ldquoThe cricket location‐support systemʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ00) Boston MA Aug 2000 pp 32‐43 [17] Holger K Willig A (2005) Protocols and Architectures for Wireless Sensor Networks London [18] IEEE (2003) Part 154 Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low‐Rate Wireless Personal Area Networks (LR‐WPANs) New York [19] Chipcon CC2420 802154 compliant radio httpwwwchipconcom [20] ZigBee Specification v10 ZigBee Alliance [21] Dimitrios Lymberopoulos Quentin Lindsey and Andreas Savvides ldquoAn Empirical Analysis of Radio Signal Strength Variability in IEEE 802154 Networks using Monopole Antennasrdquo [22] Kiran Yedavalli Bhaskar Krishnamachari Sharmila Ravula and Bhaskar Srinivasan ldquoEcolocation A sequence based technique for RF localization in wireless sensor networksrdquo Robert Bosch Research and Technology Centre Palo Alto CA

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

List of Figures10102006

vii

List of Figures Figure 1 Schema of thesis work 5 Figure 2 Block diagram representing hardware module 8 Figure 3 Taxonomy of positioning system [7] 10 Figure 4 Star and peer‐to‐peer topologies in LR‐WPAN [18] 15 Figure 5 WPAN architecture [18] 16 Figure 6 Example of super frame structure [18] 19 Figure 7 Communication from a device to a PAN coordinator in (a) a beacon‐enabled network and (b) a non‐beacon enabled network [18] 20 Figure 8 Communication from a PAN coordinator to a device in (a) a beacon‐enabled network and (b) a non‐beacon enabled network [18] 21 Figure 9 Overview of the coverage for different wireless communication standards [17] 21 Figure 10 IEEE 802154ZigBee stack [20] 22 Figure 11 IEEE 802154ZigBee stack [17] 23 Figure 12 ZigBee secure frame in MAC layer [17] 24 Figure 13 ZigBee secure frame in network layer [17] 24 Figure 14 ZigBee secure frame in application layer [17] 25 Figure 15 SENTIO Platform [8] 31 Figure 16 Layout of Masterʹs Room 32 Figure 17 Flow chart representing the algorithm of fixed sensors 35 Figure 18 Flow chart representing the algorithm of main sensor 37 Figure 19 Test bed for one dimensional test in Masters Room 38 Figure 20 Histogram plot of RSSI values at 1m and 4m 39 Figure 21 Histogram plot of RSSI values at 6m and 8m 39 Figure 22 Distance vs RSSI 40 Figure 23 Error Rate vs Number of samples 42 Figure 24 RSS distribution at 214 pm 42 Figure 25 RSS distribution at 100pm and 1100 am 43

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

Terminology Notation10102006

viii

Terminology Notation

GPS ‐ Global Positioning System

RSSI ‐ Received Signal Strength Indication

MS ‐ Mobile Station

RF ‐ Radio Frequency

WSN ‐ Wireless Sensor Network

AP ‐ Access Point

RSS ‐ Received Signal Strength

PC ‐ Personal Computer

WLAN ‐ Wireless local Area Network

SNR ‐ Signal to Noise Ratio

RFD ‐ Reduced‐Function Device

WPAN ‐ Wireless Personal Area Network

FFD ‐ Full Functional Device

MAC ‐ Medium Access Control

LR‐WPAN ‐ Low Rate WPAN

PAN ‐ Personal Area Network

OSI ‐ Open Systems Interconnection

LLC ‐ Logical Link Control

SSCS ‐ Service Specific Convergence Sub layer

PHY ‐ Physical Layer

PPDU ‐ PHY Protocol Data Unit

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

Terminology Notation10102006

ix

ED ‐ Energy Detection

CCA ‐ Clear Channel Assessment

LQI ‐ Link Quality Indication

ISM ‐ Industrial Scientific and Medical

DSSS ‐ Direct‐Sequence Spread Spectrum technique

LSB ‐ Least Significant Bit

SHR ‐ Synchronization Header

SFD ‐ Start of Frame Delimiter

PHR ‐ PHY Header

MPDU ‐ MAC Protocol Data Units

GTS ‐ Guaranteed Time Slot

MHR ‐ MAC Header

MFC ‐ MAC Footer

FCS ‐ Frame Check Sequence

CAP ‐ Contention Access Period

CFP ‐ Contention Free Period

ACL ‐ Access Control List

AES ‐ Advance Encryption Standard

MIC ‐ Message Integrity Code

CBC ‐ Cipher Block Chaining

ZDO ‐ ZigBee Device Objects

OS ‐ Operating System

LOS ‐ Line of Sight

NLOS ‐ Non Line of Sight

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

Terminology Notation10102006

x

FPGA ‐ Field Programmable Gate Array

BS ‐ Base Station

AOA ‐ Angle of Arrival

TDOA ‐ Time difference of arrival

TOA ‐ Time of Arrival

TOF ‐ Time of flight

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

1

1 INTRODUCTION 11 Introduction to the study Advancement in wireless sensing technologies has encouraged in developing a model for indoor object‐tracking application using low power IEEE 802154 compliant radios Stimulus used in this model is RSS This application is not only limited to object tracking in an indoor environment Concierge services enable users to become aware of nearest supporting facilities For example smart home applications such as multimedia appliances that forward multimedia stream to the nearest video screen can be achieved with a home positioning system [3] In the field of robotics a robot can navigate by itself using the assistance of indoor positioning system [2] Granularity of location information is most important in location determination applications For example to locate a book in a library requires coarse grained granularity whereas locating a user requires coarse grained granularity Position estimation refers to a process used to obtain location information of a MS with respect to a set of reference positions within a pre‐defined space A system deployed to determine or estimate the location of an entity is called a position location system or positioning system A wireless indoor positioning system refers to a wireless network infrastructure that provides indoor location information to any requesting end user One of the key technical elements allowing the realisation of this vision will be the knowledge of the accurate position of the personobjects Already today accurate location mechanisms such as GPS are commercially available Many more will become part of our daily life with the introduction of third generation mobile phones But none of these techniques will allow the meter or sub‐meter level of accuracy within buildings which is necessary for smart houses Hence this report introduces an object tracking application in an indoor environment using IEEE 802154 compliant radios which gives meter level accuracy First this chapter presents the background of indoor positioning systems identifies the challenges of such systems and briefly describes the scope of indoor positioning systems based on location fingerprints Next the objective of study the overview of approaches and the contributions are presented Finally the scope and organization of thesis work is outlined

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

2

12 Background The success of outdoor positioning and applications based on the GPS provides an incentive to the research and development of indoor positioning systems It has been developed over several decades and it relies on constellation of satellites Ranges to several satellites are used in multi‐lateration procedure to infer the position of the receiver This localization scheme requires hardware that is both expensive and consumes significant power As a result indoor positioning systems require alternative means to detect the MS location without relying on the direct signal from GPS satellites Infrared RF and ultra sound signals are major technologies used for indoor positioning systems Unlike outdoor areas the indoor environment imposes different challenges on location discovery due to the dense multi‐path effect and building material dependent propagation effect Thus an in‐depth understanding of indoor radio propagation for positioning is crucial for efficient design and deployment The existence of radio connectivity and the attenuation of radio signal with distance are attractive properties that could potentially be exploited to estimate the positions of small wireless devices featuring low‐power radios RSSI a standard feature in most radios has attracted a lot of attention in the recent literature for obvious reasons RSSI eliminates the need for additional hardware in small wireless devices and exhibits favourable properties with respect to power consumption size and cost As mentioned previously RF‐based location tracking has been widely studied [5 6] Given a model of radio signal propagation in a building or other environment RSSI can be used to estimate the distance from a transmitter to a receiver and thereby estimate the position of a mobile node However this approach requires detailed models of RF propagation and does not account for variations in receiver sensitivity and orientation Table 1 below briefly summarizes performance of existing indoor positioning system based on location fingerprints and WLANrsquos All the systems mentioned below in table 1 follow IEEE 80211 protocol standards The major performance metrics studied by all systems are positioning accuracy which is a form of error measurement The accuracy of the location information is usually reported as an error distance between the estimated location and the actual mobile location However the report of accuracy should include the confidence interval or percentage of successful location detection which is called the precision The system parameters of existing systems are summarized in table 1 and the best reported performance of these systems are listed in table 2

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

3

System Spacing(m) Positions SamplesPos APs Orientation Environment

RADAR [6] Non

uniform 70 80 3 4 Hall-way Saha et al [9] 3 19 1200 3 NA 1-floor

Roos et al [10] 2 155 40 10 NA 1-floor Battiti et at [11] NA 257 NA 6 NA 1-floor Ladd et al [2] 3 11 1307 5 2 Hall-way

Prasithsangaree et al [12] 153 60 40

2 to 7 4 1-floor

Youssef et al[13] 15 110 300 4 NA Hall-way Xiang et al [14] NA 100 300 5 4 1-floor

Table 1 Comparison of Indoor Positioning Systems [7]

System Algorithm Type Accuracy and Precision RADAR [6] Nearest Neighbour within 7 feet 38

Saha et al [9] Nearest neighbour amp Neural Network no specified accuracy

90 Roos et al [10] Bayesian best within 828 feet 90

Battiti et at [11] Neural Network amp Weighted k-Nearest

Neighbour all within 16-17 feet 90 Ladd et al [2] Bayesian within 5 feet 77

Prasithsangaree et al [12] Weighted k-Nearest Neighbour

25 feet at 75 amp 40 fet at 95

Youssef et al [13] Bayesian within 7 feet more than

90 Xiang et al [14] Bayesian with RSS distribution model within 6 feet 90 Table 2 Performance comparison of Indoor Positioning Systems [7]

Based on the study of results obtained in various positioning systems as in table 2 we have developed a model for an indoor positioning system using SENTIO [8] platform based on RSSI

13 Objectives In the last two or three years a number of theoretical practical andor simulation studies were done on the topic of object‐tracking While these studies are useful they are too general and provide little guidance for the actual deployment of sensor networks for real life location‐tracking of an object This thesis focuses on developing an object‐tracking application and prescribes sensor network configurations that work well with our algorithms I implemented my software using SENTIO [8] platform for sensor networking The major issue addressed in this project are the accuracy and precision in tracking an object in a real‐world application and efficient ways to algorithmically analyze the collected raw data from the specific wireless sensor product

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

4

Although the focus is to develop a real‐world application using WSN it also provides great opportunity to explore the new area of wireless communication overall

14 Research Questions My primary target is to find a method to determine the position of an object inside a building using ZigBee protocol standards based on RSSI The study addresses the following questions What is WSN What requirements do WSN need What are their main ideas and concepts What are the associated technologies What are the standards that sensor networks use

What are the existing hardware implementations that use this particular technology How can we build our new applications using WSN What do sensor network systems require for general or specific applications Are they applicable And what is the appropriate design for sensor network implementations in order to combine them with other existing systems At this point we describe and evaluate the specific object‐tracking application to be built

15 Scope The study area of the thesis is an overview of the WSN technology study of SENTIO platform and implementation of an object tracking application Thus the study is divided into two parts Part one is an overview of WSN it includes study of SENTIO platform and IEEE 802154 protocol standards The second part describes the implementation of the object tracking algorithm The object tracking application receives and uses the raw values returned by the sensor network system to produce clear and meaningful outputs Schema of thesis work is as shown in figure 1

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

5

Figure 1 Schema of thesis work

16 Methodology The ldquoIndoor Local Positioning System for ZigBee based on RSSI ldquois based on ldquolocation fingerprintingrdquo technique Location fingerprinting refers to a technique that exploits the relation between any measurable physical stimulus and a specific location This approach uses empirical measurements of RSSI to estimate location By recording a database of radio ldquosignaturesrdquo along with their known locations a mobile node can estimate its position by acquiring a signature and comparing it to the known signatures in the database All of these systems require the database of signatures which should be collected manually prior to the system installation and rely on central server (or the userrsquos mobile node) to perform location calculation For example in RADAR [6] technology the RSSI is the stimulus This type of positioning system does not require specialized hardware other than the common wireless network interfaces with received signal strength measurement capability thus it is simple to deploy compared to other techniques Unlike outdoor counterpart systems which can use AOA and TDOA techniques effectively indoor positioning systems encounter the problem of NLOS and the dense multi path effect that render these two techniques ineffective or complex for practical implementation It is also difficult for a MS to always see three or more access points or base stations in indoor environment which is essential for triangulation by AOA and TDOA Location fingerprinting can also be implemented as software based positioning system which can reduce

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

6

complexity and cost Generally the deployment of fingerprinting based positioning systems can be divided into two phases First in the off‐line phase the locations where the fingerprints are required is pre‐determined and then the fingerprints are collected by performing a site‐survey of the RSS from multiple APs The distance between two closest positions is called grid spacing and usually reported in meters or feet However some points may be omitted due to inaccessibility The RSS is measured at each predetermined location to create a database of RSS patterns of the indoor environment The database of RSS patterns is called a radio map The vector of RSS values at a point on the grid is called the location fingerprint of that point Second in the on‐line phase a MS will report a sample measured vector of RSS from different APs to a base station The base station uses a positioning algorithm to estimate the location of the MS The most common algorithm used to estimate the location is Euclidean algorithm It computes the Euclidean distance between the measured RSS vector and each fingerprint in the database and then the coordinates associated with the fingerprint that provides the smallest Euclidean distance is returned as the estimate of the position It is clear from the above discussion that the designer should not start out to do the site survey without proper system design objectives and guideline If the performance is not sufficient collecting more fingerprints in between previous location fingerprints may help It is a problem of choice as to how many access points are required for the system and what the minimum distance between physical positions on the grid should be in order to provide a good position resolution and best system performance The system parameters and factors that improve the accuracy and the precision performance are still not clear

17 Outline Chapter 2 provides literature review of ldquoIndoor Local Positioning System for ZigBee based on RSSIrdquo it introduces the basic components required for a positioning system general classification of indoor positioning system Finally it explains ldquoLocation Fingerprintrdquo technology used in this thesis work to determine the position of an object in a real‐world application It briefly explains the protocols and industry standards for wireless sensor networks Chapter 3 describes some properties of RSS which is a stimulus to determine

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

7

the position of an object RSS relation with energy and on what parameters is the RSS value dependent Chapter 4 describes the model of an indoor local positioning system and its probability of returning the correct location It also explains its performance evaluation and its error distance distribution Chapter 5 describes results obtained from the model of positioning system Chapter 6 includes conclusions from the experimental analysis and future work

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

8

2 LITERATURE REVIEW This chapter reviews the literature on wireless indoor positioning system as a means of providing an intellectual background for the thesis work First in section 21 the common components of indoor positioning system are described Then in section 22 discusses different means to classify indoor positioning systems In section 23 reviews of all relevant literature on location fingerprint technique Finally in section 24 protocols and industry standards for wireless sensor networks are explained

21 Common components of indoor positioning system Basically a wireless local positioning system consists of at least two separate hardware components a measurable unit that usually carries the major part of the system intelligence and a signal transmitterreceiver The transmitter in the simplest case is just a beacon Figure 2 represents basic hardware model of a positioning system

Figure 2 Block diagram representing hardware module

First the location sensing devices detect the signals transmitted by or received at known reference points using radio sensing technologies The sensing technique is based on signal strength level that converts the sensed signal into RSS Given a set of reference points the relative position of the MS can be derived from the signal characteristics of RSS The signal characteristics of RSS are unique for a location Then the microcontroller processes the position algorithm and estimates the position of object and displays it on PC

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

9

22 Classification of Indoor positioning systems Indoor positioning system can be categorized based on the stimulus used in particular application and measurement principle

221 Classification based on Stimulus Based on the stimulus used in an application WSN inherit certain characteristics and limitations on sensorrsquos signal The propagation delay diffraction reflection and scattering are the basic signal characteristics which affect all signal types The effective range available bandwidth regulatory constraints interference power constraints safety and cost of technology limitations The wireless signals commonly used for indoor positioning systems are infrared radio frequency and ultrasound Brief descriptions of the three major technologies are as follows

Infrared Infrared signal is an electromagnetic signal of a wavelength longer than that of visible light but shorter than that of radio waves Infrared is just below the visible spectrum of light in frequency and is radiated strongly by hot bodies Many objects such as people vehicle engines and aircraft generate and retain heat and as such are especially visible in the infrared wavelengths of light compared to objects in the background It cannot pass through walls or obstructions therefore it has a rather limited range of indoor environments Thus it requires a more sophisticated circuitry than ultrasound signals Indoor lighting interferes with this type of signal and causes problems in accurate sensing It generally has a range of around 5 m The infrared devices are usually small in size compared to ultrasound devices

Radio frequency The RF signal can penetrate through most of the building material therefore it has an excellent range in indoor environments The propagation speed is also high approximately 3108 ms There are unlicensed frequencies available freely for use This type of signal has the longest range compared to infrared and ultrasound Since the propagation waves are of higher frequency (24GHz) interference with other frequency components is less

Ultrasound Although ultrasound operates at low frequency bands (typical 40 kHz) compared to the other two signaling technologies it possesses a good precision for location sensing at a slow propagation speed of sound (343 ms) The advantages of ultrasound devices are their simplicity and that they are inexpensive However ultrasound does not penetrate walls

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

10

but reflects off most of the indoor obstructions It has a short range around 3 m to 10 m but has a 1 cm resolution of distance measurement The operating temperature influences the performance of ultrasound

222 Classification based on measurement principle Wireless positioning systems can be categorized by measurement techniques which are used to derive the position of mobile stations Mainly three different measurement principles are used today AOA RSS and propagation‐time based systems that can further be divided into two different sub‐classes TOA and TDOA Figure 3 shows taxonomy based on the technology and the technique classifications

Figure 3 Taxonomy of positioning system [7]

In AOA systems the position is calculated via goniometry With the use of directional antennas or antenna arrays the angle or bearing relative to points located at known positions is measured The intersection of several measured directions pointers the yields the position value The accuracy of this approach is limited by the possible directivity of the measuring aperture by shadowing and by multi‐path reflections arriving from misleading directions In TOA and TDOA techniques rely on the precision of timing between the signal transmitter and the receiver in order to use the propagation delay or TOF to calculate the distance between transmitter and receiver Therefore a precise synchronization is also very important in such systems By combining at least three distances from three reference positions triangulation can be used to estimate the mobile stationʹs location This type of technique will

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

11

require a high accuracy clock in the communication system TDOA is more practical [1] Examples of location‐sensing systems that use time of flight are GPS the Active Bats [15] and the Cricket [16] Besides these time delay‐based techniques the distance between transmitter and receiver can also be determined from signal strength attenuation and direct distance measurement (such as dead reckoning) Fingerprinting or Location Pattern Matching this technique generally requires only measurement of RSS or other non‐geometric features at several locations to form a database of location fingerprints To estimate the mobile location the system needs to first measure the received signal strength at particular locations and then search for the pattern or fingerprint with the closest match in the database This technique does not require the mobile station to see at least three base stations or access points in order to determine the location The disadvantage of this technique is that it is very time‐consuming to perform an exhaustive data collection for a wide area network such as in outdoor positioning systems

23 Indoor local positioning using ZigBee based on RSSI This section reviews relevant RF‐based indoor positioning systems which can be used to locate stationary objects and track mobile users The impressive growth of IEEE 802154 wireless PANs (WPAN) in recent years suggests an interesting future for the location fingerprinting technique The system is quite flexible because system designers can select whether to have a centralized positioning server or let the mobile to determine its own position However the fingerprinting technique requires a training phase (off‐line phase) to collect location fingerprints for all positions in the operating area before the actual deployment (on‐line phase) [7] The following discussion is divided according to the positioning system components First the common form of location fingerprinting and its relationship with physical locations are explained Finally location estimation algorithm is reviewed

231 Location Fingerprint A location fingerprint based on RF characteristics such as RSS is the basis for representing a unique location It is created under the assumption that each position or location inside a building has unique RF signature [4] Generally a fingerprint F is labelled with a location information L The location fingerprints and their labels (eg location information) are maintained in

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

12

database and used during the on‐line phase to estimate the location The label and fingerprint are usually denoted as a tuple of (L F) Battiti et al [11] point out that the location information L for indoor location can be recorded in two forms as either a tuple of coordinates or an indicator variable The tuple of real coordinates can vary from one dimension to five dimensions which include the three dimension space and two orientation variables expressed in spherical coordinates For instance a location information of a two‐dimension system with an orientation could be expressed as a triplet L = (x y d) | x y є R 2 d є North East South West The RSS was found to be more location‐dependent than the SNR because the noise component is rather random in nature However the RSS itself fluctuates over time for each access point and location Each RSS element can be considered as a random variable therefore it can be captured by recording its descriptive statistics parameters approximating its distribution or maintaining the whole measurement dataset These approaches of RSS representation result in different procedures for location estimation algorithm Regardless of the approach the location fingerprint is usually denoted as an array or vector of signal strength (random variables) received at any position in the location‐based service area The size of the vector is determined by the number of access points that can be heard To create a basis fingerprint such as in a number of samples of vectors of signal strength are collected over a window of time for each position This basis is called a prototype Then the average RSS of each access point is calculated and recorded as an element in the location fingerprint For an area that can receive signals from N access points the location fingerprint can be expressed as a vector of average RSS elements βi

F = (β1 β2hellip βN)T Besides the basis location fingerprint the samples of location fingerprint measured during the on‐line phase are also important for the system that tracks the mobile object The number of samples should be selected appropriately to represent the location fingerprint for the mobile application A step in statistical analysis method called pre‐processing is another important issue needed to be considered because it can impact the estimation of dependency between location fingerprint and the location information easier The pre‐processing refers to a step that cleans the raw data (in this case the training set) before any further operations or analysis The cleaning may consist of encoding dimensionality reduction (reduce unnecessary elements) feature extractionselection clustering and outlier elimination Roos et al [10] point out that the pre‐processing enables faster location estimation and

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

13

reduce the noise from the training data The process that creates the basis of location fingerprint discussed above could be considered as a part of pre‐processing

232 Location Estimation Algorithm Location estimation algorithms or positioning algorithms are procedures that exploit dependency between location information and location fingerprint basis in order to determine a position or location from samples of RSS signals Position algorithms can be classified into deterministic and probabilistic types based on the approaches that model the relationship between location fingerprints and location information The deterministic type of algorithms are those that based in the nearest neighbour classifiers and neural network classifiers The probabilistic type of algorithms is those that are based on the Bayesian inference and statistical learning theory such as support vector machines (SVR) The algorithm used in this project is discussed briefly

Euclidean distance method The Euclidean distance method is a deterministic approach of position estimation because they require only a set of constant location fingerprints which includes mean vectors In order to determine the location a form of discriminate function is used to classify a sample of RSS fingerprint into a position The mean or average RSS vector is a centre of a mass which represents each class of location fingerprint Suppose a set of l location fingerprints is denoted by F1 F2hellip Fl exist and each fingerprint has a one‐to‐one mapping to a set of positions L1 L2hellip Ll A sample of an RSS fingerprint measured during an on‐line phase is denoted as S which can be another mean or average RSS vector of small window of RSS samples Assuming that an indoor positioning system only considers the average RSS from N access points as a location fingerprint the sample of RSS vector is S = (s1 s2 helliphellip sN)T and each location fingerprint i in the database can be expressed as Fi = (β1 β2helliphellip βN)T The simplest closeness metric is a distance measurement in signal space denoted as the Dist () function [6] Thus the simple procedure of nearest neighbour algorithm is expressed as picking the fingerprint j that has the shortest signal distance

Dist(S Fj) le Dist(S Fk) for all k ne j A generalized weighted distance Lp summarized by [12] can be used to calculate different forms of distance in signal space as

Lp = 1N pN

i

pPiSiWi 1

1)||1(sum

=

minus

where N is the number of access points wi is a weighting factor (wi le 1) and p is the norm parameter starting from 1 The weighting factor wi is a bias

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

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parameter that can demote or promote an important RSS component in the fingerprint

24 Protocols and Industry standards for WSN Although many applications for WSN are proposed the communication protocols supporting them remain mainly unexplored and diverged This is because the area of interest is new each implementation is application specific and the components of WSNrsquos have a lot of constraints Wireless sensor networks use the wireless medium to communicate However the traditional communication protocols that support wireless communications especially ad‐hoc mesh networks may not be well suited for them WSN communication is an active research area and many algorithms and protocols have already been proposed This section introduces two proposed standards the IEEE 802154 and the ZigBee The IEEE 802154 covers the physical layer and the MAC layer of low‐rate WPAN The ZigBee is ldquoan emerging standard that is based on the IEEE 802154 and adds network construction (star networks peer‐to‐peermesh networks and cluster‐tree networks) application services and morerdquo

241 IEEE 802154 Holger and Willig (2005) [17] mention home automation home networking and home security as possible applications for IEEE 802154 They add that ldquomost of these applications require only low‐to‐medium bitrates (up to some few hundreds of kbps) moderate average delays without too stringent delay guarantees and for certain nodes it is highly desirable to reduce the energy consumption to a minimumrdquo The IEEE 802154 standard (2003) defines the device types that can be used in a LR‐WPAN A device can be a FFD or a RFD The RFD can be used in simple applications in which they do not need to transmit large amounts of data and they have to communicate only with a specific FFD The FFD can work as a PAN coordinator as a coordinator or as a simple device It can communicate with either another FFD or a RFD In keeping with the application requirements the LR‐WPAN operates in a star or peer‐to‐peer topology (figure 4) In the star topology the RFD communicates with a single controller the PAN coordinator The PAN coordinator can perform the same function as the RFD but it is also responsible for controlling the PAN ldquoit initiates terminates or routes communication around the networkrdquo [18] The peer‐to‐peer topology

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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supports ad‐hoc mesh multi‐hop networking Any device in the peer‐to‐peer topology can communicate with any other device within its communication range however this topology also has a PAN coordinator All the devices in a LR‐WPAN have a unique 64‐bit address This or a short address allocated by the PAN coordinator can be used inside a PAN Additionally each PAN has a unique identifier The combination of the PAN identifier and the sort addresses allows communication across different PANs (IEEE 802154 Standard 2003)

Figure 4 Star and peer‐to‐peer topologies in LR‐WPAN [18]

The LR‐WPAN based on the OSI seven‐layer model has the layered architecture presented in figure 5 The application and the network layer are the upper layers in the LR‐WPAN architecture but are outside the scope of the IEEE 802154 standard Only the physical layer ldquowhich contains the RF transceiver along with its low‐level control mechanismrdquo and the MAC layer ldquothat provides access to the physical channelrdquo are included in the standard and will be introduced in the following sections Finally the Type I 8022 LLC and the SSCS are intermediate sub layers supporting communication with the above layers (IEEE 802154 Standard 2003)

Physical Layer The 802154 standard specifies two different services that the PHY provides The PHY data service controls the radio and thus the transmission and reception of the PPDUs In addition the management service performs ED in the channel The management service also performs CCA before sending the messages and provides LQI for the received packets

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Figure 5 WPAN architecture [18]

Three different bands are defined by the standard The 868‐8686 MHz for Europe the 902‐928 MHz for North America and the 2400‐24835 MHz worldwide All of them belong in the ISM radio bands and they are using DSSS Each of the bands supports different a data rate and it uses a different modulation technique chip rate and number of channels Moreover if the system does not use the 2450 MHz frequency it operates in both the 802 MHz and 902 MHz frequencies Table 3 summarizes the supported bands and data rates (IEEE 802154 standard 2003)

PHY (MHz)

Frequency Band (MHz) Modulation

Bit Rate Number of Channels

868-8686 BPSK 20 1 868 915 902-928 BPSK 40 10

2450 2400-24835 O-QPSK 230 16 Table 3 Frequency bands and data rates for IEEE 802154 based on the IEEE

802154 standard [18] The physical layer uses PPDU packets to communicate Table 4 demonstrates its structure The LSB is always transmitted and received first The SHR contains the preamble and the SFD fields which helps receiver

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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17

synchronization The 8‐bytes preamble contains only zero and is used for synchronization The SFD contains a specific sequence of one and zeros and specifies the beginning of the frame The PHR contains the payload length Packets with a length of 9 or more bytes are MPDU as the next section further explains Packets with length 5 are used for MPDU acknowledgements The payload part of the PPDU encloses the MAC layer packet Finally the PPDU size can be up to 127 bytes [18]

4 bytes 1 byte 1 byte Variable

Preamble SFD Frame length

(7 bits) Reserved

(1 bit) Payload (PSU)

SHR PHR PHY Payload Table 4 Format based on the IEEE 802154 standard [18]

MAC Layer The MAC layer is the interface between the SSCS and the PHY layer Similar to the PHY layer the MAC layer supports two services The MAC data service is responsible for the transmission and reception of the MPDUs through the PHY data service The MAC management service if the device is a coordinator manages the network beacons It is also responsible for PAN association and disassociation frame validation and acknowledgment providing ldquoa reliable link between two peer MAC entitiesrdquo In addition it uses the CSMA‐CA for channel access and handles and maintains the GTS mechanism Finally it supports device security [18] The IEEE 802154 standard defines four different frame types the beacon data acknowledgment and MAC command frame All frame types are based on the general MAC frame format (table 5) The frame control field describes and specifies the above different frame types Every MAC frame comprises a MHR which consists of a frame control sequence number and the information field It also contains the MAC payload different frame types have different MAC payload fields The acknowledgment type does not have a payload Finally each frame includes a MFR which contains a FCS The data in the MPDU follows the same order as the PPDU the least significant bits are left in the frame and are transited first Octets2 1 02 028 02 028 Variable 2

Destination PAN Identifier

Destination Address

Source PAN

Identifier Source Address Frame

Control Sequence Number Addressing Field

Frame Payload FCS

MHR MAC

Payload MFRTable 5 General MAC frame format IEEE 802154 standard [18]

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Table 6 7 8 and 9 present the four different MAC frame types The beacon frame is transmitted periodically by the PAN coordinator It provides information about the network management through the super frame and GTS fields which are analyzed later in the section It also synchronizes the network devices and indicates the proper communication period for them The data frame payload encapsulates data from the higher layers When a device receives a packet it is not obliged to response with an acknowledgement packet Finally the command frame identifier and command payload fields of the command frame are used for communication between the network devices The command identifier specifies actions like association disassociation and data GTS or beacon request Octets2 1 410 2 Variable Variable Variable 2

Frame Control

Sequence Number

Addressing Fields

Super frame Specification

GTS fields

Pending Addressing

Fields Beacon Payload FCS

MHR MAC Payload MFRTable 6 Beacon frame format [18]

Octets 2 1 Variable 2

Frame Control

Sequence Number

Addressing Field

Data Payload FCS

MHR MAC

Payload MFR Table 7 Data frame format [18]

Octets 2 1 2

Frame Control

Sequence Number FCS

MHR MFR Table 8 Acknowledgement frame format [18]

Octets2 1 1 Variable 2

Frame Control

Sequence Number

Addressing Fields

Command Frame Identifier

Command Payload FCS

MHR MAC Payload MFR Table 9 Command frame format [18]

In the LR‐WPAN every PAN has its own coordinator The PAN coordinator manages the communication in the local area it has two options to use or not use the super frame structure The super frame (figure 6) uses network beacons If the coordinator does not want to use a super frame structure it suspends the beacon transmission However the beacon is important for device association and disassociation If the coordinator wishes to maintain close communication control in the PAN and to support low‐latency devices it usually uses the super frame A super frame determines a specific time

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

19

period beacons bound it The beacon is transmitted in the first of the sixteen equal time slots that the super frame has It is used to ldquosynchronize the attached devices to identify the PAN and to describe the structure of the super framerdquo The super frame can have active and inactive periods All the communications have to be finished inside the super frame period In the inactive periods the devices can switch to the sleep mode but they have to be ready for the next beacon The active period is further divided into a CAP and a CFP During the CAP any device can communicate competing with the other devices in the PAN using slotted CSMA‐CA The CFP contains GTSs and follows the CAP A CFP may maintain up to seven GTSs and each GTS can reserve more than one time slot However the CAP should always be sufficient to allow new devices t join the PAN The GTSs are allocated to specific devices All the transactions must be completed inside the assigned time period (CAP GTS and CFP) (IEEE 802154 standard 2003)

Figure 6 Example of super frame structure [18]

In accordance with the 802154 standard (2003) three different types of data‐transfer exist In addition the types differ if the coordinator uses or does not beacons Data transfer from a device to the PAN coordinator is the first type (figure 7) For a ldquonon‐beacon‐enable networkrdquo it first senses the medium by using ldquoun‐slotted CSMA‐CArdquo and then a simple transmit to the data frame In a ldquobeacon‐enabled networkrdquo the sender waits for the beacon when it finds it ldquothe device synchronizes to the super fame structurerdquo In the specified time frame the sender again senses the medium by using ldquoslotted CSMA‐CArdquo and transmits the data to the coordinator In either case the coordinator has the option to acknowledge or not acknowledge the data reception after that the transaction is completed Data transfer from the coordinator is the next type described in the standard (figure 8) In a beacon‐enabled network the coordinator indicates a pending message through the beacon The messagersquos target device receives the beacon and if the message is pending it responses with a MAC command request

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20

message using slotted CSMA‐CA the coordinator may or may not acknowledge the command message and through slotted CSMA‐CA sends the pending message The device acknowledges the received message After that the coordinator removes the message from the beaconrsquos pending list and completes the transaction In a non‐beacon enable network the device periodically sends a MAC command‐request frame to the coordinator The coordinator acknowledges the data request Then by using un‐slotted CSMA‐CA if it has a pending message it transmits it If it has not it respond with a data frame with a ldquozero‐length payloadrdquo To complete the transaction the device acknowledges the data reception

Figure 7 Communication from a device to a PAN coordinator in (a) a beacon‐enabled network and (b) a non‐beacon enabled network [18]

Peer‐to‐peer is the last type of data transfer In this situation the devices are free to communicate with any other device within their communication range In a peer‐to‐peer PAN the devices can ldquoeither receive constantly or synchronize with each otherrdquo If they are receiving constantly to transmit data they use un‐slotted CSAM‐CA In the second case synchronization must be achieved first (IEEE 802154 standard [IEEE 2003]) The IEEE 802154 (2003) standard establishes MAC and PHY standards for low‐cost low‐power and high‐density node deployments In addition to the above PHY and MAC characteristics IEEE 802154 provides a security baseline including ldquothe ability to maintain an ACL and use symmetric cryptographyrdquo for data encryption The algorithm that is used for encryption is the AES However the higher level layers decide when security is need The upper layers are in general responsible for device authentication and key management The next section introduces the ZigBee standard which

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2 LITERATURE REVIEW10102006

21

encapsulates the IEEE 802154 and provides additional standardization for the higher levels

Figure 8 Communication from a PAN coordinator to a device in (a) a beacon‐enabled network and (b) a non‐beacon enabled network [18]

242 ZigBee Heily (2004) defines ZigBee as ldquoa rapidly growing worldwide non‐profit industry consortiumrdquo whose mission is ldquoto define a reliable cost‐effective low‐power wirelessly networked monitoring and control product based on an open global standardrdquo Figure 9 illustrates the areas of interest for different wireless communication standards

Figure 9 Overview of the coverage for different wireless communication

standards [17]

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22

ZigBee a new standard which became publicly available in June 2005 is based on the IEEE 802154 standard It expands the IEEE 802154 by adding the framework for ldquothe network security and applicationrdquo (Craig 2005) Figure 10 presents the IEEE 802154ZigBee stack and the areas of responsibilities

Figure 10 IEEE 802154ZigBee stack [20]

Craig (2005) mentions three networking topologies that the standard covers the star mesh and cluster tree (figure 11) The ZigBee standard works on top of the IEEE 802154 addressing schema by using the standard 64‐bit and the short 16‐bit addressing Kinney (2005) summarizes the ZigBee network layer responsibilities the successful establishment of a new the network and successful new device configuration addressing assignment network synchronization frames security and message routing ZigBee further distinguishes the concept of the physical devices (RFD FFD) by using the notion of ldquological devicesrdquo ldquoZigBee Coordinatorrdquo is the first type of logical devices It is responsible for initializing maintaining and managing the network Under the coordinator in the network hierarchy is the ldquoZigBee routerrdquo which is responsible for controlling the message routing between the nodes Finally the ldquoZigBee End Devicerdquo acts as the end point of the network

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2 LITERATURE REVIEW10102006

23

structure The tasks that an end device can perform are specified in the ldquoApplication Profilesrdquo (Craig 2005)

Figure 11 IEEE 802154ZigBee stack [17]

The ZigBee specifications (2005) summarize the security services provided by ZigBee ldquokey establishment key transport frame protection and device managementrdquo ZigBee builds its security mechanism using symmetric key cryptography The security services also depend on the associated layer thus as the figure 10 shows the security mechanism covers the network and the application layer In addition if a MAC frame needs security protection the MAC layer is able to secure it Moreover the notion of end‐to‐end security is supported the source and destination devices have access and use the same share key In the MAC layer the 802154 AES mechanism provides the proper security The mechanism protects ldquothe confidentiality integrity and authenticity of the MAC framesrdquo (Kinney 2005) An auxiliary header field in front of the MAC payload indicates if the frame is encrypted or not The MAC framesrsquo integrity is supported by calculating and using a MIC at the end of the MAC payload In addition to the AES nonce is used to provide MAC confidentiality and authenticity Figure 12 illustrates a MAC frame with security For different security aspects the MAC layer uses different mode of the AES for the encryption it uses the AES in Counter (CTR) mode and for the integrity the

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

24

CBC‐MAC Finally the combination (CCM) of the above two modes is available providing both encryption and integrity (Kinney 2005)

Figure 12 ZigBee secure frame in MAC layer [17]

In the network layer the CCM (a modified MAC layer CCM mode) is used for encryption Because the network layer uses only the CCM mode a single key is used for all different security options The network layer security message format is similar to the MAC frame it is presented in the figure 13 Finally although the network layer is responsible for securing its layer messages the above layers specify the keys and the CCM option for each frame (Kinney 2005)

Figure 13 ZigBee secure frame in network layer [17]

Security in the application layer works similar to the network and MAC layers It uses the ldquolink keyrdquo or the ldquonetwork keyrdquo to secure the message and then encapsulate it inside a set of fields similar to the network format (figure 14) Other security responsibilities that the application layer has are to provide the ZDO and the applications with device management services key establishment and key transport (ZigBee specifications 2005)

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

25

Figure 14 ZigBee secure frame in application layer [17]

The ZigBee application layer contains the manufacturer‐defined application objects the ZDO and the application sub‐layer In addition to the security responsibilities the application sub‐layer binds devices based on their duties and needs maintains the binding tables and forwards messages between them The application sub‐layer also discovers the neighbour devices for a given device The ZDO is responsible for determining the devicersquos duty in the network for communicating using binding requests and for supporting security as was mentioned above The sub‐layer that implements the actual application is the manufacturer‐defined application object (Kinney 2005) The ZigBee specifications are a great step toward the wireless sensors networks standardization They cover all the OSI layers from the physical to the application providing guidance to the developers The specifications are the product of the ZigBee alliances a consortium of companies and academic institutes Thus they not only produce the specifications but also are the first use them The next section goes beyond the layer architecture and standardization and introduces the OS field another crucial element of WSNs

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

26

3 RECEIVED SIGNAL STRENGTH (RSS) Majority of the existing methods leverage the existence of IEEE 80211 base stations with powerful radio transmit powers of approximately 100mW per base station Such radios are in a different class from the low power IEEE 802154 compliant radios that typically transmit at low power levels ranging from 52mW to 29mW [19] The wide availability of larger number of IEEE 802154 radios in the research community has revived the interest for signal strength based localization in sensor network Despite of rapidly increasing popularity of IEEE 802154 radios and signal strength localization there is a lack of detailed characterization of the fundamental factors contributing to large signal strength variation This chapter investigates some of the properties of RSS observed during testing and analysis of research work collected during the period of thesis The analysis of RSS values is needed to understand the underlying features of location dependent RSS patterns and location fingerprints An understanding of the properties of the RSS values for location fingerprinting can assist in improving the design of positioning algorithms and in deployment of indoor positioning systems

31 Parameters that affect RSS Different factors that affect the indoor RSS are the userrsquos presenceabsence hisher orientation time of the day building type and material distance from transmitter and type of radio These factors are by no means exhaustive Other possible factors are antenna orientation directionality and type Table 10 classify the parameters which affect RSS [7]

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

27

Effect on Factors Options

1 Proximity of user Userrsquos presence or absence

2 Orientation of user and terminal

North East South West

Data Collection

3 Make of Radio 4 Time of measurement Time of day amp days of

week 5 Period of measurement

Second minute hour

7 Interference Co‐channel adjacent co‐channel

Statistics

8 Building environment Small offices or large hall

Table 10 Classification of factors the affect RSS [7]

Transmitter variability Different transmitters behave differently even when they are configured exactly in the same way In practice this means that when a transmitter is configured to send packets at a power level of d dBm then the transmitter will send these packets at a power level that is very close to d dBm but not necessarily exactly equal to d dBm This can alter the received signal strength indication and thus it can lead to inaccurate distance estimation

Receiver variability The sensitivity of the receivers across different radio chips is different In practice this means that the RSSI value recorded at different receivers can be different even when all the other parameters that affect the received signal strength are kept constant

Antenna orientation Each antenna has its own radiation pattern that is not uniform In practice this means that the RSSI value recorded at the receiver for a given pair of communicating nodes and for a given distance between them varies as the pair wise antenna orientations of the transmitter and the receiver are changed

Multi‐path fading and shadowing in the RF channel In indoor environments the transmitted signals get reflected after hitting on the walls andor on other objects in the room such as furniture Both the original signal and the reflected signal reach the receiver almost at the same time since they both travel at the speed of light As a result of this the receiver is not able to

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

28

distinguish the two signals and it measures the received signal strength for both of them

32 Related work analysis A number of indoor location tracking systems have been proposed in the literature based on RF signals ultrasound infrared and some combinations of these As mentioned previously RF based positioning system are widely studied as shown in Table 2 Given a model of radio signal propagation in a building or other environment RSS can be used to estimate the distance between the transmitter and receiver Most of the algorithms (Table 2) have been evaluated in practice using 80211b compliant wireless radios and seem to produce a location error of 15ft to 20ft for an indoor network area of approximately 225ft x 144ft According to the results of this study all the localization techniques produce approximately the same location error over a range of environments Ecolocation [22] Mote Track [5] are two of the few localization algorithms that have been evaluated on a real sensor network that uses a low power wireless radio Ecolocation reports a location error of 10ft for a very small outdoor network deployment area (26ft x 49ft) Mote Track reports a location error of approximately 13ft for an indoor network deployment area of 18751ft2 In general the RSSI based localization algorithms can be divided into two broad categories the map based and the distance (or area) prediction based algorithms In the case of the map based algorithms the deployment area is divided into a large set of sample points that cover the whole area Most techniques collect one or more signal strength measurements from all visible beacon nodes and for each sample point Each sample point is then mapped to either a signal strength vector or a signal strength probability distribution This process generates an RSSI fingerprint map which is unique with respect to the beacon locations and the deployment area This fingerprint map is stored to a central base station A node that is not aware of its location collects one or more signal strength measurements from all the visible beacon nodes and creates its own RSSI fingerprint that is sent to the central base station The base station computes nodersquos location as the sample pointrsquos location whose RSSI fingerprint distance from the unknown nodersquos RSSI fingerprint is the minimum among all the sample points Despite the fact that the map based RSSI localization methods produce relatively small location errors they are not attractive mainly because they require an excessive amount of profiling data and because they are not fully distributed The detailed profiling of a building is a major holdback because it is complex time consuming and depends on beacon locations Therefore for different buildings or even for the

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

29

same building and different beacon locations different RSSI maps have to be obtained The second category of RSSI based algorithms are the distance (or area) prediction algorithms which focus on minimizing the amount of profiling data In distance prediction based methods raw RSSI data are directly mapped to distances through a signal propagation model In some cases distances are not explicitly measured but they are taken into consideration through assumptions on the underlying distributions In the case of outdoor deployments where there are no obstacles a single signal propagation model is all the profiling data required In the case of indoor deployments the same applies but now the signal propagation model might be different across different buildings with different furniture and wall arrangements Given an underlying signal propagation model raw RSSI data can be mapped to specific distances These distances can be either used directly in order to perform localization or assigned a probability and used in a learning basedprobabilistic localization algorithm Obviously the accuracy of the distance prediction based methods depends heavily on the accuracy of the signal propagation model used for translating RSSI values to distances

33 Properties of RSSI Changes in signal due to propagation indoors are difficult to predict because of dense environment and propagation effects such as reflection diffraction and scattering The multi‐path fading effect which is the result of either constructive or destructive combination of multiple signal copies at the receiver causes the received signal to fluctuate around a mean value at a particular location The received signal is usually modelled by the combined effects of large‐scale fading and small‐scale fading The large‐scale fading component describes the signal attenuation as the signal travels over a distance and is absorbed by material such as walls and floors along the way to the receiver This component predicts the mean of the RSS and usually has a lognormal distribution On the other hand the small scale fading component explains the dramatic fluctuation of the signal due to multi‐path fading On the other hand the small scale‐fading component explains the dramatic fluctuation of the signal due to multi‐path fading If there is no LOS component the small‐scale‐fading is often modelled with a Rayleigh distribution Such scenarios are commonly called NLOS If there is a LOS component the small‐scale‐fading is typically modelled by a Rician distribution Note that when the RSS is measured the measurement averages out the small‐scale‐fading effects

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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4 OBJECT TRACKING 41 Introduction This chapter presents the object‐tracking application the main objective of this thesis work The application is an algorithmic‐software implementation written in Embedded C which is capable of receiving data messages and generating object position estimation data The implementation of the object‐tracking application makes use of the SENTIO platform and uses the background knowledge introduced in the preceding chapters The motivation for the object‐tracking application is to determine the position of an object in indoor environments (Eg Home) The application acts as an interface which collects the raw data that the sensors generate (numerical values) It makes the proper comparisons and algorithmic processes to identify and track the object while it is static inside the area covered by the wireless sensor network Finally with the detection and tracking data it alerts the user or another system The following sections provide an overview of the object‐tracking application

42 Overview of hardware product Before describing the object tracking application it is important to know the characteristics of hardware product The SENTIO platform (figure 15) is built in modular fashion and has the following key features [8]

Flexibility This platform is very flexible in such a way that it is made easy to add or change the hardware for each sensor task depending on the requirement in the WSN For a specific platform this can be hard or sometimes even impossible to accomplish

Sensor resources Resources in a sensor module includes the ability of usage of controllerrsquos resources as much as possible such as ADC‐channels interrupts and digital Input‐Outputrsquos Local voltage regulation should also be possible since many sensors require higher supply voltages than digital Integrated Circuits do Further support of a shut‐down mechanism to power down the sensor modules when not needed

Computation and memory The processor module based on microcontroller should support many devices in a microcontroller family and support different power supply voltages enabling full range of clock frequencies All power‐down modes of the devices should be

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

31

supported The platform should support different processor modules including FPGArsquos and very simple microcontrollers

Communication The focus should be on enabling an IEEE 802154 compliant communication interface With or without ZigBee stack this will provide a flexible platform for exploring different network topologies Support for power‐down of the entire communication module should be supported

Cost and form factor High flexibility is in conflict with low cost and small size The primary objective should be flexibility but the size should be considered in order to enable sensor device integration in systems where space is limited

Figure 15 SENTIO Platform [8]

The communication subsystem of the SENTIO sensor node was designed so that the correct operation of the radio chip is ensured The radio chip is powered by the on‐board voltage regulator and thus fluctuations in the battery voltage level do not affect the operation of the communication subsystem This platform uses Chipcon CC2420 IEEE 802154 radio transceiver operates in the 24GHz ISM band and includes a digital direct sequence spread spectrum (DSSS) modem providing a spreading gain of 9dB and an effective data rate of 250Kbps It was specifically designed for low power wireless applications and supports 8 discrete power levels 0dBm minus1dBm minus3dBm minus5dBm minus7dBm minus10dBm minus15dBm and minus25dBm at which its power consumption varies from 29mW to 52mW [21] A built‐in received signal strength indicator gives an 8‐bit digital value RSSIVAL The RSSIVAL is always averaged over 8 symbol periods (128μs) and a status bit indicates when the RSSIVAL is valid (meaning that the receiver was enabled for at least 8 symbol periods) The

Antenna

Communication Module

Processing Module

Sensing Module

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

32

power P at the RF pins can be obtained directly from RSSIVAL using the following equation

P = RSSIVAL + RSSIOFFSET [dBm] where the RSSIOFFSET is found empirically from the front‐end gain and it is approximately equal to minus45dBm In the next sections when we refer to the RSSI value we refer to the RSSIVAL and not the power P unless otherwise stated

43 Experimental Testbed The experimental test bed for object tracking application is as shown in figure 16 Experiment was conducted in Masterrsquos room at Mid Sweden University A DELL laptop is connected to SENTIO platform and software were primarily used to collect samples of RSS from APrsquos The software monitors IEEE 802154 radio frequency channels which operate in the 24GHz band

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

1030

BC - 1030

AP 3 AP 1

AP 2

AP 4

BS

HEWLETT

PACKARD

Figure 16 Layout of Masterʹs Room

A three dimensional scalable testbed is used for indoor sensor network deployments The testbed illustrated in figure 16 is a 3‐D structure measuring 16m (L) x 12m (W) x 3m (H) and it is designed to host a large number of static nodes as shown in figure 16 Arrow marks in the above figure represent the location where fingerprints were taken The centrepiece of our infrastructure

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

33

is the SENTIO sensor node [8] an open‐source general purpose sensing platform designed with AT Mega 128 microprocessor and the IEEE 802154 compliant CC2420 radio from Chipcon [19]

44 Obtaining data from the sensor network The next important step in the development of the object tracking application is to obtain the data from the sensor network and send it to the base station From there the data is transmitted through a serial port to the computer that runs the object tracking application When the data arrives at the serial port the application must capture it This paragraph gives a brief explanation of how and what kind data is transmitted by the static object fixed sensors and how this data is being used to estimate the position of the object First the static object transmits Beacon packet continuously which is received by all the fixed APrsquos The beacon packet format is as shown in table 6 Then the fixed APrsquos calculates the 8‐bit value of RSSI from the transmitted beacon packet and then sends the resultant RSSI value as a form of data packet (table 7) to the BS which calculates the position and sends back to the object

45 Object tracking application Testing and Evaluation The purpose of this section is to discuss and evaluate the object tracking application Finally we are ready with the experimental testbed as shown in figure 16 As shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors placed at the corners of the masterrsquos room BS is placed at the centre of the room connected to a PC All the nodes are power connected before an actual measurement takes place At first the object is placed at random position inside the room and transmits beacon packets continuously Then these packets are received by the fixed APrsquos which calculate the RSSI from the packet and then averages it until it receives 50 beacon packets and then transmits the data to the BS (figure 17) Then the BS waits until it receives the data from all the APrsquos Once it receives from all the APrsquos it calculates the position of the object (figure 18) The application was developed to demonstrate a real world use of a WSN in object tracking In evaluating the final position obtained with respect to the actual position we conclude that the object‐tracking was successful Although

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

34

it is a research prototype object tracking application is a simple stable stand‐alone system capable of demonstrating an important application of WSN It takes raw data from the WSN aggregates it evaluates it and based on the evaluation tracks the object The accuracy of estimating the position is less than 5m Table 11 below shows a log of readings taken during test

Start

Intialize micro‐controller and transeciever in receieve mode

If received_packet=

Beacon

Average RSSI values

Increase the counter

If countergt1000050

A

No

Yes

B

B

Yes

No

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

35

A

Reset counter

Set transceiever in transmit mode

Transmit averaged RSSI to Main sensor and set it back to receive mode

10000 samples ‐ Offline

50 samples ‐ Online

Figure 17 Flow chart representing the algorithm of fixed sensors

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

36

Start

Intialize micro‐controller and transeciever in receieve mode

Received RSSI values from all fixed Sensors

Open radio map

Calculate Euclidean distance between obtained RSSI values from fixed sensors and fingerprints

in radio map

No

Yes

Bubble sort for least error in signal strength values

A

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

37

A

Get the location of the object from the radiousing the least error value

Display location of object on PC

End

Figure 18 Flow chart representing the algorithm of main sensor

Table 11 Accuracy of positioning system

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

38

5 RESULTS This chapter presents how the system parameters such as number of samples mean or median etchellipare finalized All the tests are performed in masterrsquos room at Mid Sweden University This chapter is divided into three subsections First in Section 51 it reviews results obtained from samples test Section 52 discusses one dimensional test results and finally section 53 discusses results of two dimensional tests

51 Measurement setup A Dell laptop was connected to a SENTIO platform and is loaded with software (Appendix 2) This setup was used to determine number of samples required during object tracking mean or median position estimation algorithm and one dimensional test The setup is as follows

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

103

0

BC - 1030

AP 1AP 2

BS

HEWLETT

PACKARD

Figure 19 Test bed for one dimensional test in Masters Room

Two SENTIO nodes (AP1 and AP2 as shown above) were separated by a distance of 10 meters and these sensors are known as fixed sensors which are fixed to the ceiling such that there antenna is faced towards the ground They are linear is position A BS was connected to Dell laptop and it was placed in such a way that its antenna was faced towards the ceiling Another SENTIO

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

39

node was used as an object and its orientation was same as BS Figure 19 represents the complete setup for object tracking application Mobile node was moved between these fixed ones at regular intervals of one meter and RSSI values were logged at each interval

52 Number of samples test This test was performed to determine number of samples required for accurate positioning and either mean of the obtained RSSI values or median of them Figure 19 represents the setup for number of samples test Once the setup is ready the measurements are taken as follows

1 An object was tagged with SENTIO node and was placed stationary linear to the fixed sensors

2 10000 samples (RSSI) were taken at regular intervals of 1 meter up to 10 meters

3 The sample values were logged at every interval 4 Using these logged samples I have plotted graphs such as distribution

of RSS values (figure 20 21) meanmedian at intervals of 1 meter using MATLAB

5 The mean and median for various numbers of samples is as shown in figure 22

Figure 20 Histogram plot of RSSI values at 1m and 4m

Figure 21 Histogram plot of RSSI values at 6m and 8m

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

40

Figure 22 Distance vs RSSI

From the above graphs following conclusions were made 1 From the histogram plots we can observe that the distribution of RSSI

values follow Gaussians distribution law 2 As we can see from figure 22 that median curve has drastic changes

when compared to mean curve Hence mean is considered while estimating the position

3 From the observations we conclude that the number of samples does not matter since mean curve tends to be same for various numbers of samples

4 Hence 10000 samples are considered in order to get a proper location fingerprint during off‐line phase and during online phase 50 samples are enough to estimate the position of object

53 0ne dimensional test Measurement setup for one dimensional test is as shown in figure 19 This test was conducted to check the performance of algorithm and how accurate it was At first ie during offline phase fingerprints at each interval averaging 10000 samples were taken During online phase Euclidean algorithm was used estimate the position averaging 50 samples of RSSI From the results we

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

41

can realize that the error was less than 2 meters Table 12 shows the result of location estimation in one dimensional case

Table 12 One dimensional position estimation results

54 Two dimensional test This test is the final stage of my thesis work Experimental testbed for this stage is as shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors fixed at the corners of the room and a BS at the centre of the room There are 18 pre‐determined locations where the fingerprints (RSSI) were taken The fingerprints are as shown in Appendix 1 All the readings were taken in presence of userrsquos The algorithm for position estimation is shown in Appendix 2 Position estimation test is performed as follows

Tagged object is placed at random inside the masterrsquos room All the sensors including the base station and object are switched on Using the algorithm (Appendix 2) the BS estimates the position of the object and displays it on the PC

This test was performed using various numbers of samples to test which has a better accuracy Figure 23 represents the error rate using various numbers of samples From figure 23 we can conclude that 50 numbers of samples is enough to estimate the position of object with an accuracy of 3m

55 Causes of error in location determination The randomness of RSS is the major cause of error in any indoor

positioning system that uses the RSS for location inference If there is no randomness in the RSS and its location fingerprint every indoor positioning systems of this type will have no location detection error For example I have recorded RSS at one particular location during different times of the day From figures 24 and 25 you can observe that RSS values are different at different times of day

The performance of indoor positioning systems depends greatly on the separation of location fingerprints A location fingerprint

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

42

corresponding to a location can be identified correctly if it is difficult to classify it as another fingerprint by a pattern classifier

Path loss is one of the basic signal propagation properties of the RSS that best describes attenuation of transmit signal power over a distance between an access point and a mobile device

Presence of Userrsquos or userrsquos orientation affects RSS

Figure 23 Error Rate vs Number of samples

Figure 24 RSS distribution at 214 pm

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

43

Figure 25 RSS distribution at 100pm and 1100 am

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

6 DISCUSSIONS10102006

44

6 DISCUSSIONS 61 Summary and Conclusion While empirical results and performance studies of positioning systems based on location fingerprint have been presented in this report analytical model that can be used as a framework for efficiently designing the positioning systems are not available This thesis design work uses general principles of position estimation such as position estimation of sensors grid spacing etc This design work focuses on accuracy and precision The concept of location fingerprint technique based on RSS us explained in detail in chapter 2 It is found that RSS is random It is possible to approximate the RSS as being normally distributed While the RSS random process is non‐stationary the mean is more or less constant and could be used as the fingerprint of the location Model designed for position estimation is as shown in chapter 4 Accuracy and precision are the two major performance matrices in this study Uniform grid spacing has been used From the experimental analysis I have obtained an accuracy of 3 meters and 35 precision The sensitivity analysis among the system parameters and the performance matrices were done using the model as shown in chapter 4 For instance our study found that the minimum distance between two adjacent positions also affects the positioning performance This is due to the randomness of RSS patterns induced by the ever changing indoor environment

62 Future work The research in this thesis provides a ground work for the future study of efficient design of indoor positioning system The RSSI based position estimation still has limitations First the RSSI value is time dependent Thus the future study should address this issue Second the movement of object is not considered Third orientations of sensors affect the performance which should be taken care off Fourth to improve the performance of the system we need to find a solution to deal with those incomplete or missing data during the off‐line and the on‐line phase Fifth position estimation on multi‐floor and three‐dimensional coordinates is not available

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 110102006

45

APPENDIX 1

Location Fingerprints of Masters Room Time 1315 Observation Time 20 minutes Number of Samples 10000

People Presence SNo Co-ordinates AP1 (0x0001) AP2 (0x0002) AP3 (0x0004) AP4 (0x0008)

1 11 -24 -16 -27 -33 2 13 -26 -8 -34 -31 3 15 -43 -9 -39 -25 4 32 -21 -11 -27 -33 5 61 -6 -23 -32 -28 6 64 -20 -31 -25 -34 7 35 -23 -35 -29 -38 8 37 -29 -35 -22 -27 9 39 -38 -29 -21 -27 10 67 -27 -23 -20 -23 11 611 -26 -29 -23 -30 12 613 -35 -32 -2 -31 13 513 -34 -31 -20 -26 14 11 -39 -22 -29 -17 15 212 -37 -42 -28 -31 16 -212 -38 -33 -29 -6 17 -21 -42 -29 -33 -19 18 -38 -40 -23 -35 -22

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 210102006

46

APPENDIX 2

Begin

Open radio map and raw data files for write

Intialize radio map in memory

Check whether the

Packet is data or Beacon packet If data packet

Save the data to register

Check if data is

Received from all

sensors

Estimate the position of static

Display the position on PC

End Yes

No

A

A

Yes

No

Flow chart representing the main algorithm for position estimation

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

47

APPENDIX 3

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

48

Distribution of RSSI

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

49

REFERENCES [1] T S Rappaport J H Reed and B D Woerner ldquoPosition location using wireless communications on highways of the futureʺ IEEE Commun Mag vol 34 no 10 pp33‐41 Oct 1996 [2] A M Ladd K E Bekris G Marceau A Rudys L E Kavraki and D S Wallach rdquoRobotics‐based location sensing using wireless ethernetʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ02) 2002 pp 227‐238 [3] I Ramani R Bharadwaja and P V Rangan ldquoLocation tracking for media appliances in wireless home networksʺ in Proc International Conference on Multimedia and Expo (ICMEʹ03) July 2003 pp 769‐772 [4] K Pahlavan X Li and J P Makela ldquoIndoor geolocation science and technologyʺ IEEE Commun Mag vol 40 no 2 pp 112‐118 Feb 2002 [5] Konard Lorincz and Matt Welsh ldquoMote Track A Robust Decentralized Approach to RF‐Based Location Trackingrdquo [6] Paramvir Bahl and Venkata N Padmanabham ldquoRADAR An In‐Building RF‐based User Location and Tracking Systemrdquo [7] Kamol Kaemarungsi ldquoDesign of Indoor positioning system based on location fingerprint techiniquerdquo University of Pittsburgh 2005 [8] Fredrik Linnarsson Cheng Peng and Bengt Oelmann ldquoSENTIO A Hardware Platform for Rapid Prototyping of Wireless Sensor Networksrdquo [9] S Saha K Chaudhuri D Sanghi and P Bhagwat ldquoLocation determination of a mobile device using IEEE 80211b access point signalsʺ in Proc IEEE Wireless Communications and Networking Conference (WCNCʹ03) New Orleans LA Mar 2003 pp 1987‐1992 [10] T Roos P Myllymaki H Tirri P Misikangas and J Sievanen ldquoA probabilistic approach to WLAN user location estimationʺ International Journal of Wireless Information Networks vol 9 no 3 pp 155‐164 July 2002 [11] R Battiti M Brunato and A Villani ldquoStatistical learning theory for location fingerprinting in wireless LANrsquosʺ Technical Report Oct 2002 [Online] Available httprtmscienceunitnitraquobattitiarchive86pdf [12] P Prasithsangaree P Krishnamurthy and P K Chrysanthis ldquoAn indoor position location with wireless LANsʺ in Proc IEEE International Symposium on Personal Indoor and Mobile Radio Communications (PIMRCʹ02) Lisbon Portugal Sept 2002 [13] M A Youssef A Agrawala and A U Shankar ldquoWLAN location determination via clustering and probability distributionsʺ in Proc IEEE

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

50

International Conference on Pervasive Computing and Communications (PerComʹ03) Dallas‐Fort Worth TX Mar 23‐26 2003 pp 23‐26 [14] Z Xiang S Song J Chen H Wang J Huang and X Gao ldquoA wireless LAN‐based indoor positioning technologyʺ IBM Journal of Research and Development vol 48 no 56 pp 617‐626 SeptNov 2004 [15] R Want A Hopper V Falcao and J Gibbons ldquoThe active badge location systemʺACM Transactions on Information Systems vol 40 no 1 pp 91‐102 Jan 1992 [16] N B Priyantha A Chakraborty and H Balakrishnan ldquoThe cricket location‐support systemʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ00) Boston MA Aug 2000 pp 32‐43 [17] Holger K Willig A (2005) Protocols and Architectures for Wireless Sensor Networks London [18] IEEE (2003) Part 154 Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low‐Rate Wireless Personal Area Networks (LR‐WPANs) New York [19] Chipcon CC2420 802154 compliant radio httpwwwchipconcom [20] ZigBee Specification v10 ZigBee Alliance [21] Dimitrios Lymberopoulos Quentin Lindsey and Andreas Savvides ldquoAn Empirical Analysis of Radio Signal Strength Variability in IEEE 802154 Networks using Monopole Antennasrdquo [22] Kiran Yedavalli Bhaskar Krishnamachari Sharmila Ravula and Bhaskar Srinivasan ldquoEcolocation A sequence based technique for RF localization in wireless sensor networksrdquo Robert Bosch Research and Technology Centre Palo Alto CA

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

Terminology Notation10102006

viii

Terminology Notation

GPS ‐ Global Positioning System

RSSI ‐ Received Signal Strength Indication

MS ‐ Mobile Station

RF ‐ Radio Frequency

WSN ‐ Wireless Sensor Network

AP ‐ Access Point

RSS ‐ Received Signal Strength

PC ‐ Personal Computer

WLAN ‐ Wireless local Area Network

SNR ‐ Signal to Noise Ratio

RFD ‐ Reduced‐Function Device

WPAN ‐ Wireless Personal Area Network

FFD ‐ Full Functional Device

MAC ‐ Medium Access Control

LR‐WPAN ‐ Low Rate WPAN

PAN ‐ Personal Area Network

OSI ‐ Open Systems Interconnection

LLC ‐ Logical Link Control

SSCS ‐ Service Specific Convergence Sub layer

PHY ‐ Physical Layer

PPDU ‐ PHY Protocol Data Unit

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

Terminology Notation10102006

ix

ED ‐ Energy Detection

CCA ‐ Clear Channel Assessment

LQI ‐ Link Quality Indication

ISM ‐ Industrial Scientific and Medical

DSSS ‐ Direct‐Sequence Spread Spectrum technique

LSB ‐ Least Significant Bit

SHR ‐ Synchronization Header

SFD ‐ Start of Frame Delimiter

PHR ‐ PHY Header

MPDU ‐ MAC Protocol Data Units

GTS ‐ Guaranteed Time Slot

MHR ‐ MAC Header

MFC ‐ MAC Footer

FCS ‐ Frame Check Sequence

CAP ‐ Contention Access Period

CFP ‐ Contention Free Period

ACL ‐ Access Control List

AES ‐ Advance Encryption Standard

MIC ‐ Message Integrity Code

CBC ‐ Cipher Block Chaining

ZDO ‐ ZigBee Device Objects

OS ‐ Operating System

LOS ‐ Line of Sight

NLOS ‐ Non Line of Sight

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

Terminology Notation10102006

x

FPGA ‐ Field Programmable Gate Array

BS ‐ Base Station

AOA ‐ Angle of Arrival

TDOA ‐ Time difference of arrival

TOA ‐ Time of Arrival

TOF ‐ Time of flight

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

1

1 INTRODUCTION 11 Introduction to the study Advancement in wireless sensing technologies has encouraged in developing a model for indoor object‐tracking application using low power IEEE 802154 compliant radios Stimulus used in this model is RSS This application is not only limited to object tracking in an indoor environment Concierge services enable users to become aware of nearest supporting facilities For example smart home applications such as multimedia appliances that forward multimedia stream to the nearest video screen can be achieved with a home positioning system [3] In the field of robotics a robot can navigate by itself using the assistance of indoor positioning system [2] Granularity of location information is most important in location determination applications For example to locate a book in a library requires coarse grained granularity whereas locating a user requires coarse grained granularity Position estimation refers to a process used to obtain location information of a MS with respect to a set of reference positions within a pre‐defined space A system deployed to determine or estimate the location of an entity is called a position location system or positioning system A wireless indoor positioning system refers to a wireless network infrastructure that provides indoor location information to any requesting end user One of the key technical elements allowing the realisation of this vision will be the knowledge of the accurate position of the personobjects Already today accurate location mechanisms such as GPS are commercially available Many more will become part of our daily life with the introduction of third generation mobile phones But none of these techniques will allow the meter or sub‐meter level of accuracy within buildings which is necessary for smart houses Hence this report introduces an object tracking application in an indoor environment using IEEE 802154 compliant radios which gives meter level accuracy First this chapter presents the background of indoor positioning systems identifies the challenges of such systems and briefly describes the scope of indoor positioning systems based on location fingerprints Next the objective of study the overview of approaches and the contributions are presented Finally the scope and organization of thesis work is outlined

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

2

12 Background The success of outdoor positioning and applications based on the GPS provides an incentive to the research and development of indoor positioning systems It has been developed over several decades and it relies on constellation of satellites Ranges to several satellites are used in multi‐lateration procedure to infer the position of the receiver This localization scheme requires hardware that is both expensive and consumes significant power As a result indoor positioning systems require alternative means to detect the MS location without relying on the direct signal from GPS satellites Infrared RF and ultra sound signals are major technologies used for indoor positioning systems Unlike outdoor areas the indoor environment imposes different challenges on location discovery due to the dense multi‐path effect and building material dependent propagation effect Thus an in‐depth understanding of indoor radio propagation for positioning is crucial for efficient design and deployment The existence of radio connectivity and the attenuation of radio signal with distance are attractive properties that could potentially be exploited to estimate the positions of small wireless devices featuring low‐power radios RSSI a standard feature in most radios has attracted a lot of attention in the recent literature for obvious reasons RSSI eliminates the need for additional hardware in small wireless devices and exhibits favourable properties with respect to power consumption size and cost As mentioned previously RF‐based location tracking has been widely studied [5 6] Given a model of radio signal propagation in a building or other environment RSSI can be used to estimate the distance from a transmitter to a receiver and thereby estimate the position of a mobile node However this approach requires detailed models of RF propagation and does not account for variations in receiver sensitivity and orientation Table 1 below briefly summarizes performance of existing indoor positioning system based on location fingerprints and WLANrsquos All the systems mentioned below in table 1 follow IEEE 80211 protocol standards The major performance metrics studied by all systems are positioning accuracy which is a form of error measurement The accuracy of the location information is usually reported as an error distance between the estimated location and the actual mobile location However the report of accuracy should include the confidence interval or percentage of successful location detection which is called the precision The system parameters of existing systems are summarized in table 1 and the best reported performance of these systems are listed in table 2

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

3

System Spacing(m) Positions SamplesPos APs Orientation Environment

RADAR [6] Non

uniform 70 80 3 4 Hall-way Saha et al [9] 3 19 1200 3 NA 1-floor

Roos et al [10] 2 155 40 10 NA 1-floor Battiti et at [11] NA 257 NA 6 NA 1-floor Ladd et al [2] 3 11 1307 5 2 Hall-way

Prasithsangaree et al [12] 153 60 40

2 to 7 4 1-floor

Youssef et al[13] 15 110 300 4 NA Hall-way Xiang et al [14] NA 100 300 5 4 1-floor

Table 1 Comparison of Indoor Positioning Systems [7]

System Algorithm Type Accuracy and Precision RADAR [6] Nearest Neighbour within 7 feet 38

Saha et al [9] Nearest neighbour amp Neural Network no specified accuracy

90 Roos et al [10] Bayesian best within 828 feet 90

Battiti et at [11] Neural Network amp Weighted k-Nearest

Neighbour all within 16-17 feet 90 Ladd et al [2] Bayesian within 5 feet 77

Prasithsangaree et al [12] Weighted k-Nearest Neighbour

25 feet at 75 amp 40 fet at 95

Youssef et al [13] Bayesian within 7 feet more than

90 Xiang et al [14] Bayesian with RSS distribution model within 6 feet 90 Table 2 Performance comparison of Indoor Positioning Systems [7]

Based on the study of results obtained in various positioning systems as in table 2 we have developed a model for an indoor positioning system using SENTIO [8] platform based on RSSI

13 Objectives In the last two or three years a number of theoretical practical andor simulation studies were done on the topic of object‐tracking While these studies are useful they are too general and provide little guidance for the actual deployment of sensor networks for real life location‐tracking of an object This thesis focuses on developing an object‐tracking application and prescribes sensor network configurations that work well with our algorithms I implemented my software using SENTIO [8] platform for sensor networking The major issue addressed in this project are the accuracy and precision in tracking an object in a real‐world application and efficient ways to algorithmically analyze the collected raw data from the specific wireless sensor product

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

4

Although the focus is to develop a real‐world application using WSN it also provides great opportunity to explore the new area of wireless communication overall

14 Research Questions My primary target is to find a method to determine the position of an object inside a building using ZigBee protocol standards based on RSSI The study addresses the following questions What is WSN What requirements do WSN need What are their main ideas and concepts What are the associated technologies What are the standards that sensor networks use

What are the existing hardware implementations that use this particular technology How can we build our new applications using WSN What do sensor network systems require for general or specific applications Are they applicable And what is the appropriate design for sensor network implementations in order to combine them with other existing systems At this point we describe and evaluate the specific object‐tracking application to be built

15 Scope The study area of the thesis is an overview of the WSN technology study of SENTIO platform and implementation of an object tracking application Thus the study is divided into two parts Part one is an overview of WSN it includes study of SENTIO platform and IEEE 802154 protocol standards The second part describes the implementation of the object tracking algorithm The object tracking application receives and uses the raw values returned by the sensor network system to produce clear and meaningful outputs Schema of thesis work is as shown in figure 1

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

5

Figure 1 Schema of thesis work

16 Methodology The ldquoIndoor Local Positioning System for ZigBee based on RSSI ldquois based on ldquolocation fingerprintingrdquo technique Location fingerprinting refers to a technique that exploits the relation between any measurable physical stimulus and a specific location This approach uses empirical measurements of RSSI to estimate location By recording a database of radio ldquosignaturesrdquo along with their known locations a mobile node can estimate its position by acquiring a signature and comparing it to the known signatures in the database All of these systems require the database of signatures which should be collected manually prior to the system installation and rely on central server (or the userrsquos mobile node) to perform location calculation For example in RADAR [6] technology the RSSI is the stimulus This type of positioning system does not require specialized hardware other than the common wireless network interfaces with received signal strength measurement capability thus it is simple to deploy compared to other techniques Unlike outdoor counterpart systems which can use AOA and TDOA techniques effectively indoor positioning systems encounter the problem of NLOS and the dense multi path effect that render these two techniques ineffective or complex for practical implementation It is also difficult for a MS to always see three or more access points or base stations in indoor environment which is essential for triangulation by AOA and TDOA Location fingerprinting can also be implemented as software based positioning system which can reduce

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

6

complexity and cost Generally the deployment of fingerprinting based positioning systems can be divided into two phases First in the off‐line phase the locations where the fingerprints are required is pre‐determined and then the fingerprints are collected by performing a site‐survey of the RSS from multiple APs The distance between two closest positions is called grid spacing and usually reported in meters or feet However some points may be omitted due to inaccessibility The RSS is measured at each predetermined location to create a database of RSS patterns of the indoor environment The database of RSS patterns is called a radio map The vector of RSS values at a point on the grid is called the location fingerprint of that point Second in the on‐line phase a MS will report a sample measured vector of RSS from different APs to a base station The base station uses a positioning algorithm to estimate the location of the MS The most common algorithm used to estimate the location is Euclidean algorithm It computes the Euclidean distance between the measured RSS vector and each fingerprint in the database and then the coordinates associated with the fingerprint that provides the smallest Euclidean distance is returned as the estimate of the position It is clear from the above discussion that the designer should not start out to do the site survey without proper system design objectives and guideline If the performance is not sufficient collecting more fingerprints in between previous location fingerprints may help It is a problem of choice as to how many access points are required for the system and what the minimum distance between physical positions on the grid should be in order to provide a good position resolution and best system performance The system parameters and factors that improve the accuracy and the precision performance are still not clear

17 Outline Chapter 2 provides literature review of ldquoIndoor Local Positioning System for ZigBee based on RSSIrdquo it introduces the basic components required for a positioning system general classification of indoor positioning system Finally it explains ldquoLocation Fingerprintrdquo technology used in this thesis work to determine the position of an object in a real‐world application It briefly explains the protocols and industry standards for wireless sensor networks Chapter 3 describes some properties of RSS which is a stimulus to determine

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

7

the position of an object RSS relation with energy and on what parameters is the RSS value dependent Chapter 4 describes the model of an indoor local positioning system and its probability of returning the correct location It also explains its performance evaluation and its error distance distribution Chapter 5 describes results obtained from the model of positioning system Chapter 6 includes conclusions from the experimental analysis and future work

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

8

2 LITERATURE REVIEW This chapter reviews the literature on wireless indoor positioning system as a means of providing an intellectual background for the thesis work First in section 21 the common components of indoor positioning system are described Then in section 22 discusses different means to classify indoor positioning systems In section 23 reviews of all relevant literature on location fingerprint technique Finally in section 24 protocols and industry standards for wireless sensor networks are explained

21 Common components of indoor positioning system Basically a wireless local positioning system consists of at least two separate hardware components a measurable unit that usually carries the major part of the system intelligence and a signal transmitterreceiver The transmitter in the simplest case is just a beacon Figure 2 represents basic hardware model of a positioning system

Figure 2 Block diagram representing hardware module

First the location sensing devices detect the signals transmitted by or received at known reference points using radio sensing technologies The sensing technique is based on signal strength level that converts the sensed signal into RSS Given a set of reference points the relative position of the MS can be derived from the signal characteristics of RSS The signal characteristics of RSS are unique for a location Then the microcontroller processes the position algorithm and estimates the position of object and displays it on PC

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

9

22 Classification of Indoor positioning systems Indoor positioning system can be categorized based on the stimulus used in particular application and measurement principle

221 Classification based on Stimulus Based on the stimulus used in an application WSN inherit certain characteristics and limitations on sensorrsquos signal The propagation delay diffraction reflection and scattering are the basic signal characteristics which affect all signal types The effective range available bandwidth regulatory constraints interference power constraints safety and cost of technology limitations The wireless signals commonly used for indoor positioning systems are infrared radio frequency and ultrasound Brief descriptions of the three major technologies are as follows

Infrared Infrared signal is an electromagnetic signal of a wavelength longer than that of visible light but shorter than that of radio waves Infrared is just below the visible spectrum of light in frequency and is radiated strongly by hot bodies Many objects such as people vehicle engines and aircraft generate and retain heat and as such are especially visible in the infrared wavelengths of light compared to objects in the background It cannot pass through walls or obstructions therefore it has a rather limited range of indoor environments Thus it requires a more sophisticated circuitry than ultrasound signals Indoor lighting interferes with this type of signal and causes problems in accurate sensing It generally has a range of around 5 m The infrared devices are usually small in size compared to ultrasound devices

Radio frequency The RF signal can penetrate through most of the building material therefore it has an excellent range in indoor environments The propagation speed is also high approximately 3108 ms There are unlicensed frequencies available freely for use This type of signal has the longest range compared to infrared and ultrasound Since the propagation waves are of higher frequency (24GHz) interference with other frequency components is less

Ultrasound Although ultrasound operates at low frequency bands (typical 40 kHz) compared to the other two signaling technologies it possesses a good precision for location sensing at a slow propagation speed of sound (343 ms) The advantages of ultrasound devices are their simplicity and that they are inexpensive However ultrasound does not penetrate walls

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

10

but reflects off most of the indoor obstructions It has a short range around 3 m to 10 m but has a 1 cm resolution of distance measurement The operating temperature influences the performance of ultrasound

222 Classification based on measurement principle Wireless positioning systems can be categorized by measurement techniques which are used to derive the position of mobile stations Mainly three different measurement principles are used today AOA RSS and propagation‐time based systems that can further be divided into two different sub‐classes TOA and TDOA Figure 3 shows taxonomy based on the technology and the technique classifications

Figure 3 Taxonomy of positioning system [7]

In AOA systems the position is calculated via goniometry With the use of directional antennas or antenna arrays the angle or bearing relative to points located at known positions is measured The intersection of several measured directions pointers the yields the position value The accuracy of this approach is limited by the possible directivity of the measuring aperture by shadowing and by multi‐path reflections arriving from misleading directions In TOA and TDOA techniques rely on the precision of timing between the signal transmitter and the receiver in order to use the propagation delay or TOF to calculate the distance between transmitter and receiver Therefore a precise synchronization is also very important in such systems By combining at least three distances from three reference positions triangulation can be used to estimate the mobile stationʹs location This type of technique will

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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require a high accuracy clock in the communication system TDOA is more practical [1] Examples of location‐sensing systems that use time of flight are GPS the Active Bats [15] and the Cricket [16] Besides these time delay‐based techniques the distance between transmitter and receiver can also be determined from signal strength attenuation and direct distance measurement (such as dead reckoning) Fingerprinting or Location Pattern Matching this technique generally requires only measurement of RSS or other non‐geometric features at several locations to form a database of location fingerprints To estimate the mobile location the system needs to first measure the received signal strength at particular locations and then search for the pattern or fingerprint with the closest match in the database This technique does not require the mobile station to see at least three base stations or access points in order to determine the location The disadvantage of this technique is that it is very time‐consuming to perform an exhaustive data collection for a wide area network such as in outdoor positioning systems

23 Indoor local positioning using ZigBee based on RSSI This section reviews relevant RF‐based indoor positioning systems which can be used to locate stationary objects and track mobile users The impressive growth of IEEE 802154 wireless PANs (WPAN) in recent years suggests an interesting future for the location fingerprinting technique The system is quite flexible because system designers can select whether to have a centralized positioning server or let the mobile to determine its own position However the fingerprinting technique requires a training phase (off‐line phase) to collect location fingerprints for all positions in the operating area before the actual deployment (on‐line phase) [7] The following discussion is divided according to the positioning system components First the common form of location fingerprinting and its relationship with physical locations are explained Finally location estimation algorithm is reviewed

231 Location Fingerprint A location fingerprint based on RF characteristics such as RSS is the basis for representing a unique location It is created under the assumption that each position or location inside a building has unique RF signature [4] Generally a fingerprint F is labelled with a location information L The location fingerprints and their labels (eg location information) are maintained in

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database and used during the on‐line phase to estimate the location The label and fingerprint are usually denoted as a tuple of (L F) Battiti et al [11] point out that the location information L for indoor location can be recorded in two forms as either a tuple of coordinates or an indicator variable The tuple of real coordinates can vary from one dimension to five dimensions which include the three dimension space and two orientation variables expressed in spherical coordinates For instance a location information of a two‐dimension system with an orientation could be expressed as a triplet L = (x y d) | x y є R 2 d є North East South West The RSS was found to be more location‐dependent than the SNR because the noise component is rather random in nature However the RSS itself fluctuates over time for each access point and location Each RSS element can be considered as a random variable therefore it can be captured by recording its descriptive statistics parameters approximating its distribution or maintaining the whole measurement dataset These approaches of RSS representation result in different procedures for location estimation algorithm Regardless of the approach the location fingerprint is usually denoted as an array or vector of signal strength (random variables) received at any position in the location‐based service area The size of the vector is determined by the number of access points that can be heard To create a basis fingerprint such as in a number of samples of vectors of signal strength are collected over a window of time for each position This basis is called a prototype Then the average RSS of each access point is calculated and recorded as an element in the location fingerprint For an area that can receive signals from N access points the location fingerprint can be expressed as a vector of average RSS elements βi

F = (β1 β2hellip βN)T Besides the basis location fingerprint the samples of location fingerprint measured during the on‐line phase are also important for the system that tracks the mobile object The number of samples should be selected appropriately to represent the location fingerprint for the mobile application A step in statistical analysis method called pre‐processing is another important issue needed to be considered because it can impact the estimation of dependency between location fingerprint and the location information easier The pre‐processing refers to a step that cleans the raw data (in this case the training set) before any further operations or analysis The cleaning may consist of encoding dimensionality reduction (reduce unnecessary elements) feature extractionselection clustering and outlier elimination Roos et al [10] point out that the pre‐processing enables faster location estimation and

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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reduce the noise from the training data The process that creates the basis of location fingerprint discussed above could be considered as a part of pre‐processing

232 Location Estimation Algorithm Location estimation algorithms or positioning algorithms are procedures that exploit dependency between location information and location fingerprint basis in order to determine a position or location from samples of RSS signals Position algorithms can be classified into deterministic and probabilistic types based on the approaches that model the relationship between location fingerprints and location information The deterministic type of algorithms are those that based in the nearest neighbour classifiers and neural network classifiers The probabilistic type of algorithms is those that are based on the Bayesian inference and statistical learning theory such as support vector machines (SVR) The algorithm used in this project is discussed briefly

Euclidean distance method The Euclidean distance method is a deterministic approach of position estimation because they require only a set of constant location fingerprints which includes mean vectors In order to determine the location a form of discriminate function is used to classify a sample of RSS fingerprint into a position The mean or average RSS vector is a centre of a mass which represents each class of location fingerprint Suppose a set of l location fingerprints is denoted by F1 F2hellip Fl exist and each fingerprint has a one‐to‐one mapping to a set of positions L1 L2hellip Ll A sample of an RSS fingerprint measured during an on‐line phase is denoted as S which can be another mean or average RSS vector of small window of RSS samples Assuming that an indoor positioning system only considers the average RSS from N access points as a location fingerprint the sample of RSS vector is S = (s1 s2 helliphellip sN)T and each location fingerprint i in the database can be expressed as Fi = (β1 β2helliphellip βN)T The simplest closeness metric is a distance measurement in signal space denoted as the Dist () function [6] Thus the simple procedure of nearest neighbour algorithm is expressed as picking the fingerprint j that has the shortest signal distance

Dist(S Fj) le Dist(S Fk) for all k ne j A generalized weighted distance Lp summarized by [12] can be used to calculate different forms of distance in signal space as

Lp = 1N pN

i

pPiSiWi 1

1)||1(sum

=

minus

where N is the number of access points wi is a weighting factor (wi le 1) and p is the norm parameter starting from 1 The weighting factor wi is a bias

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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parameter that can demote or promote an important RSS component in the fingerprint

24 Protocols and Industry standards for WSN Although many applications for WSN are proposed the communication protocols supporting them remain mainly unexplored and diverged This is because the area of interest is new each implementation is application specific and the components of WSNrsquos have a lot of constraints Wireless sensor networks use the wireless medium to communicate However the traditional communication protocols that support wireless communications especially ad‐hoc mesh networks may not be well suited for them WSN communication is an active research area and many algorithms and protocols have already been proposed This section introduces two proposed standards the IEEE 802154 and the ZigBee The IEEE 802154 covers the physical layer and the MAC layer of low‐rate WPAN The ZigBee is ldquoan emerging standard that is based on the IEEE 802154 and adds network construction (star networks peer‐to‐peermesh networks and cluster‐tree networks) application services and morerdquo

241 IEEE 802154 Holger and Willig (2005) [17] mention home automation home networking and home security as possible applications for IEEE 802154 They add that ldquomost of these applications require only low‐to‐medium bitrates (up to some few hundreds of kbps) moderate average delays without too stringent delay guarantees and for certain nodes it is highly desirable to reduce the energy consumption to a minimumrdquo The IEEE 802154 standard (2003) defines the device types that can be used in a LR‐WPAN A device can be a FFD or a RFD The RFD can be used in simple applications in which they do not need to transmit large amounts of data and they have to communicate only with a specific FFD The FFD can work as a PAN coordinator as a coordinator or as a simple device It can communicate with either another FFD or a RFD In keeping with the application requirements the LR‐WPAN operates in a star or peer‐to‐peer topology (figure 4) In the star topology the RFD communicates with a single controller the PAN coordinator The PAN coordinator can perform the same function as the RFD but it is also responsible for controlling the PAN ldquoit initiates terminates or routes communication around the networkrdquo [18] The peer‐to‐peer topology

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supports ad‐hoc mesh multi‐hop networking Any device in the peer‐to‐peer topology can communicate with any other device within its communication range however this topology also has a PAN coordinator All the devices in a LR‐WPAN have a unique 64‐bit address This or a short address allocated by the PAN coordinator can be used inside a PAN Additionally each PAN has a unique identifier The combination of the PAN identifier and the sort addresses allows communication across different PANs (IEEE 802154 Standard 2003)

Figure 4 Star and peer‐to‐peer topologies in LR‐WPAN [18]

The LR‐WPAN based on the OSI seven‐layer model has the layered architecture presented in figure 5 The application and the network layer are the upper layers in the LR‐WPAN architecture but are outside the scope of the IEEE 802154 standard Only the physical layer ldquowhich contains the RF transceiver along with its low‐level control mechanismrdquo and the MAC layer ldquothat provides access to the physical channelrdquo are included in the standard and will be introduced in the following sections Finally the Type I 8022 LLC and the SSCS are intermediate sub layers supporting communication with the above layers (IEEE 802154 Standard 2003)

Physical Layer The 802154 standard specifies two different services that the PHY provides The PHY data service controls the radio and thus the transmission and reception of the PPDUs In addition the management service performs ED in the channel The management service also performs CCA before sending the messages and provides LQI for the received packets

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Figure 5 WPAN architecture [18]

Three different bands are defined by the standard The 868‐8686 MHz for Europe the 902‐928 MHz for North America and the 2400‐24835 MHz worldwide All of them belong in the ISM radio bands and they are using DSSS Each of the bands supports different a data rate and it uses a different modulation technique chip rate and number of channels Moreover if the system does not use the 2450 MHz frequency it operates in both the 802 MHz and 902 MHz frequencies Table 3 summarizes the supported bands and data rates (IEEE 802154 standard 2003)

PHY (MHz)

Frequency Band (MHz) Modulation

Bit Rate Number of Channels

868-8686 BPSK 20 1 868 915 902-928 BPSK 40 10

2450 2400-24835 O-QPSK 230 16 Table 3 Frequency bands and data rates for IEEE 802154 based on the IEEE

802154 standard [18] The physical layer uses PPDU packets to communicate Table 4 demonstrates its structure The LSB is always transmitted and received first The SHR contains the preamble and the SFD fields which helps receiver

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synchronization The 8‐bytes preamble contains only zero and is used for synchronization The SFD contains a specific sequence of one and zeros and specifies the beginning of the frame The PHR contains the payload length Packets with a length of 9 or more bytes are MPDU as the next section further explains Packets with length 5 are used for MPDU acknowledgements The payload part of the PPDU encloses the MAC layer packet Finally the PPDU size can be up to 127 bytes [18]

4 bytes 1 byte 1 byte Variable

Preamble SFD Frame length

(7 bits) Reserved

(1 bit) Payload (PSU)

SHR PHR PHY Payload Table 4 Format based on the IEEE 802154 standard [18]

MAC Layer The MAC layer is the interface between the SSCS and the PHY layer Similar to the PHY layer the MAC layer supports two services The MAC data service is responsible for the transmission and reception of the MPDUs through the PHY data service The MAC management service if the device is a coordinator manages the network beacons It is also responsible for PAN association and disassociation frame validation and acknowledgment providing ldquoa reliable link between two peer MAC entitiesrdquo In addition it uses the CSMA‐CA for channel access and handles and maintains the GTS mechanism Finally it supports device security [18] The IEEE 802154 standard defines four different frame types the beacon data acknowledgment and MAC command frame All frame types are based on the general MAC frame format (table 5) The frame control field describes and specifies the above different frame types Every MAC frame comprises a MHR which consists of a frame control sequence number and the information field It also contains the MAC payload different frame types have different MAC payload fields The acknowledgment type does not have a payload Finally each frame includes a MFR which contains a FCS The data in the MPDU follows the same order as the PPDU the least significant bits are left in the frame and are transited first Octets2 1 02 028 02 028 Variable 2

Destination PAN Identifier

Destination Address

Source PAN

Identifier Source Address Frame

Control Sequence Number Addressing Field

Frame Payload FCS

MHR MAC

Payload MFRTable 5 General MAC frame format IEEE 802154 standard [18]

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Table 6 7 8 and 9 present the four different MAC frame types The beacon frame is transmitted periodically by the PAN coordinator It provides information about the network management through the super frame and GTS fields which are analyzed later in the section It also synchronizes the network devices and indicates the proper communication period for them The data frame payload encapsulates data from the higher layers When a device receives a packet it is not obliged to response with an acknowledgement packet Finally the command frame identifier and command payload fields of the command frame are used for communication between the network devices The command identifier specifies actions like association disassociation and data GTS or beacon request Octets2 1 410 2 Variable Variable Variable 2

Frame Control

Sequence Number

Addressing Fields

Super frame Specification

GTS fields

Pending Addressing

Fields Beacon Payload FCS

MHR MAC Payload MFRTable 6 Beacon frame format [18]

Octets 2 1 Variable 2

Frame Control

Sequence Number

Addressing Field

Data Payload FCS

MHR MAC

Payload MFR Table 7 Data frame format [18]

Octets 2 1 2

Frame Control

Sequence Number FCS

MHR MFR Table 8 Acknowledgement frame format [18]

Octets2 1 1 Variable 2

Frame Control

Sequence Number

Addressing Fields

Command Frame Identifier

Command Payload FCS

MHR MAC Payload MFR Table 9 Command frame format [18]

In the LR‐WPAN every PAN has its own coordinator The PAN coordinator manages the communication in the local area it has two options to use or not use the super frame structure The super frame (figure 6) uses network beacons If the coordinator does not want to use a super frame structure it suspends the beacon transmission However the beacon is important for device association and disassociation If the coordinator wishes to maintain close communication control in the PAN and to support low‐latency devices it usually uses the super frame A super frame determines a specific time

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period beacons bound it The beacon is transmitted in the first of the sixteen equal time slots that the super frame has It is used to ldquosynchronize the attached devices to identify the PAN and to describe the structure of the super framerdquo The super frame can have active and inactive periods All the communications have to be finished inside the super frame period In the inactive periods the devices can switch to the sleep mode but they have to be ready for the next beacon The active period is further divided into a CAP and a CFP During the CAP any device can communicate competing with the other devices in the PAN using slotted CSMA‐CA The CFP contains GTSs and follows the CAP A CFP may maintain up to seven GTSs and each GTS can reserve more than one time slot However the CAP should always be sufficient to allow new devices t join the PAN The GTSs are allocated to specific devices All the transactions must be completed inside the assigned time period (CAP GTS and CFP) (IEEE 802154 standard 2003)

Figure 6 Example of super frame structure [18]

In accordance with the 802154 standard (2003) three different types of data‐transfer exist In addition the types differ if the coordinator uses or does not beacons Data transfer from a device to the PAN coordinator is the first type (figure 7) For a ldquonon‐beacon‐enable networkrdquo it first senses the medium by using ldquoun‐slotted CSMA‐CArdquo and then a simple transmit to the data frame In a ldquobeacon‐enabled networkrdquo the sender waits for the beacon when it finds it ldquothe device synchronizes to the super fame structurerdquo In the specified time frame the sender again senses the medium by using ldquoslotted CSMA‐CArdquo and transmits the data to the coordinator In either case the coordinator has the option to acknowledge or not acknowledge the data reception after that the transaction is completed Data transfer from the coordinator is the next type described in the standard (figure 8) In a beacon‐enabled network the coordinator indicates a pending message through the beacon The messagersquos target device receives the beacon and if the message is pending it responses with a MAC command request

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message using slotted CSMA‐CA the coordinator may or may not acknowledge the command message and through slotted CSMA‐CA sends the pending message The device acknowledges the received message After that the coordinator removes the message from the beaconrsquos pending list and completes the transaction In a non‐beacon enable network the device periodically sends a MAC command‐request frame to the coordinator The coordinator acknowledges the data request Then by using un‐slotted CSMA‐CA if it has a pending message it transmits it If it has not it respond with a data frame with a ldquozero‐length payloadrdquo To complete the transaction the device acknowledges the data reception

Figure 7 Communication from a device to a PAN coordinator in (a) a beacon‐enabled network and (b) a non‐beacon enabled network [18]

Peer‐to‐peer is the last type of data transfer In this situation the devices are free to communicate with any other device within their communication range In a peer‐to‐peer PAN the devices can ldquoeither receive constantly or synchronize with each otherrdquo If they are receiving constantly to transmit data they use un‐slotted CSAM‐CA In the second case synchronization must be achieved first (IEEE 802154 standard [IEEE 2003]) The IEEE 802154 (2003) standard establishes MAC and PHY standards for low‐cost low‐power and high‐density node deployments In addition to the above PHY and MAC characteristics IEEE 802154 provides a security baseline including ldquothe ability to maintain an ACL and use symmetric cryptographyrdquo for data encryption The algorithm that is used for encryption is the AES However the higher level layers decide when security is need The upper layers are in general responsible for device authentication and key management The next section introduces the ZigBee standard which

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encapsulates the IEEE 802154 and provides additional standardization for the higher levels

Figure 8 Communication from a PAN coordinator to a device in (a) a beacon‐enabled network and (b) a non‐beacon enabled network [18]

242 ZigBee Heily (2004) defines ZigBee as ldquoa rapidly growing worldwide non‐profit industry consortiumrdquo whose mission is ldquoto define a reliable cost‐effective low‐power wirelessly networked monitoring and control product based on an open global standardrdquo Figure 9 illustrates the areas of interest for different wireless communication standards

Figure 9 Overview of the coverage for different wireless communication

standards [17]

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ZigBee a new standard which became publicly available in June 2005 is based on the IEEE 802154 standard It expands the IEEE 802154 by adding the framework for ldquothe network security and applicationrdquo (Craig 2005) Figure 10 presents the IEEE 802154ZigBee stack and the areas of responsibilities

Figure 10 IEEE 802154ZigBee stack [20]

Craig (2005) mentions three networking topologies that the standard covers the star mesh and cluster tree (figure 11) The ZigBee standard works on top of the IEEE 802154 addressing schema by using the standard 64‐bit and the short 16‐bit addressing Kinney (2005) summarizes the ZigBee network layer responsibilities the successful establishment of a new the network and successful new device configuration addressing assignment network synchronization frames security and message routing ZigBee further distinguishes the concept of the physical devices (RFD FFD) by using the notion of ldquological devicesrdquo ldquoZigBee Coordinatorrdquo is the first type of logical devices It is responsible for initializing maintaining and managing the network Under the coordinator in the network hierarchy is the ldquoZigBee routerrdquo which is responsible for controlling the message routing between the nodes Finally the ldquoZigBee End Devicerdquo acts as the end point of the network

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structure The tasks that an end device can perform are specified in the ldquoApplication Profilesrdquo (Craig 2005)

Figure 11 IEEE 802154ZigBee stack [17]

The ZigBee specifications (2005) summarize the security services provided by ZigBee ldquokey establishment key transport frame protection and device managementrdquo ZigBee builds its security mechanism using symmetric key cryptography The security services also depend on the associated layer thus as the figure 10 shows the security mechanism covers the network and the application layer In addition if a MAC frame needs security protection the MAC layer is able to secure it Moreover the notion of end‐to‐end security is supported the source and destination devices have access and use the same share key In the MAC layer the 802154 AES mechanism provides the proper security The mechanism protects ldquothe confidentiality integrity and authenticity of the MAC framesrdquo (Kinney 2005) An auxiliary header field in front of the MAC payload indicates if the frame is encrypted or not The MAC framesrsquo integrity is supported by calculating and using a MIC at the end of the MAC payload In addition to the AES nonce is used to provide MAC confidentiality and authenticity Figure 12 illustrates a MAC frame with security For different security aspects the MAC layer uses different mode of the AES for the encryption it uses the AES in Counter (CTR) mode and for the integrity the

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

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CBC‐MAC Finally the combination (CCM) of the above two modes is available providing both encryption and integrity (Kinney 2005)

Figure 12 ZigBee secure frame in MAC layer [17]

In the network layer the CCM (a modified MAC layer CCM mode) is used for encryption Because the network layer uses only the CCM mode a single key is used for all different security options The network layer security message format is similar to the MAC frame it is presented in the figure 13 Finally although the network layer is responsible for securing its layer messages the above layers specify the keys and the CCM option for each frame (Kinney 2005)

Figure 13 ZigBee secure frame in network layer [17]

Security in the application layer works similar to the network and MAC layers It uses the ldquolink keyrdquo or the ldquonetwork keyrdquo to secure the message and then encapsulate it inside a set of fields similar to the network format (figure 14) Other security responsibilities that the application layer has are to provide the ZDO and the applications with device management services key establishment and key transport (ZigBee specifications 2005)

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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Figure 14 ZigBee secure frame in application layer [17]

The ZigBee application layer contains the manufacturer‐defined application objects the ZDO and the application sub‐layer In addition to the security responsibilities the application sub‐layer binds devices based on their duties and needs maintains the binding tables and forwards messages between them The application sub‐layer also discovers the neighbour devices for a given device The ZDO is responsible for determining the devicersquos duty in the network for communicating using binding requests and for supporting security as was mentioned above The sub‐layer that implements the actual application is the manufacturer‐defined application object (Kinney 2005) The ZigBee specifications are a great step toward the wireless sensors networks standardization They cover all the OSI layers from the physical to the application providing guidance to the developers The specifications are the product of the ZigBee alliances a consortium of companies and academic institutes Thus they not only produce the specifications but also are the first use them The next section goes beyond the layer architecture and standardization and introduces the OS field another crucial element of WSNs

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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26

3 RECEIVED SIGNAL STRENGTH (RSS) Majority of the existing methods leverage the existence of IEEE 80211 base stations with powerful radio transmit powers of approximately 100mW per base station Such radios are in a different class from the low power IEEE 802154 compliant radios that typically transmit at low power levels ranging from 52mW to 29mW [19] The wide availability of larger number of IEEE 802154 radios in the research community has revived the interest for signal strength based localization in sensor network Despite of rapidly increasing popularity of IEEE 802154 radios and signal strength localization there is a lack of detailed characterization of the fundamental factors contributing to large signal strength variation This chapter investigates some of the properties of RSS observed during testing and analysis of research work collected during the period of thesis The analysis of RSS values is needed to understand the underlying features of location dependent RSS patterns and location fingerprints An understanding of the properties of the RSS values for location fingerprinting can assist in improving the design of positioning algorithms and in deployment of indoor positioning systems

31 Parameters that affect RSS Different factors that affect the indoor RSS are the userrsquos presenceabsence hisher orientation time of the day building type and material distance from transmitter and type of radio These factors are by no means exhaustive Other possible factors are antenna orientation directionality and type Table 10 classify the parameters which affect RSS [7]

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

27

Effect on Factors Options

1 Proximity of user Userrsquos presence or absence

2 Orientation of user and terminal

North East South West

Data Collection

3 Make of Radio 4 Time of measurement Time of day amp days of

week 5 Period of measurement

Second minute hour

7 Interference Co‐channel adjacent co‐channel

Statistics

8 Building environment Small offices or large hall

Table 10 Classification of factors the affect RSS [7]

Transmitter variability Different transmitters behave differently even when they are configured exactly in the same way In practice this means that when a transmitter is configured to send packets at a power level of d dBm then the transmitter will send these packets at a power level that is very close to d dBm but not necessarily exactly equal to d dBm This can alter the received signal strength indication and thus it can lead to inaccurate distance estimation

Receiver variability The sensitivity of the receivers across different radio chips is different In practice this means that the RSSI value recorded at different receivers can be different even when all the other parameters that affect the received signal strength are kept constant

Antenna orientation Each antenna has its own radiation pattern that is not uniform In practice this means that the RSSI value recorded at the receiver for a given pair of communicating nodes and for a given distance between them varies as the pair wise antenna orientations of the transmitter and the receiver are changed

Multi‐path fading and shadowing in the RF channel In indoor environments the transmitted signals get reflected after hitting on the walls andor on other objects in the room such as furniture Both the original signal and the reflected signal reach the receiver almost at the same time since they both travel at the speed of light As a result of this the receiver is not able to

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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28

distinguish the two signals and it measures the received signal strength for both of them

32 Related work analysis A number of indoor location tracking systems have been proposed in the literature based on RF signals ultrasound infrared and some combinations of these As mentioned previously RF based positioning system are widely studied as shown in Table 2 Given a model of radio signal propagation in a building or other environment RSS can be used to estimate the distance between the transmitter and receiver Most of the algorithms (Table 2) have been evaluated in practice using 80211b compliant wireless radios and seem to produce a location error of 15ft to 20ft for an indoor network area of approximately 225ft x 144ft According to the results of this study all the localization techniques produce approximately the same location error over a range of environments Ecolocation [22] Mote Track [5] are two of the few localization algorithms that have been evaluated on a real sensor network that uses a low power wireless radio Ecolocation reports a location error of 10ft for a very small outdoor network deployment area (26ft x 49ft) Mote Track reports a location error of approximately 13ft for an indoor network deployment area of 18751ft2 In general the RSSI based localization algorithms can be divided into two broad categories the map based and the distance (or area) prediction based algorithms In the case of the map based algorithms the deployment area is divided into a large set of sample points that cover the whole area Most techniques collect one or more signal strength measurements from all visible beacon nodes and for each sample point Each sample point is then mapped to either a signal strength vector or a signal strength probability distribution This process generates an RSSI fingerprint map which is unique with respect to the beacon locations and the deployment area This fingerprint map is stored to a central base station A node that is not aware of its location collects one or more signal strength measurements from all the visible beacon nodes and creates its own RSSI fingerprint that is sent to the central base station The base station computes nodersquos location as the sample pointrsquos location whose RSSI fingerprint distance from the unknown nodersquos RSSI fingerprint is the minimum among all the sample points Despite the fact that the map based RSSI localization methods produce relatively small location errors they are not attractive mainly because they require an excessive amount of profiling data and because they are not fully distributed The detailed profiling of a building is a major holdback because it is complex time consuming and depends on beacon locations Therefore for different buildings or even for the

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

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29

same building and different beacon locations different RSSI maps have to be obtained The second category of RSSI based algorithms are the distance (or area) prediction algorithms which focus on minimizing the amount of profiling data In distance prediction based methods raw RSSI data are directly mapped to distances through a signal propagation model In some cases distances are not explicitly measured but they are taken into consideration through assumptions on the underlying distributions In the case of outdoor deployments where there are no obstacles a single signal propagation model is all the profiling data required In the case of indoor deployments the same applies but now the signal propagation model might be different across different buildings with different furniture and wall arrangements Given an underlying signal propagation model raw RSSI data can be mapped to specific distances These distances can be either used directly in order to perform localization or assigned a probability and used in a learning basedprobabilistic localization algorithm Obviously the accuracy of the distance prediction based methods depends heavily on the accuracy of the signal propagation model used for translating RSSI values to distances

33 Properties of RSSI Changes in signal due to propagation indoors are difficult to predict because of dense environment and propagation effects such as reflection diffraction and scattering The multi‐path fading effect which is the result of either constructive or destructive combination of multiple signal copies at the receiver causes the received signal to fluctuate around a mean value at a particular location The received signal is usually modelled by the combined effects of large‐scale fading and small‐scale fading The large‐scale fading component describes the signal attenuation as the signal travels over a distance and is absorbed by material such as walls and floors along the way to the receiver This component predicts the mean of the RSS and usually has a lognormal distribution On the other hand the small scale fading component explains the dramatic fluctuation of the signal due to multi‐path fading On the other hand the small scale‐fading component explains the dramatic fluctuation of the signal due to multi‐path fading If there is no LOS component the small‐scale‐fading is often modelled with a Rayleigh distribution Such scenarios are commonly called NLOS If there is a LOS component the small‐scale‐fading is typically modelled by a Rician distribution Note that when the RSS is measured the measurement averages out the small‐scale‐fading effects

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

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4 OBJECT TRACKING 41 Introduction This chapter presents the object‐tracking application the main objective of this thesis work The application is an algorithmic‐software implementation written in Embedded C which is capable of receiving data messages and generating object position estimation data The implementation of the object‐tracking application makes use of the SENTIO platform and uses the background knowledge introduced in the preceding chapters The motivation for the object‐tracking application is to determine the position of an object in indoor environments (Eg Home) The application acts as an interface which collects the raw data that the sensors generate (numerical values) It makes the proper comparisons and algorithmic processes to identify and track the object while it is static inside the area covered by the wireless sensor network Finally with the detection and tracking data it alerts the user or another system The following sections provide an overview of the object‐tracking application

42 Overview of hardware product Before describing the object tracking application it is important to know the characteristics of hardware product The SENTIO platform (figure 15) is built in modular fashion and has the following key features [8]

Flexibility This platform is very flexible in such a way that it is made easy to add or change the hardware for each sensor task depending on the requirement in the WSN For a specific platform this can be hard or sometimes even impossible to accomplish

Sensor resources Resources in a sensor module includes the ability of usage of controllerrsquos resources as much as possible such as ADC‐channels interrupts and digital Input‐Outputrsquos Local voltage regulation should also be possible since many sensors require higher supply voltages than digital Integrated Circuits do Further support of a shut‐down mechanism to power down the sensor modules when not needed

Computation and memory The processor module based on microcontroller should support many devices in a microcontroller family and support different power supply voltages enabling full range of clock frequencies All power‐down modes of the devices should be

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

31

supported The platform should support different processor modules including FPGArsquos and very simple microcontrollers

Communication The focus should be on enabling an IEEE 802154 compliant communication interface With or without ZigBee stack this will provide a flexible platform for exploring different network topologies Support for power‐down of the entire communication module should be supported

Cost and form factor High flexibility is in conflict with low cost and small size The primary objective should be flexibility but the size should be considered in order to enable sensor device integration in systems where space is limited

Figure 15 SENTIO Platform [8]

The communication subsystem of the SENTIO sensor node was designed so that the correct operation of the radio chip is ensured The radio chip is powered by the on‐board voltage regulator and thus fluctuations in the battery voltage level do not affect the operation of the communication subsystem This platform uses Chipcon CC2420 IEEE 802154 radio transceiver operates in the 24GHz ISM band and includes a digital direct sequence spread spectrum (DSSS) modem providing a spreading gain of 9dB and an effective data rate of 250Kbps It was specifically designed for low power wireless applications and supports 8 discrete power levels 0dBm minus1dBm minus3dBm minus5dBm minus7dBm minus10dBm minus15dBm and minus25dBm at which its power consumption varies from 29mW to 52mW [21] A built‐in received signal strength indicator gives an 8‐bit digital value RSSIVAL The RSSIVAL is always averaged over 8 symbol periods (128μs) and a status bit indicates when the RSSIVAL is valid (meaning that the receiver was enabled for at least 8 symbol periods) The

Antenna

Communication Module

Processing Module

Sensing Module

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

32

power P at the RF pins can be obtained directly from RSSIVAL using the following equation

P = RSSIVAL + RSSIOFFSET [dBm] where the RSSIOFFSET is found empirically from the front‐end gain and it is approximately equal to minus45dBm In the next sections when we refer to the RSSI value we refer to the RSSIVAL and not the power P unless otherwise stated

43 Experimental Testbed The experimental test bed for object tracking application is as shown in figure 16 Experiment was conducted in Masterrsquos room at Mid Sweden University A DELL laptop is connected to SENTIO platform and software were primarily used to collect samples of RSS from APrsquos The software monitors IEEE 802154 radio frequency channels which operate in the 24GHz band

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

1030

BC - 1030

AP 3 AP 1

AP 2

AP 4

BS

HEWLETT

PACKARD

Figure 16 Layout of Masterʹs Room

A three dimensional scalable testbed is used for indoor sensor network deployments The testbed illustrated in figure 16 is a 3‐D structure measuring 16m (L) x 12m (W) x 3m (H) and it is designed to host a large number of static nodes as shown in figure 16 Arrow marks in the above figure represent the location where fingerprints were taken The centrepiece of our infrastructure

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

33

is the SENTIO sensor node [8] an open‐source general purpose sensing platform designed with AT Mega 128 microprocessor and the IEEE 802154 compliant CC2420 radio from Chipcon [19]

44 Obtaining data from the sensor network The next important step in the development of the object tracking application is to obtain the data from the sensor network and send it to the base station From there the data is transmitted through a serial port to the computer that runs the object tracking application When the data arrives at the serial port the application must capture it This paragraph gives a brief explanation of how and what kind data is transmitted by the static object fixed sensors and how this data is being used to estimate the position of the object First the static object transmits Beacon packet continuously which is received by all the fixed APrsquos The beacon packet format is as shown in table 6 Then the fixed APrsquos calculates the 8‐bit value of RSSI from the transmitted beacon packet and then sends the resultant RSSI value as a form of data packet (table 7) to the BS which calculates the position and sends back to the object

45 Object tracking application Testing and Evaluation The purpose of this section is to discuss and evaluate the object tracking application Finally we are ready with the experimental testbed as shown in figure 16 As shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors placed at the corners of the masterrsquos room BS is placed at the centre of the room connected to a PC All the nodes are power connected before an actual measurement takes place At first the object is placed at random position inside the room and transmits beacon packets continuously Then these packets are received by the fixed APrsquos which calculate the RSSI from the packet and then averages it until it receives 50 beacon packets and then transmits the data to the BS (figure 17) Then the BS waits until it receives the data from all the APrsquos Once it receives from all the APrsquos it calculates the position of the object (figure 18) The application was developed to demonstrate a real world use of a WSN in object tracking In evaluating the final position obtained with respect to the actual position we conclude that the object‐tracking was successful Although

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

34

it is a research prototype object tracking application is a simple stable stand‐alone system capable of demonstrating an important application of WSN It takes raw data from the WSN aggregates it evaluates it and based on the evaluation tracks the object The accuracy of estimating the position is less than 5m Table 11 below shows a log of readings taken during test

Start

Intialize micro‐controller and transeciever in receieve mode

If received_packet=

Beacon

Average RSSI values

Increase the counter

If countergt1000050

A

No

Yes

B

B

Yes

No

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

35

A

Reset counter

Set transceiever in transmit mode

Transmit averaged RSSI to Main sensor and set it back to receive mode

10000 samples ‐ Offline

50 samples ‐ Online

Figure 17 Flow chart representing the algorithm of fixed sensors

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

36

Start

Intialize micro‐controller and transeciever in receieve mode

Received RSSI values from all fixed Sensors

Open radio map

Calculate Euclidean distance between obtained RSSI values from fixed sensors and fingerprints

in radio map

No

Yes

Bubble sort for least error in signal strength values

A

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

37

A

Get the location of the object from the radiousing the least error value

Display location of object on PC

End

Figure 18 Flow chart representing the algorithm of main sensor

Table 11 Accuracy of positioning system

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

38

5 RESULTS This chapter presents how the system parameters such as number of samples mean or median etchellipare finalized All the tests are performed in masterrsquos room at Mid Sweden University This chapter is divided into three subsections First in Section 51 it reviews results obtained from samples test Section 52 discusses one dimensional test results and finally section 53 discusses results of two dimensional tests

51 Measurement setup A Dell laptop was connected to a SENTIO platform and is loaded with software (Appendix 2) This setup was used to determine number of samples required during object tracking mean or median position estimation algorithm and one dimensional test The setup is as follows

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

103

0

BC - 1030

AP 1AP 2

BS

HEWLETT

PACKARD

Figure 19 Test bed for one dimensional test in Masters Room

Two SENTIO nodes (AP1 and AP2 as shown above) were separated by a distance of 10 meters and these sensors are known as fixed sensors which are fixed to the ceiling such that there antenna is faced towards the ground They are linear is position A BS was connected to Dell laptop and it was placed in such a way that its antenna was faced towards the ceiling Another SENTIO

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

39

node was used as an object and its orientation was same as BS Figure 19 represents the complete setup for object tracking application Mobile node was moved between these fixed ones at regular intervals of one meter and RSSI values were logged at each interval

52 Number of samples test This test was performed to determine number of samples required for accurate positioning and either mean of the obtained RSSI values or median of them Figure 19 represents the setup for number of samples test Once the setup is ready the measurements are taken as follows

1 An object was tagged with SENTIO node and was placed stationary linear to the fixed sensors

2 10000 samples (RSSI) were taken at regular intervals of 1 meter up to 10 meters

3 The sample values were logged at every interval 4 Using these logged samples I have plotted graphs such as distribution

of RSS values (figure 20 21) meanmedian at intervals of 1 meter using MATLAB

5 The mean and median for various numbers of samples is as shown in figure 22

Figure 20 Histogram plot of RSSI values at 1m and 4m

Figure 21 Histogram plot of RSSI values at 6m and 8m

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

40

Figure 22 Distance vs RSSI

From the above graphs following conclusions were made 1 From the histogram plots we can observe that the distribution of RSSI

values follow Gaussians distribution law 2 As we can see from figure 22 that median curve has drastic changes

when compared to mean curve Hence mean is considered while estimating the position

3 From the observations we conclude that the number of samples does not matter since mean curve tends to be same for various numbers of samples

4 Hence 10000 samples are considered in order to get a proper location fingerprint during off‐line phase and during online phase 50 samples are enough to estimate the position of object

53 0ne dimensional test Measurement setup for one dimensional test is as shown in figure 19 This test was conducted to check the performance of algorithm and how accurate it was At first ie during offline phase fingerprints at each interval averaging 10000 samples were taken During online phase Euclidean algorithm was used estimate the position averaging 50 samples of RSSI From the results we

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

41

can realize that the error was less than 2 meters Table 12 shows the result of location estimation in one dimensional case

Table 12 One dimensional position estimation results

54 Two dimensional test This test is the final stage of my thesis work Experimental testbed for this stage is as shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors fixed at the corners of the room and a BS at the centre of the room There are 18 pre‐determined locations where the fingerprints (RSSI) were taken The fingerprints are as shown in Appendix 1 All the readings were taken in presence of userrsquos The algorithm for position estimation is shown in Appendix 2 Position estimation test is performed as follows

Tagged object is placed at random inside the masterrsquos room All the sensors including the base station and object are switched on Using the algorithm (Appendix 2) the BS estimates the position of the object and displays it on the PC

This test was performed using various numbers of samples to test which has a better accuracy Figure 23 represents the error rate using various numbers of samples From figure 23 we can conclude that 50 numbers of samples is enough to estimate the position of object with an accuracy of 3m

55 Causes of error in location determination The randomness of RSS is the major cause of error in any indoor

positioning system that uses the RSS for location inference If there is no randomness in the RSS and its location fingerprint every indoor positioning systems of this type will have no location detection error For example I have recorded RSS at one particular location during different times of the day From figures 24 and 25 you can observe that RSS values are different at different times of day

The performance of indoor positioning systems depends greatly on the separation of location fingerprints A location fingerprint

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

42

corresponding to a location can be identified correctly if it is difficult to classify it as another fingerprint by a pattern classifier

Path loss is one of the basic signal propagation properties of the RSS that best describes attenuation of transmit signal power over a distance between an access point and a mobile device

Presence of Userrsquos or userrsquos orientation affects RSS

Figure 23 Error Rate vs Number of samples

Figure 24 RSS distribution at 214 pm

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

43

Figure 25 RSS distribution at 100pm and 1100 am

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

6 DISCUSSIONS10102006

44

6 DISCUSSIONS 61 Summary and Conclusion While empirical results and performance studies of positioning systems based on location fingerprint have been presented in this report analytical model that can be used as a framework for efficiently designing the positioning systems are not available This thesis design work uses general principles of position estimation such as position estimation of sensors grid spacing etc This design work focuses on accuracy and precision The concept of location fingerprint technique based on RSS us explained in detail in chapter 2 It is found that RSS is random It is possible to approximate the RSS as being normally distributed While the RSS random process is non‐stationary the mean is more or less constant and could be used as the fingerprint of the location Model designed for position estimation is as shown in chapter 4 Accuracy and precision are the two major performance matrices in this study Uniform grid spacing has been used From the experimental analysis I have obtained an accuracy of 3 meters and 35 precision The sensitivity analysis among the system parameters and the performance matrices were done using the model as shown in chapter 4 For instance our study found that the minimum distance between two adjacent positions also affects the positioning performance This is due to the randomness of RSS patterns induced by the ever changing indoor environment

62 Future work The research in this thesis provides a ground work for the future study of efficient design of indoor positioning system The RSSI based position estimation still has limitations First the RSSI value is time dependent Thus the future study should address this issue Second the movement of object is not considered Third orientations of sensors affect the performance which should be taken care off Fourth to improve the performance of the system we need to find a solution to deal with those incomplete or missing data during the off‐line and the on‐line phase Fifth position estimation on multi‐floor and three‐dimensional coordinates is not available

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 110102006

45

APPENDIX 1

Location Fingerprints of Masters Room Time 1315 Observation Time 20 minutes Number of Samples 10000

People Presence SNo Co-ordinates AP1 (0x0001) AP2 (0x0002) AP3 (0x0004) AP4 (0x0008)

1 11 -24 -16 -27 -33 2 13 -26 -8 -34 -31 3 15 -43 -9 -39 -25 4 32 -21 -11 -27 -33 5 61 -6 -23 -32 -28 6 64 -20 -31 -25 -34 7 35 -23 -35 -29 -38 8 37 -29 -35 -22 -27 9 39 -38 -29 -21 -27 10 67 -27 -23 -20 -23 11 611 -26 -29 -23 -30 12 613 -35 -32 -2 -31 13 513 -34 -31 -20 -26 14 11 -39 -22 -29 -17 15 212 -37 -42 -28 -31 16 -212 -38 -33 -29 -6 17 -21 -42 -29 -33 -19 18 -38 -40 -23 -35 -22

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 210102006

46

APPENDIX 2

Begin

Open radio map and raw data files for write

Intialize radio map in memory

Check whether the

Packet is data or Beacon packet If data packet

Save the data to register

Check if data is

Received from all

sensors

Estimate the position of static

Display the position on PC

End Yes

No

A

A

Yes

No

Flow chart representing the main algorithm for position estimation

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

47

APPENDIX 3

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

48

Distribution of RSSI

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

49

REFERENCES [1] T S Rappaport J H Reed and B D Woerner ldquoPosition location using wireless communications on highways of the futureʺ IEEE Commun Mag vol 34 no 10 pp33‐41 Oct 1996 [2] A M Ladd K E Bekris G Marceau A Rudys L E Kavraki and D S Wallach rdquoRobotics‐based location sensing using wireless ethernetʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ02) 2002 pp 227‐238 [3] I Ramani R Bharadwaja and P V Rangan ldquoLocation tracking for media appliances in wireless home networksʺ in Proc International Conference on Multimedia and Expo (ICMEʹ03) July 2003 pp 769‐772 [4] K Pahlavan X Li and J P Makela ldquoIndoor geolocation science and technologyʺ IEEE Commun Mag vol 40 no 2 pp 112‐118 Feb 2002 [5] Konard Lorincz and Matt Welsh ldquoMote Track A Robust Decentralized Approach to RF‐Based Location Trackingrdquo [6] Paramvir Bahl and Venkata N Padmanabham ldquoRADAR An In‐Building RF‐based User Location and Tracking Systemrdquo [7] Kamol Kaemarungsi ldquoDesign of Indoor positioning system based on location fingerprint techiniquerdquo University of Pittsburgh 2005 [8] Fredrik Linnarsson Cheng Peng and Bengt Oelmann ldquoSENTIO A Hardware Platform for Rapid Prototyping of Wireless Sensor Networksrdquo [9] S Saha K Chaudhuri D Sanghi and P Bhagwat ldquoLocation determination of a mobile device using IEEE 80211b access point signalsʺ in Proc IEEE Wireless Communications and Networking Conference (WCNCʹ03) New Orleans LA Mar 2003 pp 1987‐1992 [10] T Roos P Myllymaki H Tirri P Misikangas and J Sievanen ldquoA probabilistic approach to WLAN user location estimationʺ International Journal of Wireless Information Networks vol 9 no 3 pp 155‐164 July 2002 [11] R Battiti M Brunato and A Villani ldquoStatistical learning theory for location fingerprinting in wireless LANrsquosʺ Technical Report Oct 2002 [Online] Available httprtmscienceunitnitraquobattitiarchive86pdf [12] P Prasithsangaree P Krishnamurthy and P K Chrysanthis ldquoAn indoor position location with wireless LANsʺ in Proc IEEE International Symposium on Personal Indoor and Mobile Radio Communications (PIMRCʹ02) Lisbon Portugal Sept 2002 [13] M A Youssef A Agrawala and A U Shankar ldquoWLAN location determination via clustering and probability distributionsʺ in Proc IEEE

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

50

International Conference on Pervasive Computing and Communications (PerComʹ03) Dallas‐Fort Worth TX Mar 23‐26 2003 pp 23‐26 [14] Z Xiang S Song J Chen H Wang J Huang and X Gao ldquoA wireless LAN‐based indoor positioning technologyʺ IBM Journal of Research and Development vol 48 no 56 pp 617‐626 SeptNov 2004 [15] R Want A Hopper V Falcao and J Gibbons ldquoThe active badge location systemʺACM Transactions on Information Systems vol 40 no 1 pp 91‐102 Jan 1992 [16] N B Priyantha A Chakraborty and H Balakrishnan ldquoThe cricket location‐support systemʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ00) Boston MA Aug 2000 pp 32‐43 [17] Holger K Willig A (2005) Protocols and Architectures for Wireless Sensor Networks London [18] IEEE (2003) Part 154 Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low‐Rate Wireless Personal Area Networks (LR‐WPANs) New York [19] Chipcon CC2420 802154 compliant radio httpwwwchipconcom [20] ZigBee Specification v10 ZigBee Alliance [21] Dimitrios Lymberopoulos Quentin Lindsey and Andreas Savvides ldquoAn Empirical Analysis of Radio Signal Strength Variability in IEEE 802154 Networks using Monopole Antennasrdquo [22] Kiran Yedavalli Bhaskar Krishnamachari Sharmila Ravula and Bhaskar Srinivasan ldquoEcolocation A sequence based technique for RF localization in wireless sensor networksrdquo Robert Bosch Research and Technology Centre Palo Alto CA

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

Terminology Notation10102006

ix

ED ‐ Energy Detection

CCA ‐ Clear Channel Assessment

LQI ‐ Link Quality Indication

ISM ‐ Industrial Scientific and Medical

DSSS ‐ Direct‐Sequence Spread Spectrum technique

LSB ‐ Least Significant Bit

SHR ‐ Synchronization Header

SFD ‐ Start of Frame Delimiter

PHR ‐ PHY Header

MPDU ‐ MAC Protocol Data Units

GTS ‐ Guaranteed Time Slot

MHR ‐ MAC Header

MFC ‐ MAC Footer

FCS ‐ Frame Check Sequence

CAP ‐ Contention Access Period

CFP ‐ Contention Free Period

ACL ‐ Access Control List

AES ‐ Advance Encryption Standard

MIC ‐ Message Integrity Code

CBC ‐ Cipher Block Chaining

ZDO ‐ ZigBee Device Objects

OS ‐ Operating System

LOS ‐ Line of Sight

NLOS ‐ Non Line of Sight

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

Terminology Notation10102006

x

FPGA ‐ Field Programmable Gate Array

BS ‐ Base Station

AOA ‐ Angle of Arrival

TDOA ‐ Time difference of arrival

TOA ‐ Time of Arrival

TOF ‐ Time of flight

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

1

1 INTRODUCTION 11 Introduction to the study Advancement in wireless sensing technologies has encouraged in developing a model for indoor object‐tracking application using low power IEEE 802154 compliant radios Stimulus used in this model is RSS This application is not only limited to object tracking in an indoor environment Concierge services enable users to become aware of nearest supporting facilities For example smart home applications such as multimedia appliances that forward multimedia stream to the nearest video screen can be achieved with a home positioning system [3] In the field of robotics a robot can navigate by itself using the assistance of indoor positioning system [2] Granularity of location information is most important in location determination applications For example to locate a book in a library requires coarse grained granularity whereas locating a user requires coarse grained granularity Position estimation refers to a process used to obtain location information of a MS with respect to a set of reference positions within a pre‐defined space A system deployed to determine or estimate the location of an entity is called a position location system or positioning system A wireless indoor positioning system refers to a wireless network infrastructure that provides indoor location information to any requesting end user One of the key technical elements allowing the realisation of this vision will be the knowledge of the accurate position of the personobjects Already today accurate location mechanisms such as GPS are commercially available Many more will become part of our daily life with the introduction of third generation mobile phones But none of these techniques will allow the meter or sub‐meter level of accuracy within buildings which is necessary for smart houses Hence this report introduces an object tracking application in an indoor environment using IEEE 802154 compliant radios which gives meter level accuracy First this chapter presents the background of indoor positioning systems identifies the challenges of such systems and briefly describes the scope of indoor positioning systems based on location fingerprints Next the objective of study the overview of approaches and the contributions are presented Finally the scope and organization of thesis work is outlined

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

2

12 Background The success of outdoor positioning and applications based on the GPS provides an incentive to the research and development of indoor positioning systems It has been developed over several decades and it relies on constellation of satellites Ranges to several satellites are used in multi‐lateration procedure to infer the position of the receiver This localization scheme requires hardware that is both expensive and consumes significant power As a result indoor positioning systems require alternative means to detect the MS location without relying on the direct signal from GPS satellites Infrared RF and ultra sound signals are major technologies used for indoor positioning systems Unlike outdoor areas the indoor environment imposes different challenges on location discovery due to the dense multi‐path effect and building material dependent propagation effect Thus an in‐depth understanding of indoor radio propagation for positioning is crucial for efficient design and deployment The existence of radio connectivity and the attenuation of radio signal with distance are attractive properties that could potentially be exploited to estimate the positions of small wireless devices featuring low‐power radios RSSI a standard feature in most radios has attracted a lot of attention in the recent literature for obvious reasons RSSI eliminates the need for additional hardware in small wireless devices and exhibits favourable properties with respect to power consumption size and cost As mentioned previously RF‐based location tracking has been widely studied [5 6] Given a model of radio signal propagation in a building or other environment RSSI can be used to estimate the distance from a transmitter to a receiver and thereby estimate the position of a mobile node However this approach requires detailed models of RF propagation and does not account for variations in receiver sensitivity and orientation Table 1 below briefly summarizes performance of existing indoor positioning system based on location fingerprints and WLANrsquos All the systems mentioned below in table 1 follow IEEE 80211 protocol standards The major performance metrics studied by all systems are positioning accuracy which is a form of error measurement The accuracy of the location information is usually reported as an error distance between the estimated location and the actual mobile location However the report of accuracy should include the confidence interval or percentage of successful location detection which is called the precision The system parameters of existing systems are summarized in table 1 and the best reported performance of these systems are listed in table 2

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

3

System Spacing(m) Positions SamplesPos APs Orientation Environment

RADAR [6] Non

uniform 70 80 3 4 Hall-way Saha et al [9] 3 19 1200 3 NA 1-floor

Roos et al [10] 2 155 40 10 NA 1-floor Battiti et at [11] NA 257 NA 6 NA 1-floor Ladd et al [2] 3 11 1307 5 2 Hall-way

Prasithsangaree et al [12] 153 60 40

2 to 7 4 1-floor

Youssef et al[13] 15 110 300 4 NA Hall-way Xiang et al [14] NA 100 300 5 4 1-floor

Table 1 Comparison of Indoor Positioning Systems [7]

System Algorithm Type Accuracy and Precision RADAR [6] Nearest Neighbour within 7 feet 38

Saha et al [9] Nearest neighbour amp Neural Network no specified accuracy

90 Roos et al [10] Bayesian best within 828 feet 90

Battiti et at [11] Neural Network amp Weighted k-Nearest

Neighbour all within 16-17 feet 90 Ladd et al [2] Bayesian within 5 feet 77

Prasithsangaree et al [12] Weighted k-Nearest Neighbour

25 feet at 75 amp 40 fet at 95

Youssef et al [13] Bayesian within 7 feet more than

90 Xiang et al [14] Bayesian with RSS distribution model within 6 feet 90 Table 2 Performance comparison of Indoor Positioning Systems [7]

Based on the study of results obtained in various positioning systems as in table 2 we have developed a model for an indoor positioning system using SENTIO [8] platform based on RSSI

13 Objectives In the last two or three years a number of theoretical practical andor simulation studies were done on the topic of object‐tracking While these studies are useful they are too general and provide little guidance for the actual deployment of sensor networks for real life location‐tracking of an object This thesis focuses on developing an object‐tracking application and prescribes sensor network configurations that work well with our algorithms I implemented my software using SENTIO [8] platform for sensor networking The major issue addressed in this project are the accuracy and precision in tracking an object in a real‐world application and efficient ways to algorithmically analyze the collected raw data from the specific wireless sensor product

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

4

Although the focus is to develop a real‐world application using WSN it also provides great opportunity to explore the new area of wireless communication overall

14 Research Questions My primary target is to find a method to determine the position of an object inside a building using ZigBee protocol standards based on RSSI The study addresses the following questions What is WSN What requirements do WSN need What are their main ideas and concepts What are the associated technologies What are the standards that sensor networks use

What are the existing hardware implementations that use this particular technology How can we build our new applications using WSN What do sensor network systems require for general or specific applications Are they applicable And what is the appropriate design for sensor network implementations in order to combine them with other existing systems At this point we describe and evaluate the specific object‐tracking application to be built

15 Scope The study area of the thesis is an overview of the WSN technology study of SENTIO platform and implementation of an object tracking application Thus the study is divided into two parts Part one is an overview of WSN it includes study of SENTIO platform and IEEE 802154 protocol standards The second part describes the implementation of the object tracking algorithm The object tracking application receives and uses the raw values returned by the sensor network system to produce clear and meaningful outputs Schema of thesis work is as shown in figure 1

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

5

Figure 1 Schema of thesis work

16 Methodology The ldquoIndoor Local Positioning System for ZigBee based on RSSI ldquois based on ldquolocation fingerprintingrdquo technique Location fingerprinting refers to a technique that exploits the relation between any measurable physical stimulus and a specific location This approach uses empirical measurements of RSSI to estimate location By recording a database of radio ldquosignaturesrdquo along with their known locations a mobile node can estimate its position by acquiring a signature and comparing it to the known signatures in the database All of these systems require the database of signatures which should be collected manually prior to the system installation and rely on central server (or the userrsquos mobile node) to perform location calculation For example in RADAR [6] technology the RSSI is the stimulus This type of positioning system does not require specialized hardware other than the common wireless network interfaces with received signal strength measurement capability thus it is simple to deploy compared to other techniques Unlike outdoor counterpart systems which can use AOA and TDOA techniques effectively indoor positioning systems encounter the problem of NLOS and the dense multi path effect that render these two techniques ineffective or complex for practical implementation It is also difficult for a MS to always see three or more access points or base stations in indoor environment which is essential for triangulation by AOA and TDOA Location fingerprinting can also be implemented as software based positioning system which can reduce

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complexity and cost Generally the deployment of fingerprinting based positioning systems can be divided into two phases First in the off‐line phase the locations where the fingerprints are required is pre‐determined and then the fingerprints are collected by performing a site‐survey of the RSS from multiple APs The distance between two closest positions is called grid spacing and usually reported in meters or feet However some points may be omitted due to inaccessibility The RSS is measured at each predetermined location to create a database of RSS patterns of the indoor environment The database of RSS patterns is called a radio map The vector of RSS values at a point on the grid is called the location fingerprint of that point Second in the on‐line phase a MS will report a sample measured vector of RSS from different APs to a base station The base station uses a positioning algorithm to estimate the location of the MS The most common algorithm used to estimate the location is Euclidean algorithm It computes the Euclidean distance between the measured RSS vector and each fingerprint in the database and then the coordinates associated with the fingerprint that provides the smallest Euclidean distance is returned as the estimate of the position It is clear from the above discussion that the designer should not start out to do the site survey without proper system design objectives and guideline If the performance is not sufficient collecting more fingerprints in between previous location fingerprints may help It is a problem of choice as to how many access points are required for the system and what the minimum distance between physical positions on the grid should be in order to provide a good position resolution and best system performance The system parameters and factors that improve the accuracy and the precision performance are still not clear

17 Outline Chapter 2 provides literature review of ldquoIndoor Local Positioning System for ZigBee based on RSSIrdquo it introduces the basic components required for a positioning system general classification of indoor positioning system Finally it explains ldquoLocation Fingerprintrdquo technology used in this thesis work to determine the position of an object in a real‐world application It briefly explains the protocols and industry standards for wireless sensor networks Chapter 3 describes some properties of RSS which is a stimulus to determine

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the position of an object RSS relation with energy and on what parameters is the RSS value dependent Chapter 4 describes the model of an indoor local positioning system and its probability of returning the correct location It also explains its performance evaluation and its error distance distribution Chapter 5 describes results obtained from the model of positioning system Chapter 6 includes conclusions from the experimental analysis and future work

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2 LITERATURE REVIEW This chapter reviews the literature on wireless indoor positioning system as a means of providing an intellectual background for the thesis work First in section 21 the common components of indoor positioning system are described Then in section 22 discusses different means to classify indoor positioning systems In section 23 reviews of all relevant literature on location fingerprint technique Finally in section 24 protocols and industry standards for wireless sensor networks are explained

21 Common components of indoor positioning system Basically a wireless local positioning system consists of at least two separate hardware components a measurable unit that usually carries the major part of the system intelligence and a signal transmitterreceiver The transmitter in the simplest case is just a beacon Figure 2 represents basic hardware model of a positioning system

Figure 2 Block diagram representing hardware module

First the location sensing devices detect the signals transmitted by or received at known reference points using radio sensing technologies The sensing technique is based on signal strength level that converts the sensed signal into RSS Given a set of reference points the relative position of the MS can be derived from the signal characteristics of RSS The signal characteristics of RSS are unique for a location Then the microcontroller processes the position algorithm and estimates the position of object and displays it on PC

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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22 Classification of Indoor positioning systems Indoor positioning system can be categorized based on the stimulus used in particular application and measurement principle

221 Classification based on Stimulus Based on the stimulus used in an application WSN inherit certain characteristics and limitations on sensorrsquos signal The propagation delay diffraction reflection and scattering are the basic signal characteristics which affect all signal types The effective range available bandwidth regulatory constraints interference power constraints safety and cost of technology limitations The wireless signals commonly used for indoor positioning systems are infrared radio frequency and ultrasound Brief descriptions of the three major technologies are as follows

Infrared Infrared signal is an electromagnetic signal of a wavelength longer than that of visible light but shorter than that of radio waves Infrared is just below the visible spectrum of light in frequency and is radiated strongly by hot bodies Many objects such as people vehicle engines and aircraft generate and retain heat and as such are especially visible in the infrared wavelengths of light compared to objects in the background It cannot pass through walls or obstructions therefore it has a rather limited range of indoor environments Thus it requires a more sophisticated circuitry than ultrasound signals Indoor lighting interferes with this type of signal and causes problems in accurate sensing It generally has a range of around 5 m The infrared devices are usually small in size compared to ultrasound devices

Radio frequency The RF signal can penetrate through most of the building material therefore it has an excellent range in indoor environments The propagation speed is also high approximately 3108 ms There are unlicensed frequencies available freely for use This type of signal has the longest range compared to infrared and ultrasound Since the propagation waves are of higher frequency (24GHz) interference with other frequency components is less

Ultrasound Although ultrasound operates at low frequency bands (typical 40 kHz) compared to the other two signaling technologies it possesses a good precision for location sensing at a slow propagation speed of sound (343 ms) The advantages of ultrasound devices are their simplicity and that they are inexpensive However ultrasound does not penetrate walls

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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10

but reflects off most of the indoor obstructions It has a short range around 3 m to 10 m but has a 1 cm resolution of distance measurement The operating temperature influences the performance of ultrasound

222 Classification based on measurement principle Wireless positioning systems can be categorized by measurement techniques which are used to derive the position of mobile stations Mainly three different measurement principles are used today AOA RSS and propagation‐time based systems that can further be divided into two different sub‐classes TOA and TDOA Figure 3 shows taxonomy based on the technology and the technique classifications

Figure 3 Taxonomy of positioning system [7]

In AOA systems the position is calculated via goniometry With the use of directional antennas or antenna arrays the angle or bearing relative to points located at known positions is measured The intersection of several measured directions pointers the yields the position value The accuracy of this approach is limited by the possible directivity of the measuring aperture by shadowing and by multi‐path reflections arriving from misleading directions In TOA and TDOA techniques rely on the precision of timing between the signal transmitter and the receiver in order to use the propagation delay or TOF to calculate the distance between transmitter and receiver Therefore a precise synchronization is also very important in such systems By combining at least three distances from three reference positions triangulation can be used to estimate the mobile stationʹs location This type of technique will

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11

require a high accuracy clock in the communication system TDOA is more practical [1] Examples of location‐sensing systems that use time of flight are GPS the Active Bats [15] and the Cricket [16] Besides these time delay‐based techniques the distance between transmitter and receiver can also be determined from signal strength attenuation and direct distance measurement (such as dead reckoning) Fingerprinting or Location Pattern Matching this technique generally requires only measurement of RSS or other non‐geometric features at several locations to form a database of location fingerprints To estimate the mobile location the system needs to first measure the received signal strength at particular locations and then search for the pattern or fingerprint with the closest match in the database This technique does not require the mobile station to see at least three base stations or access points in order to determine the location The disadvantage of this technique is that it is very time‐consuming to perform an exhaustive data collection for a wide area network such as in outdoor positioning systems

23 Indoor local positioning using ZigBee based on RSSI This section reviews relevant RF‐based indoor positioning systems which can be used to locate stationary objects and track mobile users The impressive growth of IEEE 802154 wireless PANs (WPAN) in recent years suggests an interesting future for the location fingerprinting technique The system is quite flexible because system designers can select whether to have a centralized positioning server or let the mobile to determine its own position However the fingerprinting technique requires a training phase (off‐line phase) to collect location fingerprints for all positions in the operating area before the actual deployment (on‐line phase) [7] The following discussion is divided according to the positioning system components First the common form of location fingerprinting and its relationship with physical locations are explained Finally location estimation algorithm is reviewed

231 Location Fingerprint A location fingerprint based on RF characteristics such as RSS is the basis for representing a unique location It is created under the assumption that each position or location inside a building has unique RF signature [4] Generally a fingerprint F is labelled with a location information L The location fingerprints and their labels (eg location information) are maintained in

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database and used during the on‐line phase to estimate the location The label and fingerprint are usually denoted as a tuple of (L F) Battiti et al [11] point out that the location information L for indoor location can be recorded in two forms as either a tuple of coordinates or an indicator variable The tuple of real coordinates can vary from one dimension to five dimensions which include the three dimension space and two orientation variables expressed in spherical coordinates For instance a location information of a two‐dimension system with an orientation could be expressed as a triplet L = (x y d) | x y є R 2 d є North East South West The RSS was found to be more location‐dependent than the SNR because the noise component is rather random in nature However the RSS itself fluctuates over time for each access point and location Each RSS element can be considered as a random variable therefore it can be captured by recording its descriptive statistics parameters approximating its distribution or maintaining the whole measurement dataset These approaches of RSS representation result in different procedures for location estimation algorithm Regardless of the approach the location fingerprint is usually denoted as an array or vector of signal strength (random variables) received at any position in the location‐based service area The size of the vector is determined by the number of access points that can be heard To create a basis fingerprint such as in a number of samples of vectors of signal strength are collected over a window of time for each position This basis is called a prototype Then the average RSS of each access point is calculated and recorded as an element in the location fingerprint For an area that can receive signals from N access points the location fingerprint can be expressed as a vector of average RSS elements βi

F = (β1 β2hellip βN)T Besides the basis location fingerprint the samples of location fingerprint measured during the on‐line phase are also important for the system that tracks the mobile object The number of samples should be selected appropriately to represent the location fingerprint for the mobile application A step in statistical analysis method called pre‐processing is another important issue needed to be considered because it can impact the estimation of dependency between location fingerprint and the location information easier The pre‐processing refers to a step that cleans the raw data (in this case the training set) before any further operations or analysis The cleaning may consist of encoding dimensionality reduction (reduce unnecessary elements) feature extractionselection clustering and outlier elimination Roos et al [10] point out that the pre‐processing enables faster location estimation and

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reduce the noise from the training data The process that creates the basis of location fingerprint discussed above could be considered as a part of pre‐processing

232 Location Estimation Algorithm Location estimation algorithms or positioning algorithms are procedures that exploit dependency between location information and location fingerprint basis in order to determine a position or location from samples of RSS signals Position algorithms can be classified into deterministic and probabilistic types based on the approaches that model the relationship between location fingerprints and location information The deterministic type of algorithms are those that based in the nearest neighbour classifiers and neural network classifiers The probabilistic type of algorithms is those that are based on the Bayesian inference and statistical learning theory such as support vector machines (SVR) The algorithm used in this project is discussed briefly

Euclidean distance method The Euclidean distance method is a deterministic approach of position estimation because they require only a set of constant location fingerprints which includes mean vectors In order to determine the location a form of discriminate function is used to classify a sample of RSS fingerprint into a position The mean or average RSS vector is a centre of a mass which represents each class of location fingerprint Suppose a set of l location fingerprints is denoted by F1 F2hellip Fl exist and each fingerprint has a one‐to‐one mapping to a set of positions L1 L2hellip Ll A sample of an RSS fingerprint measured during an on‐line phase is denoted as S which can be another mean or average RSS vector of small window of RSS samples Assuming that an indoor positioning system only considers the average RSS from N access points as a location fingerprint the sample of RSS vector is S = (s1 s2 helliphellip sN)T and each location fingerprint i in the database can be expressed as Fi = (β1 β2helliphellip βN)T The simplest closeness metric is a distance measurement in signal space denoted as the Dist () function [6] Thus the simple procedure of nearest neighbour algorithm is expressed as picking the fingerprint j that has the shortest signal distance

Dist(S Fj) le Dist(S Fk) for all k ne j A generalized weighted distance Lp summarized by [12] can be used to calculate different forms of distance in signal space as

Lp = 1N pN

i

pPiSiWi 1

1)||1(sum

=

minus

where N is the number of access points wi is a weighting factor (wi le 1) and p is the norm parameter starting from 1 The weighting factor wi is a bias

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14

parameter that can demote or promote an important RSS component in the fingerprint

24 Protocols and Industry standards for WSN Although many applications for WSN are proposed the communication protocols supporting them remain mainly unexplored and diverged This is because the area of interest is new each implementation is application specific and the components of WSNrsquos have a lot of constraints Wireless sensor networks use the wireless medium to communicate However the traditional communication protocols that support wireless communications especially ad‐hoc mesh networks may not be well suited for them WSN communication is an active research area and many algorithms and protocols have already been proposed This section introduces two proposed standards the IEEE 802154 and the ZigBee The IEEE 802154 covers the physical layer and the MAC layer of low‐rate WPAN The ZigBee is ldquoan emerging standard that is based on the IEEE 802154 and adds network construction (star networks peer‐to‐peermesh networks and cluster‐tree networks) application services and morerdquo

241 IEEE 802154 Holger and Willig (2005) [17] mention home automation home networking and home security as possible applications for IEEE 802154 They add that ldquomost of these applications require only low‐to‐medium bitrates (up to some few hundreds of kbps) moderate average delays without too stringent delay guarantees and for certain nodes it is highly desirable to reduce the energy consumption to a minimumrdquo The IEEE 802154 standard (2003) defines the device types that can be used in a LR‐WPAN A device can be a FFD or a RFD The RFD can be used in simple applications in which they do not need to transmit large amounts of data and they have to communicate only with a specific FFD The FFD can work as a PAN coordinator as a coordinator or as a simple device It can communicate with either another FFD or a RFD In keeping with the application requirements the LR‐WPAN operates in a star or peer‐to‐peer topology (figure 4) In the star topology the RFD communicates with a single controller the PAN coordinator The PAN coordinator can perform the same function as the RFD but it is also responsible for controlling the PAN ldquoit initiates terminates or routes communication around the networkrdquo [18] The peer‐to‐peer topology

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supports ad‐hoc mesh multi‐hop networking Any device in the peer‐to‐peer topology can communicate with any other device within its communication range however this topology also has a PAN coordinator All the devices in a LR‐WPAN have a unique 64‐bit address This or a short address allocated by the PAN coordinator can be used inside a PAN Additionally each PAN has a unique identifier The combination of the PAN identifier and the sort addresses allows communication across different PANs (IEEE 802154 Standard 2003)

Figure 4 Star and peer‐to‐peer topologies in LR‐WPAN [18]

The LR‐WPAN based on the OSI seven‐layer model has the layered architecture presented in figure 5 The application and the network layer are the upper layers in the LR‐WPAN architecture but are outside the scope of the IEEE 802154 standard Only the physical layer ldquowhich contains the RF transceiver along with its low‐level control mechanismrdquo and the MAC layer ldquothat provides access to the physical channelrdquo are included in the standard and will be introduced in the following sections Finally the Type I 8022 LLC and the SSCS are intermediate sub layers supporting communication with the above layers (IEEE 802154 Standard 2003)

Physical Layer The 802154 standard specifies two different services that the PHY provides The PHY data service controls the radio and thus the transmission and reception of the PPDUs In addition the management service performs ED in the channel The management service also performs CCA before sending the messages and provides LQI for the received packets

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Figure 5 WPAN architecture [18]

Three different bands are defined by the standard The 868‐8686 MHz for Europe the 902‐928 MHz for North America and the 2400‐24835 MHz worldwide All of them belong in the ISM radio bands and they are using DSSS Each of the bands supports different a data rate and it uses a different modulation technique chip rate and number of channels Moreover if the system does not use the 2450 MHz frequency it operates in both the 802 MHz and 902 MHz frequencies Table 3 summarizes the supported bands and data rates (IEEE 802154 standard 2003)

PHY (MHz)

Frequency Band (MHz) Modulation

Bit Rate Number of Channels

868-8686 BPSK 20 1 868 915 902-928 BPSK 40 10

2450 2400-24835 O-QPSK 230 16 Table 3 Frequency bands and data rates for IEEE 802154 based on the IEEE

802154 standard [18] The physical layer uses PPDU packets to communicate Table 4 demonstrates its structure The LSB is always transmitted and received first The SHR contains the preamble and the SFD fields which helps receiver

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synchronization The 8‐bytes preamble contains only zero and is used for synchronization The SFD contains a specific sequence of one and zeros and specifies the beginning of the frame The PHR contains the payload length Packets with a length of 9 or more bytes are MPDU as the next section further explains Packets with length 5 are used for MPDU acknowledgements The payload part of the PPDU encloses the MAC layer packet Finally the PPDU size can be up to 127 bytes [18]

4 bytes 1 byte 1 byte Variable

Preamble SFD Frame length

(7 bits) Reserved

(1 bit) Payload (PSU)

SHR PHR PHY Payload Table 4 Format based on the IEEE 802154 standard [18]

MAC Layer The MAC layer is the interface between the SSCS and the PHY layer Similar to the PHY layer the MAC layer supports two services The MAC data service is responsible for the transmission and reception of the MPDUs through the PHY data service The MAC management service if the device is a coordinator manages the network beacons It is also responsible for PAN association and disassociation frame validation and acknowledgment providing ldquoa reliable link between two peer MAC entitiesrdquo In addition it uses the CSMA‐CA for channel access and handles and maintains the GTS mechanism Finally it supports device security [18] The IEEE 802154 standard defines four different frame types the beacon data acknowledgment and MAC command frame All frame types are based on the general MAC frame format (table 5) The frame control field describes and specifies the above different frame types Every MAC frame comprises a MHR which consists of a frame control sequence number and the information field It also contains the MAC payload different frame types have different MAC payload fields The acknowledgment type does not have a payload Finally each frame includes a MFR which contains a FCS The data in the MPDU follows the same order as the PPDU the least significant bits are left in the frame and are transited first Octets2 1 02 028 02 028 Variable 2

Destination PAN Identifier

Destination Address

Source PAN

Identifier Source Address Frame

Control Sequence Number Addressing Field

Frame Payload FCS

MHR MAC

Payload MFRTable 5 General MAC frame format IEEE 802154 standard [18]

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Table 6 7 8 and 9 present the four different MAC frame types The beacon frame is transmitted periodically by the PAN coordinator It provides information about the network management through the super frame and GTS fields which are analyzed later in the section It also synchronizes the network devices and indicates the proper communication period for them The data frame payload encapsulates data from the higher layers When a device receives a packet it is not obliged to response with an acknowledgement packet Finally the command frame identifier and command payload fields of the command frame are used for communication between the network devices The command identifier specifies actions like association disassociation and data GTS or beacon request Octets2 1 410 2 Variable Variable Variable 2

Frame Control

Sequence Number

Addressing Fields

Super frame Specification

GTS fields

Pending Addressing

Fields Beacon Payload FCS

MHR MAC Payload MFRTable 6 Beacon frame format [18]

Octets 2 1 Variable 2

Frame Control

Sequence Number

Addressing Field

Data Payload FCS

MHR MAC

Payload MFR Table 7 Data frame format [18]

Octets 2 1 2

Frame Control

Sequence Number FCS

MHR MFR Table 8 Acknowledgement frame format [18]

Octets2 1 1 Variable 2

Frame Control

Sequence Number

Addressing Fields

Command Frame Identifier

Command Payload FCS

MHR MAC Payload MFR Table 9 Command frame format [18]

In the LR‐WPAN every PAN has its own coordinator The PAN coordinator manages the communication in the local area it has two options to use or not use the super frame structure The super frame (figure 6) uses network beacons If the coordinator does not want to use a super frame structure it suspends the beacon transmission However the beacon is important for device association and disassociation If the coordinator wishes to maintain close communication control in the PAN and to support low‐latency devices it usually uses the super frame A super frame determines a specific time

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period beacons bound it The beacon is transmitted in the first of the sixteen equal time slots that the super frame has It is used to ldquosynchronize the attached devices to identify the PAN and to describe the structure of the super framerdquo The super frame can have active and inactive periods All the communications have to be finished inside the super frame period In the inactive periods the devices can switch to the sleep mode but they have to be ready for the next beacon The active period is further divided into a CAP and a CFP During the CAP any device can communicate competing with the other devices in the PAN using slotted CSMA‐CA The CFP contains GTSs and follows the CAP A CFP may maintain up to seven GTSs and each GTS can reserve more than one time slot However the CAP should always be sufficient to allow new devices t join the PAN The GTSs are allocated to specific devices All the transactions must be completed inside the assigned time period (CAP GTS and CFP) (IEEE 802154 standard 2003)

Figure 6 Example of super frame structure [18]

In accordance with the 802154 standard (2003) three different types of data‐transfer exist In addition the types differ if the coordinator uses or does not beacons Data transfer from a device to the PAN coordinator is the first type (figure 7) For a ldquonon‐beacon‐enable networkrdquo it first senses the medium by using ldquoun‐slotted CSMA‐CArdquo and then a simple transmit to the data frame In a ldquobeacon‐enabled networkrdquo the sender waits for the beacon when it finds it ldquothe device synchronizes to the super fame structurerdquo In the specified time frame the sender again senses the medium by using ldquoslotted CSMA‐CArdquo and transmits the data to the coordinator In either case the coordinator has the option to acknowledge or not acknowledge the data reception after that the transaction is completed Data transfer from the coordinator is the next type described in the standard (figure 8) In a beacon‐enabled network the coordinator indicates a pending message through the beacon The messagersquos target device receives the beacon and if the message is pending it responses with a MAC command request

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message using slotted CSMA‐CA the coordinator may or may not acknowledge the command message and through slotted CSMA‐CA sends the pending message The device acknowledges the received message After that the coordinator removes the message from the beaconrsquos pending list and completes the transaction In a non‐beacon enable network the device periodically sends a MAC command‐request frame to the coordinator The coordinator acknowledges the data request Then by using un‐slotted CSMA‐CA if it has a pending message it transmits it If it has not it respond with a data frame with a ldquozero‐length payloadrdquo To complete the transaction the device acknowledges the data reception

Figure 7 Communication from a device to a PAN coordinator in (a) a beacon‐enabled network and (b) a non‐beacon enabled network [18]

Peer‐to‐peer is the last type of data transfer In this situation the devices are free to communicate with any other device within their communication range In a peer‐to‐peer PAN the devices can ldquoeither receive constantly or synchronize with each otherrdquo If they are receiving constantly to transmit data they use un‐slotted CSAM‐CA In the second case synchronization must be achieved first (IEEE 802154 standard [IEEE 2003]) The IEEE 802154 (2003) standard establishes MAC and PHY standards for low‐cost low‐power and high‐density node deployments In addition to the above PHY and MAC characteristics IEEE 802154 provides a security baseline including ldquothe ability to maintain an ACL and use symmetric cryptographyrdquo for data encryption The algorithm that is used for encryption is the AES However the higher level layers decide when security is need The upper layers are in general responsible for device authentication and key management The next section introduces the ZigBee standard which

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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21

encapsulates the IEEE 802154 and provides additional standardization for the higher levels

Figure 8 Communication from a PAN coordinator to a device in (a) a beacon‐enabled network and (b) a non‐beacon enabled network [18]

242 ZigBee Heily (2004) defines ZigBee as ldquoa rapidly growing worldwide non‐profit industry consortiumrdquo whose mission is ldquoto define a reliable cost‐effective low‐power wirelessly networked monitoring and control product based on an open global standardrdquo Figure 9 illustrates the areas of interest for different wireless communication standards

Figure 9 Overview of the coverage for different wireless communication

standards [17]

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ZigBee a new standard which became publicly available in June 2005 is based on the IEEE 802154 standard It expands the IEEE 802154 by adding the framework for ldquothe network security and applicationrdquo (Craig 2005) Figure 10 presents the IEEE 802154ZigBee stack and the areas of responsibilities

Figure 10 IEEE 802154ZigBee stack [20]

Craig (2005) mentions three networking topologies that the standard covers the star mesh and cluster tree (figure 11) The ZigBee standard works on top of the IEEE 802154 addressing schema by using the standard 64‐bit and the short 16‐bit addressing Kinney (2005) summarizes the ZigBee network layer responsibilities the successful establishment of a new the network and successful new device configuration addressing assignment network synchronization frames security and message routing ZigBee further distinguishes the concept of the physical devices (RFD FFD) by using the notion of ldquological devicesrdquo ldquoZigBee Coordinatorrdquo is the first type of logical devices It is responsible for initializing maintaining and managing the network Under the coordinator in the network hierarchy is the ldquoZigBee routerrdquo which is responsible for controlling the message routing between the nodes Finally the ldquoZigBee End Devicerdquo acts as the end point of the network

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structure The tasks that an end device can perform are specified in the ldquoApplication Profilesrdquo (Craig 2005)

Figure 11 IEEE 802154ZigBee stack [17]

The ZigBee specifications (2005) summarize the security services provided by ZigBee ldquokey establishment key transport frame protection and device managementrdquo ZigBee builds its security mechanism using symmetric key cryptography The security services also depend on the associated layer thus as the figure 10 shows the security mechanism covers the network and the application layer In addition if a MAC frame needs security protection the MAC layer is able to secure it Moreover the notion of end‐to‐end security is supported the source and destination devices have access and use the same share key In the MAC layer the 802154 AES mechanism provides the proper security The mechanism protects ldquothe confidentiality integrity and authenticity of the MAC framesrdquo (Kinney 2005) An auxiliary header field in front of the MAC payload indicates if the frame is encrypted or not The MAC framesrsquo integrity is supported by calculating and using a MIC at the end of the MAC payload In addition to the AES nonce is used to provide MAC confidentiality and authenticity Figure 12 illustrates a MAC frame with security For different security aspects the MAC layer uses different mode of the AES for the encryption it uses the AES in Counter (CTR) mode and for the integrity the

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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24

CBC‐MAC Finally the combination (CCM) of the above two modes is available providing both encryption and integrity (Kinney 2005)

Figure 12 ZigBee secure frame in MAC layer [17]

In the network layer the CCM (a modified MAC layer CCM mode) is used for encryption Because the network layer uses only the CCM mode a single key is used for all different security options The network layer security message format is similar to the MAC frame it is presented in the figure 13 Finally although the network layer is responsible for securing its layer messages the above layers specify the keys and the CCM option for each frame (Kinney 2005)

Figure 13 ZigBee secure frame in network layer [17]

Security in the application layer works similar to the network and MAC layers It uses the ldquolink keyrdquo or the ldquonetwork keyrdquo to secure the message and then encapsulate it inside a set of fields similar to the network format (figure 14) Other security responsibilities that the application layer has are to provide the ZDO and the applications with device management services key establishment and key transport (ZigBee specifications 2005)

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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Figure 14 ZigBee secure frame in application layer [17]

The ZigBee application layer contains the manufacturer‐defined application objects the ZDO and the application sub‐layer In addition to the security responsibilities the application sub‐layer binds devices based on their duties and needs maintains the binding tables and forwards messages between them The application sub‐layer also discovers the neighbour devices for a given device The ZDO is responsible for determining the devicersquos duty in the network for communicating using binding requests and for supporting security as was mentioned above The sub‐layer that implements the actual application is the manufacturer‐defined application object (Kinney 2005) The ZigBee specifications are a great step toward the wireless sensors networks standardization They cover all the OSI layers from the physical to the application providing guidance to the developers The specifications are the product of the ZigBee alliances a consortium of companies and academic institutes Thus they not only produce the specifications but also are the first use them The next section goes beyond the layer architecture and standardization and introduces the OS field another crucial element of WSNs

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3 RECEIVED SIGNAL STRENGTH (RSS) Majority of the existing methods leverage the existence of IEEE 80211 base stations with powerful radio transmit powers of approximately 100mW per base station Such radios are in a different class from the low power IEEE 802154 compliant radios that typically transmit at low power levels ranging from 52mW to 29mW [19] The wide availability of larger number of IEEE 802154 radios in the research community has revived the interest for signal strength based localization in sensor network Despite of rapidly increasing popularity of IEEE 802154 radios and signal strength localization there is a lack of detailed characterization of the fundamental factors contributing to large signal strength variation This chapter investigates some of the properties of RSS observed during testing and analysis of research work collected during the period of thesis The analysis of RSS values is needed to understand the underlying features of location dependent RSS patterns and location fingerprints An understanding of the properties of the RSS values for location fingerprinting can assist in improving the design of positioning algorithms and in deployment of indoor positioning systems

31 Parameters that affect RSS Different factors that affect the indoor RSS are the userrsquos presenceabsence hisher orientation time of the day building type and material distance from transmitter and type of radio These factors are by no means exhaustive Other possible factors are antenna orientation directionality and type Table 10 classify the parameters which affect RSS [7]

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

27

Effect on Factors Options

1 Proximity of user Userrsquos presence or absence

2 Orientation of user and terminal

North East South West

Data Collection

3 Make of Radio 4 Time of measurement Time of day amp days of

week 5 Period of measurement

Second minute hour

7 Interference Co‐channel adjacent co‐channel

Statistics

8 Building environment Small offices or large hall

Table 10 Classification of factors the affect RSS [7]

Transmitter variability Different transmitters behave differently even when they are configured exactly in the same way In practice this means that when a transmitter is configured to send packets at a power level of d dBm then the transmitter will send these packets at a power level that is very close to d dBm but not necessarily exactly equal to d dBm This can alter the received signal strength indication and thus it can lead to inaccurate distance estimation

Receiver variability The sensitivity of the receivers across different radio chips is different In practice this means that the RSSI value recorded at different receivers can be different even when all the other parameters that affect the received signal strength are kept constant

Antenna orientation Each antenna has its own radiation pattern that is not uniform In practice this means that the RSSI value recorded at the receiver for a given pair of communicating nodes and for a given distance between them varies as the pair wise antenna orientations of the transmitter and the receiver are changed

Multi‐path fading and shadowing in the RF channel In indoor environments the transmitted signals get reflected after hitting on the walls andor on other objects in the room such as furniture Both the original signal and the reflected signal reach the receiver almost at the same time since they both travel at the speed of light As a result of this the receiver is not able to

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

28

distinguish the two signals and it measures the received signal strength for both of them

32 Related work analysis A number of indoor location tracking systems have been proposed in the literature based on RF signals ultrasound infrared and some combinations of these As mentioned previously RF based positioning system are widely studied as shown in Table 2 Given a model of radio signal propagation in a building or other environment RSS can be used to estimate the distance between the transmitter and receiver Most of the algorithms (Table 2) have been evaluated in practice using 80211b compliant wireless radios and seem to produce a location error of 15ft to 20ft for an indoor network area of approximately 225ft x 144ft According to the results of this study all the localization techniques produce approximately the same location error over a range of environments Ecolocation [22] Mote Track [5] are two of the few localization algorithms that have been evaluated on a real sensor network that uses a low power wireless radio Ecolocation reports a location error of 10ft for a very small outdoor network deployment area (26ft x 49ft) Mote Track reports a location error of approximately 13ft for an indoor network deployment area of 18751ft2 In general the RSSI based localization algorithms can be divided into two broad categories the map based and the distance (or area) prediction based algorithms In the case of the map based algorithms the deployment area is divided into a large set of sample points that cover the whole area Most techniques collect one or more signal strength measurements from all visible beacon nodes and for each sample point Each sample point is then mapped to either a signal strength vector or a signal strength probability distribution This process generates an RSSI fingerprint map which is unique with respect to the beacon locations and the deployment area This fingerprint map is stored to a central base station A node that is not aware of its location collects one or more signal strength measurements from all the visible beacon nodes and creates its own RSSI fingerprint that is sent to the central base station The base station computes nodersquos location as the sample pointrsquos location whose RSSI fingerprint distance from the unknown nodersquos RSSI fingerprint is the minimum among all the sample points Despite the fact that the map based RSSI localization methods produce relatively small location errors they are not attractive mainly because they require an excessive amount of profiling data and because they are not fully distributed The detailed profiling of a building is a major holdback because it is complex time consuming and depends on beacon locations Therefore for different buildings or even for the

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

29

same building and different beacon locations different RSSI maps have to be obtained The second category of RSSI based algorithms are the distance (or area) prediction algorithms which focus on minimizing the amount of profiling data In distance prediction based methods raw RSSI data are directly mapped to distances through a signal propagation model In some cases distances are not explicitly measured but they are taken into consideration through assumptions on the underlying distributions In the case of outdoor deployments where there are no obstacles a single signal propagation model is all the profiling data required In the case of indoor deployments the same applies but now the signal propagation model might be different across different buildings with different furniture and wall arrangements Given an underlying signal propagation model raw RSSI data can be mapped to specific distances These distances can be either used directly in order to perform localization or assigned a probability and used in a learning basedprobabilistic localization algorithm Obviously the accuracy of the distance prediction based methods depends heavily on the accuracy of the signal propagation model used for translating RSSI values to distances

33 Properties of RSSI Changes in signal due to propagation indoors are difficult to predict because of dense environment and propagation effects such as reflection diffraction and scattering The multi‐path fading effect which is the result of either constructive or destructive combination of multiple signal copies at the receiver causes the received signal to fluctuate around a mean value at a particular location The received signal is usually modelled by the combined effects of large‐scale fading and small‐scale fading The large‐scale fading component describes the signal attenuation as the signal travels over a distance and is absorbed by material such as walls and floors along the way to the receiver This component predicts the mean of the RSS and usually has a lognormal distribution On the other hand the small scale fading component explains the dramatic fluctuation of the signal due to multi‐path fading On the other hand the small scale‐fading component explains the dramatic fluctuation of the signal due to multi‐path fading If there is no LOS component the small‐scale‐fading is often modelled with a Rayleigh distribution Such scenarios are commonly called NLOS If there is a LOS component the small‐scale‐fading is typically modelled by a Rician distribution Note that when the RSS is measured the measurement averages out the small‐scale‐fading effects

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

30

4 OBJECT TRACKING 41 Introduction This chapter presents the object‐tracking application the main objective of this thesis work The application is an algorithmic‐software implementation written in Embedded C which is capable of receiving data messages and generating object position estimation data The implementation of the object‐tracking application makes use of the SENTIO platform and uses the background knowledge introduced in the preceding chapters The motivation for the object‐tracking application is to determine the position of an object in indoor environments (Eg Home) The application acts as an interface which collects the raw data that the sensors generate (numerical values) It makes the proper comparisons and algorithmic processes to identify and track the object while it is static inside the area covered by the wireless sensor network Finally with the detection and tracking data it alerts the user or another system The following sections provide an overview of the object‐tracking application

42 Overview of hardware product Before describing the object tracking application it is important to know the characteristics of hardware product The SENTIO platform (figure 15) is built in modular fashion and has the following key features [8]

Flexibility This platform is very flexible in such a way that it is made easy to add or change the hardware for each sensor task depending on the requirement in the WSN For a specific platform this can be hard or sometimes even impossible to accomplish

Sensor resources Resources in a sensor module includes the ability of usage of controllerrsquos resources as much as possible such as ADC‐channels interrupts and digital Input‐Outputrsquos Local voltage regulation should also be possible since many sensors require higher supply voltages than digital Integrated Circuits do Further support of a shut‐down mechanism to power down the sensor modules when not needed

Computation and memory The processor module based on microcontroller should support many devices in a microcontroller family and support different power supply voltages enabling full range of clock frequencies All power‐down modes of the devices should be

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

31

supported The platform should support different processor modules including FPGArsquos and very simple microcontrollers

Communication The focus should be on enabling an IEEE 802154 compliant communication interface With or without ZigBee stack this will provide a flexible platform for exploring different network topologies Support for power‐down of the entire communication module should be supported

Cost and form factor High flexibility is in conflict with low cost and small size The primary objective should be flexibility but the size should be considered in order to enable sensor device integration in systems where space is limited

Figure 15 SENTIO Platform [8]

The communication subsystem of the SENTIO sensor node was designed so that the correct operation of the radio chip is ensured The radio chip is powered by the on‐board voltage regulator and thus fluctuations in the battery voltage level do not affect the operation of the communication subsystem This platform uses Chipcon CC2420 IEEE 802154 radio transceiver operates in the 24GHz ISM band and includes a digital direct sequence spread spectrum (DSSS) modem providing a spreading gain of 9dB and an effective data rate of 250Kbps It was specifically designed for low power wireless applications and supports 8 discrete power levels 0dBm minus1dBm minus3dBm minus5dBm minus7dBm minus10dBm minus15dBm and minus25dBm at which its power consumption varies from 29mW to 52mW [21] A built‐in received signal strength indicator gives an 8‐bit digital value RSSIVAL The RSSIVAL is always averaged over 8 symbol periods (128μs) and a status bit indicates when the RSSIVAL is valid (meaning that the receiver was enabled for at least 8 symbol periods) The

Antenna

Communication Module

Processing Module

Sensing Module

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

32

power P at the RF pins can be obtained directly from RSSIVAL using the following equation

P = RSSIVAL + RSSIOFFSET [dBm] where the RSSIOFFSET is found empirically from the front‐end gain and it is approximately equal to minus45dBm In the next sections when we refer to the RSSI value we refer to the RSSIVAL and not the power P unless otherwise stated

43 Experimental Testbed The experimental test bed for object tracking application is as shown in figure 16 Experiment was conducted in Masterrsquos room at Mid Sweden University A DELL laptop is connected to SENTIO platform and software were primarily used to collect samples of RSS from APrsquos The software monitors IEEE 802154 radio frequency channels which operate in the 24GHz band

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

1030

BC - 1030

AP 3 AP 1

AP 2

AP 4

BS

HEWLETT

PACKARD

Figure 16 Layout of Masterʹs Room

A three dimensional scalable testbed is used for indoor sensor network deployments The testbed illustrated in figure 16 is a 3‐D structure measuring 16m (L) x 12m (W) x 3m (H) and it is designed to host a large number of static nodes as shown in figure 16 Arrow marks in the above figure represent the location where fingerprints were taken The centrepiece of our infrastructure

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

33

is the SENTIO sensor node [8] an open‐source general purpose sensing platform designed with AT Mega 128 microprocessor and the IEEE 802154 compliant CC2420 radio from Chipcon [19]

44 Obtaining data from the sensor network The next important step in the development of the object tracking application is to obtain the data from the sensor network and send it to the base station From there the data is transmitted through a serial port to the computer that runs the object tracking application When the data arrives at the serial port the application must capture it This paragraph gives a brief explanation of how and what kind data is transmitted by the static object fixed sensors and how this data is being used to estimate the position of the object First the static object transmits Beacon packet continuously which is received by all the fixed APrsquos The beacon packet format is as shown in table 6 Then the fixed APrsquos calculates the 8‐bit value of RSSI from the transmitted beacon packet and then sends the resultant RSSI value as a form of data packet (table 7) to the BS which calculates the position and sends back to the object

45 Object tracking application Testing and Evaluation The purpose of this section is to discuss and evaluate the object tracking application Finally we are ready with the experimental testbed as shown in figure 16 As shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors placed at the corners of the masterrsquos room BS is placed at the centre of the room connected to a PC All the nodes are power connected before an actual measurement takes place At first the object is placed at random position inside the room and transmits beacon packets continuously Then these packets are received by the fixed APrsquos which calculate the RSSI from the packet and then averages it until it receives 50 beacon packets and then transmits the data to the BS (figure 17) Then the BS waits until it receives the data from all the APrsquos Once it receives from all the APrsquos it calculates the position of the object (figure 18) The application was developed to demonstrate a real world use of a WSN in object tracking In evaluating the final position obtained with respect to the actual position we conclude that the object‐tracking was successful Although

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

34

it is a research prototype object tracking application is a simple stable stand‐alone system capable of demonstrating an important application of WSN It takes raw data from the WSN aggregates it evaluates it and based on the evaluation tracks the object The accuracy of estimating the position is less than 5m Table 11 below shows a log of readings taken during test

Start

Intialize micro‐controller and transeciever in receieve mode

If received_packet=

Beacon

Average RSSI values

Increase the counter

If countergt1000050

A

No

Yes

B

B

Yes

No

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

35

A

Reset counter

Set transceiever in transmit mode

Transmit averaged RSSI to Main sensor and set it back to receive mode

10000 samples ‐ Offline

50 samples ‐ Online

Figure 17 Flow chart representing the algorithm of fixed sensors

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

36

Start

Intialize micro‐controller and transeciever in receieve mode

Received RSSI values from all fixed Sensors

Open radio map

Calculate Euclidean distance between obtained RSSI values from fixed sensors and fingerprints

in radio map

No

Yes

Bubble sort for least error in signal strength values

A

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

37

A

Get the location of the object from the radiousing the least error value

Display location of object on PC

End

Figure 18 Flow chart representing the algorithm of main sensor

Table 11 Accuracy of positioning system

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

38

5 RESULTS This chapter presents how the system parameters such as number of samples mean or median etchellipare finalized All the tests are performed in masterrsquos room at Mid Sweden University This chapter is divided into three subsections First in Section 51 it reviews results obtained from samples test Section 52 discusses one dimensional test results and finally section 53 discusses results of two dimensional tests

51 Measurement setup A Dell laptop was connected to a SENTIO platform and is loaded with software (Appendix 2) This setup was used to determine number of samples required during object tracking mean or median position estimation algorithm and one dimensional test The setup is as follows

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

103

0

BC - 1030

AP 1AP 2

BS

HEWLETT

PACKARD

Figure 19 Test bed for one dimensional test in Masters Room

Two SENTIO nodes (AP1 and AP2 as shown above) were separated by a distance of 10 meters and these sensors are known as fixed sensors which are fixed to the ceiling such that there antenna is faced towards the ground They are linear is position A BS was connected to Dell laptop and it was placed in such a way that its antenna was faced towards the ceiling Another SENTIO

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

39

node was used as an object and its orientation was same as BS Figure 19 represents the complete setup for object tracking application Mobile node was moved between these fixed ones at regular intervals of one meter and RSSI values were logged at each interval

52 Number of samples test This test was performed to determine number of samples required for accurate positioning and either mean of the obtained RSSI values or median of them Figure 19 represents the setup for number of samples test Once the setup is ready the measurements are taken as follows

1 An object was tagged with SENTIO node and was placed stationary linear to the fixed sensors

2 10000 samples (RSSI) were taken at regular intervals of 1 meter up to 10 meters

3 The sample values were logged at every interval 4 Using these logged samples I have plotted graphs such as distribution

of RSS values (figure 20 21) meanmedian at intervals of 1 meter using MATLAB

5 The mean and median for various numbers of samples is as shown in figure 22

Figure 20 Histogram plot of RSSI values at 1m and 4m

Figure 21 Histogram plot of RSSI values at 6m and 8m

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

40

Figure 22 Distance vs RSSI

From the above graphs following conclusions were made 1 From the histogram plots we can observe that the distribution of RSSI

values follow Gaussians distribution law 2 As we can see from figure 22 that median curve has drastic changes

when compared to mean curve Hence mean is considered while estimating the position

3 From the observations we conclude that the number of samples does not matter since mean curve tends to be same for various numbers of samples

4 Hence 10000 samples are considered in order to get a proper location fingerprint during off‐line phase and during online phase 50 samples are enough to estimate the position of object

53 0ne dimensional test Measurement setup for one dimensional test is as shown in figure 19 This test was conducted to check the performance of algorithm and how accurate it was At first ie during offline phase fingerprints at each interval averaging 10000 samples were taken During online phase Euclidean algorithm was used estimate the position averaging 50 samples of RSSI From the results we

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

41

can realize that the error was less than 2 meters Table 12 shows the result of location estimation in one dimensional case

Table 12 One dimensional position estimation results

54 Two dimensional test This test is the final stage of my thesis work Experimental testbed for this stage is as shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors fixed at the corners of the room and a BS at the centre of the room There are 18 pre‐determined locations where the fingerprints (RSSI) were taken The fingerprints are as shown in Appendix 1 All the readings were taken in presence of userrsquos The algorithm for position estimation is shown in Appendix 2 Position estimation test is performed as follows

Tagged object is placed at random inside the masterrsquos room All the sensors including the base station and object are switched on Using the algorithm (Appendix 2) the BS estimates the position of the object and displays it on the PC

This test was performed using various numbers of samples to test which has a better accuracy Figure 23 represents the error rate using various numbers of samples From figure 23 we can conclude that 50 numbers of samples is enough to estimate the position of object with an accuracy of 3m

55 Causes of error in location determination The randomness of RSS is the major cause of error in any indoor

positioning system that uses the RSS for location inference If there is no randomness in the RSS and its location fingerprint every indoor positioning systems of this type will have no location detection error For example I have recorded RSS at one particular location during different times of the day From figures 24 and 25 you can observe that RSS values are different at different times of day

The performance of indoor positioning systems depends greatly on the separation of location fingerprints A location fingerprint

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

42

corresponding to a location can be identified correctly if it is difficult to classify it as another fingerprint by a pattern classifier

Path loss is one of the basic signal propagation properties of the RSS that best describes attenuation of transmit signal power over a distance between an access point and a mobile device

Presence of Userrsquos or userrsquos orientation affects RSS

Figure 23 Error Rate vs Number of samples

Figure 24 RSS distribution at 214 pm

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

43

Figure 25 RSS distribution at 100pm and 1100 am

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

6 DISCUSSIONS10102006

44

6 DISCUSSIONS 61 Summary and Conclusion While empirical results and performance studies of positioning systems based on location fingerprint have been presented in this report analytical model that can be used as a framework for efficiently designing the positioning systems are not available This thesis design work uses general principles of position estimation such as position estimation of sensors grid spacing etc This design work focuses on accuracy and precision The concept of location fingerprint technique based on RSS us explained in detail in chapter 2 It is found that RSS is random It is possible to approximate the RSS as being normally distributed While the RSS random process is non‐stationary the mean is more or less constant and could be used as the fingerprint of the location Model designed for position estimation is as shown in chapter 4 Accuracy and precision are the two major performance matrices in this study Uniform grid spacing has been used From the experimental analysis I have obtained an accuracy of 3 meters and 35 precision The sensitivity analysis among the system parameters and the performance matrices were done using the model as shown in chapter 4 For instance our study found that the minimum distance between two adjacent positions also affects the positioning performance This is due to the randomness of RSS patterns induced by the ever changing indoor environment

62 Future work The research in this thesis provides a ground work for the future study of efficient design of indoor positioning system The RSSI based position estimation still has limitations First the RSSI value is time dependent Thus the future study should address this issue Second the movement of object is not considered Third orientations of sensors affect the performance which should be taken care off Fourth to improve the performance of the system we need to find a solution to deal with those incomplete or missing data during the off‐line and the on‐line phase Fifth position estimation on multi‐floor and three‐dimensional coordinates is not available

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 110102006

45

APPENDIX 1

Location Fingerprints of Masters Room Time 1315 Observation Time 20 minutes Number of Samples 10000

People Presence SNo Co-ordinates AP1 (0x0001) AP2 (0x0002) AP3 (0x0004) AP4 (0x0008)

1 11 -24 -16 -27 -33 2 13 -26 -8 -34 -31 3 15 -43 -9 -39 -25 4 32 -21 -11 -27 -33 5 61 -6 -23 -32 -28 6 64 -20 -31 -25 -34 7 35 -23 -35 -29 -38 8 37 -29 -35 -22 -27 9 39 -38 -29 -21 -27 10 67 -27 -23 -20 -23 11 611 -26 -29 -23 -30 12 613 -35 -32 -2 -31 13 513 -34 -31 -20 -26 14 11 -39 -22 -29 -17 15 212 -37 -42 -28 -31 16 -212 -38 -33 -29 -6 17 -21 -42 -29 -33 -19 18 -38 -40 -23 -35 -22

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 210102006

46

APPENDIX 2

Begin

Open radio map and raw data files for write

Intialize radio map in memory

Check whether the

Packet is data or Beacon packet If data packet

Save the data to register

Check if data is

Received from all

sensors

Estimate the position of static

Display the position on PC

End Yes

No

A

A

Yes

No

Flow chart representing the main algorithm for position estimation

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

47

APPENDIX 3

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

48

Distribution of RSSI

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

49

REFERENCES [1] T S Rappaport J H Reed and B D Woerner ldquoPosition location using wireless communications on highways of the futureʺ IEEE Commun Mag vol 34 no 10 pp33‐41 Oct 1996 [2] A M Ladd K E Bekris G Marceau A Rudys L E Kavraki and D S Wallach rdquoRobotics‐based location sensing using wireless ethernetʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ02) 2002 pp 227‐238 [3] I Ramani R Bharadwaja and P V Rangan ldquoLocation tracking for media appliances in wireless home networksʺ in Proc International Conference on Multimedia and Expo (ICMEʹ03) July 2003 pp 769‐772 [4] K Pahlavan X Li and J P Makela ldquoIndoor geolocation science and technologyʺ IEEE Commun Mag vol 40 no 2 pp 112‐118 Feb 2002 [5] Konard Lorincz and Matt Welsh ldquoMote Track A Robust Decentralized Approach to RF‐Based Location Trackingrdquo [6] Paramvir Bahl and Venkata N Padmanabham ldquoRADAR An In‐Building RF‐based User Location and Tracking Systemrdquo [7] Kamol Kaemarungsi ldquoDesign of Indoor positioning system based on location fingerprint techiniquerdquo University of Pittsburgh 2005 [8] Fredrik Linnarsson Cheng Peng and Bengt Oelmann ldquoSENTIO A Hardware Platform for Rapid Prototyping of Wireless Sensor Networksrdquo [9] S Saha K Chaudhuri D Sanghi and P Bhagwat ldquoLocation determination of a mobile device using IEEE 80211b access point signalsʺ in Proc IEEE Wireless Communications and Networking Conference (WCNCʹ03) New Orleans LA Mar 2003 pp 1987‐1992 [10] T Roos P Myllymaki H Tirri P Misikangas and J Sievanen ldquoA probabilistic approach to WLAN user location estimationʺ International Journal of Wireless Information Networks vol 9 no 3 pp 155‐164 July 2002 [11] R Battiti M Brunato and A Villani ldquoStatistical learning theory for location fingerprinting in wireless LANrsquosʺ Technical Report Oct 2002 [Online] Available httprtmscienceunitnitraquobattitiarchive86pdf [12] P Prasithsangaree P Krishnamurthy and P K Chrysanthis ldquoAn indoor position location with wireless LANsʺ in Proc IEEE International Symposium on Personal Indoor and Mobile Radio Communications (PIMRCʹ02) Lisbon Portugal Sept 2002 [13] M A Youssef A Agrawala and A U Shankar ldquoWLAN location determination via clustering and probability distributionsʺ in Proc IEEE

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

50

International Conference on Pervasive Computing and Communications (PerComʹ03) Dallas‐Fort Worth TX Mar 23‐26 2003 pp 23‐26 [14] Z Xiang S Song J Chen H Wang J Huang and X Gao ldquoA wireless LAN‐based indoor positioning technologyʺ IBM Journal of Research and Development vol 48 no 56 pp 617‐626 SeptNov 2004 [15] R Want A Hopper V Falcao and J Gibbons ldquoThe active badge location systemʺACM Transactions on Information Systems vol 40 no 1 pp 91‐102 Jan 1992 [16] N B Priyantha A Chakraborty and H Balakrishnan ldquoThe cricket location‐support systemʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ00) Boston MA Aug 2000 pp 32‐43 [17] Holger K Willig A (2005) Protocols and Architectures for Wireless Sensor Networks London [18] IEEE (2003) Part 154 Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low‐Rate Wireless Personal Area Networks (LR‐WPANs) New York [19] Chipcon CC2420 802154 compliant radio httpwwwchipconcom [20] ZigBee Specification v10 ZigBee Alliance [21] Dimitrios Lymberopoulos Quentin Lindsey and Andreas Savvides ldquoAn Empirical Analysis of Radio Signal Strength Variability in IEEE 802154 Networks using Monopole Antennasrdquo [22] Kiran Yedavalli Bhaskar Krishnamachari Sharmila Ravula and Bhaskar Srinivasan ldquoEcolocation A sequence based technique for RF localization in wireless sensor networksrdquo Robert Bosch Research and Technology Centre Palo Alto CA

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

Terminology Notation10102006

x

FPGA ‐ Field Programmable Gate Array

BS ‐ Base Station

AOA ‐ Angle of Arrival

TDOA ‐ Time difference of arrival

TOA ‐ Time of Arrival

TOF ‐ Time of flight

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

1

1 INTRODUCTION 11 Introduction to the study Advancement in wireless sensing technologies has encouraged in developing a model for indoor object‐tracking application using low power IEEE 802154 compliant radios Stimulus used in this model is RSS This application is not only limited to object tracking in an indoor environment Concierge services enable users to become aware of nearest supporting facilities For example smart home applications such as multimedia appliances that forward multimedia stream to the nearest video screen can be achieved with a home positioning system [3] In the field of robotics a robot can navigate by itself using the assistance of indoor positioning system [2] Granularity of location information is most important in location determination applications For example to locate a book in a library requires coarse grained granularity whereas locating a user requires coarse grained granularity Position estimation refers to a process used to obtain location information of a MS with respect to a set of reference positions within a pre‐defined space A system deployed to determine or estimate the location of an entity is called a position location system or positioning system A wireless indoor positioning system refers to a wireless network infrastructure that provides indoor location information to any requesting end user One of the key technical elements allowing the realisation of this vision will be the knowledge of the accurate position of the personobjects Already today accurate location mechanisms such as GPS are commercially available Many more will become part of our daily life with the introduction of third generation mobile phones But none of these techniques will allow the meter or sub‐meter level of accuracy within buildings which is necessary for smart houses Hence this report introduces an object tracking application in an indoor environment using IEEE 802154 compliant radios which gives meter level accuracy First this chapter presents the background of indoor positioning systems identifies the challenges of such systems and briefly describes the scope of indoor positioning systems based on location fingerprints Next the objective of study the overview of approaches and the contributions are presented Finally the scope and organization of thesis work is outlined

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

2

12 Background The success of outdoor positioning and applications based on the GPS provides an incentive to the research and development of indoor positioning systems It has been developed over several decades and it relies on constellation of satellites Ranges to several satellites are used in multi‐lateration procedure to infer the position of the receiver This localization scheme requires hardware that is both expensive and consumes significant power As a result indoor positioning systems require alternative means to detect the MS location without relying on the direct signal from GPS satellites Infrared RF and ultra sound signals are major technologies used for indoor positioning systems Unlike outdoor areas the indoor environment imposes different challenges on location discovery due to the dense multi‐path effect and building material dependent propagation effect Thus an in‐depth understanding of indoor radio propagation for positioning is crucial for efficient design and deployment The existence of radio connectivity and the attenuation of radio signal with distance are attractive properties that could potentially be exploited to estimate the positions of small wireless devices featuring low‐power radios RSSI a standard feature in most radios has attracted a lot of attention in the recent literature for obvious reasons RSSI eliminates the need for additional hardware in small wireless devices and exhibits favourable properties with respect to power consumption size and cost As mentioned previously RF‐based location tracking has been widely studied [5 6] Given a model of radio signal propagation in a building or other environment RSSI can be used to estimate the distance from a transmitter to a receiver and thereby estimate the position of a mobile node However this approach requires detailed models of RF propagation and does not account for variations in receiver sensitivity and orientation Table 1 below briefly summarizes performance of existing indoor positioning system based on location fingerprints and WLANrsquos All the systems mentioned below in table 1 follow IEEE 80211 protocol standards The major performance metrics studied by all systems are positioning accuracy which is a form of error measurement The accuracy of the location information is usually reported as an error distance between the estimated location and the actual mobile location However the report of accuracy should include the confidence interval or percentage of successful location detection which is called the precision The system parameters of existing systems are summarized in table 1 and the best reported performance of these systems are listed in table 2

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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System Spacing(m) Positions SamplesPos APs Orientation Environment

RADAR [6] Non

uniform 70 80 3 4 Hall-way Saha et al [9] 3 19 1200 3 NA 1-floor

Roos et al [10] 2 155 40 10 NA 1-floor Battiti et at [11] NA 257 NA 6 NA 1-floor Ladd et al [2] 3 11 1307 5 2 Hall-way

Prasithsangaree et al [12] 153 60 40

2 to 7 4 1-floor

Youssef et al[13] 15 110 300 4 NA Hall-way Xiang et al [14] NA 100 300 5 4 1-floor

Table 1 Comparison of Indoor Positioning Systems [7]

System Algorithm Type Accuracy and Precision RADAR [6] Nearest Neighbour within 7 feet 38

Saha et al [9] Nearest neighbour amp Neural Network no specified accuracy

90 Roos et al [10] Bayesian best within 828 feet 90

Battiti et at [11] Neural Network amp Weighted k-Nearest

Neighbour all within 16-17 feet 90 Ladd et al [2] Bayesian within 5 feet 77

Prasithsangaree et al [12] Weighted k-Nearest Neighbour

25 feet at 75 amp 40 fet at 95

Youssef et al [13] Bayesian within 7 feet more than

90 Xiang et al [14] Bayesian with RSS distribution model within 6 feet 90 Table 2 Performance comparison of Indoor Positioning Systems [7]

Based on the study of results obtained in various positioning systems as in table 2 we have developed a model for an indoor positioning system using SENTIO [8] platform based on RSSI

13 Objectives In the last two or three years a number of theoretical practical andor simulation studies were done on the topic of object‐tracking While these studies are useful they are too general and provide little guidance for the actual deployment of sensor networks for real life location‐tracking of an object This thesis focuses on developing an object‐tracking application and prescribes sensor network configurations that work well with our algorithms I implemented my software using SENTIO [8] platform for sensor networking The major issue addressed in this project are the accuracy and precision in tracking an object in a real‐world application and efficient ways to algorithmically analyze the collected raw data from the specific wireless sensor product

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Although the focus is to develop a real‐world application using WSN it also provides great opportunity to explore the new area of wireless communication overall

14 Research Questions My primary target is to find a method to determine the position of an object inside a building using ZigBee protocol standards based on RSSI The study addresses the following questions What is WSN What requirements do WSN need What are their main ideas and concepts What are the associated technologies What are the standards that sensor networks use

What are the existing hardware implementations that use this particular technology How can we build our new applications using WSN What do sensor network systems require for general or specific applications Are they applicable And what is the appropriate design for sensor network implementations in order to combine them with other existing systems At this point we describe and evaluate the specific object‐tracking application to be built

15 Scope The study area of the thesis is an overview of the WSN technology study of SENTIO platform and implementation of an object tracking application Thus the study is divided into two parts Part one is an overview of WSN it includes study of SENTIO platform and IEEE 802154 protocol standards The second part describes the implementation of the object tracking algorithm The object tracking application receives and uses the raw values returned by the sensor network system to produce clear and meaningful outputs Schema of thesis work is as shown in figure 1

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Figure 1 Schema of thesis work

16 Methodology The ldquoIndoor Local Positioning System for ZigBee based on RSSI ldquois based on ldquolocation fingerprintingrdquo technique Location fingerprinting refers to a technique that exploits the relation between any measurable physical stimulus and a specific location This approach uses empirical measurements of RSSI to estimate location By recording a database of radio ldquosignaturesrdquo along with their known locations a mobile node can estimate its position by acquiring a signature and comparing it to the known signatures in the database All of these systems require the database of signatures which should be collected manually prior to the system installation and rely on central server (or the userrsquos mobile node) to perform location calculation For example in RADAR [6] technology the RSSI is the stimulus This type of positioning system does not require specialized hardware other than the common wireless network interfaces with received signal strength measurement capability thus it is simple to deploy compared to other techniques Unlike outdoor counterpart systems which can use AOA and TDOA techniques effectively indoor positioning systems encounter the problem of NLOS and the dense multi path effect that render these two techniques ineffective or complex for practical implementation It is also difficult for a MS to always see three or more access points or base stations in indoor environment which is essential for triangulation by AOA and TDOA Location fingerprinting can also be implemented as software based positioning system which can reduce

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complexity and cost Generally the deployment of fingerprinting based positioning systems can be divided into two phases First in the off‐line phase the locations where the fingerprints are required is pre‐determined and then the fingerprints are collected by performing a site‐survey of the RSS from multiple APs The distance between two closest positions is called grid spacing and usually reported in meters or feet However some points may be omitted due to inaccessibility The RSS is measured at each predetermined location to create a database of RSS patterns of the indoor environment The database of RSS patterns is called a radio map The vector of RSS values at a point on the grid is called the location fingerprint of that point Second in the on‐line phase a MS will report a sample measured vector of RSS from different APs to a base station The base station uses a positioning algorithm to estimate the location of the MS The most common algorithm used to estimate the location is Euclidean algorithm It computes the Euclidean distance between the measured RSS vector and each fingerprint in the database and then the coordinates associated with the fingerprint that provides the smallest Euclidean distance is returned as the estimate of the position It is clear from the above discussion that the designer should not start out to do the site survey without proper system design objectives and guideline If the performance is not sufficient collecting more fingerprints in between previous location fingerprints may help It is a problem of choice as to how many access points are required for the system and what the minimum distance between physical positions on the grid should be in order to provide a good position resolution and best system performance The system parameters and factors that improve the accuracy and the precision performance are still not clear

17 Outline Chapter 2 provides literature review of ldquoIndoor Local Positioning System for ZigBee based on RSSIrdquo it introduces the basic components required for a positioning system general classification of indoor positioning system Finally it explains ldquoLocation Fingerprintrdquo technology used in this thesis work to determine the position of an object in a real‐world application It briefly explains the protocols and industry standards for wireless sensor networks Chapter 3 describes some properties of RSS which is a stimulus to determine

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the position of an object RSS relation with energy and on what parameters is the RSS value dependent Chapter 4 describes the model of an indoor local positioning system and its probability of returning the correct location It also explains its performance evaluation and its error distance distribution Chapter 5 describes results obtained from the model of positioning system Chapter 6 includes conclusions from the experimental analysis and future work

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2 LITERATURE REVIEW This chapter reviews the literature on wireless indoor positioning system as a means of providing an intellectual background for the thesis work First in section 21 the common components of indoor positioning system are described Then in section 22 discusses different means to classify indoor positioning systems In section 23 reviews of all relevant literature on location fingerprint technique Finally in section 24 protocols and industry standards for wireless sensor networks are explained

21 Common components of indoor positioning system Basically a wireless local positioning system consists of at least two separate hardware components a measurable unit that usually carries the major part of the system intelligence and a signal transmitterreceiver The transmitter in the simplest case is just a beacon Figure 2 represents basic hardware model of a positioning system

Figure 2 Block diagram representing hardware module

First the location sensing devices detect the signals transmitted by or received at known reference points using radio sensing technologies The sensing technique is based on signal strength level that converts the sensed signal into RSS Given a set of reference points the relative position of the MS can be derived from the signal characteristics of RSS The signal characteristics of RSS are unique for a location Then the microcontroller processes the position algorithm and estimates the position of object and displays it on PC

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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22 Classification of Indoor positioning systems Indoor positioning system can be categorized based on the stimulus used in particular application and measurement principle

221 Classification based on Stimulus Based on the stimulus used in an application WSN inherit certain characteristics and limitations on sensorrsquos signal The propagation delay diffraction reflection and scattering are the basic signal characteristics which affect all signal types The effective range available bandwidth regulatory constraints interference power constraints safety and cost of technology limitations The wireless signals commonly used for indoor positioning systems are infrared radio frequency and ultrasound Brief descriptions of the three major technologies are as follows

Infrared Infrared signal is an electromagnetic signal of a wavelength longer than that of visible light but shorter than that of radio waves Infrared is just below the visible spectrum of light in frequency and is radiated strongly by hot bodies Many objects such as people vehicle engines and aircraft generate and retain heat and as such are especially visible in the infrared wavelengths of light compared to objects in the background It cannot pass through walls or obstructions therefore it has a rather limited range of indoor environments Thus it requires a more sophisticated circuitry than ultrasound signals Indoor lighting interferes with this type of signal and causes problems in accurate sensing It generally has a range of around 5 m The infrared devices are usually small in size compared to ultrasound devices

Radio frequency The RF signal can penetrate through most of the building material therefore it has an excellent range in indoor environments The propagation speed is also high approximately 3108 ms There are unlicensed frequencies available freely for use This type of signal has the longest range compared to infrared and ultrasound Since the propagation waves are of higher frequency (24GHz) interference with other frequency components is less

Ultrasound Although ultrasound operates at low frequency bands (typical 40 kHz) compared to the other two signaling technologies it possesses a good precision for location sensing at a slow propagation speed of sound (343 ms) The advantages of ultrasound devices are their simplicity and that they are inexpensive However ultrasound does not penetrate walls

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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10

but reflects off most of the indoor obstructions It has a short range around 3 m to 10 m but has a 1 cm resolution of distance measurement The operating temperature influences the performance of ultrasound

222 Classification based on measurement principle Wireless positioning systems can be categorized by measurement techniques which are used to derive the position of mobile stations Mainly three different measurement principles are used today AOA RSS and propagation‐time based systems that can further be divided into two different sub‐classes TOA and TDOA Figure 3 shows taxonomy based on the technology and the technique classifications

Figure 3 Taxonomy of positioning system [7]

In AOA systems the position is calculated via goniometry With the use of directional antennas or antenna arrays the angle or bearing relative to points located at known positions is measured The intersection of several measured directions pointers the yields the position value The accuracy of this approach is limited by the possible directivity of the measuring aperture by shadowing and by multi‐path reflections arriving from misleading directions In TOA and TDOA techniques rely on the precision of timing between the signal transmitter and the receiver in order to use the propagation delay or TOF to calculate the distance between transmitter and receiver Therefore a precise synchronization is also very important in such systems By combining at least three distances from three reference positions triangulation can be used to estimate the mobile stationʹs location This type of technique will

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11

require a high accuracy clock in the communication system TDOA is more practical [1] Examples of location‐sensing systems that use time of flight are GPS the Active Bats [15] and the Cricket [16] Besides these time delay‐based techniques the distance between transmitter and receiver can also be determined from signal strength attenuation and direct distance measurement (such as dead reckoning) Fingerprinting or Location Pattern Matching this technique generally requires only measurement of RSS or other non‐geometric features at several locations to form a database of location fingerprints To estimate the mobile location the system needs to first measure the received signal strength at particular locations and then search for the pattern or fingerprint with the closest match in the database This technique does not require the mobile station to see at least three base stations or access points in order to determine the location The disadvantage of this technique is that it is very time‐consuming to perform an exhaustive data collection for a wide area network such as in outdoor positioning systems

23 Indoor local positioning using ZigBee based on RSSI This section reviews relevant RF‐based indoor positioning systems which can be used to locate stationary objects and track mobile users The impressive growth of IEEE 802154 wireless PANs (WPAN) in recent years suggests an interesting future for the location fingerprinting technique The system is quite flexible because system designers can select whether to have a centralized positioning server or let the mobile to determine its own position However the fingerprinting technique requires a training phase (off‐line phase) to collect location fingerprints for all positions in the operating area before the actual deployment (on‐line phase) [7] The following discussion is divided according to the positioning system components First the common form of location fingerprinting and its relationship with physical locations are explained Finally location estimation algorithm is reviewed

231 Location Fingerprint A location fingerprint based on RF characteristics such as RSS is the basis for representing a unique location It is created under the assumption that each position or location inside a building has unique RF signature [4] Generally a fingerprint F is labelled with a location information L The location fingerprints and their labels (eg location information) are maintained in

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database and used during the on‐line phase to estimate the location The label and fingerprint are usually denoted as a tuple of (L F) Battiti et al [11] point out that the location information L for indoor location can be recorded in two forms as either a tuple of coordinates or an indicator variable The tuple of real coordinates can vary from one dimension to five dimensions which include the three dimension space and two orientation variables expressed in spherical coordinates For instance a location information of a two‐dimension system with an orientation could be expressed as a triplet L = (x y d) | x y є R 2 d є North East South West The RSS was found to be more location‐dependent than the SNR because the noise component is rather random in nature However the RSS itself fluctuates over time for each access point and location Each RSS element can be considered as a random variable therefore it can be captured by recording its descriptive statistics parameters approximating its distribution or maintaining the whole measurement dataset These approaches of RSS representation result in different procedures for location estimation algorithm Regardless of the approach the location fingerprint is usually denoted as an array or vector of signal strength (random variables) received at any position in the location‐based service area The size of the vector is determined by the number of access points that can be heard To create a basis fingerprint such as in a number of samples of vectors of signal strength are collected over a window of time for each position This basis is called a prototype Then the average RSS of each access point is calculated and recorded as an element in the location fingerprint For an area that can receive signals from N access points the location fingerprint can be expressed as a vector of average RSS elements βi

F = (β1 β2hellip βN)T Besides the basis location fingerprint the samples of location fingerprint measured during the on‐line phase are also important for the system that tracks the mobile object The number of samples should be selected appropriately to represent the location fingerprint for the mobile application A step in statistical analysis method called pre‐processing is another important issue needed to be considered because it can impact the estimation of dependency between location fingerprint and the location information easier The pre‐processing refers to a step that cleans the raw data (in this case the training set) before any further operations or analysis The cleaning may consist of encoding dimensionality reduction (reduce unnecessary elements) feature extractionselection clustering and outlier elimination Roos et al [10] point out that the pre‐processing enables faster location estimation and

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reduce the noise from the training data The process that creates the basis of location fingerprint discussed above could be considered as a part of pre‐processing

232 Location Estimation Algorithm Location estimation algorithms or positioning algorithms are procedures that exploit dependency between location information and location fingerprint basis in order to determine a position or location from samples of RSS signals Position algorithms can be classified into deterministic and probabilistic types based on the approaches that model the relationship between location fingerprints and location information The deterministic type of algorithms are those that based in the nearest neighbour classifiers and neural network classifiers The probabilistic type of algorithms is those that are based on the Bayesian inference and statistical learning theory such as support vector machines (SVR) The algorithm used in this project is discussed briefly

Euclidean distance method The Euclidean distance method is a deterministic approach of position estimation because they require only a set of constant location fingerprints which includes mean vectors In order to determine the location a form of discriminate function is used to classify a sample of RSS fingerprint into a position The mean or average RSS vector is a centre of a mass which represents each class of location fingerprint Suppose a set of l location fingerprints is denoted by F1 F2hellip Fl exist and each fingerprint has a one‐to‐one mapping to a set of positions L1 L2hellip Ll A sample of an RSS fingerprint measured during an on‐line phase is denoted as S which can be another mean or average RSS vector of small window of RSS samples Assuming that an indoor positioning system only considers the average RSS from N access points as a location fingerprint the sample of RSS vector is S = (s1 s2 helliphellip sN)T and each location fingerprint i in the database can be expressed as Fi = (β1 β2helliphellip βN)T The simplest closeness metric is a distance measurement in signal space denoted as the Dist () function [6] Thus the simple procedure of nearest neighbour algorithm is expressed as picking the fingerprint j that has the shortest signal distance

Dist(S Fj) le Dist(S Fk) for all k ne j A generalized weighted distance Lp summarized by [12] can be used to calculate different forms of distance in signal space as

Lp = 1N pN

i

pPiSiWi 1

1)||1(sum

=

minus

where N is the number of access points wi is a weighting factor (wi le 1) and p is the norm parameter starting from 1 The weighting factor wi is a bias

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parameter that can demote or promote an important RSS component in the fingerprint

24 Protocols and Industry standards for WSN Although many applications for WSN are proposed the communication protocols supporting them remain mainly unexplored and diverged This is because the area of interest is new each implementation is application specific and the components of WSNrsquos have a lot of constraints Wireless sensor networks use the wireless medium to communicate However the traditional communication protocols that support wireless communications especially ad‐hoc mesh networks may not be well suited for them WSN communication is an active research area and many algorithms and protocols have already been proposed This section introduces two proposed standards the IEEE 802154 and the ZigBee The IEEE 802154 covers the physical layer and the MAC layer of low‐rate WPAN The ZigBee is ldquoan emerging standard that is based on the IEEE 802154 and adds network construction (star networks peer‐to‐peermesh networks and cluster‐tree networks) application services and morerdquo

241 IEEE 802154 Holger and Willig (2005) [17] mention home automation home networking and home security as possible applications for IEEE 802154 They add that ldquomost of these applications require only low‐to‐medium bitrates (up to some few hundreds of kbps) moderate average delays without too stringent delay guarantees and for certain nodes it is highly desirable to reduce the energy consumption to a minimumrdquo The IEEE 802154 standard (2003) defines the device types that can be used in a LR‐WPAN A device can be a FFD or a RFD The RFD can be used in simple applications in which they do not need to transmit large amounts of data and they have to communicate only with a specific FFD The FFD can work as a PAN coordinator as a coordinator or as a simple device It can communicate with either another FFD or a RFD In keeping with the application requirements the LR‐WPAN operates in a star or peer‐to‐peer topology (figure 4) In the star topology the RFD communicates with a single controller the PAN coordinator The PAN coordinator can perform the same function as the RFD but it is also responsible for controlling the PAN ldquoit initiates terminates or routes communication around the networkrdquo [18] The peer‐to‐peer topology

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supports ad‐hoc mesh multi‐hop networking Any device in the peer‐to‐peer topology can communicate with any other device within its communication range however this topology also has a PAN coordinator All the devices in a LR‐WPAN have a unique 64‐bit address This or a short address allocated by the PAN coordinator can be used inside a PAN Additionally each PAN has a unique identifier The combination of the PAN identifier and the sort addresses allows communication across different PANs (IEEE 802154 Standard 2003)

Figure 4 Star and peer‐to‐peer topologies in LR‐WPAN [18]

The LR‐WPAN based on the OSI seven‐layer model has the layered architecture presented in figure 5 The application and the network layer are the upper layers in the LR‐WPAN architecture but are outside the scope of the IEEE 802154 standard Only the physical layer ldquowhich contains the RF transceiver along with its low‐level control mechanismrdquo and the MAC layer ldquothat provides access to the physical channelrdquo are included in the standard and will be introduced in the following sections Finally the Type I 8022 LLC and the SSCS are intermediate sub layers supporting communication with the above layers (IEEE 802154 Standard 2003)

Physical Layer The 802154 standard specifies two different services that the PHY provides The PHY data service controls the radio and thus the transmission and reception of the PPDUs In addition the management service performs ED in the channel The management service also performs CCA before sending the messages and provides LQI for the received packets

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Figure 5 WPAN architecture [18]

Three different bands are defined by the standard The 868‐8686 MHz for Europe the 902‐928 MHz for North America and the 2400‐24835 MHz worldwide All of them belong in the ISM radio bands and they are using DSSS Each of the bands supports different a data rate and it uses a different modulation technique chip rate and number of channels Moreover if the system does not use the 2450 MHz frequency it operates in both the 802 MHz and 902 MHz frequencies Table 3 summarizes the supported bands and data rates (IEEE 802154 standard 2003)

PHY (MHz)

Frequency Band (MHz) Modulation

Bit Rate Number of Channels

868-8686 BPSK 20 1 868 915 902-928 BPSK 40 10

2450 2400-24835 O-QPSK 230 16 Table 3 Frequency bands and data rates for IEEE 802154 based on the IEEE

802154 standard [18] The physical layer uses PPDU packets to communicate Table 4 demonstrates its structure The LSB is always transmitted and received first The SHR contains the preamble and the SFD fields which helps receiver

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synchronization The 8‐bytes preamble contains only zero and is used for synchronization The SFD contains a specific sequence of one and zeros and specifies the beginning of the frame The PHR contains the payload length Packets with a length of 9 or more bytes are MPDU as the next section further explains Packets with length 5 are used for MPDU acknowledgements The payload part of the PPDU encloses the MAC layer packet Finally the PPDU size can be up to 127 bytes [18]

4 bytes 1 byte 1 byte Variable

Preamble SFD Frame length

(7 bits) Reserved

(1 bit) Payload (PSU)

SHR PHR PHY Payload Table 4 Format based on the IEEE 802154 standard [18]

MAC Layer The MAC layer is the interface between the SSCS and the PHY layer Similar to the PHY layer the MAC layer supports two services The MAC data service is responsible for the transmission and reception of the MPDUs through the PHY data service The MAC management service if the device is a coordinator manages the network beacons It is also responsible for PAN association and disassociation frame validation and acknowledgment providing ldquoa reliable link between two peer MAC entitiesrdquo In addition it uses the CSMA‐CA for channel access and handles and maintains the GTS mechanism Finally it supports device security [18] The IEEE 802154 standard defines four different frame types the beacon data acknowledgment and MAC command frame All frame types are based on the general MAC frame format (table 5) The frame control field describes and specifies the above different frame types Every MAC frame comprises a MHR which consists of a frame control sequence number and the information field It also contains the MAC payload different frame types have different MAC payload fields The acknowledgment type does not have a payload Finally each frame includes a MFR which contains a FCS The data in the MPDU follows the same order as the PPDU the least significant bits are left in the frame and are transited first Octets2 1 02 028 02 028 Variable 2

Destination PAN Identifier

Destination Address

Source PAN

Identifier Source Address Frame

Control Sequence Number Addressing Field

Frame Payload FCS

MHR MAC

Payload MFRTable 5 General MAC frame format IEEE 802154 standard [18]

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Table 6 7 8 and 9 present the four different MAC frame types The beacon frame is transmitted periodically by the PAN coordinator It provides information about the network management through the super frame and GTS fields which are analyzed later in the section It also synchronizes the network devices and indicates the proper communication period for them The data frame payload encapsulates data from the higher layers When a device receives a packet it is not obliged to response with an acknowledgement packet Finally the command frame identifier and command payload fields of the command frame are used for communication between the network devices The command identifier specifies actions like association disassociation and data GTS or beacon request Octets2 1 410 2 Variable Variable Variable 2

Frame Control

Sequence Number

Addressing Fields

Super frame Specification

GTS fields

Pending Addressing

Fields Beacon Payload FCS

MHR MAC Payload MFRTable 6 Beacon frame format [18]

Octets 2 1 Variable 2

Frame Control

Sequence Number

Addressing Field

Data Payload FCS

MHR MAC

Payload MFR Table 7 Data frame format [18]

Octets 2 1 2

Frame Control

Sequence Number FCS

MHR MFR Table 8 Acknowledgement frame format [18]

Octets2 1 1 Variable 2

Frame Control

Sequence Number

Addressing Fields

Command Frame Identifier

Command Payload FCS

MHR MAC Payload MFR Table 9 Command frame format [18]

In the LR‐WPAN every PAN has its own coordinator The PAN coordinator manages the communication in the local area it has two options to use or not use the super frame structure The super frame (figure 6) uses network beacons If the coordinator does not want to use a super frame structure it suspends the beacon transmission However the beacon is important for device association and disassociation If the coordinator wishes to maintain close communication control in the PAN and to support low‐latency devices it usually uses the super frame A super frame determines a specific time

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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period beacons bound it The beacon is transmitted in the first of the sixteen equal time slots that the super frame has It is used to ldquosynchronize the attached devices to identify the PAN and to describe the structure of the super framerdquo The super frame can have active and inactive periods All the communications have to be finished inside the super frame period In the inactive periods the devices can switch to the sleep mode but they have to be ready for the next beacon The active period is further divided into a CAP and a CFP During the CAP any device can communicate competing with the other devices in the PAN using slotted CSMA‐CA The CFP contains GTSs and follows the CAP A CFP may maintain up to seven GTSs and each GTS can reserve more than one time slot However the CAP should always be sufficient to allow new devices t join the PAN The GTSs are allocated to specific devices All the transactions must be completed inside the assigned time period (CAP GTS and CFP) (IEEE 802154 standard 2003)

Figure 6 Example of super frame structure [18]

In accordance with the 802154 standard (2003) three different types of data‐transfer exist In addition the types differ if the coordinator uses or does not beacons Data transfer from a device to the PAN coordinator is the first type (figure 7) For a ldquonon‐beacon‐enable networkrdquo it first senses the medium by using ldquoun‐slotted CSMA‐CArdquo and then a simple transmit to the data frame In a ldquobeacon‐enabled networkrdquo the sender waits for the beacon when it finds it ldquothe device synchronizes to the super fame structurerdquo In the specified time frame the sender again senses the medium by using ldquoslotted CSMA‐CArdquo and transmits the data to the coordinator In either case the coordinator has the option to acknowledge or not acknowledge the data reception after that the transaction is completed Data transfer from the coordinator is the next type described in the standard (figure 8) In a beacon‐enabled network the coordinator indicates a pending message through the beacon The messagersquos target device receives the beacon and if the message is pending it responses with a MAC command request

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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message using slotted CSMA‐CA the coordinator may or may not acknowledge the command message and through slotted CSMA‐CA sends the pending message The device acknowledges the received message After that the coordinator removes the message from the beaconrsquos pending list and completes the transaction In a non‐beacon enable network the device periodically sends a MAC command‐request frame to the coordinator The coordinator acknowledges the data request Then by using un‐slotted CSMA‐CA if it has a pending message it transmits it If it has not it respond with a data frame with a ldquozero‐length payloadrdquo To complete the transaction the device acknowledges the data reception

Figure 7 Communication from a device to a PAN coordinator in (a) a beacon‐enabled network and (b) a non‐beacon enabled network [18]

Peer‐to‐peer is the last type of data transfer In this situation the devices are free to communicate with any other device within their communication range In a peer‐to‐peer PAN the devices can ldquoeither receive constantly or synchronize with each otherrdquo If they are receiving constantly to transmit data they use un‐slotted CSAM‐CA In the second case synchronization must be achieved first (IEEE 802154 standard [IEEE 2003]) The IEEE 802154 (2003) standard establishes MAC and PHY standards for low‐cost low‐power and high‐density node deployments In addition to the above PHY and MAC characteristics IEEE 802154 provides a security baseline including ldquothe ability to maintain an ACL and use symmetric cryptographyrdquo for data encryption The algorithm that is used for encryption is the AES However the higher level layers decide when security is need The upper layers are in general responsible for device authentication and key management The next section introduces the ZigBee standard which

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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21

encapsulates the IEEE 802154 and provides additional standardization for the higher levels

Figure 8 Communication from a PAN coordinator to a device in (a) a beacon‐enabled network and (b) a non‐beacon enabled network [18]

242 ZigBee Heily (2004) defines ZigBee as ldquoa rapidly growing worldwide non‐profit industry consortiumrdquo whose mission is ldquoto define a reliable cost‐effective low‐power wirelessly networked monitoring and control product based on an open global standardrdquo Figure 9 illustrates the areas of interest for different wireless communication standards

Figure 9 Overview of the coverage for different wireless communication

standards [17]

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

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ZigBee a new standard which became publicly available in June 2005 is based on the IEEE 802154 standard It expands the IEEE 802154 by adding the framework for ldquothe network security and applicationrdquo (Craig 2005) Figure 10 presents the IEEE 802154ZigBee stack and the areas of responsibilities

Figure 10 IEEE 802154ZigBee stack [20]

Craig (2005) mentions three networking topologies that the standard covers the star mesh and cluster tree (figure 11) The ZigBee standard works on top of the IEEE 802154 addressing schema by using the standard 64‐bit and the short 16‐bit addressing Kinney (2005) summarizes the ZigBee network layer responsibilities the successful establishment of a new the network and successful new device configuration addressing assignment network synchronization frames security and message routing ZigBee further distinguishes the concept of the physical devices (RFD FFD) by using the notion of ldquological devicesrdquo ldquoZigBee Coordinatorrdquo is the first type of logical devices It is responsible for initializing maintaining and managing the network Under the coordinator in the network hierarchy is the ldquoZigBee routerrdquo which is responsible for controlling the message routing between the nodes Finally the ldquoZigBee End Devicerdquo acts as the end point of the network

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

23

structure The tasks that an end device can perform are specified in the ldquoApplication Profilesrdquo (Craig 2005)

Figure 11 IEEE 802154ZigBee stack [17]

The ZigBee specifications (2005) summarize the security services provided by ZigBee ldquokey establishment key transport frame protection and device managementrdquo ZigBee builds its security mechanism using symmetric key cryptography The security services also depend on the associated layer thus as the figure 10 shows the security mechanism covers the network and the application layer In addition if a MAC frame needs security protection the MAC layer is able to secure it Moreover the notion of end‐to‐end security is supported the source and destination devices have access and use the same share key In the MAC layer the 802154 AES mechanism provides the proper security The mechanism protects ldquothe confidentiality integrity and authenticity of the MAC framesrdquo (Kinney 2005) An auxiliary header field in front of the MAC payload indicates if the frame is encrypted or not The MAC framesrsquo integrity is supported by calculating and using a MIC at the end of the MAC payload In addition to the AES nonce is used to provide MAC confidentiality and authenticity Figure 12 illustrates a MAC frame with security For different security aspects the MAC layer uses different mode of the AES for the encryption it uses the AES in Counter (CTR) mode and for the integrity the

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

24

CBC‐MAC Finally the combination (CCM) of the above two modes is available providing both encryption and integrity (Kinney 2005)

Figure 12 ZigBee secure frame in MAC layer [17]

In the network layer the CCM (a modified MAC layer CCM mode) is used for encryption Because the network layer uses only the CCM mode a single key is used for all different security options The network layer security message format is similar to the MAC frame it is presented in the figure 13 Finally although the network layer is responsible for securing its layer messages the above layers specify the keys and the CCM option for each frame (Kinney 2005)

Figure 13 ZigBee secure frame in network layer [17]

Security in the application layer works similar to the network and MAC layers It uses the ldquolink keyrdquo or the ldquonetwork keyrdquo to secure the message and then encapsulate it inside a set of fields similar to the network format (figure 14) Other security responsibilities that the application layer has are to provide the ZDO and the applications with device management services key establishment and key transport (ZigBee specifications 2005)

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

25

Figure 14 ZigBee secure frame in application layer [17]

The ZigBee application layer contains the manufacturer‐defined application objects the ZDO and the application sub‐layer In addition to the security responsibilities the application sub‐layer binds devices based on their duties and needs maintains the binding tables and forwards messages between them The application sub‐layer also discovers the neighbour devices for a given device The ZDO is responsible for determining the devicersquos duty in the network for communicating using binding requests and for supporting security as was mentioned above The sub‐layer that implements the actual application is the manufacturer‐defined application object (Kinney 2005) The ZigBee specifications are a great step toward the wireless sensors networks standardization They cover all the OSI layers from the physical to the application providing guidance to the developers The specifications are the product of the ZigBee alliances a consortium of companies and academic institutes Thus they not only produce the specifications but also are the first use them The next section goes beyond the layer architecture and standardization and introduces the OS field another crucial element of WSNs

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

26

3 RECEIVED SIGNAL STRENGTH (RSS) Majority of the existing methods leverage the existence of IEEE 80211 base stations with powerful radio transmit powers of approximately 100mW per base station Such radios are in a different class from the low power IEEE 802154 compliant radios that typically transmit at low power levels ranging from 52mW to 29mW [19] The wide availability of larger number of IEEE 802154 radios in the research community has revived the interest for signal strength based localization in sensor network Despite of rapidly increasing popularity of IEEE 802154 radios and signal strength localization there is a lack of detailed characterization of the fundamental factors contributing to large signal strength variation This chapter investigates some of the properties of RSS observed during testing and analysis of research work collected during the period of thesis The analysis of RSS values is needed to understand the underlying features of location dependent RSS patterns and location fingerprints An understanding of the properties of the RSS values for location fingerprinting can assist in improving the design of positioning algorithms and in deployment of indoor positioning systems

31 Parameters that affect RSS Different factors that affect the indoor RSS are the userrsquos presenceabsence hisher orientation time of the day building type and material distance from transmitter and type of radio These factors are by no means exhaustive Other possible factors are antenna orientation directionality and type Table 10 classify the parameters which affect RSS [7]

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

27

Effect on Factors Options

1 Proximity of user Userrsquos presence or absence

2 Orientation of user and terminal

North East South West

Data Collection

3 Make of Radio 4 Time of measurement Time of day amp days of

week 5 Period of measurement

Second minute hour

7 Interference Co‐channel adjacent co‐channel

Statistics

8 Building environment Small offices or large hall

Table 10 Classification of factors the affect RSS [7]

Transmitter variability Different transmitters behave differently even when they are configured exactly in the same way In practice this means that when a transmitter is configured to send packets at a power level of d dBm then the transmitter will send these packets at a power level that is very close to d dBm but not necessarily exactly equal to d dBm This can alter the received signal strength indication and thus it can lead to inaccurate distance estimation

Receiver variability The sensitivity of the receivers across different radio chips is different In practice this means that the RSSI value recorded at different receivers can be different even when all the other parameters that affect the received signal strength are kept constant

Antenna orientation Each antenna has its own radiation pattern that is not uniform In practice this means that the RSSI value recorded at the receiver for a given pair of communicating nodes and for a given distance between them varies as the pair wise antenna orientations of the transmitter and the receiver are changed

Multi‐path fading and shadowing in the RF channel In indoor environments the transmitted signals get reflected after hitting on the walls andor on other objects in the room such as furniture Both the original signal and the reflected signal reach the receiver almost at the same time since they both travel at the speed of light As a result of this the receiver is not able to

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

28

distinguish the two signals and it measures the received signal strength for both of them

32 Related work analysis A number of indoor location tracking systems have been proposed in the literature based on RF signals ultrasound infrared and some combinations of these As mentioned previously RF based positioning system are widely studied as shown in Table 2 Given a model of radio signal propagation in a building or other environment RSS can be used to estimate the distance between the transmitter and receiver Most of the algorithms (Table 2) have been evaluated in practice using 80211b compliant wireless radios and seem to produce a location error of 15ft to 20ft for an indoor network area of approximately 225ft x 144ft According to the results of this study all the localization techniques produce approximately the same location error over a range of environments Ecolocation [22] Mote Track [5] are two of the few localization algorithms that have been evaluated on a real sensor network that uses a low power wireless radio Ecolocation reports a location error of 10ft for a very small outdoor network deployment area (26ft x 49ft) Mote Track reports a location error of approximately 13ft for an indoor network deployment area of 18751ft2 In general the RSSI based localization algorithms can be divided into two broad categories the map based and the distance (or area) prediction based algorithms In the case of the map based algorithms the deployment area is divided into a large set of sample points that cover the whole area Most techniques collect one or more signal strength measurements from all visible beacon nodes and for each sample point Each sample point is then mapped to either a signal strength vector or a signal strength probability distribution This process generates an RSSI fingerprint map which is unique with respect to the beacon locations and the deployment area This fingerprint map is stored to a central base station A node that is not aware of its location collects one or more signal strength measurements from all the visible beacon nodes and creates its own RSSI fingerprint that is sent to the central base station The base station computes nodersquos location as the sample pointrsquos location whose RSSI fingerprint distance from the unknown nodersquos RSSI fingerprint is the minimum among all the sample points Despite the fact that the map based RSSI localization methods produce relatively small location errors they are not attractive mainly because they require an excessive amount of profiling data and because they are not fully distributed The detailed profiling of a building is a major holdback because it is complex time consuming and depends on beacon locations Therefore for different buildings or even for the

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

29

same building and different beacon locations different RSSI maps have to be obtained The second category of RSSI based algorithms are the distance (or area) prediction algorithms which focus on minimizing the amount of profiling data In distance prediction based methods raw RSSI data are directly mapped to distances through a signal propagation model In some cases distances are not explicitly measured but they are taken into consideration through assumptions on the underlying distributions In the case of outdoor deployments where there are no obstacles a single signal propagation model is all the profiling data required In the case of indoor deployments the same applies but now the signal propagation model might be different across different buildings with different furniture and wall arrangements Given an underlying signal propagation model raw RSSI data can be mapped to specific distances These distances can be either used directly in order to perform localization or assigned a probability and used in a learning basedprobabilistic localization algorithm Obviously the accuracy of the distance prediction based methods depends heavily on the accuracy of the signal propagation model used for translating RSSI values to distances

33 Properties of RSSI Changes in signal due to propagation indoors are difficult to predict because of dense environment and propagation effects such as reflection diffraction and scattering The multi‐path fading effect which is the result of either constructive or destructive combination of multiple signal copies at the receiver causes the received signal to fluctuate around a mean value at a particular location The received signal is usually modelled by the combined effects of large‐scale fading and small‐scale fading The large‐scale fading component describes the signal attenuation as the signal travels over a distance and is absorbed by material such as walls and floors along the way to the receiver This component predicts the mean of the RSS and usually has a lognormal distribution On the other hand the small scale fading component explains the dramatic fluctuation of the signal due to multi‐path fading On the other hand the small scale‐fading component explains the dramatic fluctuation of the signal due to multi‐path fading If there is no LOS component the small‐scale‐fading is often modelled with a Rayleigh distribution Such scenarios are commonly called NLOS If there is a LOS component the small‐scale‐fading is typically modelled by a Rician distribution Note that when the RSS is measured the measurement averages out the small‐scale‐fading effects

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

30

4 OBJECT TRACKING 41 Introduction This chapter presents the object‐tracking application the main objective of this thesis work The application is an algorithmic‐software implementation written in Embedded C which is capable of receiving data messages and generating object position estimation data The implementation of the object‐tracking application makes use of the SENTIO platform and uses the background knowledge introduced in the preceding chapters The motivation for the object‐tracking application is to determine the position of an object in indoor environments (Eg Home) The application acts as an interface which collects the raw data that the sensors generate (numerical values) It makes the proper comparisons and algorithmic processes to identify and track the object while it is static inside the area covered by the wireless sensor network Finally with the detection and tracking data it alerts the user or another system The following sections provide an overview of the object‐tracking application

42 Overview of hardware product Before describing the object tracking application it is important to know the characteristics of hardware product The SENTIO platform (figure 15) is built in modular fashion and has the following key features [8]

Flexibility This platform is very flexible in such a way that it is made easy to add or change the hardware for each sensor task depending on the requirement in the WSN For a specific platform this can be hard or sometimes even impossible to accomplish

Sensor resources Resources in a sensor module includes the ability of usage of controllerrsquos resources as much as possible such as ADC‐channels interrupts and digital Input‐Outputrsquos Local voltage regulation should also be possible since many sensors require higher supply voltages than digital Integrated Circuits do Further support of a shut‐down mechanism to power down the sensor modules when not needed

Computation and memory The processor module based on microcontroller should support many devices in a microcontroller family and support different power supply voltages enabling full range of clock frequencies All power‐down modes of the devices should be

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

31

supported The platform should support different processor modules including FPGArsquos and very simple microcontrollers

Communication The focus should be on enabling an IEEE 802154 compliant communication interface With or without ZigBee stack this will provide a flexible platform for exploring different network topologies Support for power‐down of the entire communication module should be supported

Cost and form factor High flexibility is in conflict with low cost and small size The primary objective should be flexibility but the size should be considered in order to enable sensor device integration in systems where space is limited

Figure 15 SENTIO Platform [8]

The communication subsystem of the SENTIO sensor node was designed so that the correct operation of the radio chip is ensured The radio chip is powered by the on‐board voltage regulator and thus fluctuations in the battery voltage level do not affect the operation of the communication subsystem This platform uses Chipcon CC2420 IEEE 802154 radio transceiver operates in the 24GHz ISM band and includes a digital direct sequence spread spectrum (DSSS) modem providing a spreading gain of 9dB and an effective data rate of 250Kbps It was specifically designed for low power wireless applications and supports 8 discrete power levels 0dBm minus1dBm minus3dBm minus5dBm minus7dBm minus10dBm minus15dBm and minus25dBm at which its power consumption varies from 29mW to 52mW [21] A built‐in received signal strength indicator gives an 8‐bit digital value RSSIVAL The RSSIVAL is always averaged over 8 symbol periods (128μs) and a status bit indicates when the RSSIVAL is valid (meaning that the receiver was enabled for at least 8 symbol periods) The

Antenna

Communication Module

Processing Module

Sensing Module

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

32

power P at the RF pins can be obtained directly from RSSIVAL using the following equation

P = RSSIVAL + RSSIOFFSET [dBm] where the RSSIOFFSET is found empirically from the front‐end gain and it is approximately equal to minus45dBm In the next sections when we refer to the RSSI value we refer to the RSSIVAL and not the power P unless otherwise stated

43 Experimental Testbed The experimental test bed for object tracking application is as shown in figure 16 Experiment was conducted in Masterrsquos room at Mid Sweden University A DELL laptop is connected to SENTIO platform and software were primarily used to collect samples of RSS from APrsquos The software monitors IEEE 802154 radio frequency channels which operate in the 24GHz band

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

1030

BC - 1030

AP 3 AP 1

AP 2

AP 4

BS

HEWLETT

PACKARD

Figure 16 Layout of Masterʹs Room

A three dimensional scalable testbed is used for indoor sensor network deployments The testbed illustrated in figure 16 is a 3‐D structure measuring 16m (L) x 12m (W) x 3m (H) and it is designed to host a large number of static nodes as shown in figure 16 Arrow marks in the above figure represent the location where fingerprints were taken The centrepiece of our infrastructure

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

33

is the SENTIO sensor node [8] an open‐source general purpose sensing platform designed with AT Mega 128 microprocessor and the IEEE 802154 compliant CC2420 radio from Chipcon [19]

44 Obtaining data from the sensor network The next important step in the development of the object tracking application is to obtain the data from the sensor network and send it to the base station From there the data is transmitted through a serial port to the computer that runs the object tracking application When the data arrives at the serial port the application must capture it This paragraph gives a brief explanation of how and what kind data is transmitted by the static object fixed sensors and how this data is being used to estimate the position of the object First the static object transmits Beacon packet continuously which is received by all the fixed APrsquos The beacon packet format is as shown in table 6 Then the fixed APrsquos calculates the 8‐bit value of RSSI from the transmitted beacon packet and then sends the resultant RSSI value as a form of data packet (table 7) to the BS which calculates the position and sends back to the object

45 Object tracking application Testing and Evaluation The purpose of this section is to discuss and evaluate the object tracking application Finally we are ready with the experimental testbed as shown in figure 16 As shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors placed at the corners of the masterrsquos room BS is placed at the centre of the room connected to a PC All the nodes are power connected before an actual measurement takes place At first the object is placed at random position inside the room and transmits beacon packets continuously Then these packets are received by the fixed APrsquos which calculate the RSSI from the packet and then averages it until it receives 50 beacon packets and then transmits the data to the BS (figure 17) Then the BS waits until it receives the data from all the APrsquos Once it receives from all the APrsquos it calculates the position of the object (figure 18) The application was developed to demonstrate a real world use of a WSN in object tracking In evaluating the final position obtained with respect to the actual position we conclude that the object‐tracking was successful Although

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

34

it is a research prototype object tracking application is a simple stable stand‐alone system capable of demonstrating an important application of WSN It takes raw data from the WSN aggregates it evaluates it and based on the evaluation tracks the object The accuracy of estimating the position is less than 5m Table 11 below shows a log of readings taken during test

Start

Intialize micro‐controller and transeciever in receieve mode

If received_packet=

Beacon

Average RSSI values

Increase the counter

If countergt1000050

A

No

Yes

B

B

Yes

No

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

35

A

Reset counter

Set transceiever in transmit mode

Transmit averaged RSSI to Main sensor and set it back to receive mode

10000 samples ‐ Offline

50 samples ‐ Online

Figure 17 Flow chart representing the algorithm of fixed sensors

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

36

Start

Intialize micro‐controller and transeciever in receieve mode

Received RSSI values from all fixed Sensors

Open radio map

Calculate Euclidean distance between obtained RSSI values from fixed sensors and fingerprints

in radio map

No

Yes

Bubble sort for least error in signal strength values

A

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

37

A

Get the location of the object from the radiousing the least error value

Display location of object on PC

End

Figure 18 Flow chart representing the algorithm of main sensor

Table 11 Accuracy of positioning system

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

38

5 RESULTS This chapter presents how the system parameters such as number of samples mean or median etchellipare finalized All the tests are performed in masterrsquos room at Mid Sweden University This chapter is divided into three subsections First in Section 51 it reviews results obtained from samples test Section 52 discusses one dimensional test results and finally section 53 discusses results of two dimensional tests

51 Measurement setup A Dell laptop was connected to a SENTIO platform and is loaded with software (Appendix 2) This setup was used to determine number of samples required during object tracking mean or median position estimation algorithm and one dimensional test The setup is as follows

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

103

0

BC - 1030

AP 1AP 2

BS

HEWLETT

PACKARD

Figure 19 Test bed for one dimensional test in Masters Room

Two SENTIO nodes (AP1 and AP2 as shown above) were separated by a distance of 10 meters and these sensors are known as fixed sensors which are fixed to the ceiling such that there antenna is faced towards the ground They are linear is position A BS was connected to Dell laptop and it was placed in such a way that its antenna was faced towards the ceiling Another SENTIO

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

39

node was used as an object and its orientation was same as BS Figure 19 represents the complete setup for object tracking application Mobile node was moved between these fixed ones at regular intervals of one meter and RSSI values were logged at each interval

52 Number of samples test This test was performed to determine number of samples required for accurate positioning and either mean of the obtained RSSI values or median of them Figure 19 represents the setup for number of samples test Once the setup is ready the measurements are taken as follows

1 An object was tagged with SENTIO node and was placed stationary linear to the fixed sensors

2 10000 samples (RSSI) were taken at regular intervals of 1 meter up to 10 meters

3 The sample values were logged at every interval 4 Using these logged samples I have plotted graphs such as distribution

of RSS values (figure 20 21) meanmedian at intervals of 1 meter using MATLAB

5 The mean and median for various numbers of samples is as shown in figure 22

Figure 20 Histogram plot of RSSI values at 1m and 4m

Figure 21 Histogram plot of RSSI values at 6m and 8m

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

40

Figure 22 Distance vs RSSI

From the above graphs following conclusions were made 1 From the histogram plots we can observe that the distribution of RSSI

values follow Gaussians distribution law 2 As we can see from figure 22 that median curve has drastic changes

when compared to mean curve Hence mean is considered while estimating the position

3 From the observations we conclude that the number of samples does not matter since mean curve tends to be same for various numbers of samples

4 Hence 10000 samples are considered in order to get a proper location fingerprint during off‐line phase and during online phase 50 samples are enough to estimate the position of object

53 0ne dimensional test Measurement setup for one dimensional test is as shown in figure 19 This test was conducted to check the performance of algorithm and how accurate it was At first ie during offline phase fingerprints at each interval averaging 10000 samples were taken During online phase Euclidean algorithm was used estimate the position averaging 50 samples of RSSI From the results we

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

41

can realize that the error was less than 2 meters Table 12 shows the result of location estimation in one dimensional case

Table 12 One dimensional position estimation results

54 Two dimensional test This test is the final stage of my thesis work Experimental testbed for this stage is as shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors fixed at the corners of the room and a BS at the centre of the room There are 18 pre‐determined locations where the fingerprints (RSSI) were taken The fingerprints are as shown in Appendix 1 All the readings were taken in presence of userrsquos The algorithm for position estimation is shown in Appendix 2 Position estimation test is performed as follows

Tagged object is placed at random inside the masterrsquos room All the sensors including the base station and object are switched on Using the algorithm (Appendix 2) the BS estimates the position of the object and displays it on the PC

This test was performed using various numbers of samples to test which has a better accuracy Figure 23 represents the error rate using various numbers of samples From figure 23 we can conclude that 50 numbers of samples is enough to estimate the position of object with an accuracy of 3m

55 Causes of error in location determination The randomness of RSS is the major cause of error in any indoor

positioning system that uses the RSS for location inference If there is no randomness in the RSS and its location fingerprint every indoor positioning systems of this type will have no location detection error For example I have recorded RSS at one particular location during different times of the day From figures 24 and 25 you can observe that RSS values are different at different times of day

The performance of indoor positioning systems depends greatly on the separation of location fingerprints A location fingerprint

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

42

corresponding to a location can be identified correctly if it is difficult to classify it as another fingerprint by a pattern classifier

Path loss is one of the basic signal propagation properties of the RSS that best describes attenuation of transmit signal power over a distance between an access point and a mobile device

Presence of Userrsquos or userrsquos orientation affects RSS

Figure 23 Error Rate vs Number of samples

Figure 24 RSS distribution at 214 pm

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

43

Figure 25 RSS distribution at 100pm and 1100 am

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

6 DISCUSSIONS10102006

44

6 DISCUSSIONS 61 Summary and Conclusion While empirical results and performance studies of positioning systems based on location fingerprint have been presented in this report analytical model that can be used as a framework for efficiently designing the positioning systems are not available This thesis design work uses general principles of position estimation such as position estimation of sensors grid spacing etc This design work focuses on accuracy and precision The concept of location fingerprint technique based on RSS us explained in detail in chapter 2 It is found that RSS is random It is possible to approximate the RSS as being normally distributed While the RSS random process is non‐stationary the mean is more or less constant and could be used as the fingerprint of the location Model designed for position estimation is as shown in chapter 4 Accuracy and precision are the two major performance matrices in this study Uniform grid spacing has been used From the experimental analysis I have obtained an accuracy of 3 meters and 35 precision The sensitivity analysis among the system parameters and the performance matrices were done using the model as shown in chapter 4 For instance our study found that the minimum distance between two adjacent positions also affects the positioning performance This is due to the randomness of RSS patterns induced by the ever changing indoor environment

62 Future work The research in this thesis provides a ground work for the future study of efficient design of indoor positioning system The RSSI based position estimation still has limitations First the RSSI value is time dependent Thus the future study should address this issue Second the movement of object is not considered Third orientations of sensors affect the performance which should be taken care off Fourth to improve the performance of the system we need to find a solution to deal with those incomplete or missing data during the off‐line and the on‐line phase Fifth position estimation on multi‐floor and three‐dimensional coordinates is not available

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 110102006

45

APPENDIX 1

Location Fingerprints of Masters Room Time 1315 Observation Time 20 minutes Number of Samples 10000

People Presence SNo Co-ordinates AP1 (0x0001) AP2 (0x0002) AP3 (0x0004) AP4 (0x0008)

1 11 -24 -16 -27 -33 2 13 -26 -8 -34 -31 3 15 -43 -9 -39 -25 4 32 -21 -11 -27 -33 5 61 -6 -23 -32 -28 6 64 -20 -31 -25 -34 7 35 -23 -35 -29 -38 8 37 -29 -35 -22 -27 9 39 -38 -29 -21 -27 10 67 -27 -23 -20 -23 11 611 -26 -29 -23 -30 12 613 -35 -32 -2 -31 13 513 -34 -31 -20 -26 14 11 -39 -22 -29 -17 15 212 -37 -42 -28 -31 16 -212 -38 -33 -29 -6 17 -21 -42 -29 -33 -19 18 -38 -40 -23 -35 -22

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 210102006

46

APPENDIX 2

Begin

Open radio map and raw data files for write

Intialize radio map in memory

Check whether the

Packet is data or Beacon packet If data packet

Save the data to register

Check if data is

Received from all

sensors

Estimate the position of static

Display the position on PC

End Yes

No

A

A

Yes

No

Flow chart representing the main algorithm for position estimation

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

47

APPENDIX 3

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

48

Distribution of RSSI

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

49

REFERENCES [1] T S Rappaport J H Reed and B D Woerner ldquoPosition location using wireless communications on highways of the futureʺ IEEE Commun Mag vol 34 no 10 pp33‐41 Oct 1996 [2] A M Ladd K E Bekris G Marceau A Rudys L E Kavraki and D S Wallach rdquoRobotics‐based location sensing using wireless ethernetʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ02) 2002 pp 227‐238 [3] I Ramani R Bharadwaja and P V Rangan ldquoLocation tracking for media appliances in wireless home networksʺ in Proc International Conference on Multimedia and Expo (ICMEʹ03) July 2003 pp 769‐772 [4] K Pahlavan X Li and J P Makela ldquoIndoor geolocation science and technologyʺ IEEE Commun Mag vol 40 no 2 pp 112‐118 Feb 2002 [5] Konard Lorincz and Matt Welsh ldquoMote Track A Robust Decentralized Approach to RF‐Based Location Trackingrdquo [6] Paramvir Bahl and Venkata N Padmanabham ldquoRADAR An In‐Building RF‐based User Location and Tracking Systemrdquo [7] Kamol Kaemarungsi ldquoDesign of Indoor positioning system based on location fingerprint techiniquerdquo University of Pittsburgh 2005 [8] Fredrik Linnarsson Cheng Peng and Bengt Oelmann ldquoSENTIO A Hardware Platform for Rapid Prototyping of Wireless Sensor Networksrdquo [9] S Saha K Chaudhuri D Sanghi and P Bhagwat ldquoLocation determination of a mobile device using IEEE 80211b access point signalsʺ in Proc IEEE Wireless Communications and Networking Conference (WCNCʹ03) New Orleans LA Mar 2003 pp 1987‐1992 [10] T Roos P Myllymaki H Tirri P Misikangas and J Sievanen ldquoA probabilistic approach to WLAN user location estimationʺ International Journal of Wireless Information Networks vol 9 no 3 pp 155‐164 July 2002 [11] R Battiti M Brunato and A Villani ldquoStatistical learning theory for location fingerprinting in wireless LANrsquosʺ Technical Report Oct 2002 [Online] Available httprtmscienceunitnitraquobattitiarchive86pdf [12] P Prasithsangaree P Krishnamurthy and P K Chrysanthis ldquoAn indoor position location with wireless LANsʺ in Proc IEEE International Symposium on Personal Indoor and Mobile Radio Communications (PIMRCʹ02) Lisbon Portugal Sept 2002 [13] M A Youssef A Agrawala and A U Shankar ldquoWLAN location determination via clustering and probability distributionsʺ in Proc IEEE

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

50

International Conference on Pervasive Computing and Communications (PerComʹ03) Dallas‐Fort Worth TX Mar 23‐26 2003 pp 23‐26 [14] Z Xiang S Song J Chen H Wang J Huang and X Gao ldquoA wireless LAN‐based indoor positioning technologyʺ IBM Journal of Research and Development vol 48 no 56 pp 617‐626 SeptNov 2004 [15] R Want A Hopper V Falcao and J Gibbons ldquoThe active badge location systemʺACM Transactions on Information Systems vol 40 no 1 pp 91‐102 Jan 1992 [16] N B Priyantha A Chakraborty and H Balakrishnan ldquoThe cricket location‐support systemʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ00) Boston MA Aug 2000 pp 32‐43 [17] Holger K Willig A (2005) Protocols and Architectures for Wireless Sensor Networks London [18] IEEE (2003) Part 154 Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low‐Rate Wireless Personal Area Networks (LR‐WPANs) New York [19] Chipcon CC2420 802154 compliant radio httpwwwchipconcom [20] ZigBee Specification v10 ZigBee Alliance [21] Dimitrios Lymberopoulos Quentin Lindsey and Andreas Savvides ldquoAn Empirical Analysis of Radio Signal Strength Variability in IEEE 802154 Networks using Monopole Antennasrdquo [22] Kiran Yedavalli Bhaskar Krishnamachari Sharmila Ravula and Bhaskar Srinivasan ldquoEcolocation A sequence based technique for RF localization in wireless sensor networksrdquo Robert Bosch Research and Technology Centre Palo Alto CA

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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1 INTRODUCTION 11 Introduction to the study Advancement in wireless sensing technologies has encouraged in developing a model for indoor object‐tracking application using low power IEEE 802154 compliant radios Stimulus used in this model is RSS This application is not only limited to object tracking in an indoor environment Concierge services enable users to become aware of nearest supporting facilities For example smart home applications such as multimedia appliances that forward multimedia stream to the nearest video screen can be achieved with a home positioning system [3] In the field of robotics a robot can navigate by itself using the assistance of indoor positioning system [2] Granularity of location information is most important in location determination applications For example to locate a book in a library requires coarse grained granularity whereas locating a user requires coarse grained granularity Position estimation refers to a process used to obtain location information of a MS with respect to a set of reference positions within a pre‐defined space A system deployed to determine or estimate the location of an entity is called a position location system or positioning system A wireless indoor positioning system refers to a wireless network infrastructure that provides indoor location information to any requesting end user One of the key technical elements allowing the realisation of this vision will be the knowledge of the accurate position of the personobjects Already today accurate location mechanisms such as GPS are commercially available Many more will become part of our daily life with the introduction of third generation mobile phones But none of these techniques will allow the meter or sub‐meter level of accuracy within buildings which is necessary for smart houses Hence this report introduces an object tracking application in an indoor environment using IEEE 802154 compliant radios which gives meter level accuracy First this chapter presents the background of indoor positioning systems identifies the challenges of such systems and briefly describes the scope of indoor positioning systems based on location fingerprints Next the objective of study the overview of approaches and the contributions are presented Finally the scope and organization of thesis work is outlined

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

2

12 Background The success of outdoor positioning and applications based on the GPS provides an incentive to the research and development of indoor positioning systems It has been developed over several decades and it relies on constellation of satellites Ranges to several satellites are used in multi‐lateration procedure to infer the position of the receiver This localization scheme requires hardware that is both expensive and consumes significant power As a result indoor positioning systems require alternative means to detect the MS location without relying on the direct signal from GPS satellites Infrared RF and ultra sound signals are major technologies used for indoor positioning systems Unlike outdoor areas the indoor environment imposes different challenges on location discovery due to the dense multi‐path effect and building material dependent propagation effect Thus an in‐depth understanding of indoor radio propagation for positioning is crucial for efficient design and deployment The existence of radio connectivity and the attenuation of radio signal with distance are attractive properties that could potentially be exploited to estimate the positions of small wireless devices featuring low‐power radios RSSI a standard feature in most radios has attracted a lot of attention in the recent literature for obvious reasons RSSI eliminates the need for additional hardware in small wireless devices and exhibits favourable properties with respect to power consumption size and cost As mentioned previously RF‐based location tracking has been widely studied [5 6] Given a model of radio signal propagation in a building or other environment RSSI can be used to estimate the distance from a transmitter to a receiver and thereby estimate the position of a mobile node However this approach requires detailed models of RF propagation and does not account for variations in receiver sensitivity and orientation Table 1 below briefly summarizes performance of existing indoor positioning system based on location fingerprints and WLANrsquos All the systems mentioned below in table 1 follow IEEE 80211 protocol standards The major performance metrics studied by all systems are positioning accuracy which is a form of error measurement The accuracy of the location information is usually reported as an error distance between the estimated location and the actual mobile location However the report of accuracy should include the confidence interval or percentage of successful location detection which is called the precision The system parameters of existing systems are summarized in table 1 and the best reported performance of these systems are listed in table 2

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

3

System Spacing(m) Positions SamplesPos APs Orientation Environment

RADAR [6] Non

uniform 70 80 3 4 Hall-way Saha et al [9] 3 19 1200 3 NA 1-floor

Roos et al [10] 2 155 40 10 NA 1-floor Battiti et at [11] NA 257 NA 6 NA 1-floor Ladd et al [2] 3 11 1307 5 2 Hall-way

Prasithsangaree et al [12] 153 60 40

2 to 7 4 1-floor

Youssef et al[13] 15 110 300 4 NA Hall-way Xiang et al [14] NA 100 300 5 4 1-floor

Table 1 Comparison of Indoor Positioning Systems [7]

System Algorithm Type Accuracy and Precision RADAR [6] Nearest Neighbour within 7 feet 38

Saha et al [9] Nearest neighbour amp Neural Network no specified accuracy

90 Roos et al [10] Bayesian best within 828 feet 90

Battiti et at [11] Neural Network amp Weighted k-Nearest

Neighbour all within 16-17 feet 90 Ladd et al [2] Bayesian within 5 feet 77

Prasithsangaree et al [12] Weighted k-Nearest Neighbour

25 feet at 75 amp 40 fet at 95

Youssef et al [13] Bayesian within 7 feet more than

90 Xiang et al [14] Bayesian with RSS distribution model within 6 feet 90 Table 2 Performance comparison of Indoor Positioning Systems [7]

Based on the study of results obtained in various positioning systems as in table 2 we have developed a model for an indoor positioning system using SENTIO [8] platform based on RSSI

13 Objectives In the last two or three years a number of theoretical practical andor simulation studies were done on the topic of object‐tracking While these studies are useful they are too general and provide little guidance for the actual deployment of sensor networks for real life location‐tracking of an object This thesis focuses on developing an object‐tracking application and prescribes sensor network configurations that work well with our algorithms I implemented my software using SENTIO [8] platform for sensor networking The major issue addressed in this project are the accuracy and precision in tracking an object in a real‐world application and efficient ways to algorithmically analyze the collected raw data from the specific wireless sensor product

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

4

Although the focus is to develop a real‐world application using WSN it also provides great opportunity to explore the new area of wireless communication overall

14 Research Questions My primary target is to find a method to determine the position of an object inside a building using ZigBee protocol standards based on RSSI The study addresses the following questions What is WSN What requirements do WSN need What are their main ideas and concepts What are the associated technologies What are the standards that sensor networks use

What are the existing hardware implementations that use this particular technology How can we build our new applications using WSN What do sensor network systems require for general or specific applications Are they applicable And what is the appropriate design for sensor network implementations in order to combine them with other existing systems At this point we describe and evaluate the specific object‐tracking application to be built

15 Scope The study area of the thesis is an overview of the WSN technology study of SENTIO platform and implementation of an object tracking application Thus the study is divided into two parts Part one is an overview of WSN it includes study of SENTIO platform and IEEE 802154 protocol standards The second part describes the implementation of the object tracking algorithm The object tracking application receives and uses the raw values returned by the sensor network system to produce clear and meaningful outputs Schema of thesis work is as shown in figure 1

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1 INTRODUCTION10102006

5

Figure 1 Schema of thesis work

16 Methodology The ldquoIndoor Local Positioning System for ZigBee based on RSSI ldquois based on ldquolocation fingerprintingrdquo technique Location fingerprinting refers to a technique that exploits the relation between any measurable physical stimulus and a specific location This approach uses empirical measurements of RSSI to estimate location By recording a database of radio ldquosignaturesrdquo along with their known locations a mobile node can estimate its position by acquiring a signature and comparing it to the known signatures in the database All of these systems require the database of signatures which should be collected manually prior to the system installation and rely on central server (or the userrsquos mobile node) to perform location calculation For example in RADAR [6] technology the RSSI is the stimulus This type of positioning system does not require specialized hardware other than the common wireless network interfaces with received signal strength measurement capability thus it is simple to deploy compared to other techniques Unlike outdoor counterpart systems which can use AOA and TDOA techniques effectively indoor positioning systems encounter the problem of NLOS and the dense multi path effect that render these two techniques ineffective or complex for practical implementation It is also difficult for a MS to always see three or more access points or base stations in indoor environment which is essential for triangulation by AOA and TDOA Location fingerprinting can also be implemented as software based positioning system which can reduce

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1 INTRODUCTION10102006

6

complexity and cost Generally the deployment of fingerprinting based positioning systems can be divided into two phases First in the off‐line phase the locations where the fingerprints are required is pre‐determined and then the fingerprints are collected by performing a site‐survey of the RSS from multiple APs The distance between two closest positions is called grid spacing and usually reported in meters or feet However some points may be omitted due to inaccessibility The RSS is measured at each predetermined location to create a database of RSS patterns of the indoor environment The database of RSS patterns is called a radio map The vector of RSS values at a point on the grid is called the location fingerprint of that point Second in the on‐line phase a MS will report a sample measured vector of RSS from different APs to a base station The base station uses a positioning algorithm to estimate the location of the MS The most common algorithm used to estimate the location is Euclidean algorithm It computes the Euclidean distance between the measured RSS vector and each fingerprint in the database and then the coordinates associated with the fingerprint that provides the smallest Euclidean distance is returned as the estimate of the position It is clear from the above discussion that the designer should not start out to do the site survey without proper system design objectives and guideline If the performance is not sufficient collecting more fingerprints in between previous location fingerprints may help It is a problem of choice as to how many access points are required for the system and what the minimum distance between physical positions on the grid should be in order to provide a good position resolution and best system performance The system parameters and factors that improve the accuracy and the precision performance are still not clear

17 Outline Chapter 2 provides literature review of ldquoIndoor Local Positioning System for ZigBee based on RSSIrdquo it introduces the basic components required for a positioning system general classification of indoor positioning system Finally it explains ldquoLocation Fingerprintrdquo technology used in this thesis work to determine the position of an object in a real‐world application It briefly explains the protocols and industry standards for wireless sensor networks Chapter 3 describes some properties of RSS which is a stimulus to determine

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1 INTRODUCTION10102006

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the position of an object RSS relation with energy and on what parameters is the RSS value dependent Chapter 4 describes the model of an indoor local positioning system and its probability of returning the correct location It also explains its performance evaluation and its error distance distribution Chapter 5 describes results obtained from the model of positioning system Chapter 6 includes conclusions from the experimental analysis and future work

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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8

2 LITERATURE REVIEW This chapter reviews the literature on wireless indoor positioning system as a means of providing an intellectual background for the thesis work First in section 21 the common components of indoor positioning system are described Then in section 22 discusses different means to classify indoor positioning systems In section 23 reviews of all relevant literature on location fingerprint technique Finally in section 24 protocols and industry standards for wireless sensor networks are explained

21 Common components of indoor positioning system Basically a wireless local positioning system consists of at least two separate hardware components a measurable unit that usually carries the major part of the system intelligence and a signal transmitterreceiver The transmitter in the simplest case is just a beacon Figure 2 represents basic hardware model of a positioning system

Figure 2 Block diagram representing hardware module

First the location sensing devices detect the signals transmitted by or received at known reference points using radio sensing technologies The sensing technique is based on signal strength level that converts the sensed signal into RSS Given a set of reference points the relative position of the MS can be derived from the signal characteristics of RSS The signal characteristics of RSS are unique for a location Then the microcontroller processes the position algorithm and estimates the position of object and displays it on PC

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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9

22 Classification of Indoor positioning systems Indoor positioning system can be categorized based on the stimulus used in particular application and measurement principle

221 Classification based on Stimulus Based on the stimulus used in an application WSN inherit certain characteristics and limitations on sensorrsquos signal The propagation delay diffraction reflection and scattering are the basic signal characteristics which affect all signal types The effective range available bandwidth regulatory constraints interference power constraints safety and cost of technology limitations The wireless signals commonly used for indoor positioning systems are infrared radio frequency and ultrasound Brief descriptions of the three major technologies are as follows

Infrared Infrared signal is an electromagnetic signal of a wavelength longer than that of visible light but shorter than that of radio waves Infrared is just below the visible spectrum of light in frequency and is radiated strongly by hot bodies Many objects such as people vehicle engines and aircraft generate and retain heat and as such are especially visible in the infrared wavelengths of light compared to objects in the background It cannot pass through walls or obstructions therefore it has a rather limited range of indoor environments Thus it requires a more sophisticated circuitry than ultrasound signals Indoor lighting interferes with this type of signal and causes problems in accurate sensing It generally has a range of around 5 m The infrared devices are usually small in size compared to ultrasound devices

Radio frequency The RF signal can penetrate through most of the building material therefore it has an excellent range in indoor environments The propagation speed is also high approximately 3108 ms There are unlicensed frequencies available freely for use This type of signal has the longest range compared to infrared and ultrasound Since the propagation waves are of higher frequency (24GHz) interference with other frequency components is less

Ultrasound Although ultrasound operates at low frequency bands (typical 40 kHz) compared to the other two signaling technologies it possesses a good precision for location sensing at a slow propagation speed of sound (343 ms) The advantages of ultrasound devices are their simplicity and that they are inexpensive However ultrasound does not penetrate walls

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

10

but reflects off most of the indoor obstructions It has a short range around 3 m to 10 m but has a 1 cm resolution of distance measurement The operating temperature influences the performance of ultrasound

222 Classification based on measurement principle Wireless positioning systems can be categorized by measurement techniques which are used to derive the position of mobile stations Mainly three different measurement principles are used today AOA RSS and propagation‐time based systems that can further be divided into two different sub‐classes TOA and TDOA Figure 3 shows taxonomy based on the technology and the technique classifications

Figure 3 Taxonomy of positioning system [7]

In AOA systems the position is calculated via goniometry With the use of directional antennas or antenna arrays the angle or bearing relative to points located at known positions is measured The intersection of several measured directions pointers the yields the position value The accuracy of this approach is limited by the possible directivity of the measuring aperture by shadowing and by multi‐path reflections arriving from misleading directions In TOA and TDOA techniques rely on the precision of timing between the signal transmitter and the receiver in order to use the propagation delay or TOF to calculate the distance between transmitter and receiver Therefore a precise synchronization is also very important in such systems By combining at least three distances from three reference positions triangulation can be used to estimate the mobile stationʹs location This type of technique will

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

11

require a high accuracy clock in the communication system TDOA is more practical [1] Examples of location‐sensing systems that use time of flight are GPS the Active Bats [15] and the Cricket [16] Besides these time delay‐based techniques the distance between transmitter and receiver can also be determined from signal strength attenuation and direct distance measurement (such as dead reckoning) Fingerprinting or Location Pattern Matching this technique generally requires only measurement of RSS or other non‐geometric features at several locations to form a database of location fingerprints To estimate the mobile location the system needs to first measure the received signal strength at particular locations and then search for the pattern or fingerprint with the closest match in the database This technique does not require the mobile station to see at least three base stations or access points in order to determine the location The disadvantage of this technique is that it is very time‐consuming to perform an exhaustive data collection for a wide area network such as in outdoor positioning systems

23 Indoor local positioning using ZigBee based on RSSI This section reviews relevant RF‐based indoor positioning systems which can be used to locate stationary objects and track mobile users The impressive growth of IEEE 802154 wireless PANs (WPAN) in recent years suggests an interesting future for the location fingerprinting technique The system is quite flexible because system designers can select whether to have a centralized positioning server or let the mobile to determine its own position However the fingerprinting technique requires a training phase (off‐line phase) to collect location fingerprints for all positions in the operating area before the actual deployment (on‐line phase) [7] The following discussion is divided according to the positioning system components First the common form of location fingerprinting and its relationship with physical locations are explained Finally location estimation algorithm is reviewed

231 Location Fingerprint A location fingerprint based on RF characteristics such as RSS is the basis for representing a unique location It is created under the assumption that each position or location inside a building has unique RF signature [4] Generally a fingerprint F is labelled with a location information L The location fingerprints and their labels (eg location information) are maintained in

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

12

database and used during the on‐line phase to estimate the location The label and fingerprint are usually denoted as a tuple of (L F) Battiti et al [11] point out that the location information L for indoor location can be recorded in two forms as either a tuple of coordinates or an indicator variable The tuple of real coordinates can vary from one dimension to five dimensions which include the three dimension space and two orientation variables expressed in spherical coordinates For instance a location information of a two‐dimension system with an orientation could be expressed as a triplet L = (x y d) | x y є R 2 d є North East South West The RSS was found to be more location‐dependent than the SNR because the noise component is rather random in nature However the RSS itself fluctuates over time for each access point and location Each RSS element can be considered as a random variable therefore it can be captured by recording its descriptive statistics parameters approximating its distribution or maintaining the whole measurement dataset These approaches of RSS representation result in different procedures for location estimation algorithm Regardless of the approach the location fingerprint is usually denoted as an array or vector of signal strength (random variables) received at any position in the location‐based service area The size of the vector is determined by the number of access points that can be heard To create a basis fingerprint such as in a number of samples of vectors of signal strength are collected over a window of time for each position This basis is called a prototype Then the average RSS of each access point is calculated and recorded as an element in the location fingerprint For an area that can receive signals from N access points the location fingerprint can be expressed as a vector of average RSS elements βi

F = (β1 β2hellip βN)T Besides the basis location fingerprint the samples of location fingerprint measured during the on‐line phase are also important for the system that tracks the mobile object The number of samples should be selected appropriately to represent the location fingerprint for the mobile application A step in statistical analysis method called pre‐processing is another important issue needed to be considered because it can impact the estimation of dependency between location fingerprint and the location information easier The pre‐processing refers to a step that cleans the raw data (in this case the training set) before any further operations or analysis The cleaning may consist of encoding dimensionality reduction (reduce unnecessary elements) feature extractionselection clustering and outlier elimination Roos et al [10] point out that the pre‐processing enables faster location estimation and

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

13

reduce the noise from the training data The process that creates the basis of location fingerprint discussed above could be considered as a part of pre‐processing

232 Location Estimation Algorithm Location estimation algorithms or positioning algorithms are procedures that exploit dependency between location information and location fingerprint basis in order to determine a position or location from samples of RSS signals Position algorithms can be classified into deterministic and probabilistic types based on the approaches that model the relationship between location fingerprints and location information The deterministic type of algorithms are those that based in the nearest neighbour classifiers and neural network classifiers The probabilistic type of algorithms is those that are based on the Bayesian inference and statistical learning theory such as support vector machines (SVR) The algorithm used in this project is discussed briefly

Euclidean distance method The Euclidean distance method is a deterministic approach of position estimation because they require only a set of constant location fingerprints which includes mean vectors In order to determine the location a form of discriminate function is used to classify a sample of RSS fingerprint into a position The mean or average RSS vector is a centre of a mass which represents each class of location fingerprint Suppose a set of l location fingerprints is denoted by F1 F2hellip Fl exist and each fingerprint has a one‐to‐one mapping to a set of positions L1 L2hellip Ll A sample of an RSS fingerprint measured during an on‐line phase is denoted as S which can be another mean or average RSS vector of small window of RSS samples Assuming that an indoor positioning system only considers the average RSS from N access points as a location fingerprint the sample of RSS vector is S = (s1 s2 helliphellip sN)T and each location fingerprint i in the database can be expressed as Fi = (β1 β2helliphellip βN)T The simplest closeness metric is a distance measurement in signal space denoted as the Dist () function [6] Thus the simple procedure of nearest neighbour algorithm is expressed as picking the fingerprint j that has the shortest signal distance

Dist(S Fj) le Dist(S Fk) for all k ne j A generalized weighted distance Lp summarized by [12] can be used to calculate different forms of distance in signal space as

Lp = 1N pN

i

pPiSiWi 1

1)||1(sum

=

minus

where N is the number of access points wi is a weighting factor (wi le 1) and p is the norm parameter starting from 1 The weighting factor wi is a bias

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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14

parameter that can demote or promote an important RSS component in the fingerprint

24 Protocols and Industry standards for WSN Although many applications for WSN are proposed the communication protocols supporting them remain mainly unexplored and diverged This is because the area of interest is new each implementation is application specific and the components of WSNrsquos have a lot of constraints Wireless sensor networks use the wireless medium to communicate However the traditional communication protocols that support wireless communications especially ad‐hoc mesh networks may not be well suited for them WSN communication is an active research area and many algorithms and protocols have already been proposed This section introduces two proposed standards the IEEE 802154 and the ZigBee The IEEE 802154 covers the physical layer and the MAC layer of low‐rate WPAN The ZigBee is ldquoan emerging standard that is based on the IEEE 802154 and adds network construction (star networks peer‐to‐peermesh networks and cluster‐tree networks) application services and morerdquo

241 IEEE 802154 Holger and Willig (2005) [17] mention home automation home networking and home security as possible applications for IEEE 802154 They add that ldquomost of these applications require only low‐to‐medium bitrates (up to some few hundreds of kbps) moderate average delays without too stringent delay guarantees and for certain nodes it is highly desirable to reduce the energy consumption to a minimumrdquo The IEEE 802154 standard (2003) defines the device types that can be used in a LR‐WPAN A device can be a FFD or a RFD The RFD can be used in simple applications in which they do not need to transmit large amounts of data and they have to communicate only with a specific FFD The FFD can work as a PAN coordinator as a coordinator or as a simple device It can communicate with either another FFD or a RFD In keeping with the application requirements the LR‐WPAN operates in a star or peer‐to‐peer topology (figure 4) In the star topology the RFD communicates with a single controller the PAN coordinator The PAN coordinator can perform the same function as the RFD but it is also responsible for controlling the PAN ldquoit initiates terminates or routes communication around the networkrdquo [18] The peer‐to‐peer topology

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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supports ad‐hoc mesh multi‐hop networking Any device in the peer‐to‐peer topology can communicate with any other device within its communication range however this topology also has a PAN coordinator All the devices in a LR‐WPAN have a unique 64‐bit address This or a short address allocated by the PAN coordinator can be used inside a PAN Additionally each PAN has a unique identifier The combination of the PAN identifier and the sort addresses allows communication across different PANs (IEEE 802154 Standard 2003)

Figure 4 Star and peer‐to‐peer topologies in LR‐WPAN [18]

The LR‐WPAN based on the OSI seven‐layer model has the layered architecture presented in figure 5 The application and the network layer are the upper layers in the LR‐WPAN architecture but are outside the scope of the IEEE 802154 standard Only the physical layer ldquowhich contains the RF transceiver along with its low‐level control mechanismrdquo and the MAC layer ldquothat provides access to the physical channelrdquo are included in the standard and will be introduced in the following sections Finally the Type I 8022 LLC and the SSCS are intermediate sub layers supporting communication with the above layers (IEEE 802154 Standard 2003)

Physical Layer The 802154 standard specifies two different services that the PHY provides The PHY data service controls the radio and thus the transmission and reception of the PPDUs In addition the management service performs ED in the channel The management service also performs CCA before sending the messages and provides LQI for the received packets

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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Figure 5 WPAN architecture [18]

Three different bands are defined by the standard The 868‐8686 MHz for Europe the 902‐928 MHz for North America and the 2400‐24835 MHz worldwide All of them belong in the ISM radio bands and they are using DSSS Each of the bands supports different a data rate and it uses a different modulation technique chip rate and number of channels Moreover if the system does not use the 2450 MHz frequency it operates in both the 802 MHz and 902 MHz frequencies Table 3 summarizes the supported bands and data rates (IEEE 802154 standard 2003)

PHY (MHz)

Frequency Band (MHz) Modulation

Bit Rate Number of Channels

868-8686 BPSK 20 1 868 915 902-928 BPSK 40 10

2450 2400-24835 O-QPSK 230 16 Table 3 Frequency bands and data rates for IEEE 802154 based on the IEEE

802154 standard [18] The physical layer uses PPDU packets to communicate Table 4 demonstrates its structure The LSB is always transmitted and received first The SHR contains the preamble and the SFD fields which helps receiver

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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17

synchronization The 8‐bytes preamble contains only zero and is used for synchronization The SFD contains a specific sequence of one and zeros and specifies the beginning of the frame The PHR contains the payload length Packets with a length of 9 or more bytes are MPDU as the next section further explains Packets with length 5 are used for MPDU acknowledgements The payload part of the PPDU encloses the MAC layer packet Finally the PPDU size can be up to 127 bytes [18]

4 bytes 1 byte 1 byte Variable

Preamble SFD Frame length

(7 bits) Reserved

(1 bit) Payload (PSU)

SHR PHR PHY Payload Table 4 Format based on the IEEE 802154 standard [18]

MAC Layer The MAC layer is the interface between the SSCS and the PHY layer Similar to the PHY layer the MAC layer supports two services The MAC data service is responsible for the transmission and reception of the MPDUs through the PHY data service The MAC management service if the device is a coordinator manages the network beacons It is also responsible for PAN association and disassociation frame validation and acknowledgment providing ldquoa reliable link between two peer MAC entitiesrdquo In addition it uses the CSMA‐CA for channel access and handles and maintains the GTS mechanism Finally it supports device security [18] The IEEE 802154 standard defines four different frame types the beacon data acknowledgment and MAC command frame All frame types are based on the general MAC frame format (table 5) The frame control field describes and specifies the above different frame types Every MAC frame comprises a MHR which consists of a frame control sequence number and the information field It also contains the MAC payload different frame types have different MAC payload fields The acknowledgment type does not have a payload Finally each frame includes a MFR which contains a FCS The data in the MPDU follows the same order as the PPDU the least significant bits are left in the frame and are transited first Octets2 1 02 028 02 028 Variable 2

Destination PAN Identifier

Destination Address

Source PAN

Identifier Source Address Frame

Control Sequence Number Addressing Field

Frame Payload FCS

MHR MAC

Payload MFRTable 5 General MAC frame format IEEE 802154 standard [18]

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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Table 6 7 8 and 9 present the four different MAC frame types The beacon frame is transmitted periodically by the PAN coordinator It provides information about the network management through the super frame and GTS fields which are analyzed later in the section It also synchronizes the network devices and indicates the proper communication period for them The data frame payload encapsulates data from the higher layers When a device receives a packet it is not obliged to response with an acknowledgement packet Finally the command frame identifier and command payload fields of the command frame are used for communication between the network devices The command identifier specifies actions like association disassociation and data GTS or beacon request Octets2 1 410 2 Variable Variable Variable 2

Frame Control

Sequence Number

Addressing Fields

Super frame Specification

GTS fields

Pending Addressing

Fields Beacon Payload FCS

MHR MAC Payload MFRTable 6 Beacon frame format [18]

Octets 2 1 Variable 2

Frame Control

Sequence Number

Addressing Field

Data Payload FCS

MHR MAC

Payload MFR Table 7 Data frame format [18]

Octets 2 1 2

Frame Control

Sequence Number FCS

MHR MFR Table 8 Acknowledgement frame format [18]

Octets2 1 1 Variable 2

Frame Control

Sequence Number

Addressing Fields

Command Frame Identifier

Command Payload FCS

MHR MAC Payload MFR Table 9 Command frame format [18]

In the LR‐WPAN every PAN has its own coordinator The PAN coordinator manages the communication in the local area it has two options to use or not use the super frame structure The super frame (figure 6) uses network beacons If the coordinator does not want to use a super frame structure it suspends the beacon transmission However the beacon is important for device association and disassociation If the coordinator wishes to maintain close communication control in the PAN and to support low‐latency devices it usually uses the super frame A super frame determines a specific time

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

19

period beacons bound it The beacon is transmitted in the first of the sixteen equal time slots that the super frame has It is used to ldquosynchronize the attached devices to identify the PAN and to describe the structure of the super framerdquo The super frame can have active and inactive periods All the communications have to be finished inside the super frame period In the inactive periods the devices can switch to the sleep mode but they have to be ready for the next beacon The active period is further divided into a CAP and a CFP During the CAP any device can communicate competing with the other devices in the PAN using slotted CSMA‐CA The CFP contains GTSs and follows the CAP A CFP may maintain up to seven GTSs and each GTS can reserve more than one time slot However the CAP should always be sufficient to allow new devices t join the PAN The GTSs are allocated to specific devices All the transactions must be completed inside the assigned time period (CAP GTS and CFP) (IEEE 802154 standard 2003)

Figure 6 Example of super frame structure [18]

In accordance with the 802154 standard (2003) three different types of data‐transfer exist In addition the types differ if the coordinator uses or does not beacons Data transfer from a device to the PAN coordinator is the first type (figure 7) For a ldquonon‐beacon‐enable networkrdquo it first senses the medium by using ldquoun‐slotted CSMA‐CArdquo and then a simple transmit to the data frame In a ldquobeacon‐enabled networkrdquo the sender waits for the beacon when it finds it ldquothe device synchronizes to the super fame structurerdquo In the specified time frame the sender again senses the medium by using ldquoslotted CSMA‐CArdquo and transmits the data to the coordinator In either case the coordinator has the option to acknowledge or not acknowledge the data reception after that the transaction is completed Data transfer from the coordinator is the next type described in the standard (figure 8) In a beacon‐enabled network the coordinator indicates a pending message through the beacon The messagersquos target device receives the beacon and if the message is pending it responses with a MAC command request

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

20

message using slotted CSMA‐CA the coordinator may or may not acknowledge the command message and through slotted CSMA‐CA sends the pending message The device acknowledges the received message After that the coordinator removes the message from the beaconrsquos pending list and completes the transaction In a non‐beacon enable network the device periodically sends a MAC command‐request frame to the coordinator The coordinator acknowledges the data request Then by using un‐slotted CSMA‐CA if it has a pending message it transmits it If it has not it respond with a data frame with a ldquozero‐length payloadrdquo To complete the transaction the device acknowledges the data reception

Figure 7 Communication from a device to a PAN coordinator in (a) a beacon‐enabled network and (b) a non‐beacon enabled network [18]

Peer‐to‐peer is the last type of data transfer In this situation the devices are free to communicate with any other device within their communication range In a peer‐to‐peer PAN the devices can ldquoeither receive constantly or synchronize with each otherrdquo If they are receiving constantly to transmit data they use un‐slotted CSAM‐CA In the second case synchronization must be achieved first (IEEE 802154 standard [IEEE 2003]) The IEEE 802154 (2003) standard establishes MAC and PHY standards for low‐cost low‐power and high‐density node deployments In addition to the above PHY and MAC characteristics IEEE 802154 provides a security baseline including ldquothe ability to maintain an ACL and use symmetric cryptographyrdquo for data encryption The algorithm that is used for encryption is the AES However the higher level layers decide when security is need The upper layers are in general responsible for device authentication and key management The next section introduces the ZigBee standard which

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

21

encapsulates the IEEE 802154 and provides additional standardization for the higher levels

Figure 8 Communication from a PAN coordinator to a device in (a) a beacon‐enabled network and (b) a non‐beacon enabled network [18]

242 ZigBee Heily (2004) defines ZigBee as ldquoa rapidly growing worldwide non‐profit industry consortiumrdquo whose mission is ldquoto define a reliable cost‐effective low‐power wirelessly networked monitoring and control product based on an open global standardrdquo Figure 9 illustrates the areas of interest for different wireless communication standards

Figure 9 Overview of the coverage for different wireless communication

standards [17]

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

22

ZigBee a new standard which became publicly available in June 2005 is based on the IEEE 802154 standard It expands the IEEE 802154 by adding the framework for ldquothe network security and applicationrdquo (Craig 2005) Figure 10 presents the IEEE 802154ZigBee stack and the areas of responsibilities

Figure 10 IEEE 802154ZigBee stack [20]

Craig (2005) mentions three networking topologies that the standard covers the star mesh and cluster tree (figure 11) The ZigBee standard works on top of the IEEE 802154 addressing schema by using the standard 64‐bit and the short 16‐bit addressing Kinney (2005) summarizes the ZigBee network layer responsibilities the successful establishment of a new the network and successful new device configuration addressing assignment network synchronization frames security and message routing ZigBee further distinguishes the concept of the physical devices (RFD FFD) by using the notion of ldquological devicesrdquo ldquoZigBee Coordinatorrdquo is the first type of logical devices It is responsible for initializing maintaining and managing the network Under the coordinator in the network hierarchy is the ldquoZigBee routerrdquo which is responsible for controlling the message routing between the nodes Finally the ldquoZigBee End Devicerdquo acts as the end point of the network

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

23

structure The tasks that an end device can perform are specified in the ldquoApplication Profilesrdquo (Craig 2005)

Figure 11 IEEE 802154ZigBee stack [17]

The ZigBee specifications (2005) summarize the security services provided by ZigBee ldquokey establishment key transport frame protection and device managementrdquo ZigBee builds its security mechanism using symmetric key cryptography The security services also depend on the associated layer thus as the figure 10 shows the security mechanism covers the network and the application layer In addition if a MAC frame needs security protection the MAC layer is able to secure it Moreover the notion of end‐to‐end security is supported the source and destination devices have access and use the same share key In the MAC layer the 802154 AES mechanism provides the proper security The mechanism protects ldquothe confidentiality integrity and authenticity of the MAC framesrdquo (Kinney 2005) An auxiliary header field in front of the MAC payload indicates if the frame is encrypted or not The MAC framesrsquo integrity is supported by calculating and using a MIC at the end of the MAC payload In addition to the AES nonce is used to provide MAC confidentiality and authenticity Figure 12 illustrates a MAC frame with security For different security aspects the MAC layer uses different mode of the AES for the encryption it uses the AES in Counter (CTR) mode and for the integrity the

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

24

CBC‐MAC Finally the combination (CCM) of the above two modes is available providing both encryption and integrity (Kinney 2005)

Figure 12 ZigBee secure frame in MAC layer [17]

In the network layer the CCM (a modified MAC layer CCM mode) is used for encryption Because the network layer uses only the CCM mode a single key is used for all different security options The network layer security message format is similar to the MAC frame it is presented in the figure 13 Finally although the network layer is responsible for securing its layer messages the above layers specify the keys and the CCM option for each frame (Kinney 2005)

Figure 13 ZigBee secure frame in network layer [17]

Security in the application layer works similar to the network and MAC layers It uses the ldquolink keyrdquo or the ldquonetwork keyrdquo to secure the message and then encapsulate it inside a set of fields similar to the network format (figure 14) Other security responsibilities that the application layer has are to provide the ZDO and the applications with device management services key establishment and key transport (ZigBee specifications 2005)

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

25

Figure 14 ZigBee secure frame in application layer [17]

The ZigBee application layer contains the manufacturer‐defined application objects the ZDO and the application sub‐layer In addition to the security responsibilities the application sub‐layer binds devices based on their duties and needs maintains the binding tables and forwards messages between them The application sub‐layer also discovers the neighbour devices for a given device The ZDO is responsible for determining the devicersquos duty in the network for communicating using binding requests and for supporting security as was mentioned above The sub‐layer that implements the actual application is the manufacturer‐defined application object (Kinney 2005) The ZigBee specifications are a great step toward the wireless sensors networks standardization They cover all the OSI layers from the physical to the application providing guidance to the developers The specifications are the product of the ZigBee alliances a consortium of companies and academic institutes Thus they not only produce the specifications but also are the first use them The next section goes beyond the layer architecture and standardization and introduces the OS field another crucial element of WSNs

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

26

3 RECEIVED SIGNAL STRENGTH (RSS) Majority of the existing methods leverage the existence of IEEE 80211 base stations with powerful radio transmit powers of approximately 100mW per base station Such radios are in a different class from the low power IEEE 802154 compliant radios that typically transmit at low power levels ranging from 52mW to 29mW [19] The wide availability of larger number of IEEE 802154 radios in the research community has revived the interest for signal strength based localization in sensor network Despite of rapidly increasing popularity of IEEE 802154 radios and signal strength localization there is a lack of detailed characterization of the fundamental factors contributing to large signal strength variation This chapter investigates some of the properties of RSS observed during testing and analysis of research work collected during the period of thesis The analysis of RSS values is needed to understand the underlying features of location dependent RSS patterns and location fingerprints An understanding of the properties of the RSS values for location fingerprinting can assist in improving the design of positioning algorithms and in deployment of indoor positioning systems

31 Parameters that affect RSS Different factors that affect the indoor RSS are the userrsquos presenceabsence hisher orientation time of the day building type and material distance from transmitter and type of radio These factors are by no means exhaustive Other possible factors are antenna orientation directionality and type Table 10 classify the parameters which affect RSS [7]

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

27

Effect on Factors Options

1 Proximity of user Userrsquos presence or absence

2 Orientation of user and terminal

North East South West

Data Collection

3 Make of Radio 4 Time of measurement Time of day amp days of

week 5 Period of measurement

Second minute hour

7 Interference Co‐channel adjacent co‐channel

Statistics

8 Building environment Small offices or large hall

Table 10 Classification of factors the affect RSS [7]

Transmitter variability Different transmitters behave differently even when they are configured exactly in the same way In practice this means that when a transmitter is configured to send packets at a power level of d dBm then the transmitter will send these packets at a power level that is very close to d dBm but not necessarily exactly equal to d dBm This can alter the received signal strength indication and thus it can lead to inaccurate distance estimation

Receiver variability The sensitivity of the receivers across different radio chips is different In practice this means that the RSSI value recorded at different receivers can be different even when all the other parameters that affect the received signal strength are kept constant

Antenna orientation Each antenna has its own radiation pattern that is not uniform In practice this means that the RSSI value recorded at the receiver for a given pair of communicating nodes and for a given distance between them varies as the pair wise antenna orientations of the transmitter and the receiver are changed

Multi‐path fading and shadowing in the RF channel In indoor environments the transmitted signals get reflected after hitting on the walls andor on other objects in the room such as furniture Both the original signal and the reflected signal reach the receiver almost at the same time since they both travel at the speed of light As a result of this the receiver is not able to

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

28

distinguish the two signals and it measures the received signal strength for both of them

32 Related work analysis A number of indoor location tracking systems have been proposed in the literature based on RF signals ultrasound infrared and some combinations of these As mentioned previously RF based positioning system are widely studied as shown in Table 2 Given a model of radio signal propagation in a building or other environment RSS can be used to estimate the distance between the transmitter and receiver Most of the algorithms (Table 2) have been evaluated in practice using 80211b compliant wireless radios and seem to produce a location error of 15ft to 20ft for an indoor network area of approximately 225ft x 144ft According to the results of this study all the localization techniques produce approximately the same location error over a range of environments Ecolocation [22] Mote Track [5] are two of the few localization algorithms that have been evaluated on a real sensor network that uses a low power wireless radio Ecolocation reports a location error of 10ft for a very small outdoor network deployment area (26ft x 49ft) Mote Track reports a location error of approximately 13ft for an indoor network deployment area of 18751ft2 In general the RSSI based localization algorithms can be divided into two broad categories the map based and the distance (or area) prediction based algorithms In the case of the map based algorithms the deployment area is divided into a large set of sample points that cover the whole area Most techniques collect one or more signal strength measurements from all visible beacon nodes and for each sample point Each sample point is then mapped to either a signal strength vector or a signal strength probability distribution This process generates an RSSI fingerprint map which is unique with respect to the beacon locations and the deployment area This fingerprint map is stored to a central base station A node that is not aware of its location collects one or more signal strength measurements from all the visible beacon nodes and creates its own RSSI fingerprint that is sent to the central base station The base station computes nodersquos location as the sample pointrsquos location whose RSSI fingerprint distance from the unknown nodersquos RSSI fingerprint is the minimum among all the sample points Despite the fact that the map based RSSI localization methods produce relatively small location errors they are not attractive mainly because they require an excessive amount of profiling data and because they are not fully distributed The detailed profiling of a building is a major holdback because it is complex time consuming and depends on beacon locations Therefore for different buildings or even for the

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

29

same building and different beacon locations different RSSI maps have to be obtained The second category of RSSI based algorithms are the distance (or area) prediction algorithms which focus on minimizing the amount of profiling data In distance prediction based methods raw RSSI data are directly mapped to distances through a signal propagation model In some cases distances are not explicitly measured but they are taken into consideration through assumptions on the underlying distributions In the case of outdoor deployments where there are no obstacles a single signal propagation model is all the profiling data required In the case of indoor deployments the same applies but now the signal propagation model might be different across different buildings with different furniture and wall arrangements Given an underlying signal propagation model raw RSSI data can be mapped to specific distances These distances can be either used directly in order to perform localization or assigned a probability and used in a learning basedprobabilistic localization algorithm Obviously the accuracy of the distance prediction based methods depends heavily on the accuracy of the signal propagation model used for translating RSSI values to distances

33 Properties of RSSI Changes in signal due to propagation indoors are difficult to predict because of dense environment and propagation effects such as reflection diffraction and scattering The multi‐path fading effect which is the result of either constructive or destructive combination of multiple signal copies at the receiver causes the received signal to fluctuate around a mean value at a particular location The received signal is usually modelled by the combined effects of large‐scale fading and small‐scale fading The large‐scale fading component describes the signal attenuation as the signal travels over a distance and is absorbed by material such as walls and floors along the way to the receiver This component predicts the mean of the RSS and usually has a lognormal distribution On the other hand the small scale fading component explains the dramatic fluctuation of the signal due to multi‐path fading On the other hand the small scale‐fading component explains the dramatic fluctuation of the signal due to multi‐path fading If there is no LOS component the small‐scale‐fading is often modelled with a Rayleigh distribution Such scenarios are commonly called NLOS If there is a LOS component the small‐scale‐fading is typically modelled by a Rician distribution Note that when the RSS is measured the measurement averages out the small‐scale‐fading effects

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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4 OBJECT TRACKING 41 Introduction This chapter presents the object‐tracking application the main objective of this thesis work The application is an algorithmic‐software implementation written in Embedded C which is capable of receiving data messages and generating object position estimation data The implementation of the object‐tracking application makes use of the SENTIO platform and uses the background knowledge introduced in the preceding chapters The motivation for the object‐tracking application is to determine the position of an object in indoor environments (Eg Home) The application acts as an interface which collects the raw data that the sensors generate (numerical values) It makes the proper comparisons and algorithmic processes to identify and track the object while it is static inside the area covered by the wireless sensor network Finally with the detection and tracking data it alerts the user or another system The following sections provide an overview of the object‐tracking application

42 Overview of hardware product Before describing the object tracking application it is important to know the characteristics of hardware product The SENTIO platform (figure 15) is built in modular fashion and has the following key features [8]

Flexibility This platform is very flexible in such a way that it is made easy to add or change the hardware for each sensor task depending on the requirement in the WSN For a specific platform this can be hard or sometimes even impossible to accomplish

Sensor resources Resources in a sensor module includes the ability of usage of controllerrsquos resources as much as possible such as ADC‐channels interrupts and digital Input‐Outputrsquos Local voltage regulation should also be possible since many sensors require higher supply voltages than digital Integrated Circuits do Further support of a shut‐down mechanism to power down the sensor modules when not needed

Computation and memory The processor module based on microcontroller should support many devices in a microcontroller family and support different power supply voltages enabling full range of clock frequencies All power‐down modes of the devices should be

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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31

supported The platform should support different processor modules including FPGArsquos and very simple microcontrollers

Communication The focus should be on enabling an IEEE 802154 compliant communication interface With or without ZigBee stack this will provide a flexible platform for exploring different network topologies Support for power‐down of the entire communication module should be supported

Cost and form factor High flexibility is in conflict with low cost and small size The primary objective should be flexibility but the size should be considered in order to enable sensor device integration in systems where space is limited

Figure 15 SENTIO Platform [8]

The communication subsystem of the SENTIO sensor node was designed so that the correct operation of the radio chip is ensured The radio chip is powered by the on‐board voltage regulator and thus fluctuations in the battery voltage level do not affect the operation of the communication subsystem This platform uses Chipcon CC2420 IEEE 802154 radio transceiver operates in the 24GHz ISM band and includes a digital direct sequence spread spectrum (DSSS) modem providing a spreading gain of 9dB and an effective data rate of 250Kbps It was specifically designed for low power wireless applications and supports 8 discrete power levels 0dBm minus1dBm minus3dBm minus5dBm minus7dBm minus10dBm minus15dBm and minus25dBm at which its power consumption varies from 29mW to 52mW [21] A built‐in received signal strength indicator gives an 8‐bit digital value RSSIVAL The RSSIVAL is always averaged over 8 symbol periods (128μs) and a status bit indicates when the RSSIVAL is valid (meaning that the receiver was enabled for at least 8 symbol periods) The

Antenna

Communication Module

Processing Module

Sensing Module

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

32

power P at the RF pins can be obtained directly from RSSIVAL using the following equation

P = RSSIVAL + RSSIOFFSET [dBm] where the RSSIOFFSET is found empirically from the front‐end gain and it is approximately equal to minus45dBm In the next sections when we refer to the RSSI value we refer to the RSSIVAL and not the power P unless otherwise stated

43 Experimental Testbed The experimental test bed for object tracking application is as shown in figure 16 Experiment was conducted in Masterrsquos room at Mid Sweden University A DELL laptop is connected to SENTIO platform and software were primarily used to collect samples of RSS from APrsquos The software monitors IEEE 802154 radio frequency channels which operate in the 24GHz band

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

1030

BC - 1030

AP 3 AP 1

AP 2

AP 4

BS

HEWLETT

PACKARD

Figure 16 Layout of Masterʹs Room

A three dimensional scalable testbed is used for indoor sensor network deployments The testbed illustrated in figure 16 is a 3‐D structure measuring 16m (L) x 12m (W) x 3m (H) and it is designed to host a large number of static nodes as shown in figure 16 Arrow marks in the above figure represent the location where fingerprints were taken The centrepiece of our infrastructure

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

33

is the SENTIO sensor node [8] an open‐source general purpose sensing platform designed with AT Mega 128 microprocessor and the IEEE 802154 compliant CC2420 radio from Chipcon [19]

44 Obtaining data from the sensor network The next important step in the development of the object tracking application is to obtain the data from the sensor network and send it to the base station From there the data is transmitted through a serial port to the computer that runs the object tracking application When the data arrives at the serial port the application must capture it This paragraph gives a brief explanation of how and what kind data is transmitted by the static object fixed sensors and how this data is being used to estimate the position of the object First the static object transmits Beacon packet continuously which is received by all the fixed APrsquos The beacon packet format is as shown in table 6 Then the fixed APrsquos calculates the 8‐bit value of RSSI from the transmitted beacon packet and then sends the resultant RSSI value as a form of data packet (table 7) to the BS which calculates the position and sends back to the object

45 Object tracking application Testing and Evaluation The purpose of this section is to discuss and evaluate the object tracking application Finally we are ready with the experimental testbed as shown in figure 16 As shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors placed at the corners of the masterrsquos room BS is placed at the centre of the room connected to a PC All the nodes are power connected before an actual measurement takes place At first the object is placed at random position inside the room and transmits beacon packets continuously Then these packets are received by the fixed APrsquos which calculate the RSSI from the packet and then averages it until it receives 50 beacon packets and then transmits the data to the BS (figure 17) Then the BS waits until it receives the data from all the APrsquos Once it receives from all the APrsquos it calculates the position of the object (figure 18) The application was developed to demonstrate a real world use of a WSN in object tracking In evaluating the final position obtained with respect to the actual position we conclude that the object‐tracking was successful Although

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

34

it is a research prototype object tracking application is a simple stable stand‐alone system capable of demonstrating an important application of WSN It takes raw data from the WSN aggregates it evaluates it and based on the evaluation tracks the object The accuracy of estimating the position is less than 5m Table 11 below shows a log of readings taken during test

Start

Intialize micro‐controller and transeciever in receieve mode

If received_packet=

Beacon

Average RSSI values

Increase the counter

If countergt1000050

A

No

Yes

B

B

Yes

No

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

35

A

Reset counter

Set transceiever in transmit mode

Transmit averaged RSSI to Main sensor and set it back to receive mode

10000 samples ‐ Offline

50 samples ‐ Online

Figure 17 Flow chart representing the algorithm of fixed sensors

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

36

Start

Intialize micro‐controller and transeciever in receieve mode

Received RSSI values from all fixed Sensors

Open radio map

Calculate Euclidean distance between obtained RSSI values from fixed sensors and fingerprints

in radio map

No

Yes

Bubble sort for least error in signal strength values

A

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

37

A

Get the location of the object from the radiousing the least error value

Display location of object on PC

End

Figure 18 Flow chart representing the algorithm of main sensor

Table 11 Accuracy of positioning system

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

38

5 RESULTS This chapter presents how the system parameters such as number of samples mean or median etchellipare finalized All the tests are performed in masterrsquos room at Mid Sweden University This chapter is divided into three subsections First in Section 51 it reviews results obtained from samples test Section 52 discusses one dimensional test results and finally section 53 discusses results of two dimensional tests

51 Measurement setup A Dell laptop was connected to a SENTIO platform and is loaded with software (Appendix 2) This setup was used to determine number of samples required during object tracking mean or median position estimation algorithm and one dimensional test The setup is as follows

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

103

0

BC - 1030

AP 1AP 2

BS

HEWLETT

PACKARD

Figure 19 Test bed for one dimensional test in Masters Room

Two SENTIO nodes (AP1 and AP2 as shown above) were separated by a distance of 10 meters and these sensors are known as fixed sensors which are fixed to the ceiling such that there antenna is faced towards the ground They are linear is position A BS was connected to Dell laptop and it was placed in such a way that its antenna was faced towards the ceiling Another SENTIO

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

39

node was used as an object and its orientation was same as BS Figure 19 represents the complete setup for object tracking application Mobile node was moved between these fixed ones at regular intervals of one meter and RSSI values were logged at each interval

52 Number of samples test This test was performed to determine number of samples required for accurate positioning and either mean of the obtained RSSI values or median of them Figure 19 represents the setup for number of samples test Once the setup is ready the measurements are taken as follows

1 An object was tagged with SENTIO node and was placed stationary linear to the fixed sensors

2 10000 samples (RSSI) were taken at regular intervals of 1 meter up to 10 meters

3 The sample values were logged at every interval 4 Using these logged samples I have plotted graphs such as distribution

of RSS values (figure 20 21) meanmedian at intervals of 1 meter using MATLAB

5 The mean and median for various numbers of samples is as shown in figure 22

Figure 20 Histogram plot of RSSI values at 1m and 4m

Figure 21 Histogram plot of RSSI values at 6m and 8m

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

40

Figure 22 Distance vs RSSI

From the above graphs following conclusions were made 1 From the histogram plots we can observe that the distribution of RSSI

values follow Gaussians distribution law 2 As we can see from figure 22 that median curve has drastic changes

when compared to mean curve Hence mean is considered while estimating the position

3 From the observations we conclude that the number of samples does not matter since mean curve tends to be same for various numbers of samples

4 Hence 10000 samples are considered in order to get a proper location fingerprint during off‐line phase and during online phase 50 samples are enough to estimate the position of object

53 0ne dimensional test Measurement setup for one dimensional test is as shown in figure 19 This test was conducted to check the performance of algorithm and how accurate it was At first ie during offline phase fingerprints at each interval averaging 10000 samples were taken During online phase Euclidean algorithm was used estimate the position averaging 50 samples of RSSI From the results we

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

41

can realize that the error was less than 2 meters Table 12 shows the result of location estimation in one dimensional case

Table 12 One dimensional position estimation results

54 Two dimensional test This test is the final stage of my thesis work Experimental testbed for this stage is as shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors fixed at the corners of the room and a BS at the centre of the room There are 18 pre‐determined locations where the fingerprints (RSSI) were taken The fingerprints are as shown in Appendix 1 All the readings were taken in presence of userrsquos The algorithm for position estimation is shown in Appendix 2 Position estimation test is performed as follows

Tagged object is placed at random inside the masterrsquos room All the sensors including the base station and object are switched on Using the algorithm (Appendix 2) the BS estimates the position of the object and displays it on the PC

This test was performed using various numbers of samples to test which has a better accuracy Figure 23 represents the error rate using various numbers of samples From figure 23 we can conclude that 50 numbers of samples is enough to estimate the position of object with an accuracy of 3m

55 Causes of error in location determination The randomness of RSS is the major cause of error in any indoor

positioning system that uses the RSS for location inference If there is no randomness in the RSS and its location fingerprint every indoor positioning systems of this type will have no location detection error For example I have recorded RSS at one particular location during different times of the day From figures 24 and 25 you can observe that RSS values are different at different times of day

The performance of indoor positioning systems depends greatly on the separation of location fingerprints A location fingerprint

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

42

corresponding to a location can be identified correctly if it is difficult to classify it as another fingerprint by a pattern classifier

Path loss is one of the basic signal propagation properties of the RSS that best describes attenuation of transmit signal power over a distance between an access point and a mobile device

Presence of Userrsquos or userrsquos orientation affects RSS

Figure 23 Error Rate vs Number of samples

Figure 24 RSS distribution at 214 pm

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

43

Figure 25 RSS distribution at 100pm and 1100 am

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

6 DISCUSSIONS10102006

44

6 DISCUSSIONS 61 Summary and Conclusion While empirical results and performance studies of positioning systems based on location fingerprint have been presented in this report analytical model that can be used as a framework for efficiently designing the positioning systems are not available This thesis design work uses general principles of position estimation such as position estimation of sensors grid spacing etc This design work focuses on accuracy and precision The concept of location fingerprint technique based on RSS us explained in detail in chapter 2 It is found that RSS is random It is possible to approximate the RSS as being normally distributed While the RSS random process is non‐stationary the mean is more or less constant and could be used as the fingerprint of the location Model designed for position estimation is as shown in chapter 4 Accuracy and precision are the two major performance matrices in this study Uniform grid spacing has been used From the experimental analysis I have obtained an accuracy of 3 meters and 35 precision The sensitivity analysis among the system parameters and the performance matrices were done using the model as shown in chapter 4 For instance our study found that the minimum distance between two adjacent positions also affects the positioning performance This is due to the randomness of RSS patterns induced by the ever changing indoor environment

62 Future work The research in this thesis provides a ground work for the future study of efficient design of indoor positioning system The RSSI based position estimation still has limitations First the RSSI value is time dependent Thus the future study should address this issue Second the movement of object is not considered Third orientations of sensors affect the performance which should be taken care off Fourth to improve the performance of the system we need to find a solution to deal with those incomplete or missing data during the off‐line and the on‐line phase Fifth position estimation on multi‐floor and three‐dimensional coordinates is not available

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 110102006

45

APPENDIX 1

Location Fingerprints of Masters Room Time 1315 Observation Time 20 minutes Number of Samples 10000

People Presence SNo Co-ordinates AP1 (0x0001) AP2 (0x0002) AP3 (0x0004) AP4 (0x0008)

1 11 -24 -16 -27 -33 2 13 -26 -8 -34 -31 3 15 -43 -9 -39 -25 4 32 -21 -11 -27 -33 5 61 -6 -23 -32 -28 6 64 -20 -31 -25 -34 7 35 -23 -35 -29 -38 8 37 -29 -35 -22 -27 9 39 -38 -29 -21 -27 10 67 -27 -23 -20 -23 11 611 -26 -29 -23 -30 12 613 -35 -32 -2 -31 13 513 -34 -31 -20 -26 14 11 -39 -22 -29 -17 15 212 -37 -42 -28 -31 16 -212 -38 -33 -29 -6 17 -21 -42 -29 -33 -19 18 -38 -40 -23 -35 -22

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 210102006

46

APPENDIX 2

Begin

Open radio map and raw data files for write

Intialize radio map in memory

Check whether the

Packet is data or Beacon packet If data packet

Save the data to register

Check if data is

Received from all

sensors

Estimate the position of static

Display the position on PC

End Yes

No

A

A

Yes

No

Flow chart representing the main algorithm for position estimation

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

47

APPENDIX 3

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

48

Distribution of RSSI

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

49

REFERENCES [1] T S Rappaport J H Reed and B D Woerner ldquoPosition location using wireless communications on highways of the futureʺ IEEE Commun Mag vol 34 no 10 pp33‐41 Oct 1996 [2] A M Ladd K E Bekris G Marceau A Rudys L E Kavraki and D S Wallach rdquoRobotics‐based location sensing using wireless ethernetʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ02) 2002 pp 227‐238 [3] I Ramani R Bharadwaja and P V Rangan ldquoLocation tracking for media appliances in wireless home networksʺ in Proc International Conference on Multimedia and Expo (ICMEʹ03) July 2003 pp 769‐772 [4] K Pahlavan X Li and J P Makela ldquoIndoor geolocation science and technologyʺ IEEE Commun Mag vol 40 no 2 pp 112‐118 Feb 2002 [5] Konard Lorincz and Matt Welsh ldquoMote Track A Robust Decentralized Approach to RF‐Based Location Trackingrdquo [6] Paramvir Bahl and Venkata N Padmanabham ldquoRADAR An In‐Building RF‐based User Location and Tracking Systemrdquo [7] Kamol Kaemarungsi ldquoDesign of Indoor positioning system based on location fingerprint techiniquerdquo University of Pittsburgh 2005 [8] Fredrik Linnarsson Cheng Peng and Bengt Oelmann ldquoSENTIO A Hardware Platform for Rapid Prototyping of Wireless Sensor Networksrdquo [9] S Saha K Chaudhuri D Sanghi and P Bhagwat ldquoLocation determination of a mobile device using IEEE 80211b access point signalsʺ in Proc IEEE Wireless Communications and Networking Conference (WCNCʹ03) New Orleans LA Mar 2003 pp 1987‐1992 [10] T Roos P Myllymaki H Tirri P Misikangas and J Sievanen ldquoA probabilistic approach to WLAN user location estimationʺ International Journal of Wireless Information Networks vol 9 no 3 pp 155‐164 July 2002 [11] R Battiti M Brunato and A Villani ldquoStatistical learning theory for location fingerprinting in wireless LANrsquosʺ Technical Report Oct 2002 [Online] Available httprtmscienceunitnitraquobattitiarchive86pdf [12] P Prasithsangaree P Krishnamurthy and P K Chrysanthis ldquoAn indoor position location with wireless LANsʺ in Proc IEEE International Symposium on Personal Indoor and Mobile Radio Communications (PIMRCʹ02) Lisbon Portugal Sept 2002 [13] M A Youssef A Agrawala and A U Shankar ldquoWLAN location determination via clustering and probability distributionsʺ in Proc IEEE

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

50

International Conference on Pervasive Computing and Communications (PerComʹ03) Dallas‐Fort Worth TX Mar 23‐26 2003 pp 23‐26 [14] Z Xiang S Song J Chen H Wang J Huang and X Gao ldquoA wireless LAN‐based indoor positioning technologyʺ IBM Journal of Research and Development vol 48 no 56 pp 617‐626 SeptNov 2004 [15] R Want A Hopper V Falcao and J Gibbons ldquoThe active badge location systemʺACM Transactions on Information Systems vol 40 no 1 pp 91‐102 Jan 1992 [16] N B Priyantha A Chakraborty and H Balakrishnan ldquoThe cricket location‐support systemʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ00) Boston MA Aug 2000 pp 32‐43 [17] Holger K Willig A (2005) Protocols and Architectures for Wireless Sensor Networks London [18] IEEE (2003) Part 154 Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low‐Rate Wireless Personal Area Networks (LR‐WPANs) New York [19] Chipcon CC2420 802154 compliant radio httpwwwchipconcom [20] ZigBee Specification v10 ZigBee Alliance [21] Dimitrios Lymberopoulos Quentin Lindsey and Andreas Savvides ldquoAn Empirical Analysis of Radio Signal Strength Variability in IEEE 802154 Networks using Monopole Antennasrdquo [22] Kiran Yedavalli Bhaskar Krishnamachari Sharmila Ravula and Bhaskar Srinivasan ldquoEcolocation A sequence based technique for RF localization in wireless sensor networksrdquo Robert Bosch Research and Technology Centre Palo Alto CA

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

2

12 Background The success of outdoor positioning and applications based on the GPS provides an incentive to the research and development of indoor positioning systems It has been developed over several decades and it relies on constellation of satellites Ranges to several satellites are used in multi‐lateration procedure to infer the position of the receiver This localization scheme requires hardware that is both expensive and consumes significant power As a result indoor positioning systems require alternative means to detect the MS location without relying on the direct signal from GPS satellites Infrared RF and ultra sound signals are major technologies used for indoor positioning systems Unlike outdoor areas the indoor environment imposes different challenges on location discovery due to the dense multi‐path effect and building material dependent propagation effect Thus an in‐depth understanding of indoor radio propagation for positioning is crucial for efficient design and deployment The existence of radio connectivity and the attenuation of radio signal with distance are attractive properties that could potentially be exploited to estimate the positions of small wireless devices featuring low‐power radios RSSI a standard feature in most radios has attracted a lot of attention in the recent literature for obvious reasons RSSI eliminates the need for additional hardware in small wireless devices and exhibits favourable properties with respect to power consumption size and cost As mentioned previously RF‐based location tracking has been widely studied [5 6] Given a model of radio signal propagation in a building or other environment RSSI can be used to estimate the distance from a transmitter to a receiver and thereby estimate the position of a mobile node However this approach requires detailed models of RF propagation and does not account for variations in receiver sensitivity and orientation Table 1 below briefly summarizes performance of existing indoor positioning system based on location fingerprints and WLANrsquos All the systems mentioned below in table 1 follow IEEE 80211 protocol standards The major performance metrics studied by all systems are positioning accuracy which is a form of error measurement The accuracy of the location information is usually reported as an error distance between the estimated location and the actual mobile location However the report of accuracy should include the confidence interval or percentage of successful location detection which is called the precision The system parameters of existing systems are summarized in table 1 and the best reported performance of these systems are listed in table 2

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

3

System Spacing(m) Positions SamplesPos APs Orientation Environment

RADAR [6] Non

uniform 70 80 3 4 Hall-way Saha et al [9] 3 19 1200 3 NA 1-floor

Roos et al [10] 2 155 40 10 NA 1-floor Battiti et at [11] NA 257 NA 6 NA 1-floor Ladd et al [2] 3 11 1307 5 2 Hall-way

Prasithsangaree et al [12] 153 60 40

2 to 7 4 1-floor

Youssef et al[13] 15 110 300 4 NA Hall-way Xiang et al [14] NA 100 300 5 4 1-floor

Table 1 Comparison of Indoor Positioning Systems [7]

System Algorithm Type Accuracy and Precision RADAR [6] Nearest Neighbour within 7 feet 38

Saha et al [9] Nearest neighbour amp Neural Network no specified accuracy

90 Roos et al [10] Bayesian best within 828 feet 90

Battiti et at [11] Neural Network amp Weighted k-Nearest

Neighbour all within 16-17 feet 90 Ladd et al [2] Bayesian within 5 feet 77

Prasithsangaree et al [12] Weighted k-Nearest Neighbour

25 feet at 75 amp 40 fet at 95

Youssef et al [13] Bayesian within 7 feet more than

90 Xiang et al [14] Bayesian with RSS distribution model within 6 feet 90 Table 2 Performance comparison of Indoor Positioning Systems [7]

Based on the study of results obtained in various positioning systems as in table 2 we have developed a model for an indoor positioning system using SENTIO [8] platform based on RSSI

13 Objectives In the last two or three years a number of theoretical practical andor simulation studies were done on the topic of object‐tracking While these studies are useful they are too general and provide little guidance for the actual deployment of sensor networks for real life location‐tracking of an object This thesis focuses on developing an object‐tracking application and prescribes sensor network configurations that work well with our algorithms I implemented my software using SENTIO [8] platform for sensor networking The major issue addressed in this project are the accuracy and precision in tracking an object in a real‐world application and efficient ways to algorithmically analyze the collected raw data from the specific wireless sensor product

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

4

Although the focus is to develop a real‐world application using WSN it also provides great opportunity to explore the new area of wireless communication overall

14 Research Questions My primary target is to find a method to determine the position of an object inside a building using ZigBee protocol standards based on RSSI The study addresses the following questions What is WSN What requirements do WSN need What are their main ideas and concepts What are the associated technologies What are the standards that sensor networks use

What are the existing hardware implementations that use this particular technology How can we build our new applications using WSN What do sensor network systems require for general or specific applications Are they applicable And what is the appropriate design for sensor network implementations in order to combine them with other existing systems At this point we describe and evaluate the specific object‐tracking application to be built

15 Scope The study area of the thesis is an overview of the WSN technology study of SENTIO platform and implementation of an object tracking application Thus the study is divided into two parts Part one is an overview of WSN it includes study of SENTIO platform and IEEE 802154 protocol standards The second part describes the implementation of the object tracking algorithm The object tracking application receives and uses the raw values returned by the sensor network system to produce clear and meaningful outputs Schema of thesis work is as shown in figure 1

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

5

Figure 1 Schema of thesis work

16 Methodology The ldquoIndoor Local Positioning System for ZigBee based on RSSI ldquois based on ldquolocation fingerprintingrdquo technique Location fingerprinting refers to a technique that exploits the relation between any measurable physical stimulus and a specific location This approach uses empirical measurements of RSSI to estimate location By recording a database of radio ldquosignaturesrdquo along with their known locations a mobile node can estimate its position by acquiring a signature and comparing it to the known signatures in the database All of these systems require the database of signatures which should be collected manually prior to the system installation and rely on central server (or the userrsquos mobile node) to perform location calculation For example in RADAR [6] technology the RSSI is the stimulus This type of positioning system does not require specialized hardware other than the common wireless network interfaces with received signal strength measurement capability thus it is simple to deploy compared to other techniques Unlike outdoor counterpart systems which can use AOA and TDOA techniques effectively indoor positioning systems encounter the problem of NLOS and the dense multi path effect that render these two techniques ineffective or complex for practical implementation It is also difficult for a MS to always see three or more access points or base stations in indoor environment which is essential for triangulation by AOA and TDOA Location fingerprinting can also be implemented as software based positioning system which can reduce

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

6

complexity and cost Generally the deployment of fingerprinting based positioning systems can be divided into two phases First in the off‐line phase the locations where the fingerprints are required is pre‐determined and then the fingerprints are collected by performing a site‐survey of the RSS from multiple APs The distance between two closest positions is called grid spacing and usually reported in meters or feet However some points may be omitted due to inaccessibility The RSS is measured at each predetermined location to create a database of RSS patterns of the indoor environment The database of RSS patterns is called a radio map The vector of RSS values at a point on the grid is called the location fingerprint of that point Second in the on‐line phase a MS will report a sample measured vector of RSS from different APs to a base station The base station uses a positioning algorithm to estimate the location of the MS The most common algorithm used to estimate the location is Euclidean algorithm It computes the Euclidean distance between the measured RSS vector and each fingerprint in the database and then the coordinates associated with the fingerprint that provides the smallest Euclidean distance is returned as the estimate of the position It is clear from the above discussion that the designer should not start out to do the site survey without proper system design objectives and guideline If the performance is not sufficient collecting more fingerprints in between previous location fingerprints may help It is a problem of choice as to how many access points are required for the system and what the minimum distance between physical positions on the grid should be in order to provide a good position resolution and best system performance The system parameters and factors that improve the accuracy and the precision performance are still not clear

17 Outline Chapter 2 provides literature review of ldquoIndoor Local Positioning System for ZigBee based on RSSIrdquo it introduces the basic components required for a positioning system general classification of indoor positioning system Finally it explains ldquoLocation Fingerprintrdquo technology used in this thesis work to determine the position of an object in a real‐world application It briefly explains the protocols and industry standards for wireless sensor networks Chapter 3 describes some properties of RSS which is a stimulus to determine

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

7

the position of an object RSS relation with energy and on what parameters is the RSS value dependent Chapter 4 describes the model of an indoor local positioning system and its probability of returning the correct location It also explains its performance evaluation and its error distance distribution Chapter 5 describes results obtained from the model of positioning system Chapter 6 includes conclusions from the experimental analysis and future work

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

8

2 LITERATURE REVIEW This chapter reviews the literature on wireless indoor positioning system as a means of providing an intellectual background for the thesis work First in section 21 the common components of indoor positioning system are described Then in section 22 discusses different means to classify indoor positioning systems In section 23 reviews of all relevant literature on location fingerprint technique Finally in section 24 protocols and industry standards for wireless sensor networks are explained

21 Common components of indoor positioning system Basically a wireless local positioning system consists of at least two separate hardware components a measurable unit that usually carries the major part of the system intelligence and a signal transmitterreceiver The transmitter in the simplest case is just a beacon Figure 2 represents basic hardware model of a positioning system

Figure 2 Block diagram representing hardware module

First the location sensing devices detect the signals transmitted by or received at known reference points using radio sensing technologies The sensing technique is based on signal strength level that converts the sensed signal into RSS Given a set of reference points the relative position of the MS can be derived from the signal characteristics of RSS The signal characteristics of RSS are unique for a location Then the microcontroller processes the position algorithm and estimates the position of object and displays it on PC

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

9

22 Classification of Indoor positioning systems Indoor positioning system can be categorized based on the stimulus used in particular application and measurement principle

221 Classification based on Stimulus Based on the stimulus used in an application WSN inherit certain characteristics and limitations on sensorrsquos signal The propagation delay diffraction reflection and scattering are the basic signal characteristics which affect all signal types The effective range available bandwidth regulatory constraints interference power constraints safety and cost of technology limitations The wireless signals commonly used for indoor positioning systems are infrared radio frequency and ultrasound Brief descriptions of the three major technologies are as follows

Infrared Infrared signal is an electromagnetic signal of a wavelength longer than that of visible light but shorter than that of radio waves Infrared is just below the visible spectrum of light in frequency and is radiated strongly by hot bodies Many objects such as people vehicle engines and aircraft generate and retain heat and as such are especially visible in the infrared wavelengths of light compared to objects in the background It cannot pass through walls or obstructions therefore it has a rather limited range of indoor environments Thus it requires a more sophisticated circuitry than ultrasound signals Indoor lighting interferes with this type of signal and causes problems in accurate sensing It generally has a range of around 5 m The infrared devices are usually small in size compared to ultrasound devices

Radio frequency The RF signal can penetrate through most of the building material therefore it has an excellent range in indoor environments The propagation speed is also high approximately 3108 ms There are unlicensed frequencies available freely for use This type of signal has the longest range compared to infrared and ultrasound Since the propagation waves are of higher frequency (24GHz) interference with other frequency components is less

Ultrasound Although ultrasound operates at low frequency bands (typical 40 kHz) compared to the other two signaling technologies it possesses a good precision for location sensing at a slow propagation speed of sound (343 ms) The advantages of ultrasound devices are their simplicity and that they are inexpensive However ultrasound does not penetrate walls

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

10

but reflects off most of the indoor obstructions It has a short range around 3 m to 10 m but has a 1 cm resolution of distance measurement The operating temperature influences the performance of ultrasound

222 Classification based on measurement principle Wireless positioning systems can be categorized by measurement techniques which are used to derive the position of mobile stations Mainly three different measurement principles are used today AOA RSS and propagation‐time based systems that can further be divided into two different sub‐classes TOA and TDOA Figure 3 shows taxonomy based on the technology and the technique classifications

Figure 3 Taxonomy of positioning system [7]

In AOA systems the position is calculated via goniometry With the use of directional antennas or antenna arrays the angle or bearing relative to points located at known positions is measured The intersection of several measured directions pointers the yields the position value The accuracy of this approach is limited by the possible directivity of the measuring aperture by shadowing and by multi‐path reflections arriving from misleading directions In TOA and TDOA techniques rely on the precision of timing between the signal transmitter and the receiver in order to use the propagation delay or TOF to calculate the distance between transmitter and receiver Therefore a precise synchronization is also very important in such systems By combining at least three distances from three reference positions triangulation can be used to estimate the mobile stationʹs location This type of technique will

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

11

require a high accuracy clock in the communication system TDOA is more practical [1] Examples of location‐sensing systems that use time of flight are GPS the Active Bats [15] and the Cricket [16] Besides these time delay‐based techniques the distance between transmitter and receiver can also be determined from signal strength attenuation and direct distance measurement (such as dead reckoning) Fingerprinting or Location Pattern Matching this technique generally requires only measurement of RSS or other non‐geometric features at several locations to form a database of location fingerprints To estimate the mobile location the system needs to first measure the received signal strength at particular locations and then search for the pattern or fingerprint with the closest match in the database This technique does not require the mobile station to see at least three base stations or access points in order to determine the location The disadvantage of this technique is that it is very time‐consuming to perform an exhaustive data collection for a wide area network such as in outdoor positioning systems

23 Indoor local positioning using ZigBee based on RSSI This section reviews relevant RF‐based indoor positioning systems which can be used to locate stationary objects and track mobile users The impressive growth of IEEE 802154 wireless PANs (WPAN) in recent years suggests an interesting future for the location fingerprinting technique The system is quite flexible because system designers can select whether to have a centralized positioning server or let the mobile to determine its own position However the fingerprinting technique requires a training phase (off‐line phase) to collect location fingerprints for all positions in the operating area before the actual deployment (on‐line phase) [7] The following discussion is divided according to the positioning system components First the common form of location fingerprinting and its relationship with physical locations are explained Finally location estimation algorithm is reviewed

231 Location Fingerprint A location fingerprint based on RF characteristics such as RSS is the basis for representing a unique location It is created under the assumption that each position or location inside a building has unique RF signature [4] Generally a fingerprint F is labelled with a location information L The location fingerprints and their labels (eg location information) are maintained in

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

12

database and used during the on‐line phase to estimate the location The label and fingerprint are usually denoted as a tuple of (L F) Battiti et al [11] point out that the location information L for indoor location can be recorded in two forms as either a tuple of coordinates or an indicator variable The tuple of real coordinates can vary from one dimension to five dimensions which include the three dimension space and two orientation variables expressed in spherical coordinates For instance a location information of a two‐dimension system with an orientation could be expressed as a triplet L = (x y d) | x y є R 2 d є North East South West The RSS was found to be more location‐dependent than the SNR because the noise component is rather random in nature However the RSS itself fluctuates over time for each access point and location Each RSS element can be considered as a random variable therefore it can be captured by recording its descriptive statistics parameters approximating its distribution or maintaining the whole measurement dataset These approaches of RSS representation result in different procedures for location estimation algorithm Regardless of the approach the location fingerprint is usually denoted as an array or vector of signal strength (random variables) received at any position in the location‐based service area The size of the vector is determined by the number of access points that can be heard To create a basis fingerprint such as in a number of samples of vectors of signal strength are collected over a window of time for each position This basis is called a prototype Then the average RSS of each access point is calculated and recorded as an element in the location fingerprint For an area that can receive signals from N access points the location fingerprint can be expressed as a vector of average RSS elements βi

F = (β1 β2hellip βN)T Besides the basis location fingerprint the samples of location fingerprint measured during the on‐line phase are also important for the system that tracks the mobile object The number of samples should be selected appropriately to represent the location fingerprint for the mobile application A step in statistical analysis method called pre‐processing is another important issue needed to be considered because it can impact the estimation of dependency between location fingerprint and the location information easier The pre‐processing refers to a step that cleans the raw data (in this case the training set) before any further operations or analysis The cleaning may consist of encoding dimensionality reduction (reduce unnecessary elements) feature extractionselection clustering and outlier elimination Roos et al [10] point out that the pre‐processing enables faster location estimation and

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

13

reduce the noise from the training data The process that creates the basis of location fingerprint discussed above could be considered as a part of pre‐processing

232 Location Estimation Algorithm Location estimation algorithms or positioning algorithms are procedures that exploit dependency between location information and location fingerprint basis in order to determine a position or location from samples of RSS signals Position algorithms can be classified into deterministic and probabilistic types based on the approaches that model the relationship between location fingerprints and location information The deterministic type of algorithms are those that based in the nearest neighbour classifiers and neural network classifiers The probabilistic type of algorithms is those that are based on the Bayesian inference and statistical learning theory such as support vector machines (SVR) The algorithm used in this project is discussed briefly

Euclidean distance method The Euclidean distance method is a deterministic approach of position estimation because they require only a set of constant location fingerprints which includes mean vectors In order to determine the location a form of discriminate function is used to classify a sample of RSS fingerprint into a position The mean or average RSS vector is a centre of a mass which represents each class of location fingerprint Suppose a set of l location fingerprints is denoted by F1 F2hellip Fl exist and each fingerprint has a one‐to‐one mapping to a set of positions L1 L2hellip Ll A sample of an RSS fingerprint measured during an on‐line phase is denoted as S which can be another mean or average RSS vector of small window of RSS samples Assuming that an indoor positioning system only considers the average RSS from N access points as a location fingerprint the sample of RSS vector is S = (s1 s2 helliphellip sN)T and each location fingerprint i in the database can be expressed as Fi = (β1 β2helliphellip βN)T The simplest closeness metric is a distance measurement in signal space denoted as the Dist () function [6] Thus the simple procedure of nearest neighbour algorithm is expressed as picking the fingerprint j that has the shortest signal distance

Dist(S Fj) le Dist(S Fk) for all k ne j A generalized weighted distance Lp summarized by [12] can be used to calculate different forms of distance in signal space as

Lp = 1N pN

i

pPiSiWi 1

1)||1(sum

=

minus

where N is the number of access points wi is a weighting factor (wi le 1) and p is the norm parameter starting from 1 The weighting factor wi is a bias

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

14

parameter that can demote or promote an important RSS component in the fingerprint

24 Protocols and Industry standards for WSN Although many applications for WSN are proposed the communication protocols supporting them remain mainly unexplored and diverged This is because the area of interest is new each implementation is application specific and the components of WSNrsquos have a lot of constraints Wireless sensor networks use the wireless medium to communicate However the traditional communication protocols that support wireless communications especially ad‐hoc mesh networks may not be well suited for them WSN communication is an active research area and many algorithms and protocols have already been proposed This section introduces two proposed standards the IEEE 802154 and the ZigBee The IEEE 802154 covers the physical layer and the MAC layer of low‐rate WPAN The ZigBee is ldquoan emerging standard that is based on the IEEE 802154 and adds network construction (star networks peer‐to‐peermesh networks and cluster‐tree networks) application services and morerdquo

241 IEEE 802154 Holger and Willig (2005) [17] mention home automation home networking and home security as possible applications for IEEE 802154 They add that ldquomost of these applications require only low‐to‐medium bitrates (up to some few hundreds of kbps) moderate average delays without too stringent delay guarantees and for certain nodes it is highly desirable to reduce the energy consumption to a minimumrdquo The IEEE 802154 standard (2003) defines the device types that can be used in a LR‐WPAN A device can be a FFD or a RFD The RFD can be used in simple applications in which they do not need to transmit large amounts of data and they have to communicate only with a specific FFD The FFD can work as a PAN coordinator as a coordinator or as a simple device It can communicate with either another FFD or a RFD In keeping with the application requirements the LR‐WPAN operates in a star or peer‐to‐peer topology (figure 4) In the star topology the RFD communicates with a single controller the PAN coordinator The PAN coordinator can perform the same function as the RFD but it is also responsible for controlling the PAN ldquoit initiates terminates or routes communication around the networkrdquo [18] The peer‐to‐peer topology

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

15

supports ad‐hoc mesh multi‐hop networking Any device in the peer‐to‐peer topology can communicate with any other device within its communication range however this topology also has a PAN coordinator All the devices in a LR‐WPAN have a unique 64‐bit address This or a short address allocated by the PAN coordinator can be used inside a PAN Additionally each PAN has a unique identifier The combination of the PAN identifier and the sort addresses allows communication across different PANs (IEEE 802154 Standard 2003)

Figure 4 Star and peer‐to‐peer topologies in LR‐WPAN [18]

The LR‐WPAN based on the OSI seven‐layer model has the layered architecture presented in figure 5 The application and the network layer are the upper layers in the LR‐WPAN architecture but are outside the scope of the IEEE 802154 standard Only the physical layer ldquowhich contains the RF transceiver along with its low‐level control mechanismrdquo and the MAC layer ldquothat provides access to the physical channelrdquo are included in the standard and will be introduced in the following sections Finally the Type I 8022 LLC and the SSCS are intermediate sub layers supporting communication with the above layers (IEEE 802154 Standard 2003)

Physical Layer The 802154 standard specifies two different services that the PHY provides The PHY data service controls the radio and thus the transmission and reception of the PPDUs In addition the management service performs ED in the channel The management service also performs CCA before sending the messages and provides LQI for the received packets

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

16

Figure 5 WPAN architecture [18]

Three different bands are defined by the standard The 868‐8686 MHz for Europe the 902‐928 MHz for North America and the 2400‐24835 MHz worldwide All of them belong in the ISM radio bands and they are using DSSS Each of the bands supports different a data rate and it uses a different modulation technique chip rate and number of channels Moreover if the system does not use the 2450 MHz frequency it operates in both the 802 MHz and 902 MHz frequencies Table 3 summarizes the supported bands and data rates (IEEE 802154 standard 2003)

PHY (MHz)

Frequency Band (MHz) Modulation

Bit Rate Number of Channels

868-8686 BPSK 20 1 868 915 902-928 BPSK 40 10

2450 2400-24835 O-QPSK 230 16 Table 3 Frequency bands and data rates for IEEE 802154 based on the IEEE

802154 standard [18] The physical layer uses PPDU packets to communicate Table 4 demonstrates its structure The LSB is always transmitted and received first The SHR contains the preamble and the SFD fields which helps receiver

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

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synchronization The 8‐bytes preamble contains only zero and is used for synchronization The SFD contains a specific sequence of one and zeros and specifies the beginning of the frame The PHR contains the payload length Packets with a length of 9 or more bytes are MPDU as the next section further explains Packets with length 5 are used for MPDU acknowledgements The payload part of the PPDU encloses the MAC layer packet Finally the PPDU size can be up to 127 bytes [18]

4 bytes 1 byte 1 byte Variable

Preamble SFD Frame length

(7 bits) Reserved

(1 bit) Payload (PSU)

SHR PHR PHY Payload Table 4 Format based on the IEEE 802154 standard [18]

MAC Layer The MAC layer is the interface between the SSCS and the PHY layer Similar to the PHY layer the MAC layer supports two services The MAC data service is responsible for the transmission and reception of the MPDUs through the PHY data service The MAC management service if the device is a coordinator manages the network beacons It is also responsible for PAN association and disassociation frame validation and acknowledgment providing ldquoa reliable link between two peer MAC entitiesrdquo In addition it uses the CSMA‐CA for channel access and handles and maintains the GTS mechanism Finally it supports device security [18] The IEEE 802154 standard defines four different frame types the beacon data acknowledgment and MAC command frame All frame types are based on the general MAC frame format (table 5) The frame control field describes and specifies the above different frame types Every MAC frame comprises a MHR which consists of a frame control sequence number and the information field It also contains the MAC payload different frame types have different MAC payload fields The acknowledgment type does not have a payload Finally each frame includes a MFR which contains a FCS The data in the MPDU follows the same order as the PPDU the least significant bits are left in the frame and are transited first Octets2 1 02 028 02 028 Variable 2

Destination PAN Identifier

Destination Address

Source PAN

Identifier Source Address Frame

Control Sequence Number Addressing Field

Frame Payload FCS

MHR MAC

Payload MFRTable 5 General MAC frame format IEEE 802154 standard [18]

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Table 6 7 8 and 9 present the four different MAC frame types The beacon frame is transmitted periodically by the PAN coordinator It provides information about the network management through the super frame and GTS fields which are analyzed later in the section It also synchronizes the network devices and indicates the proper communication period for them The data frame payload encapsulates data from the higher layers When a device receives a packet it is not obliged to response with an acknowledgement packet Finally the command frame identifier and command payload fields of the command frame are used for communication between the network devices The command identifier specifies actions like association disassociation and data GTS or beacon request Octets2 1 410 2 Variable Variable Variable 2

Frame Control

Sequence Number

Addressing Fields

Super frame Specification

GTS fields

Pending Addressing

Fields Beacon Payload FCS

MHR MAC Payload MFRTable 6 Beacon frame format [18]

Octets 2 1 Variable 2

Frame Control

Sequence Number

Addressing Field

Data Payload FCS

MHR MAC

Payload MFR Table 7 Data frame format [18]

Octets 2 1 2

Frame Control

Sequence Number FCS

MHR MFR Table 8 Acknowledgement frame format [18]

Octets2 1 1 Variable 2

Frame Control

Sequence Number

Addressing Fields

Command Frame Identifier

Command Payload FCS

MHR MAC Payload MFR Table 9 Command frame format [18]

In the LR‐WPAN every PAN has its own coordinator The PAN coordinator manages the communication in the local area it has two options to use or not use the super frame structure The super frame (figure 6) uses network beacons If the coordinator does not want to use a super frame structure it suspends the beacon transmission However the beacon is important for device association and disassociation If the coordinator wishes to maintain close communication control in the PAN and to support low‐latency devices it usually uses the super frame A super frame determines a specific time

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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19

period beacons bound it The beacon is transmitted in the first of the sixteen equal time slots that the super frame has It is used to ldquosynchronize the attached devices to identify the PAN and to describe the structure of the super framerdquo The super frame can have active and inactive periods All the communications have to be finished inside the super frame period In the inactive periods the devices can switch to the sleep mode but they have to be ready for the next beacon The active period is further divided into a CAP and a CFP During the CAP any device can communicate competing with the other devices in the PAN using slotted CSMA‐CA The CFP contains GTSs and follows the CAP A CFP may maintain up to seven GTSs and each GTS can reserve more than one time slot However the CAP should always be sufficient to allow new devices t join the PAN The GTSs are allocated to specific devices All the transactions must be completed inside the assigned time period (CAP GTS and CFP) (IEEE 802154 standard 2003)

Figure 6 Example of super frame structure [18]

In accordance with the 802154 standard (2003) three different types of data‐transfer exist In addition the types differ if the coordinator uses or does not beacons Data transfer from a device to the PAN coordinator is the first type (figure 7) For a ldquonon‐beacon‐enable networkrdquo it first senses the medium by using ldquoun‐slotted CSMA‐CArdquo and then a simple transmit to the data frame In a ldquobeacon‐enabled networkrdquo the sender waits for the beacon when it finds it ldquothe device synchronizes to the super fame structurerdquo In the specified time frame the sender again senses the medium by using ldquoslotted CSMA‐CArdquo and transmits the data to the coordinator In either case the coordinator has the option to acknowledge or not acknowledge the data reception after that the transaction is completed Data transfer from the coordinator is the next type described in the standard (figure 8) In a beacon‐enabled network the coordinator indicates a pending message through the beacon The messagersquos target device receives the beacon and if the message is pending it responses with a MAC command request

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2 LITERATURE REVIEW10102006

20

message using slotted CSMA‐CA the coordinator may or may not acknowledge the command message and through slotted CSMA‐CA sends the pending message The device acknowledges the received message After that the coordinator removes the message from the beaconrsquos pending list and completes the transaction In a non‐beacon enable network the device periodically sends a MAC command‐request frame to the coordinator The coordinator acknowledges the data request Then by using un‐slotted CSMA‐CA if it has a pending message it transmits it If it has not it respond with a data frame with a ldquozero‐length payloadrdquo To complete the transaction the device acknowledges the data reception

Figure 7 Communication from a device to a PAN coordinator in (a) a beacon‐enabled network and (b) a non‐beacon enabled network [18]

Peer‐to‐peer is the last type of data transfer In this situation the devices are free to communicate with any other device within their communication range In a peer‐to‐peer PAN the devices can ldquoeither receive constantly or synchronize with each otherrdquo If they are receiving constantly to transmit data they use un‐slotted CSAM‐CA In the second case synchronization must be achieved first (IEEE 802154 standard [IEEE 2003]) The IEEE 802154 (2003) standard establishes MAC and PHY standards for low‐cost low‐power and high‐density node deployments In addition to the above PHY and MAC characteristics IEEE 802154 provides a security baseline including ldquothe ability to maintain an ACL and use symmetric cryptographyrdquo for data encryption The algorithm that is used for encryption is the AES However the higher level layers decide when security is need The upper layers are in general responsible for device authentication and key management The next section introduces the ZigBee standard which

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

21

encapsulates the IEEE 802154 and provides additional standardization for the higher levels

Figure 8 Communication from a PAN coordinator to a device in (a) a beacon‐enabled network and (b) a non‐beacon enabled network [18]

242 ZigBee Heily (2004) defines ZigBee as ldquoa rapidly growing worldwide non‐profit industry consortiumrdquo whose mission is ldquoto define a reliable cost‐effective low‐power wirelessly networked monitoring and control product based on an open global standardrdquo Figure 9 illustrates the areas of interest for different wireless communication standards

Figure 9 Overview of the coverage for different wireless communication

standards [17]

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

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ZigBee a new standard which became publicly available in June 2005 is based on the IEEE 802154 standard It expands the IEEE 802154 by adding the framework for ldquothe network security and applicationrdquo (Craig 2005) Figure 10 presents the IEEE 802154ZigBee stack and the areas of responsibilities

Figure 10 IEEE 802154ZigBee stack [20]

Craig (2005) mentions three networking topologies that the standard covers the star mesh and cluster tree (figure 11) The ZigBee standard works on top of the IEEE 802154 addressing schema by using the standard 64‐bit and the short 16‐bit addressing Kinney (2005) summarizes the ZigBee network layer responsibilities the successful establishment of a new the network and successful new device configuration addressing assignment network synchronization frames security and message routing ZigBee further distinguishes the concept of the physical devices (RFD FFD) by using the notion of ldquological devicesrdquo ldquoZigBee Coordinatorrdquo is the first type of logical devices It is responsible for initializing maintaining and managing the network Under the coordinator in the network hierarchy is the ldquoZigBee routerrdquo which is responsible for controlling the message routing between the nodes Finally the ldquoZigBee End Devicerdquo acts as the end point of the network

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

23

structure The tasks that an end device can perform are specified in the ldquoApplication Profilesrdquo (Craig 2005)

Figure 11 IEEE 802154ZigBee stack [17]

The ZigBee specifications (2005) summarize the security services provided by ZigBee ldquokey establishment key transport frame protection and device managementrdquo ZigBee builds its security mechanism using symmetric key cryptography The security services also depend on the associated layer thus as the figure 10 shows the security mechanism covers the network and the application layer In addition if a MAC frame needs security protection the MAC layer is able to secure it Moreover the notion of end‐to‐end security is supported the source and destination devices have access and use the same share key In the MAC layer the 802154 AES mechanism provides the proper security The mechanism protects ldquothe confidentiality integrity and authenticity of the MAC framesrdquo (Kinney 2005) An auxiliary header field in front of the MAC payload indicates if the frame is encrypted or not The MAC framesrsquo integrity is supported by calculating and using a MIC at the end of the MAC payload In addition to the AES nonce is used to provide MAC confidentiality and authenticity Figure 12 illustrates a MAC frame with security For different security aspects the MAC layer uses different mode of the AES for the encryption it uses the AES in Counter (CTR) mode and for the integrity the

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

24

CBC‐MAC Finally the combination (CCM) of the above two modes is available providing both encryption and integrity (Kinney 2005)

Figure 12 ZigBee secure frame in MAC layer [17]

In the network layer the CCM (a modified MAC layer CCM mode) is used for encryption Because the network layer uses only the CCM mode a single key is used for all different security options The network layer security message format is similar to the MAC frame it is presented in the figure 13 Finally although the network layer is responsible for securing its layer messages the above layers specify the keys and the CCM option for each frame (Kinney 2005)

Figure 13 ZigBee secure frame in network layer [17]

Security in the application layer works similar to the network and MAC layers It uses the ldquolink keyrdquo or the ldquonetwork keyrdquo to secure the message and then encapsulate it inside a set of fields similar to the network format (figure 14) Other security responsibilities that the application layer has are to provide the ZDO and the applications with device management services key establishment and key transport (ZigBee specifications 2005)

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

25

Figure 14 ZigBee secure frame in application layer [17]

The ZigBee application layer contains the manufacturer‐defined application objects the ZDO and the application sub‐layer In addition to the security responsibilities the application sub‐layer binds devices based on their duties and needs maintains the binding tables and forwards messages between them The application sub‐layer also discovers the neighbour devices for a given device The ZDO is responsible for determining the devicersquos duty in the network for communicating using binding requests and for supporting security as was mentioned above The sub‐layer that implements the actual application is the manufacturer‐defined application object (Kinney 2005) The ZigBee specifications are a great step toward the wireless sensors networks standardization They cover all the OSI layers from the physical to the application providing guidance to the developers The specifications are the product of the ZigBee alliances a consortium of companies and academic institutes Thus they not only produce the specifications but also are the first use them The next section goes beyond the layer architecture and standardization and introduces the OS field another crucial element of WSNs

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

26

3 RECEIVED SIGNAL STRENGTH (RSS) Majority of the existing methods leverage the existence of IEEE 80211 base stations with powerful radio transmit powers of approximately 100mW per base station Such radios are in a different class from the low power IEEE 802154 compliant radios that typically transmit at low power levels ranging from 52mW to 29mW [19] The wide availability of larger number of IEEE 802154 radios in the research community has revived the interest for signal strength based localization in sensor network Despite of rapidly increasing popularity of IEEE 802154 radios and signal strength localization there is a lack of detailed characterization of the fundamental factors contributing to large signal strength variation This chapter investigates some of the properties of RSS observed during testing and analysis of research work collected during the period of thesis The analysis of RSS values is needed to understand the underlying features of location dependent RSS patterns and location fingerprints An understanding of the properties of the RSS values for location fingerprinting can assist in improving the design of positioning algorithms and in deployment of indoor positioning systems

31 Parameters that affect RSS Different factors that affect the indoor RSS are the userrsquos presenceabsence hisher orientation time of the day building type and material distance from transmitter and type of radio These factors are by no means exhaustive Other possible factors are antenna orientation directionality and type Table 10 classify the parameters which affect RSS [7]

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

27

Effect on Factors Options

1 Proximity of user Userrsquos presence or absence

2 Orientation of user and terminal

North East South West

Data Collection

3 Make of Radio 4 Time of measurement Time of day amp days of

week 5 Period of measurement

Second minute hour

7 Interference Co‐channel adjacent co‐channel

Statistics

8 Building environment Small offices or large hall

Table 10 Classification of factors the affect RSS [7]

Transmitter variability Different transmitters behave differently even when they are configured exactly in the same way In practice this means that when a transmitter is configured to send packets at a power level of d dBm then the transmitter will send these packets at a power level that is very close to d dBm but not necessarily exactly equal to d dBm This can alter the received signal strength indication and thus it can lead to inaccurate distance estimation

Receiver variability The sensitivity of the receivers across different radio chips is different In practice this means that the RSSI value recorded at different receivers can be different even when all the other parameters that affect the received signal strength are kept constant

Antenna orientation Each antenna has its own radiation pattern that is not uniform In practice this means that the RSSI value recorded at the receiver for a given pair of communicating nodes and for a given distance between them varies as the pair wise antenna orientations of the transmitter and the receiver are changed

Multi‐path fading and shadowing in the RF channel In indoor environments the transmitted signals get reflected after hitting on the walls andor on other objects in the room such as furniture Both the original signal and the reflected signal reach the receiver almost at the same time since they both travel at the speed of light As a result of this the receiver is not able to

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

28

distinguish the two signals and it measures the received signal strength for both of them

32 Related work analysis A number of indoor location tracking systems have been proposed in the literature based on RF signals ultrasound infrared and some combinations of these As mentioned previously RF based positioning system are widely studied as shown in Table 2 Given a model of radio signal propagation in a building or other environment RSS can be used to estimate the distance between the transmitter and receiver Most of the algorithms (Table 2) have been evaluated in practice using 80211b compliant wireless radios and seem to produce a location error of 15ft to 20ft for an indoor network area of approximately 225ft x 144ft According to the results of this study all the localization techniques produce approximately the same location error over a range of environments Ecolocation [22] Mote Track [5] are two of the few localization algorithms that have been evaluated on a real sensor network that uses a low power wireless radio Ecolocation reports a location error of 10ft for a very small outdoor network deployment area (26ft x 49ft) Mote Track reports a location error of approximately 13ft for an indoor network deployment area of 18751ft2 In general the RSSI based localization algorithms can be divided into two broad categories the map based and the distance (or area) prediction based algorithms In the case of the map based algorithms the deployment area is divided into a large set of sample points that cover the whole area Most techniques collect one or more signal strength measurements from all visible beacon nodes and for each sample point Each sample point is then mapped to either a signal strength vector or a signal strength probability distribution This process generates an RSSI fingerprint map which is unique with respect to the beacon locations and the deployment area This fingerprint map is stored to a central base station A node that is not aware of its location collects one or more signal strength measurements from all the visible beacon nodes and creates its own RSSI fingerprint that is sent to the central base station The base station computes nodersquos location as the sample pointrsquos location whose RSSI fingerprint distance from the unknown nodersquos RSSI fingerprint is the minimum among all the sample points Despite the fact that the map based RSSI localization methods produce relatively small location errors they are not attractive mainly because they require an excessive amount of profiling data and because they are not fully distributed The detailed profiling of a building is a major holdback because it is complex time consuming and depends on beacon locations Therefore for different buildings or even for the

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

29

same building and different beacon locations different RSSI maps have to be obtained The second category of RSSI based algorithms are the distance (or area) prediction algorithms which focus on minimizing the amount of profiling data In distance prediction based methods raw RSSI data are directly mapped to distances through a signal propagation model In some cases distances are not explicitly measured but they are taken into consideration through assumptions on the underlying distributions In the case of outdoor deployments where there are no obstacles a single signal propagation model is all the profiling data required In the case of indoor deployments the same applies but now the signal propagation model might be different across different buildings with different furniture and wall arrangements Given an underlying signal propagation model raw RSSI data can be mapped to specific distances These distances can be either used directly in order to perform localization or assigned a probability and used in a learning basedprobabilistic localization algorithm Obviously the accuracy of the distance prediction based methods depends heavily on the accuracy of the signal propagation model used for translating RSSI values to distances

33 Properties of RSSI Changes in signal due to propagation indoors are difficult to predict because of dense environment and propagation effects such as reflection diffraction and scattering The multi‐path fading effect which is the result of either constructive or destructive combination of multiple signal copies at the receiver causes the received signal to fluctuate around a mean value at a particular location The received signal is usually modelled by the combined effects of large‐scale fading and small‐scale fading The large‐scale fading component describes the signal attenuation as the signal travels over a distance and is absorbed by material such as walls and floors along the way to the receiver This component predicts the mean of the RSS and usually has a lognormal distribution On the other hand the small scale fading component explains the dramatic fluctuation of the signal due to multi‐path fading On the other hand the small scale‐fading component explains the dramatic fluctuation of the signal due to multi‐path fading If there is no LOS component the small‐scale‐fading is often modelled with a Rayleigh distribution Such scenarios are commonly called NLOS If there is a LOS component the small‐scale‐fading is typically modelled by a Rician distribution Note that when the RSS is measured the measurement averages out the small‐scale‐fading effects

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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4 OBJECT TRACKING 41 Introduction This chapter presents the object‐tracking application the main objective of this thesis work The application is an algorithmic‐software implementation written in Embedded C which is capable of receiving data messages and generating object position estimation data The implementation of the object‐tracking application makes use of the SENTIO platform and uses the background knowledge introduced in the preceding chapters The motivation for the object‐tracking application is to determine the position of an object in indoor environments (Eg Home) The application acts as an interface which collects the raw data that the sensors generate (numerical values) It makes the proper comparisons and algorithmic processes to identify and track the object while it is static inside the area covered by the wireless sensor network Finally with the detection and tracking data it alerts the user or another system The following sections provide an overview of the object‐tracking application

42 Overview of hardware product Before describing the object tracking application it is important to know the characteristics of hardware product The SENTIO platform (figure 15) is built in modular fashion and has the following key features [8]

Flexibility This platform is very flexible in such a way that it is made easy to add or change the hardware for each sensor task depending on the requirement in the WSN For a specific platform this can be hard or sometimes even impossible to accomplish

Sensor resources Resources in a sensor module includes the ability of usage of controllerrsquos resources as much as possible such as ADC‐channels interrupts and digital Input‐Outputrsquos Local voltage regulation should also be possible since many sensors require higher supply voltages than digital Integrated Circuits do Further support of a shut‐down mechanism to power down the sensor modules when not needed

Computation and memory The processor module based on microcontroller should support many devices in a microcontroller family and support different power supply voltages enabling full range of clock frequencies All power‐down modes of the devices should be

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

31

supported The platform should support different processor modules including FPGArsquos and very simple microcontrollers

Communication The focus should be on enabling an IEEE 802154 compliant communication interface With or without ZigBee stack this will provide a flexible platform for exploring different network topologies Support for power‐down of the entire communication module should be supported

Cost and form factor High flexibility is in conflict with low cost and small size The primary objective should be flexibility but the size should be considered in order to enable sensor device integration in systems where space is limited

Figure 15 SENTIO Platform [8]

The communication subsystem of the SENTIO sensor node was designed so that the correct operation of the radio chip is ensured The radio chip is powered by the on‐board voltage regulator and thus fluctuations in the battery voltage level do not affect the operation of the communication subsystem This platform uses Chipcon CC2420 IEEE 802154 radio transceiver operates in the 24GHz ISM band and includes a digital direct sequence spread spectrum (DSSS) modem providing a spreading gain of 9dB and an effective data rate of 250Kbps It was specifically designed for low power wireless applications and supports 8 discrete power levels 0dBm minus1dBm minus3dBm minus5dBm minus7dBm minus10dBm minus15dBm and minus25dBm at which its power consumption varies from 29mW to 52mW [21] A built‐in received signal strength indicator gives an 8‐bit digital value RSSIVAL The RSSIVAL is always averaged over 8 symbol periods (128μs) and a status bit indicates when the RSSIVAL is valid (meaning that the receiver was enabled for at least 8 symbol periods) The

Antenna

Communication Module

Processing Module

Sensing Module

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

32

power P at the RF pins can be obtained directly from RSSIVAL using the following equation

P = RSSIVAL + RSSIOFFSET [dBm] where the RSSIOFFSET is found empirically from the front‐end gain and it is approximately equal to minus45dBm In the next sections when we refer to the RSSI value we refer to the RSSIVAL and not the power P unless otherwise stated

43 Experimental Testbed The experimental test bed for object tracking application is as shown in figure 16 Experiment was conducted in Masterrsquos room at Mid Sweden University A DELL laptop is connected to SENTIO platform and software were primarily used to collect samples of RSS from APrsquos The software monitors IEEE 802154 radio frequency channels which operate in the 24GHz band

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

1030

BC - 1030

AP 3 AP 1

AP 2

AP 4

BS

HEWLETT

PACKARD

Figure 16 Layout of Masterʹs Room

A three dimensional scalable testbed is used for indoor sensor network deployments The testbed illustrated in figure 16 is a 3‐D structure measuring 16m (L) x 12m (W) x 3m (H) and it is designed to host a large number of static nodes as shown in figure 16 Arrow marks in the above figure represent the location where fingerprints were taken The centrepiece of our infrastructure

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

33

is the SENTIO sensor node [8] an open‐source general purpose sensing platform designed with AT Mega 128 microprocessor and the IEEE 802154 compliant CC2420 radio from Chipcon [19]

44 Obtaining data from the sensor network The next important step in the development of the object tracking application is to obtain the data from the sensor network and send it to the base station From there the data is transmitted through a serial port to the computer that runs the object tracking application When the data arrives at the serial port the application must capture it This paragraph gives a brief explanation of how and what kind data is transmitted by the static object fixed sensors and how this data is being used to estimate the position of the object First the static object transmits Beacon packet continuously which is received by all the fixed APrsquos The beacon packet format is as shown in table 6 Then the fixed APrsquos calculates the 8‐bit value of RSSI from the transmitted beacon packet and then sends the resultant RSSI value as a form of data packet (table 7) to the BS which calculates the position and sends back to the object

45 Object tracking application Testing and Evaluation The purpose of this section is to discuss and evaluate the object tracking application Finally we are ready with the experimental testbed as shown in figure 16 As shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors placed at the corners of the masterrsquos room BS is placed at the centre of the room connected to a PC All the nodes are power connected before an actual measurement takes place At first the object is placed at random position inside the room and transmits beacon packets continuously Then these packets are received by the fixed APrsquos which calculate the RSSI from the packet and then averages it until it receives 50 beacon packets and then transmits the data to the BS (figure 17) Then the BS waits until it receives the data from all the APrsquos Once it receives from all the APrsquos it calculates the position of the object (figure 18) The application was developed to demonstrate a real world use of a WSN in object tracking In evaluating the final position obtained with respect to the actual position we conclude that the object‐tracking was successful Although

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

34

it is a research prototype object tracking application is a simple stable stand‐alone system capable of demonstrating an important application of WSN It takes raw data from the WSN aggregates it evaluates it and based on the evaluation tracks the object The accuracy of estimating the position is less than 5m Table 11 below shows a log of readings taken during test

Start

Intialize micro‐controller and transeciever in receieve mode

If received_packet=

Beacon

Average RSSI values

Increase the counter

If countergt1000050

A

No

Yes

B

B

Yes

No

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

35

A

Reset counter

Set transceiever in transmit mode

Transmit averaged RSSI to Main sensor and set it back to receive mode

10000 samples ‐ Offline

50 samples ‐ Online

Figure 17 Flow chart representing the algorithm of fixed sensors

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

36

Start

Intialize micro‐controller and transeciever in receieve mode

Received RSSI values from all fixed Sensors

Open radio map

Calculate Euclidean distance between obtained RSSI values from fixed sensors and fingerprints

in radio map

No

Yes

Bubble sort for least error in signal strength values

A

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

37

A

Get the location of the object from the radiousing the least error value

Display location of object on PC

End

Figure 18 Flow chart representing the algorithm of main sensor

Table 11 Accuracy of positioning system

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

38

5 RESULTS This chapter presents how the system parameters such as number of samples mean or median etchellipare finalized All the tests are performed in masterrsquos room at Mid Sweden University This chapter is divided into three subsections First in Section 51 it reviews results obtained from samples test Section 52 discusses one dimensional test results and finally section 53 discusses results of two dimensional tests

51 Measurement setup A Dell laptop was connected to a SENTIO platform and is loaded with software (Appendix 2) This setup was used to determine number of samples required during object tracking mean or median position estimation algorithm and one dimensional test The setup is as follows

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

103

0

BC - 1030

AP 1AP 2

BS

HEWLETT

PACKARD

Figure 19 Test bed for one dimensional test in Masters Room

Two SENTIO nodes (AP1 and AP2 as shown above) were separated by a distance of 10 meters and these sensors are known as fixed sensors which are fixed to the ceiling such that there antenna is faced towards the ground They are linear is position A BS was connected to Dell laptop and it was placed in such a way that its antenna was faced towards the ceiling Another SENTIO

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

39

node was used as an object and its orientation was same as BS Figure 19 represents the complete setup for object tracking application Mobile node was moved between these fixed ones at regular intervals of one meter and RSSI values were logged at each interval

52 Number of samples test This test was performed to determine number of samples required for accurate positioning and either mean of the obtained RSSI values or median of them Figure 19 represents the setup for number of samples test Once the setup is ready the measurements are taken as follows

1 An object was tagged with SENTIO node and was placed stationary linear to the fixed sensors

2 10000 samples (RSSI) were taken at regular intervals of 1 meter up to 10 meters

3 The sample values were logged at every interval 4 Using these logged samples I have plotted graphs such as distribution

of RSS values (figure 20 21) meanmedian at intervals of 1 meter using MATLAB

5 The mean and median for various numbers of samples is as shown in figure 22

Figure 20 Histogram plot of RSSI values at 1m and 4m

Figure 21 Histogram plot of RSSI values at 6m and 8m

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

40

Figure 22 Distance vs RSSI

From the above graphs following conclusions were made 1 From the histogram plots we can observe that the distribution of RSSI

values follow Gaussians distribution law 2 As we can see from figure 22 that median curve has drastic changes

when compared to mean curve Hence mean is considered while estimating the position

3 From the observations we conclude that the number of samples does not matter since mean curve tends to be same for various numbers of samples

4 Hence 10000 samples are considered in order to get a proper location fingerprint during off‐line phase and during online phase 50 samples are enough to estimate the position of object

53 0ne dimensional test Measurement setup for one dimensional test is as shown in figure 19 This test was conducted to check the performance of algorithm and how accurate it was At first ie during offline phase fingerprints at each interval averaging 10000 samples were taken During online phase Euclidean algorithm was used estimate the position averaging 50 samples of RSSI From the results we

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

41

can realize that the error was less than 2 meters Table 12 shows the result of location estimation in one dimensional case

Table 12 One dimensional position estimation results

54 Two dimensional test This test is the final stage of my thesis work Experimental testbed for this stage is as shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors fixed at the corners of the room and a BS at the centre of the room There are 18 pre‐determined locations where the fingerprints (RSSI) were taken The fingerprints are as shown in Appendix 1 All the readings were taken in presence of userrsquos The algorithm for position estimation is shown in Appendix 2 Position estimation test is performed as follows

Tagged object is placed at random inside the masterrsquos room All the sensors including the base station and object are switched on Using the algorithm (Appendix 2) the BS estimates the position of the object and displays it on the PC

This test was performed using various numbers of samples to test which has a better accuracy Figure 23 represents the error rate using various numbers of samples From figure 23 we can conclude that 50 numbers of samples is enough to estimate the position of object with an accuracy of 3m

55 Causes of error in location determination The randomness of RSS is the major cause of error in any indoor

positioning system that uses the RSS for location inference If there is no randomness in the RSS and its location fingerprint every indoor positioning systems of this type will have no location detection error For example I have recorded RSS at one particular location during different times of the day From figures 24 and 25 you can observe that RSS values are different at different times of day

The performance of indoor positioning systems depends greatly on the separation of location fingerprints A location fingerprint

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

42

corresponding to a location can be identified correctly if it is difficult to classify it as another fingerprint by a pattern classifier

Path loss is one of the basic signal propagation properties of the RSS that best describes attenuation of transmit signal power over a distance between an access point and a mobile device

Presence of Userrsquos or userrsquos orientation affects RSS

Figure 23 Error Rate vs Number of samples

Figure 24 RSS distribution at 214 pm

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

43

Figure 25 RSS distribution at 100pm and 1100 am

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

6 DISCUSSIONS10102006

44

6 DISCUSSIONS 61 Summary and Conclusion While empirical results and performance studies of positioning systems based on location fingerprint have been presented in this report analytical model that can be used as a framework for efficiently designing the positioning systems are not available This thesis design work uses general principles of position estimation such as position estimation of sensors grid spacing etc This design work focuses on accuracy and precision The concept of location fingerprint technique based on RSS us explained in detail in chapter 2 It is found that RSS is random It is possible to approximate the RSS as being normally distributed While the RSS random process is non‐stationary the mean is more or less constant and could be used as the fingerprint of the location Model designed for position estimation is as shown in chapter 4 Accuracy and precision are the two major performance matrices in this study Uniform grid spacing has been used From the experimental analysis I have obtained an accuracy of 3 meters and 35 precision The sensitivity analysis among the system parameters and the performance matrices were done using the model as shown in chapter 4 For instance our study found that the minimum distance between two adjacent positions also affects the positioning performance This is due to the randomness of RSS patterns induced by the ever changing indoor environment

62 Future work The research in this thesis provides a ground work for the future study of efficient design of indoor positioning system The RSSI based position estimation still has limitations First the RSSI value is time dependent Thus the future study should address this issue Second the movement of object is not considered Third orientations of sensors affect the performance which should be taken care off Fourth to improve the performance of the system we need to find a solution to deal with those incomplete or missing data during the off‐line and the on‐line phase Fifth position estimation on multi‐floor and three‐dimensional coordinates is not available

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 110102006

45

APPENDIX 1

Location Fingerprints of Masters Room Time 1315 Observation Time 20 minutes Number of Samples 10000

People Presence SNo Co-ordinates AP1 (0x0001) AP2 (0x0002) AP3 (0x0004) AP4 (0x0008)

1 11 -24 -16 -27 -33 2 13 -26 -8 -34 -31 3 15 -43 -9 -39 -25 4 32 -21 -11 -27 -33 5 61 -6 -23 -32 -28 6 64 -20 -31 -25 -34 7 35 -23 -35 -29 -38 8 37 -29 -35 -22 -27 9 39 -38 -29 -21 -27 10 67 -27 -23 -20 -23 11 611 -26 -29 -23 -30 12 613 -35 -32 -2 -31 13 513 -34 -31 -20 -26 14 11 -39 -22 -29 -17 15 212 -37 -42 -28 -31 16 -212 -38 -33 -29 -6 17 -21 -42 -29 -33 -19 18 -38 -40 -23 -35 -22

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 210102006

46

APPENDIX 2

Begin

Open radio map and raw data files for write

Intialize radio map in memory

Check whether the

Packet is data or Beacon packet If data packet

Save the data to register

Check if data is

Received from all

sensors

Estimate the position of static

Display the position on PC

End Yes

No

A

A

Yes

No

Flow chart representing the main algorithm for position estimation

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

47

APPENDIX 3

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

48

Distribution of RSSI

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

49

REFERENCES [1] T S Rappaport J H Reed and B D Woerner ldquoPosition location using wireless communications on highways of the futureʺ IEEE Commun Mag vol 34 no 10 pp33‐41 Oct 1996 [2] A M Ladd K E Bekris G Marceau A Rudys L E Kavraki and D S Wallach rdquoRobotics‐based location sensing using wireless ethernetʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ02) 2002 pp 227‐238 [3] I Ramani R Bharadwaja and P V Rangan ldquoLocation tracking for media appliances in wireless home networksʺ in Proc International Conference on Multimedia and Expo (ICMEʹ03) July 2003 pp 769‐772 [4] K Pahlavan X Li and J P Makela ldquoIndoor geolocation science and technologyʺ IEEE Commun Mag vol 40 no 2 pp 112‐118 Feb 2002 [5] Konard Lorincz and Matt Welsh ldquoMote Track A Robust Decentralized Approach to RF‐Based Location Trackingrdquo [6] Paramvir Bahl and Venkata N Padmanabham ldquoRADAR An In‐Building RF‐based User Location and Tracking Systemrdquo [7] Kamol Kaemarungsi ldquoDesign of Indoor positioning system based on location fingerprint techiniquerdquo University of Pittsburgh 2005 [8] Fredrik Linnarsson Cheng Peng and Bengt Oelmann ldquoSENTIO A Hardware Platform for Rapid Prototyping of Wireless Sensor Networksrdquo [9] S Saha K Chaudhuri D Sanghi and P Bhagwat ldquoLocation determination of a mobile device using IEEE 80211b access point signalsʺ in Proc IEEE Wireless Communications and Networking Conference (WCNCʹ03) New Orleans LA Mar 2003 pp 1987‐1992 [10] T Roos P Myllymaki H Tirri P Misikangas and J Sievanen ldquoA probabilistic approach to WLAN user location estimationʺ International Journal of Wireless Information Networks vol 9 no 3 pp 155‐164 July 2002 [11] R Battiti M Brunato and A Villani ldquoStatistical learning theory for location fingerprinting in wireless LANrsquosʺ Technical Report Oct 2002 [Online] Available httprtmscienceunitnitraquobattitiarchive86pdf [12] P Prasithsangaree P Krishnamurthy and P K Chrysanthis ldquoAn indoor position location with wireless LANsʺ in Proc IEEE International Symposium on Personal Indoor and Mobile Radio Communications (PIMRCʹ02) Lisbon Portugal Sept 2002 [13] M A Youssef A Agrawala and A U Shankar ldquoWLAN location determination via clustering and probability distributionsʺ in Proc IEEE

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

50

International Conference on Pervasive Computing and Communications (PerComʹ03) Dallas‐Fort Worth TX Mar 23‐26 2003 pp 23‐26 [14] Z Xiang S Song J Chen H Wang J Huang and X Gao ldquoA wireless LAN‐based indoor positioning technologyʺ IBM Journal of Research and Development vol 48 no 56 pp 617‐626 SeptNov 2004 [15] R Want A Hopper V Falcao and J Gibbons ldquoThe active badge location systemʺACM Transactions on Information Systems vol 40 no 1 pp 91‐102 Jan 1992 [16] N B Priyantha A Chakraborty and H Balakrishnan ldquoThe cricket location‐support systemʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ00) Boston MA Aug 2000 pp 32‐43 [17] Holger K Willig A (2005) Protocols and Architectures for Wireless Sensor Networks London [18] IEEE (2003) Part 154 Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low‐Rate Wireless Personal Area Networks (LR‐WPANs) New York [19] Chipcon CC2420 802154 compliant radio httpwwwchipconcom [20] ZigBee Specification v10 ZigBee Alliance [21] Dimitrios Lymberopoulos Quentin Lindsey and Andreas Savvides ldquoAn Empirical Analysis of Radio Signal Strength Variability in IEEE 802154 Networks using Monopole Antennasrdquo [22] Kiran Yedavalli Bhaskar Krishnamachari Sharmila Ravula and Bhaskar Srinivasan ldquoEcolocation A sequence based technique for RF localization in wireless sensor networksrdquo Robert Bosch Research and Technology Centre Palo Alto CA

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

3

System Spacing(m) Positions SamplesPos APs Orientation Environment

RADAR [6] Non

uniform 70 80 3 4 Hall-way Saha et al [9] 3 19 1200 3 NA 1-floor

Roos et al [10] 2 155 40 10 NA 1-floor Battiti et at [11] NA 257 NA 6 NA 1-floor Ladd et al [2] 3 11 1307 5 2 Hall-way

Prasithsangaree et al [12] 153 60 40

2 to 7 4 1-floor

Youssef et al[13] 15 110 300 4 NA Hall-way Xiang et al [14] NA 100 300 5 4 1-floor

Table 1 Comparison of Indoor Positioning Systems [7]

System Algorithm Type Accuracy and Precision RADAR [6] Nearest Neighbour within 7 feet 38

Saha et al [9] Nearest neighbour amp Neural Network no specified accuracy

90 Roos et al [10] Bayesian best within 828 feet 90

Battiti et at [11] Neural Network amp Weighted k-Nearest

Neighbour all within 16-17 feet 90 Ladd et al [2] Bayesian within 5 feet 77

Prasithsangaree et al [12] Weighted k-Nearest Neighbour

25 feet at 75 amp 40 fet at 95

Youssef et al [13] Bayesian within 7 feet more than

90 Xiang et al [14] Bayesian with RSS distribution model within 6 feet 90 Table 2 Performance comparison of Indoor Positioning Systems [7]

Based on the study of results obtained in various positioning systems as in table 2 we have developed a model for an indoor positioning system using SENTIO [8] platform based on RSSI

13 Objectives In the last two or three years a number of theoretical practical andor simulation studies were done on the topic of object‐tracking While these studies are useful they are too general and provide little guidance for the actual deployment of sensor networks for real life location‐tracking of an object This thesis focuses on developing an object‐tracking application and prescribes sensor network configurations that work well with our algorithms I implemented my software using SENTIO [8] platform for sensor networking The major issue addressed in this project are the accuracy and precision in tracking an object in a real‐world application and efficient ways to algorithmically analyze the collected raw data from the specific wireless sensor product

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

4

Although the focus is to develop a real‐world application using WSN it also provides great opportunity to explore the new area of wireless communication overall

14 Research Questions My primary target is to find a method to determine the position of an object inside a building using ZigBee protocol standards based on RSSI The study addresses the following questions What is WSN What requirements do WSN need What are their main ideas and concepts What are the associated technologies What are the standards that sensor networks use

What are the existing hardware implementations that use this particular technology How can we build our new applications using WSN What do sensor network systems require for general or specific applications Are they applicable And what is the appropriate design for sensor network implementations in order to combine them with other existing systems At this point we describe and evaluate the specific object‐tracking application to be built

15 Scope The study area of the thesis is an overview of the WSN technology study of SENTIO platform and implementation of an object tracking application Thus the study is divided into two parts Part one is an overview of WSN it includes study of SENTIO platform and IEEE 802154 protocol standards The second part describes the implementation of the object tracking algorithm The object tracking application receives and uses the raw values returned by the sensor network system to produce clear and meaningful outputs Schema of thesis work is as shown in figure 1

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

5

Figure 1 Schema of thesis work

16 Methodology The ldquoIndoor Local Positioning System for ZigBee based on RSSI ldquois based on ldquolocation fingerprintingrdquo technique Location fingerprinting refers to a technique that exploits the relation between any measurable physical stimulus and a specific location This approach uses empirical measurements of RSSI to estimate location By recording a database of radio ldquosignaturesrdquo along with their known locations a mobile node can estimate its position by acquiring a signature and comparing it to the known signatures in the database All of these systems require the database of signatures which should be collected manually prior to the system installation and rely on central server (or the userrsquos mobile node) to perform location calculation For example in RADAR [6] technology the RSSI is the stimulus This type of positioning system does not require specialized hardware other than the common wireless network interfaces with received signal strength measurement capability thus it is simple to deploy compared to other techniques Unlike outdoor counterpart systems which can use AOA and TDOA techniques effectively indoor positioning systems encounter the problem of NLOS and the dense multi path effect that render these two techniques ineffective or complex for practical implementation It is also difficult for a MS to always see three or more access points or base stations in indoor environment which is essential for triangulation by AOA and TDOA Location fingerprinting can also be implemented as software based positioning system which can reduce

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

6

complexity and cost Generally the deployment of fingerprinting based positioning systems can be divided into two phases First in the off‐line phase the locations where the fingerprints are required is pre‐determined and then the fingerprints are collected by performing a site‐survey of the RSS from multiple APs The distance between two closest positions is called grid spacing and usually reported in meters or feet However some points may be omitted due to inaccessibility The RSS is measured at each predetermined location to create a database of RSS patterns of the indoor environment The database of RSS patterns is called a radio map The vector of RSS values at a point on the grid is called the location fingerprint of that point Second in the on‐line phase a MS will report a sample measured vector of RSS from different APs to a base station The base station uses a positioning algorithm to estimate the location of the MS The most common algorithm used to estimate the location is Euclidean algorithm It computes the Euclidean distance between the measured RSS vector and each fingerprint in the database and then the coordinates associated with the fingerprint that provides the smallest Euclidean distance is returned as the estimate of the position It is clear from the above discussion that the designer should not start out to do the site survey without proper system design objectives and guideline If the performance is not sufficient collecting more fingerprints in between previous location fingerprints may help It is a problem of choice as to how many access points are required for the system and what the minimum distance between physical positions on the grid should be in order to provide a good position resolution and best system performance The system parameters and factors that improve the accuracy and the precision performance are still not clear

17 Outline Chapter 2 provides literature review of ldquoIndoor Local Positioning System for ZigBee based on RSSIrdquo it introduces the basic components required for a positioning system general classification of indoor positioning system Finally it explains ldquoLocation Fingerprintrdquo technology used in this thesis work to determine the position of an object in a real‐world application It briefly explains the protocols and industry standards for wireless sensor networks Chapter 3 describes some properties of RSS which is a stimulus to determine

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

7

the position of an object RSS relation with energy and on what parameters is the RSS value dependent Chapter 4 describes the model of an indoor local positioning system and its probability of returning the correct location It also explains its performance evaluation and its error distance distribution Chapter 5 describes results obtained from the model of positioning system Chapter 6 includes conclusions from the experimental analysis and future work

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

8

2 LITERATURE REVIEW This chapter reviews the literature on wireless indoor positioning system as a means of providing an intellectual background for the thesis work First in section 21 the common components of indoor positioning system are described Then in section 22 discusses different means to classify indoor positioning systems In section 23 reviews of all relevant literature on location fingerprint technique Finally in section 24 protocols and industry standards for wireless sensor networks are explained

21 Common components of indoor positioning system Basically a wireless local positioning system consists of at least two separate hardware components a measurable unit that usually carries the major part of the system intelligence and a signal transmitterreceiver The transmitter in the simplest case is just a beacon Figure 2 represents basic hardware model of a positioning system

Figure 2 Block diagram representing hardware module

First the location sensing devices detect the signals transmitted by or received at known reference points using radio sensing technologies The sensing technique is based on signal strength level that converts the sensed signal into RSS Given a set of reference points the relative position of the MS can be derived from the signal characteristics of RSS The signal characteristics of RSS are unique for a location Then the microcontroller processes the position algorithm and estimates the position of object and displays it on PC

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

9

22 Classification of Indoor positioning systems Indoor positioning system can be categorized based on the stimulus used in particular application and measurement principle

221 Classification based on Stimulus Based on the stimulus used in an application WSN inherit certain characteristics and limitations on sensorrsquos signal The propagation delay diffraction reflection and scattering are the basic signal characteristics which affect all signal types The effective range available bandwidth regulatory constraints interference power constraints safety and cost of technology limitations The wireless signals commonly used for indoor positioning systems are infrared radio frequency and ultrasound Brief descriptions of the three major technologies are as follows

Infrared Infrared signal is an electromagnetic signal of a wavelength longer than that of visible light but shorter than that of radio waves Infrared is just below the visible spectrum of light in frequency and is radiated strongly by hot bodies Many objects such as people vehicle engines and aircraft generate and retain heat and as such are especially visible in the infrared wavelengths of light compared to objects in the background It cannot pass through walls or obstructions therefore it has a rather limited range of indoor environments Thus it requires a more sophisticated circuitry than ultrasound signals Indoor lighting interferes with this type of signal and causes problems in accurate sensing It generally has a range of around 5 m The infrared devices are usually small in size compared to ultrasound devices

Radio frequency The RF signal can penetrate through most of the building material therefore it has an excellent range in indoor environments The propagation speed is also high approximately 3108 ms There are unlicensed frequencies available freely for use This type of signal has the longest range compared to infrared and ultrasound Since the propagation waves are of higher frequency (24GHz) interference with other frequency components is less

Ultrasound Although ultrasound operates at low frequency bands (typical 40 kHz) compared to the other two signaling technologies it possesses a good precision for location sensing at a slow propagation speed of sound (343 ms) The advantages of ultrasound devices are their simplicity and that they are inexpensive However ultrasound does not penetrate walls

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

10

but reflects off most of the indoor obstructions It has a short range around 3 m to 10 m but has a 1 cm resolution of distance measurement The operating temperature influences the performance of ultrasound

222 Classification based on measurement principle Wireless positioning systems can be categorized by measurement techniques which are used to derive the position of mobile stations Mainly three different measurement principles are used today AOA RSS and propagation‐time based systems that can further be divided into two different sub‐classes TOA and TDOA Figure 3 shows taxonomy based on the technology and the technique classifications

Figure 3 Taxonomy of positioning system [7]

In AOA systems the position is calculated via goniometry With the use of directional antennas or antenna arrays the angle or bearing relative to points located at known positions is measured The intersection of several measured directions pointers the yields the position value The accuracy of this approach is limited by the possible directivity of the measuring aperture by shadowing and by multi‐path reflections arriving from misleading directions In TOA and TDOA techniques rely on the precision of timing between the signal transmitter and the receiver in order to use the propagation delay or TOF to calculate the distance between transmitter and receiver Therefore a precise synchronization is also very important in such systems By combining at least three distances from three reference positions triangulation can be used to estimate the mobile stationʹs location This type of technique will

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

11

require a high accuracy clock in the communication system TDOA is more practical [1] Examples of location‐sensing systems that use time of flight are GPS the Active Bats [15] and the Cricket [16] Besides these time delay‐based techniques the distance between transmitter and receiver can also be determined from signal strength attenuation and direct distance measurement (such as dead reckoning) Fingerprinting or Location Pattern Matching this technique generally requires only measurement of RSS or other non‐geometric features at several locations to form a database of location fingerprints To estimate the mobile location the system needs to first measure the received signal strength at particular locations and then search for the pattern or fingerprint with the closest match in the database This technique does not require the mobile station to see at least three base stations or access points in order to determine the location The disadvantage of this technique is that it is very time‐consuming to perform an exhaustive data collection for a wide area network such as in outdoor positioning systems

23 Indoor local positioning using ZigBee based on RSSI This section reviews relevant RF‐based indoor positioning systems which can be used to locate stationary objects and track mobile users The impressive growth of IEEE 802154 wireless PANs (WPAN) in recent years suggests an interesting future for the location fingerprinting technique The system is quite flexible because system designers can select whether to have a centralized positioning server or let the mobile to determine its own position However the fingerprinting technique requires a training phase (off‐line phase) to collect location fingerprints for all positions in the operating area before the actual deployment (on‐line phase) [7] The following discussion is divided according to the positioning system components First the common form of location fingerprinting and its relationship with physical locations are explained Finally location estimation algorithm is reviewed

231 Location Fingerprint A location fingerprint based on RF characteristics such as RSS is the basis for representing a unique location It is created under the assumption that each position or location inside a building has unique RF signature [4] Generally a fingerprint F is labelled with a location information L The location fingerprints and their labels (eg location information) are maintained in

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

12

database and used during the on‐line phase to estimate the location The label and fingerprint are usually denoted as a tuple of (L F) Battiti et al [11] point out that the location information L for indoor location can be recorded in two forms as either a tuple of coordinates or an indicator variable The tuple of real coordinates can vary from one dimension to five dimensions which include the three dimension space and two orientation variables expressed in spherical coordinates For instance a location information of a two‐dimension system with an orientation could be expressed as a triplet L = (x y d) | x y є R 2 d є North East South West The RSS was found to be more location‐dependent than the SNR because the noise component is rather random in nature However the RSS itself fluctuates over time for each access point and location Each RSS element can be considered as a random variable therefore it can be captured by recording its descriptive statistics parameters approximating its distribution or maintaining the whole measurement dataset These approaches of RSS representation result in different procedures for location estimation algorithm Regardless of the approach the location fingerprint is usually denoted as an array or vector of signal strength (random variables) received at any position in the location‐based service area The size of the vector is determined by the number of access points that can be heard To create a basis fingerprint such as in a number of samples of vectors of signal strength are collected over a window of time for each position This basis is called a prototype Then the average RSS of each access point is calculated and recorded as an element in the location fingerprint For an area that can receive signals from N access points the location fingerprint can be expressed as a vector of average RSS elements βi

F = (β1 β2hellip βN)T Besides the basis location fingerprint the samples of location fingerprint measured during the on‐line phase are also important for the system that tracks the mobile object The number of samples should be selected appropriately to represent the location fingerprint for the mobile application A step in statistical analysis method called pre‐processing is another important issue needed to be considered because it can impact the estimation of dependency between location fingerprint and the location information easier The pre‐processing refers to a step that cleans the raw data (in this case the training set) before any further operations or analysis The cleaning may consist of encoding dimensionality reduction (reduce unnecessary elements) feature extractionselection clustering and outlier elimination Roos et al [10] point out that the pre‐processing enables faster location estimation and

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

13

reduce the noise from the training data The process that creates the basis of location fingerprint discussed above could be considered as a part of pre‐processing

232 Location Estimation Algorithm Location estimation algorithms or positioning algorithms are procedures that exploit dependency between location information and location fingerprint basis in order to determine a position or location from samples of RSS signals Position algorithms can be classified into deterministic and probabilistic types based on the approaches that model the relationship between location fingerprints and location information The deterministic type of algorithms are those that based in the nearest neighbour classifiers and neural network classifiers The probabilistic type of algorithms is those that are based on the Bayesian inference and statistical learning theory such as support vector machines (SVR) The algorithm used in this project is discussed briefly

Euclidean distance method The Euclidean distance method is a deterministic approach of position estimation because they require only a set of constant location fingerprints which includes mean vectors In order to determine the location a form of discriminate function is used to classify a sample of RSS fingerprint into a position The mean or average RSS vector is a centre of a mass which represents each class of location fingerprint Suppose a set of l location fingerprints is denoted by F1 F2hellip Fl exist and each fingerprint has a one‐to‐one mapping to a set of positions L1 L2hellip Ll A sample of an RSS fingerprint measured during an on‐line phase is denoted as S which can be another mean or average RSS vector of small window of RSS samples Assuming that an indoor positioning system only considers the average RSS from N access points as a location fingerprint the sample of RSS vector is S = (s1 s2 helliphellip sN)T and each location fingerprint i in the database can be expressed as Fi = (β1 β2helliphellip βN)T The simplest closeness metric is a distance measurement in signal space denoted as the Dist () function [6] Thus the simple procedure of nearest neighbour algorithm is expressed as picking the fingerprint j that has the shortest signal distance

Dist(S Fj) le Dist(S Fk) for all k ne j A generalized weighted distance Lp summarized by [12] can be used to calculate different forms of distance in signal space as

Lp = 1N pN

i

pPiSiWi 1

1)||1(sum

=

minus

where N is the number of access points wi is a weighting factor (wi le 1) and p is the norm parameter starting from 1 The weighting factor wi is a bias

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

14

parameter that can demote or promote an important RSS component in the fingerprint

24 Protocols and Industry standards for WSN Although many applications for WSN are proposed the communication protocols supporting them remain mainly unexplored and diverged This is because the area of interest is new each implementation is application specific and the components of WSNrsquos have a lot of constraints Wireless sensor networks use the wireless medium to communicate However the traditional communication protocols that support wireless communications especially ad‐hoc mesh networks may not be well suited for them WSN communication is an active research area and many algorithms and protocols have already been proposed This section introduces two proposed standards the IEEE 802154 and the ZigBee The IEEE 802154 covers the physical layer and the MAC layer of low‐rate WPAN The ZigBee is ldquoan emerging standard that is based on the IEEE 802154 and adds network construction (star networks peer‐to‐peermesh networks and cluster‐tree networks) application services and morerdquo

241 IEEE 802154 Holger and Willig (2005) [17] mention home automation home networking and home security as possible applications for IEEE 802154 They add that ldquomost of these applications require only low‐to‐medium bitrates (up to some few hundreds of kbps) moderate average delays without too stringent delay guarantees and for certain nodes it is highly desirable to reduce the energy consumption to a minimumrdquo The IEEE 802154 standard (2003) defines the device types that can be used in a LR‐WPAN A device can be a FFD or a RFD The RFD can be used in simple applications in which they do not need to transmit large amounts of data and they have to communicate only with a specific FFD The FFD can work as a PAN coordinator as a coordinator or as a simple device It can communicate with either another FFD or a RFD In keeping with the application requirements the LR‐WPAN operates in a star or peer‐to‐peer topology (figure 4) In the star topology the RFD communicates with a single controller the PAN coordinator The PAN coordinator can perform the same function as the RFD but it is also responsible for controlling the PAN ldquoit initiates terminates or routes communication around the networkrdquo [18] The peer‐to‐peer topology

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

15

supports ad‐hoc mesh multi‐hop networking Any device in the peer‐to‐peer topology can communicate with any other device within its communication range however this topology also has a PAN coordinator All the devices in a LR‐WPAN have a unique 64‐bit address This or a short address allocated by the PAN coordinator can be used inside a PAN Additionally each PAN has a unique identifier The combination of the PAN identifier and the sort addresses allows communication across different PANs (IEEE 802154 Standard 2003)

Figure 4 Star and peer‐to‐peer topologies in LR‐WPAN [18]

The LR‐WPAN based on the OSI seven‐layer model has the layered architecture presented in figure 5 The application and the network layer are the upper layers in the LR‐WPAN architecture but are outside the scope of the IEEE 802154 standard Only the physical layer ldquowhich contains the RF transceiver along with its low‐level control mechanismrdquo and the MAC layer ldquothat provides access to the physical channelrdquo are included in the standard and will be introduced in the following sections Finally the Type I 8022 LLC and the SSCS are intermediate sub layers supporting communication with the above layers (IEEE 802154 Standard 2003)

Physical Layer The 802154 standard specifies two different services that the PHY provides The PHY data service controls the radio and thus the transmission and reception of the PPDUs In addition the management service performs ED in the channel The management service also performs CCA before sending the messages and provides LQI for the received packets

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Figure 5 WPAN architecture [18]

Three different bands are defined by the standard The 868‐8686 MHz for Europe the 902‐928 MHz for North America and the 2400‐24835 MHz worldwide All of them belong in the ISM radio bands and they are using DSSS Each of the bands supports different a data rate and it uses a different modulation technique chip rate and number of channels Moreover if the system does not use the 2450 MHz frequency it operates in both the 802 MHz and 902 MHz frequencies Table 3 summarizes the supported bands and data rates (IEEE 802154 standard 2003)

PHY (MHz)

Frequency Band (MHz) Modulation

Bit Rate Number of Channels

868-8686 BPSK 20 1 868 915 902-928 BPSK 40 10

2450 2400-24835 O-QPSK 230 16 Table 3 Frequency bands and data rates for IEEE 802154 based on the IEEE

802154 standard [18] The physical layer uses PPDU packets to communicate Table 4 demonstrates its structure The LSB is always transmitted and received first The SHR contains the preamble and the SFD fields which helps receiver

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synchronization The 8‐bytes preamble contains only zero and is used for synchronization The SFD contains a specific sequence of one and zeros and specifies the beginning of the frame The PHR contains the payload length Packets with a length of 9 or more bytes are MPDU as the next section further explains Packets with length 5 are used for MPDU acknowledgements The payload part of the PPDU encloses the MAC layer packet Finally the PPDU size can be up to 127 bytes [18]

4 bytes 1 byte 1 byte Variable

Preamble SFD Frame length

(7 bits) Reserved

(1 bit) Payload (PSU)

SHR PHR PHY Payload Table 4 Format based on the IEEE 802154 standard [18]

MAC Layer The MAC layer is the interface between the SSCS and the PHY layer Similar to the PHY layer the MAC layer supports two services The MAC data service is responsible for the transmission and reception of the MPDUs through the PHY data service The MAC management service if the device is a coordinator manages the network beacons It is also responsible for PAN association and disassociation frame validation and acknowledgment providing ldquoa reliable link between two peer MAC entitiesrdquo In addition it uses the CSMA‐CA for channel access and handles and maintains the GTS mechanism Finally it supports device security [18] The IEEE 802154 standard defines four different frame types the beacon data acknowledgment and MAC command frame All frame types are based on the general MAC frame format (table 5) The frame control field describes and specifies the above different frame types Every MAC frame comprises a MHR which consists of a frame control sequence number and the information field It also contains the MAC payload different frame types have different MAC payload fields The acknowledgment type does not have a payload Finally each frame includes a MFR which contains a FCS The data in the MPDU follows the same order as the PPDU the least significant bits are left in the frame and are transited first Octets2 1 02 028 02 028 Variable 2

Destination PAN Identifier

Destination Address

Source PAN

Identifier Source Address Frame

Control Sequence Number Addressing Field

Frame Payload FCS

MHR MAC

Payload MFRTable 5 General MAC frame format IEEE 802154 standard [18]

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Table 6 7 8 and 9 present the four different MAC frame types The beacon frame is transmitted periodically by the PAN coordinator It provides information about the network management through the super frame and GTS fields which are analyzed later in the section It also synchronizes the network devices and indicates the proper communication period for them The data frame payload encapsulates data from the higher layers When a device receives a packet it is not obliged to response with an acknowledgement packet Finally the command frame identifier and command payload fields of the command frame are used for communication between the network devices The command identifier specifies actions like association disassociation and data GTS or beacon request Octets2 1 410 2 Variable Variable Variable 2

Frame Control

Sequence Number

Addressing Fields

Super frame Specification

GTS fields

Pending Addressing

Fields Beacon Payload FCS

MHR MAC Payload MFRTable 6 Beacon frame format [18]

Octets 2 1 Variable 2

Frame Control

Sequence Number

Addressing Field

Data Payload FCS

MHR MAC

Payload MFR Table 7 Data frame format [18]

Octets 2 1 2

Frame Control

Sequence Number FCS

MHR MFR Table 8 Acknowledgement frame format [18]

Octets2 1 1 Variable 2

Frame Control

Sequence Number

Addressing Fields

Command Frame Identifier

Command Payload FCS

MHR MAC Payload MFR Table 9 Command frame format [18]

In the LR‐WPAN every PAN has its own coordinator The PAN coordinator manages the communication in the local area it has two options to use or not use the super frame structure The super frame (figure 6) uses network beacons If the coordinator does not want to use a super frame structure it suspends the beacon transmission However the beacon is important for device association and disassociation If the coordinator wishes to maintain close communication control in the PAN and to support low‐latency devices it usually uses the super frame A super frame determines a specific time

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period beacons bound it The beacon is transmitted in the first of the sixteen equal time slots that the super frame has It is used to ldquosynchronize the attached devices to identify the PAN and to describe the structure of the super framerdquo The super frame can have active and inactive periods All the communications have to be finished inside the super frame period In the inactive periods the devices can switch to the sleep mode but they have to be ready for the next beacon The active period is further divided into a CAP and a CFP During the CAP any device can communicate competing with the other devices in the PAN using slotted CSMA‐CA The CFP contains GTSs and follows the CAP A CFP may maintain up to seven GTSs and each GTS can reserve more than one time slot However the CAP should always be sufficient to allow new devices t join the PAN The GTSs are allocated to specific devices All the transactions must be completed inside the assigned time period (CAP GTS and CFP) (IEEE 802154 standard 2003)

Figure 6 Example of super frame structure [18]

In accordance with the 802154 standard (2003) three different types of data‐transfer exist In addition the types differ if the coordinator uses or does not beacons Data transfer from a device to the PAN coordinator is the first type (figure 7) For a ldquonon‐beacon‐enable networkrdquo it first senses the medium by using ldquoun‐slotted CSMA‐CArdquo and then a simple transmit to the data frame In a ldquobeacon‐enabled networkrdquo the sender waits for the beacon when it finds it ldquothe device synchronizes to the super fame structurerdquo In the specified time frame the sender again senses the medium by using ldquoslotted CSMA‐CArdquo and transmits the data to the coordinator In either case the coordinator has the option to acknowledge or not acknowledge the data reception after that the transaction is completed Data transfer from the coordinator is the next type described in the standard (figure 8) In a beacon‐enabled network the coordinator indicates a pending message through the beacon The messagersquos target device receives the beacon and if the message is pending it responses with a MAC command request

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message using slotted CSMA‐CA the coordinator may or may not acknowledge the command message and through slotted CSMA‐CA sends the pending message The device acknowledges the received message After that the coordinator removes the message from the beaconrsquos pending list and completes the transaction In a non‐beacon enable network the device periodically sends a MAC command‐request frame to the coordinator The coordinator acknowledges the data request Then by using un‐slotted CSMA‐CA if it has a pending message it transmits it If it has not it respond with a data frame with a ldquozero‐length payloadrdquo To complete the transaction the device acknowledges the data reception

Figure 7 Communication from a device to a PAN coordinator in (a) a beacon‐enabled network and (b) a non‐beacon enabled network [18]

Peer‐to‐peer is the last type of data transfer In this situation the devices are free to communicate with any other device within their communication range In a peer‐to‐peer PAN the devices can ldquoeither receive constantly or synchronize with each otherrdquo If they are receiving constantly to transmit data they use un‐slotted CSAM‐CA In the second case synchronization must be achieved first (IEEE 802154 standard [IEEE 2003]) The IEEE 802154 (2003) standard establishes MAC and PHY standards for low‐cost low‐power and high‐density node deployments In addition to the above PHY and MAC characteristics IEEE 802154 provides a security baseline including ldquothe ability to maintain an ACL and use symmetric cryptographyrdquo for data encryption The algorithm that is used for encryption is the AES However the higher level layers decide when security is need The upper layers are in general responsible for device authentication and key management The next section introduces the ZigBee standard which

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2 LITERATURE REVIEW10102006

21

encapsulates the IEEE 802154 and provides additional standardization for the higher levels

Figure 8 Communication from a PAN coordinator to a device in (a) a beacon‐enabled network and (b) a non‐beacon enabled network [18]

242 ZigBee Heily (2004) defines ZigBee as ldquoa rapidly growing worldwide non‐profit industry consortiumrdquo whose mission is ldquoto define a reliable cost‐effective low‐power wirelessly networked monitoring and control product based on an open global standardrdquo Figure 9 illustrates the areas of interest for different wireless communication standards

Figure 9 Overview of the coverage for different wireless communication

standards [17]

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ZigBee a new standard which became publicly available in June 2005 is based on the IEEE 802154 standard It expands the IEEE 802154 by adding the framework for ldquothe network security and applicationrdquo (Craig 2005) Figure 10 presents the IEEE 802154ZigBee stack and the areas of responsibilities

Figure 10 IEEE 802154ZigBee stack [20]

Craig (2005) mentions three networking topologies that the standard covers the star mesh and cluster tree (figure 11) The ZigBee standard works on top of the IEEE 802154 addressing schema by using the standard 64‐bit and the short 16‐bit addressing Kinney (2005) summarizes the ZigBee network layer responsibilities the successful establishment of a new the network and successful new device configuration addressing assignment network synchronization frames security and message routing ZigBee further distinguishes the concept of the physical devices (RFD FFD) by using the notion of ldquological devicesrdquo ldquoZigBee Coordinatorrdquo is the first type of logical devices It is responsible for initializing maintaining and managing the network Under the coordinator in the network hierarchy is the ldquoZigBee routerrdquo which is responsible for controlling the message routing between the nodes Finally the ldquoZigBee End Devicerdquo acts as the end point of the network

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

23

structure The tasks that an end device can perform are specified in the ldquoApplication Profilesrdquo (Craig 2005)

Figure 11 IEEE 802154ZigBee stack [17]

The ZigBee specifications (2005) summarize the security services provided by ZigBee ldquokey establishment key transport frame protection and device managementrdquo ZigBee builds its security mechanism using symmetric key cryptography The security services also depend on the associated layer thus as the figure 10 shows the security mechanism covers the network and the application layer In addition if a MAC frame needs security protection the MAC layer is able to secure it Moreover the notion of end‐to‐end security is supported the source and destination devices have access and use the same share key In the MAC layer the 802154 AES mechanism provides the proper security The mechanism protects ldquothe confidentiality integrity and authenticity of the MAC framesrdquo (Kinney 2005) An auxiliary header field in front of the MAC payload indicates if the frame is encrypted or not The MAC framesrsquo integrity is supported by calculating and using a MIC at the end of the MAC payload In addition to the AES nonce is used to provide MAC confidentiality and authenticity Figure 12 illustrates a MAC frame with security For different security aspects the MAC layer uses different mode of the AES for the encryption it uses the AES in Counter (CTR) mode and for the integrity the

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

24

CBC‐MAC Finally the combination (CCM) of the above two modes is available providing both encryption and integrity (Kinney 2005)

Figure 12 ZigBee secure frame in MAC layer [17]

In the network layer the CCM (a modified MAC layer CCM mode) is used for encryption Because the network layer uses only the CCM mode a single key is used for all different security options The network layer security message format is similar to the MAC frame it is presented in the figure 13 Finally although the network layer is responsible for securing its layer messages the above layers specify the keys and the CCM option for each frame (Kinney 2005)

Figure 13 ZigBee secure frame in network layer [17]

Security in the application layer works similar to the network and MAC layers It uses the ldquolink keyrdquo or the ldquonetwork keyrdquo to secure the message and then encapsulate it inside a set of fields similar to the network format (figure 14) Other security responsibilities that the application layer has are to provide the ZDO and the applications with device management services key establishment and key transport (ZigBee specifications 2005)

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

25

Figure 14 ZigBee secure frame in application layer [17]

The ZigBee application layer contains the manufacturer‐defined application objects the ZDO and the application sub‐layer In addition to the security responsibilities the application sub‐layer binds devices based on their duties and needs maintains the binding tables and forwards messages between them The application sub‐layer also discovers the neighbour devices for a given device The ZDO is responsible for determining the devicersquos duty in the network for communicating using binding requests and for supporting security as was mentioned above The sub‐layer that implements the actual application is the manufacturer‐defined application object (Kinney 2005) The ZigBee specifications are a great step toward the wireless sensors networks standardization They cover all the OSI layers from the physical to the application providing guidance to the developers The specifications are the product of the ZigBee alliances a consortium of companies and academic institutes Thus they not only produce the specifications but also are the first use them The next section goes beyond the layer architecture and standardization and introduces the OS field another crucial element of WSNs

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

26

3 RECEIVED SIGNAL STRENGTH (RSS) Majority of the existing methods leverage the existence of IEEE 80211 base stations with powerful radio transmit powers of approximately 100mW per base station Such radios are in a different class from the low power IEEE 802154 compliant radios that typically transmit at low power levels ranging from 52mW to 29mW [19] The wide availability of larger number of IEEE 802154 radios in the research community has revived the interest for signal strength based localization in sensor network Despite of rapidly increasing popularity of IEEE 802154 radios and signal strength localization there is a lack of detailed characterization of the fundamental factors contributing to large signal strength variation This chapter investigates some of the properties of RSS observed during testing and analysis of research work collected during the period of thesis The analysis of RSS values is needed to understand the underlying features of location dependent RSS patterns and location fingerprints An understanding of the properties of the RSS values for location fingerprinting can assist in improving the design of positioning algorithms and in deployment of indoor positioning systems

31 Parameters that affect RSS Different factors that affect the indoor RSS are the userrsquos presenceabsence hisher orientation time of the day building type and material distance from transmitter and type of radio These factors are by no means exhaustive Other possible factors are antenna orientation directionality and type Table 10 classify the parameters which affect RSS [7]

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

27

Effect on Factors Options

1 Proximity of user Userrsquos presence or absence

2 Orientation of user and terminal

North East South West

Data Collection

3 Make of Radio 4 Time of measurement Time of day amp days of

week 5 Period of measurement

Second minute hour

7 Interference Co‐channel adjacent co‐channel

Statistics

8 Building environment Small offices or large hall

Table 10 Classification of factors the affect RSS [7]

Transmitter variability Different transmitters behave differently even when they are configured exactly in the same way In practice this means that when a transmitter is configured to send packets at a power level of d dBm then the transmitter will send these packets at a power level that is very close to d dBm but not necessarily exactly equal to d dBm This can alter the received signal strength indication and thus it can lead to inaccurate distance estimation

Receiver variability The sensitivity of the receivers across different radio chips is different In practice this means that the RSSI value recorded at different receivers can be different even when all the other parameters that affect the received signal strength are kept constant

Antenna orientation Each antenna has its own radiation pattern that is not uniform In practice this means that the RSSI value recorded at the receiver for a given pair of communicating nodes and for a given distance between them varies as the pair wise antenna orientations of the transmitter and the receiver are changed

Multi‐path fading and shadowing in the RF channel In indoor environments the transmitted signals get reflected after hitting on the walls andor on other objects in the room such as furniture Both the original signal and the reflected signal reach the receiver almost at the same time since they both travel at the speed of light As a result of this the receiver is not able to

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

28

distinguish the two signals and it measures the received signal strength for both of them

32 Related work analysis A number of indoor location tracking systems have been proposed in the literature based on RF signals ultrasound infrared and some combinations of these As mentioned previously RF based positioning system are widely studied as shown in Table 2 Given a model of radio signal propagation in a building or other environment RSS can be used to estimate the distance between the transmitter and receiver Most of the algorithms (Table 2) have been evaluated in practice using 80211b compliant wireless radios and seem to produce a location error of 15ft to 20ft for an indoor network area of approximately 225ft x 144ft According to the results of this study all the localization techniques produce approximately the same location error over a range of environments Ecolocation [22] Mote Track [5] are two of the few localization algorithms that have been evaluated on a real sensor network that uses a low power wireless radio Ecolocation reports a location error of 10ft for a very small outdoor network deployment area (26ft x 49ft) Mote Track reports a location error of approximately 13ft for an indoor network deployment area of 18751ft2 In general the RSSI based localization algorithms can be divided into two broad categories the map based and the distance (or area) prediction based algorithms In the case of the map based algorithms the deployment area is divided into a large set of sample points that cover the whole area Most techniques collect one or more signal strength measurements from all visible beacon nodes and for each sample point Each sample point is then mapped to either a signal strength vector or a signal strength probability distribution This process generates an RSSI fingerprint map which is unique with respect to the beacon locations and the deployment area This fingerprint map is stored to a central base station A node that is not aware of its location collects one or more signal strength measurements from all the visible beacon nodes and creates its own RSSI fingerprint that is sent to the central base station The base station computes nodersquos location as the sample pointrsquos location whose RSSI fingerprint distance from the unknown nodersquos RSSI fingerprint is the minimum among all the sample points Despite the fact that the map based RSSI localization methods produce relatively small location errors they are not attractive mainly because they require an excessive amount of profiling data and because they are not fully distributed The detailed profiling of a building is a major holdback because it is complex time consuming and depends on beacon locations Therefore for different buildings or even for the

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

29

same building and different beacon locations different RSSI maps have to be obtained The second category of RSSI based algorithms are the distance (or area) prediction algorithms which focus on minimizing the amount of profiling data In distance prediction based methods raw RSSI data are directly mapped to distances through a signal propagation model In some cases distances are not explicitly measured but they are taken into consideration through assumptions on the underlying distributions In the case of outdoor deployments where there are no obstacles a single signal propagation model is all the profiling data required In the case of indoor deployments the same applies but now the signal propagation model might be different across different buildings with different furniture and wall arrangements Given an underlying signal propagation model raw RSSI data can be mapped to specific distances These distances can be either used directly in order to perform localization or assigned a probability and used in a learning basedprobabilistic localization algorithm Obviously the accuracy of the distance prediction based methods depends heavily on the accuracy of the signal propagation model used for translating RSSI values to distances

33 Properties of RSSI Changes in signal due to propagation indoors are difficult to predict because of dense environment and propagation effects such as reflection diffraction and scattering The multi‐path fading effect which is the result of either constructive or destructive combination of multiple signal copies at the receiver causes the received signal to fluctuate around a mean value at a particular location The received signal is usually modelled by the combined effects of large‐scale fading and small‐scale fading The large‐scale fading component describes the signal attenuation as the signal travels over a distance and is absorbed by material such as walls and floors along the way to the receiver This component predicts the mean of the RSS and usually has a lognormal distribution On the other hand the small scale fading component explains the dramatic fluctuation of the signal due to multi‐path fading On the other hand the small scale‐fading component explains the dramatic fluctuation of the signal due to multi‐path fading If there is no LOS component the small‐scale‐fading is often modelled with a Rayleigh distribution Such scenarios are commonly called NLOS If there is a LOS component the small‐scale‐fading is typically modelled by a Rician distribution Note that when the RSS is measured the measurement averages out the small‐scale‐fading effects

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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4 OBJECT TRACKING 41 Introduction This chapter presents the object‐tracking application the main objective of this thesis work The application is an algorithmic‐software implementation written in Embedded C which is capable of receiving data messages and generating object position estimation data The implementation of the object‐tracking application makes use of the SENTIO platform and uses the background knowledge introduced in the preceding chapters The motivation for the object‐tracking application is to determine the position of an object in indoor environments (Eg Home) The application acts as an interface which collects the raw data that the sensors generate (numerical values) It makes the proper comparisons and algorithmic processes to identify and track the object while it is static inside the area covered by the wireless sensor network Finally with the detection and tracking data it alerts the user or another system The following sections provide an overview of the object‐tracking application

42 Overview of hardware product Before describing the object tracking application it is important to know the characteristics of hardware product The SENTIO platform (figure 15) is built in modular fashion and has the following key features [8]

Flexibility This platform is very flexible in such a way that it is made easy to add or change the hardware for each sensor task depending on the requirement in the WSN For a specific platform this can be hard or sometimes even impossible to accomplish

Sensor resources Resources in a sensor module includes the ability of usage of controllerrsquos resources as much as possible such as ADC‐channels interrupts and digital Input‐Outputrsquos Local voltage regulation should also be possible since many sensors require higher supply voltages than digital Integrated Circuits do Further support of a shut‐down mechanism to power down the sensor modules when not needed

Computation and memory The processor module based on microcontroller should support many devices in a microcontroller family and support different power supply voltages enabling full range of clock frequencies All power‐down modes of the devices should be

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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31

supported The platform should support different processor modules including FPGArsquos and very simple microcontrollers

Communication The focus should be on enabling an IEEE 802154 compliant communication interface With or without ZigBee stack this will provide a flexible platform for exploring different network topologies Support for power‐down of the entire communication module should be supported

Cost and form factor High flexibility is in conflict with low cost and small size The primary objective should be flexibility but the size should be considered in order to enable sensor device integration in systems where space is limited

Figure 15 SENTIO Platform [8]

The communication subsystem of the SENTIO sensor node was designed so that the correct operation of the radio chip is ensured The radio chip is powered by the on‐board voltage regulator and thus fluctuations in the battery voltage level do not affect the operation of the communication subsystem This platform uses Chipcon CC2420 IEEE 802154 radio transceiver operates in the 24GHz ISM band and includes a digital direct sequence spread spectrum (DSSS) modem providing a spreading gain of 9dB and an effective data rate of 250Kbps It was specifically designed for low power wireless applications and supports 8 discrete power levels 0dBm minus1dBm minus3dBm minus5dBm minus7dBm minus10dBm minus15dBm and minus25dBm at which its power consumption varies from 29mW to 52mW [21] A built‐in received signal strength indicator gives an 8‐bit digital value RSSIVAL The RSSIVAL is always averaged over 8 symbol periods (128μs) and a status bit indicates when the RSSIVAL is valid (meaning that the receiver was enabled for at least 8 symbol periods) The

Antenna

Communication Module

Processing Module

Sensing Module

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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32

power P at the RF pins can be obtained directly from RSSIVAL using the following equation

P = RSSIVAL + RSSIOFFSET [dBm] where the RSSIOFFSET is found empirically from the front‐end gain and it is approximately equal to minus45dBm In the next sections when we refer to the RSSI value we refer to the RSSIVAL and not the power P unless otherwise stated

43 Experimental Testbed The experimental test bed for object tracking application is as shown in figure 16 Experiment was conducted in Masterrsquos room at Mid Sweden University A DELL laptop is connected to SENTIO platform and software were primarily used to collect samples of RSS from APrsquos The software monitors IEEE 802154 radio frequency channels which operate in the 24GHz band

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

1030

BC - 1030

AP 3 AP 1

AP 2

AP 4

BS

HEWLETT

PACKARD

Figure 16 Layout of Masterʹs Room

A three dimensional scalable testbed is used for indoor sensor network deployments The testbed illustrated in figure 16 is a 3‐D structure measuring 16m (L) x 12m (W) x 3m (H) and it is designed to host a large number of static nodes as shown in figure 16 Arrow marks in the above figure represent the location where fingerprints were taken The centrepiece of our infrastructure

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

33

is the SENTIO sensor node [8] an open‐source general purpose sensing platform designed with AT Mega 128 microprocessor and the IEEE 802154 compliant CC2420 radio from Chipcon [19]

44 Obtaining data from the sensor network The next important step in the development of the object tracking application is to obtain the data from the sensor network and send it to the base station From there the data is transmitted through a serial port to the computer that runs the object tracking application When the data arrives at the serial port the application must capture it This paragraph gives a brief explanation of how and what kind data is transmitted by the static object fixed sensors and how this data is being used to estimate the position of the object First the static object transmits Beacon packet continuously which is received by all the fixed APrsquos The beacon packet format is as shown in table 6 Then the fixed APrsquos calculates the 8‐bit value of RSSI from the transmitted beacon packet and then sends the resultant RSSI value as a form of data packet (table 7) to the BS which calculates the position and sends back to the object

45 Object tracking application Testing and Evaluation The purpose of this section is to discuss and evaluate the object tracking application Finally we are ready with the experimental testbed as shown in figure 16 As shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors placed at the corners of the masterrsquos room BS is placed at the centre of the room connected to a PC All the nodes are power connected before an actual measurement takes place At first the object is placed at random position inside the room and transmits beacon packets continuously Then these packets are received by the fixed APrsquos which calculate the RSSI from the packet and then averages it until it receives 50 beacon packets and then transmits the data to the BS (figure 17) Then the BS waits until it receives the data from all the APrsquos Once it receives from all the APrsquos it calculates the position of the object (figure 18) The application was developed to demonstrate a real world use of a WSN in object tracking In evaluating the final position obtained with respect to the actual position we conclude that the object‐tracking was successful Although

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

34

it is a research prototype object tracking application is a simple stable stand‐alone system capable of demonstrating an important application of WSN It takes raw data from the WSN aggregates it evaluates it and based on the evaluation tracks the object The accuracy of estimating the position is less than 5m Table 11 below shows a log of readings taken during test

Start

Intialize micro‐controller and transeciever in receieve mode

If received_packet=

Beacon

Average RSSI values

Increase the counter

If countergt1000050

A

No

Yes

B

B

Yes

No

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

35

A

Reset counter

Set transceiever in transmit mode

Transmit averaged RSSI to Main sensor and set it back to receive mode

10000 samples ‐ Offline

50 samples ‐ Online

Figure 17 Flow chart representing the algorithm of fixed sensors

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

36

Start

Intialize micro‐controller and transeciever in receieve mode

Received RSSI values from all fixed Sensors

Open radio map

Calculate Euclidean distance between obtained RSSI values from fixed sensors and fingerprints

in radio map

No

Yes

Bubble sort for least error in signal strength values

A

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

37

A

Get the location of the object from the radiousing the least error value

Display location of object on PC

End

Figure 18 Flow chart representing the algorithm of main sensor

Table 11 Accuracy of positioning system

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

38

5 RESULTS This chapter presents how the system parameters such as number of samples mean or median etchellipare finalized All the tests are performed in masterrsquos room at Mid Sweden University This chapter is divided into three subsections First in Section 51 it reviews results obtained from samples test Section 52 discusses one dimensional test results and finally section 53 discusses results of two dimensional tests

51 Measurement setup A Dell laptop was connected to a SENTIO platform and is loaded with software (Appendix 2) This setup was used to determine number of samples required during object tracking mean or median position estimation algorithm and one dimensional test The setup is as follows

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

103

0

BC - 1030

AP 1AP 2

BS

HEWLETT

PACKARD

Figure 19 Test bed for one dimensional test in Masters Room

Two SENTIO nodes (AP1 and AP2 as shown above) were separated by a distance of 10 meters and these sensors are known as fixed sensors which are fixed to the ceiling such that there antenna is faced towards the ground They are linear is position A BS was connected to Dell laptop and it was placed in such a way that its antenna was faced towards the ceiling Another SENTIO

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

39

node was used as an object and its orientation was same as BS Figure 19 represents the complete setup for object tracking application Mobile node was moved between these fixed ones at regular intervals of one meter and RSSI values were logged at each interval

52 Number of samples test This test was performed to determine number of samples required for accurate positioning and either mean of the obtained RSSI values or median of them Figure 19 represents the setup for number of samples test Once the setup is ready the measurements are taken as follows

1 An object was tagged with SENTIO node and was placed stationary linear to the fixed sensors

2 10000 samples (RSSI) were taken at regular intervals of 1 meter up to 10 meters

3 The sample values were logged at every interval 4 Using these logged samples I have plotted graphs such as distribution

of RSS values (figure 20 21) meanmedian at intervals of 1 meter using MATLAB

5 The mean and median for various numbers of samples is as shown in figure 22

Figure 20 Histogram plot of RSSI values at 1m and 4m

Figure 21 Histogram plot of RSSI values at 6m and 8m

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

40

Figure 22 Distance vs RSSI

From the above graphs following conclusions were made 1 From the histogram plots we can observe that the distribution of RSSI

values follow Gaussians distribution law 2 As we can see from figure 22 that median curve has drastic changes

when compared to mean curve Hence mean is considered while estimating the position

3 From the observations we conclude that the number of samples does not matter since mean curve tends to be same for various numbers of samples

4 Hence 10000 samples are considered in order to get a proper location fingerprint during off‐line phase and during online phase 50 samples are enough to estimate the position of object

53 0ne dimensional test Measurement setup for one dimensional test is as shown in figure 19 This test was conducted to check the performance of algorithm and how accurate it was At first ie during offline phase fingerprints at each interval averaging 10000 samples were taken During online phase Euclidean algorithm was used estimate the position averaging 50 samples of RSSI From the results we

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

41

can realize that the error was less than 2 meters Table 12 shows the result of location estimation in one dimensional case

Table 12 One dimensional position estimation results

54 Two dimensional test This test is the final stage of my thesis work Experimental testbed for this stage is as shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors fixed at the corners of the room and a BS at the centre of the room There are 18 pre‐determined locations where the fingerprints (RSSI) were taken The fingerprints are as shown in Appendix 1 All the readings were taken in presence of userrsquos The algorithm for position estimation is shown in Appendix 2 Position estimation test is performed as follows

Tagged object is placed at random inside the masterrsquos room All the sensors including the base station and object are switched on Using the algorithm (Appendix 2) the BS estimates the position of the object and displays it on the PC

This test was performed using various numbers of samples to test which has a better accuracy Figure 23 represents the error rate using various numbers of samples From figure 23 we can conclude that 50 numbers of samples is enough to estimate the position of object with an accuracy of 3m

55 Causes of error in location determination The randomness of RSS is the major cause of error in any indoor

positioning system that uses the RSS for location inference If there is no randomness in the RSS and its location fingerprint every indoor positioning systems of this type will have no location detection error For example I have recorded RSS at one particular location during different times of the day From figures 24 and 25 you can observe that RSS values are different at different times of day

The performance of indoor positioning systems depends greatly on the separation of location fingerprints A location fingerprint

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

42

corresponding to a location can be identified correctly if it is difficult to classify it as another fingerprint by a pattern classifier

Path loss is one of the basic signal propagation properties of the RSS that best describes attenuation of transmit signal power over a distance between an access point and a mobile device

Presence of Userrsquos or userrsquos orientation affects RSS

Figure 23 Error Rate vs Number of samples

Figure 24 RSS distribution at 214 pm

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

43

Figure 25 RSS distribution at 100pm and 1100 am

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

6 DISCUSSIONS10102006

44

6 DISCUSSIONS 61 Summary and Conclusion While empirical results and performance studies of positioning systems based on location fingerprint have been presented in this report analytical model that can be used as a framework for efficiently designing the positioning systems are not available This thesis design work uses general principles of position estimation such as position estimation of sensors grid spacing etc This design work focuses on accuracy and precision The concept of location fingerprint technique based on RSS us explained in detail in chapter 2 It is found that RSS is random It is possible to approximate the RSS as being normally distributed While the RSS random process is non‐stationary the mean is more or less constant and could be used as the fingerprint of the location Model designed for position estimation is as shown in chapter 4 Accuracy and precision are the two major performance matrices in this study Uniform grid spacing has been used From the experimental analysis I have obtained an accuracy of 3 meters and 35 precision The sensitivity analysis among the system parameters and the performance matrices were done using the model as shown in chapter 4 For instance our study found that the minimum distance between two adjacent positions also affects the positioning performance This is due to the randomness of RSS patterns induced by the ever changing indoor environment

62 Future work The research in this thesis provides a ground work for the future study of efficient design of indoor positioning system The RSSI based position estimation still has limitations First the RSSI value is time dependent Thus the future study should address this issue Second the movement of object is not considered Third orientations of sensors affect the performance which should be taken care off Fourth to improve the performance of the system we need to find a solution to deal with those incomplete or missing data during the off‐line and the on‐line phase Fifth position estimation on multi‐floor and three‐dimensional coordinates is not available

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 110102006

45

APPENDIX 1

Location Fingerprints of Masters Room Time 1315 Observation Time 20 minutes Number of Samples 10000

People Presence SNo Co-ordinates AP1 (0x0001) AP2 (0x0002) AP3 (0x0004) AP4 (0x0008)

1 11 -24 -16 -27 -33 2 13 -26 -8 -34 -31 3 15 -43 -9 -39 -25 4 32 -21 -11 -27 -33 5 61 -6 -23 -32 -28 6 64 -20 -31 -25 -34 7 35 -23 -35 -29 -38 8 37 -29 -35 -22 -27 9 39 -38 -29 -21 -27 10 67 -27 -23 -20 -23 11 611 -26 -29 -23 -30 12 613 -35 -32 -2 -31 13 513 -34 -31 -20 -26 14 11 -39 -22 -29 -17 15 212 -37 -42 -28 -31 16 -212 -38 -33 -29 -6 17 -21 -42 -29 -33 -19 18 -38 -40 -23 -35 -22

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 210102006

46

APPENDIX 2

Begin

Open radio map and raw data files for write

Intialize radio map in memory

Check whether the

Packet is data or Beacon packet If data packet

Save the data to register

Check if data is

Received from all

sensors

Estimate the position of static

Display the position on PC

End Yes

No

A

A

Yes

No

Flow chart representing the main algorithm for position estimation

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

47

APPENDIX 3

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

48

Distribution of RSSI

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

49

REFERENCES [1] T S Rappaport J H Reed and B D Woerner ldquoPosition location using wireless communications on highways of the futureʺ IEEE Commun Mag vol 34 no 10 pp33‐41 Oct 1996 [2] A M Ladd K E Bekris G Marceau A Rudys L E Kavraki and D S Wallach rdquoRobotics‐based location sensing using wireless ethernetʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ02) 2002 pp 227‐238 [3] I Ramani R Bharadwaja and P V Rangan ldquoLocation tracking for media appliances in wireless home networksʺ in Proc International Conference on Multimedia and Expo (ICMEʹ03) July 2003 pp 769‐772 [4] K Pahlavan X Li and J P Makela ldquoIndoor geolocation science and technologyʺ IEEE Commun Mag vol 40 no 2 pp 112‐118 Feb 2002 [5] Konard Lorincz and Matt Welsh ldquoMote Track A Robust Decentralized Approach to RF‐Based Location Trackingrdquo [6] Paramvir Bahl and Venkata N Padmanabham ldquoRADAR An In‐Building RF‐based User Location and Tracking Systemrdquo [7] Kamol Kaemarungsi ldquoDesign of Indoor positioning system based on location fingerprint techiniquerdquo University of Pittsburgh 2005 [8] Fredrik Linnarsson Cheng Peng and Bengt Oelmann ldquoSENTIO A Hardware Platform for Rapid Prototyping of Wireless Sensor Networksrdquo [9] S Saha K Chaudhuri D Sanghi and P Bhagwat ldquoLocation determination of a mobile device using IEEE 80211b access point signalsʺ in Proc IEEE Wireless Communications and Networking Conference (WCNCʹ03) New Orleans LA Mar 2003 pp 1987‐1992 [10] T Roos P Myllymaki H Tirri P Misikangas and J Sievanen ldquoA probabilistic approach to WLAN user location estimationʺ International Journal of Wireless Information Networks vol 9 no 3 pp 155‐164 July 2002 [11] R Battiti M Brunato and A Villani ldquoStatistical learning theory for location fingerprinting in wireless LANrsquosʺ Technical Report Oct 2002 [Online] Available httprtmscienceunitnitraquobattitiarchive86pdf [12] P Prasithsangaree P Krishnamurthy and P K Chrysanthis ldquoAn indoor position location with wireless LANsʺ in Proc IEEE International Symposium on Personal Indoor and Mobile Radio Communications (PIMRCʹ02) Lisbon Portugal Sept 2002 [13] M A Youssef A Agrawala and A U Shankar ldquoWLAN location determination via clustering and probability distributionsʺ in Proc IEEE

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

50

International Conference on Pervasive Computing and Communications (PerComʹ03) Dallas‐Fort Worth TX Mar 23‐26 2003 pp 23‐26 [14] Z Xiang S Song J Chen H Wang J Huang and X Gao ldquoA wireless LAN‐based indoor positioning technologyʺ IBM Journal of Research and Development vol 48 no 56 pp 617‐626 SeptNov 2004 [15] R Want A Hopper V Falcao and J Gibbons ldquoThe active badge location systemʺACM Transactions on Information Systems vol 40 no 1 pp 91‐102 Jan 1992 [16] N B Priyantha A Chakraborty and H Balakrishnan ldquoThe cricket location‐support systemʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ00) Boston MA Aug 2000 pp 32‐43 [17] Holger K Willig A (2005) Protocols and Architectures for Wireless Sensor Networks London [18] IEEE (2003) Part 154 Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low‐Rate Wireless Personal Area Networks (LR‐WPANs) New York [19] Chipcon CC2420 802154 compliant radio httpwwwchipconcom [20] ZigBee Specification v10 ZigBee Alliance [21] Dimitrios Lymberopoulos Quentin Lindsey and Andreas Savvides ldquoAn Empirical Analysis of Radio Signal Strength Variability in IEEE 802154 Networks using Monopole Antennasrdquo [22] Kiran Yedavalli Bhaskar Krishnamachari Sharmila Ravula and Bhaskar Srinivasan ldquoEcolocation A sequence based technique for RF localization in wireless sensor networksrdquo Robert Bosch Research and Technology Centre Palo Alto CA

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

4

Although the focus is to develop a real‐world application using WSN it also provides great opportunity to explore the new area of wireless communication overall

14 Research Questions My primary target is to find a method to determine the position of an object inside a building using ZigBee protocol standards based on RSSI The study addresses the following questions What is WSN What requirements do WSN need What are their main ideas and concepts What are the associated technologies What are the standards that sensor networks use

What are the existing hardware implementations that use this particular technology How can we build our new applications using WSN What do sensor network systems require for general or specific applications Are they applicable And what is the appropriate design for sensor network implementations in order to combine them with other existing systems At this point we describe and evaluate the specific object‐tracking application to be built

15 Scope The study area of the thesis is an overview of the WSN technology study of SENTIO platform and implementation of an object tracking application Thus the study is divided into two parts Part one is an overview of WSN it includes study of SENTIO platform and IEEE 802154 protocol standards The second part describes the implementation of the object tracking algorithm The object tracking application receives and uses the raw values returned by the sensor network system to produce clear and meaningful outputs Schema of thesis work is as shown in figure 1

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

5

Figure 1 Schema of thesis work

16 Methodology The ldquoIndoor Local Positioning System for ZigBee based on RSSI ldquois based on ldquolocation fingerprintingrdquo technique Location fingerprinting refers to a technique that exploits the relation between any measurable physical stimulus and a specific location This approach uses empirical measurements of RSSI to estimate location By recording a database of radio ldquosignaturesrdquo along with their known locations a mobile node can estimate its position by acquiring a signature and comparing it to the known signatures in the database All of these systems require the database of signatures which should be collected manually prior to the system installation and rely on central server (or the userrsquos mobile node) to perform location calculation For example in RADAR [6] technology the RSSI is the stimulus This type of positioning system does not require specialized hardware other than the common wireless network interfaces with received signal strength measurement capability thus it is simple to deploy compared to other techniques Unlike outdoor counterpart systems which can use AOA and TDOA techniques effectively indoor positioning systems encounter the problem of NLOS and the dense multi path effect that render these two techniques ineffective or complex for practical implementation It is also difficult for a MS to always see three or more access points or base stations in indoor environment which is essential for triangulation by AOA and TDOA Location fingerprinting can also be implemented as software based positioning system which can reduce

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

6

complexity and cost Generally the deployment of fingerprinting based positioning systems can be divided into two phases First in the off‐line phase the locations where the fingerprints are required is pre‐determined and then the fingerprints are collected by performing a site‐survey of the RSS from multiple APs The distance between two closest positions is called grid spacing and usually reported in meters or feet However some points may be omitted due to inaccessibility The RSS is measured at each predetermined location to create a database of RSS patterns of the indoor environment The database of RSS patterns is called a radio map The vector of RSS values at a point on the grid is called the location fingerprint of that point Second in the on‐line phase a MS will report a sample measured vector of RSS from different APs to a base station The base station uses a positioning algorithm to estimate the location of the MS The most common algorithm used to estimate the location is Euclidean algorithm It computes the Euclidean distance between the measured RSS vector and each fingerprint in the database and then the coordinates associated with the fingerprint that provides the smallest Euclidean distance is returned as the estimate of the position It is clear from the above discussion that the designer should not start out to do the site survey without proper system design objectives and guideline If the performance is not sufficient collecting more fingerprints in between previous location fingerprints may help It is a problem of choice as to how many access points are required for the system and what the minimum distance between physical positions on the grid should be in order to provide a good position resolution and best system performance The system parameters and factors that improve the accuracy and the precision performance are still not clear

17 Outline Chapter 2 provides literature review of ldquoIndoor Local Positioning System for ZigBee based on RSSIrdquo it introduces the basic components required for a positioning system general classification of indoor positioning system Finally it explains ldquoLocation Fingerprintrdquo technology used in this thesis work to determine the position of an object in a real‐world application It briefly explains the protocols and industry standards for wireless sensor networks Chapter 3 describes some properties of RSS which is a stimulus to determine

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

7

the position of an object RSS relation with energy and on what parameters is the RSS value dependent Chapter 4 describes the model of an indoor local positioning system and its probability of returning the correct location It also explains its performance evaluation and its error distance distribution Chapter 5 describes results obtained from the model of positioning system Chapter 6 includes conclusions from the experimental analysis and future work

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

8

2 LITERATURE REVIEW This chapter reviews the literature on wireless indoor positioning system as a means of providing an intellectual background for the thesis work First in section 21 the common components of indoor positioning system are described Then in section 22 discusses different means to classify indoor positioning systems In section 23 reviews of all relevant literature on location fingerprint technique Finally in section 24 protocols and industry standards for wireless sensor networks are explained

21 Common components of indoor positioning system Basically a wireless local positioning system consists of at least two separate hardware components a measurable unit that usually carries the major part of the system intelligence and a signal transmitterreceiver The transmitter in the simplest case is just a beacon Figure 2 represents basic hardware model of a positioning system

Figure 2 Block diagram representing hardware module

First the location sensing devices detect the signals transmitted by or received at known reference points using radio sensing technologies The sensing technique is based on signal strength level that converts the sensed signal into RSS Given a set of reference points the relative position of the MS can be derived from the signal characteristics of RSS The signal characteristics of RSS are unique for a location Then the microcontroller processes the position algorithm and estimates the position of object and displays it on PC

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

9

22 Classification of Indoor positioning systems Indoor positioning system can be categorized based on the stimulus used in particular application and measurement principle

221 Classification based on Stimulus Based on the stimulus used in an application WSN inherit certain characteristics and limitations on sensorrsquos signal The propagation delay diffraction reflection and scattering are the basic signal characteristics which affect all signal types The effective range available bandwidth regulatory constraints interference power constraints safety and cost of technology limitations The wireless signals commonly used for indoor positioning systems are infrared radio frequency and ultrasound Brief descriptions of the three major technologies are as follows

Infrared Infrared signal is an electromagnetic signal of a wavelength longer than that of visible light but shorter than that of radio waves Infrared is just below the visible spectrum of light in frequency and is radiated strongly by hot bodies Many objects such as people vehicle engines and aircraft generate and retain heat and as such are especially visible in the infrared wavelengths of light compared to objects in the background It cannot pass through walls or obstructions therefore it has a rather limited range of indoor environments Thus it requires a more sophisticated circuitry than ultrasound signals Indoor lighting interferes with this type of signal and causes problems in accurate sensing It generally has a range of around 5 m The infrared devices are usually small in size compared to ultrasound devices

Radio frequency The RF signal can penetrate through most of the building material therefore it has an excellent range in indoor environments The propagation speed is also high approximately 3108 ms There are unlicensed frequencies available freely for use This type of signal has the longest range compared to infrared and ultrasound Since the propagation waves are of higher frequency (24GHz) interference with other frequency components is less

Ultrasound Although ultrasound operates at low frequency bands (typical 40 kHz) compared to the other two signaling technologies it possesses a good precision for location sensing at a slow propagation speed of sound (343 ms) The advantages of ultrasound devices are their simplicity and that they are inexpensive However ultrasound does not penetrate walls

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

10

but reflects off most of the indoor obstructions It has a short range around 3 m to 10 m but has a 1 cm resolution of distance measurement The operating temperature influences the performance of ultrasound

222 Classification based on measurement principle Wireless positioning systems can be categorized by measurement techniques which are used to derive the position of mobile stations Mainly three different measurement principles are used today AOA RSS and propagation‐time based systems that can further be divided into two different sub‐classes TOA and TDOA Figure 3 shows taxonomy based on the technology and the technique classifications

Figure 3 Taxonomy of positioning system [7]

In AOA systems the position is calculated via goniometry With the use of directional antennas or antenna arrays the angle or bearing relative to points located at known positions is measured The intersection of several measured directions pointers the yields the position value The accuracy of this approach is limited by the possible directivity of the measuring aperture by shadowing and by multi‐path reflections arriving from misleading directions In TOA and TDOA techniques rely on the precision of timing between the signal transmitter and the receiver in order to use the propagation delay or TOF to calculate the distance between transmitter and receiver Therefore a precise synchronization is also very important in such systems By combining at least three distances from three reference positions triangulation can be used to estimate the mobile stationʹs location This type of technique will

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

11

require a high accuracy clock in the communication system TDOA is more practical [1] Examples of location‐sensing systems that use time of flight are GPS the Active Bats [15] and the Cricket [16] Besides these time delay‐based techniques the distance between transmitter and receiver can also be determined from signal strength attenuation and direct distance measurement (such as dead reckoning) Fingerprinting or Location Pattern Matching this technique generally requires only measurement of RSS or other non‐geometric features at several locations to form a database of location fingerprints To estimate the mobile location the system needs to first measure the received signal strength at particular locations and then search for the pattern or fingerprint with the closest match in the database This technique does not require the mobile station to see at least three base stations or access points in order to determine the location The disadvantage of this technique is that it is very time‐consuming to perform an exhaustive data collection for a wide area network such as in outdoor positioning systems

23 Indoor local positioning using ZigBee based on RSSI This section reviews relevant RF‐based indoor positioning systems which can be used to locate stationary objects and track mobile users The impressive growth of IEEE 802154 wireless PANs (WPAN) in recent years suggests an interesting future for the location fingerprinting technique The system is quite flexible because system designers can select whether to have a centralized positioning server or let the mobile to determine its own position However the fingerprinting technique requires a training phase (off‐line phase) to collect location fingerprints for all positions in the operating area before the actual deployment (on‐line phase) [7] The following discussion is divided according to the positioning system components First the common form of location fingerprinting and its relationship with physical locations are explained Finally location estimation algorithm is reviewed

231 Location Fingerprint A location fingerprint based on RF characteristics such as RSS is the basis for representing a unique location It is created under the assumption that each position or location inside a building has unique RF signature [4] Generally a fingerprint F is labelled with a location information L The location fingerprints and their labels (eg location information) are maintained in

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

12

database and used during the on‐line phase to estimate the location The label and fingerprint are usually denoted as a tuple of (L F) Battiti et al [11] point out that the location information L for indoor location can be recorded in two forms as either a tuple of coordinates or an indicator variable The tuple of real coordinates can vary from one dimension to five dimensions which include the three dimension space and two orientation variables expressed in spherical coordinates For instance a location information of a two‐dimension system with an orientation could be expressed as a triplet L = (x y d) | x y є R 2 d є North East South West The RSS was found to be more location‐dependent than the SNR because the noise component is rather random in nature However the RSS itself fluctuates over time for each access point and location Each RSS element can be considered as a random variable therefore it can be captured by recording its descriptive statistics parameters approximating its distribution or maintaining the whole measurement dataset These approaches of RSS representation result in different procedures for location estimation algorithm Regardless of the approach the location fingerprint is usually denoted as an array or vector of signal strength (random variables) received at any position in the location‐based service area The size of the vector is determined by the number of access points that can be heard To create a basis fingerprint such as in a number of samples of vectors of signal strength are collected over a window of time for each position This basis is called a prototype Then the average RSS of each access point is calculated and recorded as an element in the location fingerprint For an area that can receive signals from N access points the location fingerprint can be expressed as a vector of average RSS elements βi

F = (β1 β2hellip βN)T Besides the basis location fingerprint the samples of location fingerprint measured during the on‐line phase are also important for the system that tracks the mobile object The number of samples should be selected appropriately to represent the location fingerprint for the mobile application A step in statistical analysis method called pre‐processing is another important issue needed to be considered because it can impact the estimation of dependency between location fingerprint and the location information easier The pre‐processing refers to a step that cleans the raw data (in this case the training set) before any further operations or analysis The cleaning may consist of encoding dimensionality reduction (reduce unnecessary elements) feature extractionselection clustering and outlier elimination Roos et al [10] point out that the pre‐processing enables faster location estimation and

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

13

reduce the noise from the training data The process that creates the basis of location fingerprint discussed above could be considered as a part of pre‐processing

232 Location Estimation Algorithm Location estimation algorithms or positioning algorithms are procedures that exploit dependency between location information and location fingerprint basis in order to determine a position or location from samples of RSS signals Position algorithms can be classified into deterministic and probabilistic types based on the approaches that model the relationship between location fingerprints and location information The deterministic type of algorithms are those that based in the nearest neighbour classifiers and neural network classifiers The probabilistic type of algorithms is those that are based on the Bayesian inference and statistical learning theory such as support vector machines (SVR) The algorithm used in this project is discussed briefly

Euclidean distance method The Euclidean distance method is a deterministic approach of position estimation because they require only a set of constant location fingerprints which includes mean vectors In order to determine the location a form of discriminate function is used to classify a sample of RSS fingerprint into a position The mean or average RSS vector is a centre of a mass which represents each class of location fingerprint Suppose a set of l location fingerprints is denoted by F1 F2hellip Fl exist and each fingerprint has a one‐to‐one mapping to a set of positions L1 L2hellip Ll A sample of an RSS fingerprint measured during an on‐line phase is denoted as S which can be another mean or average RSS vector of small window of RSS samples Assuming that an indoor positioning system only considers the average RSS from N access points as a location fingerprint the sample of RSS vector is S = (s1 s2 helliphellip sN)T and each location fingerprint i in the database can be expressed as Fi = (β1 β2helliphellip βN)T The simplest closeness metric is a distance measurement in signal space denoted as the Dist () function [6] Thus the simple procedure of nearest neighbour algorithm is expressed as picking the fingerprint j that has the shortest signal distance

Dist(S Fj) le Dist(S Fk) for all k ne j A generalized weighted distance Lp summarized by [12] can be used to calculate different forms of distance in signal space as

Lp = 1N pN

i

pPiSiWi 1

1)||1(sum

=

minus

where N is the number of access points wi is a weighting factor (wi le 1) and p is the norm parameter starting from 1 The weighting factor wi is a bias

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

14

parameter that can demote or promote an important RSS component in the fingerprint

24 Protocols and Industry standards for WSN Although many applications for WSN are proposed the communication protocols supporting them remain mainly unexplored and diverged This is because the area of interest is new each implementation is application specific and the components of WSNrsquos have a lot of constraints Wireless sensor networks use the wireless medium to communicate However the traditional communication protocols that support wireless communications especially ad‐hoc mesh networks may not be well suited for them WSN communication is an active research area and many algorithms and protocols have already been proposed This section introduces two proposed standards the IEEE 802154 and the ZigBee The IEEE 802154 covers the physical layer and the MAC layer of low‐rate WPAN The ZigBee is ldquoan emerging standard that is based on the IEEE 802154 and adds network construction (star networks peer‐to‐peermesh networks and cluster‐tree networks) application services and morerdquo

241 IEEE 802154 Holger and Willig (2005) [17] mention home automation home networking and home security as possible applications for IEEE 802154 They add that ldquomost of these applications require only low‐to‐medium bitrates (up to some few hundreds of kbps) moderate average delays without too stringent delay guarantees and for certain nodes it is highly desirable to reduce the energy consumption to a minimumrdquo The IEEE 802154 standard (2003) defines the device types that can be used in a LR‐WPAN A device can be a FFD or a RFD The RFD can be used in simple applications in which they do not need to transmit large amounts of data and they have to communicate only with a specific FFD The FFD can work as a PAN coordinator as a coordinator or as a simple device It can communicate with either another FFD or a RFD In keeping with the application requirements the LR‐WPAN operates in a star or peer‐to‐peer topology (figure 4) In the star topology the RFD communicates with a single controller the PAN coordinator The PAN coordinator can perform the same function as the RFD but it is also responsible for controlling the PAN ldquoit initiates terminates or routes communication around the networkrdquo [18] The peer‐to‐peer topology

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

15

supports ad‐hoc mesh multi‐hop networking Any device in the peer‐to‐peer topology can communicate with any other device within its communication range however this topology also has a PAN coordinator All the devices in a LR‐WPAN have a unique 64‐bit address This or a short address allocated by the PAN coordinator can be used inside a PAN Additionally each PAN has a unique identifier The combination of the PAN identifier and the sort addresses allows communication across different PANs (IEEE 802154 Standard 2003)

Figure 4 Star and peer‐to‐peer topologies in LR‐WPAN [18]

The LR‐WPAN based on the OSI seven‐layer model has the layered architecture presented in figure 5 The application and the network layer are the upper layers in the LR‐WPAN architecture but are outside the scope of the IEEE 802154 standard Only the physical layer ldquowhich contains the RF transceiver along with its low‐level control mechanismrdquo and the MAC layer ldquothat provides access to the physical channelrdquo are included in the standard and will be introduced in the following sections Finally the Type I 8022 LLC and the SSCS are intermediate sub layers supporting communication with the above layers (IEEE 802154 Standard 2003)

Physical Layer The 802154 standard specifies two different services that the PHY provides The PHY data service controls the radio and thus the transmission and reception of the PPDUs In addition the management service performs ED in the channel The management service also performs CCA before sending the messages and provides LQI for the received packets

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

16

Figure 5 WPAN architecture [18]

Three different bands are defined by the standard The 868‐8686 MHz for Europe the 902‐928 MHz for North America and the 2400‐24835 MHz worldwide All of them belong in the ISM radio bands and they are using DSSS Each of the bands supports different a data rate and it uses a different modulation technique chip rate and number of channels Moreover if the system does not use the 2450 MHz frequency it operates in both the 802 MHz and 902 MHz frequencies Table 3 summarizes the supported bands and data rates (IEEE 802154 standard 2003)

PHY (MHz)

Frequency Band (MHz) Modulation

Bit Rate Number of Channels

868-8686 BPSK 20 1 868 915 902-928 BPSK 40 10

2450 2400-24835 O-QPSK 230 16 Table 3 Frequency bands and data rates for IEEE 802154 based on the IEEE

802154 standard [18] The physical layer uses PPDU packets to communicate Table 4 demonstrates its structure The LSB is always transmitted and received first The SHR contains the preamble and the SFD fields which helps receiver

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

17

synchronization The 8‐bytes preamble contains only zero and is used for synchronization The SFD contains a specific sequence of one and zeros and specifies the beginning of the frame The PHR contains the payload length Packets with a length of 9 or more bytes are MPDU as the next section further explains Packets with length 5 are used for MPDU acknowledgements The payload part of the PPDU encloses the MAC layer packet Finally the PPDU size can be up to 127 bytes [18]

4 bytes 1 byte 1 byte Variable

Preamble SFD Frame length

(7 bits) Reserved

(1 bit) Payload (PSU)

SHR PHR PHY Payload Table 4 Format based on the IEEE 802154 standard [18]

MAC Layer The MAC layer is the interface between the SSCS and the PHY layer Similar to the PHY layer the MAC layer supports two services The MAC data service is responsible for the transmission and reception of the MPDUs through the PHY data service The MAC management service if the device is a coordinator manages the network beacons It is also responsible for PAN association and disassociation frame validation and acknowledgment providing ldquoa reliable link between two peer MAC entitiesrdquo In addition it uses the CSMA‐CA for channel access and handles and maintains the GTS mechanism Finally it supports device security [18] The IEEE 802154 standard defines four different frame types the beacon data acknowledgment and MAC command frame All frame types are based on the general MAC frame format (table 5) The frame control field describes and specifies the above different frame types Every MAC frame comprises a MHR which consists of a frame control sequence number and the information field It also contains the MAC payload different frame types have different MAC payload fields The acknowledgment type does not have a payload Finally each frame includes a MFR which contains a FCS The data in the MPDU follows the same order as the PPDU the least significant bits are left in the frame and are transited first Octets2 1 02 028 02 028 Variable 2

Destination PAN Identifier

Destination Address

Source PAN

Identifier Source Address Frame

Control Sequence Number Addressing Field

Frame Payload FCS

MHR MAC

Payload MFRTable 5 General MAC frame format IEEE 802154 standard [18]

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Table 6 7 8 and 9 present the four different MAC frame types The beacon frame is transmitted periodically by the PAN coordinator It provides information about the network management through the super frame and GTS fields which are analyzed later in the section It also synchronizes the network devices and indicates the proper communication period for them The data frame payload encapsulates data from the higher layers When a device receives a packet it is not obliged to response with an acknowledgement packet Finally the command frame identifier and command payload fields of the command frame are used for communication between the network devices The command identifier specifies actions like association disassociation and data GTS or beacon request Octets2 1 410 2 Variable Variable Variable 2

Frame Control

Sequence Number

Addressing Fields

Super frame Specification

GTS fields

Pending Addressing

Fields Beacon Payload FCS

MHR MAC Payload MFRTable 6 Beacon frame format [18]

Octets 2 1 Variable 2

Frame Control

Sequence Number

Addressing Field

Data Payload FCS

MHR MAC

Payload MFR Table 7 Data frame format [18]

Octets 2 1 2

Frame Control

Sequence Number FCS

MHR MFR Table 8 Acknowledgement frame format [18]

Octets2 1 1 Variable 2

Frame Control

Sequence Number

Addressing Fields

Command Frame Identifier

Command Payload FCS

MHR MAC Payload MFR Table 9 Command frame format [18]

In the LR‐WPAN every PAN has its own coordinator The PAN coordinator manages the communication in the local area it has two options to use or not use the super frame structure The super frame (figure 6) uses network beacons If the coordinator does not want to use a super frame structure it suspends the beacon transmission However the beacon is important for device association and disassociation If the coordinator wishes to maintain close communication control in the PAN and to support low‐latency devices it usually uses the super frame A super frame determines a specific time

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

19

period beacons bound it The beacon is transmitted in the first of the sixteen equal time slots that the super frame has It is used to ldquosynchronize the attached devices to identify the PAN and to describe the structure of the super framerdquo The super frame can have active and inactive periods All the communications have to be finished inside the super frame period In the inactive periods the devices can switch to the sleep mode but they have to be ready for the next beacon The active period is further divided into a CAP and a CFP During the CAP any device can communicate competing with the other devices in the PAN using slotted CSMA‐CA The CFP contains GTSs and follows the CAP A CFP may maintain up to seven GTSs and each GTS can reserve more than one time slot However the CAP should always be sufficient to allow new devices t join the PAN The GTSs are allocated to specific devices All the transactions must be completed inside the assigned time period (CAP GTS and CFP) (IEEE 802154 standard 2003)

Figure 6 Example of super frame structure [18]

In accordance with the 802154 standard (2003) three different types of data‐transfer exist In addition the types differ if the coordinator uses or does not beacons Data transfer from a device to the PAN coordinator is the first type (figure 7) For a ldquonon‐beacon‐enable networkrdquo it first senses the medium by using ldquoun‐slotted CSMA‐CArdquo and then a simple transmit to the data frame In a ldquobeacon‐enabled networkrdquo the sender waits for the beacon when it finds it ldquothe device synchronizes to the super fame structurerdquo In the specified time frame the sender again senses the medium by using ldquoslotted CSMA‐CArdquo and transmits the data to the coordinator In either case the coordinator has the option to acknowledge or not acknowledge the data reception after that the transaction is completed Data transfer from the coordinator is the next type described in the standard (figure 8) In a beacon‐enabled network the coordinator indicates a pending message through the beacon The messagersquos target device receives the beacon and if the message is pending it responses with a MAC command request

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2 LITERATURE REVIEW10102006

20

message using slotted CSMA‐CA the coordinator may or may not acknowledge the command message and through slotted CSMA‐CA sends the pending message The device acknowledges the received message After that the coordinator removes the message from the beaconrsquos pending list and completes the transaction In a non‐beacon enable network the device periodically sends a MAC command‐request frame to the coordinator The coordinator acknowledges the data request Then by using un‐slotted CSMA‐CA if it has a pending message it transmits it If it has not it respond with a data frame with a ldquozero‐length payloadrdquo To complete the transaction the device acknowledges the data reception

Figure 7 Communication from a device to a PAN coordinator in (a) a beacon‐enabled network and (b) a non‐beacon enabled network [18]

Peer‐to‐peer is the last type of data transfer In this situation the devices are free to communicate with any other device within their communication range In a peer‐to‐peer PAN the devices can ldquoeither receive constantly or synchronize with each otherrdquo If they are receiving constantly to transmit data they use un‐slotted CSAM‐CA In the second case synchronization must be achieved first (IEEE 802154 standard [IEEE 2003]) The IEEE 802154 (2003) standard establishes MAC and PHY standards for low‐cost low‐power and high‐density node deployments In addition to the above PHY and MAC characteristics IEEE 802154 provides a security baseline including ldquothe ability to maintain an ACL and use symmetric cryptographyrdquo for data encryption The algorithm that is used for encryption is the AES However the higher level layers decide when security is need The upper layers are in general responsible for device authentication and key management The next section introduces the ZigBee standard which

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

21

encapsulates the IEEE 802154 and provides additional standardization for the higher levels

Figure 8 Communication from a PAN coordinator to a device in (a) a beacon‐enabled network and (b) a non‐beacon enabled network [18]

242 ZigBee Heily (2004) defines ZigBee as ldquoa rapidly growing worldwide non‐profit industry consortiumrdquo whose mission is ldquoto define a reliable cost‐effective low‐power wirelessly networked monitoring and control product based on an open global standardrdquo Figure 9 illustrates the areas of interest for different wireless communication standards

Figure 9 Overview of the coverage for different wireless communication

standards [17]

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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ZigBee a new standard which became publicly available in June 2005 is based on the IEEE 802154 standard It expands the IEEE 802154 by adding the framework for ldquothe network security and applicationrdquo (Craig 2005) Figure 10 presents the IEEE 802154ZigBee stack and the areas of responsibilities

Figure 10 IEEE 802154ZigBee stack [20]

Craig (2005) mentions three networking topologies that the standard covers the star mesh and cluster tree (figure 11) The ZigBee standard works on top of the IEEE 802154 addressing schema by using the standard 64‐bit and the short 16‐bit addressing Kinney (2005) summarizes the ZigBee network layer responsibilities the successful establishment of a new the network and successful new device configuration addressing assignment network synchronization frames security and message routing ZigBee further distinguishes the concept of the physical devices (RFD FFD) by using the notion of ldquological devicesrdquo ldquoZigBee Coordinatorrdquo is the first type of logical devices It is responsible for initializing maintaining and managing the network Under the coordinator in the network hierarchy is the ldquoZigBee routerrdquo which is responsible for controlling the message routing between the nodes Finally the ldquoZigBee End Devicerdquo acts as the end point of the network

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

23

structure The tasks that an end device can perform are specified in the ldquoApplication Profilesrdquo (Craig 2005)

Figure 11 IEEE 802154ZigBee stack [17]

The ZigBee specifications (2005) summarize the security services provided by ZigBee ldquokey establishment key transport frame protection and device managementrdquo ZigBee builds its security mechanism using symmetric key cryptography The security services also depend on the associated layer thus as the figure 10 shows the security mechanism covers the network and the application layer In addition if a MAC frame needs security protection the MAC layer is able to secure it Moreover the notion of end‐to‐end security is supported the source and destination devices have access and use the same share key In the MAC layer the 802154 AES mechanism provides the proper security The mechanism protects ldquothe confidentiality integrity and authenticity of the MAC framesrdquo (Kinney 2005) An auxiliary header field in front of the MAC payload indicates if the frame is encrypted or not The MAC framesrsquo integrity is supported by calculating and using a MIC at the end of the MAC payload In addition to the AES nonce is used to provide MAC confidentiality and authenticity Figure 12 illustrates a MAC frame with security For different security aspects the MAC layer uses different mode of the AES for the encryption it uses the AES in Counter (CTR) mode and for the integrity the

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

24

CBC‐MAC Finally the combination (CCM) of the above two modes is available providing both encryption and integrity (Kinney 2005)

Figure 12 ZigBee secure frame in MAC layer [17]

In the network layer the CCM (a modified MAC layer CCM mode) is used for encryption Because the network layer uses only the CCM mode a single key is used for all different security options The network layer security message format is similar to the MAC frame it is presented in the figure 13 Finally although the network layer is responsible for securing its layer messages the above layers specify the keys and the CCM option for each frame (Kinney 2005)

Figure 13 ZigBee secure frame in network layer [17]

Security in the application layer works similar to the network and MAC layers It uses the ldquolink keyrdquo or the ldquonetwork keyrdquo to secure the message and then encapsulate it inside a set of fields similar to the network format (figure 14) Other security responsibilities that the application layer has are to provide the ZDO and the applications with device management services key establishment and key transport (ZigBee specifications 2005)

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

25

Figure 14 ZigBee secure frame in application layer [17]

The ZigBee application layer contains the manufacturer‐defined application objects the ZDO and the application sub‐layer In addition to the security responsibilities the application sub‐layer binds devices based on their duties and needs maintains the binding tables and forwards messages between them The application sub‐layer also discovers the neighbour devices for a given device The ZDO is responsible for determining the devicersquos duty in the network for communicating using binding requests and for supporting security as was mentioned above The sub‐layer that implements the actual application is the manufacturer‐defined application object (Kinney 2005) The ZigBee specifications are a great step toward the wireless sensors networks standardization They cover all the OSI layers from the physical to the application providing guidance to the developers The specifications are the product of the ZigBee alliances a consortium of companies and academic institutes Thus they not only produce the specifications but also are the first use them The next section goes beyond the layer architecture and standardization and introduces the OS field another crucial element of WSNs

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

26

3 RECEIVED SIGNAL STRENGTH (RSS) Majority of the existing methods leverage the existence of IEEE 80211 base stations with powerful radio transmit powers of approximately 100mW per base station Such radios are in a different class from the low power IEEE 802154 compliant radios that typically transmit at low power levels ranging from 52mW to 29mW [19] The wide availability of larger number of IEEE 802154 radios in the research community has revived the interest for signal strength based localization in sensor network Despite of rapidly increasing popularity of IEEE 802154 radios and signal strength localization there is a lack of detailed characterization of the fundamental factors contributing to large signal strength variation This chapter investigates some of the properties of RSS observed during testing and analysis of research work collected during the period of thesis The analysis of RSS values is needed to understand the underlying features of location dependent RSS patterns and location fingerprints An understanding of the properties of the RSS values for location fingerprinting can assist in improving the design of positioning algorithms and in deployment of indoor positioning systems

31 Parameters that affect RSS Different factors that affect the indoor RSS are the userrsquos presenceabsence hisher orientation time of the day building type and material distance from transmitter and type of radio These factors are by no means exhaustive Other possible factors are antenna orientation directionality and type Table 10 classify the parameters which affect RSS [7]

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

27

Effect on Factors Options

1 Proximity of user Userrsquos presence or absence

2 Orientation of user and terminal

North East South West

Data Collection

3 Make of Radio 4 Time of measurement Time of day amp days of

week 5 Period of measurement

Second minute hour

7 Interference Co‐channel adjacent co‐channel

Statistics

8 Building environment Small offices or large hall

Table 10 Classification of factors the affect RSS [7]

Transmitter variability Different transmitters behave differently even when they are configured exactly in the same way In practice this means that when a transmitter is configured to send packets at a power level of d dBm then the transmitter will send these packets at a power level that is very close to d dBm but not necessarily exactly equal to d dBm This can alter the received signal strength indication and thus it can lead to inaccurate distance estimation

Receiver variability The sensitivity of the receivers across different radio chips is different In practice this means that the RSSI value recorded at different receivers can be different even when all the other parameters that affect the received signal strength are kept constant

Antenna orientation Each antenna has its own radiation pattern that is not uniform In practice this means that the RSSI value recorded at the receiver for a given pair of communicating nodes and for a given distance between them varies as the pair wise antenna orientations of the transmitter and the receiver are changed

Multi‐path fading and shadowing in the RF channel In indoor environments the transmitted signals get reflected after hitting on the walls andor on other objects in the room such as furniture Both the original signal and the reflected signal reach the receiver almost at the same time since they both travel at the speed of light As a result of this the receiver is not able to

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

28

distinguish the two signals and it measures the received signal strength for both of them

32 Related work analysis A number of indoor location tracking systems have been proposed in the literature based on RF signals ultrasound infrared and some combinations of these As mentioned previously RF based positioning system are widely studied as shown in Table 2 Given a model of radio signal propagation in a building or other environment RSS can be used to estimate the distance between the transmitter and receiver Most of the algorithms (Table 2) have been evaluated in practice using 80211b compliant wireless radios and seem to produce a location error of 15ft to 20ft for an indoor network area of approximately 225ft x 144ft According to the results of this study all the localization techniques produce approximately the same location error over a range of environments Ecolocation [22] Mote Track [5] are two of the few localization algorithms that have been evaluated on a real sensor network that uses a low power wireless radio Ecolocation reports a location error of 10ft for a very small outdoor network deployment area (26ft x 49ft) Mote Track reports a location error of approximately 13ft for an indoor network deployment area of 18751ft2 In general the RSSI based localization algorithms can be divided into two broad categories the map based and the distance (or area) prediction based algorithms In the case of the map based algorithms the deployment area is divided into a large set of sample points that cover the whole area Most techniques collect one or more signal strength measurements from all visible beacon nodes and for each sample point Each sample point is then mapped to either a signal strength vector or a signal strength probability distribution This process generates an RSSI fingerprint map which is unique with respect to the beacon locations and the deployment area This fingerprint map is stored to a central base station A node that is not aware of its location collects one or more signal strength measurements from all the visible beacon nodes and creates its own RSSI fingerprint that is sent to the central base station The base station computes nodersquos location as the sample pointrsquos location whose RSSI fingerprint distance from the unknown nodersquos RSSI fingerprint is the minimum among all the sample points Despite the fact that the map based RSSI localization methods produce relatively small location errors they are not attractive mainly because they require an excessive amount of profiling data and because they are not fully distributed The detailed profiling of a building is a major holdback because it is complex time consuming and depends on beacon locations Therefore for different buildings or even for the

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

29

same building and different beacon locations different RSSI maps have to be obtained The second category of RSSI based algorithms are the distance (or area) prediction algorithms which focus on minimizing the amount of profiling data In distance prediction based methods raw RSSI data are directly mapped to distances through a signal propagation model In some cases distances are not explicitly measured but they are taken into consideration through assumptions on the underlying distributions In the case of outdoor deployments where there are no obstacles a single signal propagation model is all the profiling data required In the case of indoor deployments the same applies but now the signal propagation model might be different across different buildings with different furniture and wall arrangements Given an underlying signal propagation model raw RSSI data can be mapped to specific distances These distances can be either used directly in order to perform localization or assigned a probability and used in a learning basedprobabilistic localization algorithm Obviously the accuracy of the distance prediction based methods depends heavily on the accuracy of the signal propagation model used for translating RSSI values to distances

33 Properties of RSSI Changes in signal due to propagation indoors are difficult to predict because of dense environment and propagation effects such as reflection diffraction and scattering The multi‐path fading effect which is the result of either constructive or destructive combination of multiple signal copies at the receiver causes the received signal to fluctuate around a mean value at a particular location The received signal is usually modelled by the combined effects of large‐scale fading and small‐scale fading The large‐scale fading component describes the signal attenuation as the signal travels over a distance and is absorbed by material such as walls and floors along the way to the receiver This component predicts the mean of the RSS and usually has a lognormal distribution On the other hand the small scale fading component explains the dramatic fluctuation of the signal due to multi‐path fading On the other hand the small scale‐fading component explains the dramatic fluctuation of the signal due to multi‐path fading If there is no LOS component the small‐scale‐fading is often modelled with a Rayleigh distribution Such scenarios are commonly called NLOS If there is a LOS component the small‐scale‐fading is typically modelled by a Rician distribution Note that when the RSS is measured the measurement averages out the small‐scale‐fading effects

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

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4 OBJECT TRACKING 41 Introduction This chapter presents the object‐tracking application the main objective of this thesis work The application is an algorithmic‐software implementation written in Embedded C which is capable of receiving data messages and generating object position estimation data The implementation of the object‐tracking application makes use of the SENTIO platform and uses the background knowledge introduced in the preceding chapters The motivation for the object‐tracking application is to determine the position of an object in indoor environments (Eg Home) The application acts as an interface which collects the raw data that the sensors generate (numerical values) It makes the proper comparisons and algorithmic processes to identify and track the object while it is static inside the area covered by the wireless sensor network Finally with the detection and tracking data it alerts the user or another system The following sections provide an overview of the object‐tracking application

42 Overview of hardware product Before describing the object tracking application it is important to know the characteristics of hardware product The SENTIO platform (figure 15) is built in modular fashion and has the following key features [8]

Flexibility This platform is very flexible in such a way that it is made easy to add or change the hardware for each sensor task depending on the requirement in the WSN For a specific platform this can be hard or sometimes even impossible to accomplish

Sensor resources Resources in a sensor module includes the ability of usage of controllerrsquos resources as much as possible such as ADC‐channels interrupts and digital Input‐Outputrsquos Local voltage regulation should also be possible since many sensors require higher supply voltages than digital Integrated Circuits do Further support of a shut‐down mechanism to power down the sensor modules when not needed

Computation and memory The processor module based on microcontroller should support many devices in a microcontroller family and support different power supply voltages enabling full range of clock frequencies All power‐down modes of the devices should be

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

31

supported The platform should support different processor modules including FPGArsquos and very simple microcontrollers

Communication The focus should be on enabling an IEEE 802154 compliant communication interface With or without ZigBee stack this will provide a flexible platform for exploring different network topologies Support for power‐down of the entire communication module should be supported

Cost and form factor High flexibility is in conflict with low cost and small size The primary objective should be flexibility but the size should be considered in order to enable sensor device integration in systems where space is limited

Figure 15 SENTIO Platform [8]

The communication subsystem of the SENTIO sensor node was designed so that the correct operation of the radio chip is ensured The radio chip is powered by the on‐board voltage regulator and thus fluctuations in the battery voltage level do not affect the operation of the communication subsystem This platform uses Chipcon CC2420 IEEE 802154 radio transceiver operates in the 24GHz ISM band and includes a digital direct sequence spread spectrum (DSSS) modem providing a spreading gain of 9dB and an effective data rate of 250Kbps It was specifically designed for low power wireless applications and supports 8 discrete power levels 0dBm minus1dBm minus3dBm minus5dBm minus7dBm minus10dBm minus15dBm and minus25dBm at which its power consumption varies from 29mW to 52mW [21] A built‐in received signal strength indicator gives an 8‐bit digital value RSSIVAL The RSSIVAL is always averaged over 8 symbol periods (128μs) and a status bit indicates when the RSSIVAL is valid (meaning that the receiver was enabled for at least 8 symbol periods) The

Antenna

Communication Module

Processing Module

Sensing Module

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

32

power P at the RF pins can be obtained directly from RSSIVAL using the following equation

P = RSSIVAL + RSSIOFFSET [dBm] where the RSSIOFFSET is found empirically from the front‐end gain and it is approximately equal to minus45dBm In the next sections when we refer to the RSSI value we refer to the RSSIVAL and not the power P unless otherwise stated

43 Experimental Testbed The experimental test bed for object tracking application is as shown in figure 16 Experiment was conducted in Masterrsquos room at Mid Sweden University A DELL laptop is connected to SENTIO platform and software were primarily used to collect samples of RSS from APrsquos The software monitors IEEE 802154 radio frequency channels which operate in the 24GHz band

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

1030

BC - 1030

AP 3 AP 1

AP 2

AP 4

BS

HEWLETT

PACKARD

Figure 16 Layout of Masterʹs Room

A three dimensional scalable testbed is used for indoor sensor network deployments The testbed illustrated in figure 16 is a 3‐D structure measuring 16m (L) x 12m (W) x 3m (H) and it is designed to host a large number of static nodes as shown in figure 16 Arrow marks in the above figure represent the location where fingerprints were taken The centrepiece of our infrastructure

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

33

is the SENTIO sensor node [8] an open‐source general purpose sensing platform designed with AT Mega 128 microprocessor and the IEEE 802154 compliant CC2420 radio from Chipcon [19]

44 Obtaining data from the sensor network The next important step in the development of the object tracking application is to obtain the data from the sensor network and send it to the base station From there the data is transmitted through a serial port to the computer that runs the object tracking application When the data arrives at the serial port the application must capture it This paragraph gives a brief explanation of how and what kind data is transmitted by the static object fixed sensors and how this data is being used to estimate the position of the object First the static object transmits Beacon packet continuously which is received by all the fixed APrsquos The beacon packet format is as shown in table 6 Then the fixed APrsquos calculates the 8‐bit value of RSSI from the transmitted beacon packet and then sends the resultant RSSI value as a form of data packet (table 7) to the BS which calculates the position and sends back to the object

45 Object tracking application Testing and Evaluation The purpose of this section is to discuss and evaluate the object tracking application Finally we are ready with the experimental testbed as shown in figure 16 As shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors placed at the corners of the masterrsquos room BS is placed at the centre of the room connected to a PC All the nodes are power connected before an actual measurement takes place At first the object is placed at random position inside the room and transmits beacon packets continuously Then these packets are received by the fixed APrsquos which calculate the RSSI from the packet and then averages it until it receives 50 beacon packets and then transmits the data to the BS (figure 17) Then the BS waits until it receives the data from all the APrsquos Once it receives from all the APrsquos it calculates the position of the object (figure 18) The application was developed to demonstrate a real world use of a WSN in object tracking In evaluating the final position obtained with respect to the actual position we conclude that the object‐tracking was successful Although

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

34

it is a research prototype object tracking application is a simple stable stand‐alone system capable of demonstrating an important application of WSN It takes raw data from the WSN aggregates it evaluates it and based on the evaluation tracks the object The accuracy of estimating the position is less than 5m Table 11 below shows a log of readings taken during test

Start

Intialize micro‐controller and transeciever in receieve mode

If received_packet=

Beacon

Average RSSI values

Increase the counter

If countergt1000050

A

No

Yes

B

B

Yes

No

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

35

A

Reset counter

Set transceiever in transmit mode

Transmit averaged RSSI to Main sensor and set it back to receive mode

10000 samples ‐ Offline

50 samples ‐ Online

Figure 17 Flow chart representing the algorithm of fixed sensors

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

36

Start

Intialize micro‐controller and transeciever in receieve mode

Received RSSI values from all fixed Sensors

Open radio map

Calculate Euclidean distance between obtained RSSI values from fixed sensors and fingerprints

in radio map

No

Yes

Bubble sort for least error in signal strength values

A

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

37

A

Get the location of the object from the radiousing the least error value

Display location of object on PC

End

Figure 18 Flow chart representing the algorithm of main sensor

Table 11 Accuracy of positioning system

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

38

5 RESULTS This chapter presents how the system parameters such as number of samples mean or median etchellipare finalized All the tests are performed in masterrsquos room at Mid Sweden University This chapter is divided into three subsections First in Section 51 it reviews results obtained from samples test Section 52 discusses one dimensional test results and finally section 53 discusses results of two dimensional tests

51 Measurement setup A Dell laptop was connected to a SENTIO platform and is loaded with software (Appendix 2) This setup was used to determine number of samples required during object tracking mean or median position estimation algorithm and one dimensional test The setup is as follows

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

103

0

BC - 1030

AP 1AP 2

BS

HEWLETT

PACKARD

Figure 19 Test bed for one dimensional test in Masters Room

Two SENTIO nodes (AP1 and AP2 as shown above) were separated by a distance of 10 meters and these sensors are known as fixed sensors which are fixed to the ceiling such that there antenna is faced towards the ground They are linear is position A BS was connected to Dell laptop and it was placed in such a way that its antenna was faced towards the ceiling Another SENTIO

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

39

node was used as an object and its orientation was same as BS Figure 19 represents the complete setup for object tracking application Mobile node was moved between these fixed ones at regular intervals of one meter and RSSI values were logged at each interval

52 Number of samples test This test was performed to determine number of samples required for accurate positioning and either mean of the obtained RSSI values or median of them Figure 19 represents the setup for number of samples test Once the setup is ready the measurements are taken as follows

1 An object was tagged with SENTIO node and was placed stationary linear to the fixed sensors

2 10000 samples (RSSI) were taken at regular intervals of 1 meter up to 10 meters

3 The sample values were logged at every interval 4 Using these logged samples I have plotted graphs such as distribution

of RSS values (figure 20 21) meanmedian at intervals of 1 meter using MATLAB

5 The mean and median for various numbers of samples is as shown in figure 22

Figure 20 Histogram plot of RSSI values at 1m and 4m

Figure 21 Histogram plot of RSSI values at 6m and 8m

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

40

Figure 22 Distance vs RSSI

From the above graphs following conclusions were made 1 From the histogram plots we can observe that the distribution of RSSI

values follow Gaussians distribution law 2 As we can see from figure 22 that median curve has drastic changes

when compared to mean curve Hence mean is considered while estimating the position

3 From the observations we conclude that the number of samples does not matter since mean curve tends to be same for various numbers of samples

4 Hence 10000 samples are considered in order to get a proper location fingerprint during off‐line phase and during online phase 50 samples are enough to estimate the position of object

53 0ne dimensional test Measurement setup for one dimensional test is as shown in figure 19 This test was conducted to check the performance of algorithm and how accurate it was At first ie during offline phase fingerprints at each interval averaging 10000 samples were taken During online phase Euclidean algorithm was used estimate the position averaging 50 samples of RSSI From the results we

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

41

can realize that the error was less than 2 meters Table 12 shows the result of location estimation in one dimensional case

Table 12 One dimensional position estimation results

54 Two dimensional test This test is the final stage of my thesis work Experimental testbed for this stage is as shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors fixed at the corners of the room and a BS at the centre of the room There are 18 pre‐determined locations where the fingerprints (RSSI) were taken The fingerprints are as shown in Appendix 1 All the readings were taken in presence of userrsquos The algorithm for position estimation is shown in Appendix 2 Position estimation test is performed as follows

Tagged object is placed at random inside the masterrsquos room All the sensors including the base station and object are switched on Using the algorithm (Appendix 2) the BS estimates the position of the object and displays it on the PC

This test was performed using various numbers of samples to test which has a better accuracy Figure 23 represents the error rate using various numbers of samples From figure 23 we can conclude that 50 numbers of samples is enough to estimate the position of object with an accuracy of 3m

55 Causes of error in location determination The randomness of RSS is the major cause of error in any indoor

positioning system that uses the RSS for location inference If there is no randomness in the RSS and its location fingerprint every indoor positioning systems of this type will have no location detection error For example I have recorded RSS at one particular location during different times of the day From figures 24 and 25 you can observe that RSS values are different at different times of day

The performance of indoor positioning systems depends greatly on the separation of location fingerprints A location fingerprint

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

42

corresponding to a location can be identified correctly if it is difficult to classify it as another fingerprint by a pattern classifier

Path loss is one of the basic signal propagation properties of the RSS that best describes attenuation of transmit signal power over a distance between an access point and a mobile device

Presence of Userrsquos or userrsquos orientation affects RSS

Figure 23 Error Rate vs Number of samples

Figure 24 RSS distribution at 214 pm

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

43

Figure 25 RSS distribution at 100pm and 1100 am

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

6 DISCUSSIONS10102006

44

6 DISCUSSIONS 61 Summary and Conclusion While empirical results and performance studies of positioning systems based on location fingerprint have been presented in this report analytical model that can be used as a framework for efficiently designing the positioning systems are not available This thesis design work uses general principles of position estimation such as position estimation of sensors grid spacing etc This design work focuses on accuracy and precision The concept of location fingerprint technique based on RSS us explained in detail in chapter 2 It is found that RSS is random It is possible to approximate the RSS as being normally distributed While the RSS random process is non‐stationary the mean is more or less constant and could be used as the fingerprint of the location Model designed for position estimation is as shown in chapter 4 Accuracy and precision are the two major performance matrices in this study Uniform grid spacing has been used From the experimental analysis I have obtained an accuracy of 3 meters and 35 precision The sensitivity analysis among the system parameters and the performance matrices were done using the model as shown in chapter 4 For instance our study found that the minimum distance between two adjacent positions also affects the positioning performance This is due to the randomness of RSS patterns induced by the ever changing indoor environment

62 Future work The research in this thesis provides a ground work for the future study of efficient design of indoor positioning system The RSSI based position estimation still has limitations First the RSSI value is time dependent Thus the future study should address this issue Second the movement of object is not considered Third orientations of sensors affect the performance which should be taken care off Fourth to improve the performance of the system we need to find a solution to deal with those incomplete or missing data during the off‐line and the on‐line phase Fifth position estimation on multi‐floor and three‐dimensional coordinates is not available

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 110102006

45

APPENDIX 1

Location Fingerprints of Masters Room Time 1315 Observation Time 20 minutes Number of Samples 10000

People Presence SNo Co-ordinates AP1 (0x0001) AP2 (0x0002) AP3 (0x0004) AP4 (0x0008)

1 11 -24 -16 -27 -33 2 13 -26 -8 -34 -31 3 15 -43 -9 -39 -25 4 32 -21 -11 -27 -33 5 61 -6 -23 -32 -28 6 64 -20 -31 -25 -34 7 35 -23 -35 -29 -38 8 37 -29 -35 -22 -27 9 39 -38 -29 -21 -27 10 67 -27 -23 -20 -23 11 611 -26 -29 -23 -30 12 613 -35 -32 -2 -31 13 513 -34 -31 -20 -26 14 11 -39 -22 -29 -17 15 212 -37 -42 -28 -31 16 -212 -38 -33 -29 -6 17 -21 -42 -29 -33 -19 18 -38 -40 -23 -35 -22

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 210102006

46

APPENDIX 2

Begin

Open radio map and raw data files for write

Intialize radio map in memory

Check whether the

Packet is data or Beacon packet If data packet

Save the data to register

Check if data is

Received from all

sensors

Estimate the position of static

Display the position on PC

End Yes

No

A

A

Yes

No

Flow chart representing the main algorithm for position estimation

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

47

APPENDIX 3

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

48

Distribution of RSSI

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

49

REFERENCES [1] T S Rappaport J H Reed and B D Woerner ldquoPosition location using wireless communications on highways of the futureʺ IEEE Commun Mag vol 34 no 10 pp33‐41 Oct 1996 [2] A M Ladd K E Bekris G Marceau A Rudys L E Kavraki and D S Wallach rdquoRobotics‐based location sensing using wireless ethernetʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ02) 2002 pp 227‐238 [3] I Ramani R Bharadwaja and P V Rangan ldquoLocation tracking for media appliances in wireless home networksʺ in Proc International Conference on Multimedia and Expo (ICMEʹ03) July 2003 pp 769‐772 [4] K Pahlavan X Li and J P Makela ldquoIndoor geolocation science and technologyʺ IEEE Commun Mag vol 40 no 2 pp 112‐118 Feb 2002 [5] Konard Lorincz and Matt Welsh ldquoMote Track A Robust Decentralized Approach to RF‐Based Location Trackingrdquo [6] Paramvir Bahl and Venkata N Padmanabham ldquoRADAR An In‐Building RF‐based User Location and Tracking Systemrdquo [7] Kamol Kaemarungsi ldquoDesign of Indoor positioning system based on location fingerprint techiniquerdquo University of Pittsburgh 2005 [8] Fredrik Linnarsson Cheng Peng and Bengt Oelmann ldquoSENTIO A Hardware Platform for Rapid Prototyping of Wireless Sensor Networksrdquo [9] S Saha K Chaudhuri D Sanghi and P Bhagwat ldquoLocation determination of a mobile device using IEEE 80211b access point signalsʺ in Proc IEEE Wireless Communications and Networking Conference (WCNCʹ03) New Orleans LA Mar 2003 pp 1987‐1992 [10] T Roos P Myllymaki H Tirri P Misikangas and J Sievanen ldquoA probabilistic approach to WLAN user location estimationʺ International Journal of Wireless Information Networks vol 9 no 3 pp 155‐164 July 2002 [11] R Battiti M Brunato and A Villani ldquoStatistical learning theory for location fingerprinting in wireless LANrsquosʺ Technical Report Oct 2002 [Online] Available httprtmscienceunitnitraquobattitiarchive86pdf [12] P Prasithsangaree P Krishnamurthy and P K Chrysanthis ldquoAn indoor position location with wireless LANsʺ in Proc IEEE International Symposium on Personal Indoor and Mobile Radio Communications (PIMRCʹ02) Lisbon Portugal Sept 2002 [13] M A Youssef A Agrawala and A U Shankar ldquoWLAN location determination via clustering and probability distributionsʺ in Proc IEEE

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

50

International Conference on Pervasive Computing and Communications (PerComʹ03) Dallas‐Fort Worth TX Mar 23‐26 2003 pp 23‐26 [14] Z Xiang S Song J Chen H Wang J Huang and X Gao ldquoA wireless LAN‐based indoor positioning technologyʺ IBM Journal of Research and Development vol 48 no 56 pp 617‐626 SeptNov 2004 [15] R Want A Hopper V Falcao and J Gibbons ldquoThe active badge location systemʺACM Transactions on Information Systems vol 40 no 1 pp 91‐102 Jan 1992 [16] N B Priyantha A Chakraborty and H Balakrishnan ldquoThe cricket location‐support systemʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ00) Boston MA Aug 2000 pp 32‐43 [17] Holger K Willig A (2005) Protocols and Architectures for Wireless Sensor Networks London [18] IEEE (2003) Part 154 Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low‐Rate Wireless Personal Area Networks (LR‐WPANs) New York [19] Chipcon CC2420 802154 compliant radio httpwwwchipconcom [20] ZigBee Specification v10 ZigBee Alliance [21] Dimitrios Lymberopoulos Quentin Lindsey and Andreas Savvides ldquoAn Empirical Analysis of Radio Signal Strength Variability in IEEE 802154 Networks using Monopole Antennasrdquo [22] Kiran Yedavalli Bhaskar Krishnamachari Sharmila Ravula and Bhaskar Srinivasan ldquoEcolocation A sequence based technique for RF localization in wireless sensor networksrdquo Robert Bosch Research and Technology Centre Palo Alto CA

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

5

Figure 1 Schema of thesis work

16 Methodology The ldquoIndoor Local Positioning System for ZigBee based on RSSI ldquois based on ldquolocation fingerprintingrdquo technique Location fingerprinting refers to a technique that exploits the relation between any measurable physical stimulus and a specific location This approach uses empirical measurements of RSSI to estimate location By recording a database of radio ldquosignaturesrdquo along with their known locations a mobile node can estimate its position by acquiring a signature and comparing it to the known signatures in the database All of these systems require the database of signatures which should be collected manually prior to the system installation and rely on central server (or the userrsquos mobile node) to perform location calculation For example in RADAR [6] technology the RSSI is the stimulus This type of positioning system does not require specialized hardware other than the common wireless network interfaces with received signal strength measurement capability thus it is simple to deploy compared to other techniques Unlike outdoor counterpart systems which can use AOA and TDOA techniques effectively indoor positioning systems encounter the problem of NLOS and the dense multi path effect that render these two techniques ineffective or complex for practical implementation It is also difficult for a MS to always see three or more access points or base stations in indoor environment which is essential for triangulation by AOA and TDOA Location fingerprinting can also be implemented as software based positioning system which can reduce

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

6

complexity and cost Generally the deployment of fingerprinting based positioning systems can be divided into two phases First in the off‐line phase the locations where the fingerprints are required is pre‐determined and then the fingerprints are collected by performing a site‐survey of the RSS from multiple APs The distance between two closest positions is called grid spacing and usually reported in meters or feet However some points may be omitted due to inaccessibility The RSS is measured at each predetermined location to create a database of RSS patterns of the indoor environment The database of RSS patterns is called a radio map The vector of RSS values at a point on the grid is called the location fingerprint of that point Second in the on‐line phase a MS will report a sample measured vector of RSS from different APs to a base station The base station uses a positioning algorithm to estimate the location of the MS The most common algorithm used to estimate the location is Euclidean algorithm It computes the Euclidean distance between the measured RSS vector and each fingerprint in the database and then the coordinates associated with the fingerprint that provides the smallest Euclidean distance is returned as the estimate of the position It is clear from the above discussion that the designer should not start out to do the site survey without proper system design objectives and guideline If the performance is not sufficient collecting more fingerprints in between previous location fingerprints may help It is a problem of choice as to how many access points are required for the system and what the minimum distance between physical positions on the grid should be in order to provide a good position resolution and best system performance The system parameters and factors that improve the accuracy and the precision performance are still not clear

17 Outline Chapter 2 provides literature review of ldquoIndoor Local Positioning System for ZigBee based on RSSIrdquo it introduces the basic components required for a positioning system general classification of indoor positioning system Finally it explains ldquoLocation Fingerprintrdquo technology used in this thesis work to determine the position of an object in a real‐world application It briefly explains the protocols and industry standards for wireless sensor networks Chapter 3 describes some properties of RSS which is a stimulus to determine

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

7

the position of an object RSS relation with energy and on what parameters is the RSS value dependent Chapter 4 describes the model of an indoor local positioning system and its probability of returning the correct location It also explains its performance evaluation and its error distance distribution Chapter 5 describes results obtained from the model of positioning system Chapter 6 includes conclusions from the experimental analysis and future work

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

8

2 LITERATURE REVIEW This chapter reviews the literature on wireless indoor positioning system as a means of providing an intellectual background for the thesis work First in section 21 the common components of indoor positioning system are described Then in section 22 discusses different means to classify indoor positioning systems In section 23 reviews of all relevant literature on location fingerprint technique Finally in section 24 protocols and industry standards for wireless sensor networks are explained

21 Common components of indoor positioning system Basically a wireless local positioning system consists of at least two separate hardware components a measurable unit that usually carries the major part of the system intelligence and a signal transmitterreceiver The transmitter in the simplest case is just a beacon Figure 2 represents basic hardware model of a positioning system

Figure 2 Block diagram representing hardware module

First the location sensing devices detect the signals transmitted by or received at known reference points using radio sensing technologies The sensing technique is based on signal strength level that converts the sensed signal into RSS Given a set of reference points the relative position of the MS can be derived from the signal characteristics of RSS The signal characteristics of RSS are unique for a location Then the microcontroller processes the position algorithm and estimates the position of object and displays it on PC

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

9

22 Classification of Indoor positioning systems Indoor positioning system can be categorized based on the stimulus used in particular application and measurement principle

221 Classification based on Stimulus Based on the stimulus used in an application WSN inherit certain characteristics and limitations on sensorrsquos signal The propagation delay diffraction reflection and scattering are the basic signal characteristics which affect all signal types The effective range available bandwidth regulatory constraints interference power constraints safety and cost of technology limitations The wireless signals commonly used for indoor positioning systems are infrared radio frequency and ultrasound Brief descriptions of the three major technologies are as follows

Infrared Infrared signal is an electromagnetic signal of a wavelength longer than that of visible light but shorter than that of radio waves Infrared is just below the visible spectrum of light in frequency and is radiated strongly by hot bodies Many objects such as people vehicle engines and aircraft generate and retain heat and as such are especially visible in the infrared wavelengths of light compared to objects in the background It cannot pass through walls or obstructions therefore it has a rather limited range of indoor environments Thus it requires a more sophisticated circuitry than ultrasound signals Indoor lighting interferes with this type of signal and causes problems in accurate sensing It generally has a range of around 5 m The infrared devices are usually small in size compared to ultrasound devices

Radio frequency The RF signal can penetrate through most of the building material therefore it has an excellent range in indoor environments The propagation speed is also high approximately 3108 ms There are unlicensed frequencies available freely for use This type of signal has the longest range compared to infrared and ultrasound Since the propagation waves are of higher frequency (24GHz) interference with other frequency components is less

Ultrasound Although ultrasound operates at low frequency bands (typical 40 kHz) compared to the other two signaling technologies it possesses a good precision for location sensing at a slow propagation speed of sound (343 ms) The advantages of ultrasound devices are their simplicity and that they are inexpensive However ultrasound does not penetrate walls

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

10

but reflects off most of the indoor obstructions It has a short range around 3 m to 10 m but has a 1 cm resolution of distance measurement The operating temperature influences the performance of ultrasound

222 Classification based on measurement principle Wireless positioning systems can be categorized by measurement techniques which are used to derive the position of mobile stations Mainly three different measurement principles are used today AOA RSS and propagation‐time based systems that can further be divided into two different sub‐classes TOA and TDOA Figure 3 shows taxonomy based on the technology and the technique classifications

Figure 3 Taxonomy of positioning system [7]

In AOA systems the position is calculated via goniometry With the use of directional antennas or antenna arrays the angle or bearing relative to points located at known positions is measured The intersection of several measured directions pointers the yields the position value The accuracy of this approach is limited by the possible directivity of the measuring aperture by shadowing and by multi‐path reflections arriving from misleading directions In TOA and TDOA techniques rely on the precision of timing between the signal transmitter and the receiver in order to use the propagation delay or TOF to calculate the distance between transmitter and receiver Therefore a precise synchronization is also very important in such systems By combining at least three distances from three reference positions triangulation can be used to estimate the mobile stationʹs location This type of technique will

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

11

require a high accuracy clock in the communication system TDOA is more practical [1] Examples of location‐sensing systems that use time of flight are GPS the Active Bats [15] and the Cricket [16] Besides these time delay‐based techniques the distance between transmitter and receiver can also be determined from signal strength attenuation and direct distance measurement (such as dead reckoning) Fingerprinting or Location Pattern Matching this technique generally requires only measurement of RSS or other non‐geometric features at several locations to form a database of location fingerprints To estimate the mobile location the system needs to first measure the received signal strength at particular locations and then search for the pattern or fingerprint with the closest match in the database This technique does not require the mobile station to see at least three base stations or access points in order to determine the location The disadvantage of this technique is that it is very time‐consuming to perform an exhaustive data collection for a wide area network such as in outdoor positioning systems

23 Indoor local positioning using ZigBee based on RSSI This section reviews relevant RF‐based indoor positioning systems which can be used to locate stationary objects and track mobile users The impressive growth of IEEE 802154 wireless PANs (WPAN) in recent years suggests an interesting future for the location fingerprinting technique The system is quite flexible because system designers can select whether to have a centralized positioning server or let the mobile to determine its own position However the fingerprinting technique requires a training phase (off‐line phase) to collect location fingerprints for all positions in the operating area before the actual deployment (on‐line phase) [7] The following discussion is divided according to the positioning system components First the common form of location fingerprinting and its relationship with physical locations are explained Finally location estimation algorithm is reviewed

231 Location Fingerprint A location fingerprint based on RF characteristics such as RSS is the basis for representing a unique location It is created under the assumption that each position or location inside a building has unique RF signature [4] Generally a fingerprint F is labelled with a location information L The location fingerprints and their labels (eg location information) are maintained in

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

12

database and used during the on‐line phase to estimate the location The label and fingerprint are usually denoted as a tuple of (L F) Battiti et al [11] point out that the location information L for indoor location can be recorded in two forms as either a tuple of coordinates or an indicator variable The tuple of real coordinates can vary from one dimension to five dimensions which include the three dimension space and two orientation variables expressed in spherical coordinates For instance a location information of a two‐dimension system with an orientation could be expressed as a triplet L = (x y d) | x y є R 2 d є North East South West The RSS was found to be more location‐dependent than the SNR because the noise component is rather random in nature However the RSS itself fluctuates over time for each access point and location Each RSS element can be considered as a random variable therefore it can be captured by recording its descriptive statistics parameters approximating its distribution or maintaining the whole measurement dataset These approaches of RSS representation result in different procedures for location estimation algorithm Regardless of the approach the location fingerprint is usually denoted as an array or vector of signal strength (random variables) received at any position in the location‐based service area The size of the vector is determined by the number of access points that can be heard To create a basis fingerprint such as in a number of samples of vectors of signal strength are collected over a window of time for each position This basis is called a prototype Then the average RSS of each access point is calculated and recorded as an element in the location fingerprint For an area that can receive signals from N access points the location fingerprint can be expressed as a vector of average RSS elements βi

F = (β1 β2hellip βN)T Besides the basis location fingerprint the samples of location fingerprint measured during the on‐line phase are also important for the system that tracks the mobile object The number of samples should be selected appropriately to represent the location fingerprint for the mobile application A step in statistical analysis method called pre‐processing is another important issue needed to be considered because it can impact the estimation of dependency between location fingerprint and the location information easier The pre‐processing refers to a step that cleans the raw data (in this case the training set) before any further operations or analysis The cleaning may consist of encoding dimensionality reduction (reduce unnecessary elements) feature extractionselection clustering and outlier elimination Roos et al [10] point out that the pre‐processing enables faster location estimation and

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

13

reduce the noise from the training data The process that creates the basis of location fingerprint discussed above could be considered as a part of pre‐processing

232 Location Estimation Algorithm Location estimation algorithms or positioning algorithms are procedures that exploit dependency between location information and location fingerprint basis in order to determine a position or location from samples of RSS signals Position algorithms can be classified into deterministic and probabilistic types based on the approaches that model the relationship between location fingerprints and location information The deterministic type of algorithms are those that based in the nearest neighbour classifiers and neural network classifiers The probabilistic type of algorithms is those that are based on the Bayesian inference and statistical learning theory such as support vector machines (SVR) The algorithm used in this project is discussed briefly

Euclidean distance method The Euclidean distance method is a deterministic approach of position estimation because they require only a set of constant location fingerprints which includes mean vectors In order to determine the location a form of discriminate function is used to classify a sample of RSS fingerprint into a position The mean or average RSS vector is a centre of a mass which represents each class of location fingerprint Suppose a set of l location fingerprints is denoted by F1 F2hellip Fl exist and each fingerprint has a one‐to‐one mapping to a set of positions L1 L2hellip Ll A sample of an RSS fingerprint measured during an on‐line phase is denoted as S which can be another mean or average RSS vector of small window of RSS samples Assuming that an indoor positioning system only considers the average RSS from N access points as a location fingerprint the sample of RSS vector is S = (s1 s2 helliphellip sN)T and each location fingerprint i in the database can be expressed as Fi = (β1 β2helliphellip βN)T The simplest closeness metric is a distance measurement in signal space denoted as the Dist () function [6] Thus the simple procedure of nearest neighbour algorithm is expressed as picking the fingerprint j that has the shortest signal distance

Dist(S Fj) le Dist(S Fk) for all k ne j A generalized weighted distance Lp summarized by [12] can be used to calculate different forms of distance in signal space as

Lp = 1N pN

i

pPiSiWi 1

1)||1(sum

=

minus

where N is the number of access points wi is a weighting factor (wi le 1) and p is the norm parameter starting from 1 The weighting factor wi is a bias

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

14

parameter that can demote or promote an important RSS component in the fingerprint

24 Protocols and Industry standards for WSN Although many applications for WSN are proposed the communication protocols supporting them remain mainly unexplored and diverged This is because the area of interest is new each implementation is application specific and the components of WSNrsquos have a lot of constraints Wireless sensor networks use the wireless medium to communicate However the traditional communication protocols that support wireless communications especially ad‐hoc mesh networks may not be well suited for them WSN communication is an active research area and many algorithms and protocols have already been proposed This section introduces two proposed standards the IEEE 802154 and the ZigBee The IEEE 802154 covers the physical layer and the MAC layer of low‐rate WPAN The ZigBee is ldquoan emerging standard that is based on the IEEE 802154 and adds network construction (star networks peer‐to‐peermesh networks and cluster‐tree networks) application services and morerdquo

241 IEEE 802154 Holger and Willig (2005) [17] mention home automation home networking and home security as possible applications for IEEE 802154 They add that ldquomost of these applications require only low‐to‐medium bitrates (up to some few hundreds of kbps) moderate average delays without too stringent delay guarantees and for certain nodes it is highly desirable to reduce the energy consumption to a minimumrdquo The IEEE 802154 standard (2003) defines the device types that can be used in a LR‐WPAN A device can be a FFD or a RFD The RFD can be used in simple applications in which they do not need to transmit large amounts of data and they have to communicate only with a specific FFD The FFD can work as a PAN coordinator as a coordinator or as a simple device It can communicate with either another FFD or a RFD In keeping with the application requirements the LR‐WPAN operates in a star or peer‐to‐peer topology (figure 4) In the star topology the RFD communicates with a single controller the PAN coordinator The PAN coordinator can perform the same function as the RFD but it is also responsible for controlling the PAN ldquoit initiates terminates or routes communication around the networkrdquo [18] The peer‐to‐peer topology

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

15

supports ad‐hoc mesh multi‐hop networking Any device in the peer‐to‐peer topology can communicate with any other device within its communication range however this topology also has a PAN coordinator All the devices in a LR‐WPAN have a unique 64‐bit address This or a short address allocated by the PAN coordinator can be used inside a PAN Additionally each PAN has a unique identifier The combination of the PAN identifier and the sort addresses allows communication across different PANs (IEEE 802154 Standard 2003)

Figure 4 Star and peer‐to‐peer topologies in LR‐WPAN [18]

The LR‐WPAN based on the OSI seven‐layer model has the layered architecture presented in figure 5 The application and the network layer are the upper layers in the LR‐WPAN architecture but are outside the scope of the IEEE 802154 standard Only the physical layer ldquowhich contains the RF transceiver along with its low‐level control mechanismrdquo and the MAC layer ldquothat provides access to the physical channelrdquo are included in the standard and will be introduced in the following sections Finally the Type I 8022 LLC and the SSCS are intermediate sub layers supporting communication with the above layers (IEEE 802154 Standard 2003)

Physical Layer The 802154 standard specifies two different services that the PHY provides The PHY data service controls the radio and thus the transmission and reception of the PPDUs In addition the management service performs ED in the channel The management service also performs CCA before sending the messages and provides LQI for the received packets

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

16

Figure 5 WPAN architecture [18]

Three different bands are defined by the standard The 868‐8686 MHz for Europe the 902‐928 MHz for North America and the 2400‐24835 MHz worldwide All of them belong in the ISM radio bands and they are using DSSS Each of the bands supports different a data rate and it uses a different modulation technique chip rate and number of channels Moreover if the system does not use the 2450 MHz frequency it operates in both the 802 MHz and 902 MHz frequencies Table 3 summarizes the supported bands and data rates (IEEE 802154 standard 2003)

PHY (MHz)

Frequency Band (MHz) Modulation

Bit Rate Number of Channels

868-8686 BPSK 20 1 868 915 902-928 BPSK 40 10

2450 2400-24835 O-QPSK 230 16 Table 3 Frequency bands and data rates for IEEE 802154 based on the IEEE

802154 standard [18] The physical layer uses PPDU packets to communicate Table 4 demonstrates its structure The LSB is always transmitted and received first The SHR contains the preamble and the SFD fields which helps receiver

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

17

synchronization The 8‐bytes preamble contains only zero and is used for synchronization The SFD contains a specific sequence of one and zeros and specifies the beginning of the frame The PHR contains the payload length Packets with a length of 9 or more bytes are MPDU as the next section further explains Packets with length 5 are used for MPDU acknowledgements The payload part of the PPDU encloses the MAC layer packet Finally the PPDU size can be up to 127 bytes [18]

4 bytes 1 byte 1 byte Variable

Preamble SFD Frame length

(7 bits) Reserved

(1 bit) Payload (PSU)

SHR PHR PHY Payload Table 4 Format based on the IEEE 802154 standard [18]

MAC Layer The MAC layer is the interface between the SSCS and the PHY layer Similar to the PHY layer the MAC layer supports two services The MAC data service is responsible for the transmission and reception of the MPDUs through the PHY data service The MAC management service if the device is a coordinator manages the network beacons It is also responsible for PAN association and disassociation frame validation and acknowledgment providing ldquoa reliable link between two peer MAC entitiesrdquo In addition it uses the CSMA‐CA for channel access and handles and maintains the GTS mechanism Finally it supports device security [18] The IEEE 802154 standard defines four different frame types the beacon data acknowledgment and MAC command frame All frame types are based on the general MAC frame format (table 5) The frame control field describes and specifies the above different frame types Every MAC frame comprises a MHR which consists of a frame control sequence number and the information field It also contains the MAC payload different frame types have different MAC payload fields The acknowledgment type does not have a payload Finally each frame includes a MFR which contains a FCS The data in the MPDU follows the same order as the PPDU the least significant bits are left in the frame and are transited first Octets2 1 02 028 02 028 Variable 2

Destination PAN Identifier

Destination Address

Source PAN

Identifier Source Address Frame

Control Sequence Number Addressing Field

Frame Payload FCS

MHR MAC

Payload MFRTable 5 General MAC frame format IEEE 802154 standard [18]

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

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Table 6 7 8 and 9 present the four different MAC frame types The beacon frame is transmitted periodically by the PAN coordinator It provides information about the network management through the super frame and GTS fields which are analyzed later in the section It also synchronizes the network devices and indicates the proper communication period for them The data frame payload encapsulates data from the higher layers When a device receives a packet it is not obliged to response with an acknowledgement packet Finally the command frame identifier and command payload fields of the command frame are used for communication between the network devices The command identifier specifies actions like association disassociation and data GTS or beacon request Octets2 1 410 2 Variable Variable Variable 2

Frame Control

Sequence Number

Addressing Fields

Super frame Specification

GTS fields

Pending Addressing

Fields Beacon Payload FCS

MHR MAC Payload MFRTable 6 Beacon frame format [18]

Octets 2 1 Variable 2

Frame Control

Sequence Number

Addressing Field

Data Payload FCS

MHR MAC

Payload MFR Table 7 Data frame format [18]

Octets 2 1 2

Frame Control

Sequence Number FCS

MHR MFR Table 8 Acknowledgement frame format [18]

Octets2 1 1 Variable 2

Frame Control

Sequence Number

Addressing Fields

Command Frame Identifier

Command Payload FCS

MHR MAC Payload MFR Table 9 Command frame format [18]

In the LR‐WPAN every PAN has its own coordinator The PAN coordinator manages the communication in the local area it has two options to use or not use the super frame structure The super frame (figure 6) uses network beacons If the coordinator does not want to use a super frame structure it suspends the beacon transmission However the beacon is important for device association and disassociation If the coordinator wishes to maintain close communication control in the PAN and to support low‐latency devices it usually uses the super frame A super frame determines a specific time

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

19

period beacons bound it The beacon is transmitted in the first of the sixteen equal time slots that the super frame has It is used to ldquosynchronize the attached devices to identify the PAN and to describe the structure of the super framerdquo The super frame can have active and inactive periods All the communications have to be finished inside the super frame period In the inactive periods the devices can switch to the sleep mode but they have to be ready for the next beacon The active period is further divided into a CAP and a CFP During the CAP any device can communicate competing with the other devices in the PAN using slotted CSMA‐CA The CFP contains GTSs and follows the CAP A CFP may maintain up to seven GTSs and each GTS can reserve more than one time slot However the CAP should always be sufficient to allow new devices t join the PAN The GTSs are allocated to specific devices All the transactions must be completed inside the assigned time period (CAP GTS and CFP) (IEEE 802154 standard 2003)

Figure 6 Example of super frame structure [18]

In accordance with the 802154 standard (2003) three different types of data‐transfer exist In addition the types differ if the coordinator uses or does not beacons Data transfer from a device to the PAN coordinator is the first type (figure 7) For a ldquonon‐beacon‐enable networkrdquo it first senses the medium by using ldquoun‐slotted CSMA‐CArdquo and then a simple transmit to the data frame In a ldquobeacon‐enabled networkrdquo the sender waits for the beacon when it finds it ldquothe device synchronizes to the super fame structurerdquo In the specified time frame the sender again senses the medium by using ldquoslotted CSMA‐CArdquo and transmits the data to the coordinator In either case the coordinator has the option to acknowledge or not acknowledge the data reception after that the transaction is completed Data transfer from the coordinator is the next type described in the standard (figure 8) In a beacon‐enabled network the coordinator indicates a pending message through the beacon The messagersquos target device receives the beacon and if the message is pending it responses with a MAC command request

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

20

message using slotted CSMA‐CA the coordinator may or may not acknowledge the command message and through slotted CSMA‐CA sends the pending message The device acknowledges the received message After that the coordinator removes the message from the beaconrsquos pending list and completes the transaction In a non‐beacon enable network the device periodically sends a MAC command‐request frame to the coordinator The coordinator acknowledges the data request Then by using un‐slotted CSMA‐CA if it has a pending message it transmits it If it has not it respond with a data frame with a ldquozero‐length payloadrdquo To complete the transaction the device acknowledges the data reception

Figure 7 Communication from a device to a PAN coordinator in (a) a beacon‐enabled network and (b) a non‐beacon enabled network [18]

Peer‐to‐peer is the last type of data transfer In this situation the devices are free to communicate with any other device within their communication range In a peer‐to‐peer PAN the devices can ldquoeither receive constantly or synchronize with each otherrdquo If they are receiving constantly to transmit data they use un‐slotted CSAM‐CA In the second case synchronization must be achieved first (IEEE 802154 standard [IEEE 2003]) The IEEE 802154 (2003) standard establishes MAC and PHY standards for low‐cost low‐power and high‐density node deployments In addition to the above PHY and MAC characteristics IEEE 802154 provides a security baseline including ldquothe ability to maintain an ACL and use symmetric cryptographyrdquo for data encryption The algorithm that is used for encryption is the AES However the higher level layers decide when security is need The upper layers are in general responsible for device authentication and key management The next section introduces the ZigBee standard which

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

21

encapsulates the IEEE 802154 and provides additional standardization for the higher levels

Figure 8 Communication from a PAN coordinator to a device in (a) a beacon‐enabled network and (b) a non‐beacon enabled network [18]

242 ZigBee Heily (2004) defines ZigBee as ldquoa rapidly growing worldwide non‐profit industry consortiumrdquo whose mission is ldquoto define a reliable cost‐effective low‐power wirelessly networked monitoring and control product based on an open global standardrdquo Figure 9 illustrates the areas of interest for different wireless communication standards

Figure 9 Overview of the coverage for different wireless communication

standards [17]

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

22

ZigBee a new standard which became publicly available in June 2005 is based on the IEEE 802154 standard It expands the IEEE 802154 by adding the framework for ldquothe network security and applicationrdquo (Craig 2005) Figure 10 presents the IEEE 802154ZigBee stack and the areas of responsibilities

Figure 10 IEEE 802154ZigBee stack [20]

Craig (2005) mentions three networking topologies that the standard covers the star mesh and cluster tree (figure 11) The ZigBee standard works on top of the IEEE 802154 addressing schema by using the standard 64‐bit and the short 16‐bit addressing Kinney (2005) summarizes the ZigBee network layer responsibilities the successful establishment of a new the network and successful new device configuration addressing assignment network synchronization frames security and message routing ZigBee further distinguishes the concept of the physical devices (RFD FFD) by using the notion of ldquological devicesrdquo ldquoZigBee Coordinatorrdquo is the first type of logical devices It is responsible for initializing maintaining and managing the network Under the coordinator in the network hierarchy is the ldquoZigBee routerrdquo which is responsible for controlling the message routing between the nodes Finally the ldquoZigBee End Devicerdquo acts as the end point of the network

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

23

structure The tasks that an end device can perform are specified in the ldquoApplication Profilesrdquo (Craig 2005)

Figure 11 IEEE 802154ZigBee stack [17]

The ZigBee specifications (2005) summarize the security services provided by ZigBee ldquokey establishment key transport frame protection and device managementrdquo ZigBee builds its security mechanism using symmetric key cryptography The security services also depend on the associated layer thus as the figure 10 shows the security mechanism covers the network and the application layer In addition if a MAC frame needs security protection the MAC layer is able to secure it Moreover the notion of end‐to‐end security is supported the source and destination devices have access and use the same share key In the MAC layer the 802154 AES mechanism provides the proper security The mechanism protects ldquothe confidentiality integrity and authenticity of the MAC framesrdquo (Kinney 2005) An auxiliary header field in front of the MAC payload indicates if the frame is encrypted or not The MAC framesrsquo integrity is supported by calculating and using a MIC at the end of the MAC payload In addition to the AES nonce is used to provide MAC confidentiality and authenticity Figure 12 illustrates a MAC frame with security For different security aspects the MAC layer uses different mode of the AES for the encryption it uses the AES in Counter (CTR) mode and for the integrity the

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

24

CBC‐MAC Finally the combination (CCM) of the above two modes is available providing both encryption and integrity (Kinney 2005)

Figure 12 ZigBee secure frame in MAC layer [17]

In the network layer the CCM (a modified MAC layer CCM mode) is used for encryption Because the network layer uses only the CCM mode a single key is used for all different security options The network layer security message format is similar to the MAC frame it is presented in the figure 13 Finally although the network layer is responsible for securing its layer messages the above layers specify the keys and the CCM option for each frame (Kinney 2005)

Figure 13 ZigBee secure frame in network layer [17]

Security in the application layer works similar to the network and MAC layers It uses the ldquolink keyrdquo or the ldquonetwork keyrdquo to secure the message and then encapsulate it inside a set of fields similar to the network format (figure 14) Other security responsibilities that the application layer has are to provide the ZDO and the applications with device management services key establishment and key transport (ZigBee specifications 2005)

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

25

Figure 14 ZigBee secure frame in application layer [17]

The ZigBee application layer contains the manufacturer‐defined application objects the ZDO and the application sub‐layer In addition to the security responsibilities the application sub‐layer binds devices based on their duties and needs maintains the binding tables and forwards messages between them The application sub‐layer also discovers the neighbour devices for a given device The ZDO is responsible for determining the devicersquos duty in the network for communicating using binding requests and for supporting security as was mentioned above The sub‐layer that implements the actual application is the manufacturer‐defined application object (Kinney 2005) The ZigBee specifications are a great step toward the wireless sensors networks standardization They cover all the OSI layers from the physical to the application providing guidance to the developers The specifications are the product of the ZigBee alliances a consortium of companies and academic institutes Thus they not only produce the specifications but also are the first use them The next section goes beyond the layer architecture and standardization and introduces the OS field another crucial element of WSNs

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

26

3 RECEIVED SIGNAL STRENGTH (RSS) Majority of the existing methods leverage the existence of IEEE 80211 base stations with powerful radio transmit powers of approximately 100mW per base station Such radios are in a different class from the low power IEEE 802154 compliant radios that typically transmit at low power levels ranging from 52mW to 29mW [19] The wide availability of larger number of IEEE 802154 radios in the research community has revived the interest for signal strength based localization in sensor network Despite of rapidly increasing popularity of IEEE 802154 radios and signal strength localization there is a lack of detailed characterization of the fundamental factors contributing to large signal strength variation This chapter investigates some of the properties of RSS observed during testing and analysis of research work collected during the period of thesis The analysis of RSS values is needed to understand the underlying features of location dependent RSS patterns and location fingerprints An understanding of the properties of the RSS values for location fingerprinting can assist in improving the design of positioning algorithms and in deployment of indoor positioning systems

31 Parameters that affect RSS Different factors that affect the indoor RSS are the userrsquos presenceabsence hisher orientation time of the day building type and material distance from transmitter and type of radio These factors are by no means exhaustive Other possible factors are antenna orientation directionality and type Table 10 classify the parameters which affect RSS [7]

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

27

Effect on Factors Options

1 Proximity of user Userrsquos presence or absence

2 Orientation of user and terminal

North East South West

Data Collection

3 Make of Radio 4 Time of measurement Time of day amp days of

week 5 Period of measurement

Second minute hour

7 Interference Co‐channel adjacent co‐channel

Statistics

8 Building environment Small offices or large hall

Table 10 Classification of factors the affect RSS [7]

Transmitter variability Different transmitters behave differently even when they are configured exactly in the same way In practice this means that when a transmitter is configured to send packets at a power level of d dBm then the transmitter will send these packets at a power level that is very close to d dBm but not necessarily exactly equal to d dBm This can alter the received signal strength indication and thus it can lead to inaccurate distance estimation

Receiver variability The sensitivity of the receivers across different radio chips is different In practice this means that the RSSI value recorded at different receivers can be different even when all the other parameters that affect the received signal strength are kept constant

Antenna orientation Each antenna has its own radiation pattern that is not uniform In practice this means that the RSSI value recorded at the receiver for a given pair of communicating nodes and for a given distance between them varies as the pair wise antenna orientations of the transmitter and the receiver are changed

Multi‐path fading and shadowing in the RF channel In indoor environments the transmitted signals get reflected after hitting on the walls andor on other objects in the room such as furniture Both the original signal and the reflected signal reach the receiver almost at the same time since they both travel at the speed of light As a result of this the receiver is not able to

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

28

distinguish the two signals and it measures the received signal strength for both of them

32 Related work analysis A number of indoor location tracking systems have been proposed in the literature based on RF signals ultrasound infrared and some combinations of these As mentioned previously RF based positioning system are widely studied as shown in Table 2 Given a model of radio signal propagation in a building or other environment RSS can be used to estimate the distance between the transmitter and receiver Most of the algorithms (Table 2) have been evaluated in practice using 80211b compliant wireless radios and seem to produce a location error of 15ft to 20ft for an indoor network area of approximately 225ft x 144ft According to the results of this study all the localization techniques produce approximately the same location error over a range of environments Ecolocation [22] Mote Track [5] are two of the few localization algorithms that have been evaluated on a real sensor network that uses a low power wireless radio Ecolocation reports a location error of 10ft for a very small outdoor network deployment area (26ft x 49ft) Mote Track reports a location error of approximately 13ft for an indoor network deployment area of 18751ft2 In general the RSSI based localization algorithms can be divided into two broad categories the map based and the distance (or area) prediction based algorithms In the case of the map based algorithms the deployment area is divided into a large set of sample points that cover the whole area Most techniques collect one or more signal strength measurements from all visible beacon nodes and for each sample point Each sample point is then mapped to either a signal strength vector or a signal strength probability distribution This process generates an RSSI fingerprint map which is unique with respect to the beacon locations and the deployment area This fingerprint map is stored to a central base station A node that is not aware of its location collects one or more signal strength measurements from all the visible beacon nodes and creates its own RSSI fingerprint that is sent to the central base station The base station computes nodersquos location as the sample pointrsquos location whose RSSI fingerprint distance from the unknown nodersquos RSSI fingerprint is the minimum among all the sample points Despite the fact that the map based RSSI localization methods produce relatively small location errors they are not attractive mainly because they require an excessive amount of profiling data and because they are not fully distributed The detailed profiling of a building is a major holdback because it is complex time consuming and depends on beacon locations Therefore for different buildings or even for the

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

29

same building and different beacon locations different RSSI maps have to be obtained The second category of RSSI based algorithms are the distance (or area) prediction algorithms which focus on minimizing the amount of profiling data In distance prediction based methods raw RSSI data are directly mapped to distances through a signal propagation model In some cases distances are not explicitly measured but they are taken into consideration through assumptions on the underlying distributions In the case of outdoor deployments where there are no obstacles a single signal propagation model is all the profiling data required In the case of indoor deployments the same applies but now the signal propagation model might be different across different buildings with different furniture and wall arrangements Given an underlying signal propagation model raw RSSI data can be mapped to specific distances These distances can be either used directly in order to perform localization or assigned a probability and used in a learning basedprobabilistic localization algorithm Obviously the accuracy of the distance prediction based methods depends heavily on the accuracy of the signal propagation model used for translating RSSI values to distances

33 Properties of RSSI Changes in signal due to propagation indoors are difficult to predict because of dense environment and propagation effects such as reflection diffraction and scattering The multi‐path fading effect which is the result of either constructive or destructive combination of multiple signal copies at the receiver causes the received signal to fluctuate around a mean value at a particular location The received signal is usually modelled by the combined effects of large‐scale fading and small‐scale fading The large‐scale fading component describes the signal attenuation as the signal travels over a distance and is absorbed by material such as walls and floors along the way to the receiver This component predicts the mean of the RSS and usually has a lognormal distribution On the other hand the small scale fading component explains the dramatic fluctuation of the signal due to multi‐path fading On the other hand the small scale‐fading component explains the dramatic fluctuation of the signal due to multi‐path fading If there is no LOS component the small‐scale‐fading is often modelled with a Rayleigh distribution Such scenarios are commonly called NLOS If there is a LOS component the small‐scale‐fading is typically modelled by a Rician distribution Note that when the RSS is measured the measurement averages out the small‐scale‐fading effects

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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4 OBJECT TRACKING 41 Introduction This chapter presents the object‐tracking application the main objective of this thesis work The application is an algorithmic‐software implementation written in Embedded C which is capable of receiving data messages and generating object position estimation data The implementation of the object‐tracking application makes use of the SENTIO platform and uses the background knowledge introduced in the preceding chapters The motivation for the object‐tracking application is to determine the position of an object in indoor environments (Eg Home) The application acts as an interface which collects the raw data that the sensors generate (numerical values) It makes the proper comparisons and algorithmic processes to identify and track the object while it is static inside the area covered by the wireless sensor network Finally with the detection and tracking data it alerts the user or another system The following sections provide an overview of the object‐tracking application

42 Overview of hardware product Before describing the object tracking application it is important to know the characteristics of hardware product The SENTIO platform (figure 15) is built in modular fashion and has the following key features [8]

Flexibility This platform is very flexible in such a way that it is made easy to add or change the hardware for each sensor task depending on the requirement in the WSN For a specific platform this can be hard or sometimes even impossible to accomplish

Sensor resources Resources in a sensor module includes the ability of usage of controllerrsquos resources as much as possible such as ADC‐channels interrupts and digital Input‐Outputrsquos Local voltage regulation should also be possible since many sensors require higher supply voltages than digital Integrated Circuits do Further support of a shut‐down mechanism to power down the sensor modules when not needed

Computation and memory The processor module based on microcontroller should support many devices in a microcontroller family and support different power supply voltages enabling full range of clock frequencies All power‐down modes of the devices should be

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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31

supported The platform should support different processor modules including FPGArsquos and very simple microcontrollers

Communication The focus should be on enabling an IEEE 802154 compliant communication interface With or without ZigBee stack this will provide a flexible platform for exploring different network topologies Support for power‐down of the entire communication module should be supported

Cost and form factor High flexibility is in conflict with low cost and small size The primary objective should be flexibility but the size should be considered in order to enable sensor device integration in systems where space is limited

Figure 15 SENTIO Platform [8]

The communication subsystem of the SENTIO sensor node was designed so that the correct operation of the radio chip is ensured The radio chip is powered by the on‐board voltage regulator and thus fluctuations in the battery voltage level do not affect the operation of the communication subsystem This platform uses Chipcon CC2420 IEEE 802154 radio transceiver operates in the 24GHz ISM band and includes a digital direct sequence spread spectrum (DSSS) modem providing a spreading gain of 9dB and an effective data rate of 250Kbps It was specifically designed for low power wireless applications and supports 8 discrete power levels 0dBm minus1dBm minus3dBm minus5dBm minus7dBm minus10dBm minus15dBm and minus25dBm at which its power consumption varies from 29mW to 52mW [21] A built‐in received signal strength indicator gives an 8‐bit digital value RSSIVAL The RSSIVAL is always averaged over 8 symbol periods (128μs) and a status bit indicates when the RSSIVAL is valid (meaning that the receiver was enabled for at least 8 symbol periods) The

Antenna

Communication Module

Processing Module

Sensing Module

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

32

power P at the RF pins can be obtained directly from RSSIVAL using the following equation

P = RSSIVAL + RSSIOFFSET [dBm] where the RSSIOFFSET is found empirically from the front‐end gain and it is approximately equal to minus45dBm In the next sections when we refer to the RSSI value we refer to the RSSIVAL and not the power P unless otherwise stated

43 Experimental Testbed The experimental test bed for object tracking application is as shown in figure 16 Experiment was conducted in Masterrsquos room at Mid Sweden University A DELL laptop is connected to SENTIO platform and software were primarily used to collect samples of RSS from APrsquos The software monitors IEEE 802154 radio frequency channels which operate in the 24GHz band

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

1030

BC - 1030

AP 3 AP 1

AP 2

AP 4

BS

HEWLETT

PACKARD

Figure 16 Layout of Masterʹs Room

A three dimensional scalable testbed is used for indoor sensor network deployments The testbed illustrated in figure 16 is a 3‐D structure measuring 16m (L) x 12m (W) x 3m (H) and it is designed to host a large number of static nodes as shown in figure 16 Arrow marks in the above figure represent the location where fingerprints were taken The centrepiece of our infrastructure

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

33

is the SENTIO sensor node [8] an open‐source general purpose sensing platform designed with AT Mega 128 microprocessor and the IEEE 802154 compliant CC2420 radio from Chipcon [19]

44 Obtaining data from the sensor network The next important step in the development of the object tracking application is to obtain the data from the sensor network and send it to the base station From there the data is transmitted through a serial port to the computer that runs the object tracking application When the data arrives at the serial port the application must capture it This paragraph gives a brief explanation of how and what kind data is transmitted by the static object fixed sensors and how this data is being used to estimate the position of the object First the static object transmits Beacon packet continuously which is received by all the fixed APrsquos The beacon packet format is as shown in table 6 Then the fixed APrsquos calculates the 8‐bit value of RSSI from the transmitted beacon packet and then sends the resultant RSSI value as a form of data packet (table 7) to the BS which calculates the position and sends back to the object

45 Object tracking application Testing and Evaluation The purpose of this section is to discuss and evaluate the object tracking application Finally we are ready with the experimental testbed as shown in figure 16 As shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors placed at the corners of the masterrsquos room BS is placed at the centre of the room connected to a PC All the nodes are power connected before an actual measurement takes place At first the object is placed at random position inside the room and transmits beacon packets continuously Then these packets are received by the fixed APrsquos which calculate the RSSI from the packet and then averages it until it receives 50 beacon packets and then transmits the data to the BS (figure 17) Then the BS waits until it receives the data from all the APrsquos Once it receives from all the APrsquos it calculates the position of the object (figure 18) The application was developed to demonstrate a real world use of a WSN in object tracking In evaluating the final position obtained with respect to the actual position we conclude that the object‐tracking was successful Although

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

34

it is a research prototype object tracking application is a simple stable stand‐alone system capable of demonstrating an important application of WSN It takes raw data from the WSN aggregates it evaluates it and based on the evaluation tracks the object The accuracy of estimating the position is less than 5m Table 11 below shows a log of readings taken during test

Start

Intialize micro‐controller and transeciever in receieve mode

If received_packet=

Beacon

Average RSSI values

Increase the counter

If countergt1000050

A

No

Yes

B

B

Yes

No

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

35

A

Reset counter

Set transceiever in transmit mode

Transmit averaged RSSI to Main sensor and set it back to receive mode

10000 samples ‐ Offline

50 samples ‐ Online

Figure 17 Flow chart representing the algorithm of fixed sensors

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

36

Start

Intialize micro‐controller and transeciever in receieve mode

Received RSSI values from all fixed Sensors

Open radio map

Calculate Euclidean distance between obtained RSSI values from fixed sensors and fingerprints

in radio map

No

Yes

Bubble sort for least error in signal strength values

A

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

37

A

Get the location of the object from the radiousing the least error value

Display location of object on PC

End

Figure 18 Flow chart representing the algorithm of main sensor

Table 11 Accuracy of positioning system

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

38

5 RESULTS This chapter presents how the system parameters such as number of samples mean or median etchellipare finalized All the tests are performed in masterrsquos room at Mid Sweden University This chapter is divided into three subsections First in Section 51 it reviews results obtained from samples test Section 52 discusses one dimensional test results and finally section 53 discusses results of two dimensional tests

51 Measurement setup A Dell laptop was connected to a SENTIO platform and is loaded with software (Appendix 2) This setup was used to determine number of samples required during object tracking mean or median position estimation algorithm and one dimensional test The setup is as follows

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

103

0

BC - 1030

AP 1AP 2

BS

HEWLETT

PACKARD

Figure 19 Test bed for one dimensional test in Masters Room

Two SENTIO nodes (AP1 and AP2 as shown above) were separated by a distance of 10 meters and these sensors are known as fixed sensors which are fixed to the ceiling such that there antenna is faced towards the ground They are linear is position A BS was connected to Dell laptop and it was placed in such a way that its antenna was faced towards the ceiling Another SENTIO

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

39

node was used as an object and its orientation was same as BS Figure 19 represents the complete setup for object tracking application Mobile node was moved between these fixed ones at regular intervals of one meter and RSSI values were logged at each interval

52 Number of samples test This test was performed to determine number of samples required for accurate positioning and either mean of the obtained RSSI values or median of them Figure 19 represents the setup for number of samples test Once the setup is ready the measurements are taken as follows

1 An object was tagged with SENTIO node and was placed stationary linear to the fixed sensors

2 10000 samples (RSSI) were taken at regular intervals of 1 meter up to 10 meters

3 The sample values were logged at every interval 4 Using these logged samples I have plotted graphs such as distribution

of RSS values (figure 20 21) meanmedian at intervals of 1 meter using MATLAB

5 The mean and median for various numbers of samples is as shown in figure 22

Figure 20 Histogram plot of RSSI values at 1m and 4m

Figure 21 Histogram plot of RSSI values at 6m and 8m

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

40

Figure 22 Distance vs RSSI

From the above graphs following conclusions were made 1 From the histogram plots we can observe that the distribution of RSSI

values follow Gaussians distribution law 2 As we can see from figure 22 that median curve has drastic changes

when compared to mean curve Hence mean is considered while estimating the position

3 From the observations we conclude that the number of samples does not matter since mean curve tends to be same for various numbers of samples

4 Hence 10000 samples are considered in order to get a proper location fingerprint during off‐line phase and during online phase 50 samples are enough to estimate the position of object

53 0ne dimensional test Measurement setup for one dimensional test is as shown in figure 19 This test was conducted to check the performance of algorithm and how accurate it was At first ie during offline phase fingerprints at each interval averaging 10000 samples were taken During online phase Euclidean algorithm was used estimate the position averaging 50 samples of RSSI From the results we

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

41

can realize that the error was less than 2 meters Table 12 shows the result of location estimation in one dimensional case

Table 12 One dimensional position estimation results

54 Two dimensional test This test is the final stage of my thesis work Experimental testbed for this stage is as shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors fixed at the corners of the room and a BS at the centre of the room There are 18 pre‐determined locations where the fingerprints (RSSI) were taken The fingerprints are as shown in Appendix 1 All the readings were taken in presence of userrsquos The algorithm for position estimation is shown in Appendix 2 Position estimation test is performed as follows

Tagged object is placed at random inside the masterrsquos room All the sensors including the base station and object are switched on Using the algorithm (Appendix 2) the BS estimates the position of the object and displays it on the PC

This test was performed using various numbers of samples to test which has a better accuracy Figure 23 represents the error rate using various numbers of samples From figure 23 we can conclude that 50 numbers of samples is enough to estimate the position of object with an accuracy of 3m

55 Causes of error in location determination The randomness of RSS is the major cause of error in any indoor

positioning system that uses the RSS for location inference If there is no randomness in the RSS and its location fingerprint every indoor positioning systems of this type will have no location detection error For example I have recorded RSS at one particular location during different times of the day From figures 24 and 25 you can observe that RSS values are different at different times of day

The performance of indoor positioning systems depends greatly on the separation of location fingerprints A location fingerprint

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

42

corresponding to a location can be identified correctly if it is difficult to classify it as another fingerprint by a pattern classifier

Path loss is one of the basic signal propagation properties of the RSS that best describes attenuation of transmit signal power over a distance between an access point and a mobile device

Presence of Userrsquos or userrsquos orientation affects RSS

Figure 23 Error Rate vs Number of samples

Figure 24 RSS distribution at 214 pm

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

43

Figure 25 RSS distribution at 100pm and 1100 am

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

6 DISCUSSIONS10102006

44

6 DISCUSSIONS 61 Summary and Conclusion While empirical results and performance studies of positioning systems based on location fingerprint have been presented in this report analytical model that can be used as a framework for efficiently designing the positioning systems are not available This thesis design work uses general principles of position estimation such as position estimation of sensors grid spacing etc This design work focuses on accuracy and precision The concept of location fingerprint technique based on RSS us explained in detail in chapter 2 It is found that RSS is random It is possible to approximate the RSS as being normally distributed While the RSS random process is non‐stationary the mean is more or less constant and could be used as the fingerprint of the location Model designed for position estimation is as shown in chapter 4 Accuracy and precision are the two major performance matrices in this study Uniform grid spacing has been used From the experimental analysis I have obtained an accuracy of 3 meters and 35 precision The sensitivity analysis among the system parameters and the performance matrices were done using the model as shown in chapter 4 For instance our study found that the minimum distance between two adjacent positions also affects the positioning performance This is due to the randomness of RSS patterns induced by the ever changing indoor environment

62 Future work The research in this thesis provides a ground work for the future study of efficient design of indoor positioning system The RSSI based position estimation still has limitations First the RSSI value is time dependent Thus the future study should address this issue Second the movement of object is not considered Third orientations of sensors affect the performance which should be taken care off Fourth to improve the performance of the system we need to find a solution to deal with those incomplete or missing data during the off‐line and the on‐line phase Fifth position estimation on multi‐floor and three‐dimensional coordinates is not available

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 110102006

45

APPENDIX 1

Location Fingerprints of Masters Room Time 1315 Observation Time 20 minutes Number of Samples 10000

People Presence SNo Co-ordinates AP1 (0x0001) AP2 (0x0002) AP3 (0x0004) AP4 (0x0008)

1 11 -24 -16 -27 -33 2 13 -26 -8 -34 -31 3 15 -43 -9 -39 -25 4 32 -21 -11 -27 -33 5 61 -6 -23 -32 -28 6 64 -20 -31 -25 -34 7 35 -23 -35 -29 -38 8 37 -29 -35 -22 -27 9 39 -38 -29 -21 -27 10 67 -27 -23 -20 -23 11 611 -26 -29 -23 -30 12 613 -35 -32 -2 -31 13 513 -34 -31 -20 -26 14 11 -39 -22 -29 -17 15 212 -37 -42 -28 -31 16 -212 -38 -33 -29 -6 17 -21 -42 -29 -33 -19 18 -38 -40 -23 -35 -22

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 210102006

46

APPENDIX 2

Begin

Open radio map and raw data files for write

Intialize radio map in memory

Check whether the

Packet is data or Beacon packet If data packet

Save the data to register

Check if data is

Received from all

sensors

Estimate the position of static

Display the position on PC

End Yes

No

A

A

Yes

No

Flow chart representing the main algorithm for position estimation

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

47

APPENDIX 3

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

48

Distribution of RSSI

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

49

REFERENCES [1] T S Rappaport J H Reed and B D Woerner ldquoPosition location using wireless communications on highways of the futureʺ IEEE Commun Mag vol 34 no 10 pp33‐41 Oct 1996 [2] A M Ladd K E Bekris G Marceau A Rudys L E Kavraki and D S Wallach rdquoRobotics‐based location sensing using wireless ethernetʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ02) 2002 pp 227‐238 [3] I Ramani R Bharadwaja and P V Rangan ldquoLocation tracking for media appliances in wireless home networksʺ in Proc International Conference on Multimedia and Expo (ICMEʹ03) July 2003 pp 769‐772 [4] K Pahlavan X Li and J P Makela ldquoIndoor geolocation science and technologyʺ IEEE Commun Mag vol 40 no 2 pp 112‐118 Feb 2002 [5] Konard Lorincz and Matt Welsh ldquoMote Track A Robust Decentralized Approach to RF‐Based Location Trackingrdquo [6] Paramvir Bahl and Venkata N Padmanabham ldquoRADAR An In‐Building RF‐based User Location and Tracking Systemrdquo [7] Kamol Kaemarungsi ldquoDesign of Indoor positioning system based on location fingerprint techiniquerdquo University of Pittsburgh 2005 [8] Fredrik Linnarsson Cheng Peng and Bengt Oelmann ldquoSENTIO A Hardware Platform for Rapid Prototyping of Wireless Sensor Networksrdquo [9] S Saha K Chaudhuri D Sanghi and P Bhagwat ldquoLocation determination of a mobile device using IEEE 80211b access point signalsʺ in Proc IEEE Wireless Communications and Networking Conference (WCNCʹ03) New Orleans LA Mar 2003 pp 1987‐1992 [10] T Roos P Myllymaki H Tirri P Misikangas and J Sievanen ldquoA probabilistic approach to WLAN user location estimationʺ International Journal of Wireless Information Networks vol 9 no 3 pp 155‐164 July 2002 [11] R Battiti M Brunato and A Villani ldquoStatistical learning theory for location fingerprinting in wireless LANrsquosʺ Technical Report Oct 2002 [Online] Available httprtmscienceunitnitraquobattitiarchive86pdf [12] P Prasithsangaree P Krishnamurthy and P K Chrysanthis ldquoAn indoor position location with wireless LANsʺ in Proc IEEE International Symposium on Personal Indoor and Mobile Radio Communications (PIMRCʹ02) Lisbon Portugal Sept 2002 [13] M A Youssef A Agrawala and A U Shankar ldquoWLAN location determination via clustering and probability distributionsʺ in Proc IEEE

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

50

International Conference on Pervasive Computing and Communications (PerComʹ03) Dallas‐Fort Worth TX Mar 23‐26 2003 pp 23‐26 [14] Z Xiang S Song J Chen H Wang J Huang and X Gao ldquoA wireless LAN‐based indoor positioning technologyʺ IBM Journal of Research and Development vol 48 no 56 pp 617‐626 SeptNov 2004 [15] R Want A Hopper V Falcao and J Gibbons ldquoThe active badge location systemʺACM Transactions on Information Systems vol 40 no 1 pp 91‐102 Jan 1992 [16] N B Priyantha A Chakraborty and H Balakrishnan ldquoThe cricket location‐support systemʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ00) Boston MA Aug 2000 pp 32‐43 [17] Holger K Willig A (2005) Protocols and Architectures for Wireless Sensor Networks London [18] IEEE (2003) Part 154 Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low‐Rate Wireless Personal Area Networks (LR‐WPANs) New York [19] Chipcon CC2420 802154 compliant radio httpwwwchipconcom [20] ZigBee Specification v10 ZigBee Alliance [21] Dimitrios Lymberopoulos Quentin Lindsey and Andreas Savvides ldquoAn Empirical Analysis of Radio Signal Strength Variability in IEEE 802154 Networks using Monopole Antennasrdquo [22] Kiran Yedavalli Bhaskar Krishnamachari Sharmila Ravula and Bhaskar Srinivasan ldquoEcolocation A sequence based technique for RF localization in wireless sensor networksrdquo Robert Bosch Research and Technology Centre Palo Alto CA

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

6

complexity and cost Generally the deployment of fingerprinting based positioning systems can be divided into two phases First in the off‐line phase the locations where the fingerprints are required is pre‐determined and then the fingerprints are collected by performing a site‐survey of the RSS from multiple APs The distance between two closest positions is called grid spacing and usually reported in meters or feet However some points may be omitted due to inaccessibility The RSS is measured at each predetermined location to create a database of RSS patterns of the indoor environment The database of RSS patterns is called a radio map The vector of RSS values at a point on the grid is called the location fingerprint of that point Second in the on‐line phase a MS will report a sample measured vector of RSS from different APs to a base station The base station uses a positioning algorithm to estimate the location of the MS The most common algorithm used to estimate the location is Euclidean algorithm It computes the Euclidean distance between the measured RSS vector and each fingerprint in the database and then the coordinates associated with the fingerprint that provides the smallest Euclidean distance is returned as the estimate of the position It is clear from the above discussion that the designer should not start out to do the site survey without proper system design objectives and guideline If the performance is not sufficient collecting more fingerprints in between previous location fingerprints may help It is a problem of choice as to how many access points are required for the system and what the minimum distance between physical positions on the grid should be in order to provide a good position resolution and best system performance The system parameters and factors that improve the accuracy and the precision performance are still not clear

17 Outline Chapter 2 provides literature review of ldquoIndoor Local Positioning System for ZigBee based on RSSIrdquo it introduces the basic components required for a positioning system general classification of indoor positioning system Finally it explains ldquoLocation Fingerprintrdquo technology used in this thesis work to determine the position of an object in a real‐world application It briefly explains the protocols and industry standards for wireless sensor networks Chapter 3 describes some properties of RSS which is a stimulus to determine

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

7

the position of an object RSS relation with energy and on what parameters is the RSS value dependent Chapter 4 describes the model of an indoor local positioning system and its probability of returning the correct location It also explains its performance evaluation and its error distance distribution Chapter 5 describes results obtained from the model of positioning system Chapter 6 includes conclusions from the experimental analysis and future work

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

8

2 LITERATURE REVIEW This chapter reviews the literature on wireless indoor positioning system as a means of providing an intellectual background for the thesis work First in section 21 the common components of indoor positioning system are described Then in section 22 discusses different means to classify indoor positioning systems In section 23 reviews of all relevant literature on location fingerprint technique Finally in section 24 protocols and industry standards for wireless sensor networks are explained

21 Common components of indoor positioning system Basically a wireless local positioning system consists of at least two separate hardware components a measurable unit that usually carries the major part of the system intelligence and a signal transmitterreceiver The transmitter in the simplest case is just a beacon Figure 2 represents basic hardware model of a positioning system

Figure 2 Block diagram representing hardware module

First the location sensing devices detect the signals transmitted by or received at known reference points using radio sensing technologies The sensing technique is based on signal strength level that converts the sensed signal into RSS Given a set of reference points the relative position of the MS can be derived from the signal characteristics of RSS The signal characteristics of RSS are unique for a location Then the microcontroller processes the position algorithm and estimates the position of object and displays it on PC

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

9

22 Classification of Indoor positioning systems Indoor positioning system can be categorized based on the stimulus used in particular application and measurement principle

221 Classification based on Stimulus Based on the stimulus used in an application WSN inherit certain characteristics and limitations on sensorrsquos signal The propagation delay diffraction reflection and scattering are the basic signal characteristics which affect all signal types The effective range available bandwidth regulatory constraints interference power constraints safety and cost of technology limitations The wireless signals commonly used for indoor positioning systems are infrared radio frequency and ultrasound Brief descriptions of the three major technologies are as follows

Infrared Infrared signal is an electromagnetic signal of a wavelength longer than that of visible light but shorter than that of radio waves Infrared is just below the visible spectrum of light in frequency and is radiated strongly by hot bodies Many objects such as people vehicle engines and aircraft generate and retain heat and as such are especially visible in the infrared wavelengths of light compared to objects in the background It cannot pass through walls or obstructions therefore it has a rather limited range of indoor environments Thus it requires a more sophisticated circuitry than ultrasound signals Indoor lighting interferes with this type of signal and causes problems in accurate sensing It generally has a range of around 5 m The infrared devices are usually small in size compared to ultrasound devices

Radio frequency The RF signal can penetrate through most of the building material therefore it has an excellent range in indoor environments The propagation speed is also high approximately 3108 ms There are unlicensed frequencies available freely for use This type of signal has the longest range compared to infrared and ultrasound Since the propagation waves are of higher frequency (24GHz) interference with other frequency components is less

Ultrasound Although ultrasound operates at low frequency bands (typical 40 kHz) compared to the other two signaling technologies it possesses a good precision for location sensing at a slow propagation speed of sound (343 ms) The advantages of ultrasound devices are their simplicity and that they are inexpensive However ultrasound does not penetrate walls

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

10

but reflects off most of the indoor obstructions It has a short range around 3 m to 10 m but has a 1 cm resolution of distance measurement The operating temperature influences the performance of ultrasound

222 Classification based on measurement principle Wireless positioning systems can be categorized by measurement techniques which are used to derive the position of mobile stations Mainly three different measurement principles are used today AOA RSS and propagation‐time based systems that can further be divided into two different sub‐classes TOA and TDOA Figure 3 shows taxonomy based on the technology and the technique classifications

Figure 3 Taxonomy of positioning system [7]

In AOA systems the position is calculated via goniometry With the use of directional antennas or antenna arrays the angle or bearing relative to points located at known positions is measured The intersection of several measured directions pointers the yields the position value The accuracy of this approach is limited by the possible directivity of the measuring aperture by shadowing and by multi‐path reflections arriving from misleading directions In TOA and TDOA techniques rely on the precision of timing between the signal transmitter and the receiver in order to use the propagation delay or TOF to calculate the distance between transmitter and receiver Therefore a precise synchronization is also very important in such systems By combining at least three distances from three reference positions triangulation can be used to estimate the mobile stationʹs location This type of technique will

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

11

require a high accuracy clock in the communication system TDOA is more practical [1] Examples of location‐sensing systems that use time of flight are GPS the Active Bats [15] and the Cricket [16] Besides these time delay‐based techniques the distance between transmitter and receiver can also be determined from signal strength attenuation and direct distance measurement (such as dead reckoning) Fingerprinting or Location Pattern Matching this technique generally requires only measurement of RSS or other non‐geometric features at several locations to form a database of location fingerprints To estimate the mobile location the system needs to first measure the received signal strength at particular locations and then search for the pattern or fingerprint with the closest match in the database This technique does not require the mobile station to see at least three base stations or access points in order to determine the location The disadvantage of this technique is that it is very time‐consuming to perform an exhaustive data collection for a wide area network such as in outdoor positioning systems

23 Indoor local positioning using ZigBee based on RSSI This section reviews relevant RF‐based indoor positioning systems which can be used to locate stationary objects and track mobile users The impressive growth of IEEE 802154 wireless PANs (WPAN) in recent years suggests an interesting future for the location fingerprinting technique The system is quite flexible because system designers can select whether to have a centralized positioning server or let the mobile to determine its own position However the fingerprinting technique requires a training phase (off‐line phase) to collect location fingerprints for all positions in the operating area before the actual deployment (on‐line phase) [7] The following discussion is divided according to the positioning system components First the common form of location fingerprinting and its relationship with physical locations are explained Finally location estimation algorithm is reviewed

231 Location Fingerprint A location fingerprint based on RF characteristics such as RSS is the basis for representing a unique location It is created under the assumption that each position or location inside a building has unique RF signature [4] Generally a fingerprint F is labelled with a location information L The location fingerprints and their labels (eg location information) are maintained in

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

12

database and used during the on‐line phase to estimate the location The label and fingerprint are usually denoted as a tuple of (L F) Battiti et al [11] point out that the location information L for indoor location can be recorded in two forms as either a tuple of coordinates or an indicator variable The tuple of real coordinates can vary from one dimension to five dimensions which include the three dimension space and two orientation variables expressed in spherical coordinates For instance a location information of a two‐dimension system with an orientation could be expressed as a triplet L = (x y d) | x y є R 2 d є North East South West The RSS was found to be more location‐dependent than the SNR because the noise component is rather random in nature However the RSS itself fluctuates over time for each access point and location Each RSS element can be considered as a random variable therefore it can be captured by recording its descriptive statistics parameters approximating its distribution or maintaining the whole measurement dataset These approaches of RSS representation result in different procedures for location estimation algorithm Regardless of the approach the location fingerprint is usually denoted as an array or vector of signal strength (random variables) received at any position in the location‐based service area The size of the vector is determined by the number of access points that can be heard To create a basis fingerprint such as in a number of samples of vectors of signal strength are collected over a window of time for each position This basis is called a prototype Then the average RSS of each access point is calculated and recorded as an element in the location fingerprint For an area that can receive signals from N access points the location fingerprint can be expressed as a vector of average RSS elements βi

F = (β1 β2hellip βN)T Besides the basis location fingerprint the samples of location fingerprint measured during the on‐line phase are also important for the system that tracks the mobile object The number of samples should be selected appropriately to represent the location fingerprint for the mobile application A step in statistical analysis method called pre‐processing is another important issue needed to be considered because it can impact the estimation of dependency between location fingerprint and the location information easier The pre‐processing refers to a step that cleans the raw data (in this case the training set) before any further operations or analysis The cleaning may consist of encoding dimensionality reduction (reduce unnecessary elements) feature extractionselection clustering and outlier elimination Roos et al [10] point out that the pre‐processing enables faster location estimation and

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

13

reduce the noise from the training data The process that creates the basis of location fingerprint discussed above could be considered as a part of pre‐processing

232 Location Estimation Algorithm Location estimation algorithms or positioning algorithms are procedures that exploit dependency between location information and location fingerprint basis in order to determine a position or location from samples of RSS signals Position algorithms can be classified into deterministic and probabilistic types based on the approaches that model the relationship between location fingerprints and location information The deterministic type of algorithms are those that based in the nearest neighbour classifiers and neural network classifiers The probabilistic type of algorithms is those that are based on the Bayesian inference and statistical learning theory such as support vector machines (SVR) The algorithm used in this project is discussed briefly

Euclidean distance method The Euclidean distance method is a deterministic approach of position estimation because they require only a set of constant location fingerprints which includes mean vectors In order to determine the location a form of discriminate function is used to classify a sample of RSS fingerprint into a position The mean or average RSS vector is a centre of a mass which represents each class of location fingerprint Suppose a set of l location fingerprints is denoted by F1 F2hellip Fl exist and each fingerprint has a one‐to‐one mapping to a set of positions L1 L2hellip Ll A sample of an RSS fingerprint measured during an on‐line phase is denoted as S which can be another mean or average RSS vector of small window of RSS samples Assuming that an indoor positioning system only considers the average RSS from N access points as a location fingerprint the sample of RSS vector is S = (s1 s2 helliphellip sN)T and each location fingerprint i in the database can be expressed as Fi = (β1 β2helliphellip βN)T The simplest closeness metric is a distance measurement in signal space denoted as the Dist () function [6] Thus the simple procedure of nearest neighbour algorithm is expressed as picking the fingerprint j that has the shortest signal distance

Dist(S Fj) le Dist(S Fk) for all k ne j A generalized weighted distance Lp summarized by [12] can be used to calculate different forms of distance in signal space as

Lp = 1N pN

i

pPiSiWi 1

1)||1(sum

=

minus

where N is the number of access points wi is a weighting factor (wi le 1) and p is the norm parameter starting from 1 The weighting factor wi is a bias

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

14

parameter that can demote or promote an important RSS component in the fingerprint

24 Protocols and Industry standards for WSN Although many applications for WSN are proposed the communication protocols supporting them remain mainly unexplored and diverged This is because the area of interest is new each implementation is application specific and the components of WSNrsquos have a lot of constraints Wireless sensor networks use the wireless medium to communicate However the traditional communication protocols that support wireless communications especially ad‐hoc mesh networks may not be well suited for them WSN communication is an active research area and many algorithms and protocols have already been proposed This section introduces two proposed standards the IEEE 802154 and the ZigBee The IEEE 802154 covers the physical layer and the MAC layer of low‐rate WPAN The ZigBee is ldquoan emerging standard that is based on the IEEE 802154 and adds network construction (star networks peer‐to‐peermesh networks and cluster‐tree networks) application services and morerdquo

241 IEEE 802154 Holger and Willig (2005) [17] mention home automation home networking and home security as possible applications for IEEE 802154 They add that ldquomost of these applications require only low‐to‐medium bitrates (up to some few hundreds of kbps) moderate average delays without too stringent delay guarantees and for certain nodes it is highly desirable to reduce the energy consumption to a minimumrdquo The IEEE 802154 standard (2003) defines the device types that can be used in a LR‐WPAN A device can be a FFD or a RFD The RFD can be used in simple applications in which they do not need to transmit large amounts of data and they have to communicate only with a specific FFD The FFD can work as a PAN coordinator as a coordinator or as a simple device It can communicate with either another FFD or a RFD In keeping with the application requirements the LR‐WPAN operates in a star or peer‐to‐peer topology (figure 4) In the star topology the RFD communicates with a single controller the PAN coordinator The PAN coordinator can perform the same function as the RFD but it is also responsible for controlling the PAN ldquoit initiates terminates or routes communication around the networkrdquo [18] The peer‐to‐peer topology

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

15

supports ad‐hoc mesh multi‐hop networking Any device in the peer‐to‐peer topology can communicate with any other device within its communication range however this topology also has a PAN coordinator All the devices in a LR‐WPAN have a unique 64‐bit address This or a short address allocated by the PAN coordinator can be used inside a PAN Additionally each PAN has a unique identifier The combination of the PAN identifier and the sort addresses allows communication across different PANs (IEEE 802154 Standard 2003)

Figure 4 Star and peer‐to‐peer topologies in LR‐WPAN [18]

The LR‐WPAN based on the OSI seven‐layer model has the layered architecture presented in figure 5 The application and the network layer are the upper layers in the LR‐WPAN architecture but are outside the scope of the IEEE 802154 standard Only the physical layer ldquowhich contains the RF transceiver along with its low‐level control mechanismrdquo and the MAC layer ldquothat provides access to the physical channelrdquo are included in the standard and will be introduced in the following sections Finally the Type I 8022 LLC and the SSCS are intermediate sub layers supporting communication with the above layers (IEEE 802154 Standard 2003)

Physical Layer The 802154 standard specifies two different services that the PHY provides The PHY data service controls the radio and thus the transmission and reception of the PPDUs In addition the management service performs ED in the channel The management service also performs CCA before sending the messages and provides LQI for the received packets

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

16

Figure 5 WPAN architecture [18]

Three different bands are defined by the standard The 868‐8686 MHz for Europe the 902‐928 MHz for North America and the 2400‐24835 MHz worldwide All of them belong in the ISM radio bands and they are using DSSS Each of the bands supports different a data rate and it uses a different modulation technique chip rate and number of channels Moreover if the system does not use the 2450 MHz frequency it operates in both the 802 MHz and 902 MHz frequencies Table 3 summarizes the supported bands and data rates (IEEE 802154 standard 2003)

PHY (MHz)

Frequency Band (MHz) Modulation

Bit Rate Number of Channels

868-8686 BPSK 20 1 868 915 902-928 BPSK 40 10

2450 2400-24835 O-QPSK 230 16 Table 3 Frequency bands and data rates for IEEE 802154 based on the IEEE

802154 standard [18] The physical layer uses PPDU packets to communicate Table 4 demonstrates its structure The LSB is always transmitted and received first The SHR contains the preamble and the SFD fields which helps receiver

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

17

synchronization The 8‐bytes preamble contains only zero and is used for synchronization The SFD contains a specific sequence of one and zeros and specifies the beginning of the frame The PHR contains the payload length Packets with a length of 9 or more bytes are MPDU as the next section further explains Packets with length 5 are used for MPDU acknowledgements The payload part of the PPDU encloses the MAC layer packet Finally the PPDU size can be up to 127 bytes [18]

4 bytes 1 byte 1 byte Variable

Preamble SFD Frame length

(7 bits) Reserved

(1 bit) Payload (PSU)

SHR PHR PHY Payload Table 4 Format based on the IEEE 802154 standard [18]

MAC Layer The MAC layer is the interface between the SSCS and the PHY layer Similar to the PHY layer the MAC layer supports two services The MAC data service is responsible for the transmission and reception of the MPDUs through the PHY data service The MAC management service if the device is a coordinator manages the network beacons It is also responsible for PAN association and disassociation frame validation and acknowledgment providing ldquoa reliable link between two peer MAC entitiesrdquo In addition it uses the CSMA‐CA for channel access and handles and maintains the GTS mechanism Finally it supports device security [18] The IEEE 802154 standard defines four different frame types the beacon data acknowledgment and MAC command frame All frame types are based on the general MAC frame format (table 5) The frame control field describes and specifies the above different frame types Every MAC frame comprises a MHR which consists of a frame control sequence number and the information field It also contains the MAC payload different frame types have different MAC payload fields The acknowledgment type does not have a payload Finally each frame includes a MFR which contains a FCS The data in the MPDU follows the same order as the PPDU the least significant bits are left in the frame and are transited first Octets2 1 02 028 02 028 Variable 2

Destination PAN Identifier

Destination Address

Source PAN

Identifier Source Address Frame

Control Sequence Number Addressing Field

Frame Payload FCS

MHR MAC

Payload MFRTable 5 General MAC frame format IEEE 802154 standard [18]

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

18

Table 6 7 8 and 9 present the four different MAC frame types The beacon frame is transmitted periodically by the PAN coordinator It provides information about the network management through the super frame and GTS fields which are analyzed later in the section It also synchronizes the network devices and indicates the proper communication period for them The data frame payload encapsulates data from the higher layers When a device receives a packet it is not obliged to response with an acknowledgement packet Finally the command frame identifier and command payload fields of the command frame are used for communication between the network devices The command identifier specifies actions like association disassociation and data GTS or beacon request Octets2 1 410 2 Variable Variable Variable 2

Frame Control

Sequence Number

Addressing Fields

Super frame Specification

GTS fields

Pending Addressing

Fields Beacon Payload FCS

MHR MAC Payload MFRTable 6 Beacon frame format [18]

Octets 2 1 Variable 2

Frame Control

Sequence Number

Addressing Field

Data Payload FCS

MHR MAC

Payload MFR Table 7 Data frame format [18]

Octets 2 1 2

Frame Control

Sequence Number FCS

MHR MFR Table 8 Acknowledgement frame format [18]

Octets2 1 1 Variable 2

Frame Control

Sequence Number

Addressing Fields

Command Frame Identifier

Command Payload FCS

MHR MAC Payload MFR Table 9 Command frame format [18]

In the LR‐WPAN every PAN has its own coordinator The PAN coordinator manages the communication in the local area it has two options to use or not use the super frame structure The super frame (figure 6) uses network beacons If the coordinator does not want to use a super frame structure it suspends the beacon transmission However the beacon is important for device association and disassociation If the coordinator wishes to maintain close communication control in the PAN and to support low‐latency devices it usually uses the super frame A super frame determines a specific time

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

19

period beacons bound it The beacon is transmitted in the first of the sixteen equal time slots that the super frame has It is used to ldquosynchronize the attached devices to identify the PAN and to describe the structure of the super framerdquo The super frame can have active and inactive periods All the communications have to be finished inside the super frame period In the inactive periods the devices can switch to the sleep mode but they have to be ready for the next beacon The active period is further divided into a CAP and a CFP During the CAP any device can communicate competing with the other devices in the PAN using slotted CSMA‐CA The CFP contains GTSs and follows the CAP A CFP may maintain up to seven GTSs and each GTS can reserve more than one time slot However the CAP should always be sufficient to allow new devices t join the PAN The GTSs are allocated to specific devices All the transactions must be completed inside the assigned time period (CAP GTS and CFP) (IEEE 802154 standard 2003)

Figure 6 Example of super frame structure [18]

In accordance with the 802154 standard (2003) three different types of data‐transfer exist In addition the types differ if the coordinator uses or does not beacons Data transfer from a device to the PAN coordinator is the first type (figure 7) For a ldquonon‐beacon‐enable networkrdquo it first senses the medium by using ldquoun‐slotted CSMA‐CArdquo and then a simple transmit to the data frame In a ldquobeacon‐enabled networkrdquo the sender waits for the beacon when it finds it ldquothe device synchronizes to the super fame structurerdquo In the specified time frame the sender again senses the medium by using ldquoslotted CSMA‐CArdquo and transmits the data to the coordinator In either case the coordinator has the option to acknowledge or not acknowledge the data reception after that the transaction is completed Data transfer from the coordinator is the next type described in the standard (figure 8) In a beacon‐enabled network the coordinator indicates a pending message through the beacon The messagersquos target device receives the beacon and if the message is pending it responses with a MAC command request

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

20

message using slotted CSMA‐CA the coordinator may or may not acknowledge the command message and through slotted CSMA‐CA sends the pending message The device acknowledges the received message After that the coordinator removes the message from the beaconrsquos pending list and completes the transaction In a non‐beacon enable network the device periodically sends a MAC command‐request frame to the coordinator The coordinator acknowledges the data request Then by using un‐slotted CSMA‐CA if it has a pending message it transmits it If it has not it respond with a data frame with a ldquozero‐length payloadrdquo To complete the transaction the device acknowledges the data reception

Figure 7 Communication from a device to a PAN coordinator in (a) a beacon‐enabled network and (b) a non‐beacon enabled network [18]

Peer‐to‐peer is the last type of data transfer In this situation the devices are free to communicate with any other device within their communication range In a peer‐to‐peer PAN the devices can ldquoeither receive constantly or synchronize with each otherrdquo If they are receiving constantly to transmit data they use un‐slotted CSAM‐CA In the second case synchronization must be achieved first (IEEE 802154 standard [IEEE 2003]) The IEEE 802154 (2003) standard establishes MAC and PHY standards for low‐cost low‐power and high‐density node deployments In addition to the above PHY and MAC characteristics IEEE 802154 provides a security baseline including ldquothe ability to maintain an ACL and use symmetric cryptographyrdquo for data encryption The algorithm that is used for encryption is the AES However the higher level layers decide when security is need The upper layers are in general responsible for device authentication and key management The next section introduces the ZigBee standard which

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

21

encapsulates the IEEE 802154 and provides additional standardization for the higher levels

Figure 8 Communication from a PAN coordinator to a device in (a) a beacon‐enabled network and (b) a non‐beacon enabled network [18]

242 ZigBee Heily (2004) defines ZigBee as ldquoa rapidly growing worldwide non‐profit industry consortiumrdquo whose mission is ldquoto define a reliable cost‐effective low‐power wirelessly networked monitoring and control product based on an open global standardrdquo Figure 9 illustrates the areas of interest for different wireless communication standards

Figure 9 Overview of the coverage for different wireless communication

standards [17]

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

22

ZigBee a new standard which became publicly available in June 2005 is based on the IEEE 802154 standard It expands the IEEE 802154 by adding the framework for ldquothe network security and applicationrdquo (Craig 2005) Figure 10 presents the IEEE 802154ZigBee stack and the areas of responsibilities

Figure 10 IEEE 802154ZigBee stack [20]

Craig (2005) mentions three networking topologies that the standard covers the star mesh and cluster tree (figure 11) The ZigBee standard works on top of the IEEE 802154 addressing schema by using the standard 64‐bit and the short 16‐bit addressing Kinney (2005) summarizes the ZigBee network layer responsibilities the successful establishment of a new the network and successful new device configuration addressing assignment network synchronization frames security and message routing ZigBee further distinguishes the concept of the physical devices (RFD FFD) by using the notion of ldquological devicesrdquo ldquoZigBee Coordinatorrdquo is the first type of logical devices It is responsible for initializing maintaining and managing the network Under the coordinator in the network hierarchy is the ldquoZigBee routerrdquo which is responsible for controlling the message routing between the nodes Finally the ldquoZigBee End Devicerdquo acts as the end point of the network

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

23

structure The tasks that an end device can perform are specified in the ldquoApplication Profilesrdquo (Craig 2005)

Figure 11 IEEE 802154ZigBee stack [17]

The ZigBee specifications (2005) summarize the security services provided by ZigBee ldquokey establishment key transport frame protection and device managementrdquo ZigBee builds its security mechanism using symmetric key cryptography The security services also depend on the associated layer thus as the figure 10 shows the security mechanism covers the network and the application layer In addition if a MAC frame needs security protection the MAC layer is able to secure it Moreover the notion of end‐to‐end security is supported the source and destination devices have access and use the same share key In the MAC layer the 802154 AES mechanism provides the proper security The mechanism protects ldquothe confidentiality integrity and authenticity of the MAC framesrdquo (Kinney 2005) An auxiliary header field in front of the MAC payload indicates if the frame is encrypted or not The MAC framesrsquo integrity is supported by calculating and using a MIC at the end of the MAC payload In addition to the AES nonce is used to provide MAC confidentiality and authenticity Figure 12 illustrates a MAC frame with security For different security aspects the MAC layer uses different mode of the AES for the encryption it uses the AES in Counter (CTR) mode and for the integrity the

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

24

CBC‐MAC Finally the combination (CCM) of the above two modes is available providing both encryption and integrity (Kinney 2005)

Figure 12 ZigBee secure frame in MAC layer [17]

In the network layer the CCM (a modified MAC layer CCM mode) is used for encryption Because the network layer uses only the CCM mode a single key is used for all different security options The network layer security message format is similar to the MAC frame it is presented in the figure 13 Finally although the network layer is responsible for securing its layer messages the above layers specify the keys and the CCM option for each frame (Kinney 2005)

Figure 13 ZigBee secure frame in network layer [17]

Security in the application layer works similar to the network and MAC layers It uses the ldquolink keyrdquo or the ldquonetwork keyrdquo to secure the message and then encapsulate it inside a set of fields similar to the network format (figure 14) Other security responsibilities that the application layer has are to provide the ZDO and the applications with device management services key establishment and key transport (ZigBee specifications 2005)

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

25

Figure 14 ZigBee secure frame in application layer [17]

The ZigBee application layer contains the manufacturer‐defined application objects the ZDO and the application sub‐layer In addition to the security responsibilities the application sub‐layer binds devices based on their duties and needs maintains the binding tables and forwards messages between them The application sub‐layer also discovers the neighbour devices for a given device The ZDO is responsible for determining the devicersquos duty in the network for communicating using binding requests and for supporting security as was mentioned above The sub‐layer that implements the actual application is the manufacturer‐defined application object (Kinney 2005) The ZigBee specifications are a great step toward the wireless sensors networks standardization They cover all the OSI layers from the physical to the application providing guidance to the developers The specifications are the product of the ZigBee alliances a consortium of companies and academic institutes Thus they not only produce the specifications but also are the first use them The next section goes beyond the layer architecture and standardization and introduces the OS field another crucial element of WSNs

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

26

3 RECEIVED SIGNAL STRENGTH (RSS) Majority of the existing methods leverage the existence of IEEE 80211 base stations with powerful radio transmit powers of approximately 100mW per base station Such radios are in a different class from the low power IEEE 802154 compliant radios that typically transmit at low power levels ranging from 52mW to 29mW [19] The wide availability of larger number of IEEE 802154 radios in the research community has revived the interest for signal strength based localization in sensor network Despite of rapidly increasing popularity of IEEE 802154 radios and signal strength localization there is a lack of detailed characterization of the fundamental factors contributing to large signal strength variation This chapter investigates some of the properties of RSS observed during testing and analysis of research work collected during the period of thesis The analysis of RSS values is needed to understand the underlying features of location dependent RSS patterns and location fingerprints An understanding of the properties of the RSS values for location fingerprinting can assist in improving the design of positioning algorithms and in deployment of indoor positioning systems

31 Parameters that affect RSS Different factors that affect the indoor RSS are the userrsquos presenceabsence hisher orientation time of the day building type and material distance from transmitter and type of radio These factors are by no means exhaustive Other possible factors are antenna orientation directionality and type Table 10 classify the parameters which affect RSS [7]

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

27

Effect on Factors Options

1 Proximity of user Userrsquos presence or absence

2 Orientation of user and terminal

North East South West

Data Collection

3 Make of Radio 4 Time of measurement Time of day amp days of

week 5 Period of measurement

Second minute hour

7 Interference Co‐channel adjacent co‐channel

Statistics

8 Building environment Small offices or large hall

Table 10 Classification of factors the affect RSS [7]

Transmitter variability Different transmitters behave differently even when they are configured exactly in the same way In practice this means that when a transmitter is configured to send packets at a power level of d dBm then the transmitter will send these packets at a power level that is very close to d dBm but not necessarily exactly equal to d dBm This can alter the received signal strength indication and thus it can lead to inaccurate distance estimation

Receiver variability The sensitivity of the receivers across different radio chips is different In practice this means that the RSSI value recorded at different receivers can be different even when all the other parameters that affect the received signal strength are kept constant

Antenna orientation Each antenna has its own radiation pattern that is not uniform In practice this means that the RSSI value recorded at the receiver for a given pair of communicating nodes and for a given distance between them varies as the pair wise antenna orientations of the transmitter and the receiver are changed

Multi‐path fading and shadowing in the RF channel In indoor environments the transmitted signals get reflected after hitting on the walls andor on other objects in the room such as furniture Both the original signal and the reflected signal reach the receiver almost at the same time since they both travel at the speed of light As a result of this the receiver is not able to

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

28

distinguish the two signals and it measures the received signal strength for both of them

32 Related work analysis A number of indoor location tracking systems have been proposed in the literature based on RF signals ultrasound infrared and some combinations of these As mentioned previously RF based positioning system are widely studied as shown in Table 2 Given a model of radio signal propagation in a building or other environment RSS can be used to estimate the distance between the transmitter and receiver Most of the algorithms (Table 2) have been evaluated in practice using 80211b compliant wireless radios and seem to produce a location error of 15ft to 20ft for an indoor network area of approximately 225ft x 144ft According to the results of this study all the localization techniques produce approximately the same location error over a range of environments Ecolocation [22] Mote Track [5] are two of the few localization algorithms that have been evaluated on a real sensor network that uses a low power wireless radio Ecolocation reports a location error of 10ft for a very small outdoor network deployment area (26ft x 49ft) Mote Track reports a location error of approximately 13ft for an indoor network deployment area of 18751ft2 In general the RSSI based localization algorithms can be divided into two broad categories the map based and the distance (or area) prediction based algorithms In the case of the map based algorithms the deployment area is divided into a large set of sample points that cover the whole area Most techniques collect one or more signal strength measurements from all visible beacon nodes and for each sample point Each sample point is then mapped to either a signal strength vector or a signal strength probability distribution This process generates an RSSI fingerprint map which is unique with respect to the beacon locations and the deployment area This fingerprint map is stored to a central base station A node that is not aware of its location collects one or more signal strength measurements from all the visible beacon nodes and creates its own RSSI fingerprint that is sent to the central base station The base station computes nodersquos location as the sample pointrsquos location whose RSSI fingerprint distance from the unknown nodersquos RSSI fingerprint is the minimum among all the sample points Despite the fact that the map based RSSI localization methods produce relatively small location errors they are not attractive mainly because they require an excessive amount of profiling data and because they are not fully distributed The detailed profiling of a building is a major holdback because it is complex time consuming and depends on beacon locations Therefore for different buildings or even for the

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

29

same building and different beacon locations different RSSI maps have to be obtained The second category of RSSI based algorithms are the distance (or area) prediction algorithms which focus on minimizing the amount of profiling data In distance prediction based methods raw RSSI data are directly mapped to distances through a signal propagation model In some cases distances are not explicitly measured but they are taken into consideration through assumptions on the underlying distributions In the case of outdoor deployments where there are no obstacles a single signal propagation model is all the profiling data required In the case of indoor deployments the same applies but now the signal propagation model might be different across different buildings with different furniture and wall arrangements Given an underlying signal propagation model raw RSSI data can be mapped to specific distances These distances can be either used directly in order to perform localization or assigned a probability and used in a learning basedprobabilistic localization algorithm Obviously the accuracy of the distance prediction based methods depends heavily on the accuracy of the signal propagation model used for translating RSSI values to distances

33 Properties of RSSI Changes in signal due to propagation indoors are difficult to predict because of dense environment and propagation effects such as reflection diffraction and scattering The multi‐path fading effect which is the result of either constructive or destructive combination of multiple signal copies at the receiver causes the received signal to fluctuate around a mean value at a particular location The received signal is usually modelled by the combined effects of large‐scale fading and small‐scale fading The large‐scale fading component describes the signal attenuation as the signal travels over a distance and is absorbed by material such as walls and floors along the way to the receiver This component predicts the mean of the RSS and usually has a lognormal distribution On the other hand the small scale fading component explains the dramatic fluctuation of the signal due to multi‐path fading On the other hand the small scale‐fading component explains the dramatic fluctuation of the signal due to multi‐path fading If there is no LOS component the small‐scale‐fading is often modelled with a Rayleigh distribution Such scenarios are commonly called NLOS If there is a LOS component the small‐scale‐fading is typically modelled by a Rician distribution Note that when the RSS is measured the measurement averages out the small‐scale‐fading effects

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

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4 OBJECT TRACKING 41 Introduction This chapter presents the object‐tracking application the main objective of this thesis work The application is an algorithmic‐software implementation written in Embedded C which is capable of receiving data messages and generating object position estimation data The implementation of the object‐tracking application makes use of the SENTIO platform and uses the background knowledge introduced in the preceding chapters The motivation for the object‐tracking application is to determine the position of an object in indoor environments (Eg Home) The application acts as an interface which collects the raw data that the sensors generate (numerical values) It makes the proper comparisons and algorithmic processes to identify and track the object while it is static inside the area covered by the wireless sensor network Finally with the detection and tracking data it alerts the user or another system The following sections provide an overview of the object‐tracking application

42 Overview of hardware product Before describing the object tracking application it is important to know the characteristics of hardware product The SENTIO platform (figure 15) is built in modular fashion and has the following key features [8]

Flexibility This platform is very flexible in such a way that it is made easy to add or change the hardware for each sensor task depending on the requirement in the WSN For a specific platform this can be hard or sometimes even impossible to accomplish

Sensor resources Resources in a sensor module includes the ability of usage of controllerrsquos resources as much as possible such as ADC‐channels interrupts and digital Input‐Outputrsquos Local voltage regulation should also be possible since many sensors require higher supply voltages than digital Integrated Circuits do Further support of a shut‐down mechanism to power down the sensor modules when not needed

Computation and memory The processor module based on microcontroller should support many devices in a microcontroller family and support different power supply voltages enabling full range of clock frequencies All power‐down modes of the devices should be

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

31

supported The platform should support different processor modules including FPGArsquos and very simple microcontrollers

Communication The focus should be on enabling an IEEE 802154 compliant communication interface With or without ZigBee stack this will provide a flexible platform for exploring different network topologies Support for power‐down of the entire communication module should be supported

Cost and form factor High flexibility is in conflict with low cost and small size The primary objective should be flexibility but the size should be considered in order to enable sensor device integration in systems where space is limited

Figure 15 SENTIO Platform [8]

The communication subsystem of the SENTIO sensor node was designed so that the correct operation of the radio chip is ensured The radio chip is powered by the on‐board voltage regulator and thus fluctuations in the battery voltage level do not affect the operation of the communication subsystem This platform uses Chipcon CC2420 IEEE 802154 radio transceiver operates in the 24GHz ISM band and includes a digital direct sequence spread spectrum (DSSS) modem providing a spreading gain of 9dB and an effective data rate of 250Kbps It was specifically designed for low power wireless applications and supports 8 discrete power levels 0dBm minus1dBm minus3dBm minus5dBm minus7dBm minus10dBm minus15dBm and minus25dBm at which its power consumption varies from 29mW to 52mW [21] A built‐in received signal strength indicator gives an 8‐bit digital value RSSIVAL The RSSIVAL is always averaged over 8 symbol periods (128μs) and a status bit indicates when the RSSIVAL is valid (meaning that the receiver was enabled for at least 8 symbol periods) The

Antenna

Communication Module

Processing Module

Sensing Module

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

32

power P at the RF pins can be obtained directly from RSSIVAL using the following equation

P = RSSIVAL + RSSIOFFSET [dBm] where the RSSIOFFSET is found empirically from the front‐end gain and it is approximately equal to minus45dBm In the next sections when we refer to the RSSI value we refer to the RSSIVAL and not the power P unless otherwise stated

43 Experimental Testbed The experimental test bed for object tracking application is as shown in figure 16 Experiment was conducted in Masterrsquos room at Mid Sweden University A DELL laptop is connected to SENTIO platform and software were primarily used to collect samples of RSS from APrsquos The software monitors IEEE 802154 radio frequency channels which operate in the 24GHz band

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

1030

BC - 1030

AP 3 AP 1

AP 2

AP 4

BS

HEWLETT

PACKARD

Figure 16 Layout of Masterʹs Room

A three dimensional scalable testbed is used for indoor sensor network deployments The testbed illustrated in figure 16 is a 3‐D structure measuring 16m (L) x 12m (W) x 3m (H) and it is designed to host a large number of static nodes as shown in figure 16 Arrow marks in the above figure represent the location where fingerprints were taken The centrepiece of our infrastructure

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

33

is the SENTIO sensor node [8] an open‐source general purpose sensing platform designed with AT Mega 128 microprocessor and the IEEE 802154 compliant CC2420 radio from Chipcon [19]

44 Obtaining data from the sensor network The next important step in the development of the object tracking application is to obtain the data from the sensor network and send it to the base station From there the data is transmitted through a serial port to the computer that runs the object tracking application When the data arrives at the serial port the application must capture it This paragraph gives a brief explanation of how and what kind data is transmitted by the static object fixed sensors and how this data is being used to estimate the position of the object First the static object transmits Beacon packet continuously which is received by all the fixed APrsquos The beacon packet format is as shown in table 6 Then the fixed APrsquos calculates the 8‐bit value of RSSI from the transmitted beacon packet and then sends the resultant RSSI value as a form of data packet (table 7) to the BS which calculates the position and sends back to the object

45 Object tracking application Testing and Evaluation The purpose of this section is to discuss and evaluate the object tracking application Finally we are ready with the experimental testbed as shown in figure 16 As shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors placed at the corners of the masterrsquos room BS is placed at the centre of the room connected to a PC All the nodes are power connected before an actual measurement takes place At first the object is placed at random position inside the room and transmits beacon packets continuously Then these packets are received by the fixed APrsquos which calculate the RSSI from the packet and then averages it until it receives 50 beacon packets and then transmits the data to the BS (figure 17) Then the BS waits until it receives the data from all the APrsquos Once it receives from all the APrsquos it calculates the position of the object (figure 18) The application was developed to demonstrate a real world use of a WSN in object tracking In evaluating the final position obtained with respect to the actual position we conclude that the object‐tracking was successful Although

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

34

it is a research prototype object tracking application is a simple stable stand‐alone system capable of demonstrating an important application of WSN It takes raw data from the WSN aggregates it evaluates it and based on the evaluation tracks the object The accuracy of estimating the position is less than 5m Table 11 below shows a log of readings taken during test

Start

Intialize micro‐controller and transeciever in receieve mode

If received_packet=

Beacon

Average RSSI values

Increase the counter

If countergt1000050

A

No

Yes

B

B

Yes

No

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

35

A

Reset counter

Set transceiever in transmit mode

Transmit averaged RSSI to Main sensor and set it back to receive mode

10000 samples ‐ Offline

50 samples ‐ Online

Figure 17 Flow chart representing the algorithm of fixed sensors

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

36

Start

Intialize micro‐controller and transeciever in receieve mode

Received RSSI values from all fixed Sensors

Open radio map

Calculate Euclidean distance between obtained RSSI values from fixed sensors and fingerprints

in radio map

No

Yes

Bubble sort for least error in signal strength values

A

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

37

A

Get the location of the object from the radiousing the least error value

Display location of object on PC

End

Figure 18 Flow chart representing the algorithm of main sensor

Table 11 Accuracy of positioning system

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

38

5 RESULTS This chapter presents how the system parameters such as number of samples mean or median etchellipare finalized All the tests are performed in masterrsquos room at Mid Sweden University This chapter is divided into three subsections First in Section 51 it reviews results obtained from samples test Section 52 discusses one dimensional test results and finally section 53 discusses results of two dimensional tests

51 Measurement setup A Dell laptop was connected to a SENTIO platform and is loaded with software (Appendix 2) This setup was used to determine number of samples required during object tracking mean or median position estimation algorithm and one dimensional test The setup is as follows

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

103

0

BC - 1030

AP 1AP 2

BS

HEWLETT

PACKARD

Figure 19 Test bed for one dimensional test in Masters Room

Two SENTIO nodes (AP1 and AP2 as shown above) were separated by a distance of 10 meters and these sensors are known as fixed sensors which are fixed to the ceiling such that there antenna is faced towards the ground They are linear is position A BS was connected to Dell laptop and it was placed in such a way that its antenna was faced towards the ceiling Another SENTIO

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

39

node was used as an object and its orientation was same as BS Figure 19 represents the complete setup for object tracking application Mobile node was moved between these fixed ones at regular intervals of one meter and RSSI values were logged at each interval

52 Number of samples test This test was performed to determine number of samples required for accurate positioning and either mean of the obtained RSSI values or median of them Figure 19 represents the setup for number of samples test Once the setup is ready the measurements are taken as follows

1 An object was tagged with SENTIO node and was placed stationary linear to the fixed sensors

2 10000 samples (RSSI) were taken at regular intervals of 1 meter up to 10 meters

3 The sample values were logged at every interval 4 Using these logged samples I have plotted graphs such as distribution

of RSS values (figure 20 21) meanmedian at intervals of 1 meter using MATLAB

5 The mean and median for various numbers of samples is as shown in figure 22

Figure 20 Histogram plot of RSSI values at 1m and 4m

Figure 21 Histogram plot of RSSI values at 6m and 8m

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

40

Figure 22 Distance vs RSSI

From the above graphs following conclusions were made 1 From the histogram plots we can observe that the distribution of RSSI

values follow Gaussians distribution law 2 As we can see from figure 22 that median curve has drastic changes

when compared to mean curve Hence mean is considered while estimating the position

3 From the observations we conclude that the number of samples does not matter since mean curve tends to be same for various numbers of samples

4 Hence 10000 samples are considered in order to get a proper location fingerprint during off‐line phase and during online phase 50 samples are enough to estimate the position of object

53 0ne dimensional test Measurement setup for one dimensional test is as shown in figure 19 This test was conducted to check the performance of algorithm and how accurate it was At first ie during offline phase fingerprints at each interval averaging 10000 samples were taken During online phase Euclidean algorithm was used estimate the position averaging 50 samples of RSSI From the results we

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

41

can realize that the error was less than 2 meters Table 12 shows the result of location estimation in one dimensional case

Table 12 One dimensional position estimation results

54 Two dimensional test This test is the final stage of my thesis work Experimental testbed for this stage is as shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors fixed at the corners of the room and a BS at the centre of the room There are 18 pre‐determined locations where the fingerprints (RSSI) were taken The fingerprints are as shown in Appendix 1 All the readings were taken in presence of userrsquos The algorithm for position estimation is shown in Appendix 2 Position estimation test is performed as follows

Tagged object is placed at random inside the masterrsquos room All the sensors including the base station and object are switched on Using the algorithm (Appendix 2) the BS estimates the position of the object and displays it on the PC

This test was performed using various numbers of samples to test which has a better accuracy Figure 23 represents the error rate using various numbers of samples From figure 23 we can conclude that 50 numbers of samples is enough to estimate the position of object with an accuracy of 3m

55 Causes of error in location determination The randomness of RSS is the major cause of error in any indoor

positioning system that uses the RSS for location inference If there is no randomness in the RSS and its location fingerprint every indoor positioning systems of this type will have no location detection error For example I have recorded RSS at one particular location during different times of the day From figures 24 and 25 you can observe that RSS values are different at different times of day

The performance of indoor positioning systems depends greatly on the separation of location fingerprints A location fingerprint

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

42

corresponding to a location can be identified correctly if it is difficult to classify it as another fingerprint by a pattern classifier

Path loss is one of the basic signal propagation properties of the RSS that best describes attenuation of transmit signal power over a distance between an access point and a mobile device

Presence of Userrsquos or userrsquos orientation affects RSS

Figure 23 Error Rate vs Number of samples

Figure 24 RSS distribution at 214 pm

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

43

Figure 25 RSS distribution at 100pm and 1100 am

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

6 DISCUSSIONS10102006

44

6 DISCUSSIONS 61 Summary and Conclusion While empirical results and performance studies of positioning systems based on location fingerprint have been presented in this report analytical model that can be used as a framework for efficiently designing the positioning systems are not available This thesis design work uses general principles of position estimation such as position estimation of sensors grid spacing etc This design work focuses on accuracy and precision The concept of location fingerprint technique based on RSS us explained in detail in chapter 2 It is found that RSS is random It is possible to approximate the RSS as being normally distributed While the RSS random process is non‐stationary the mean is more or less constant and could be used as the fingerprint of the location Model designed for position estimation is as shown in chapter 4 Accuracy and precision are the two major performance matrices in this study Uniform grid spacing has been used From the experimental analysis I have obtained an accuracy of 3 meters and 35 precision The sensitivity analysis among the system parameters and the performance matrices were done using the model as shown in chapter 4 For instance our study found that the minimum distance between two adjacent positions also affects the positioning performance This is due to the randomness of RSS patterns induced by the ever changing indoor environment

62 Future work The research in this thesis provides a ground work for the future study of efficient design of indoor positioning system The RSSI based position estimation still has limitations First the RSSI value is time dependent Thus the future study should address this issue Second the movement of object is not considered Third orientations of sensors affect the performance which should be taken care off Fourth to improve the performance of the system we need to find a solution to deal with those incomplete or missing data during the off‐line and the on‐line phase Fifth position estimation on multi‐floor and three‐dimensional coordinates is not available

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 110102006

45

APPENDIX 1

Location Fingerprints of Masters Room Time 1315 Observation Time 20 minutes Number of Samples 10000

People Presence SNo Co-ordinates AP1 (0x0001) AP2 (0x0002) AP3 (0x0004) AP4 (0x0008)

1 11 -24 -16 -27 -33 2 13 -26 -8 -34 -31 3 15 -43 -9 -39 -25 4 32 -21 -11 -27 -33 5 61 -6 -23 -32 -28 6 64 -20 -31 -25 -34 7 35 -23 -35 -29 -38 8 37 -29 -35 -22 -27 9 39 -38 -29 -21 -27 10 67 -27 -23 -20 -23 11 611 -26 -29 -23 -30 12 613 -35 -32 -2 -31 13 513 -34 -31 -20 -26 14 11 -39 -22 -29 -17 15 212 -37 -42 -28 -31 16 -212 -38 -33 -29 -6 17 -21 -42 -29 -33 -19 18 -38 -40 -23 -35 -22

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 210102006

46

APPENDIX 2

Begin

Open radio map and raw data files for write

Intialize radio map in memory

Check whether the

Packet is data or Beacon packet If data packet

Save the data to register

Check if data is

Received from all

sensors

Estimate the position of static

Display the position on PC

End Yes

No

A

A

Yes

No

Flow chart representing the main algorithm for position estimation

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

47

APPENDIX 3

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

48

Distribution of RSSI

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

49

REFERENCES [1] T S Rappaport J H Reed and B D Woerner ldquoPosition location using wireless communications on highways of the futureʺ IEEE Commun Mag vol 34 no 10 pp33‐41 Oct 1996 [2] A M Ladd K E Bekris G Marceau A Rudys L E Kavraki and D S Wallach rdquoRobotics‐based location sensing using wireless ethernetʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ02) 2002 pp 227‐238 [3] I Ramani R Bharadwaja and P V Rangan ldquoLocation tracking for media appliances in wireless home networksʺ in Proc International Conference on Multimedia and Expo (ICMEʹ03) July 2003 pp 769‐772 [4] K Pahlavan X Li and J P Makela ldquoIndoor geolocation science and technologyʺ IEEE Commun Mag vol 40 no 2 pp 112‐118 Feb 2002 [5] Konard Lorincz and Matt Welsh ldquoMote Track A Robust Decentralized Approach to RF‐Based Location Trackingrdquo [6] Paramvir Bahl and Venkata N Padmanabham ldquoRADAR An In‐Building RF‐based User Location and Tracking Systemrdquo [7] Kamol Kaemarungsi ldquoDesign of Indoor positioning system based on location fingerprint techiniquerdquo University of Pittsburgh 2005 [8] Fredrik Linnarsson Cheng Peng and Bengt Oelmann ldquoSENTIO A Hardware Platform for Rapid Prototyping of Wireless Sensor Networksrdquo [9] S Saha K Chaudhuri D Sanghi and P Bhagwat ldquoLocation determination of a mobile device using IEEE 80211b access point signalsʺ in Proc IEEE Wireless Communications and Networking Conference (WCNCʹ03) New Orleans LA Mar 2003 pp 1987‐1992 [10] T Roos P Myllymaki H Tirri P Misikangas and J Sievanen ldquoA probabilistic approach to WLAN user location estimationʺ International Journal of Wireless Information Networks vol 9 no 3 pp 155‐164 July 2002 [11] R Battiti M Brunato and A Villani ldquoStatistical learning theory for location fingerprinting in wireless LANrsquosʺ Technical Report Oct 2002 [Online] Available httprtmscienceunitnitraquobattitiarchive86pdf [12] P Prasithsangaree P Krishnamurthy and P K Chrysanthis ldquoAn indoor position location with wireless LANsʺ in Proc IEEE International Symposium on Personal Indoor and Mobile Radio Communications (PIMRCʹ02) Lisbon Portugal Sept 2002 [13] M A Youssef A Agrawala and A U Shankar ldquoWLAN location determination via clustering and probability distributionsʺ in Proc IEEE

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

50

International Conference on Pervasive Computing and Communications (PerComʹ03) Dallas‐Fort Worth TX Mar 23‐26 2003 pp 23‐26 [14] Z Xiang S Song J Chen H Wang J Huang and X Gao ldquoA wireless LAN‐based indoor positioning technologyʺ IBM Journal of Research and Development vol 48 no 56 pp 617‐626 SeptNov 2004 [15] R Want A Hopper V Falcao and J Gibbons ldquoThe active badge location systemʺACM Transactions on Information Systems vol 40 no 1 pp 91‐102 Jan 1992 [16] N B Priyantha A Chakraborty and H Balakrishnan ldquoThe cricket location‐support systemʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ00) Boston MA Aug 2000 pp 32‐43 [17] Holger K Willig A (2005) Protocols and Architectures for Wireless Sensor Networks London [18] IEEE (2003) Part 154 Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low‐Rate Wireless Personal Area Networks (LR‐WPANs) New York [19] Chipcon CC2420 802154 compliant radio httpwwwchipconcom [20] ZigBee Specification v10 ZigBee Alliance [21] Dimitrios Lymberopoulos Quentin Lindsey and Andreas Savvides ldquoAn Empirical Analysis of Radio Signal Strength Variability in IEEE 802154 Networks using Monopole Antennasrdquo [22] Kiran Yedavalli Bhaskar Krishnamachari Sharmila Ravula and Bhaskar Srinivasan ldquoEcolocation A sequence based technique for RF localization in wireless sensor networksrdquo Robert Bosch Research and Technology Centre Palo Alto CA

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

1 INTRODUCTION10102006

7

the position of an object RSS relation with energy and on what parameters is the RSS value dependent Chapter 4 describes the model of an indoor local positioning system and its probability of returning the correct location It also explains its performance evaluation and its error distance distribution Chapter 5 describes results obtained from the model of positioning system Chapter 6 includes conclusions from the experimental analysis and future work

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

8

2 LITERATURE REVIEW This chapter reviews the literature on wireless indoor positioning system as a means of providing an intellectual background for the thesis work First in section 21 the common components of indoor positioning system are described Then in section 22 discusses different means to classify indoor positioning systems In section 23 reviews of all relevant literature on location fingerprint technique Finally in section 24 protocols and industry standards for wireless sensor networks are explained

21 Common components of indoor positioning system Basically a wireless local positioning system consists of at least two separate hardware components a measurable unit that usually carries the major part of the system intelligence and a signal transmitterreceiver The transmitter in the simplest case is just a beacon Figure 2 represents basic hardware model of a positioning system

Figure 2 Block diagram representing hardware module

First the location sensing devices detect the signals transmitted by or received at known reference points using radio sensing technologies The sensing technique is based on signal strength level that converts the sensed signal into RSS Given a set of reference points the relative position of the MS can be derived from the signal characteristics of RSS The signal characteristics of RSS are unique for a location Then the microcontroller processes the position algorithm and estimates the position of object and displays it on PC

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

9

22 Classification of Indoor positioning systems Indoor positioning system can be categorized based on the stimulus used in particular application and measurement principle

221 Classification based on Stimulus Based on the stimulus used in an application WSN inherit certain characteristics and limitations on sensorrsquos signal The propagation delay diffraction reflection and scattering are the basic signal characteristics which affect all signal types The effective range available bandwidth regulatory constraints interference power constraints safety and cost of technology limitations The wireless signals commonly used for indoor positioning systems are infrared radio frequency and ultrasound Brief descriptions of the three major technologies are as follows

Infrared Infrared signal is an electromagnetic signal of a wavelength longer than that of visible light but shorter than that of radio waves Infrared is just below the visible spectrum of light in frequency and is radiated strongly by hot bodies Many objects such as people vehicle engines and aircraft generate and retain heat and as such are especially visible in the infrared wavelengths of light compared to objects in the background It cannot pass through walls or obstructions therefore it has a rather limited range of indoor environments Thus it requires a more sophisticated circuitry than ultrasound signals Indoor lighting interferes with this type of signal and causes problems in accurate sensing It generally has a range of around 5 m The infrared devices are usually small in size compared to ultrasound devices

Radio frequency The RF signal can penetrate through most of the building material therefore it has an excellent range in indoor environments The propagation speed is also high approximately 3108 ms There are unlicensed frequencies available freely for use This type of signal has the longest range compared to infrared and ultrasound Since the propagation waves are of higher frequency (24GHz) interference with other frequency components is less

Ultrasound Although ultrasound operates at low frequency bands (typical 40 kHz) compared to the other two signaling technologies it possesses a good precision for location sensing at a slow propagation speed of sound (343 ms) The advantages of ultrasound devices are their simplicity and that they are inexpensive However ultrasound does not penetrate walls

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

10

but reflects off most of the indoor obstructions It has a short range around 3 m to 10 m but has a 1 cm resolution of distance measurement The operating temperature influences the performance of ultrasound

222 Classification based on measurement principle Wireless positioning systems can be categorized by measurement techniques which are used to derive the position of mobile stations Mainly three different measurement principles are used today AOA RSS and propagation‐time based systems that can further be divided into two different sub‐classes TOA and TDOA Figure 3 shows taxonomy based on the technology and the technique classifications

Figure 3 Taxonomy of positioning system [7]

In AOA systems the position is calculated via goniometry With the use of directional antennas or antenna arrays the angle or bearing relative to points located at known positions is measured The intersection of several measured directions pointers the yields the position value The accuracy of this approach is limited by the possible directivity of the measuring aperture by shadowing and by multi‐path reflections arriving from misleading directions In TOA and TDOA techniques rely on the precision of timing between the signal transmitter and the receiver in order to use the propagation delay or TOF to calculate the distance between transmitter and receiver Therefore a precise synchronization is also very important in such systems By combining at least three distances from three reference positions triangulation can be used to estimate the mobile stationʹs location This type of technique will

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

11

require a high accuracy clock in the communication system TDOA is more practical [1] Examples of location‐sensing systems that use time of flight are GPS the Active Bats [15] and the Cricket [16] Besides these time delay‐based techniques the distance between transmitter and receiver can also be determined from signal strength attenuation and direct distance measurement (such as dead reckoning) Fingerprinting or Location Pattern Matching this technique generally requires only measurement of RSS or other non‐geometric features at several locations to form a database of location fingerprints To estimate the mobile location the system needs to first measure the received signal strength at particular locations and then search for the pattern or fingerprint with the closest match in the database This technique does not require the mobile station to see at least three base stations or access points in order to determine the location The disadvantage of this technique is that it is very time‐consuming to perform an exhaustive data collection for a wide area network such as in outdoor positioning systems

23 Indoor local positioning using ZigBee based on RSSI This section reviews relevant RF‐based indoor positioning systems which can be used to locate stationary objects and track mobile users The impressive growth of IEEE 802154 wireless PANs (WPAN) in recent years suggests an interesting future for the location fingerprinting technique The system is quite flexible because system designers can select whether to have a centralized positioning server or let the mobile to determine its own position However the fingerprinting technique requires a training phase (off‐line phase) to collect location fingerprints for all positions in the operating area before the actual deployment (on‐line phase) [7] The following discussion is divided according to the positioning system components First the common form of location fingerprinting and its relationship with physical locations are explained Finally location estimation algorithm is reviewed

231 Location Fingerprint A location fingerprint based on RF characteristics such as RSS is the basis for representing a unique location It is created under the assumption that each position or location inside a building has unique RF signature [4] Generally a fingerprint F is labelled with a location information L The location fingerprints and their labels (eg location information) are maintained in

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

12

database and used during the on‐line phase to estimate the location The label and fingerprint are usually denoted as a tuple of (L F) Battiti et al [11] point out that the location information L for indoor location can be recorded in two forms as either a tuple of coordinates or an indicator variable The tuple of real coordinates can vary from one dimension to five dimensions which include the three dimension space and two orientation variables expressed in spherical coordinates For instance a location information of a two‐dimension system with an orientation could be expressed as a triplet L = (x y d) | x y є R 2 d є North East South West The RSS was found to be more location‐dependent than the SNR because the noise component is rather random in nature However the RSS itself fluctuates over time for each access point and location Each RSS element can be considered as a random variable therefore it can be captured by recording its descriptive statistics parameters approximating its distribution or maintaining the whole measurement dataset These approaches of RSS representation result in different procedures for location estimation algorithm Regardless of the approach the location fingerprint is usually denoted as an array or vector of signal strength (random variables) received at any position in the location‐based service area The size of the vector is determined by the number of access points that can be heard To create a basis fingerprint such as in a number of samples of vectors of signal strength are collected over a window of time for each position This basis is called a prototype Then the average RSS of each access point is calculated and recorded as an element in the location fingerprint For an area that can receive signals from N access points the location fingerprint can be expressed as a vector of average RSS elements βi

F = (β1 β2hellip βN)T Besides the basis location fingerprint the samples of location fingerprint measured during the on‐line phase are also important for the system that tracks the mobile object The number of samples should be selected appropriately to represent the location fingerprint for the mobile application A step in statistical analysis method called pre‐processing is another important issue needed to be considered because it can impact the estimation of dependency between location fingerprint and the location information easier The pre‐processing refers to a step that cleans the raw data (in this case the training set) before any further operations or analysis The cleaning may consist of encoding dimensionality reduction (reduce unnecessary elements) feature extractionselection clustering and outlier elimination Roos et al [10] point out that the pre‐processing enables faster location estimation and

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

13

reduce the noise from the training data The process that creates the basis of location fingerprint discussed above could be considered as a part of pre‐processing

232 Location Estimation Algorithm Location estimation algorithms or positioning algorithms are procedures that exploit dependency between location information and location fingerprint basis in order to determine a position or location from samples of RSS signals Position algorithms can be classified into deterministic and probabilistic types based on the approaches that model the relationship between location fingerprints and location information The deterministic type of algorithms are those that based in the nearest neighbour classifiers and neural network classifiers The probabilistic type of algorithms is those that are based on the Bayesian inference and statistical learning theory such as support vector machines (SVR) The algorithm used in this project is discussed briefly

Euclidean distance method The Euclidean distance method is a deterministic approach of position estimation because they require only a set of constant location fingerprints which includes mean vectors In order to determine the location a form of discriminate function is used to classify a sample of RSS fingerprint into a position The mean or average RSS vector is a centre of a mass which represents each class of location fingerprint Suppose a set of l location fingerprints is denoted by F1 F2hellip Fl exist and each fingerprint has a one‐to‐one mapping to a set of positions L1 L2hellip Ll A sample of an RSS fingerprint measured during an on‐line phase is denoted as S which can be another mean or average RSS vector of small window of RSS samples Assuming that an indoor positioning system only considers the average RSS from N access points as a location fingerprint the sample of RSS vector is S = (s1 s2 helliphellip sN)T and each location fingerprint i in the database can be expressed as Fi = (β1 β2helliphellip βN)T The simplest closeness metric is a distance measurement in signal space denoted as the Dist () function [6] Thus the simple procedure of nearest neighbour algorithm is expressed as picking the fingerprint j that has the shortest signal distance

Dist(S Fj) le Dist(S Fk) for all k ne j A generalized weighted distance Lp summarized by [12] can be used to calculate different forms of distance in signal space as

Lp = 1N pN

i

pPiSiWi 1

1)||1(sum

=

minus

where N is the number of access points wi is a weighting factor (wi le 1) and p is the norm parameter starting from 1 The weighting factor wi is a bias

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

14

parameter that can demote or promote an important RSS component in the fingerprint

24 Protocols and Industry standards for WSN Although many applications for WSN are proposed the communication protocols supporting them remain mainly unexplored and diverged This is because the area of interest is new each implementation is application specific and the components of WSNrsquos have a lot of constraints Wireless sensor networks use the wireless medium to communicate However the traditional communication protocols that support wireless communications especially ad‐hoc mesh networks may not be well suited for them WSN communication is an active research area and many algorithms and protocols have already been proposed This section introduces two proposed standards the IEEE 802154 and the ZigBee The IEEE 802154 covers the physical layer and the MAC layer of low‐rate WPAN The ZigBee is ldquoan emerging standard that is based on the IEEE 802154 and adds network construction (star networks peer‐to‐peermesh networks and cluster‐tree networks) application services and morerdquo

241 IEEE 802154 Holger and Willig (2005) [17] mention home automation home networking and home security as possible applications for IEEE 802154 They add that ldquomost of these applications require only low‐to‐medium bitrates (up to some few hundreds of kbps) moderate average delays without too stringent delay guarantees and for certain nodes it is highly desirable to reduce the energy consumption to a minimumrdquo The IEEE 802154 standard (2003) defines the device types that can be used in a LR‐WPAN A device can be a FFD or a RFD The RFD can be used in simple applications in which they do not need to transmit large amounts of data and they have to communicate only with a specific FFD The FFD can work as a PAN coordinator as a coordinator or as a simple device It can communicate with either another FFD or a RFD In keeping with the application requirements the LR‐WPAN operates in a star or peer‐to‐peer topology (figure 4) In the star topology the RFD communicates with a single controller the PAN coordinator The PAN coordinator can perform the same function as the RFD but it is also responsible for controlling the PAN ldquoit initiates terminates or routes communication around the networkrdquo [18] The peer‐to‐peer topology

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

15

supports ad‐hoc mesh multi‐hop networking Any device in the peer‐to‐peer topology can communicate with any other device within its communication range however this topology also has a PAN coordinator All the devices in a LR‐WPAN have a unique 64‐bit address This or a short address allocated by the PAN coordinator can be used inside a PAN Additionally each PAN has a unique identifier The combination of the PAN identifier and the sort addresses allows communication across different PANs (IEEE 802154 Standard 2003)

Figure 4 Star and peer‐to‐peer topologies in LR‐WPAN [18]

The LR‐WPAN based on the OSI seven‐layer model has the layered architecture presented in figure 5 The application and the network layer are the upper layers in the LR‐WPAN architecture but are outside the scope of the IEEE 802154 standard Only the physical layer ldquowhich contains the RF transceiver along with its low‐level control mechanismrdquo and the MAC layer ldquothat provides access to the physical channelrdquo are included in the standard and will be introduced in the following sections Finally the Type I 8022 LLC and the SSCS are intermediate sub layers supporting communication with the above layers (IEEE 802154 Standard 2003)

Physical Layer The 802154 standard specifies two different services that the PHY provides The PHY data service controls the radio and thus the transmission and reception of the PPDUs In addition the management service performs ED in the channel The management service also performs CCA before sending the messages and provides LQI for the received packets

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

16

Figure 5 WPAN architecture [18]

Three different bands are defined by the standard The 868‐8686 MHz for Europe the 902‐928 MHz for North America and the 2400‐24835 MHz worldwide All of them belong in the ISM radio bands and they are using DSSS Each of the bands supports different a data rate and it uses a different modulation technique chip rate and number of channels Moreover if the system does not use the 2450 MHz frequency it operates in both the 802 MHz and 902 MHz frequencies Table 3 summarizes the supported bands and data rates (IEEE 802154 standard 2003)

PHY (MHz)

Frequency Band (MHz) Modulation

Bit Rate Number of Channels

868-8686 BPSK 20 1 868 915 902-928 BPSK 40 10

2450 2400-24835 O-QPSK 230 16 Table 3 Frequency bands and data rates for IEEE 802154 based on the IEEE

802154 standard [18] The physical layer uses PPDU packets to communicate Table 4 demonstrates its structure The LSB is always transmitted and received first The SHR contains the preamble and the SFD fields which helps receiver

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

17

synchronization The 8‐bytes preamble contains only zero and is used for synchronization The SFD contains a specific sequence of one and zeros and specifies the beginning of the frame The PHR contains the payload length Packets with a length of 9 or more bytes are MPDU as the next section further explains Packets with length 5 are used for MPDU acknowledgements The payload part of the PPDU encloses the MAC layer packet Finally the PPDU size can be up to 127 bytes [18]

4 bytes 1 byte 1 byte Variable

Preamble SFD Frame length

(7 bits) Reserved

(1 bit) Payload (PSU)

SHR PHR PHY Payload Table 4 Format based on the IEEE 802154 standard [18]

MAC Layer The MAC layer is the interface between the SSCS and the PHY layer Similar to the PHY layer the MAC layer supports two services The MAC data service is responsible for the transmission and reception of the MPDUs through the PHY data service The MAC management service if the device is a coordinator manages the network beacons It is also responsible for PAN association and disassociation frame validation and acknowledgment providing ldquoa reliable link between two peer MAC entitiesrdquo In addition it uses the CSMA‐CA for channel access and handles and maintains the GTS mechanism Finally it supports device security [18] The IEEE 802154 standard defines four different frame types the beacon data acknowledgment and MAC command frame All frame types are based on the general MAC frame format (table 5) The frame control field describes and specifies the above different frame types Every MAC frame comprises a MHR which consists of a frame control sequence number and the information field It also contains the MAC payload different frame types have different MAC payload fields The acknowledgment type does not have a payload Finally each frame includes a MFR which contains a FCS The data in the MPDU follows the same order as the PPDU the least significant bits are left in the frame and are transited first Octets2 1 02 028 02 028 Variable 2

Destination PAN Identifier

Destination Address

Source PAN

Identifier Source Address Frame

Control Sequence Number Addressing Field

Frame Payload FCS

MHR MAC

Payload MFRTable 5 General MAC frame format IEEE 802154 standard [18]

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

18

Table 6 7 8 and 9 present the four different MAC frame types The beacon frame is transmitted periodically by the PAN coordinator It provides information about the network management through the super frame and GTS fields which are analyzed later in the section It also synchronizes the network devices and indicates the proper communication period for them The data frame payload encapsulates data from the higher layers When a device receives a packet it is not obliged to response with an acknowledgement packet Finally the command frame identifier and command payload fields of the command frame are used for communication between the network devices The command identifier specifies actions like association disassociation and data GTS or beacon request Octets2 1 410 2 Variable Variable Variable 2

Frame Control

Sequence Number

Addressing Fields

Super frame Specification

GTS fields

Pending Addressing

Fields Beacon Payload FCS

MHR MAC Payload MFRTable 6 Beacon frame format [18]

Octets 2 1 Variable 2

Frame Control

Sequence Number

Addressing Field

Data Payload FCS

MHR MAC

Payload MFR Table 7 Data frame format [18]

Octets 2 1 2

Frame Control

Sequence Number FCS

MHR MFR Table 8 Acknowledgement frame format [18]

Octets2 1 1 Variable 2

Frame Control

Sequence Number

Addressing Fields

Command Frame Identifier

Command Payload FCS

MHR MAC Payload MFR Table 9 Command frame format [18]

In the LR‐WPAN every PAN has its own coordinator The PAN coordinator manages the communication in the local area it has two options to use or not use the super frame structure The super frame (figure 6) uses network beacons If the coordinator does not want to use a super frame structure it suspends the beacon transmission However the beacon is important for device association and disassociation If the coordinator wishes to maintain close communication control in the PAN and to support low‐latency devices it usually uses the super frame A super frame determines a specific time

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

19

period beacons bound it The beacon is transmitted in the first of the sixteen equal time slots that the super frame has It is used to ldquosynchronize the attached devices to identify the PAN and to describe the structure of the super framerdquo The super frame can have active and inactive periods All the communications have to be finished inside the super frame period In the inactive periods the devices can switch to the sleep mode but they have to be ready for the next beacon The active period is further divided into a CAP and a CFP During the CAP any device can communicate competing with the other devices in the PAN using slotted CSMA‐CA The CFP contains GTSs and follows the CAP A CFP may maintain up to seven GTSs and each GTS can reserve more than one time slot However the CAP should always be sufficient to allow new devices t join the PAN The GTSs are allocated to specific devices All the transactions must be completed inside the assigned time period (CAP GTS and CFP) (IEEE 802154 standard 2003)

Figure 6 Example of super frame structure [18]

In accordance with the 802154 standard (2003) three different types of data‐transfer exist In addition the types differ if the coordinator uses or does not beacons Data transfer from a device to the PAN coordinator is the first type (figure 7) For a ldquonon‐beacon‐enable networkrdquo it first senses the medium by using ldquoun‐slotted CSMA‐CArdquo and then a simple transmit to the data frame In a ldquobeacon‐enabled networkrdquo the sender waits for the beacon when it finds it ldquothe device synchronizes to the super fame structurerdquo In the specified time frame the sender again senses the medium by using ldquoslotted CSMA‐CArdquo and transmits the data to the coordinator In either case the coordinator has the option to acknowledge or not acknowledge the data reception after that the transaction is completed Data transfer from the coordinator is the next type described in the standard (figure 8) In a beacon‐enabled network the coordinator indicates a pending message through the beacon The messagersquos target device receives the beacon and if the message is pending it responses with a MAC command request

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

20

message using slotted CSMA‐CA the coordinator may or may not acknowledge the command message and through slotted CSMA‐CA sends the pending message The device acknowledges the received message After that the coordinator removes the message from the beaconrsquos pending list and completes the transaction In a non‐beacon enable network the device periodically sends a MAC command‐request frame to the coordinator The coordinator acknowledges the data request Then by using un‐slotted CSMA‐CA if it has a pending message it transmits it If it has not it respond with a data frame with a ldquozero‐length payloadrdquo To complete the transaction the device acknowledges the data reception

Figure 7 Communication from a device to a PAN coordinator in (a) a beacon‐enabled network and (b) a non‐beacon enabled network [18]

Peer‐to‐peer is the last type of data transfer In this situation the devices are free to communicate with any other device within their communication range In a peer‐to‐peer PAN the devices can ldquoeither receive constantly or synchronize with each otherrdquo If they are receiving constantly to transmit data they use un‐slotted CSAM‐CA In the second case synchronization must be achieved first (IEEE 802154 standard [IEEE 2003]) The IEEE 802154 (2003) standard establishes MAC and PHY standards for low‐cost low‐power and high‐density node deployments In addition to the above PHY and MAC characteristics IEEE 802154 provides a security baseline including ldquothe ability to maintain an ACL and use symmetric cryptographyrdquo for data encryption The algorithm that is used for encryption is the AES However the higher level layers decide when security is need The upper layers are in general responsible for device authentication and key management The next section introduces the ZigBee standard which

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

21

encapsulates the IEEE 802154 and provides additional standardization for the higher levels

Figure 8 Communication from a PAN coordinator to a device in (a) a beacon‐enabled network and (b) a non‐beacon enabled network [18]

242 ZigBee Heily (2004) defines ZigBee as ldquoa rapidly growing worldwide non‐profit industry consortiumrdquo whose mission is ldquoto define a reliable cost‐effective low‐power wirelessly networked monitoring and control product based on an open global standardrdquo Figure 9 illustrates the areas of interest for different wireless communication standards

Figure 9 Overview of the coverage for different wireless communication

standards [17]

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

22

ZigBee a new standard which became publicly available in June 2005 is based on the IEEE 802154 standard It expands the IEEE 802154 by adding the framework for ldquothe network security and applicationrdquo (Craig 2005) Figure 10 presents the IEEE 802154ZigBee stack and the areas of responsibilities

Figure 10 IEEE 802154ZigBee stack [20]

Craig (2005) mentions three networking topologies that the standard covers the star mesh and cluster tree (figure 11) The ZigBee standard works on top of the IEEE 802154 addressing schema by using the standard 64‐bit and the short 16‐bit addressing Kinney (2005) summarizes the ZigBee network layer responsibilities the successful establishment of a new the network and successful new device configuration addressing assignment network synchronization frames security and message routing ZigBee further distinguishes the concept of the physical devices (RFD FFD) by using the notion of ldquological devicesrdquo ldquoZigBee Coordinatorrdquo is the first type of logical devices It is responsible for initializing maintaining and managing the network Under the coordinator in the network hierarchy is the ldquoZigBee routerrdquo which is responsible for controlling the message routing between the nodes Finally the ldquoZigBee End Devicerdquo acts as the end point of the network

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

23

structure The tasks that an end device can perform are specified in the ldquoApplication Profilesrdquo (Craig 2005)

Figure 11 IEEE 802154ZigBee stack [17]

The ZigBee specifications (2005) summarize the security services provided by ZigBee ldquokey establishment key transport frame protection and device managementrdquo ZigBee builds its security mechanism using symmetric key cryptography The security services also depend on the associated layer thus as the figure 10 shows the security mechanism covers the network and the application layer In addition if a MAC frame needs security protection the MAC layer is able to secure it Moreover the notion of end‐to‐end security is supported the source and destination devices have access and use the same share key In the MAC layer the 802154 AES mechanism provides the proper security The mechanism protects ldquothe confidentiality integrity and authenticity of the MAC framesrdquo (Kinney 2005) An auxiliary header field in front of the MAC payload indicates if the frame is encrypted or not The MAC framesrsquo integrity is supported by calculating and using a MIC at the end of the MAC payload In addition to the AES nonce is used to provide MAC confidentiality and authenticity Figure 12 illustrates a MAC frame with security For different security aspects the MAC layer uses different mode of the AES for the encryption it uses the AES in Counter (CTR) mode and for the integrity the

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

24

CBC‐MAC Finally the combination (CCM) of the above two modes is available providing both encryption and integrity (Kinney 2005)

Figure 12 ZigBee secure frame in MAC layer [17]

In the network layer the CCM (a modified MAC layer CCM mode) is used for encryption Because the network layer uses only the CCM mode a single key is used for all different security options The network layer security message format is similar to the MAC frame it is presented in the figure 13 Finally although the network layer is responsible for securing its layer messages the above layers specify the keys and the CCM option for each frame (Kinney 2005)

Figure 13 ZigBee secure frame in network layer [17]

Security in the application layer works similar to the network and MAC layers It uses the ldquolink keyrdquo or the ldquonetwork keyrdquo to secure the message and then encapsulate it inside a set of fields similar to the network format (figure 14) Other security responsibilities that the application layer has are to provide the ZDO and the applications with device management services key establishment and key transport (ZigBee specifications 2005)

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

25

Figure 14 ZigBee secure frame in application layer [17]

The ZigBee application layer contains the manufacturer‐defined application objects the ZDO and the application sub‐layer In addition to the security responsibilities the application sub‐layer binds devices based on their duties and needs maintains the binding tables and forwards messages between them The application sub‐layer also discovers the neighbour devices for a given device The ZDO is responsible for determining the devicersquos duty in the network for communicating using binding requests and for supporting security as was mentioned above The sub‐layer that implements the actual application is the manufacturer‐defined application object (Kinney 2005) The ZigBee specifications are a great step toward the wireless sensors networks standardization They cover all the OSI layers from the physical to the application providing guidance to the developers The specifications are the product of the ZigBee alliances a consortium of companies and academic institutes Thus they not only produce the specifications but also are the first use them The next section goes beyond the layer architecture and standardization and introduces the OS field another crucial element of WSNs

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

26

3 RECEIVED SIGNAL STRENGTH (RSS) Majority of the existing methods leverage the existence of IEEE 80211 base stations with powerful radio transmit powers of approximately 100mW per base station Such radios are in a different class from the low power IEEE 802154 compliant radios that typically transmit at low power levels ranging from 52mW to 29mW [19] The wide availability of larger number of IEEE 802154 radios in the research community has revived the interest for signal strength based localization in sensor network Despite of rapidly increasing popularity of IEEE 802154 radios and signal strength localization there is a lack of detailed characterization of the fundamental factors contributing to large signal strength variation This chapter investigates some of the properties of RSS observed during testing and analysis of research work collected during the period of thesis The analysis of RSS values is needed to understand the underlying features of location dependent RSS patterns and location fingerprints An understanding of the properties of the RSS values for location fingerprinting can assist in improving the design of positioning algorithms and in deployment of indoor positioning systems

31 Parameters that affect RSS Different factors that affect the indoor RSS are the userrsquos presenceabsence hisher orientation time of the day building type and material distance from transmitter and type of radio These factors are by no means exhaustive Other possible factors are antenna orientation directionality and type Table 10 classify the parameters which affect RSS [7]

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

27

Effect on Factors Options

1 Proximity of user Userrsquos presence or absence

2 Orientation of user and terminal

North East South West

Data Collection

3 Make of Radio 4 Time of measurement Time of day amp days of

week 5 Period of measurement

Second minute hour

7 Interference Co‐channel adjacent co‐channel

Statistics

8 Building environment Small offices or large hall

Table 10 Classification of factors the affect RSS [7]

Transmitter variability Different transmitters behave differently even when they are configured exactly in the same way In practice this means that when a transmitter is configured to send packets at a power level of d dBm then the transmitter will send these packets at a power level that is very close to d dBm but not necessarily exactly equal to d dBm This can alter the received signal strength indication and thus it can lead to inaccurate distance estimation

Receiver variability The sensitivity of the receivers across different radio chips is different In practice this means that the RSSI value recorded at different receivers can be different even when all the other parameters that affect the received signal strength are kept constant

Antenna orientation Each antenna has its own radiation pattern that is not uniform In practice this means that the RSSI value recorded at the receiver for a given pair of communicating nodes and for a given distance between them varies as the pair wise antenna orientations of the transmitter and the receiver are changed

Multi‐path fading and shadowing in the RF channel In indoor environments the transmitted signals get reflected after hitting on the walls andor on other objects in the room such as furniture Both the original signal and the reflected signal reach the receiver almost at the same time since they both travel at the speed of light As a result of this the receiver is not able to

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

28

distinguish the two signals and it measures the received signal strength for both of them

32 Related work analysis A number of indoor location tracking systems have been proposed in the literature based on RF signals ultrasound infrared and some combinations of these As mentioned previously RF based positioning system are widely studied as shown in Table 2 Given a model of radio signal propagation in a building or other environment RSS can be used to estimate the distance between the transmitter and receiver Most of the algorithms (Table 2) have been evaluated in practice using 80211b compliant wireless radios and seem to produce a location error of 15ft to 20ft for an indoor network area of approximately 225ft x 144ft According to the results of this study all the localization techniques produce approximately the same location error over a range of environments Ecolocation [22] Mote Track [5] are two of the few localization algorithms that have been evaluated on a real sensor network that uses a low power wireless radio Ecolocation reports a location error of 10ft for a very small outdoor network deployment area (26ft x 49ft) Mote Track reports a location error of approximately 13ft for an indoor network deployment area of 18751ft2 In general the RSSI based localization algorithms can be divided into two broad categories the map based and the distance (or area) prediction based algorithms In the case of the map based algorithms the deployment area is divided into a large set of sample points that cover the whole area Most techniques collect one or more signal strength measurements from all visible beacon nodes and for each sample point Each sample point is then mapped to either a signal strength vector or a signal strength probability distribution This process generates an RSSI fingerprint map which is unique with respect to the beacon locations and the deployment area This fingerprint map is stored to a central base station A node that is not aware of its location collects one or more signal strength measurements from all the visible beacon nodes and creates its own RSSI fingerprint that is sent to the central base station The base station computes nodersquos location as the sample pointrsquos location whose RSSI fingerprint distance from the unknown nodersquos RSSI fingerprint is the minimum among all the sample points Despite the fact that the map based RSSI localization methods produce relatively small location errors they are not attractive mainly because they require an excessive amount of profiling data and because they are not fully distributed The detailed profiling of a building is a major holdback because it is complex time consuming and depends on beacon locations Therefore for different buildings or even for the

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

29

same building and different beacon locations different RSSI maps have to be obtained The second category of RSSI based algorithms are the distance (or area) prediction algorithms which focus on minimizing the amount of profiling data In distance prediction based methods raw RSSI data are directly mapped to distances through a signal propagation model In some cases distances are not explicitly measured but they are taken into consideration through assumptions on the underlying distributions In the case of outdoor deployments where there are no obstacles a single signal propagation model is all the profiling data required In the case of indoor deployments the same applies but now the signal propagation model might be different across different buildings with different furniture and wall arrangements Given an underlying signal propagation model raw RSSI data can be mapped to specific distances These distances can be either used directly in order to perform localization or assigned a probability and used in a learning basedprobabilistic localization algorithm Obviously the accuracy of the distance prediction based methods depends heavily on the accuracy of the signal propagation model used for translating RSSI values to distances

33 Properties of RSSI Changes in signal due to propagation indoors are difficult to predict because of dense environment and propagation effects such as reflection diffraction and scattering The multi‐path fading effect which is the result of either constructive or destructive combination of multiple signal copies at the receiver causes the received signal to fluctuate around a mean value at a particular location The received signal is usually modelled by the combined effects of large‐scale fading and small‐scale fading The large‐scale fading component describes the signal attenuation as the signal travels over a distance and is absorbed by material such as walls and floors along the way to the receiver This component predicts the mean of the RSS and usually has a lognormal distribution On the other hand the small scale fading component explains the dramatic fluctuation of the signal due to multi‐path fading On the other hand the small scale‐fading component explains the dramatic fluctuation of the signal due to multi‐path fading If there is no LOS component the small‐scale‐fading is often modelled with a Rayleigh distribution Such scenarios are commonly called NLOS If there is a LOS component the small‐scale‐fading is typically modelled by a Rician distribution Note that when the RSS is measured the measurement averages out the small‐scale‐fading effects

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

30

4 OBJECT TRACKING 41 Introduction This chapter presents the object‐tracking application the main objective of this thesis work The application is an algorithmic‐software implementation written in Embedded C which is capable of receiving data messages and generating object position estimation data The implementation of the object‐tracking application makes use of the SENTIO platform and uses the background knowledge introduced in the preceding chapters The motivation for the object‐tracking application is to determine the position of an object in indoor environments (Eg Home) The application acts as an interface which collects the raw data that the sensors generate (numerical values) It makes the proper comparisons and algorithmic processes to identify and track the object while it is static inside the area covered by the wireless sensor network Finally with the detection and tracking data it alerts the user or another system The following sections provide an overview of the object‐tracking application

42 Overview of hardware product Before describing the object tracking application it is important to know the characteristics of hardware product The SENTIO platform (figure 15) is built in modular fashion and has the following key features [8]

Flexibility This platform is very flexible in such a way that it is made easy to add or change the hardware for each sensor task depending on the requirement in the WSN For a specific platform this can be hard or sometimes even impossible to accomplish

Sensor resources Resources in a sensor module includes the ability of usage of controllerrsquos resources as much as possible such as ADC‐channels interrupts and digital Input‐Outputrsquos Local voltage regulation should also be possible since many sensors require higher supply voltages than digital Integrated Circuits do Further support of a shut‐down mechanism to power down the sensor modules when not needed

Computation and memory The processor module based on microcontroller should support many devices in a microcontroller family and support different power supply voltages enabling full range of clock frequencies All power‐down modes of the devices should be

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

31

supported The platform should support different processor modules including FPGArsquos and very simple microcontrollers

Communication The focus should be on enabling an IEEE 802154 compliant communication interface With or without ZigBee stack this will provide a flexible platform for exploring different network topologies Support for power‐down of the entire communication module should be supported

Cost and form factor High flexibility is in conflict with low cost and small size The primary objective should be flexibility but the size should be considered in order to enable sensor device integration in systems where space is limited

Figure 15 SENTIO Platform [8]

The communication subsystem of the SENTIO sensor node was designed so that the correct operation of the radio chip is ensured The radio chip is powered by the on‐board voltage regulator and thus fluctuations in the battery voltage level do not affect the operation of the communication subsystem This platform uses Chipcon CC2420 IEEE 802154 radio transceiver operates in the 24GHz ISM band and includes a digital direct sequence spread spectrum (DSSS) modem providing a spreading gain of 9dB and an effective data rate of 250Kbps It was specifically designed for low power wireless applications and supports 8 discrete power levels 0dBm minus1dBm minus3dBm minus5dBm minus7dBm minus10dBm minus15dBm and minus25dBm at which its power consumption varies from 29mW to 52mW [21] A built‐in received signal strength indicator gives an 8‐bit digital value RSSIVAL The RSSIVAL is always averaged over 8 symbol periods (128μs) and a status bit indicates when the RSSIVAL is valid (meaning that the receiver was enabled for at least 8 symbol periods) The

Antenna

Communication Module

Processing Module

Sensing Module

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

32

power P at the RF pins can be obtained directly from RSSIVAL using the following equation

P = RSSIVAL + RSSIOFFSET [dBm] where the RSSIOFFSET is found empirically from the front‐end gain and it is approximately equal to minus45dBm In the next sections when we refer to the RSSI value we refer to the RSSIVAL and not the power P unless otherwise stated

43 Experimental Testbed The experimental test bed for object tracking application is as shown in figure 16 Experiment was conducted in Masterrsquos room at Mid Sweden University A DELL laptop is connected to SENTIO platform and software were primarily used to collect samples of RSS from APrsquos The software monitors IEEE 802154 radio frequency channels which operate in the 24GHz band

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

1030

BC - 1030

AP 3 AP 1

AP 2

AP 4

BS

HEWLETT

PACKARD

Figure 16 Layout of Masterʹs Room

A three dimensional scalable testbed is used for indoor sensor network deployments The testbed illustrated in figure 16 is a 3‐D structure measuring 16m (L) x 12m (W) x 3m (H) and it is designed to host a large number of static nodes as shown in figure 16 Arrow marks in the above figure represent the location where fingerprints were taken The centrepiece of our infrastructure

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

33

is the SENTIO sensor node [8] an open‐source general purpose sensing platform designed with AT Mega 128 microprocessor and the IEEE 802154 compliant CC2420 radio from Chipcon [19]

44 Obtaining data from the sensor network The next important step in the development of the object tracking application is to obtain the data from the sensor network and send it to the base station From there the data is transmitted through a serial port to the computer that runs the object tracking application When the data arrives at the serial port the application must capture it This paragraph gives a brief explanation of how and what kind data is transmitted by the static object fixed sensors and how this data is being used to estimate the position of the object First the static object transmits Beacon packet continuously which is received by all the fixed APrsquos The beacon packet format is as shown in table 6 Then the fixed APrsquos calculates the 8‐bit value of RSSI from the transmitted beacon packet and then sends the resultant RSSI value as a form of data packet (table 7) to the BS which calculates the position and sends back to the object

45 Object tracking application Testing and Evaluation The purpose of this section is to discuss and evaluate the object tracking application Finally we are ready with the experimental testbed as shown in figure 16 As shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors placed at the corners of the masterrsquos room BS is placed at the centre of the room connected to a PC All the nodes are power connected before an actual measurement takes place At first the object is placed at random position inside the room and transmits beacon packets continuously Then these packets are received by the fixed APrsquos which calculate the RSSI from the packet and then averages it until it receives 50 beacon packets and then transmits the data to the BS (figure 17) Then the BS waits until it receives the data from all the APrsquos Once it receives from all the APrsquos it calculates the position of the object (figure 18) The application was developed to demonstrate a real world use of a WSN in object tracking In evaluating the final position obtained with respect to the actual position we conclude that the object‐tracking was successful Although

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

34

it is a research prototype object tracking application is a simple stable stand‐alone system capable of demonstrating an important application of WSN It takes raw data from the WSN aggregates it evaluates it and based on the evaluation tracks the object The accuracy of estimating the position is less than 5m Table 11 below shows a log of readings taken during test

Start

Intialize micro‐controller and transeciever in receieve mode

If received_packet=

Beacon

Average RSSI values

Increase the counter

If countergt1000050

A

No

Yes

B

B

Yes

No

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

35

A

Reset counter

Set transceiever in transmit mode

Transmit averaged RSSI to Main sensor and set it back to receive mode

10000 samples ‐ Offline

50 samples ‐ Online

Figure 17 Flow chart representing the algorithm of fixed sensors

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

36

Start

Intialize micro‐controller and transeciever in receieve mode

Received RSSI values from all fixed Sensors

Open radio map

Calculate Euclidean distance between obtained RSSI values from fixed sensors and fingerprints

in radio map

No

Yes

Bubble sort for least error in signal strength values

A

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

37

A

Get the location of the object from the radiousing the least error value

Display location of object on PC

End

Figure 18 Flow chart representing the algorithm of main sensor

Table 11 Accuracy of positioning system

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

38

5 RESULTS This chapter presents how the system parameters such as number of samples mean or median etchellipare finalized All the tests are performed in masterrsquos room at Mid Sweden University This chapter is divided into three subsections First in Section 51 it reviews results obtained from samples test Section 52 discusses one dimensional test results and finally section 53 discusses results of two dimensional tests

51 Measurement setup A Dell laptop was connected to a SENTIO platform and is loaded with software (Appendix 2) This setup was used to determine number of samples required during object tracking mean or median position estimation algorithm and one dimensional test The setup is as follows

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

103

0

BC - 1030

AP 1AP 2

BS

HEWLETT

PACKARD

Figure 19 Test bed for one dimensional test in Masters Room

Two SENTIO nodes (AP1 and AP2 as shown above) were separated by a distance of 10 meters and these sensors are known as fixed sensors which are fixed to the ceiling such that there antenna is faced towards the ground They are linear is position A BS was connected to Dell laptop and it was placed in such a way that its antenna was faced towards the ceiling Another SENTIO

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

39

node was used as an object and its orientation was same as BS Figure 19 represents the complete setup for object tracking application Mobile node was moved between these fixed ones at regular intervals of one meter and RSSI values were logged at each interval

52 Number of samples test This test was performed to determine number of samples required for accurate positioning and either mean of the obtained RSSI values or median of them Figure 19 represents the setup for number of samples test Once the setup is ready the measurements are taken as follows

1 An object was tagged with SENTIO node and was placed stationary linear to the fixed sensors

2 10000 samples (RSSI) were taken at regular intervals of 1 meter up to 10 meters

3 The sample values were logged at every interval 4 Using these logged samples I have plotted graphs such as distribution

of RSS values (figure 20 21) meanmedian at intervals of 1 meter using MATLAB

5 The mean and median for various numbers of samples is as shown in figure 22

Figure 20 Histogram plot of RSSI values at 1m and 4m

Figure 21 Histogram plot of RSSI values at 6m and 8m

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

40

Figure 22 Distance vs RSSI

From the above graphs following conclusions were made 1 From the histogram plots we can observe that the distribution of RSSI

values follow Gaussians distribution law 2 As we can see from figure 22 that median curve has drastic changes

when compared to mean curve Hence mean is considered while estimating the position

3 From the observations we conclude that the number of samples does not matter since mean curve tends to be same for various numbers of samples

4 Hence 10000 samples are considered in order to get a proper location fingerprint during off‐line phase and during online phase 50 samples are enough to estimate the position of object

53 0ne dimensional test Measurement setup for one dimensional test is as shown in figure 19 This test was conducted to check the performance of algorithm and how accurate it was At first ie during offline phase fingerprints at each interval averaging 10000 samples were taken During online phase Euclidean algorithm was used estimate the position averaging 50 samples of RSSI From the results we

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

41

can realize that the error was less than 2 meters Table 12 shows the result of location estimation in one dimensional case

Table 12 One dimensional position estimation results

54 Two dimensional test This test is the final stage of my thesis work Experimental testbed for this stage is as shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors fixed at the corners of the room and a BS at the centre of the room There are 18 pre‐determined locations where the fingerprints (RSSI) were taken The fingerprints are as shown in Appendix 1 All the readings were taken in presence of userrsquos The algorithm for position estimation is shown in Appendix 2 Position estimation test is performed as follows

Tagged object is placed at random inside the masterrsquos room All the sensors including the base station and object are switched on Using the algorithm (Appendix 2) the BS estimates the position of the object and displays it on the PC

This test was performed using various numbers of samples to test which has a better accuracy Figure 23 represents the error rate using various numbers of samples From figure 23 we can conclude that 50 numbers of samples is enough to estimate the position of object with an accuracy of 3m

55 Causes of error in location determination The randomness of RSS is the major cause of error in any indoor

positioning system that uses the RSS for location inference If there is no randomness in the RSS and its location fingerprint every indoor positioning systems of this type will have no location detection error For example I have recorded RSS at one particular location during different times of the day From figures 24 and 25 you can observe that RSS values are different at different times of day

The performance of indoor positioning systems depends greatly on the separation of location fingerprints A location fingerprint

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

42

corresponding to a location can be identified correctly if it is difficult to classify it as another fingerprint by a pattern classifier

Path loss is one of the basic signal propagation properties of the RSS that best describes attenuation of transmit signal power over a distance between an access point and a mobile device

Presence of Userrsquos or userrsquos orientation affects RSS

Figure 23 Error Rate vs Number of samples

Figure 24 RSS distribution at 214 pm

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

43

Figure 25 RSS distribution at 100pm and 1100 am

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

6 DISCUSSIONS10102006

44

6 DISCUSSIONS 61 Summary and Conclusion While empirical results and performance studies of positioning systems based on location fingerprint have been presented in this report analytical model that can be used as a framework for efficiently designing the positioning systems are not available This thesis design work uses general principles of position estimation such as position estimation of sensors grid spacing etc This design work focuses on accuracy and precision The concept of location fingerprint technique based on RSS us explained in detail in chapter 2 It is found that RSS is random It is possible to approximate the RSS as being normally distributed While the RSS random process is non‐stationary the mean is more or less constant and could be used as the fingerprint of the location Model designed for position estimation is as shown in chapter 4 Accuracy and precision are the two major performance matrices in this study Uniform grid spacing has been used From the experimental analysis I have obtained an accuracy of 3 meters and 35 precision The sensitivity analysis among the system parameters and the performance matrices were done using the model as shown in chapter 4 For instance our study found that the minimum distance between two adjacent positions also affects the positioning performance This is due to the randomness of RSS patterns induced by the ever changing indoor environment

62 Future work The research in this thesis provides a ground work for the future study of efficient design of indoor positioning system The RSSI based position estimation still has limitations First the RSSI value is time dependent Thus the future study should address this issue Second the movement of object is not considered Third orientations of sensors affect the performance which should be taken care off Fourth to improve the performance of the system we need to find a solution to deal with those incomplete or missing data during the off‐line and the on‐line phase Fifth position estimation on multi‐floor and three‐dimensional coordinates is not available

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 110102006

45

APPENDIX 1

Location Fingerprints of Masters Room Time 1315 Observation Time 20 minutes Number of Samples 10000

People Presence SNo Co-ordinates AP1 (0x0001) AP2 (0x0002) AP3 (0x0004) AP4 (0x0008)

1 11 -24 -16 -27 -33 2 13 -26 -8 -34 -31 3 15 -43 -9 -39 -25 4 32 -21 -11 -27 -33 5 61 -6 -23 -32 -28 6 64 -20 -31 -25 -34 7 35 -23 -35 -29 -38 8 37 -29 -35 -22 -27 9 39 -38 -29 -21 -27 10 67 -27 -23 -20 -23 11 611 -26 -29 -23 -30 12 613 -35 -32 -2 -31 13 513 -34 -31 -20 -26 14 11 -39 -22 -29 -17 15 212 -37 -42 -28 -31 16 -212 -38 -33 -29 -6 17 -21 -42 -29 -33 -19 18 -38 -40 -23 -35 -22

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 210102006

46

APPENDIX 2

Begin

Open radio map and raw data files for write

Intialize radio map in memory

Check whether the

Packet is data or Beacon packet If data packet

Save the data to register

Check if data is

Received from all

sensors

Estimate the position of static

Display the position on PC

End Yes

No

A

A

Yes

No

Flow chart representing the main algorithm for position estimation

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

47

APPENDIX 3

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

48

Distribution of RSSI

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

49

REFERENCES [1] T S Rappaport J H Reed and B D Woerner ldquoPosition location using wireless communications on highways of the futureʺ IEEE Commun Mag vol 34 no 10 pp33‐41 Oct 1996 [2] A M Ladd K E Bekris G Marceau A Rudys L E Kavraki and D S Wallach rdquoRobotics‐based location sensing using wireless ethernetʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ02) 2002 pp 227‐238 [3] I Ramani R Bharadwaja and P V Rangan ldquoLocation tracking for media appliances in wireless home networksʺ in Proc International Conference on Multimedia and Expo (ICMEʹ03) July 2003 pp 769‐772 [4] K Pahlavan X Li and J P Makela ldquoIndoor geolocation science and technologyʺ IEEE Commun Mag vol 40 no 2 pp 112‐118 Feb 2002 [5] Konard Lorincz and Matt Welsh ldquoMote Track A Robust Decentralized Approach to RF‐Based Location Trackingrdquo [6] Paramvir Bahl and Venkata N Padmanabham ldquoRADAR An In‐Building RF‐based User Location and Tracking Systemrdquo [7] Kamol Kaemarungsi ldquoDesign of Indoor positioning system based on location fingerprint techiniquerdquo University of Pittsburgh 2005 [8] Fredrik Linnarsson Cheng Peng and Bengt Oelmann ldquoSENTIO A Hardware Platform for Rapid Prototyping of Wireless Sensor Networksrdquo [9] S Saha K Chaudhuri D Sanghi and P Bhagwat ldquoLocation determination of a mobile device using IEEE 80211b access point signalsʺ in Proc IEEE Wireless Communications and Networking Conference (WCNCʹ03) New Orleans LA Mar 2003 pp 1987‐1992 [10] T Roos P Myllymaki H Tirri P Misikangas and J Sievanen ldquoA probabilistic approach to WLAN user location estimationʺ International Journal of Wireless Information Networks vol 9 no 3 pp 155‐164 July 2002 [11] R Battiti M Brunato and A Villani ldquoStatistical learning theory for location fingerprinting in wireless LANrsquosʺ Technical Report Oct 2002 [Online] Available httprtmscienceunitnitraquobattitiarchive86pdf [12] P Prasithsangaree P Krishnamurthy and P K Chrysanthis ldquoAn indoor position location with wireless LANsʺ in Proc IEEE International Symposium on Personal Indoor and Mobile Radio Communications (PIMRCʹ02) Lisbon Portugal Sept 2002 [13] M A Youssef A Agrawala and A U Shankar ldquoWLAN location determination via clustering and probability distributionsʺ in Proc IEEE

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

50

International Conference on Pervasive Computing and Communications (PerComʹ03) Dallas‐Fort Worth TX Mar 23‐26 2003 pp 23‐26 [14] Z Xiang S Song J Chen H Wang J Huang and X Gao ldquoA wireless LAN‐based indoor positioning technologyʺ IBM Journal of Research and Development vol 48 no 56 pp 617‐626 SeptNov 2004 [15] R Want A Hopper V Falcao and J Gibbons ldquoThe active badge location systemʺACM Transactions on Information Systems vol 40 no 1 pp 91‐102 Jan 1992 [16] N B Priyantha A Chakraborty and H Balakrishnan ldquoThe cricket location‐support systemʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ00) Boston MA Aug 2000 pp 32‐43 [17] Holger K Willig A (2005) Protocols and Architectures for Wireless Sensor Networks London [18] IEEE (2003) Part 154 Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low‐Rate Wireless Personal Area Networks (LR‐WPANs) New York [19] Chipcon CC2420 802154 compliant radio httpwwwchipconcom [20] ZigBee Specification v10 ZigBee Alliance [21] Dimitrios Lymberopoulos Quentin Lindsey and Andreas Savvides ldquoAn Empirical Analysis of Radio Signal Strength Variability in IEEE 802154 Networks using Monopole Antennasrdquo [22] Kiran Yedavalli Bhaskar Krishnamachari Sharmila Ravula and Bhaskar Srinivasan ldquoEcolocation A sequence based technique for RF localization in wireless sensor networksrdquo Robert Bosch Research and Technology Centre Palo Alto CA

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

8

2 LITERATURE REVIEW This chapter reviews the literature on wireless indoor positioning system as a means of providing an intellectual background for the thesis work First in section 21 the common components of indoor positioning system are described Then in section 22 discusses different means to classify indoor positioning systems In section 23 reviews of all relevant literature on location fingerprint technique Finally in section 24 protocols and industry standards for wireless sensor networks are explained

21 Common components of indoor positioning system Basically a wireless local positioning system consists of at least two separate hardware components a measurable unit that usually carries the major part of the system intelligence and a signal transmitterreceiver The transmitter in the simplest case is just a beacon Figure 2 represents basic hardware model of a positioning system

Figure 2 Block diagram representing hardware module

First the location sensing devices detect the signals transmitted by or received at known reference points using radio sensing technologies The sensing technique is based on signal strength level that converts the sensed signal into RSS Given a set of reference points the relative position of the MS can be derived from the signal characteristics of RSS The signal characteristics of RSS are unique for a location Then the microcontroller processes the position algorithm and estimates the position of object and displays it on PC

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

9

22 Classification of Indoor positioning systems Indoor positioning system can be categorized based on the stimulus used in particular application and measurement principle

221 Classification based on Stimulus Based on the stimulus used in an application WSN inherit certain characteristics and limitations on sensorrsquos signal The propagation delay diffraction reflection and scattering are the basic signal characteristics which affect all signal types The effective range available bandwidth regulatory constraints interference power constraints safety and cost of technology limitations The wireless signals commonly used for indoor positioning systems are infrared radio frequency and ultrasound Brief descriptions of the three major technologies are as follows

Infrared Infrared signal is an electromagnetic signal of a wavelength longer than that of visible light but shorter than that of radio waves Infrared is just below the visible spectrum of light in frequency and is radiated strongly by hot bodies Many objects such as people vehicle engines and aircraft generate and retain heat and as such are especially visible in the infrared wavelengths of light compared to objects in the background It cannot pass through walls or obstructions therefore it has a rather limited range of indoor environments Thus it requires a more sophisticated circuitry than ultrasound signals Indoor lighting interferes with this type of signal and causes problems in accurate sensing It generally has a range of around 5 m The infrared devices are usually small in size compared to ultrasound devices

Radio frequency The RF signal can penetrate through most of the building material therefore it has an excellent range in indoor environments The propagation speed is also high approximately 3108 ms There are unlicensed frequencies available freely for use This type of signal has the longest range compared to infrared and ultrasound Since the propagation waves are of higher frequency (24GHz) interference with other frequency components is less

Ultrasound Although ultrasound operates at low frequency bands (typical 40 kHz) compared to the other two signaling technologies it possesses a good precision for location sensing at a slow propagation speed of sound (343 ms) The advantages of ultrasound devices are their simplicity and that they are inexpensive However ultrasound does not penetrate walls

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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but reflects off most of the indoor obstructions It has a short range around 3 m to 10 m but has a 1 cm resolution of distance measurement The operating temperature influences the performance of ultrasound

222 Classification based on measurement principle Wireless positioning systems can be categorized by measurement techniques which are used to derive the position of mobile stations Mainly three different measurement principles are used today AOA RSS and propagation‐time based systems that can further be divided into two different sub‐classes TOA and TDOA Figure 3 shows taxonomy based on the technology and the technique classifications

Figure 3 Taxonomy of positioning system [7]

In AOA systems the position is calculated via goniometry With the use of directional antennas or antenna arrays the angle or bearing relative to points located at known positions is measured The intersection of several measured directions pointers the yields the position value The accuracy of this approach is limited by the possible directivity of the measuring aperture by shadowing and by multi‐path reflections arriving from misleading directions In TOA and TDOA techniques rely on the precision of timing between the signal transmitter and the receiver in order to use the propagation delay or TOF to calculate the distance between transmitter and receiver Therefore a precise synchronization is also very important in such systems By combining at least three distances from three reference positions triangulation can be used to estimate the mobile stationʹs location This type of technique will

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

11

require a high accuracy clock in the communication system TDOA is more practical [1] Examples of location‐sensing systems that use time of flight are GPS the Active Bats [15] and the Cricket [16] Besides these time delay‐based techniques the distance between transmitter and receiver can also be determined from signal strength attenuation and direct distance measurement (such as dead reckoning) Fingerprinting or Location Pattern Matching this technique generally requires only measurement of RSS or other non‐geometric features at several locations to form a database of location fingerprints To estimate the mobile location the system needs to first measure the received signal strength at particular locations and then search for the pattern or fingerprint with the closest match in the database This technique does not require the mobile station to see at least three base stations or access points in order to determine the location The disadvantage of this technique is that it is very time‐consuming to perform an exhaustive data collection for a wide area network such as in outdoor positioning systems

23 Indoor local positioning using ZigBee based on RSSI This section reviews relevant RF‐based indoor positioning systems which can be used to locate stationary objects and track mobile users The impressive growth of IEEE 802154 wireless PANs (WPAN) in recent years suggests an interesting future for the location fingerprinting technique The system is quite flexible because system designers can select whether to have a centralized positioning server or let the mobile to determine its own position However the fingerprinting technique requires a training phase (off‐line phase) to collect location fingerprints for all positions in the operating area before the actual deployment (on‐line phase) [7] The following discussion is divided according to the positioning system components First the common form of location fingerprinting and its relationship with physical locations are explained Finally location estimation algorithm is reviewed

231 Location Fingerprint A location fingerprint based on RF characteristics such as RSS is the basis for representing a unique location It is created under the assumption that each position or location inside a building has unique RF signature [4] Generally a fingerprint F is labelled with a location information L The location fingerprints and their labels (eg location information) are maintained in

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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database and used during the on‐line phase to estimate the location The label and fingerprint are usually denoted as a tuple of (L F) Battiti et al [11] point out that the location information L for indoor location can be recorded in two forms as either a tuple of coordinates or an indicator variable The tuple of real coordinates can vary from one dimension to five dimensions which include the three dimension space and two orientation variables expressed in spherical coordinates For instance a location information of a two‐dimension system with an orientation could be expressed as a triplet L = (x y d) | x y є R 2 d є North East South West The RSS was found to be more location‐dependent than the SNR because the noise component is rather random in nature However the RSS itself fluctuates over time for each access point and location Each RSS element can be considered as a random variable therefore it can be captured by recording its descriptive statistics parameters approximating its distribution or maintaining the whole measurement dataset These approaches of RSS representation result in different procedures for location estimation algorithm Regardless of the approach the location fingerprint is usually denoted as an array or vector of signal strength (random variables) received at any position in the location‐based service area The size of the vector is determined by the number of access points that can be heard To create a basis fingerprint such as in a number of samples of vectors of signal strength are collected over a window of time for each position This basis is called a prototype Then the average RSS of each access point is calculated and recorded as an element in the location fingerprint For an area that can receive signals from N access points the location fingerprint can be expressed as a vector of average RSS elements βi

F = (β1 β2hellip βN)T Besides the basis location fingerprint the samples of location fingerprint measured during the on‐line phase are also important for the system that tracks the mobile object The number of samples should be selected appropriately to represent the location fingerprint for the mobile application A step in statistical analysis method called pre‐processing is another important issue needed to be considered because it can impact the estimation of dependency between location fingerprint and the location information easier The pre‐processing refers to a step that cleans the raw data (in this case the training set) before any further operations or analysis The cleaning may consist of encoding dimensionality reduction (reduce unnecessary elements) feature extractionselection clustering and outlier elimination Roos et al [10] point out that the pre‐processing enables faster location estimation and

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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reduce the noise from the training data The process that creates the basis of location fingerprint discussed above could be considered as a part of pre‐processing

232 Location Estimation Algorithm Location estimation algorithms or positioning algorithms are procedures that exploit dependency between location information and location fingerprint basis in order to determine a position or location from samples of RSS signals Position algorithms can be classified into deterministic and probabilistic types based on the approaches that model the relationship between location fingerprints and location information The deterministic type of algorithms are those that based in the nearest neighbour classifiers and neural network classifiers The probabilistic type of algorithms is those that are based on the Bayesian inference and statistical learning theory such as support vector machines (SVR) The algorithm used in this project is discussed briefly

Euclidean distance method The Euclidean distance method is a deterministic approach of position estimation because they require only a set of constant location fingerprints which includes mean vectors In order to determine the location a form of discriminate function is used to classify a sample of RSS fingerprint into a position The mean or average RSS vector is a centre of a mass which represents each class of location fingerprint Suppose a set of l location fingerprints is denoted by F1 F2hellip Fl exist and each fingerprint has a one‐to‐one mapping to a set of positions L1 L2hellip Ll A sample of an RSS fingerprint measured during an on‐line phase is denoted as S which can be another mean or average RSS vector of small window of RSS samples Assuming that an indoor positioning system only considers the average RSS from N access points as a location fingerprint the sample of RSS vector is S = (s1 s2 helliphellip sN)T and each location fingerprint i in the database can be expressed as Fi = (β1 β2helliphellip βN)T The simplest closeness metric is a distance measurement in signal space denoted as the Dist () function [6] Thus the simple procedure of nearest neighbour algorithm is expressed as picking the fingerprint j that has the shortest signal distance

Dist(S Fj) le Dist(S Fk) for all k ne j A generalized weighted distance Lp summarized by [12] can be used to calculate different forms of distance in signal space as

Lp = 1N pN

i

pPiSiWi 1

1)||1(sum

=

minus

where N is the number of access points wi is a weighting factor (wi le 1) and p is the norm parameter starting from 1 The weighting factor wi is a bias

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

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parameter that can demote or promote an important RSS component in the fingerprint

24 Protocols and Industry standards for WSN Although many applications for WSN are proposed the communication protocols supporting them remain mainly unexplored and diverged This is because the area of interest is new each implementation is application specific and the components of WSNrsquos have a lot of constraints Wireless sensor networks use the wireless medium to communicate However the traditional communication protocols that support wireless communications especially ad‐hoc mesh networks may not be well suited for them WSN communication is an active research area and many algorithms and protocols have already been proposed This section introduces two proposed standards the IEEE 802154 and the ZigBee The IEEE 802154 covers the physical layer and the MAC layer of low‐rate WPAN The ZigBee is ldquoan emerging standard that is based on the IEEE 802154 and adds network construction (star networks peer‐to‐peermesh networks and cluster‐tree networks) application services and morerdquo

241 IEEE 802154 Holger and Willig (2005) [17] mention home automation home networking and home security as possible applications for IEEE 802154 They add that ldquomost of these applications require only low‐to‐medium bitrates (up to some few hundreds of kbps) moderate average delays without too stringent delay guarantees and for certain nodes it is highly desirable to reduce the energy consumption to a minimumrdquo The IEEE 802154 standard (2003) defines the device types that can be used in a LR‐WPAN A device can be a FFD or a RFD The RFD can be used in simple applications in which they do not need to transmit large amounts of data and they have to communicate only with a specific FFD The FFD can work as a PAN coordinator as a coordinator or as a simple device It can communicate with either another FFD or a RFD In keeping with the application requirements the LR‐WPAN operates in a star or peer‐to‐peer topology (figure 4) In the star topology the RFD communicates with a single controller the PAN coordinator The PAN coordinator can perform the same function as the RFD but it is also responsible for controlling the PAN ldquoit initiates terminates or routes communication around the networkrdquo [18] The peer‐to‐peer topology

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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supports ad‐hoc mesh multi‐hop networking Any device in the peer‐to‐peer topology can communicate with any other device within its communication range however this topology also has a PAN coordinator All the devices in a LR‐WPAN have a unique 64‐bit address This or a short address allocated by the PAN coordinator can be used inside a PAN Additionally each PAN has a unique identifier The combination of the PAN identifier and the sort addresses allows communication across different PANs (IEEE 802154 Standard 2003)

Figure 4 Star and peer‐to‐peer topologies in LR‐WPAN [18]

The LR‐WPAN based on the OSI seven‐layer model has the layered architecture presented in figure 5 The application and the network layer are the upper layers in the LR‐WPAN architecture but are outside the scope of the IEEE 802154 standard Only the physical layer ldquowhich contains the RF transceiver along with its low‐level control mechanismrdquo and the MAC layer ldquothat provides access to the physical channelrdquo are included in the standard and will be introduced in the following sections Finally the Type I 8022 LLC and the SSCS are intermediate sub layers supporting communication with the above layers (IEEE 802154 Standard 2003)

Physical Layer The 802154 standard specifies two different services that the PHY provides The PHY data service controls the radio and thus the transmission and reception of the PPDUs In addition the management service performs ED in the channel The management service also performs CCA before sending the messages and provides LQI for the received packets

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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Figure 5 WPAN architecture [18]

Three different bands are defined by the standard The 868‐8686 MHz for Europe the 902‐928 MHz for North America and the 2400‐24835 MHz worldwide All of them belong in the ISM radio bands and they are using DSSS Each of the bands supports different a data rate and it uses a different modulation technique chip rate and number of channels Moreover if the system does not use the 2450 MHz frequency it operates in both the 802 MHz and 902 MHz frequencies Table 3 summarizes the supported bands and data rates (IEEE 802154 standard 2003)

PHY (MHz)

Frequency Band (MHz) Modulation

Bit Rate Number of Channels

868-8686 BPSK 20 1 868 915 902-928 BPSK 40 10

2450 2400-24835 O-QPSK 230 16 Table 3 Frequency bands and data rates for IEEE 802154 based on the IEEE

802154 standard [18] The physical layer uses PPDU packets to communicate Table 4 demonstrates its structure The LSB is always transmitted and received first The SHR contains the preamble and the SFD fields which helps receiver

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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17

synchronization The 8‐bytes preamble contains only zero and is used for synchronization The SFD contains a specific sequence of one and zeros and specifies the beginning of the frame The PHR contains the payload length Packets with a length of 9 or more bytes are MPDU as the next section further explains Packets with length 5 are used for MPDU acknowledgements The payload part of the PPDU encloses the MAC layer packet Finally the PPDU size can be up to 127 bytes [18]

4 bytes 1 byte 1 byte Variable

Preamble SFD Frame length

(7 bits) Reserved

(1 bit) Payload (PSU)

SHR PHR PHY Payload Table 4 Format based on the IEEE 802154 standard [18]

MAC Layer The MAC layer is the interface between the SSCS and the PHY layer Similar to the PHY layer the MAC layer supports two services The MAC data service is responsible for the transmission and reception of the MPDUs through the PHY data service The MAC management service if the device is a coordinator manages the network beacons It is also responsible for PAN association and disassociation frame validation and acknowledgment providing ldquoa reliable link between two peer MAC entitiesrdquo In addition it uses the CSMA‐CA for channel access and handles and maintains the GTS mechanism Finally it supports device security [18] The IEEE 802154 standard defines four different frame types the beacon data acknowledgment and MAC command frame All frame types are based on the general MAC frame format (table 5) The frame control field describes and specifies the above different frame types Every MAC frame comprises a MHR which consists of a frame control sequence number and the information field It also contains the MAC payload different frame types have different MAC payload fields The acknowledgment type does not have a payload Finally each frame includes a MFR which contains a FCS The data in the MPDU follows the same order as the PPDU the least significant bits are left in the frame and are transited first Octets2 1 02 028 02 028 Variable 2

Destination PAN Identifier

Destination Address

Source PAN

Identifier Source Address Frame

Control Sequence Number Addressing Field

Frame Payload FCS

MHR MAC

Payload MFRTable 5 General MAC frame format IEEE 802154 standard [18]

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Table 6 7 8 and 9 present the four different MAC frame types The beacon frame is transmitted periodically by the PAN coordinator It provides information about the network management through the super frame and GTS fields which are analyzed later in the section It also synchronizes the network devices and indicates the proper communication period for them The data frame payload encapsulates data from the higher layers When a device receives a packet it is not obliged to response with an acknowledgement packet Finally the command frame identifier and command payload fields of the command frame are used for communication between the network devices The command identifier specifies actions like association disassociation and data GTS or beacon request Octets2 1 410 2 Variable Variable Variable 2

Frame Control

Sequence Number

Addressing Fields

Super frame Specification

GTS fields

Pending Addressing

Fields Beacon Payload FCS

MHR MAC Payload MFRTable 6 Beacon frame format [18]

Octets 2 1 Variable 2

Frame Control

Sequence Number

Addressing Field

Data Payload FCS

MHR MAC

Payload MFR Table 7 Data frame format [18]

Octets 2 1 2

Frame Control

Sequence Number FCS

MHR MFR Table 8 Acknowledgement frame format [18]

Octets2 1 1 Variable 2

Frame Control

Sequence Number

Addressing Fields

Command Frame Identifier

Command Payload FCS

MHR MAC Payload MFR Table 9 Command frame format [18]

In the LR‐WPAN every PAN has its own coordinator The PAN coordinator manages the communication in the local area it has two options to use or not use the super frame structure The super frame (figure 6) uses network beacons If the coordinator does not want to use a super frame structure it suspends the beacon transmission However the beacon is important for device association and disassociation If the coordinator wishes to maintain close communication control in the PAN and to support low‐latency devices it usually uses the super frame A super frame determines a specific time

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period beacons bound it The beacon is transmitted in the first of the sixteen equal time slots that the super frame has It is used to ldquosynchronize the attached devices to identify the PAN and to describe the structure of the super framerdquo The super frame can have active and inactive periods All the communications have to be finished inside the super frame period In the inactive periods the devices can switch to the sleep mode but they have to be ready for the next beacon The active period is further divided into a CAP and a CFP During the CAP any device can communicate competing with the other devices in the PAN using slotted CSMA‐CA The CFP contains GTSs and follows the CAP A CFP may maintain up to seven GTSs and each GTS can reserve more than one time slot However the CAP should always be sufficient to allow new devices t join the PAN The GTSs are allocated to specific devices All the transactions must be completed inside the assigned time period (CAP GTS and CFP) (IEEE 802154 standard 2003)

Figure 6 Example of super frame structure [18]

In accordance with the 802154 standard (2003) three different types of data‐transfer exist In addition the types differ if the coordinator uses or does not beacons Data transfer from a device to the PAN coordinator is the first type (figure 7) For a ldquonon‐beacon‐enable networkrdquo it first senses the medium by using ldquoun‐slotted CSMA‐CArdquo and then a simple transmit to the data frame In a ldquobeacon‐enabled networkrdquo the sender waits for the beacon when it finds it ldquothe device synchronizes to the super fame structurerdquo In the specified time frame the sender again senses the medium by using ldquoslotted CSMA‐CArdquo and transmits the data to the coordinator In either case the coordinator has the option to acknowledge or not acknowledge the data reception after that the transaction is completed Data transfer from the coordinator is the next type described in the standard (figure 8) In a beacon‐enabled network the coordinator indicates a pending message through the beacon The messagersquos target device receives the beacon and if the message is pending it responses with a MAC command request

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message using slotted CSMA‐CA the coordinator may or may not acknowledge the command message and through slotted CSMA‐CA sends the pending message The device acknowledges the received message After that the coordinator removes the message from the beaconrsquos pending list and completes the transaction In a non‐beacon enable network the device periodically sends a MAC command‐request frame to the coordinator The coordinator acknowledges the data request Then by using un‐slotted CSMA‐CA if it has a pending message it transmits it If it has not it respond with a data frame with a ldquozero‐length payloadrdquo To complete the transaction the device acknowledges the data reception

Figure 7 Communication from a device to a PAN coordinator in (a) a beacon‐enabled network and (b) a non‐beacon enabled network [18]

Peer‐to‐peer is the last type of data transfer In this situation the devices are free to communicate with any other device within their communication range In a peer‐to‐peer PAN the devices can ldquoeither receive constantly or synchronize with each otherrdquo If they are receiving constantly to transmit data they use un‐slotted CSAM‐CA In the second case synchronization must be achieved first (IEEE 802154 standard [IEEE 2003]) The IEEE 802154 (2003) standard establishes MAC and PHY standards for low‐cost low‐power and high‐density node deployments In addition to the above PHY and MAC characteristics IEEE 802154 provides a security baseline including ldquothe ability to maintain an ACL and use symmetric cryptographyrdquo for data encryption The algorithm that is used for encryption is the AES However the higher level layers decide when security is need The upper layers are in general responsible for device authentication and key management The next section introduces the ZigBee standard which

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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encapsulates the IEEE 802154 and provides additional standardization for the higher levels

Figure 8 Communication from a PAN coordinator to a device in (a) a beacon‐enabled network and (b) a non‐beacon enabled network [18]

242 ZigBee Heily (2004) defines ZigBee as ldquoa rapidly growing worldwide non‐profit industry consortiumrdquo whose mission is ldquoto define a reliable cost‐effective low‐power wirelessly networked monitoring and control product based on an open global standardrdquo Figure 9 illustrates the areas of interest for different wireless communication standards

Figure 9 Overview of the coverage for different wireless communication

standards [17]

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ZigBee a new standard which became publicly available in June 2005 is based on the IEEE 802154 standard It expands the IEEE 802154 by adding the framework for ldquothe network security and applicationrdquo (Craig 2005) Figure 10 presents the IEEE 802154ZigBee stack and the areas of responsibilities

Figure 10 IEEE 802154ZigBee stack [20]

Craig (2005) mentions three networking topologies that the standard covers the star mesh and cluster tree (figure 11) The ZigBee standard works on top of the IEEE 802154 addressing schema by using the standard 64‐bit and the short 16‐bit addressing Kinney (2005) summarizes the ZigBee network layer responsibilities the successful establishment of a new the network and successful new device configuration addressing assignment network synchronization frames security and message routing ZigBee further distinguishes the concept of the physical devices (RFD FFD) by using the notion of ldquological devicesrdquo ldquoZigBee Coordinatorrdquo is the first type of logical devices It is responsible for initializing maintaining and managing the network Under the coordinator in the network hierarchy is the ldquoZigBee routerrdquo which is responsible for controlling the message routing between the nodes Finally the ldquoZigBee End Devicerdquo acts as the end point of the network

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23

structure The tasks that an end device can perform are specified in the ldquoApplication Profilesrdquo (Craig 2005)

Figure 11 IEEE 802154ZigBee stack [17]

The ZigBee specifications (2005) summarize the security services provided by ZigBee ldquokey establishment key transport frame protection and device managementrdquo ZigBee builds its security mechanism using symmetric key cryptography The security services also depend on the associated layer thus as the figure 10 shows the security mechanism covers the network and the application layer In addition if a MAC frame needs security protection the MAC layer is able to secure it Moreover the notion of end‐to‐end security is supported the source and destination devices have access and use the same share key In the MAC layer the 802154 AES mechanism provides the proper security The mechanism protects ldquothe confidentiality integrity and authenticity of the MAC framesrdquo (Kinney 2005) An auxiliary header field in front of the MAC payload indicates if the frame is encrypted or not The MAC framesrsquo integrity is supported by calculating and using a MIC at the end of the MAC payload In addition to the AES nonce is used to provide MAC confidentiality and authenticity Figure 12 illustrates a MAC frame with security For different security aspects the MAC layer uses different mode of the AES for the encryption it uses the AES in Counter (CTR) mode and for the integrity the

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

24

CBC‐MAC Finally the combination (CCM) of the above two modes is available providing both encryption and integrity (Kinney 2005)

Figure 12 ZigBee secure frame in MAC layer [17]

In the network layer the CCM (a modified MAC layer CCM mode) is used for encryption Because the network layer uses only the CCM mode a single key is used for all different security options The network layer security message format is similar to the MAC frame it is presented in the figure 13 Finally although the network layer is responsible for securing its layer messages the above layers specify the keys and the CCM option for each frame (Kinney 2005)

Figure 13 ZigBee secure frame in network layer [17]

Security in the application layer works similar to the network and MAC layers It uses the ldquolink keyrdquo or the ldquonetwork keyrdquo to secure the message and then encapsulate it inside a set of fields similar to the network format (figure 14) Other security responsibilities that the application layer has are to provide the ZDO and the applications with device management services key establishment and key transport (ZigBee specifications 2005)

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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25

Figure 14 ZigBee secure frame in application layer [17]

The ZigBee application layer contains the manufacturer‐defined application objects the ZDO and the application sub‐layer In addition to the security responsibilities the application sub‐layer binds devices based on their duties and needs maintains the binding tables and forwards messages between them The application sub‐layer also discovers the neighbour devices for a given device The ZDO is responsible for determining the devicersquos duty in the network for communicating using binding requests and for supporting security as was mentioned above The sub‐layer that implements the actual application is the manufacturer‐defined application object (Kinney 2005) The ZigBee specifications are a great step toward the wireless sensors networks standardization They cover all the OSI layers from the physical to the application providing guidance to the developers The specifications are the product of the ZigBee alliances a consortium of companies and academic institutes Thus they not only produce the specifications but also are the first use them The next section goes beyond the layer architecture and standardization and introduces the OS field another crucial element of WSNs

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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3 RECEIVED SIGNAL STRENGTH (RSS) Majority of the existing methods leverage the existence of IEEE 80211 base stations with powerful radio transmit powers of approximately 100mW per base station Such radios are in a different class from the low power IEEE 802154 compliant radios that typically transmit at low power levels ranging from 52mW to 29mW [19] The wide availability of larger number of IEEE 802154 radios in the research community has revived the interest for signal strength based localization in sensor network Despite of rapidly increasing popularity of IEEE 802154 radios and signal strength localization there is a lack of detailed characterization of the fundamental factors contributing to large signal strength variation This chapter investigates some of the properties of RSS observed during testing and analysis of research work collected during the period of thesis The analysis of RSS values is needed to understand the underlying features of location dependent RSS patterns and location fingerprints An understanding of the properties of the RSS values for location fingerprinting can assist in improving the design of positioning algorithms and in deployment of indoor positioning systems

31 Parameters that affect RSS Different factors that affect the indoor RSS are the userrsquos presenceabsence hisher orientation time of the day building type and material distance from transmitter and type of radio These factors are by no means exhaustive Other possible factors are antenna orientation directionality and type Table 10 classify the parameters which affect RSS [7]

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

27

Effect on Factors Options

1 Proximity of user Userrsquos presence or absence

2 Orientation of user and terminal

North East South West

Data Collection

3 Make of Radio 4 Time of measurement Time of day amp days of

week 5 Period of measurement

Second minute hour

7 Interference Co‐channel adjacent co‐channel

Statistics

8 Building environment Small offices or large hall

Table 10 Classification of factors the affect RSS [7]

Transmitter variability Different transmitters behave differently even when they are configured exactly in the same way In practice this means that when a transmitter is configured to send packets at a power level of d dBm then the transmitter will send these packets at a power level that is very close to d dBm but not necessarily exactly equal to d dBm This can alter the received signal strength indication and thus it can lead to inaccurate distance estimation

Receiver variability The sensitivity of the receivers across different radio chips is different In practice this means that the RSSI value recorded at different receivers can be different even when all the other parameters that affect the received signal strength are kept constant

Antenna orientation Each antenna has its own radiation pattern that is not uniform In practice this means that the RSSI value recorded at the receiver for a given pair of communicating nodes and for a given distance between them varies as the pair wise antenna orientations of the transmitter and the receiver are changed

Multi‐path fading and shadowing in the RF channel In indoor environments the transmitted signals get reflected after hitting on the walls andor on other objects in the room such as furniture Both the original signal and the reflected signal reach the receiver almost at the same time since they both travel at the speed of light As a result of this the receiver is not able to

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

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28

distinguish the two signals and it measures the received signal strength for both of them

32 Related work analysis A number of indoor location tracking systems have been proposed in the literature based on RF signals ultrasound infrared and some combinations of these As mentioned previously RF based positioning system are widely studied as shown in Table 2 Given a model of radio signal propagation in a building or other environment RSS can be used to estimate the distance between the transmitter and receiver Most of the algorithms (Table 2) have been evaluated in practice using 80211b compliant wireless radios and seem to produce a location error of 15ft to 20ft for an indoor network area of approximately 225ft x 144ft According to the results of this study all the localization techniques produce approximately the same location error over a range of environments Ecolocation [22] Mote Track [5] are two of the few localization algorithms that have been evaluated on a real sensor network that uses a low power wireless radio Ecolocation reports a location error of 10ft for a very small outdoor network deployment area (26ft x 49ft) Mote Track reports a location error of approximately 13ft for an indoor network deployment area of 18751ft2 In general the RSSI based localization algorithms can be divided into two broad categories the map based and the distance (or area) prediction based algorithms In the case of the map based algorithms the deployment area is divided into a large set of sample points that cover the whole area Most techniques collect one or more signal strength measurements from all visible beacon nodes and for each sample point Each sample point is then mapped to either a signal strength vector or a signal strength probability distribution This process generates an RSSI fingerprint map which is unique with respect to the beacon locations and the deployment area This fingerprint map is stored to a central base station A node that is not aware of its location collects one or more signal strength measurements from all the visible beacon nodes and creates its own RSSI fingerprint that is sent to the central base station The base station computes nodersquos location as the sample pointrsquos location whose RSSI fingerprint distance from the unknown nodersquos RSSI fingerprint is the minimum among all the sample points Despite the fact that the map based RSSI localization methods produce relatively small location errors they are not attractive mainly because they require an excessive amount of profiling data and because they are not fully distributed The detailed profiling of a building is a major holdback because it is complex time consuming and depends on beacon locations Therefore for different buildings or even for the

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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10102006

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same building and different beacon locations different RSSI maps have to be obtained The second category of RSSI based algorithms are the distance (or area) prediction algorithms which focus on minimizing the amount of profiling data In distance prediction based methods raw RSSI data are directly mapped to distances through a signal propagation model In some cases distances are not explicitly measured but they are taken into consideration through assumptions on the underlying distributions In the case of outdoor deployments where there are no obstacles a single signal propagation model is all the profiling data required In the case of indoor deployments the same applies but now the signal propagation model might be different across different buildings with different furniture and wall arrangements Given an underlying signal propagation model raw RSSI data can be mapped to specific distances These distances can be either used directly in order to perform localization or assigned a probability and used in a learning basedprobabilistic localization algorithm Obviously the accuracy of the distance prediction based methods depends heavily on the accuracy of the signal propagation model used for translating RSSI values to distances

33 Properties of RSSI Changes in signal due to propagation indoors are difficult to predict because of dense environment and propagation effects such as reflection diffraction and scattering The multi‐path fading effect which is the result of either constructive or destructive combination of multiple signal copies at the receiver causes the received signal to fluctuate around a mean value at a particular location The received signal is usually modelled by the combined effects of large‐scale fading and small‐scale fading The large‐scale fading component describes the signal attenuation as the signal travels over a distance and is absorbed by material such as walls and floors along the way to the receiver This component predicts the mean of the RSS and usually has a lognormal distribution On the other hand the small scale fading component explains the dramatic fluctuation of the signal due to multi‐path fading On the other hand the small scale‐fading component explains the dramatic fluctuation of the signal due to multi‐path fading If there is no LOS component the small‐scale‐fading is often modelled with a Rayleigh distribution Such scenarios are commonly called NLOS If there is a LOS component the small‐scale‐fading is typically modelled by a Rician distribution Note that when the RSS is measured the measurement averages out the small‐scale‐fading effects

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

30

4 OBJECT TRACKING 41 Introduction This chapter presents the object‐tracking application the main objective of this thesis work The application is an algorithmic‐software implementation written in Embedded C which is capable of receiving data messages and generating object position estimation data The implementation of the object‐tracking application makes use of the SENTIO platform and uses the background knowledge introduced in the preceding chapters The motivation for the object‐tracking application is to determine the position of an object in indoor environments (Eg Home) The application acts as an interface which collects the raw data that the sensors generate (numerical values) It makes the proper comparisons and algorithmic processes to identify and track the object while it is static inside the area covered by the wireless sensor network Finally with the detection and tracking data it alerts the user or another system The following sections provide an overview of the object‐tracking application

42 Overview of hardware product Before describing the object tracking application it is important to know the characteristics of hardware product The SENTIO platform (figure 15) is built in modular fashion and has the following key features [8]

Flexibility This platform is very flexible in such a way that it is made easy to add or change the hardware for each sensor task depending on the requirement in the WSN For a specific platform this can be hard or sometimes even impossible to accomplish

Sensor resources Resources in a sensor module includes the ability of usage of controllerrsquos resources as much as possible such as ADC‐channels interrupts and digital Input‐Outputrsquos Local voltage regulation should also be possible since many sensors require higher supply voltages than digital Integrated Circuits do Further support of a shut‐down mechanism to power down the sensor modules when not needed

Computation and memory The processor module based on microcontroller should support many devices in a microcontroller family and support different power supply voltages enabling full range of clock frequencies All power‐down modes of the devices should be

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

31

supported The platform should support different processor modules including FPGArsquos and very simple microcontrollers

Communication The focus should be on enabling an IEEE 802154 compliant communication interface With or without ZigBee stack this will provide a flexible platform for exploring different network topologies Support for power‐down of the entire communication module should be supported

Cost and form factor High flexibility is in conflict with low cost and small size The primary objective should be flexibility but the size should be considered in order to enable sensor device integration in systems where space is limited

Figure 15 SENTIO Platform [8]

The communication subsystem of the SENTIO sensor node was designed so that the correct operation of the radio chip is ensured The radio chip is powered by the on‐board voltage regulator and thus fluctuations in the battery voltage level do not affect the operation of the communication subsystem This platform uses Chipcon CC2420 IEEE 802154 radio transceiver operates in the 24GHz ISM band and includes a digital direct sequence spread spectrum (DSSS) modem providing a spreading gain of 9dB and an effective data rate of 250Kbps It was specifically designed for low power wireless applications and supports 8 discrete power levels 0dBm minus1dBm minus3dBm minus5dBm minus7dBm minus10dBm minus15dBm and minus25dBm at which its power consumption varies from 29mW to 52mW [21] A built‐in received signal strength indicator gives an 8‐bit digital value RSSIVAL The RSSIVAL is always averaged over 8 symbol periods (128μs) and a status bit indicates when the RSSIVAL is valid (meaning that the receiver was enabled for at least 8 symbol periods) The

Antenna

Communication Module

Processing Module

Sensing Module

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

32

power P at the RF pins can be obtained directly from RSSIVAL using the following equation

P = RSSIVAL + RSSIOFFSET [dBm] where the RSSIOFFSET is found empirically from the front‐end gain and it is approximately equal to minus45dBm In the next sections when we refer to the RSSI value we refer to the RSSIVAL and not the power P unless otherwise stated

43 Experimental Testbed The experimental test bed for object tracking application is as shown in figure 16 Experiment was conducted in Masterrsquos room at Mid Sweden University A DELL laptop is connected to SENTIO platform and software were primarily used to collect samples of RSS from APrsquos The software monitors IEEE 802154 radio frequency channels which operate in the 24GHz band

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

1030

BC - 1030

AP 3 AP 1

AP 2

AP 4

BS

HEWLETT

PACKARD

Figure 16 Layout of Masterʹs Room

A three dimensional scalable testbed is used for indoor sensor network deployments The testbed illustrated in figure 16 is a 3‐D structure measuring 16m (L) x 12m (W) x 3m (H) and it is designed to host a large number of static nodes as shown in figure 16 Arrow marks in the above figure represent the location where fingerprints were taken The centrepiece of our infrastructure

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

33

is the SENTIO sensor node [8] an open‐source general purpose sensing platform designed with AT Mega 128 microprocessor and the IEEE 802154 compliant CC2420 radio from Chipcon [19]

44 Obtaining data from the sensor network The next important step in the development of the object tracking application is to obtain the data from the sensor network and send it to the base station From there the data is transmitted through a serial port to the computer that runs the object tracking application When the data arrives at the serial port the application must capture it This paragraph gives a brief explanation of how and what kind data is transmitted by the static object fixed sensors and how this data is being used to estimate the position of the object First the static object transmits Beacon packet continuously which is received by all the fixed APrsquos The beacon packet format is as shown in table 6 Then the fixed APrsquos calculates the 8‐bit value of RSSI from the transmitted beacon packet and then sends the resultant RSSI value as a form of data packet (table 7) to the BS which calculates the position and sends back to the object

45 Object tracking application Testing and Evaluation The purpose of this section is to discuss and evaluate the object tracking application Finally we are ready with the experimental testbed as shown in figure 16 As shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors placed at the corners of the masterrsquos room BS is placed at the centre of the room connected to a PC All the nodes are power connected before an actual measurement takes place At first the object is placed at random position inside the room and transmits beacon packets continuously Then these packets are received by the fixed APrsquos which calculate the RSSI from the packet and then averages it until it receives 50 beacon packets and then transmits the data to the BS (figure 17) Then the BS waits until it receives the data from all the APrsquos Once it receives from all the APrsquos it calculates the position of the object (figure 18) The application was developed to demonstrate a real world use of a WSN in object tracking In evaluating the final position obtained with respect to the actual position we conclude that the object‐tracking was successful Although

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

34

it is a research prototype object tracking application is a simple stable stand‐alone system capable of demonstrating an important application of WSN It takes raw data from the WSN aggregates it evaluates it and based on the evaluation tracks the object The accuracy of estimating the position is less than 5m Table 11 below shows a log of readings taken during test

Start

Intialize micro‐controller and transeciever in receieve mode

If received_packet=

Beacon

Average RSSI values

Increase the counter

If countergt1000050

A

No

Yes

B

B

Yes

No

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

35

A

Reset counter

Set transceiever in transmit mode

Transmit averaged RSSI to Main sensor and set it back to receive mode

10000 samples ‐ Offline

50 samples ‐ Online

Figure 17 Flow chart representing the algorithm of fixed sensors

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

36

Start

Intialize micro‐controller and transeciever in receieve mode

Received RSSI values from all fixed Sensors

Open radio map

Calculate Euclidean distance between obtained RSSI values from fixed sensors and fingerprints

in radio map

No

Yes

Bubble sort for least error in signal strength values

A

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

37

A

Get the location of the object from the radiousing the least error value

Display location of object on PC

End

Figure 18 Flow chart representing the algorithm of main sensor

Table 11 Accuracy of positioning system

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

38

5 RESULTS This chapter presents how the system parameters such as number of samples mean or median etchellipare finalized All the tests are performed in masterrsquos room at Mid Sweden University This chapter is divided into three subsections First in Section 51 it reviews results obtained from samples test Section 52 discusses one dimensional test results and finally section 53 discusses results of two dimensional tests

51 Measurement setup A Dell laptop was connected to a SENTIO platform and is loaded with software (Appendix 2) This setup was used to determine number of samples required during object tracking mean or median position estimation algorithm and one dimensional test The setup is as follows

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

103

0

BC - 1030

AP 1AP 2

BS

HEWLETT

PACKARD

Figure 19 Test bed for one dimensional test in Masters Room

Two SENTIO nodes (AP1 and AP2 as shown above) were separated by a distance of 10 meters and these sensors are known as fixed sensors which are fixed to the ceiling such that there antenna is faced towards the ground They are linear is position A BS was connected to Dell laptop and it was placed in such a way that its antenna was faced towards the ceiling Another SENTIO

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

39

node was used as an object and its orientation was same as BS Figure 19 represents the complete setup for object tracking application Mobile node was moved between these fixed ones at regular intervals of one meter and RSSI values were logged at each interval

52 Number of samples test This test was performed to determine number of samples required for accurate positioning and either mean of the obtained RSSI values or median of them Figure 19 represents the setup for number of samples test Once the setup is ready the measurements are taken as follows

1 An object was tagged with SENTIO node and was placed stationary linear to the fixed sensors

2 10000 samples (RSSI) were taken at regular intervals of 1 meter up to 10 meters

3 The sample values were logged at every interval 4 Using these logged samples I have plotted graphs such as distribution

of RSS values (figure 20 21) meanmedian at intervals of 1 meter using MATLAB

5 The mean and median for various numbers of samples is as shown in figure 22

Figure 20 Histogram plot of RSSI values at 1m and 4m

Figure 21 Histogram plot of RSSI values at 6m and 8m

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

40

Figure 22 Distance vs RSSI

From the above graphs following conclusions were made 1 From the histogram plots we can observe that the distribution of RSSI

values follow Gaussians distribution law 2 As we can see from figure 22 that median curve has drastic changes

when compared to mean curve Hence mean is considered while estimating the position

3 From the observations we conclude that the number of samples does not matter since mean curve tends to be same for various numbers of samples

4 Hence 10000 samples are considered in order to get a proper location fingerprint during off‐line phase and during online phase 50 samples are enough to estimate the position of object

53 0ne dimensional test Measurement setup for one dimensional test is as shown in figure 19 This test was conducted to check the performance of algorithm and how accurate it was At first ie during offline phase fingerprints at each interval averaging 10000 samples were taken During online phase Euclidean algorithm was used estimate the position averaging 50 samples of RSSI From the results we

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

41

can realize that the error was less than 2 meters Table 12 shows the result of location estimation in one dimensional case

Table 12 One dimensional position estimation results

54 Two dimensional test This test is the final stage of my thesis work Experimental testbed for this stage is as shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors fixed at the corners of the room and a BS at the centre of the room There are 18 pre‐determined locations where the fingerprints (RSSI) were taken The fingerprints are as shown in Appendix 1 All the readings were taken in presence of userrsquos The algorithm for position estimation is shown in Appendix 2 Position estimation test is performed as follows

Tagged object is placed at random inside the masterrsquos room All the sensors including the base station and object are switched on Using the algorithm (Appendix 2) the BS estimates the position of the object and displays it on the PC

This test was performed using various numbers of samples to test which has a better accuracy Figure 23 represents the error rate using various numbers of samples From figure 23 we can conclude that 50 numbers of samples is enough to estimate the position of object with an accuracy of 3m

55 Causes of error in location determination The randomness of RSS is the major cause of error in any indoor

positioning system that uses the RSS for location inference If there is no randomness in the RSS and its location fingerprint every indoor positioning systems of this type will have no location detection error For example I have recorded RSS at one particular location during different times of the day From figures 24 and 25 you can observe that RSS values are different at different times of day

The performance of indoor positioning systems depends greatly on the separation of location fingerprints A location fingerprint

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

42

corresponding to a location can be identified correctly if it is difficult to classify it as another fingerprint by a pattern classifier

Path loss is one of the basic signal propagation properties of the RSS that best describes attenuation of transmit signal power over a distance between an access point and a mobile device

Presence of Userrsquos or userrsquos orientation affects RSS

Figure 23 Error Rate vs Number of samples

Figure 24 RSS distribution at 214 pm

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

43

Figure 25 RSS distribution at 100pm and 1100 am

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

6 DISCUSSIONS10102006

44

6 DISCUSSIONS 61 Summary and Conclusion While empirical results and performance studies of positioning systems based on location fingerprint have been presented in this report analytical model that can be used as a framework for efficiently designing the positioning systems are not available This thesis design work uses general principles of position estimation such as position estimation of sensors grid spacing etc This design work focuses on accuracy and precision The concept of location fingerprint technique based on RSS us explained in detail in chapter 2 It is found that RSS is random It is possible to approximate the RSS as being normally distributed While the RSS random process is non‐stationary the mean is more or less constant and could be used as the fingerprint of the location Model designed for position estimation is as shown in chapter 4 Accuracy and precision are the two major performance matrices in this study Uniform grid spacing has been used From the experimental analysis I have obtained an accuracy of 3 meters and 35 precision The sensitivity analysis among the system parameters and the performance matrices were done using the model as shown in chapter 4 For instance our study found that the minimum distance between two adjacent positions also affects the positioning performance This is due to the randomness of RSS patterns induced by the ever changing indoor environment

62 Future work The research in this thesis provides a ground work for the future study of efficient design of indoor positioning system The RSSI based position estimation still has limitations First the RSSI value is time dependent Thus the future study should address this issue Second the movement of object is not considered Third orientations of sensors affect the performance which should be taken care off Fourth to improve the performance of the system we need to find a solution to deal with those incomplete or missing data during the off‐line and the on‐line phase Fifth position estimation on multi‐floor and three‐dimensional coordinates is not available

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 110102006

45

APPENDIX 1

Location Fingerprints of Masters Room Time 1315 Observation Time 20 minutes Number of Samples 10000

People Presence SNo Co-ordinates AP1 (0x0001) AP2 (0x0002) AP3 (0x0004) AP4 (0x0008)

1 11 -24 -16 -27 -33 2 13 -26 -8 -34 -31 3 15 -43 -9 -39 -25 4 32 -21 -11 -27 -33 5 61 -6 -23 -32 -28 6 64 -20 -31 -25 -34 7 35 -23 -35 -29 -38 8 37 -29 -35 -22 -27 9 39 -38 -29 -21 -27 10 67 -27 -23 -20 -23 11 611 -26 -29 -23 -30 12 613 -35 -32 -2 -31 13 513 -34 -31 -20 -26 14 11 -39 -22 -29 -17 15 212 -37 -42 -28 -31 16 -212 -38 -33 -29 -6 17 -21 -42 -29 -33 -19 18 -38 -40 -23 -35 -22

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 210102006

46

APPENDIX 2

Begin

Open radio map and raw data files for write

Intialize radio map in memory

Check whether the

Packet is data or Beacon packet If data packet

Save the data to register

Check if data is

Received from all

sensors

Estimate the position of static

Display the position on PC

End Yes

No

A

A

Yes

No

Flow chart representing the main algorithm for position estimation

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

47

APPENDIX 3

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

48

Distribution of RSSI

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

49

REFERENCES [1] T S Rappaport J H Reed and B D Woerner ldquoPosition location using wireless communications on highways of the futureʺ IEEE Commun Mag vol 34 no 10 pp33‐41 Oct 1996 [2] A M Ladd K E Bekris G Marceau A Rudys L E Kavraki and D S Wallach rdquoRobotics‐based location sensing using wireless ethernetʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ02) 2002 pp 227‐238 [3] I Ramani R Bharadwaja and P V Rangan ldquoLocation tracking for media appliances in wireless home networksʺ in Proc International Conference on Multimedia and Expo (ICMEʹ03) July 2003 pp 769‐772 [4] K Pahlavan X Li and J P Makela ldquoIndoor geolocation science and technologyʺ IEEE Commun Mag vol 40 no 2 pp 112‐118 Feb 2002 [5] Konard Lorincz and Matt Welsh ldquoMote Track A Robust Decentralized Approach to RF‐Based Location Trackingrdquo [6] Paramvir Bahl and Venkata N Padmanabham ldquoRADAR An In‐Building RF‐based User Location and Tracking Systemrdquo [7] Kamol Kaemarungsi ldquoDesign of Indoor positioning system based on location fingerprint techiniquerdquo University of Pittsburgh 2005 [8] Fredrik Linnarsson Cheng Peng and Bengt Oelmann ldquoSENTIO A Hardware Platform for Rapid Prototyping of Wireless Sensor Networksrdquo [9] S Saha K Chaudhuri D Sanghi and P Bhagwat ldquoLocation determination of a mobile device using IEEE 80211b access point signalsʺ in Proc IEEE Wireless Communications and Networking Conference (WCNCʹ03) New Orleans LA Mar 2003 pp 1987‐1992 [10] T Roos P Myllymaki H Tirri P Misikangas and J Sievanen ldquoA probabilistic approach to WLAN user location estimationʺ International Journal of Wireless Information Networks vol 9 no 3 pp 155‐164 July 2002 [11] R Battiti M Brunato and A Villani ldquoStatistical learning theory for location fingerprinting in wireless LANrsquosʺ Technical Report Oct 2002 [Online] Available httprtmscienceunitnitraquobattitiarchive86pdf [12] P Prasithsangaree P Krishnamurthy and P K Chrysanthis ldquoAn indoor position location with wireless LANsʺ in Proc IEEE International Symposium on Personal Indoor and Mobile Radio Communications (PIMRCʹ02) Lisbon Portugal Sept 2002 [13] M A Youssef A Agrawala and A U Shankar ldquoWLAN location determination via clustering and probability distributionsʺ in Proc IEEE

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

50

International Conference on Pervasive Computing and Communications (PerComʹ03) Dallas‐Fort Worth TX Mar 23‐26 2003 pp 23‐26 [14] Z Xiang S Song J Chen H Wang J Huang and X Gao ldquoA wireless LAN‐based indoor positioning technologyʺ IBM Journal of Research and Development vol 48 no 56 pp 617‐626 SeptNov 2004 [15] R Want A Hopper V Falcao and J Gibbons ldquoThe active badge location systemʺACM Transactions on Information Systems vol 40 no 1 pp 91‐102 Jan 1992 [16] N B Priyantha A Chakraborty and H Balakrishnan ldquoThe cricket location‐support systemʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ00) Boston MA Aug 2000 pp 32‐43 [17] Holger K Willig A (2005) Protocols and Architectures for Wireless Sensor Networks London [18] IEEE (2003) Part 154 Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low‐Rate Wireless Personal Area Networks (LR‐WPANs) New York [19] Chipcon CC2420 802154 compliant radio httpwwwchipconcom [20] ZigBee Specification v10 ZigBee Alliance [21] Dimitrios Lymberopoulos Quentin Lindsey and Andreas Savvides ldquoAn Empirical Analysis of Radio Signal Strength Variability in IEEE 802154 Networks using Monopole Antennasrdquo [22] Kiran Yedavalli Bhaskar Krishnamachari Sharmila Ravula and Bhaskar Srinivasan ldquoEcolocation A sequence based technique for RF localization in wireless sensor networksrdquo Robert Bosch Research and Technology Centre Palo Alto CA

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

9

22 Classification of Indoor positioning systems Indoor positioning system can be categorized based on the stimulus used in particular application and measurement principle

221 Classification based on Stimulus Based on the stimulus used in an application WSN inherit certain characteristics and limitations on sensorrsquos signal The propagation delay diffraction reflection and scattering are the basic signal characteristics which affect all signal types The effective range available bandwidth regulatory constraints interference power constraints safety and cost of technology limitations The wireless signals commonly used for indoor positioning systems are infrared radio frequency and ultrasound Brief descriptions of the three major technologies are as follows

Infrared Infrared signal is an electromagnetic signal of a wavelength longer than that of visible light but shorter than that of radio waves Infrared is just below the visible spectrum of light in frequency and is radiated strongly by hot bodies Many objects such as people vehicle engines and aircraft generate and retain heat and as such are especially visible in the infrared wavelengths of light compared to objects in the background It cannot pass through walls or obstructions therefore it has a rather limited range of indoor environments Thus it requires a more sophisticated circuitry than ultrasound signals Indoor lighting interferes with this type of signal and causes problems in accurate sensing It generally has a range of around 5 m The infrared devices are usually small in size compared to ultrasound devices

Radio frequency The RF signal can penetrate through most of the building material therefore it has an excellent range in indoor environments The propagation speed is also high approximately 3108 ms There are unlicensed frequencies available freely for use This type of signal has the longest range compared to infrared and ultrasound Since the propagation waves are of higher frequency (24GHz) interference with other frequency components is less

Ultrasound Although ultrasound operates at low frequency bands (typical 40 kHz) compared to the other two signaling technologies it possesses a good precision for location sensing at a slow propagation speed of sound (343 ms) The advantages of ultrasound devices are their simplicity and that they are inexpensive However ultrasound does not penetrate walls

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

10

but reflects off most of the indoor obstructions It has a short range around 3 m to 10 m but has a 1 cm resolution of distance measurement The operating temperature influences the performance of ultrasound

222 Classification based on measurement principle Wireless positioning systems can be categorized by measurement techniques which are used to derive the position of mobile stations Mainly three different measurement principles are used today AOA RSS and propagation‐time based systems that can further be divided into two different sub‐classes TOA and TDOA Figure 3 shows taxonomy based on the technology and the technique classifications

Figure 3 Taxonomy of positioning system [7]

In AOA systems the position is calculated via goniometry With the use of directional antennas or antenna arrays the angle or bearing relative to points located at known positions is measured The intersection of several measured directions pointers the yields the position value The accuracy of this approach is limited by the possible directivity of the measuring aperture by shadowing and by multi‐path reflections arriving from misleading directions In TOA and TDOA techniques rely on the precision of timing between the signal transmitter and the receiver in order to use the propagation delay or TOF to calculate the distance between transmitter and receiver Therefore a precise synchronization is also very important in such systems By combining at least three distances from three reference positions triangulation can be used to estimate the mobile stationʹs location This type of technique will

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

11

require a high accuracy clock in the communication system TDOA is more practical [1] Examples of location‐sensing systems that use time of flight are GPS the Active Bats [15] and the Cricket [16] Besides these time delay‐based techniques the distance between transmitter and receiver can also be determined from signal strength attenuation and direct distance measurement (such as dead reckoning) Fingerprinting or Location Pattern Matching this technique generally requires only measurement of RSS or other non‐geometric features at several locations to form a database of location fingerprints To estimate the mobile location the system needs to first measure the received signal strength at particular locations and then search for the pattern or fingerprint with the closest match in the database This technique does not require the mobile station to see at least three base stations or access points in order to determine the location The disadvantage of this technique is that it is very time‐consuming to perform an exhaustive data collection for a wide area network such as in outdoor positioning systems

23 Indoor local positioning using ZigBee based on RSSI This section reviews relevant RF‐based indoor positioning systems which can be used to locate stationary objects and track mobile users The impressive growth of IEEE 802154 wireless PANs (WPAN) in recent years suggests an interesting future for the location fingerprinting technique The system is quite flexible because system designers can select whether to have a centralized positioning server or let the mobile to determine its own position However the fingerprinting technique requires a training phase (off‐line phase) to collect location fingerprints for all positions in the operating area before the actual deployment (on‐line phase) [7] The following discussion is divided according to the positioning system components First the common form of location fingerprinting and its relationship with physical locations are explained Finally location estimation algorithm is reviewed

231 Location Fingerprint A location fingerprint based on RF characteristics such as RSS is the basis for representing a unique location It is created under the assumption that each position or location inside a building has unique RF signature [4] Generally a fingerprint F is labelled with a location information L The location fingerprints and their labels (eg location information) are maintained in

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

12

database and used during the on‐line phase to estimate the location The label and fingerprint are usually denoted as a tuple of (L F) Battiti et al [11] point out that the location information L for indoor location can be recorded in two forms as either a tuple of coordinates or an indicator variable The tuple of real coordinates can vary from one dimension to five dimensions which include the three dimension space and two orientation variables expressed in spherical coordinates For instance a location information of a two‐dimension system with an orientation could be expressed as a triplet L = (x y d) | x y є R 2 d є North East South West The RSS was found to be more location‐dependent than the SNR because the noise component is rather random in nature However the RSS itself fluctuates over time for each access point and location Each RSS element can be considered as a random variable therefore it can be captured by recording its descriptive statistics parameters approximating its distribution or maintaining the whole measurement dataset These approaches of RSS representation result in different procedures for location estimation algorithm Regardless of the approach the location fingerprint is usually denoted as an array or vector of signal strength (random variables) received at any position in the location‐based service area The size of the vector is determined by the number of access points that can be heard To create a basis fingerprint such as in a number of samples of vectors of signal strength are collected over a window of time for each position This basis is called a prototype Then the average RSS of each access point is calculated and recorded as an element in the location fingerprint For an area that can receive signals from N access points the location fingerprint can be expressed as a vector of average RSS elements βi

F = (β1 β2hellip βN)T Besides the basis location fingerprint the samples of location fingerprint measured during the on‐line phase are also important for the system that tracks the mobile object The number of samples should be selected appropriately to represent the location fingerprint for the mobile application A step in statistical analysis method called pre‐processing is another important issue needed to be considered because it can impact the estimation of dependency between location fingerprint and the location information easier The pre‐processing refers to a step that cleans the raw data (in this case the training set) before any further operations or analysis The cleaning may consist of encoding dimensionality reduction (reduce unnecessary elements) feature extractionselection clustering and outlier elimination Roos et al [10] point out that the pre‐processing enables faster location estimation and

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

13

reduce the noise from the training data The process that creates the basis of location fingerprint discussed above could be considered as a part of pre‐processing

232 Location Estimation Algorithm Location estimation algorithms or positioning algorithms are procedures that exploit dependency between location information and location fingerprint basis in order to determine a position or location from samples of RSS signals Position algorithms can be classified into deterministic and probabilistic types based on the approaches that model the relationship between location fingerprints and location information The deterministic type of algorithms are those that based in the nearest neighbour classifiers and neural network classifiers The probabilistic type of algorithms is those that are based on the Bayesian inference and statistical learning theory such as support vector machines (SVR) The algorithm used in this project is discussed briefly

Euclidean distance method The Euclidean distance method is a deterministic approach of position estimation because they require only a set of constant location fingerprints which includes mean vectors In order to determine the location a form of discriminate function is used to classify a sample of RSS fingerprint into a position The mean or average RSS vector is a centre of a mass which represents each class of location fingerprint Suppose a set of l location fingerprints is denoted by F1 F2hellip Fl exist and each fingerprint has a one‐to‐one mapping to a set of positions L1 L2hellip Ll A sample of an RSS fingerprint measured during an on‐line phase is denoted as S which can be another mean or average RSS vector of small window of RSS samples Assuming that an indoor positioning system only considers the average RSS from N access points as a location fingerprint the sample of RSS vector is S = (s1 s2 helliphellip sN)T and each location fingerprint i in the database can be expressed as Fi = (β1 β2helliphellip βN)T The simplest closeness metric is a distance measurement in signal space denoted as the Dist () function [6] Thus the simple procedure of nearest neighbour algorithm is expressed as picking the fingerprint j that has the shortest signal distance

Dist(S Fj) le Dist(S Fk) for all k ne j A generalized weighted distance Lp summarized by [12] can be used to calculate different forms of distance in signal space as

Lp = 1N pN

i

pPiSiWi 1

1)||1(sum

=

minus

where N is the number of access points wi is a weighting factor (wi le 1) and p is the norm parameter starting from 1 The weighting factor wi is a bias

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

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parameter that can demote or promote an important RSS component in the fingerprint

24 Protocols and Industry standards for WSN Although many applications for WSN are proposed the communication protocols supporting them remain mainly unexplored and diverged This is because the area of interest is new each implementation is application specific and the components of WSNrsquos have a lot of constraints Wireless sensor networks use the wireless medium to communicate However the traditional communication protocols that support wireless communications especially ad‐hoc mesh networks may not be well suited for them WSN communication is an active research area and many algorithms and protocols have already been proposed This section introduces two proposed standards the IEEE 802154 and the ZigBee The IEEE 802154 covers the physical layer and the MAC layer of low‐rate WPAN The ZigBee is ldquoan emerging standard that is based on the IEEE 802154 and adds network construction (star networks peer‐to‐peermesh networks and cluster‐tree networks) application services and morerdquo

241 IEEE 802154 Holger and Willig (2005) [17] mention home automation home networking and home security as possible applications for IEEE 802154 They add that ldquomost of these applications require only low‐to‐medium bitrates (up to some few hundreds of kbps) moderate average delays without too stringent delay guarantees and for certain nodes it is highly desirable to reduce the energy consumption to a minimumrdquo The IEEE 802154 standard (2003) defines the device types that can be used in a LR‐WPAN A device can be a FFD or a RFD The RFD can be used in simple applications in which they do not need to transmit large amounts of data and they have to communicate only with a specific FFD The FFD can work as a PAN coordinator as a coordinator or as a simple device It can communicate with either another FFD or a RFD In keeping with the application requirements the LR‐WPAN operates in a star or peer‐to‐peer topology (figure 4) In the star topology the RFD communicates with a single controller the PAN coordinator The PAN coordinator can perform the same function as the RFD but it is also responsible for controlling the PAN ldquoit initiates terminates or routes communication around the networkrdquo [18] The peer‐to‐peer topology

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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supports ad‐hoc mesh multi‐hop networking Any device in the peer‐to‐peer topology can communicate with any other device within its communication range however this topology also has a PAN coordinator All the devices in a LR‐WPAN have a unique 64‐bit address This or a short address allocated by the PAN coordinator can be used inside a PAN Additionally each PAN has a unique identifier The combination of the PAN identifier and the sort addresses allows communication across different PANs (IEEE 802154 Standard 2003)

Figure 4 Star and peer‐to‐peer topologies in LR‐WPAN [18]

The LR‐WPAN based on the OSI seven‐layer model has the layered architecture presented in figure 5 The application and the network layer are the upper layers in the LR‐WPAN architecture but are outside the scope of the IEEE 802154 standard Only the physical layer ldquowhich contains the RF transceiver along with its low‐level control mechanismrdquo and the MAC layer ldquothat provides access to the physical channelrdquo are included in the standard and will be introduced in the following sections Finally the Type I 8022 LLC and the SSCS are intermediate sub layers supporting communication with the above layers (IEEE 802154 Standard 2003)

Physical Layer The 802154 standard specifies two different services that the PHY provides The PHY data service controls the radio and thus the transmission and reception of the PPDUs In addition the management service performs ED in the channel The management service also performs CCA before sending the messages and provides LQI for the received packets

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

16

Figure 5 WPAN architecture [18]

Three different bands are defined by the standard The 868‐8686 MHz for Europe the 902‐928 MHz for North America and the 2400‐24835 MHz worldwide All of them belong in the ISM radio bands and they are using DSSS Each of the bands supports different a data rate and it uses a different modulation technique chip rate and number of channels Moreover if the system does not use the 2450 MHz frequency it operates in both the 802 MHz and 902 MHz frequencies Table 3 summarizes the supported bands and data rates (IEEE 802154 standard 2003)

PHY (MHz)

Frequency Band (MHz) Modulation

Bit Rate Number of Channels

868-8686 BPSK 20 1 868 915 902-928 BPSK 40 10

2450 2400-24835 O-QPSK 230 16 Table 3 Frequency bands and data rates for IEEE 802154 based on the IEEE

802154 standard [18] The physical layer uses PPDU packets to communicate Table 4 demonstrates its structure The LSB is always transmitted and received first The SHR contains the preamble and the SFD fields which helps receiver

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

17

synchronization The 8‐bytes preamble contains only zero and is used for synchronization The SFD contains a specific sequence of one and zeros and specifies the beginning of the frame The PHR contains the payload length Packets with a length of 9 or more bytes are MPDU as the next section further explains Packets with length 5 are used for MPDU acknowledgements The payload part of the PPDU encloses the MAC layer packet Finally the PPDU size can be up to 127 bytes [18]

4 bytes 1 byte 1 byte Variable

Preamble SFD Frame length

(7 bits) Reserved

(1 bit) Payload (PSU)

SHR PHR PHY Payload Table 4 Format based on the IEEE 802154 standard [18]

MAC Layer The MAC layer is the interface between the SSCS and the PHY layer Similar to the PHY layer the MAC layer supports two services The MAC data service is responsible for the transmission and reception of the MPDUs through the PHY data service The MAC management service if the device is a coordinator manages the network beacons It is also responsible for PAN association and disassociation frame validation and acknowledgment providing ldquoa reliable link between two peer MAC entitiesrdquo In addition it uses the CSMA‐CA for channel access and handles and maintains the GTS mechanism Finally it supports device security [18] The IEEE 802154 standard defines four different frame types the beacon data acknowledgment and MAC command frame All frame types are based on the general MAC frame format (table 5) The frame control field describes and specifies the above different frame types Every MAC frame comprises a MHR which consists of a frame control sequence number and the information field It also contains the MAC payload different frame types have different MAC payload fields The acknowledgment type does not have a payload Finally each frame includes a MFR which contains a FCS The data in the MPDU follows the same order as the PPDU the least significant bits are left in the frame and are transited first Octets2 1 02 028 02 028 Variable 2

Destination PAN Identifier

Destination Address

Source PAN

Identifier Source Address Frame

Control Sequence Number Addressing Field

Frame Payload FCS

MHR MAC

Payload MFRTable 5 General MAC frame format IEEE 802154 standard [18]

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Table 6 7 8 and 9 present the four different MAC frame types The beacon frame is transmitted periodically by the PAN coordinator It provides information about the network management through the super frame and GTS fields which are analyzed later in the section It also synchronizes the network devices and indicates the proper communication period for them The data frame payload encapsulates data from the higher layers When a device receives a packet it is not obliged to response with an acknowledgement packet Finally the command frame identifier and command payload fields of the command frame are used for communication between the network devices The command identifier specifies actions like association disassociation and data GTS or beacon request Octets2 1 410 2 Variable Variable Variable 2

Frame Control

Sequence Number

Addressing Fields

Super frame Specification

GTS fields

Pending Addressing

Fields Beacon Payload FCS

MHR MAC Payload MFRTable 6 Beacon frame format [18]

Octets 2 1 Variable 2

Frame Control

Sequence Number

Addressing Field

Data Payload FCS

MHR MAC

Payload MFR Table 7 Data frame format [18]

Octets 2 1 2

Frame Control

Sequence Number FCS

MHR MFR Table 8 Acknowledgement frame format [18]

Octets2 1 1 Variable 2

Frame Control

Sequence Number

Addressing Fields

Command Frame Identifier

Command Payload FCS

MHR MAC Payload MFR Table 9 Command frame format [18]

In the LR‐WPAN every PAN has its own coordinator The PAN coordinator manages the communication in the local area it has two options to use or not use the super frame structure The super frame (figure 6) uses network beacons If the coordinator does not want to use a super frame structure it suspends the beacon transmission However the beacon is important for device association and disassociation If the coordinator wishes to maintain close communication control in the PAN and to support low‐latency devices it usually uses the super frame A super frame determines a specific time

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

19

period beacons bound it The beacon is transmitted in the first of the sixteen equal time slots that the super frame has It is used to ldquosynchronize the attached devices to identify the PAN and to describe the structure of the super framerdquo The super frame can have active and inactive periods All the communications have to be finished inside the super frame period In the inactive periods the devices can switch to the sleep mode but they have to be ready for the next beacon The active period is further divided into a CAP and a CFP During the CAP any device can communicate competing with the other devices in the PAN using slotted CSMA‐CA The CFP contains GTSs and follows the CAP A CFP may maintain up to seven GTSs and each GTS can reserve more than one time slot However the CAP should always be sufficient to allow new devices t join the PAN The GTSs are allocated to specific devices All the transactions must be completed inside the assigned time period (CAP GTS and CFP) (IEEE 802154 standard 2003)

Figure 6 Example of super frame structure [18]

In accordance with the 802154 standard (2003) three different types of data‐transfer exist In addition the types differ if the coordinator uses or does not beacons Data transfer from a device to the PAN coordinator is the first type (figure 7) For a ldquonon‐beacon‐enable networkrdquo it first senses the medium by using ldquoun‐slotted CSMA‐CArdquo and then a simple transmit to the data frame In a ldquobeacon‐enabled networkrdquo the sender waits for the beacon when it finds it ldquothe device synchronizes to the super fame structurerdquo In the specified time frame the sender again senses the medium by using ldquoslotted CSMA‐CArdquo and transmits the data to the coordinator In either case the coordinator has the option to acknowledge or not acknowledge the data reception after that the transaction is completed Data transfer from the coordinator is the next type described in the standard (figure 8) In a beacon‐enabled network the coordinator indicates a pending message through the beacon The messagersquos target device receives the beacon and if the message is pending it responses with a MAC command request

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

20

message using slotted CSMA‐CA the coordinator may or may not acknowledge the command message and through slotted CSMA‐CA sends the pending message The device acknowledges the received message After that the coordinator removes the message from the beaconrsquos pending list and completes the transaction In a non‐beacon enable network the device periodically sends a MAC command‐request frame to the coordinator The coordinator acknowledges the data request Then by using un‐slotted CSMA‐CA if it has a pending message it transmits it If it has not it respond with a data frame with a ldquozero‐length payloadrdquo To complete the transaction the device acknowledges the data reception

Figure 7 Communication from a device to a PAN coordinator in (a) a beacon‐enabled network and (b) a non‐beacon enabled network [18]

Peer‐to‐peer is the last type of data transfer In this situation the devices are free to communicate with any other device within their communication range In a peer‐to‐peer PAN the devices can ldquoeither receive constantly or synchronize with each otherrdquo If they are receiving constantly to transmit data they use un‐slotted CSAM‐CA In the second case synchronization must be achieved first (IEEE 802154 standard [IEEE 2003]) The IEEE 802154 (2003) standard establishes MAC and PHY standards for low‐cost low‐power and high‐density node deployments In addition to the above PHY and MAC characteristics IEEE 802154 provides a security baseline including ldquothe ability to maintain an ACL and use symmetric cryptographyrdquo for data encryption The algorithm that is used for encryption is the AES However the higher level layers decide when security is need The upper layers are in general responsible for device authentication and key management The next section introduces the ZigBee standard which

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

21

encapsulates the IEEE 802154 and provides additional standardization for the higher levels

Figure 8 Communication from a PAN coordinator to a device in (a) a beacon‐enabled network and (b) a non‐beacon enabled network [18]

242 ZigBee Heily (2004) defines ZigBee as ldquoa rapidly growing worldwide non‐profit industry consortiumrdquo whose mission is ldquoto define a reliable cost‐effective low‐power wirelessly networked monitoring and control product based on an open global standardrdquo Figure 9 illustrates the areas of interest for different wireless communication standards

Figure 9 Overview of the coverage for different wireless communication

standards [17]

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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22

ZigBee a new standard which became publicly available in June 2005 is based on the IEEE 802154 standard It expands the IEEE 802154 by adding the framework for ldquothe network security and applicationrdquo (Craig 2005) Figure 10 presents the IEEE 802154ZigBee stack and the areas of responsibilities

Figure 10 IEEE 802154ZigBee stack [20]

Craig (2005) mentions three networking topologies that the standard covers the star mesh and cluster tree (figure 11) The ZigBee standard works on top of the IEEE 802154 addressing schema by using the standard 64‐bit and the short 16‐bit addressing Kinney (2005) summarizes the ZigBee network layer responsibilities the successful establishment of a new the network and successful new device configuration addressing assignment network synchronization frames security and message routing ZigBee further distinguishes the concept of the physical devices (RFD FFD) by using the notion of ldquological devicesrdquo ldquoZigBee Coordinatorrdquo is the first type of logical devices It is responsible for initializing maintaining and managing the network Under the coordinator in the network hierarchy is the ldquoZigBee routerrdquo which is responsible for controlling the message routing between the nodes Finally the ldquoZigBee End Devicerdquo acts as the end point of the network

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

23

structure The tasks that an end device can perform are specified in the ldquoApplication Profilesrdquo (Craig 2005)

Figure 11 IEEE 802154ZigBee stack [17]

The ZigBee specifications (2005) summarize the security services provided by ZigBee ldquokey establishment key transport frame protection and device managementrdquo ZigBee builds its security mechanism using symmetric key cryptography The security services also depend on the associated layer thus as the figure 10 shows the security mechanism covers the network and the application layer In addition if a MAC frame needs security protection the MAC layer is able to secure it Moreover the notion of end‐to‐end security is supported the source and destination devices have access and use the same share key In the MAC layer the 802154 AES mechanism provides the proper security The mechanism protects ldquothe confidentiality integrity and authenticity of the MAC framesrdquo (Kinney 2005) An auxiliary header field in front of the MAC payload indicates if the frame is encrypted or not The MAC framesrsquo integrity is supported by calculating and using a MIC at the end of the MAC payload In addition to the AES nonce is used to provide MAC confidentiality and authenticity Figure 12 illustrates a MAC frame with security For different security aspects the MAC layer uses different mode of the AES for the encryption it uses the AES in Counter (CTR) mode and for the integrity the

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

24

CBC‐MAC Finally the combination (CCM) of the above two modes is available providing both encryption and integrity (Kinney 2005)

Figure 12 ZigBee secure frame in MAC layer [17]

In the network layer the CCM (a modified MAC layer CCM mode) is used for encryption Because the network layer uses only the CCM mode a single key is used for all different security options The network layer security message format is similar to the MAC frame it is presented in the figure 13 Finally although the network layer is responsible for securing its layer messages the above layers specify the keys and the CCM option for each frame (Kinney 2005)

Figure 13 ZigBee secure frame in network layer [17]

Security in the application layer works similar to the network and MAC layers It uses the ldquolink keyrdquo or the ldquonetwork keyrdquo to secure the message and then encapsulate it inside a set of fields similar to the network format (figure 14) Other security responsibilities that the application layer has are to provide the ZDO and the applications with device management services key establishment and key transport (ZigBee specifications 2005)

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

25

Figure 14 ZigBee secure frame in application layer [17]

The ZigBee application layer contains the manufacturer‐defined application objects the ZDO and the application sub‐layer In addition to the security responsibilities the application sub‐layer binds devices based on their duties and needs maintains the binding tables and forwards messages between them The application sub‐layer also discovers the neighbour devices for a given device The ZDO is responsible for determining the devicersquos duty in the network for communicating using binding requests and for supporting security as was mentioned above The sub‐layer that implements the actual application is the manufacturer‐defined application object (Kinney 2005) The ZigBee specifications are a great step toward the wireless sensors networks standardization They cover all the OSI layers from the physical to the application providing guidance to the developers The specifications are the product of the ZigBee alliances a consortium of companies and academic institutes Thus they not only produce the specifications but also are the first use them The next section goes beyond the layer architecture and standardization and introduces the OS field another crucial element of WSNs

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

26

3 RECEIVED SIGNAL STRENGTH (RSS) Majority of the existing methods leverage the existence of IEEE 80211 base stations with powerful radio transmit powers of approximately 100mW per base station Such radios are in a different class from the low power IEEE 802154 compliant radios that typically transmit at low power levels ranging from 52mW to 29mW [19] The wide availability of larger number of IEEE 802154 radios in the research community has revived the interest for signal strength based localization in sensor network Despite of rapidly increasing popularity of IEEE 802154 radios and signal strength localization there is a lack of detailed characterization of the fundamental factors contributing to large signal strength variation This chapter investigates some of the properties of RSS observed during testing and analysis of research work collected during the period of thesis The analysis of RSS values is needed to understand the underlying features of location dependent RSS patterns and location fingerprints An understanding of the properties of the RSS values for location fingerprinting can assist in improving the design of positioning algorithms and in deployment of indoor positioning systems

31 Parameters that affect RSS Different factors that affect the indoor RSS are the userrsquos presenceabsence hisher orientation time of the day building type and material distance from transmitter and type of radio These factors are by no means exhaustive Other possible factors are antenna orientation directionality and type Table 10 classify the parameters which affect RSS [7]

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

27

Effect on Factors Options

1 Proximity of user Userrsquos presence or absence

2 Orientation of user and terminal

North East South West

Data Collection

3 Make of Radio 4 Time of measurement Time of day amp days of

week 5 Period of measurement

Second minute hour

7 Interference Co‐channel adjacent co‐channel

Statistics

8 Building environment Small offices or large hall

Table 10 Classification of factors the affect RSS [7]

Transmitter variability Different transmitters behave differently even when they are configured exactly in the same way In practice this means that when a transmitter is configured to send packets at a power level of d dBm then the transmitter will send these packets at a power level that is very close to d dBm but not necessarily exactly equal to d dBm This can alter the received signal strength indication and thus it can lead to inaccurate distance estimation

Receiver variability The sensitivity of the receivers across different radio chips is different In practice this means that the RSSI value recorded at different receivers can be different even when all the other parameters that affect the received signal strength are kept constant

Antenna orientation Each antenna has its own radiation pattern that is not uniform In practice this means that the RSSI value recorded at the receiver for a given pair of communicating nodes and for a given distance between them varies as the pair wise antenna orientations of the transmitter and the receiver are changed

Multi‐path fading and shadowing in the RF channel In indoor environments the transmitted signals get reflected after hitting on the walls andor on other objects in the room such as furniture Both the original signal and the reflected signal reach the receiver almost at the same time since they both travel at the speed of light As a result of this the receiver is not able to

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

28

distinguish the two signals and it measures the received signal strength for both of them

32 Related work analysis A number of indoor location tracking systems have been proposed in the literature based on RF signals ultrasound infrared and some combinations of these As mentioned previously RF based positioning system are widely studied as shown in Table 2 Given a model of radio signal propagation in a building or other environment RSS can be used to estimate the distance between the transmitter and receiver Most of the algorithms (Table 2) have been evaluated in practice using 80211b compliant wireless radios and seem to produce a location error of 15ft to 20ft for an indoor network area of approximately 225ft x 144ft According to the results of this study all the localization techniques produce approximately the same location error over a range of environments Ecolocation [22] Mote Track [5] are two of the few localization algorithms that have been evaluated on a real sensor network that uses a low power wireless radio Ecolocation reports a location error of 10ft for a very small outdoor network deployment area (26ft x 49ft) Mote Track reports a location error of approximately 13ft for an indoor network deployment area of 18751ft2 In general the RSSI based localization algorithms can be divided into two broad categories the map based and the distance (or area) prediction based algorithms In the case of the map based algorithms the deployment area is divided into a large set of sample points that cover the whole area Most techniques collect one or more signal strength measurements from all visible beacon nodes and for each sample point Each sample point is then mapped to either a signal strength vector or a signal strength probability distribution This process generates an RSSI fingerprint map which is unique with respect to the beacon locations and the deployment area This fingerprint map is stored to a central base station A node that is not aware of its location collects one or more signal strength measurements from all the visible beacon nodes and creates its own RSSI fingerprint that is sent to the central base station The base station computes nodersquos location as the sample pointrsquos location whose RSSI fingerprint distance from the unknown nodersquos RSSI fingerprint is the minimum among all the sample points Despite the fact that the map based RSSI localization methods produce relatively small location errors they are not attractive mainly because they require an excessive amount of profiling data and because they are not fully distributed The detailed profiling of a building is a major holdback because it is complex time consuming and depends on beacon locations Therefore for different buildings or even for the

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

29

same building and different beacon locations different RSSI maps have to be obtained The second category of RSSI based algorithms are the distance (or area) prediction algorithms which focus on minimizing the amount of profiling data In distance prediction based methods raw RSSI data are directly mapped to distances through a signal propagation model In some cases distances are not explicitly measured but they are taken into consideration through assumptions on the underlying distributions In the case of outdoor deployments where there are no obstacles a single signal propagation model is all the profiling data required In the case of indoor deployments the same applies but now the signal propagation model might be different across different buildings with different furniture and wall arrangements Given an underlying signal propagation model raw RSSI data can be mapped to specific distances These distances can be either used directly in order to perform localization or assigned a probability and used in a learning basedprobabilistic localization algorithm Obviously the accuracy of the distance prediction based methods depends heavily on the accuracy of the signal propagation model used for translating RSSI values to distances

33 Properties of RSSI Changes in signal due to propagation indoors are difficult to predict because of dense environment and propagation effects such as reflection diffraction and scattering The multi‐path fading effect which is the result of either constructive or destructive combination of multiple signal copies at the receiver causes the received signal to fluctuate around a mean value at a particular location The received signal is usually modelled by the combined effects of large‐scale fading and small‐scale fading The large‐scale fading component describes the signal attenuation as the signal travels over a distance and is absorbed by material such as walls and floors along the way to the receiver This component predicts the mean of the RSS and usually has a lognormal distribution On the other hand the small scale fading component explains the dramatic fluctuation of the signal due to multi‐path fading On the other hand the small scale‐fading component explains the dramatic fluctuation of the signal due to multi‐path fading If there is no LOS component the small‐scale‐fading is often modelled with a Rayleigh distribution Such scenarios are commonly called NLOS If there is a LOS component the small‐scale‐fading is typically modelled by a Rician distribution Note that when the RSS is measured the measurement averages out the small‐scale‐fading effects

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

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4 OBJECT TRACKING 41 Introduction This chapter presents the object‐tracking application the main objective of this thesis work The application is an algorithmic‐software implementation written in Embedded C which is capable of receiving data messages and generating object position estimation data The implementation of the object‐tracking application makes use of the SENTIO platform and uses the background knowledge introduced in the preceding chapters The motivation for the object‐tracking application is to determine the position of an object in indoor environments (Eg Home) The application acts as an interface which collects the raw data that the sensors generate (numerical values) It makes the proper comparisons and algorithmic processes to identify and track the object while it is static inside the area covered by the wireless sensor network Finally with the detection and tracking data it alerts the user or another system The following sections provide an overview of the object‐tracking application

42 Overview of hardware product Before describing the object tracking application it is important to know the characteristics of hardware product The SENTIO platform (figure 15) is built in modular fashion and has the following key features [8]

Flexibility This platform is very flexible in such a way that it is made easy to add or change the hardware for each sensor task depending on the requirement in the WSN For a specific platform this can be hard or sometimes even impossible to accomplish

Sensor resources Resources in a sensor module includes the ability of usage of controllerrsquos resources as much as possible such as ADC‐channels interrupts and digital Input‐Outputrsquos Local voltage regulation should also be possible since many sensors require higher supply voltages than digital Integrated Circuits do Further support of a shut‐down mechanism to power down the sensor modules when not needed

Computation and memory The processor module based on microcontroller should support many devices in a microcontroller family and support different power supply voltages enabling full range of clock frequencies All power‐down modes of the devices should be

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

31

supported The platform should support different processor modules including FPGArsquos and very simple microcontrollers

Communication The focus should be on enabling an IEEE 802154 compliant communication interface With or without ZigBee stack this will provide a flexible platform for exploring different network topologies Support for power‐down of the entire communication module should be supported

Cost and form factor High flexibility is in conflict with low cost and small size The primary objective should be flexibility but the size should be considered in order to enable sensor device integration in systems where space is limited

Figure 15 SENTIO Platform [8]

The communication subsystem of the SENTIO sensor node was designed so that the correct operation of the radio chip is ensured The radio chip is powered by the on‐board voltage regulator and thus fluctuations in the battery voltage level do not affect the operation of the communication subsystem This platform uses Chipcon CC2420 IEEE 802154 radio transceiver operates in the 24GHz ISM band and includes a digital direct sequence spread spectrum (DSSS) modem providing a spreading gain of 9dB and an effective data rate of 250Kbps It was specifically designed for low power wireless applications and supports 8 discrete power levels 0dBm minus1dBm minus3dBm minus5dBm minus7dBm minus10dBm minus15dBm and minus25dBm at which its power consumption varies from 29mW to 52mW [21] A built‐in received signal strength indicator gives an 8‐bit digital value RSSIVAL The RSSIVAL is always averaged over 8 symbol periods (128μs) and a status bit indicates when the RSSIVAL is valid (meaning that the receiver was enabled for at least 8 symbol periods) The

Antenna

Communication Module

Processing Module

Sensing Module

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

32

power P at the RF pins can be obtained directly from RSSIVAL using the following equation

P = RSSIVAL + RSSIOFFSET [dBm] where the RSSIOFFSET is found empirically from the front‐end gain and it is approximately equal to minus45dBm In the next sections when we refer to the RSSI value we refer to the RSSIVAL and not the power P unless otherwise stated

43 Experimental Testbed The experimental test bed for object tracking application is as shown in figure 16 Experiment was conducted in Masterrsquos room at Mid Sweden University A DELL laptop is connected to SENTIO platform and software were primarily used to collect samples of RSS from APrsquos The software monitors IEEE 802154 radio frequency channels which operate in the 24GHz band

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

1030

BC - 1030

AP 3 AP 1

AP 2

AP 4

BS

HEWLETT

PACKARD

Figure 16 Layout of Masterʹs Room

A three dimensional scalable testbed is used for indoor sensor network deployments The testbed illustrated in figure 16 is a 3‐D structure measuring 16m (L) x 12m (W) x 3m (H) and it is designed to host a large number of static nodes as shown in figure 16 Arrow marks in the above figure represent the location where fingerprints were taken The centrepiece of our infrastructure

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

33

is the SENTIO sensor node [8] an open‐source general purpose sensing platform designed with AT Mega 128 microprocessor and the IEEE 802154 compliant CC2420 radio from Chipcon [19]

44 Obtaining data from the sensor network The next important step in the development of the object tracking application is to obtain the data from the sensor network and send it to the base station From there the data is transmitted through a serial port to the computer that runs the object tracking application When the data arrives at the serial port the application must capture it This paragraph gives a brief explanation of how and what kind data is transmitted by the static object fixed sensors and how this data is being used to estimate the position of the object First the static object transmits Beacon packet continuously which is received by all the fixed APrsquos The beacon packet format is as shown in table 6 Then the fixed APrsquos calculates the 8‐bit value of RSSI from the transmitted beacon packet and then sends the resultant RSSI value as a form of data packet (table 7) to the BS which calculates the position and sends back to the object

45 Object tracking application Testing and Evaluation The purpose of this section is to discuss and evaluate the object tracking application Finally we are ready with the experimental testbed as shown in figure 16 As shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors placed at the corners of the masterrsquos room BS is placed at the centre of the room connected to a PC All the nodes are power connected before an actual measurement takes place At first the object is placed at random position inside the room and transmits beacon packets continuously Then these packets are received by the fixed APrsquos which calculate the RSSI from the packet and then averages it until it receives 50 beacon packets and then transmits the data to the BS (figure 17) Then the BS waits until it receives the data from all the APrsquos Once it receives from all the APrsquos it calculates the position of the object (figure 18) The application was developed to demonstrate a real world use of a WSN in object tracking In evaluating the final position obtained with respect to the actual position we conclude that the object‐tracking was successful Although

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

34

it is a research prototype object tracking application is a simple stable stand‐alone system capable of demonstrating an important application of WSN It takes raw data from the WSN aggregates it evaluates it and based on the evaluation tracks the object The accuracy of estimating the position is less than 5m Table 11 below shows a log of readings taken during test

Start

Intialize micro‐controller and transeciever in receieve mode

If received_packet=

Beacon

Average RSSI values

Increase the counter

If countergt1000050

A

No

Yes

B

B

Yes

No

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

35

A

Reset counter

Set transceiever in transmit mode

Transmit averaged RSSI to Main sensor and set it back to receive mode

10000 samples ‐ Offline

50 samples ‐ Online

Figure 17 Flow chart representing the algorithm of fixed sensors

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

36

Start

Intialize micro‐controller and transeciever in receieve mode

Received RSSI values from all fixed Sensors

Open radio map

Calculate Euclidean distance between obtained RSSI values from fixed sensors and fingerprints

in radio map

No

Yes

Bubble sort for least error in signal strength values

A

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

37

A

Get the location of the object from the radiousing the least error value

Display location of object on PC

End

Figure 18 Flow chart representing the algorithm of main sensor

Table 11 Accuracy of positioning system

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

38

5 RESULTS This chapter presents how the system parameters such as number of samples mean or median etchellipare finalized All the tests are performed in masterrsquos room at Mid Sweden University This chapter is divided into three subsections First in Section 51 it reviews results obtained from samples test Section 52 discusses one dimensional test results and finally section 53 discusses results of two dimensional tests

51 Measurement setup A Dell laptop was connected to a SENTIO platform and is loaded with software (Appendix 2) This setup was used to determine number of samples required during object tracking mean or median position estimation algorithm and one dimensional test The setup is as follows

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

103

0

BC - 1030

AP 1AP 2

BS

HEWLETT

PACKARD

Figure 19 Test bed for one dimensional test in Masters Room

Two SENTIO nodes (AP1 and AP2 as shown above) were separated by a distance of 10 meters and these sensors are known as fixed sensors which are fixed to the ceiling such that there antenna is faced towards the ground They are linear is position A BS was connected to Dell laptop and it was placed in such a way that its antenna was faced towards the ceiling Another SENTIO

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

39

node was used as an object and its orientation was same as BS Figure 19 represents the complete setup for object tracking application Mobile node was moved between these fixed ones at regular intervals of one meter and RSSI values were logged at each interval

52 Number of samples test This test was performed to determine number of samples required for accurate positioning and either mean of the obtained RSSI values or median of them Figure 19 represents the setup for number of samples test Once the setup is ready the measurements are taken as follows

1 An object was tagged with SENTIO node and was placed stationary linear to the fixed sensors

2 10000 samples (RSSI) were taken at regular intervals of 1 meter up to 10 meters

3 The sample values were logged at every interval 4 Using these logged samples I have plotted graphs such as distribution

of RSS values (figure 20 21) meanmedian at intervals of 1 meter using MATLAB

5 The mean and median for various numbers of samples is as shown in figure 22

Figure 20 Histogram plot of RSSI values at 1m and 4m

Figure 21 Histogram plot of RSSI values at 6m and 8m

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

40

Figure 22 Distance vs RSSI

From the above graphs following conclusions were made 1 From the histogram plots we can observe that the distribution of RSSI

values follow Gaussians distribution law 2 As we can see from figure 22 that median curve has drastic changes

when compared to mean curve Hence mean is considered while estimating the position

3 From the observations we conclude that the number of samples does not matter since mean curve tends to be same for various numbers of samples

4 Hence 10000 samples are considered in order to get a proper location fingerprint during off‐line phase and during online phase 50 samples are enough to estimate the position of object

53 0ne dimensional test Measurement setup for one dimensional test is as shown in figure 19 This test was conducted to check the performance of algorithm and how accurate it was At first ie during offline phase fingerprints at each interval averaging 10000 samples were taken During online phase Euclidean algorithm was used estimate the position averaging 50 samples of RSSI From the results we

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

41

can realize that the error was less than 2 meters Table 12 shows the result of location estimation in one dimensional case

Table 12 One dimensional position estimation results

54 Two dimensional test This test is the final stage of my thesis work Experimental testbed for this stage is as shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors fixed at the corners of the room and a BS at the centre of the room There are 18 pre‐determined locations where the fingerprints (RSSI) were taken The fingerprints are as shown in Appendix 1 All the readings were taken in presence of userrsquos The algorithm for position estimation is shown in Appendix 2 Position estimation test is performed as follows

Tagged object is placed at random inside the masterrsquos room All the sensors including the base station and object are switched on Using the algorithm (Appendix 2) the BS estimates the position of the object and displays it on the PC

This test was performed using various numbers of samples to test which has a better accuracy Figure 23 represents the error rate using various numbers of samples From figure 23 we can conclude that 50 numbers of samples is enough to estimate the position of object with an accuracy of 3m

55 Causes of error in location determination The randomness of RSS is the major cause of error in any indoor

positioning system that uses the RSS for location inference If there is no randomness in the RSS and its location fingerprint every indoor positioning systems of this type will have no location detection error For example I have recorded RSS at one particular location during different times of the day From figures 24 and 25 you can observe that RSS values are different at different times of day

The performance of indoor positioning systems depends greatly on the separation of location fingerprints A location fingerprint

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

42

corresponding to a location can be identified correctly if it is difficult to classify it as another fingerprint by a pattern classifier

Path loss is one of the basic signal propagation properties of the RSS that best describes attenuation of transmit signal power over a distance between an access point and a mobile device

Presence of Userrsquos or userrsquos orientation affects RSS

Figure 23 Error Rate vs Number of samples

Figure 24 RSS distribution at 214 pm

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

43

Figure 25 RSS distribution at 100pm and 1100 am

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

6 DISCUSSIONS10102006

44

6 DISCUSSIONS 61 Summary and Conclusion While empirical results and performance studies of positioning systems based on location fingerprint have been presented in this report analytical model that can be used as a framework for efficiently designing the positioning systems are not available This thesis design work uses general principles of position estimation such as position estimation of sensors grid spacing etc This design work focuses on accuracy and precision The concept of location fingerprint technique based on RSS us explained in detail in chapter 2 It is found that RSS is random It is possible to approximate the RSS as being normally distributed While the RSS random process is non‐stationary the mean is more or less constant and could be used as the fingerprint of the location Model designed for position estimation is as shown in chapter 4 Accuracy and precision are the two major performance matrices in this study Uniform grid spacing has been used From the experimental analysis I have obtained an accuracy of 3 meters and 35 precision The sensitivity analysis among the system parameters and the performance matrices were done using the model as shown in chapter 4 For instance our study found that the minimum distance between two adjacent positions also affects the positioning performance This is due to the randomness of RSS patterns induced by the ever changing indoor environment

62 Future work The research in this thesis provides a ground work for the future study of efficient design of indoor positioning system The RSSI based position estimation still has limitations First the RSSI value is time dependent Thus the future study should address this issue Second the movement of object is not considered Third orientations of sensors affect the performance which should be taken care off Fourth to improve the performance of the system we need to find a solution to deal with those incomplete or missing data during the off‐line and the on‐line phase Fifth position estimation on multi‐floor and three‐dimensional coordinates is not available

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 110102006

45

APPENDIX 1

Location Fingerprints of Masters Room Time 1315 Observation Time 20 minutes Number of Samples 10000

People Presence SNo Co-ordinates AP1 (0x0001) AP2 (0x0002) AP3 (0x0004) AP4 (0x0008)

1 11 -24 -16 -27 -33 2 13 -26 -8 -34 -31 3 15 -43 -9 -39 -25 4 32 -21 -11 -27 -33 5 61 -6 -23 -32 -28 6 64 -20 -31 -25 -34 7 35 -23 -35 -29 -38 8 37 -29 -35 -22 -27 9 39 -38 -29 -21 -27 10 67 -27 -23 -20 -23 11 611 -26 -29 -23 -30 12 613 -35 -32 -2 -31 13 513 -34 -31 -20 -26 14 11 -39 -22 -29 -17 15 212 -37 -42 -28 -31 16 -212 -38 -33 -29 -6 17 -21 -42 -29 -33 -19 18 -38 -40 -23 -35 -22

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 210102006

46

APPENDIX 2

Begin

Open radio map and raw data files for write

Intialize radio map in memory

Check whether the

Packet is data or Beacon packet If data packet

Save the data to register

Check if data is

Received from all

sensors

Estimate the position of static

Display the position on PC

End Yes

No

A

A

Yes

No

Flow chart representing the main algorithm for position estimation

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

47

APPENDIX 3

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

48

Distribution of RSSI

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

49

REFERENCES [1] T S Rappaport J H Reed and B D Woerner ldquoPosition location using wireless communications on highways of the futureʺ IEEE Commun Mag vol 34 no 10 pp33‐41 Oct 1996 [2] A M Ladd K E Bekris G Marceau A Rudys L E Kavraki and D S Wallach rdquoRobotics‐based location sensing using wireless ethernetʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ02) 2002 pp 227‐238 [3] I Ramani R Bharadwaja and P V Rangan ldquoLocation tracking for media appliances in wireless home networksʺ in Proc International Conference on Multimedia and Expo (ICMEʹ03) July 2003 pp 769‐772 [4] K Pahlavan X Li and J P Makela ldquoIndoor geolocation science and technologyʺ IEEE Commun Mag vol 40 no 2 pp 112‐118 Feb 2002 [5] Konard Lorincz and Matt Welsh ldquoMote Track A Robust Decentralized Approach to RF‐Based Location Trackingrdquo [6] Paramvir Bahl and Venkata N Padmanabham ldquoRADAR An In‐Building RF‐based User Location and Tracking Systemrdquo [7] Kamol Kaemarungsi ldquoDesign of Indoor positioning system based on location fingerprint techiniquerdquo University of Pittsburgh 2005 [8] Fredrik Linnarsson Cheng Peng and Bengt Oelmann ldquoSENTIO A Hardware Platform for Rapid Prototyping of Wireless Sensor Networksrdquo [9] S Saha K Chaudhuri D Sanghi and P Bhagwat ldquoLocation determination of a mobile device using IEEE 80211b access point signalsʺ in Proc IEEE Wireless Communications and Networking Conference (WCNCʹ03) New Orleans LA Mar 2003 pp 1987‐1992 [10] T Roos P Myllymaki H Tirri P Misikangas and J Sievanen ldquoA probabilistic approach to WLAN user location estimationʺ International Journal of Wireless Information Networks vol 9 no 3 pp 155‐164 July 2002 [11] R Battiti M Brunato and A Villani ldquoStatistical learning theory for location fingerprinting in wireless LANrsquosʺ Technical Report Oct 2002 [Online] Available httprtmscienceunitnitraquobattitiarchive86pdf [12] P Prasithsangaree P Krishnamurthy and P K Chrysanthis ldquoAn indoor position location with wireless LANsʺ in Proc IEEE International Symposium on Personal Indoor and Mobile Radio Communications (PIMRCʹ02) Lisbon Portugal Sept 2002 [13] M A Youssef A Agrawala and A U Shankar ldquoWLAN location determination via clustering and probability distributionsʺ in Proc IEEE

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

50

International Conference on Pervasive Computing and Communications (PerComʹ03) Dallas‐Fort Worth TX Mar 23‐26 2003 pp 23‐26 [14] Z Xiang S Song J Chen H Wang J Huang and X Gao ldquoA wireless LAN‐based indoor positioning technologyʺ IBM Journal of Research and Development vol 48 no 56 pp 617‐626 SeptNov 2004 [15] R Want A Hopper V Falcao and J Gibbons ldquoThe active badge location systemʺACM Transactions on Information Systems vol 40 no 1 pp 91‐102 Jan 1992 [16] N B Priyantha A Chakraborty and H Balakrishnan ldquoThe cricket location‐support systemʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ00) Boston MA Aug 2000 pp 32‐43 [17] Holger K Willig A (2005) Protocols and Architectures for Wireless Sensor Networks London [18] IEEE (2003) Part 154 Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low‐Rate Wireless Personal Area Networks (LR‐WPANs) New York [19] Chipcon CC2420 802154 compliant radio httpwwwchipconcom [20] ZigBee Specification v10 ZigBee Alliance [21] Dimitrios Lymberopoulos Quentin Lindsey and Andreas Savvides ldquoAn Empirical Analysis of Radio Signal Strength Variability in IEEE 802154 Networks using Monopole Antennasrdquo [22] Kiran Yedavalli Bhaskar Krishnamachari Sharmila Ravula and Bhaskar Srinivasan ldquoEcolocation A sequence based technique for RF localization in wireless sensor networksrdquo Robert Bosch Research and Technology Centre Palo Alto CA

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

10

but reflects off most of the indoor obstructions It has a short range around 3 m to 10 m but has a 1 cm resolution of distance measurement The operating temperature influences the performance of ultrasound

222 Classification based on measurement principle Wireless positioning systems can be categorized by measurement techniques which are used to derive the position of mobile stations Mainly three different measurement principles are used today AOA RSS and propagation‐time based systems that can further be divided into two different sub‐classes TOA and TDOA Figure 3 shows taxonomy based on the technology and the technique classifications

Figure 3 Taxonomy of positioning system [7]

In AOA systems the position is calculated via goniometry With the use of directional antennas or antenna arrays the angle or bearing relative to points located at known positions is measured The intersection of several measured directions pointers the yields the position value The accuracy of this approach is limited by the possible directivity of the measuring aperture by shadowing and by multi‐path reflections arriving from misleading directions In TOA and TDOA techniques rely on the precision of timing between the signal transmitter and the receiver in order to use the propagation delay or TOF to calculate the distance between transmitter and receiver Therefore a precise synchronization is also very important in such systems By combining at least three distances from three reference positions triangulation can be used to estimate the mobile stationʹs location This type of technique will

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

11

require a high accuracy clock in the communication system TDOA is more practical [1] Examples of location‐sensing systems that use time of flight are GPS the Active Bats [15] and the Cricket [16] Besides these time delay‐based techniques the distance between transmitter and receiver can also be determined from signal strength attenuation and direct distance measurement (such as dead reckoning) Fingerprinting or Location Pattern Matching this technique generally requires only measurement of RSS or other non‐geometric features at several locations to form a database of location fingerprints To estimate the mobile location the system needs to first measure the received signal strength at particular locations and then search for the pattern or fingerprint with the closest match in the database This technique does not require the mobile station to see at least three base stations or access points in order to determine the location The disadvantage of this technique is that it is very time‐consuming to perform an exhaustive data collection for a wide area network such as in outdoor positioning systems

23 Indoor local positioning using ZigBee based on RSSI This section reviews relevant RF‐based indoor positioning systems which can be used to locate stationary objects and track mobile users The impressive growth of IEEE 802154 wireless PANs (WPAN) in recent years suggests an interesting future for the location fingerprinting technique The system is quite flexible because system designers can select whether to have a centralized positioning server or let the mobile to determine its own position However the fingerprinting technique requires a training phase (off‐line phase) to collect location fingerprints for all positions in the operating area before the actual deployment (on‐line phase) [7] The following discussion is divided according to the positioning system components First the common form of location fingerprinting and its relationship with physical locations are explained Finally location estimation algorithm is reviewed

231 Location Fingerprint A location fingerprint based on RF characteristics such as RSS is the basis for representing a unique location It is created under the assumption that each position or location inside a building has unique RF signature [4] Generally a fingerprint F is labelled with a location information L The location fingerprints and their labels (eg location information) are maintained in

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

12

database and used during the on‐line phase to estimate the location The label and fingerprint are usually denoted as a tuple of (L F) Battiti et al [11] point out that the location information L for indoor location can be recorded in two forms as either a tuple of coordinates or an indicator variable The tuple of real coordinates can vary from one dimension to five dimensions which include the three dimension space and two orientation variables expressed in spherical coordinates For instance a location information of a two‐dimension system with an orientation could be expressed as a triplet L = (x y d) | x y є R 2 d є North East South West The RSS was found to be more location‐dependent than the SNR because the noise component is rather random in nature However the RSS itself fluctuates over time for each access point and location Each RSS element can be considered as a random variable therefore it can be captured by recording its descriptive statistics parameters approximating its distribution or maintaining the whole measurement dataset These approaches of RSS representation result in different procedures for location estimation algorithm Regardless of the approach the location fingerprint is usually denoted as an array or vector of signal strength (random variables) received at any position in the location‐based service area The size of the vector is determined by the number of access points that can be heard To create a basis fingerprint such as in a number of samples of vectors of signal strength are collected over a window of time for each position This basis is called a prototype Then the average RSS of each access point is calculated and recorded as an element in the location fingerprint For an area that can receive signals from N access points the location fingerprint can be expressed as a vector of average RSS elements βi

F = (β1 β2hellip βN)T Besides the basis location fingerprint the samples of location fingerprint measured during the on‐line phase are also important for the system that tracks the mobile object The number of samples should be selected appropriately to represent the location fingerprint for the mobile application A step in statistical analysis method called pre‐processing is another important issue needed to be considered because it can impact the estimation of dependency between location fingerprint and the location information easier The pre‐processing refers to a step that cleans the raw data (in this case the training set) before any further operations or analysis The cleaning may consist of encoding dimensionality reduction (reduce unnecessary elements) feature extractionselection clustering and outlier elimination Roos et al [10] point out that the pre‐processing enables faster location estimation and

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

13

reduce the noise from the training data The process that creates the basis of location fingerprint discussed above could be considered as a part of pre‐processing

232 Location Estimation Algorithm Location estimation algorithms or positioning algorithms are procedures that exploit dependency between location information and location fingerprint basis in order to determine a position or location from samples of RSS signals Position algorithms can be classified into deterministic and probabilistic types based on the approaches that model the relationship between location fingerprints and location information The deterministic type of algorithms are those that based in the nearest neighbour classifiers and neural network classifiers The probabilistic type of algorithms is those that are based on the Bayesian inference and statistical learning theory such as support vector machines (SVR) The algorithm used in this project is discussed briefly

Euclidean distance method The Euclidean distance method is a deterministic approach of position estimation because they require only a set of constant location fingerprints which includes mean vectors In order to determine the location a form of discriminate function is used to classify a sample of RSS fingerprint into a position The mean or average RSS vector is a centre of a mass which represents each class of location fingerprint Suppose a set of l location fingerprints is denoted by F1 F2hellip Fl exist and each fingerprint has a one‐to‐one mapping to a set of positions L1 L2hellip Ll A sample of an RSS fingerprint measured during an on‐line phase is denoted as S which can be another mean or average RSS vector of small window of RSS samples Assuming that an indoor positioning system only considers the average RSS from N access points as a location fingerprint the sample of RSS vector is S = (s1 s2 helliphellip sN)T and each location fingerprint i in the database can be expressed as Fi = (β1 β2helliphellip βN)T The simplest closeness metric is a distance measurement in signal space denoted as the Dist () function [6] Thus the simple procedure of nearest neighbour algorithm is expressed as picking the fingerprint j that has the shortest signal distance

Dist(S Fj) le Dist(S Fk) for all k ne j A generalized weighted distance Lp summarized by [12] can be used to calculate different forms of distance in signal space as

Lp = 1N pN

i

pPiSiWi 1

1)||1(sum

=

minus

where N is the number of access points wi is a weighting factor (wi le 1) and p is the norm parameter starting from 1 The weighting factor wi is a bias

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

14

parameter that can demote or promote an important RSS component in the fingerprint

24 Protocols and Industry standards for WSN Although many applications for WSN are proposed the communication protocols supporting them remain mainly unexplored and diverged This is because the area of interest is new each implementation is application specific and the components of WSNrsquos have a lot of constraints Wireless sensor networks use the wireless medium to communicate However the traditional communication protocols that support wireless communications especially ad‐hoc mesh networks may not be well suited for them WSN communication is an active research area and many algorithms and protocols have already been proposed This section introduces two proposed standards the IEEE 802154 and the ZigBee The IEEE 802154 covers the physical layer and the MAC layer of low‐rate WPAN The ZigBee is ldquoan emerging standard that is based on the IEEE 802154 and adds network construction (star networks peer‐to‐peermesh networks and cluster‐tree networks) application services and morerdquo

241 IEEE 802154 Holger and Willig (2005) [17] mention home automation home networking and home security as possible applications for IEEE 802154 They add that ldquomost of these applications require only low‐to‐medium bitrates (up to some few hundreds of kbps) moderate average delays without too stringent delay guarantees and for certain nodes it is highly desirable to reduce the energy consumption to a minimumrdquo The IEEE 802154 standard (2003) defines the device types that can be used in a LR‐WPAN A device can be a FFD or a RFD The RFD can be used in simple applications in which they do not need to transmit large amounts of data and they have to communicate only with a specific FFD The FFD can work as a PAN coordinator as a coordinator or as a simple device It can communicate with either another FFD or a RFD In keeping with the application requirements the LR‐WPAN operates in a star or peer‐to‐peer topology (figure 4) In the star topology the RFD communicates with a single controller the PAN coordinator The PAN coordinator can perform the same function as the RFD but it is also responsible for controlling the PAN ldquoit initiates terminates or routes communication around the networkrdquo [18] The peer‐to‐peer topology

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

15

supports ad‐hoc mesh multi‐hop networking Any device in the peer‐to‐peer topology can communicate with any other device within its communication range however this topology also has a PAN coordinator All the devices in a LR‐WPAN have a unique 64‐bit address This or a short address allocated by the PAN coordinator can be used inside a PAN Additionally each PAN has a unique identifier The combination of the PAN identifier and the sort addresses allows communication across different PANs (IEEE 802154 Standard 2003)

Figure 4 Star and peer‐to‐peer topologies in LR‐WPAN [18]

The LR‐WPAN based on the OSI seven‐layer model has the layered architecture presented in figure 5 The application and the network layer are the upper layers in the LR‐WPAN architecture but are outside the scope of the IEEE 802154 standard Only the physical layer ldquowhich contains the RF transceiver along with its low‐level control mechanismrdquo and the MAC layer ldquothat provides access to the physical channelrdquo are included in the standard and will be introduced in the following sections Finally the Type I 8022 LLC and the SSCS are intermediate sub layers supporting communication with the above layers (IEEE 802154 Standard 2003)

Physical Layer The 802154 standard specifies two different services that the PHY provides The PHY data service controls the radio and thus the transmission and reception of the PPDUs In addition the management service performs ED in the channel The management service also performs CCA before sending the messages and provides LQI for the received packets

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

16

Figure 5 WPAN architecture [18]

Three different bands are defined by the standard The 868‐8686 MHz for Europe the 902‐928 MHz for North America and the 2400‐24835 MHz worldwide All of them belong in the ISM radio bands and they are using DSSS Each of the bands supports different a data rate and it uses a different modulation technique chip rate and number of channels Moreover if the system does not use the 2450 MHz frequency it operates in both the 802 MHz and 902 MHz frequencies Table 3 summarizes the supported bands and data rates (IEEE 802154 standard 2003)

PHY (MHz)

Frequency Band (MHz) Modulation

Bit Rate Number of Channels

868-8686 BPSK 20 1 868 915 902-928 BPSK 40 10

2450 2400-24835 O-QPSK 230 16 Table 3 Frequency bands and data rates for IEEE 802154 based on the IEEE

802154 standard [18] The physical layer uses PPDU packets to communicate Table 4 demonstrates its structure The LSB is always transmitted and received first The SHR contains the preamble and the SFD fields which helps receiver

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

17

synchronization The 8‐bytes preamble contains only zero and is used for synchronization The SFD contains a specific sequence of one and zeros and specifies the beginning of the frame The PHR contains the payload length Packets with a length of 9 or more bytes are MPDU as the next section further explains Packets with length 5 are used for MPDU acknowledgements The payload part of the PPDU encloses the MAC layer packet Finally the PPDU size can be up to 127 bytes [18]

4 bytes 1 byte 1 byte Variable

Preamble SFD Frame length

(7 bits) Reserved

(1 bit) Payload (PSU)

SHR PHR PHY Payload Table 4 Format based on the IEEE 802154 standard [18]

MAC Layer The MAC layer is the interface between the SSCS and the PHY layer Similar to the PHY layer the MAC layer supports two services The MAC data service is responsible for the transmission and reception of the MPDUs through the PHY data service The MAC management service if the device is a coordinator manages the network beacons It is also responsible for PAN association and disassociation frame validation and acknowledgment providing ldquoa reliable link between two peer MAC entitiesrdquo In addition it uses the CSMA‐CA for channel access and handles and maintains the GTS mechanism Finally it supports device security [18] The IEEE 802154 standard defines four different frame types the beacon data acknowledgment and MAC command frame All frame types are based on the general MAC frame format (table 5) The frame control field describes and specifies the above different frame types Every MAC frame comprises a MHR which consists of a frame control sequence number and the information field It also contains the MAC payload different frame types have different MAC payload fields The acknowledgment type does not have a payload Finally each frame includes a MFR which contains a FCS The data in the MPDU follows the same order as the PPDU the least significant bits are left in the frame and are transited first Octets2 1 02 028 02 028 Variable 2

Destination PAN Identifier

Destination Address

Source PAN

Identifier Source Address Frame

Control Sequence Number Addressing Field

Frame Payload FCS

MHR MAC

Payload MFRTable 5 General MAC frame format IEEE 802154 standard [18]

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

18

Table 6 7 8 and 9 present the four different MAC frame types The beacon frame is transmitted periodically by the PAN coordinator It provides information about the network management through the super frame and GTS fields which are analyzed later in the section It also synchronizes the network devices and indicates the proper communication period for them The data frame payload encapsulates data from the higher layers When a device receives a packet it is not obliged to response with an acknowledgement packet Finally the command frame identifier and command payload fields of the command frame are used for communication between the network devices The command identifier specifies actions like association disassociation and data GTS or beacon request Octets2 1 410 2 Variable Variable Variable 2

Frame Control

Sequence Number

Addressing Fields

Super frame Specification

GTS fields

Pending Addressing

Fields Beacon Payload FCS

MHR MAC Payload MFRTable 6 Beacon frame format [18]

Octets 2 1 Variable 2

Frame Control

Sequence Number

Addressing Field

Data Payload FCS

MHR MAC

Payload MFR Table 7 Data frame format [18]

Octets 2 1 2

Frame Control

Sequence Number FCS

MHR MFR Table 8 Acknowledgement frame format [18]

Octets2 1 1 Variable 2

Frame Control

Sequence Number

Addressing Fields

Command Frame Identifier

Command Payload FCS

MHR MAC Payload MFR Table 9 Command frame format [18]

In the LR‐WPAN every PAN has its own coordinator The PAN coordinator manages the communication in the local area it has two options to use or not use the super frame structure The super frame (figure 6) uses network beacons If the coordinator does not want to use a super frame structure it suspends the beacon transmission However the beacon is important for device association and disassociation If the coordinator wishes to maintain close communication control in the PAN and to support low‐latency devices it usually uses the super frame A super frame determines a specific time

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

19

period beacons bound it The beacon is transmitted in the first of the sixteen equal time slots that the super frame has It is used to ldquosynchronize the attached devices to identify the PAN and to describe the structure of the super framerdquo The super frame can have active and inactive periods All the communications have to be finished inside the super frame period In the inactive periods the devices can switch to the sleep mode but they have to be ready for the next beacon The active period is further divided into a CAP and a CFP During the CAP any device can communicate competing with the other devices in the PAN using slotted CSMA‐CA The CFP contains GTSs and follows the CAP A CFP may maintain up to seven GTSs and each GTS can reserve more than one time slot However the CAP should always be sufficient to allow new devices t join the PAN The GTSs are allocated to specific devices All the transactions must be completed inside the assigned time period (CAP GTS and CFP) (IEEE 802154 standard 2003)

Figure 6 Example of super frame structure [18]

In accordance with the 802154 standard (2003) three different types of data‐transfer exist In addition the types differ if the coordinator uses or does not beacons Data transfer from a device to the PAN coordinator is the first type (figure 7) For a ldquonon‐beacon‐enable networkrdquo it first senses the medium by using ldquoun‐slotted CSMA‐CArdquo and then a simple transmit to the data frame In a ldquobeacon‐enabled networkrdquo the sender waits for the beacon when it finds it ldquothe device synchronizes to the super fame structurerdquo In the specified time frame the sender again senses the medium by using ldquoslotted CSMA‐CArdquo and transmits the data to the coordinator In either case the coordinator has the option to acknowledge or not acknowledge the data reception after that the transaction is completed Data transfer from the coordinator is the next type described in the standard (figure 8) In a beacon‐enabled network the coordinator indicates a pending message through the beacon The messagersquos target device receives the beacon and if the message is pending it responses with a MAC command request

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

20

message using slotted CSMA‐CA the coordinator may or may not acknowledge the command message and through slotted CSMA‐CA sends the pending message The device acknowledges the received message After that the coordinator removes the message from the beaconrsquos pending list and completes the transaction In a non‐beacon enable network the device periodically sends a MAC command‐request frame to the coordinator The coordinator acknowledges the data request Then by using un‐slotted CSMA‐CA if it has a pending message it transmits it If it has not it respond with a data frame with a ldquozero‐length payloadrdquo To complete the transaction the device acknowledges the data reception

Figure 7 Communication from a device to a PAN coordinator in (a) a beacon‐enabled network and (b) a non‐beacon enabled network [18]

Peer‐to‐peer is the last type of data transfer In this situation the devices are free to communicate with any other device within their communication range In a peer‐to‐peer PAN the devices can ldquoeither receive constantly or synchronize with each otherrdquo If they are receiving constantly to transmit data they use un‐slotted CSAM‐CA In the second case synchronization must be achieved first (IEEE 802154 standard [IEEE 2003]) The IEEE 802154 (2003) standard establishes MAC and PHY standards for low‐cost low‐power and high‐density node deployments In addition to the above PHY and MAC characteristics IEEE 802154 provides a security baseline including ldquothe ability to maintain an ACL and use symmetric cryptographyrdquo for data encryption The algorithm that is used for encryption is the AES However the higher level layers decide when security is need The upper layers are in general responsible for device authentication and key management The next section introduces the ZigBee standard which

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

21

encapsulates the IEEE 802154 and provides additional standardization for the higher levels

Figure 8 Communication from a PAN coordinator to a device in (a) a beacon‐enabled network and (b) a non‐beacon enabled network [18]

242 ZigBee Heily (2004) defines ZigBee as ldquoa rapidly growing worldwide non‐profit industry consortiumrdquo whose mission is ldquoto define a reliable cost‐effective low‐power wirelessly networked monitoring and control product based on an open global standardrdquo Figure 9 illustrates the areas of interest for different wireless communication standards

Figure 9 Overview of the coverage for different wireless communication

standards [17]

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

22

ZigBee a new standard which became publicly available in June 2005 is based on the IEEE 802154 standard It expands the IEEE 802154 by adding the framework for ldquothe network security and applicationrdquo (Craig 2005) Figure 10 presents the IEEE 802154ZigBee stack and the areas of responsibilities

Figure 10 IEEE 802154ZigBee stack [20]

Craig (2005) mentions three networking topologies that the standard covers the star mesh and cluster tree (figure 11) The ZigBee standard works on top of the IEEE 802154 addressing schema by using the standard 64‐bit and the short 16‐bit addressing Kinney (2005) summarizes the ZigBee network layer responsibilities the successful establishment of a new the network and successful new device configuration addressing assignment network synchronization frames security and message routing ZigBee further distinguishes the concept of the physical devices (RFD FFD) by using the notion of ldquological devicesrdquo ldquoZigBee Coordinatorrdquo is the first type of logical devices It is responsible for initializing maintaining and managing the network Under the coordinator in the network hierarchy is the ldquoZigBee routerrdquo which is responsible for controlling the message routing between the nodes Finally the ldquoZigBee End Devicerdquo acts as the end point of the network

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

23

structure The tasks that an end device can perform are specified in the ldquoApplication Profilesrdquo (Craig 2005)

Figure 11 IEEE 802154ZigBee stack [17]

The ZigBee specifications (2005) summarize the security services provided by ZigBee ldquokey establishment key transport frame protection and device managementrdquo ZigBee builds its security mechanism using symmetric key cryptography The security services also depend on the associated layer thus as the figure 10 shows the security mechanism covers the network and the application layer In addition if a MAC frame needs security protection the MAC layer is able to secure it Moreover the notion of end‐to‐end security is supported the source and destination devices have access and use the same share key In the MAC layer the 802154 AES mechanism provides the proper security The mechanism protects ldquothe confidentiality integrity and authenticity of the MAC framesrdquo (Kinney 2005) An auxiliary header field in front of the MAC payload indicates if the frame is encrypted or not The MAC framesrsquo integrity is supported by calculating and using a MIC at the end of the MAC payload In addition to the AES nonce is used to provide MAC confidentiality and authenticity Figure 12 illustrates a MAC frame with security For different security aspects the MAC layer uses different mode of the AES for the encryption it uses the AES in Counter (CTR) mode and for the integrity the

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

24

CBC‐MAC Finally the combination (CCM) of the above two modes is available providing both encryption and integrity (Kinney 2005)

Figure 12 ZigBee secure frame in MAC layer [17]

In the network layer the CCM (a modified MAC layer CCM mode) is used for encryption Because the network layer uses only the CCM mode a single key is used for all different security options The network layer security message format is similar to the MAC frame it is presented in the figure 13 Finally although the network layer is responsible for securing its layer messages the above layers specify the keys and the CCM option for each frame (Kinney 2005)

Figure 13 ZigBee secure frame in network layer [17]

Security in the application layer works similar to the network and MAC layers It uses the ldquolink keyrdquo or the ldquonetwork keyrdquo to secure the message and then encapsulate it inside a set of fields similar to the network format (figure 14) Other security responsibilities that the application layer has are to provide the ZDO and the applications with device management services key establishment and key transport (ZigBee specifications 2005)

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

25

Figure 14 ZigBee secure frame in application layer [17]

The ZigBee application layer contains the manufacturer‐defined application objects the ZDO and the application sub‐layer In addition to the security responsibilities the application sub‐layer binds devices based on their duties and needs maintains the binding tables and forwards messages between them The application sub‐layer also discovers the neighbour devices for a given device The ZDO is responsible for determining the devicersquos duty in the network for communicating using binding requests and for supporting security as was mentioned above The sub‐layer that implements the actual application is the manufacturer‐defined application object (Kinney 2005) The ZigBee specifications are a great step toward the wireless sensors networks standardization They cover all the OSI layers from the physical to the application providing guidance to the developers The specifications are the product of the ZigBee alliances a consortium of companies and academic institutes Thus they not only produce the specifications but also are the first use them The next section goes beyond the layer architecture and standardization and introduces the OS field another crucial element of WSNs

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

26

3 RECEIVED SIGNAL STRENGTH (RSS) Majority of the existing methods leverage the existence of IEEE 80211 base stations with powerful radio transmit powers of approximately 100mW per base station Such radios are in a different class from the low power IEEE 802154 compliant radios that typically transmit at low power levels ranging from 52mW to 29mW [19] The wide availability of larger number of IEEE 802154 radios in the research community has revived the interest for signal strength based localization in sensor network Despite of rapidly increasing popularity of IEEE 802154 radios and signal strength localization there is a lack of detailed characterization of the fundamental factors contributing to large signal strength variation This chapter investigates some of the properties of RSS observed during testing and analysis of research work collected during the period of thesis The analysis of RSS values is needed to understand the underlying features of location dependent RSS patterns and location fingerprints An understanding of the properties of the RSS values for location fingerprinting can assist in improving the design of positioning algorithms and in deployment of indoor positioning systems

31 Parameters that affect RSS Different factors that affect the indoor RSS are the userrsquos presenceabsence hisher orientation time of the day building type and material distance from transmitter and type of radio These factors are by no means exhaustive Other possible factors are antenna orientation directionality and type Table 10 classify the parameters which affect RSS [7]

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

27

Effect on Factors Options

1 Proximity of user Userrsquos presence or absence

2 Orientation of user and terminal

North East South West

Data Collection

3 Make of Radio 4 Time of measurement Time of day amp days of

week 5 Period of measurement

Second minute hour

7 Interference Co‐channel adjacent co‐channel

Statistics

8 Building environment Small offices or large hall

Table 10 Classification of factors the affect RSS [7]

Transmitter variability Different transmitters behave differently even when they are configured exactly in the same way In practice this means that when a transmitter is configured to send packets at a power level of d dBm then the transmitter will send these packets at a power level that is very close to d dBm but not necessarily exactly equal to d dBm This can alter the received signal strength indication and thus it can lead to inaccurate distance estimation

Receiver variability The sensitivity of the receivers across different radio chips is different In practice this means that the RSSI value recorded at different receivers can be different even when all the other parameters that affect the received signal strength are kept constant

Antenna orientation Each antenna has its own radiation pattern that is not uniform In practice this means that the RSSI value recorded at the receiver for a given pair of communicating nodes and for a given distance between them varies as the pair wise antenna orientations of the transmitter and the receiver are changed

Multi‐path fading and shadowing in the RF channel In indoor environments the transmitted signals get reflected after hitting on the walls andor on other objects in the room such as furniture Both the original signal and the reflected signal reach the receiver almost at the same time since they both travel at the speed of light As a result of this the receiver is not able to

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

28

distinguish the two signals and it measures the received signal strength for both of them

32 Related work analysis A number of indoor location tracking systems have been proposed in the literature based on RF signals ultrasound infrared and some combinations of these As mentioned previously RF based positioning system are widely studied as shown in Table 2 Given a model of radio signal propagation in a building or other environment RSS can be used to estimate the distance between the transmitter and receiver Most of the algorithms (Table 2) have been evaluated in practice using 80211b compliant wireless radios and seem to produce a location error of 15ft to 20ft for an indoor network area of approximately 225ft x 144ft According to the results of this study all the localization techniques produce approximately the same location error over a range of environments Ecolocation [22] Mote Track [5] are two of the few localization algorithms that have been evaluated on a real sensor network that uses a low power wireless radio Ecolocation reports a location error of 10ft for a very small outdoor network deployment area (26ft x 49ft) Mote Track reports a location error of approximately 13ft for an indoor network deployment area of 18751ft2 In general the RSSI based localization algorithms can be divided into two broad categories the map based and the distance (or area) prediction based algorithms In the case of the map based algorithms the deployment area is divided into a large set of sample points that cover the whole area Most techniques collect one or more signal strength measurements from all visible beacon nodes and for each sample point Each sample point is then mapped to either a signal strength vector or a signal strength probability distribution This process generates an RSSI fingerprint map which is unique with respect to the beacon locations and the deployment area This fingerprint map is stored to a central base station A node that is not aware of its location collects one or more signal strength measurements from all the visible beacon nodes and creates its own RSSI fingerprint that is sent to the central base station The base station computes nodersquos location as the sample pointrsquos location whose RSSI fingerprint distance from the unknown nodersquos RSSI fingerprint is the minimum among all the sample points Despite the fact that the map based RSSI localization methods produce relatively small location errors they are not attractive mainly because they require an excessive amount of profiling data and because they are not fully distributed The detailed profiling of a building is a major holdback because it is complex time consuming and depends on beacon locations Therefore for different buildings or even for the

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

29

same building and different beacon locations different RSSI maps have to be obtained The second category of RSSI based algorithms are the distance (or area) prediction algorithms which focus on minimizing the amount of profiling data In distance prediction based methods raw RSSI data are directly mapped to distances through a signal propagation model In some cases distances are not explicitly measured but they are taken into consideration through assumptions on the underlying distributions In the case of outdoor deployments where there are no obstacles a single signal propagation model is all the profiling data required In the case of indoor deployments the same applies but now the signal propagation model might be different across different buildings with different furniture and wall arrangements Given an underlying signal propagation model raw RSSI data can be mapped to specific distances These distances can be either used directly in order to perform localization or assigned a probability and used in a learning basedprobabilistic localization algorithm Obviously the accuracy of the distance prediction based methods depends heavily on the accuracy of the signal propagation model used for translating RSSI values to distances

33 Properties of RSSI Changes in signal due to propagation indoors are difficult to predict because of dense environment and propagation effects such as reflection diffraction and scattering The multi‐path fading effect which is the result of either constructive or destructive combination of multiple signal copies at the receiver causes the received signal to fluctuate around a mean value at a particular location The received signal is usually modelled by the combined effects of large‐scale fading and small‐scale fading The large‐scale fading component describes the signal attenuation as the signal travels over a distance and is absorbed by material such as walls and floors along the way to the receiver This component predicts the mean of the RSS and usually has a lognormal distribution On the other hand the small scale fading component explains the dramatic fluctuation of the signal due to multi‐path fading On the other hand the small scale‐fading component explains the dramatic fluctuation of the signal due to multi‐path fading If there is no LOS component the small‐scale‐fading is often modelled with a Rayleigh distribution Such scenarios are commonly called NLOS If there is a LOS component the small‐scale‐fading is typically modelled by a Rician distribution Note that when the RSS is measured the measurement averages out the small‐scale‐fading effects

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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4 OBJECT TRACKING 41 Introduction This chapter presents the object‐tracking application the main objective of this thesis work The application is an algorithmic‐software implementation written in Embedded C which is capable of receiving data messages and generating object position estimation data The implementation of the object‐tracking application makes use of the SENTIO platform and uses the background knowledge introduced in the preceding chapters The motivation for the object‐tracking application is to determine the position of an object in indoor environments (Eg Home) The application acts as an interface which collects the raw data that the sensors generate (numerical values) It makes the proper comparisons and algorithmic processes to identify and track the object while it is static inside the area covered by the wireless sensor network Finally with the detection and tracking data it alerts the user or another system The following sections provide an overview of the object‐tracking application

42 Overview of hardware product Before describing the object tracking application it is important to know the characteristics of hardware product The SENTIO platform (figure 15) is built in modular fashion and has the following key features [8]

Flexibility This platform is very flexible in such a way that it is made easy to add or change the hardware for each sensor task depending on the requirement in the WSN For a specific platform this can be hard or sometimes even impossible to accomplish

Sensor resources Resources in a sensor module includes the ability of usage of controllerrsquos resources as much as possible such as ADC‐channels interrupts and digital Input‐Outputrsquos Local voltage regulation should also be possible since many sensors require higher supply voltages than digital Integrated Circuits do Further support of a shut‐down mechanism to power down the sensor modules when not needed

Computation and memory The processor module based on microcontroller should support many devices in a microcontroller family and support different power supply voltages enabling full range of clock frequencies All power‐down modes of the devices should be

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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31

supported The platform should support different processor modules including FPGArsquos and very simple microcontrollers

Communication The focus should be on enabling an IEEE 802154 compliant communication interface With or without ZigBee stack this will provide a flexible platform for exploring different network topologies Support for power‐down of the entire communication module should be supported

Cost and form factor High flexibility is in conflict with low cost and small size The primary objective should be flexibility but the size should be considered in order to enable sensor device integration in systems where space is limited

Figure 15 SENTIO Platform [8]

The communication subsystem of the SENTIO sensor node was designed so that the correct operation of the radio chip is ensured The radio chip is powered by the on‐board voltage regulator and thus fluctuations in the battery voltage level do not affect the operation of the communication subsystem This platform uses Chipcon CC2420 IEEE 802154 radio transceiver operates in the 24GHz ISM band and includes a digital direct sequence spread spectrum (DSSS) modem providing a spreading gain of 9dB and an effective data rate of 250Kbps It was specifically designed for low power wireless applications and supports 8 discrete power levels 0dBm minus1dBm minus3dBm minus5dBm minus7dBm minus10dBm minus15dBm and minus25dBm at which its power consumption varies from 29mW to 52mW [21] A built‐in received signal strength indicator gives an 8‐bit digital value RSSIVAL The RSSIVAL is always averaged over 8 symbol periods (128μs) and a status bit indicates when the RSSIVAL is valid (meaning that the receiver was enabled for at least 8 symbol periods) The

Antenna

Communication Module

Processing Module

Sensing Module

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

32

power P at the RF pins can be obtained directly from RSSIVAL using the following equation

P = RSSIVAL + RSSIOFFSET [dBm] where the RSSIOFFSET is found empirically from the front‐end gain and it is approximately equal to minus45dBm In the next sections when we refer to the RSSI value we refer to the RSSIVAL and not the power P unless otherwise stated

43 Experimental Testbed The experimental test bed for object tracking application is as shown in figure 16 Experiment was conducted in Masterrsquos room at Mid Sweden University A DELL laptop is connected to SENTIO platform and software were primarily used to collect samples of RSS from APrsquos The software monitors IEEE 802154 radio frequency channels which operate in the 24GHz band

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

1030

BC - 1030

AP 3 AP 1

AP 2

AP 4

BS

HEWLETT

PACKARD

Figure 16 Layout of Masterʹs Room

A three dimensional scalable testbed is used for indoor sensor network deployments The testbed illustrated in figure 16 is a 3‐D structure measuring 16m (L) x 12m (W) x 3m (H) and it is designed to host a large number of static nodes as shown in figure 16 Arrow marks in the above figure represent the location where fingerprints were taken The centrepiece of our infrastructure

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

33

is the SENTIO sensor node [8] an open‐source general purpose sensing platform designed with AT Mega 128 microprocessor and the IEEE 802154 compliant CC2420 radio from Chipcon [19]

44 Obtaining data from the sensor network The next important step in the development of the object tracking application is to obtain the data from the sensor network and send it to the base station From there the data is transmitted through a serial port to the computer that runs the object tracking application When the data arrives at the serial port the application must capture it This paragraph gives a brief explanation of how and what kind data is transmitted by the static object fixed sensors and how this data is being used to estimate the position of the object First the static object transmits Beacon packet continuously which is received by all the fixed APrsquos The beacon packet format is as shown in table 6 Then the fixed APrsquos calculates the 8‐bit value of RSSI from the transmitted beacon packet and then sends the resultant RSSI value as a form of data packet (table 7) to the BS which calculates the position and sends back to the object

45 Object tracking application Testing and Evaluation The purpose of this section is to discuss and evaluate the object tracking application Finally we are ready with the experimental testbed as shown in figure 16 As shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors placed at the corners of the masterrsquos room BS is placed at the centre of the room connected to a PC All the nodes are power connected before an actual measurement takes place At first the object is placed at random position inside the room and transmits beacon packets continuously Then these packets are received by the fixed APrsquos which calculate the RSSI from the packet and then averages it until it receives 50 beacon packets and then transmits the data to the BS (figure 17) Then the BS waits until it receives the data from all the APrsquos Once it receives from all the APrsquos it calculates the position of the object (figure 18) The application was developed to demonstrate a real world use of a WSN in object tracking In evaluating the final position obtained with respect to the actual position we conclude that the object‐tracking was successful Although

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

34

it is a research prototype object tracking application is a simple stable stand‐alone system capable of demonstrating an important application of WSN It takes raw data from the WSN aggregates it evaluates it and based on the evaluation tracks the object The accuracy of estimating the position is less than 5m Table 11 below shows a log of readings taken during test

Start

Intialize micro‐controller and transeciever in receieve mode

If received_packet=

Beacon

Average RSSI values

Increase the counter

If countergt1000050

A

No

Yes

B

B

Yes

No

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

35

A

Reset counter

Set transceiever in transmit mode

Transmit averaged RSSI to Main sensor and set it back to receive mode

10000 samples ‐ Offline

50 samples ‐ Online

Figure 17 Flow chart representing the algorithm of fixed sensors

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

36

Start

Intialize micro‐controller and transeciever in receieve mode

Received RSSI values from all fixed Sensors

Open radio map

Calculate Euclidean distance between obtained RSSI values from fixed sensors and fingerprints

in radio map

No

Yes

Bubble sort for least error in signal strength values

A

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

37

A

Get the location of the object from the radiousing the least error value

Display location of object on PC

End

Figure 18 Flow chart representing the algorithm of main sensor

Table 11 Accuracy of positioning system

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

38

5 RESULTS This chapter presents how the system parameters such as number of samples mean or median etchellipare finalized All the tests are performed in masterrsquos room at Mid Sweden University This chapter is divided into three subsections First in Section 51 it reviews results obtained from samples test Section 52 discusses one dimensional test results and finally section 53 discusses results of two dimensional tests

51 Measurement setup A Dell laptop was connected to a SENTIO platform and is loaded with software (Appendix 2) This setup was used to determine number of samples required during object tracking mean or median position estimation algorithm and one dimensional test The setup is as follows

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

103

0

BC - 1030

AP 1AP 2

BS

HEWLETT

PACKARD

Figure 19 Test bed for one dimensional test in Masters Room

Two SENTIO nodes (AP1 and AP2 as shown above) were separated by a distance of 10 meters and these sensors are known as fixed sensors which are fixed to the ceiling such that there antenna is faced towards the ground They are linear is position A BS was connected to Dell laptop and it was placed in such a way that its antenna was faced towards the ceiling Another SENTIO

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

39

node was used as an object and its orientation was same as BS Figure 19 represents the complete setup for object tracking application Mobile node was moved between these fixed ones at regular intervals of one meter and RSSI values were logged at each interval

52 Number of samples test This test was performed to determine number of samples required for accurate positioning and either mean of the obtained RSSI values or median of them Figure 19 represents the setup for number of samples test Once the setup is ready the measurements are taken as follows

1 An object was tagged with SENTIO node and was placed stationary linear to the fixed sensors

2 10000 samples (RSSI) were taken at regular intervals of 1 meter up to 10 meters

3 The sample values were logged at every interval 4 Using these logged samples I have plotted graphs such as distribution

of RSS values (figure 20 21) meanmedian at intervals of 1 meter using MATLAB

5 The mean and median for various numbers of samples is as shown in figure 22

Figure 20 Histogram plot of RSSI values at 1m and 4m

Figure 21 Histogram plot of RSSI values at 6m and 8m

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

40

Figure 22 Distance vs RSSI

From the above graphs following conclusions were made 1 From the histogram plots we can observe that the distribution of RSSI

values follow Gaussians distribution law 2 As we can see from figure 22 that median curve has drastic changes

when compared to mean curve Hence mean is considered while estimating the position

3 From the observations we conclude that the number of samples does not matter since mean curve tends to be same for various numbers of samples

4 Hence 10000 samples are considered in order to get a proper location fingerprint during off‐line phase and during online phase 50 samples are enough to estimate the position of object

53 0ne dimensional test Measurement setup for one dimensional test is as shown in figure 19 This test was conducted to check the performance of algorithm and how accurate it was At first ie during offline phase fingerprints at each interval averaging 10000 samples were taken During online phase Euclidean algorithm was used estimate the position averaging 50 samples of RSSI From the results we

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

41

can realize that the error was less than 2 meters Table 12 shows the result of location estimation in one dimensional case

Table 12 One dimensional position estimation results

54 Two dimensional test This test is the final stage of my thesis work Experimental testbed for this stage is as shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors fixed at the corners of the room and a BS at the centre of the room There are 18 pre‐determined locations where the fingerprints (RSSI) were taken The fingerprints are as shown in Appendix 1 All the readings were taken in presence of userrsquos The algorithm for position estimation is shown in Appendix 2 Position estimation test is performed as follows

Tagged object is placed at random inside the masterrsquos room All the sensors including the base station and object are switched on Using the algorithm (Appendix 2) the BS estimates the position of the object and displays it on the PC

This test was performed using various numbers of samples to test which has a better accuracy Figure 23 represents the error rate using various numbers of samples From figure 23 we can conclude that 50 numbers of samples is enough to estimate the position of object with an accuracy of 3m

55 Causes of error in location determination The randomness of RSS is the major cause of error in any indoor

positioning system that uses the RSS for location inference If there is no randomness in the RSS and its location fingerprint every indoor positioning systems of this type will have no location detection error For example I have recorded RSS at one particular location during different times of the day From figures 24 and 25 you can observe that RSS values are different at different times of day

The performance of indoor positioning systems depends greatly on the separation of location fingerprints A location fingerprint

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

42

corresponding to a location can be identified correctly if it is difficult to classify it as another fingerprint by a pattern classifier

Path loss is one of the basic signal propagation properties of the RSS that best describes attenuation of transmit signal power over a distance between an access point and a mobile device

Presence of Userrsquos or userrsquos orientation affects RSS

Figure 23 Error Rate vs Number of samples

Figure 24 RSS distribution at 214 pm

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

43

Figure 25 RSS distribution at 100pm and 1100 am

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

6 DISCUSSIONS10102006

44

6 DISCUSSIONS 61 Summary and Conclusion While empirical results and performance studies of positioning systems based on location fingerprint have been presented in this report analytical model that can be used as a framework for efficiently designing the positioning systems are not available This thesis design work uses general principles of position estimation such as position estimation of sensors grid spacing etc This design work focuses on accuracy and precision The concept of location fingerprint technique based on RSS us explained in detail in chapter 2 It is found that RSS is random It is possible to approximate the RSS as being normally distributed While the RSS random process is non‐stationary the mean is more or less constant and could be used as the fingerprint of the location Model designed for position estimation is as shown in chapter 4 Accuracy and precision are the two major performance matrices in this study Uniform grid spacing has been used From the experimental analysis I have obtained an accuracy of 3 meters and 35 precision The sensitivity analysis among the system parameters and the performance matrices were done using the model as shown in chapter 4 For instance our study found that the minimum distance between two adjacent positions also affects the positioning performance This is due to the randomness of RSS patterns induced by the ever changing indoor environment

62 Future work The research in this thesis provides a ground work for the future study of efficient design of indoor positioning system The RSSI based position estimation still has limitations First the RSSI value is time dependent Thus the future study should address this issue Second the movement of object is not considered Third orientations of sensors affect the performance which should be taken care off Fourth to improve the performance of the system we need to find a solution to deal with those incomplete or missing data during the off‐line and the on‐line phase Fifth position estimation on multi‐floor and three‐dimensional coordinates is not available

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 110102006

45

APPENDIX 1

Location Fingerprints of Masters Room Time 1315 Observation Time 20 minutes Number of Samples 10000

People Presence SNo Co-ordinates AP1 (0x0001) AP2 (0x0002) AP3 (0x0004) AP4 (0x0008)

1 11 -24 -16 -27 -33 2 13 -26 -8 -34 -31 3 15 -43 -9 -39 -25 4 32 -21 -11 -27 -33 5 61 -6 -23 -32 -28 6 64 -20 -31 -25 -34 7 35 -23 -35 -29 -38 8 37 -29 -35 -22 -27 9 39 -38 -29 -21 -27 10 67 -27 -23 -20 -23 11 611 -26 -29 -23 -30 12 613 -35 -32 -2 -31 13 513 -34 -31 -20 -26 14 11 -39 -22 -29 -17 15 212 -37 -42 -28 -31 16 -212 -38 -33 -29 -6 17 -21 -42 -29 -33 -19 18 -38 -40 -23 -35 -22

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 210102006

46

APPENDIX 2

Begin

Open radio map and raw data files for write

Intialize radio map in memory

Check whether the

Packet is data or Beacon packet If data packet

Save the data to register

Check if data is

Received from all

sensors

Estimate the position of static

Display the position on PC

End Yes

No

A

A

Yes

No

Flow chart representing the main algorithm for position estimation

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

47

APPENDIX 3

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

48

Distribution of RSSI

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

49

REFERENCES [1] T S Rappaport J H Reed and B D Woerner ldquoPosition location using wireless communications on highways of the futureʺ IEEE Commun Mag vol 34 no 10 pp33‐41 Oct 1996 [2] A M Ladd K E Bekris G Marceau A Rudys L E Kavraki and D S Wallach rdquoRobotics‐based location sensing using wireless ethernetʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ02) 2002 pp 227‐238 [3] I Ramani R Bharadwaja and P V Rangan ldquoLocation tracking for media appliances in wireless home networksʺ in Proc International Conference on Multimedia and Expo (ICMEʹ03) July 2003 pp 769‐772 [4] K Pahlavan X Li and J P Makela ldquoIndoor geolocation science and technologyʺ IEEE Commun Mag vol 40 no 2 pp 112‐118 Feb 2002 [5] Konard Lorincz and Matt Welsh ldquoMote Track A Robust Decentralized Approach to RF‐Based Location Trackingrdquo [6] Paramvir Bahl and Venkata N Padmanabham ldquoRADAR An In‐Building RF‐based User Location and Tracking Systemrdquo [7] Kamol Kaemarungsi ldquoDesign of Indoor positioning system based on location fingerprint techiniquerdquo University of Pittsburgh 2005 [8] Fredrik Linnarsson Cheng Peng and Bengt Oelmann ldquoSENTIO A Hardware Platform for Rapid Prototyping of Wireless Sensor Networksrdquo [9] S Saha K Chaudhuri D Sanghi and P Bhagwat ldquoLocation determination of a mobile device using IEEE 80211b access point signalsʺ in Proc IEEE Wireless Communications and Networking Conference (WCNCʹ03) New Orleans LA Mar 2003 pp 1987‐1992 [10] T Roos P Myllymaki H Tirri P Misikangas and J Sievanen ldquoA probabilistic approach to WLAN user location estimationʺ International Journal of Wireless Information Networks vol 9 no 3 pp 155‐164 July 2002 [11] R Battiti M Brunato and A Villani ldquoStatistical learning theory for location fingerprinting in wireless LANrsquosʺ Technical Report Oct 2002 [Online] Available httprtmscienceunitnitraquobattitiarchive86pdf [12] P Prasithsangaree P Krishnamurthy and P K Chrysanthis ldquoAn indoor position location with wireless LANsʺ in Proc IEEE International Symposium on Personal Indoor and Mobile Radio Communications (PIMRCʹ02) Lisbon Portugal Sept 2002 [13] M A Youssef A Agrawala and A U Shankar ldquoWLAN location determination via clustering and probability distributionsʺ in Proc IEEE

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

50

International Conference on Pervasive Computing and Communications (PerComʹ03) Dallas‐Fort Worth TX Mar 23‐26 2003 pp 23‐26 [14] Z Xiang S Song J Chen H Wang J Huang and X Gao ldquoA wireless LAN‐based indoor positioning technologyʺ IBM Journal of Research and Development vol 48 no 56 pp 617‐626 SeptNov 2004 [15] R Want A Hopper V Falcao and J Gibbons ldquoThe active badge location systemʺACM Transactions on Information Systems vol 40 no 1 pp 91‐102 Jan 1992 [16] N B Priyantha A Chakraborty and H Balakrishnan ldquoThe cricket location‐support systemʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ00) Boston MA Aug 2000 pp 32‐43 [17] Holger K Willig A (2005) Protocols and Architectures for Wireless Sensor Networks London [18] IEEE (2003) Part 154 Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low‐Rate Wireless Personal Area Networks (LR‐WPANs) New York [19] Chipcon CC2420 802154 compliant radio httpwwwchipconcom [20] ZigBee Specification v10 ZigBee Alliance [21] Dimitrios Lymberopoulos Quentin Lindsey and Andreas Savvides ldquoAn Empirical Analysis of Radio Signal Strength Variability in IEEE 802154 Networks using Monopole Antennasrdquo [22] Kiran Yedavalli Bhaskar Krishnamachari Sharmila Ravula and Bhaskar Srinivasan ldquoEcolocation A sequence based technique for RF localization in wireless sensor networksrdquo Robert Bosch Research and Technology Centre Palo Alto CA

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

11

require a high accuracy clock in the communication system TDOA is more practical [1] Examples of location‐sensing systems that use time of flight are GPS the Active Bats [15] and the Cricket [16] Besides these time delay‐based techniques the distance between transmitter and receiver can also be determined from signal strength attenuation and direct distance measurement (such as dead reckoning) Fingerprinting or Location Pattern Matching this technique generally requires only measurement of RSS or other non‐geometric features at several locations to form a database of location fingerprints To estimate the mobile location the system needs to first measure the received signal strength at particular locations and then search for the pattern or fingerprint with the closest match in the database This technique does not require the mobile station to see at least three base stations or access points in order to determine the location The disadvantage of this technique is that it is very time‐consuming to perform an exhaustive data collection for a wide area network such as in outdoor positioning systems

23 Indoor local positioning using ZigBee based on RSSI This section reviews relevant RF‐based indoor positioning systems which can be used to locate stationary objects and track mobile users The impressive growth of IEEE 802154 wireless PANs (WPAN) in recent years suggests an interesting future for the location fingerprinting technique The system is quite flexible because system designers can select whether to have a centralized positioning server or let the mobile to determine its own position However the fingerprinting technique requires a training phase (off‐line phase) to collect location fingerprints for all positions in the operating area before the actual deployment (on‐line phase) [7] The following discussion is divided according to the positioning system components First the common form of location fingerprinting and its relationship with physical locations are explained Finally location estimation algorithm is reviewed

231 Location Fingerprint A location fingerprint based on RF characteristics such as RSS is the basis for representing a unique location It is created under the assumption that each position or location inside a building has unique RF signature [4] Generally a fingerprint F is labelled with a location information L The location fingerprints and their labels (eg location information) are maintained in

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

12

database and used during the on‐line phase to estimate the location The label and fingerprint are usually denoted as a tuple of (L F) Battiti et al [11] point out that the location information L for indoor location can be recorded in two forms as either a tuple of coordinates or an indicator variable The tuple of real coordinates can vary from one dimension to five dimensions which include the three dimension space and two orientation variables expressed in spherical coordinates For instance a location information of a two‐dimension system with an orientation could be expressed as a triplet L = (x y d) | x y є R 2 d є North East South West The RSS was found to be more location‐dependent than the SNR because the noise component is rather random in nature However the RSS itself fluctuates over time for each access point and location Each RSS element can be considered as a random variable therefore it can be captured by recording its descriptive statistics parameters approximating its distribution or maintaining the whole measurement dataset These approaches of RSS representation result in different procedures for location estimation algorithm Regardless of the approach the location fingerprint is usually denoted as an array or vector of signal strength (random variables) received at any position in the location‐based service area The size of the vector is determined by the number of access points that can be heard To create a basis fingerprint such as in a number of samples of vectors of signal strength are collected over a window of time for each position This basis is called a prototype Then the average RSS of each access point is calculated and recorded as an element in the location fingerprint For an area that can receive signals from N access points the location fingerprint can be expressed as a vector of average RSS elements βi

F = (β1 β2hellip βN)T Besides the basis location fingerprint the samples of location fingerprint measured during the on‐line phase are also important for the system that tracks the mobile object The number of samples should be selected appropriately to represent the location fingerprint for the mobile application A step in statistical analysis method called pre‐processing is another important issue needed to be considered because it can impact the estimation of dependency between location fingerprint and the location information easier The pre‐processing refers to a step that cleans the raw data (in this case the training set) before any further operations or analysis The cleaning may consist of encoding dimensionality reduction (reduce unnecessary elements) feature extractionselection clustering and outlier elimination Roos et al [10] point out that the pre‐processing enables faster location estimation and

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

13

reduce the noise from the training data The process that creates the basis of location fingerprint discussed above could be considered as a part of pre‐processing

232 Location Estimation Algorithm Location estimation algorithms or positioning algorithms are procedures that exploit dependency between location information and location fingerprint basis in order to determine a position or location from samples of RSS signals Position algorithms can be classified into deterministic and probabilistic types based on the approaches that model the relationship between location fingerprints and location information The deterministic type of algorithms are those that based in the nearest neighbour classifiers and neural network classifiers The probabilistic type of algorithms is those that are based on the Bayesian inference and statistical learning theory such as support vector machines (SVR) The algorithm used in this project is discussed briefly

Euclidean distance method The Euclidean distance method is a deterministic approach of position estimation because they require only a set of constant location fingerprints which includes mean vectors In order to determine the location a form of discriminate function is used to classify a sample of RSS fingerprint into a position The mean or average RSS vector is a centre of a mass which represents each class of location fingerprint Suppose a set of l location fingerprints is denoted by F1 F2hellip Fl exist and each fingerprint has a one‐to‐one mapping to a set of positions L1 L2hellip Ll A sample of an RSS fingerprint measured during an on‐line phase is denoted as S which can be another mean or average RSS vector of small window of RSS samples Assuming that an indoor positioning system only considers the average RSS from N access points as a location fingerprint the sample of RSS vector is S = (s1 s2 helliphellip sN)T and each location fingerprint i in the database can be expressed as Fi = (β1 β2helliphellip βN)T The simplest closeness metric is a distance measurement in signal space denoted as the Dist () function [6] Thus the simple procedure of nearest neighbour algorithm is expressed as picking the fingerprint j that has the shortest signal distance

Dist(S Fj) le Dist(S Fk) for all k ne j A generalized weighted distance Lp summarized by [12] can be used to calculate different forms of distance in signal space as

Lp = 1N pN

i

pPiSiWi 1

1)||1(sum

=

minus

where N is the number of access points wi is a weighting factor (wi le 1) and p is the norm parameter starting from 1 The weighting factor wi is a bias

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

14

parameter that can demote or promote an important RSS component in the fingerprint

24 Protocols and Industry standards for WSN Although many applications for WSN are proposed the communication protocols supporting them remain mainly unexplored and diverged This is because the area of interest is new each implementation is application specific and the components of WSNrsquos have a lot of constraints Wireless sensor networks use the wireless medium to communicate However the traditional communication protocols that support wireless communications especially ad‐hoc mesh networks may not be well suited for them WSN communication is an active research area and many algorithms and protocols have already been proposed This section introduces two proposed standards the IEEE 802154 and the ZigBee The IEEE 802154 covers the physical layer and the MAC layer of low‐rate WPAN The ZigBee is ldquoan emerging standard that is based on the IEEE 802154 and adds network construction (star networks peer‐to‐peermesh networks and cluster‐tree networks) application services and morerdquo

241 IEEE 802154 Holger and Willig (2005) [17] mention home automation home networking and home security as possible applications for IEEE 802154 They add that ldquomost of these applications require only low‐to‐medium bitrates (up to some few hundreds of kbps) moderate average delays without too stringent delay guarantees and for certain nodes it is highly desirable to reduce the energy consumption to a minimumrdquo The IEEE 802154 standard (2003) defines the device types that can be used in a LR‐WPAN A device can be a FFD or a RFD The RFD can be used in simple applications in which they do not need to transmit large amounts of data and they have to communicate only with a specific FFD The FFD can work as a PAN coordinator as a coordinator or as a simple device It can communicate with either another FFD or a RFD In keeping with the application requirements the LR‐WPAN operates in a star or peer‐to‐peer topology (figure 4) In the star topology the RFD communicates with a single controller the PAN coordinator The PAN coordinator can perform the same function as the RFD but it is also responsible for controlling the PAN ldquoit initiates terminates or routes communication around the networkrdquo [18] The peer‐to‐peer topology

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

15

supports ad‐hoc mesh multi‐hop networking Any device in the peer‐to‐peer topology can communicate with any other device within its communication range however this topology also has a PAN coordinator All the devices in a LR‐WPAN have a unique 64‐bit address This or a short address allocated by the PAN coordinator can be used inside a PAN Additionally each PAN has a unique identifier The combination of the PAN identifier and the sort addresses allows communication across different PANs (IEEE 802154 Standard 2003)

Figure 4 Star and peer‐to‐peer topologies in LR‐WPAN [18]

The LR‐WPAN based on the OSI seven‐layer model has the layered architecture presented in figure 5 The application and the network layer are the upper layers in the LR‐WPAN architecture but are outside the scope of the IEEE 802154 standard Only the physical layer ldquowhich contains the RF transceiver along with its low‐level control mechanismrdquo and the MAC layer ldquothat provides access to the physical channelrdquo are included in the standard and will be introduced in the following sections Finally the Type I 8022 LLC and the SSCS are intermediate sub layers supporting communication with the above layers (IEEE 802154 Standard 2003)

Physical Layer The 802154 standard specifies two different services that the PHY provides The PHY data service controls the radio and thus the transmission and reception of the PPDUs In addition the management service performs ED in the channel The management service also performs CCA before sending the messages and provides LQI for the received packets

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

16

Figure 5 WPAN architecture [18]

Three different bands are defined by the standard The 868‐8686 MHz for Europe the 902‐928 MHz for North America and the 2400‐24835 MHz worldwide All of them belong in the ISM radio bands and they are using DSSS Each of the bands supports different a data rate and it uses a different modulation technique chip rate and number of channels Moreover if the system does not use the 2450 MHz frequency it operates in both the 802 MHz and 902 MHz frequencies Table 3 summarizes the supported bands and data rates (IEEE 802154 standard 2003)

PHY (MHz)

Frequency Band (MHz) Modulation

Bit Rate Number of Channels

868-8686 BPSK 20 1 868 915 902-928 BPSK 40 10

2450 2400-24835 O-QPSK 230 16 Table 3 Frequency bands and data rates for IEEE 802154 based on the IEEE

802154 standard [18] The physical layer uses PPDU packets to communicate Table 4 demonstrates its structure The LSB is always transmitted and received first The SHR contains the preamble and the SFD fields which helps receiver

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

17

synchronization The 8‐bytes preamble contains only zero and is used for synchronization The SFD contains a specific sequence of one and zeros and specifies the beginning of the frame The PHR contains the payload length Packets with a length of 9 or more bytes are MPDU as the next section further explains Packets with length 5 are used for MPDU acknowledgements The payload part of the PPDU encloses the MAC layer packet Finally the PPDU size can be up to 127 bytes [18]

4 bytes 1 byte 1 byte Variable

Preamble SFD Frame length

(7 bits) Reserved

(1 bit) Payload (PSU)

SHR PHR PHY Payload Table 4 Format based on the IEEE 802154 standard [18]

MAC Layer The MAC layer is the interface between the SSCS and the PHY layer Similar to the PHY layer the MAC layer supports two services The MAC data service is responsible for the transmission and reception of the MPDUs through the PHY data service The MAC management service if the device is a coordinator manages the network beacons It is also responsible for PAN association and disassociation frame validation and acknowledgment providing ldquoa reliable link between two peer MAC entitiesrdquo In addition it uses the CSMA‐CA for channel access and handles and maintains the GTS mechanism Finally it supports device security [18] The IEEE 802154 standard defines four different frame types the beacon data acknowledgment and MAC command frame All frame types are based on the general MAC frame format (table 5) The frame control field describes and specifies the above different frame types Every MAC frame comprises a MHR which consists of a frame control sequence number and the information field It also contains the MAC payload different frame types have different MAC payload fields The acknowledgment type does not have a payload Finally each frame includes a MFR which contains a FCS The data in the MPDU follows the same order as the PPDU the least significant bits are left in the frame and are transited first Octets2 1 02 028 02 028 Variable 2

Destination PAN Identifier

Destination Address

Source PAN

Identifier Source Address Frame

Control Sequence Number Addressing Field

Frame Payload FCS

MHR MAC

Payload MFRTable 5 General MAC frame format IEEE 802154 standard [18]

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

18

Table 6 7 8 and 9 present the four different MAC frame types The beacon frame is transmitted periodically by the PAN coordinator It provides information about the network management through the super frame and GTS fields which are analyzed later in the section It also synchronizes the network devices and indicates the proper communication period for them The data frame payload encapsulates data from the higher layers When a device receives a packet it is not obliged to response with an acknowledgement packet Finally the command frame identifier and command payload fields of the command frame are used for communication between the network devices The command identifier specifies actions like association disassociation and data GTS or beacon request Octets2 1 410 2 Variable Variable Variable 2

Frame Control

Sequence Number

Addressing Fields

Super frame Specification

GTS fields

Pending Addressing

Fields Beacon Payload FCS

MHR MAC Payload MFRTable 6 Beacon frame format [18]

Octets 2 1 Variable 2

Frame Control

Sequence Number

Addressing Field

Data Payload FCS

MHR MAC

Payload MFR Table 7 Data frame format [18]

Octets 2 1 2

Frame Control

Sequence Number FCS

MHR MFR Table 8 Acknowledgement frame format [18]

Octets2 1 1 Variable 2

Frame Control

Sequence Number

Addressing Fields

Command Frame Identifier

Command Payload FCS

MHR MAC Payload MFR Table 9 Command frame format [18]

In the LR‐WPAN every PAN has its own coordinator The PAN coordinator manages the communication in the local area it has two options to use or not use the super frame structure The super frame (figure 6) uses network beacons If the coordinator does not want to use a super frame structure it suspends the beacon transmission However the beacon is important for device association and disassociation If the coordinator wishes to maintain close communication control in the PAN and to support low‐latency devices it usually uses the super frame A super frame determines a specific time

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

19

period beacons bound it The beacon is transmitted in the first of the sixteen equal time slots that the super frame has It is used to ldquosynchronize the attached devices to identify the PAN and to describe the structure of the super framerdquo The super frame can have active and inactive periods All the communications have to be finished inside the super frame period In the inactive periods the devices can switch to the sleep mode but they have to be ready for the next beacon The active period is further divided into a CAP and a CFP During the CAP any device can communicate competing with the other devices in the PAN using slotted CSMA‐CA The CFP contains GTSs and follows the CAP A CFP may maintain up to seven GTSs and each GTS can reserve more than one time slot However the CAP should always be sufficient to allow new devices t join the PAN The GTSs are allocated to specific devices All the transactions must be completed inside the assigned time period (CAP GTS and CFP) (IEEE 802154 standard 2003)

Figure 6 Example of super frame structure [18]

In accordance with the 802154 standard (2003) three different types of data‐transfer exist In addition the types differ if the coordinator uses or does not beacons Data transfer from a device to the PAN coordinator is the first type (figure 7) For a ldquonon‐beacon‐enable networkrdquo it first senses the medium by using ldquoun‐slotted CSMA‐CArdquo and then a simple transmit to the data frame In a ldquobeacon‐enabled networkrdquo the sender waits for the beacon when it finds it ldquothe device synchronizes to the super fame structurerdquo In the specified time frame the sender again senses the medium by using ldquoslotted CSMA‐CArdquo and transmits the data to the coordinator In either case the coordinator has the option to acknowledge or not acknowledge the data reception after that the transaction is completed Data transfer from the coordinator is the next type described in the standard (figure 8) In a beacon‐enabled network the coordinator indicates a pending message through the beacon The messagersquos target device receives the beacon and if the message is pending it responses with a MAC command request

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

20

message using slotted CSMA‐CA the coordinator may or may not acknowledge the command message and through slotted CSMA‐CA sends the pending message The device acknowledges the received message After that the coordinator removes the message from the beaconrsquos pending list and completes the transaction In a non‐beacon enable network the device periodically sends a MAC command‐request frame to the coordinator The coordinator acknowledges the data request Then by using un‐slotted CSMA‐CA if it has a pending message it transmits it If it has not it respond with a data frame with a ldquozero‐length payloadrdquo To complete the transaction the device acknowledges the data reception

Figure 7 Communication from a device to a PAN coordinator in (a) a beacon‐enabled network and (b) a non‐beacon enabled network [18]

Peer‐to‐peer is the last type of data transfer In this situation the devices are free to communicate with any other device within their communication range In a peer‐to‐peer PAN the devices can ldquoeither receive constantly or synchronize with each otherrdquo If they are receiving constantly to transmit data they use un‐slotted CSAM‐CA In the second case synchronization must be achieved first (IEEE 802154 standard [IEEE 2003]) The IEEE 802154 (2003) standard establishes MAC and PHY standards for low‐cost low‐power and high‐density node deployments In addition to the above PHY and MAC characteristics IEEE 802154 provides a security baseline including ldquothe ability to maintain an ACL and use symmetric cryptographyrdquo for data encryption The algorithm that is used for encryption is the AES However the higher level layers decide when security is need The upper layers are in general responsible for device authentication and key management The next section introduces the ZigBee standard which

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

21

encapsulates the IEEE 802154 and provides additional standardization for the higher levels

Figure 8 Communication from a PAN coordinator to a device in (a) a beacon‐enabled network and (b) a non‐beacon enabled network [18]

242 ZigBee Heily (2004) defines ZigBee as ldquoa rapidly growing worldwide non‐profit industry consortiumrdquo whose mission is ldquoto define a reliable cost‐effective low‐power wirelessly networked monitoring and control product based on an open global standardrdquo Figure 9 illustrates the areas of interest for different wireless communication standards

Figure 9 Overview of the coverage for different wireless communication

standards [17]

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

22

ZigBee a new standard which became publicly available in June 2005 is based on the IEEE 802154 standard It expands the IEEE 802154 by adding the framework for ldquothe network security and applicationrdquo (Craig 2005) Figure 10 presents the IEEE 802154ZigBee stack and the areas of responsibilities

Figure 10 IEEE 802154ZigBee stack [20]

Craig (2005) mentions three networking topologies that the standard covers the star mesh and cluster tree (figure 11) The ZigBee standard works on top of the IEEE 802154 addressing schema by using the standard 64‐bit and the short 16‐bit addressing Kinney (2005) summarizes the ZigBee network layer responsibilities the successful establishment of a new the network and successful new device configuration addressing assignment network synchronization frames security and message routing ZigBee further distinguishes the concept of the physical devices (RFD FFD) by using the notion of ldquological devicesrdquo ldquoZigBee Coordinatorrdquo is the first type of logical devices It is responsible for initializing maintaining and managing the network Under the coordinator in the network hierarchy is the ldquoZigBee routerrdquo which is responsible for controlling the message routing between the nodes Finally the ldquoZigBee End Devicerdquo acts as the end point of the network

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

23

structure The tasks that an end device can perform are specified in the ldquoApplication Profilesrdquo (Craig 2005)

Figure 11 IEEE 802154ZigBee stack [17]

The ZigBee specifications (2005) summarize the security services provided by ZigBee ldquokey establishment key transport frame protection and device managementrdquo ZigBee builds its security mechanism using symmetric key cryptography The security services also depend on the associated layer thus as the figure 10 shows the security mechanism covers the network and the application layer In addition if a MAC frame needs security protection the MAC layer is able to secure it Moreover the notion of end‐to‐end security is supported the source and destination devices have access and use the same share key In the MAC layer the 802154 AES mechanism provides the proper security The mechanism protects ldquothe confidentiality integrity and authenticity of the MAC framesrdquo (Kinney 2005) An auxiliary header field in front of the MAC payload indicates if the frame is encrypted or not The MAC framesrsquo integrity is supported by calculating and using a MIC at the end of the MAC payload In addition to the AES nonce is used to provide MAC confidentiality and authenticity Figure 12 illustrates a MAC frame with security For different security aspects the MAC layer uses different mode of the AES for the encryption it uses the AES in Counter (CTR) mode and for the integrity the

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

24

CBC‐MAC Finally the combination (CCM) of the above two modes is available providing both encryption and integrity (Kinney 2005)

Figure 12 ZigBee secure frame in MAC layer [17]

In the network layer the CCM (a modified MAC layer CCM mode) is used for encryption Because the network layer uses only the CCM mode a single key is used for all different security options The network layer security message format is similar to the MAC frame it is presented in the figure 13 Finally although the network layer is responsible for securing its layer messages the above layers specify the keys and the CCM option for each frame (Kinney 2005)

Figure 13 ZigBee secure frame in network layer [17]

Security in the application layer works similar to the network and MAC layers It uses the ldquolink keyrdquo or the ldquonetwork keyrdquo to secure the message and then encapsulate it inside a set of fields similar to the network format (figure 14) Other security responsibilities that the application layer has are to provide the ZDO and the applications with device management services key establishment and key transport (ZigBee specifications 2005)

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

25

Figure 14 ZigBee secure frame in application layer [17]

The ZigBee application layer contains the manufacturer‐defined application objects the ZDO and the application sub‐layer In addition to the security responsibilities the application sub‐layer binds devices based on their duties and needs maintains the binding tables and forwards messages between them The application sub‐layer also discovers the neighbour devices for a given device The ZDO is responsible for determining the devicersquos duty in the network for communicating using binding requests and for supporting security as was mentioned above The sub‐layer that implements the actual application is the manufacturer‐defined application object (Kinney 2005) The ZigBee specifications are a great step toward the wireless sensors networks standardization They cover all the OSI layers from the physical to the application providing guidance to the developers The specifications are the product of the ZigBee alliances a consortium of companies and academic institutes Thus they not only produce the specifications but also are the first use them The next section goes beyond the layer architecture and standardization and introduces the OS field another crucial element of WSNs

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

26

3 RECEIVED SIGNAL STRENGTH (RSS) Majority of the existing methods leverage the existence of IEEE 80211 base stations with powerful radio transmit powers of approximately 100mW per base station Such radios are in a different class from the low power IEEE 802154 compliant radios that typically transmit at low power levels ranging from 52mW to 29mW [19] The wide availability of larger number of IEEE 802154 radios in the research community has revived the interest for signal strength based localization in sensor network Despite of rapidly increasing popularity of IEEE 802154 radios and signal strength localization there is a lack of detailed characterization of the fundamental factors contributing to large signal strength variation This chapter investigates some of the properties of RSS observed during testing and analysis of research work collected during the period of thesis The analysis of RSS values is needed to understand the underlying features of location dependent RSS patterns and location fingerprints An understanding of the properties of the RSS values for location fingerprinting can assist in improving the design of positioning algorithms and in deployment of indoor positioning systems

31 Parameters that affect RSS Different factors that affect the indoor RSS are the userrsquos presenceabsence hisher orientation time of the day building type and material distance from transmitter and type of radio These factors are by no means exhaustive Other possible factors are antenna orientation directionality and type Table 10 classify the parameters which affect RSS [7]

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

27

Effect on Factors Options

1 Proximity of user Userrsquos presence or absence

2 Orientation of user and terminal

North East South West

Data Collection

3 Make of Radio 4 Time of measurement Time of day amp days of

week 5 Period of measurement

Second minute hour

7 Interference Co‐channel adjacent co‐channel

Statistics

8 Building environment Small offices or large hall

Table 10 Classification of factors the affect RSS [7]

Transmitter variability Different transmitters behave differently even when they are configured exactly in the same way In practice this means that when a transmitter is configured to send packets at a power level of d dBm then the transmitter will send these packets at a power level that is very close to d dBm but not necessarily exactly equal to d dBm This can alter the received signal strength indication and thus it can lead to inaccurate distance estimation

Receiver variability The sensitivity of the receivers across different radio chips is different In practice this means that the RSSI value recorded at different receivers can be different even when all the other parameters that affect the received signal strength are kept constant

Antenna orientation Each antenna has its own radiation pattern that is not uniform In practice this means that the RSSI value recorded at the receiver for a given pair of communicating nodes and for a given distance between them varies as the pair wise antenna orientations of the transmitter and the receiver are changed

Multi‐path fading and shadowing in the RF channel In indoor environments the transmitted signals get reflected after hitting on the walls andor on other objects in the room such as furniture Both the original signal and the reflected signal reach the receiver almost at the same time since they both travel at the speed of light As a result of this the receiver is not able to

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

28

distinguish the two signals and it measures the received signal strength for both of them

32 Related work analysis A number of indoor location tracking systems have been proposed in the literature based on RF signals ultrasound infrared and some combinations of these As mentioned previously RF based positioning system are widely studied as shown in Table 2 Given a model of radio signal propagation in a building or other environment RSS can be used to estimate the distance between the transmitter and receiver Most of the algorithms (Table 2) have been evaluated in practice using 80211b compliant wireless radios and seem to produce a location error of 15ft to 20ft for an indoor network area of approximately 225ft x 144ft According to the results of this study all the localization techniques produce approximately the same location error over a range of environments Ecolocation [22] Mote Track [5] are two of the few localization algorithms that have been evaluated on a real sensor network that uses a low power wireless radio Ecolocation reports a location error of 10ft for a very small outdoor network deployment area (26ft x 49ft) Mote Track reports a location error of approximately 13ft for an indoor network deployment area of 18751ft2 In general the RSSI based localization algorithms can be divided into two broad categories the map based and the distance (or area) prediction based algorithms In the case of the map based algorithms the deployment area is divided into a large set of sample points that cover the whole area Most techniques collect one or more signal strength measurements from all visible beacon nodes and for each sample point Each sample point is then mapped to either a signal strength vector or a signal strength probability distribution This process generates an RSSI fingerprint map which is unique with respect to the beacon locations and the deployment area This fingerprint map is stored to a central base station A node that is not aware of its location collects one or more signal strength measurements from all the visible beacon nodes and creates its own RSSI fingerprint that is sent to the central base station The base station computes nodersquos location as the sample pointrsquos location whose RSSI fingerprint distance from the unknown nodersquos RSSI fingerprint is the minimum among all the sample points Despite the fact that the map based RSSI localization methods produce relatively small location errors they are not attractive mainly because they require an excessive amount of profiling data and because they are not fully distributed The detailed profiling of a building is a major holdback because it is complex time consuming and depends on beacon locations Therefore for different buildings or even for the

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

29

same building and different beacon locations different RSSI maps have to be obtained The second category of RSSI based algorithms are the distance (or area) prediction algorithms which focus on minimizing the amount of profiling data In distance prediction based methods raw RSSI data are directly mapped to distances through a signal propagation model In some cases distances are not explicitly measured but they are taken into consideration through assumptions on the underlying distributions In the case of outdoor deployments where there are no obstacles a single signal propagation model is all the profiling data required In the case of indoor deployments the same applies but now the signal propagation model might be different across different buildings with different furniture and wall arrangements Given an underlying signal propagation model raw RSSI data can be mapped to specific distances These distances can be either used directly in order to perform localization or assigned a probability and used in a learning basedprobabilistic localization algorithm Obviously the accuracy of the distance prediction based methods depends heavily on the accuracy of the signal propagation model used for translating RSSI values to distances

33 Properties of RSSI Changes in signal due to propagation indoors are difficult to predict because of dense environment and propagation effects such as reflection diffraction and scattering The multi‐path fading effect which is the result of either constructive or destructive combination of multiple signal copies at the receiver causes the received signal to fluctuate around a mean value at a particular location The received signal is usually modelled by the combined effects of large‐scale fading and small‐scale fading The large‐scale fading component describes the signal attenuation as the signal travels over a distance and is absorbed by material such as walls and floors along the way to the receiver This component predicts the mean of the RSS and usually has a lognormal distribution On the other hand the small scale fading component explains the dramatic fluctuation of the signal due to multi‐path fading On the other hand the small scale‐fading component explains the dramatic fluctuation of the signal due to multi‐path fading If there is no LOS component the small‐scale‐fading is often modelled with a Rayleigh distribution Such scenarios are commonly called NLOS If there is a LOS component the small‐scale‐fading is typically modelled by a Rician distribution Note that when the RSS is measured the measurement averages out the small‐scale‐fading effects

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

30

4 OBJECT TRACKING 41 Introduction This chapter presents the object‐tracking application the main objective of this thesis work The application is an algorithmic‐software implementation written in Embedded C which is capable of receiving data messages and generating object position estimation data The implementation of the object‐tracking application makes use of the SENTIO platform and uses the background knowledge introduced in the preceding chapters The motivation for the object‐tracking application is to determine the position of an object in indoor environments (Eg Home) The application acts as an interface which collects the raw data that the sensors generate (numerical values) It makes the proper comparisons and algorithmic processes to identify and track the object while it is static inside the area covered by the wireless sensor network Finally with the detection and tracking data it alerts the user or another system The following sections provide an overview of the object‐tracking application

42 Overview of hardware product Before describing the object tracking application it is important to know the characteristics of hardware product The SENTIO platform (figure 15) is built in modular fashion and has the following key features [8]

Flexibility This platform is very flexible in such a way that it is made easy to add or change the hardware for each sensor task depending on the requirement in the WSN For a specific platform this can be hard or sometimes even impossible to accomplish

Sensor resources Resources in a sensor module includes the ability of usage of controllerrsquos resources as much as possible such as ADC‐channels interrupts and digital Input‐Outputrsquos Local voltage regulation should also be possible since many sensors require higher supply voltages than digital Integrated Circuits do Further support of a shut‐down mechanism to power down the sensor modules when not needed

Computation and memory The processor module based on microcontroller should support many devices in a microcontroller family and support different power supply voltages enabling full range of clock frequencies All power‐down modes of the devices should be

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

31

supported The platform should support different processor modules including FPGArsquos and very simple microcontrollers

Communication The focus should be on enabling an IEEE 802154 compliant communication interface With or without ZigBee stack this will provide a flexible platform for exploring different network topologies Support for power‐down of the entire communication module should be supported

Cost and form factor High flexibility is in conflict with low cost and small size The primary objective should be flexibility but the size should be considered in order to enable sensor device integration in systems where space is limited

Figure 15 SENTIO Platform [8]

The communication subsystem of the SENTIO sensor node was designed so that the correct operation of the radio chip is ensured The radio chip is powered by the on‐board voltage regulator and thus fluctuations in the battery voltage level do not affect the operation of the communication subsystem This platform uses Chipcon CC2420 IEEE 802154 radio transceiver operates in the 24GHz ISM band and includes a digital direct sequence spread spectrum (DSSS) modem providing a spreading gain of 9dB and an effective data rate of 250Kbps It was specifically designed for low power wireless applications and supports 8 discrete power levels 0dBm minus1dBm minus3dBm minus5dBm minus7dBm minus10dBm minus15dBm and minus25dBm at which its power consumption varies from 29mW to 52mW [21] A built‐in received signal strength indicator gives an 8‐bit digital value RSSIVAL The RSSIVAL is always averaged over 8 symbol periods (128μs) and a status bit indicates when the RSSIVAL is valid (meaning that the receiver was enabled for at least 8 symbol periods) The

Antenna

Communication Module

Processing Module

Sensing Module

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

32

power P at the RF pins can be obtained directly from RSSIVAL using the following equation

P = RSSIVAL + RSSIOFFSET [dBm] where the RSSIOFFSET is found empirically from the front‐end gain and it is approximately equal to minus45dBm In the next sections when we refer to the RSSI value we refer to the RSSIVAL and not the power P unless otherwise stated

43 Experimental Testbed The experimental test bed for object tracking application is as shown in figure 16 Experiment was conducted in Masterrsquos room at Mid Sweden University A DELL laptop is connected to SENTIO platform and software were primarily used to collect samples of RSS from APrsquos The software monitors IEEE 802154 radio frequency channels which operate in the 24GHz band

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

1030

BC - 1030

AP 3 AP 1

AP 2

AP 4

BS

HEWLETT

PACKARD

Figure 16 Layout of Masterʹs Room

A three dimensional scalable testbed is used for indoor sensor network deployments The testbed illustrated in figure 16 is a 3‐D structure measuring 16m (L) x 12m (W) x 3m (H) and it is designed to host a large number of static nodes as shown in figure 16 Arrow marks in the above figure represent the location where fingerprints were taken The centrepiece of our infrastructure

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

33

is the SENTIO sensor node [8] an open‐source general purpose sensing platform designed with AT Mega 128 microprocessor and the IEEE 802154 compliant CC2420 radio from Chipcon [19]

44 Obtaining data from the sensor network The next important step in the development of the object tracking application is to obtain the data from the sensor network and send it to the base station From there the data is transmitted through a serial port to the computer that runs the object tracking application When the data arrives at the serial port the application must capture it This paragraph gives a brief explanation of how and what kind data is transmitted by the static object fixed sensors and how this data is being used to estimate the position of the object First the static object transmits Beacon packet continuously which is received by all the fixed APrsquos The beacon packet format is as shown in table 6 Then the fixed APrsquos calculates the 8‐bit value of RSSI from the transmitted beacon packet and then sends the resultant RSSI value as a form of data packet (table 7) to the BS which calculates the position and sends back to the object

45 Object tracking application Testing and Evaluation The purpose of this section is to discuss and evaluate the object tracking application Finally we are ready with the experimental testbed as shown in figure 16 As shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors placed at the corners of the masterrsquos room BS is placed at the centre of the room connected to a PC All the nodes are power connected before an actual measurement takes place At first the object is placed at random position inside the room and transmits beacon packets continuously Then these packets are received by the fixed APrsquos which calculate the RSSI from the packet and then averages it until it receives 50 beacon packets and then transmits the data to the BS (figure 17) Then the BS waits until it receives the data from all the APrsquos Once it receives from all the APrsquos it calculates the position of the object (figure 18) The application was developed to demonstrate a real world use of a WSN in object tracking In evaluating the final position obtained with respect to the actual position we conclude that the object‐tracking was successful Although

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

34

it is a research prototype object tracking application is a simple stable stand‐alone system capable of demonstrating an important application of WSN It takes raw data from the WSN aggregates it evaluates it and based on the evaluation tracks the object The accuracy of estimating the position is less than 5m Table 11 below shows a log of readings taken during test

Start

Intialize micro‐controller and transeciever in receieve mode

If received_packet=

Beacon

Average RSSI values

Increase the counter

If countergt1000050

A

No

Yes

B

B

Yes

No

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

35

A

Reset counter

Set transceiever in transmit mode

Transmit averaged RSSI to Main sensor and set it back to receive mode

10000 samples ‐ Offline

50 samples ‐ Online

Figure 17 Flow chart representing the algorithm of fixed sensors

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

36

Start

Intialize micro‐controller and transeciever in receieve mode

Received RSSI values from all fixed Sensors

Open radio map

Calculate Euclidean distance between obtained RSSI values from fixed sensors and fingerprints

in radio map

No

Yes

Bubble sort for least error in signal strength values

A

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

37

A

Get the location of the object from the radiousing the least error value

Display location of object on PC

End

Figure 18 Flow chart representing the algorithm of main sensor

Table 11 Accuracy of positioning system

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

38

5 RESULTS This chapter presents how the system parameters such as number of samples mean or median etchellipare finalized All the tests are performed in masterrsquos room at Mid Sweden University This chapter is divided into three subsections First in Section 51 it reviews results obtained from samples test Section 52 discusses one dimensional test results and finally section 53 discusses results of two dimensional tests

51 Measurement setup A Dell laptop was connected to a SENTIO platform and is loaded with software (Appendix 2) This setup was used to determine number of samples required during object tracking mean or median position estimation algorithm and one dimensional test The setup is as follows

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

103

0

BC - 1030

AP 1AP 2

BS

HEWLETT

PACKARD

Figure 19 Test bed for one dimensional test in Masters Room

Two SENTIO nodes (AP1 and AP2 as shown above) were separated by a distance of 10 meters and these sensors are known as fixed sensors which are fixed to the ceiling such that there antenna is faced towards the ground They are linear is position A BS was connected to Dell laptop and it was placed in such a way that its antenna was faced towards the ceiling Another SENTIO

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

39

node was used as an object and its orientation was same as BS Figure 19 represents the complete setup for object tracking application Mobile node was moved between these fixed ones at regular intervals of one meter and RSSI values were logged at each interval

52 Number of samples test This test was performed to determine number of samples required for accurate positioning and either mean of the obtained RSSI values or median of them Figure 19 represents the setup for number of samples test Once the setup is ready the measurements are taken as follows

1 An object was tagged with SENTIO node and was placed stationary linear to the fixed sensors

2 10000 samples (RSSI) were taken at regular intervals of 1 meter up to 10 meters

3 The sample values were logged at every interval 4 Using these logged samples I have plotted graphs such as distribution

of RSS values (figure 20 21) meanmedian at intervals of 1 meter using MATLAB

5 The mean and median for various numbers of samples is as shown in figure 22

Figure 20 Histogram plot of RSSI values at 1m and 4m

Figure 21 Histogram plot of RSSI values at 6m and 8m

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

40

Figure 22 Distance vs RSSI

From the above graphs following conclusions were made 1 From the histogram plots we can observe that the distribution of RSSI

values follow Gaussians distribution law 2 As we can see from figure 22 that median curve has drastic changes

when compared to mean curve Hence mean is considered while estimating the position

3 From the observations we conclude that the number of samples does not matter since mean curve tends to be same for various numbers of samples

4 Hence 10000 samples are considered in order to get a proper location fingerprint during off‐line phase and during online phase 50 samples are enough to estimate the position of object

53 0ne dimensional test Measurement setup for one dimensional test is as shown in figure 19 This test was conducted to check the performance of algorithm and how accurate it was At first ie during offline phase fingerprints at each interval averaging 10000 samples were taken During online phase Euclidean algorithm was used estimate the position averaging 50 samples of RSSI From the results we

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

41

can realize that the error was less than 2 meters Table 12 shows the result of location estimation in one dimensional case

Table 12 One dimensional position estimation results

54 Two dimensional test This test is the final stage of my thesis work Experimental testbed for this stage is as shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors fixed at the corners of the room and a BS at the centre of the room There are 18 pre‐determined locations where the fingerprints (RSSI) were taken The fingerprints are as shown in Appendix 1 All the readings were taken in presence of userrsquos The algorithm for position estimation is shown in Appendix 2 Position estimation test is performed as follows

Tagged object is placed at random inside the masterrsquos room All the sensors including the base station and object are switched on Using the algorithm (Appendix 2) the BS estimates the position of the object and displays it on the PC

This test was performed using various numbers of samples to test which has a better accuracy Figure 23 represents the error rate using various numbers of samples From figure 23 we can conclude that 50 numbers of samples is enough to estimate the position of object with an accuracy of 3m

55 Causes of error in location determination The randomness of RSS is the major cause of error in any indoor

positioning system that uses the RSS for location inference If there is no randomness in the RSS and its location fingerprint every indoor positioning systems of this type will have no location detection error For example I have recorded RSS at one particular location during different times of the day From figures 24 and 25 you can observe that RSS values are different at different times of day

The performance of indoor positioning systems depends greatly on the separation of location fingerprints A location fingerprint

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

42

corresponding to a location can be identified correctly if it is difficult to classify it as another fingerprint by a pattern classifier

Path loss is one of the basic signal propagation properties of the RSS that best describes attenuation of transmit signal power over a distance between an access point and a mobile device

Presence of Userrsquos or userrsquos orientation affects RSS

Figure 23 Error Rate vs Number of samples

Figure 24 RSS distribution at 214 pm

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

43

Figure 25 RSS distribution at 100pm and 1100 am

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

6 DISCUSSIONS10102006

44

6 DISCUSSIONS 61 Summary and Conclusion While empirical results and performance studies of positioning systems based on location fingerprint have been presented in this report analytical model that can be used as a framework for efficiently designing the positioning systems are not available This thesis design work uses general principles of position estimation such as position estimation of sensors grid spacing etc This design work focuses on accuracy and precision The concept of location fingerprint technique based on RSS us explained in detail in chapter 2 It is found that RSS is random It is possible to approximate the RSS as being normally distributed While the RSS random process is non‐stationary the mean is more or less constant and could be used as the fingerprint of the location Model designed for position estimation is as shown in chapter 4 Accuracy and precision are the two major performance matrices in this study Uniform grid spacing has been used From the experimental analysis I have obtained an accuracy of 3 meters and 35 precision The sensitivity analysis among the system parameters and the performance matrices were done using the model as shown in chapter 4 For instance our study found that the minimum distance between two adjacent positions also affects the positioning performance This is due to the randomness of RSS patterns induced by the ever changing indoor environment

62 Future work The research in this thesis provides a ground work for the future study of efficient design of indoor positioning system The RSSI based position estimation still has limitations First the RSSI value is time dependent Thus the future study should address this issue Second the movement of object is not considered Third orientations of sensors affect the performance which should be taken care off Fourth to improve the performance of the system we need to find a solution to deal with those incomplete or missing data during the off‐line and the on‐line phase Fifth position estimation on multi‐floor and three‐dimensional coordinates is not available

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 110102006

45

APPENDIX 1

Location Fingerprints of Masters Room Time 1315 Observation Time 20 minutes Number of Samples 10000

People Presence SNo Co-ordinates AP1 (0x0001) AP2 (0x0002) AP3 (0x0004) AP4 (0x0008)

1 11 -24 -16 -27 -33 2 13 -26 -8 -34 -31 3 15 -43 -9 -39 -25 4 32 -21 -11 -27 -33 5 61 -6 -23 -32 -28 6 64 -20 -31 -25 -34 7 35 -23 -35 -29 -38 8 37 -29 -35 -22 -27 9 39 -38 -29 -21 -27 10 67 -27 -23 -20 -23 11 611 -26 -29 -23 -30 12 613 -35 -32 -2 -31 13 513 -34 -31 -20 -26 14 11 -39 -22 -29 -17 15 212 -37 -42 -28 -31 16 -212 -38 -33 -29 -6 17 -21 -42 -29 -33 -19 18 -38 -40 -23 -35 -22

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 210102006

46

APPENDIX 2

Begin

Open radio map and raw data files for write

Intialize radio map in memory

Check whether the

Packet is data or Beacon packet If data packet

Save the data to register

Check if data is

Received from all

sensors

Estimate the position of static

Display the position on PC

End Yes

No

A

A

Yes

No

Flow chart representing the main algorithm for position estimation

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

47

APPENDIX 3

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

48

Distribution of RSSI

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

49

REFERENCES [1] T S Rappaport J H Reed and B D Woerner ldquoPosition location using wireless communications on highways of the futureʺ IEEE Commun Mag vol 34 no 10 pp33‐41 Oct 1996 [2] A M Ladd K E Bekris G Marceau A Rudys L E Kavraki and D S Wallach rdquoRobotics‐based location sensing using wireless ethernetʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ02) 2002 pp 227‐238 [3] I Ramani R Bharadwaja and P V Rangan ldquoLocation tracking for media appliances in wireless home networksʺ in Proc International Conference on Multimedia and Expo (ICMEʹ03) July 2003 pp 769‐772 [4] K Pahlavan X Li and J P Makela ldquoIndoor geolocation science and technologyʺ IEEE Commun Mag vol 40 no 2 pp 112‐118 Feb 2002 [5] Konard Lorincz and Matt Welsh ldquoMote Track A Robust Decentralized Approach to RF‐Based Location Trackingrdquo [6] Paramvir Bahl and Venkata N Padmanabham ldquoRADAR An In‐Building RF‐based User Location and Tracking Systemrdquo [7] Kamol Kaemarungsi ldquoDesign of Indoor positioning system based on location fingerprint techiniquerdquo University of Pittsburgh 2005 [8] Fredrik Linnarsson Cheng Peng and Bengt Oelmann ldquoSENTIO A Hardware Platform for Rapid Prototyping of Wireless Sensor Networksrdquo [9] S Saha K Chaudhuri D Sanghi and P Bhagwat ldquoLocation determination of a mobile device using IEEE 80211b access point signalsʺ in Proc IEEE Wireless Communications and Networking Conference (WCNCʹ03) New Orleans LA Mar 2003 pp 1987‐1992 [10] T Roos P Myllymaki H Tirri P Misikangas and J Sievanen ldquoA probabilistic approach to WLAN user location estimationʺ International Journal of Wireless Information Networks vol 9 no 3 pp 155‐164 July 2002 [11] R Battiti M Brunato and A Villani ldquoStatistical learning theory for location fingerprinting in wireless LANrsquosʺ Technical Report Oct 2002 [Online] Available httprtmscienceunitnitraquobattitiarchive86pdf [12] P Prasithsangaree P Krishnamurthy and P K Chrysanthis ldquoAn indoor position location with wireless LANsʺ in Proc IEEE International Symposium on Personal Indoor and Mobile Radio Communications (PIMRCʹ02) Lisbon Portugal Sept 2002 [13] M A Youssef A Agrawala and A U Shankar ldquoWLAN location determination via clustering and probability distributionsʺ in Proc IEEE

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

50

International Conference on Pervasive Computing and Communications (PerComʹ03) Dallas‐Fort Worth TX Mar 23‐26 2003 pp 23‐26 [14] Z Xiang S Song J Chen H Wang J Huang and X Gao ldquoA wireless LAN‐based indoor positioning technologyʺ IBM Journal of Research and Development vol 48 no 56 pp 617‐626 SeptNov 2004 [15] R Want A Hopper V Falcao and J Gibbons ldquoThe active badge location systemʺACM Transactions on Information Systems vol 40 no 1 pp 91‐102 Jan 1992 [16] N B Priyantha A Chakraborty and H Balakrishnan ldquoThe cricket location‐support systemʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ00) Boston MA Aug 2000 pp 32‐43 [17] Holger K Willig A (2005) Protocols and Architectures for Wireless Sensor Networks London [18] IEEE (2003) Part 154 Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low‐Rate Wireless Personal Area Networks (LR‐WPANs) New York [19] Chipcon CC2420 802154 compliant radio httpwwwchipconcom [20] ZigBee Specification v10 ZigBee Alliance [21] Dimitrios Lymberopoulos Quentin Lindsey and Andreas Savvides ldquoAn Empirical Analysis of Radio Signal Strength Variability in IEEE 802154 Networks using Monopole Antennasrdquo [22] Kiran Yedavalli Bhaskar Krishnamachari Sharmila Ravula and Bhaskar Srinivasan ldquoEcolocation A sequence based technique for RF localization in wireless sensor networksrdquo Robert Bosch Research and Technology Centre Palo Alto CA

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

12

database and used during the on‐line phase to estimate the location The label and fingerprint are usually denoted as a tuple of (L F) Battiti et al [11] point out that the location information L for indoor location can be recorded in two forms as either a tuple of coordinates or an indicator variable The tuple of real coordinates can vary from one dimension to five dimensions which include the three dimension space and two orientation variables expressed in spherical coordinates For instance a location information of a two‐dimension system with an orientation could be expressed as a triplet L = (x y d) | x y є R 2 d є North East South West The RSS was found to be more location‐dependent than the SNR because the noise component is rather random in nature However the RSS itself fluctuates over time for each access point and location Each RSS element can be considered as a random variable therefore it can be captured by recording its descriptive statistics parameters approximating its distribution or maintaining the whole measurement dataset These approaches of RSS representation result in different procedures for location estimation algorithm Regardless of the approach the location fingerprint is usually denoted as an array or vector of signal strength (random variables) received at any position in the location‐based service area The size of the vector is determined by the number of access points that can be heard To create a basis fingerprint such as in a number of samples of vectors of signal strength are collected over a window of time for each position This basis is called a prototype Then the average RSS of each access point is calculated and recorded as an element in the location fingerprint For an area that can receive signals from N access points the location fingerprint can be expressed as a vector of average RSS elements βi

F = (β1 β2hellip βN)T Besides the basis location fingerprint the samples of location fingerprint measured during the on‐line phase are also important for the system that tracks the mobile object The number of samples should be selected appropriately to represent the location fingerprint for the mobile application A step in statistical analysis method called pre‐processing is another important issue needed to be considered because it can impact the estimation of dependency between location fingerprint and the location information easier The pre‐processing refers to a step that cleans the raw data (in this case the training set) before any further operations or analysis The cleaning may consist of encoding dimensionality reduction (reduce unnecessary elements) feature extractionselection clustering and outlier elimination Roos et al [10] point out that the pre‐processing enables faster location estimation and

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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reduce the noise from the training data The process that creates the basis of location fingerprint discussed above could be considered as a part of pre‐processing

232 Location Estimation Algorithm Location estimation algorithms or positioning algorithms are procedures that exploit dependency between location information and location fingerprint basis in order to determine a position or location from samples of RSS signals Position algorithms can be classified into deterministic and probabilistic types based on the approaches that model the relationship between location fingerprints and location information The deterministic type of algorithms are those that based in the nearest neighbour classifiers and neural network classifiers The probabilistic type of algorithms is those that are based on the Bayesian inference and statistical learning theory such as support vector machines (SVR) The algorithm used in this project is discussed briefly

Euclidean distance method The Euclidean distance method is a deterministic approach of position estimation because they require only a set of constant location fingerprints which includes mean vectors In order to determine the location a form of discriminate function is used to classify a sample of RSS fingerprint into a position The mean or average RSS vector is a centre of a mass which represents each class of location fingerprint Suppose a set of l location fingerprints is denoted by F1 F2hellip Fl exist and each fingerprint has a one‐to‐one mapping to a set of positions L1 L2hellip Ll A sample of an RSS fingerprint measured during an on‐line phase is denoted as S which can be another mean or average RSS vector of small window of RSS samples Assuming that an indoor positioning system only considers the average RSS from N access points as a location fingerprint the sample of RSS vector is S = (s1 s2 helliphellip sN)T and each location fingerprint i in the database can be expressed as Fi = (β1 β2helliphellip βN)T The simplest closeness metric is a distance measurement in signal space denoted as the Dist () function [6] Thus the simple procedure of nearest neighbour algorithm is expressed as picking the fingerprint j that has the shortest signal distance

Dist(S Fj) le Dist(S Fk) for all k ne j A generalized weighted distance Lp summarized by [12] can be used to calculate different forms of distance in signal space as

Lp = 1N pN

i

pPiSiWi 1

1)||1(sum

=

minus

where N is the number of access points wi is a weighting factor (wi le 1) and p is the norm parameter starting from 1 The weighting factor wi is a bias

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

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parameter that can demote or promote an important RSS component in the fingerprint

24 Protocols and Industry standards for WSN Although many applications for WSN are proposed the communication protocols supporting them remain mainly unexplored and diverged This is because the area of interest is new each implementation is application specific and the components of WSNrsquos have a lot of constraints Wireless sensor networks use the wireless medium to communicate However the traditional communication protocols that support wireless communications especially ad‐hoc mesh networks may not be well suited for them WSN communication is an active research area and many algorithms and protocols have already been proposed This section introduces two proposed standards the IEEE 802154 and the ZigBee The IEEE 802154 covers the physical layer and the MAC layer of low‐rate WPAN The ZigBee is ldquoan emerging standard that is based on the IEEE 802154 and adds network construction (star networks peer‐to‐peermesh networks and cluster‐tree networks) application services and morerdquo

241 IEEE 802154 Holger and Willig (2005) [17] mention home automation home networking and home security as possible applications for IEEE 802154 They add that ldquomost of these applications require only low‐to‐medium bitrates (up to some few hundreds of kbps) moderate average delays without too stringent delay guarantees and for certain nodes it is highly desirable to reduce the energy consumption to a minimumrdquo The IEEE 802154 standard (2003) defines the device types that can be used in a LR‐WPAN A device can be a FFD or a RFD The RFD can be used in simple applications in which they do not need to transmit large amounts of data and they have to communicate only with a specific FFD The FFD can work as a PAN coordinator as a coordinator or as a simple device It can communicate with either another FFD or a RFD In keeping with the application requirements the LR‐WPAN operates in a star or peer‐to‐peer topology (figure 4) In the star topology the RFD communicates with a single controller the PAN coordinator The PAN coordinator can perform the same function as the RFD but it is also responsible for controlling the PAN ldquoit initiates terminates or routes communication around the networkrdquo [18] The peer‐to‐peer topology

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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supports ad‐hoc mesh multi‐hop networking Any device in the peer‐to‐peer topology can communicate with any other device within its communication range however this topology also has a PAN coordinator All the devices in a LR‐WPAN have a unique 64‐bit address This or a short address allocated by the PAN coordinator can be used inside a PAN Additionally each PAN has a unique identifier The combination of the PAN identifier and the sort addresses allows communication across different PANs (IEEE 802154 Standard 2003)

Figure 4 Star and peer‐to‐peer topologies in LR‐WPAN [18]

The LR‐WPAN based on the OSI seven‐layer model has the layered architecture presented in figure 5 The application and the network layer are the upper layers in the LR‐WPAN architecture but are outside the scope of the IEEE 802154 standard Only the physical layer ldquowhich contains the RF transceiver along with its low‐level control mechanismrdquo and the MAC layer ldquothat provides access to the physical channelrdquo are included in the standard and will be introduced in the following sections Finally the Type I 8022 LLC and the SSCS are intermediate sub layers supporting communication with the above layers (IEEE 802154 Standard 2003)

Physical Layer The 802154 standard specifies two different services that the PHY provides The PHY data service controls the radio and thus the transmission and reception of the PPDUs In addition the management service performs ED in the channel The management service also performs CCA before sending the messages and provides LQI for the received packets

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2 LITERATURE REVIEW10102006

16

Figure 5 WPAN architecture [18]

Three different bands are defined by the standard The 868‐8686 MHz for Europe the 902‐928 MHz for North America and the 2400‐24835 MHz worldwide All of them belong in the ISM radio bands and they are using DSSS Each of the bands supports different a data rate and it uses a different modulation technique chip rate and number of channels Moreover if the system does not use the 2450 MHz frequency it operates in both the 802 MHz and 902 MHz frequencies Table 3 summarizes the supported bands and data rates (IEEE 802154 standard 2003)

PHY (MHz)

Frequency Band (MHz) Modulation

Bit Rate Number of Channels

868-8686 BPSK 20 1 868 915 902-928 BPSK 40 10

2450 2400-24835 O-QPSK 230 16 Table 3 Frequency bands and data rates for IEEE 802154 based on the IEEE

802154 standard [18] The physical layer uses PPDU packets to communicate Table 4 demonstrates its structure The LSB is always transmitted and received first The SHR contains the preamble and the SFD fields which helps receiver

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

17

synchronization The 8‐bytes preamble contains only zero and is used for synchronization The SFD contains a specific sequence of one and zeros and specifies the beginning of the frame The PHR contains the payload length Packets with a length of 9 or more bytes are MPDU as the next section further explains Packets with length 5 are used for MPDU acknowledgements The payload part of the PPDU encloses the MAC layer packet Finally the PPDU size can be up to 127 bytes [18]

4 bytes 1 byte 1 byte Variable

Preamble SFD Frame length

(7 bits) Reserved

(1 bit) Payload (PSU)

SHR PHR PHY Payload Table 4 Format based on the IEEE 802154 standard [18]

MAC Layer The MAC layer is the interface between the SSCS and the PHY layer Similar to the PHY layer the MAC layer supports two services The MAC data service is responsible for the transmission and reception of the MPDUs through the PHY data service The MAC management service if the device is a coordinator manages the network beacons It is also responsible for PAN association and disassociation frame validation and acknowledgment providing ldquoa reliable link between two peer MAC entitiesrdquo In addition it uses the CSMA‐CA for channel access and handles and maintains the GTS mechanism Finally it supports device security [18] The IEEE 802154 standard defines four different frame types the beacon data acknowledgment and MAC command frame All frame types are based on the general MAC frame format (table 5) The frame control field describes and specifies the above different frame types Every MAC frame comprises a MHR which consists of a frame control sequence number and the information field It also contains the MAC payload different frame types have different MAC payload fields The acknowledgment type does not have a payload Finally each frame includes a MFR which contains a FCS The data in the MPDU follows the same order as the PPDU the least significant bits are left in the frame and are transited first Octets2 1 02 028 02 028 Variable 2

Destination PAN Identifier

Destination Address

Source PAN

Identifier Source Address Frame

Control Sequence Number Addressing Field

Frame Payload FCS

MHR MAC

Payload MFRTable 5 General MAC frame format IEEE 802154 standard [18]

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Table 6 7 8 and 9 present the four different MAC frame types The beacon frame is transmitted periodically by the PAN coordinator It provides information about the network management through the super frame and GTS fields which are analyzed later in the section It also synchronizes the network devices and indicates the proper communication period for them The data frame payload encapsulates data from the higher layers When a device receives a packet it is not obliged to response with an acknowledgement packet Finally the command frame identifier and command payload fields of the command frame are used for communication between the network devices The command identifier specifies actions like association disassociation and data GTS or beacon request Octets2 1 410 2 Variable Variable Variable 2

Frame Control

Sequence Number

Addressing Fields

Super frame Specification

GTS fields

Pending Addressing

Fields Beacon Payload FCS

MHR MAC Payload MFRTable 6 Beacon frame format [18]

Octets 2 1 Variable 2

Frame Control

Sequence Number

Addressing Field

Data Payload FCS

MHR MAC

Payload MFR Table 7 Data frame format [18]

Octets 2 1 2

Frame Control

Sequence Number FCS

MHR MFR Table 8 Acknowledgement frame format [18]

Octets2 1 1 Variable 2

Frame Control

Sequence Number

Addressing Fields

Command Frame Identifier

Command Payload FCS

MHR MAC Payload MFR Table 9 Command frame format [18]

In the LR‐WPAN every PAN has its own coordinator The PAN coordinator manages the communication in the local area it has two options to use or not use the super frame structure The super frame (figure 6) uses network beacons If the coordinator does not want to use a super frame structure it suspends the beacon transmission However the beacon is important for device association and disassociation If the coordinator wishes to maintain close communication control in the PAN and to support low‐latency devices it usually uses the super frame A super frame determines a specific time

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

19

period beacons bound it The beacon is transmitted in the first of the sixteen equal time slots that the super frame has It is used to ldquosynchronize the attached devices to identify the PAN and to describe the structure of the super framerdquo The super frame can have active and inactive periods All the communications have to be finished inside the super frame period In the inactive periods the devices can switch to the sleep mode but they have to be ready for the next beacon The active period is further divided into a CAP and a CFP During the CAP any device can communicate competing with the other devices in the PAN using slotted CSMA‐CA The CFP contains GTSs and follows the CAP A CFP may maintain up to seven GTSs and each GTS can reserve more than one time slot However the CAP should always be sufficient to allow new devices t join the PAN The GTSs are allocated to specific devices All the transactions must be completed inside the assigned time period (CAP GTS and CFP) (IEEE 802154 standard 2003)

Figure 6 Example of super frame structure [18]

In accordance with the 802154 standard (2003) three different types of data‐transfer exist In addition the types differ if the coordinator uses or does not beacons Data transfer from a device to the PAN coordinator is the first type (figure 7) For a ldquonon‐beacon‐enable networkrdquo it first senses the medium by using ldquoun‐slotted CSMA‐CArdquo and then a simple transmit to the data frame In a ldquobeacon‐enabled networkrdquo the sender waits for the beacon when it finds it ldquothe device synchronizes to the super fame structurerdquo In the specified time frame the sender again senses the medium by using ldquoslotted CSMA‐CArdquo and transmits the data to the coordinator In either case the coordinator has the option to acknowledge or not acknowledge the data reception after that the transaction is completed Data transfer from the coordinator is the next type described in the standard (figure 8) In a beacon‐enabled network the coordinator indicates a pending message through the beacon The messagersquos target device receives the beacon and if the message is pending it responses with a MAC command request

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

20

message using slotted CSMA‐CA the coordinator may or may not acknowledge the command message and through slotted CSMA‐CA sends the pending message The device acknowledges the received message After that the coordinator removes the message from the beaconrsquos pending list and completes the transaction In a non‐beacon enable network the device periodically sends a MAC command‐request frame to the coordinator The coordinator acknowledges the data request Then by using un‐slotted CSMA‐CA if it has a pending message it transmits it If it has not it respond with a data frame with a ldquozero‐length payloadrdquo To complete the transaction the device acknowledges the data reception

Figure 7 Communication from a device to a PAN coordinator in (a) a beacon‐enabled network and (b) a non‐beacon enabled network [18]

Peer‐to‐peer is the last type of data transfer In this situation the devices are free to communicate with any other device within their communication range In a peer‐to‐peer PAN the devices can ldquoeither receive constantly or synchronize with each otherrdquo If they are receiving constantly to transmit data they use un‐slotted CSAM‐CA In the second case synchronization must be achieved first (IEEE 802154 standard [IEEE 2003]) The IEEE 802154 (2003) standard establishes MAC and PHY standards for low‐cost low‐power and high‐density node deployments In addition to the above PHY and MAC characteristics IEEE 802154 provides a security baseline including ldquothe ability to maintain an ACL and use symmetric cryptographyrdquo for data encryption The algorithm that is used for encryption is the AES However the higher level layers decide when security is need The upper layers are in general responsible for device authentication and key management The next section introduces the ZigBee standard which

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

21

encapsulates the IEEE 802154 and provides additional standardization for the higher levels

Figure 8 Communication from a PAN coordinator to a device in (a) a beacon‐enabled network and (b) a non‐beacon enabled network [18]

242 ZigBee Heily (2004) defines ZigBee as ldquoa rapidly growing worldwide non‐profit industry consortiumrdquo whose mission is ldquoto define a reliable cost‐effective low‐power wirelessly networked monitoring and control product based on an open global standardrdquo Figure 9 illustrates the areas of interest for different wireless communication standards

Figure 9 Overview of the coverage for different wireless communication

standards [17]

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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22

ZigBee a new standard which became publicly available in June 2005 is based on the IEEE 802154 standard It expands the IEEE 802154 by adding the framework for ldquothe network security and applicationrdquo (Craig 2005) Figure 10 presents the IEEE 802154ZigBee stack and the areas of responsibilities

Figure 10 IEEE 802154ZigBee stack [20]

Craig (2005) mentions three networking topologies that the standard covers the star mesh and cluster tree (figure 11) The ZigBee standard works on top of the IEEE 802154 addressing schema by using the standard 64‐bit and the short 16‐bit addressing Kinney (2005) summarizes the ZigBee network layer responsibilities the successful establishment of a new the network and successful new device configuration addressing assignment network synchronization frames security and message routing ZigBee further distinguishes the concept of the physical devices (RFD FFD) by using the notion of ldquological devicesrdquo ldquoZigBee Coordinatorrdquo is the first type of logical devices It is responsible for initializing maintaining and managing the network Under the coordinator in the network hierarchy is the ldquoZigBee routerrdquo which is responsible for controlling the message routing between the nodes Finally the ldquoZigBee End Devicerdquo acts as the end point of the network

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2 LITERATURE REVIEW10102006

23

structure The tasks that an end device can perform are specified in the ldquoApplication Profilesrdquo (Craig 2005)

Figure 11 IEEE 802154ZigBee stack [17]

The ZigBee specifications (2005) summarize the security services provided by ZigBee ldquokey establishment key transport frame protection and device managementrdquo ZigBee builds its security mechanism using symmetric key cryptography The security services also depend on the associated layer thus as the figure 10 shows the security mechanism covers the network and the application layer In addition if a MAC frame needs security protection the MAC layer is able to secure it Moreover the notion of end‐to‐end security is supported the source and destination devices have access and use the same share key In the MAC layer the 802154 AES mechanism provides the proper security The mechanism protects ldquothe confidentiality integrity and authenticity of the MAC framesrdquo (Kinney 2005) An auxiliary header field in front of the MAC payload indicates if the frame is encrypted or not The MAC framesrsquo integrity is supported by calculating and using a MIC at the end of the MAC payload In addition to the AES nonce is used to provide MAC confidentiality and authenticity Figure 12 illustrates a MAC frame with security For different security aspects the MAC layer uses different mode of the AES for the encryption it uses the AES in Counter (CTR) mode and for the integrity the

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

24

CBC‐MAC Finally the combination (CCM) of the above two modes is available providing both encryption and integrity (Kinney 2005)

Figure 12 ZigBee secure frame in MAC layer [17]

In the network layer the CCM (a modified MAC layer CCM mode) is used for encryption Because the network layer uses only the CCM mode a single key is used for all different security options The network layer security message format is similar to the MAC frame it is presented in the figure 13 Finally although the network layer is responsible for securing its layer messages the above layers specify the keys and the CCM option for each frame (Kinney 2005)

Figure 13 ZigBee secure frame in network layer [17]

Security in the application layer works similar to the network and MAC layers It uses the ldquolink keyrdquo or the ldquonetwork keyrdquo to secure the message and then encapsulate it inside a set of fields similar to the network format (figure 14) Other security responsibilities that the application layer has are to provide the ZDO and the applications with device management services key establishment and key transport (ZigBee specifications 2005)

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

25

Figure 14 ZigBee secure frame in application layer [17]

The ZigBee application layer contains the manufacturer‐defined application objects the ZDO and the application sub‐layer In addition to the security responsibilities the application sub‐layer binds devices based on their duties and needs maintains the binding tables and forwards messages between them The application sub‐layer also discovers the neighbour devices for a given device The ZDO is responsible for determining the devicersquos duty in the network for communicating using binding requests and for supporting security as was mentioned above The sub‐layer that implements the actual application is the manufacturer‐defined application object (Kinney 2005) The ZigBee specifications are a great step toward the wireless sensors networks standardization They cover all the OSI layers from the physical to the application providing guidance to the developers The specifications are the product of the ZigBee alliances a consortium of companies and academic institutes Thus they not only produce the specifications but also are the first use them The next section goes beyond the layer architecture and standardization and introduces the OS field another crucial element of WSNs

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

26

3 RECEIVED SIGNAL STRENGTH (RSS) Majority of the existing methods leverage the existence of IEEE 80211 base stations with powerful radio transmit powers of approximately 100mW per base station Such radios are in a different class from the low power IEEE 802154 compliant radios that typically transmit at low power levels ranging from 52mW to 29mW [19] The wide availability of larger number of IEEE 802154 radios in the research community has revived the interest for signal strength based localization in sensor network Despite of rapidly increasing popularity of IEEE 802154 radios and signal strength localization there is a lack of detailed characterization of the fundamental factors contributing to large signal strength variation This chapter investigates some of the properties of RSS observed during testing and analysis of research work collected during the period of thesis The analysis of RSS values is needed to understand the underlying features of location dependent RSS patterns and location fingerprints An understanding of the properties of the RSS values for location fingerprinting can assist in improving the design of positioning algorithms and in deployment of indoor positioning systems

31 Parameters that affect RSS Different factors that affect the indoor RSS are the userrsquos presenceabsence hisher orientation time of the day building type and material distance from transmitter and type of radio These factors are by no means exhaustive Other possible factors are antenna orientation directionality and type Table 10 classify the parameters which affect RSS [7]

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

27

Effect on Factors Options

1 Proximity of user Userrsquos presence or absence

2 Orientation of user and terminal

North East South West

Data Collection

3 Make of Radio 4 Time of measurement Time of day amp days of

week 5 Period of measurement

Second minute hour

7 Interference Co‐channel adjacent co‐channel

Statistics

8 Building environment Small offices or large hall

Table 10 Classification of factors the affect RSS [7]

Transmitter variability Different transmitters behave differently even when they are configured exactly in the same way In practice this means that when a transmitter is configured to send packets at a power level of d dBm then the transmitter will send these packets at a power level that is very close to d dBm but not necessarily exactly equal to d dBm This can alter the received signal strength indication and thus it can lead to inaccurate distance estimation

Receiver variability The sensitivity of the receivers across different radio chips is different In practice this means that the RSSI value recorded at different receivers can be different even when all the other parameters that affect the received signal strength are kept constant

Antenna orientation Each antenna has its own radiation pattern that is not uniform In practice this means that the RSSI value recorded at the receiver for a given pair of communicating nodes and for a given distance between them varies as the pair wise antenna orientations of the transmitter and the receiver are changed

Multi‐path fading and shadowing in the RF channel In indoor environments the transmitted signals get reflected after hitting on the walls andor on other objects in the room such as furniture Both the original signal and the reflected signal reach the receiver almost at the same time since they both travel at the speed of light As a result of this the receiver is not able to

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

28

distinguish the two signals and it measures the received signal strength for both of them

32 Related work analysis A number of indoor location tracking systems have been proposed in the literature based on RF signals ultrasound infrared and some combinations of these As mentioned previously RF based positioning system are widely studied as shown in Table 2 Given a model of radio signal propagation in a building or other environment RSS can be used to estimate the distance between the transmitter and receiver Most of the algorithms (Table 2) have been evaluated in practice using 80211b compliant wireless radios and seem to produce a location error of 15ft to 20ft for an indoor network area of approximately 225ft x 144ft According to the results of this study all the localization techniques produce approximately the same location error over a range of environments Ecolocation [22] Mote Track [5] are two of the few localization algorithms that have been evaluated on a real sensor network that uses a low power wireless radio Ecolocation reports a location error of 10ft for a very small outdoor network deployment area (26ft x 49ft) Mote Track reports a location error of approximately 13ft for an indoor network deployment area of 18751ft2 In general the RSSI based localization algorithms can be divided into two broad categories the map based and the distance (or area) prediction based algorithms In the case of the map based algorithms the deployment area is divided into a large set of sample points that cover the whole area Most techniques collect one or more signal strength measurements from all visible beacon nodes and for each sample point Each sample point is then mapped to either a signal strength vector or a signal strength probability distribution This process generates an RSSI fingerprint map which is unique with respect to the beacon locations and the deployment area This fingerprint map is stored to a central base station A node that is not aware of its location collects one or more signal strength measurements from all the visible beacon nodes and creates its own RSSI fingerprint that is sent to the central base station The base station computes nodersquos location as the sample pointrsquos location whose RSSI fingerprint distance from the unknown nodersquos RSSI fingerprint is the minimum among all the sample points Despite the fact that the map based RSSI localization methods produce relatively small location errors they are not attractive mainly because they require an excessive amount of profiling data and because they are not fully distributed The detailed profiling of a building is a major holdback because it is complex time consuming and depends on beacon locations Therefore for different buildings or even for the

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

29

same building and different beacon locations different RSSI maps have to be obtained The second category of RSSI based algorithms are the distance (or area) prediction algorithms which focus on minimizing the amount of profiling data In distance prediction based methods raw RSSI data are directly mapped to distances through a signal propagation model In some cases distances are not explicitly measured but they are taken into consideration through assumptions on the underlying distributions In the case of outdoor deployments where there are no obstacles a single signal propagation model is all the profiling data required In the case of indoor deployments the same applies but now the signal propagation model might be different across different buildings with different furniture and wall arrangements Given an underlying signal propagation model raw RSSI data can be mapped to specific distances These distances can be either used directly in order to perform localization or assigned a probability and used in a learning basedprobabilistic localization algorithm Obviously the accuracy of the distance prediction based methods depends heavily on the accuracy of the signal propagation model used for translating RSSI values to distances

33 Properties of RSSI Changes in signal due to propagation indoors are difficult to predict because of dense environment and propagation effects such as reflection diffraction and scattering The multi‐path fading effect which is the result of either constructive or destructive combination of multiple signal copies at the receiver causes the received signal to fluctuate around a mean value at a particular location The received signal is usually modelled by the combined effects of large‐scale fading and small‐scale fading The large‐scale fading component describes the signal attenuation as the signal travels over a distance and is absorbed by material such as walls and floors along the way to the receiver This component predicts the mean of the RSS and usually has a lognormal distribution On the other hand the small scale fading component explains the dramatic fluctuation of the signal due to multi‐path fading On the other hand the small scale‐fading component explains the dramatic fluctuation of the signal due to multi‐path fading If there is no LOS component the small‐scale‐fading is often modelled with a Rayleigh distribution Such scenarios are commonly called NLOS If there is a LOS component the small‐scale‐fading is typically modelled by a Rician distribution Note that when the RSS is measured the measurement averages out the small‐scale‐fading effects

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

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4 OBJECT TRACKING 41 Introduction This chapter presents the object‐tracking application the main objective of this thesis work The application is an algorithmic‐software implementation written in Embedded C which is capable of receiving data messages and generating object position estimation data The implementation of the object‐tracking application makes use of the SENTIO platform and uses the background knowledge introduced in the preceding chapters The motivation for the object‐tracking application is to determine the position of an object in indoor environments (Eg Home) The application acts as an interface which collects the raw data that the sensors generate (numerical values) It makes the proper comparisons and algorithmic processes to identify and track the object while it is static inside the area covered by the wireless sensor network Finally with the detection and tracking data it alerts the user or another system The following sections provide an overview of the object‐tracking application

42 Overview of hardware product Before describing the object tracking application it is important to know the characteristics of hardware product The SENTIO platform (figure 15) is built in modular fashion and has the following key features [8]

Flexibility This platform is very flexible in such a way that it is made easy to add or change the hardware for each sensor task depending on the requirement in the WSN For a specific platform this can be hard or sometimes even impossible to accomplish

Sensor resources Resources in a sensor module includes the ability of usage of controllerrsquos resources as much as possible such as ADC‐channels interrupts and digital Input‐Outputrsquos Local voltage regulation should also be possible since many sensors require higher supply voltages than digital Integrated Circuits do Further support of a shut‐down mechanism to power down the sensor modules when not needed

Computation and memory The processor module based on microcontroller should support many devices in a microcontroller family and support different power supply voltages enabling full range of clock frequencies All power‐down modes of the devices should be

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

31

supported The platform should support different processor modules including FPGArsquos and very simple microcontrollers

Communication The focus should be on enabling an IEEE 802154 compliant communication interface With or without ZigBee stack this will provide a flexible platform for exploring different network topologies Support for power‐down of the entire communication module should be supported

Cost and form factor High flexibility is in conflict with low cost and small size The primary objective should be flexibility but the size should be considered in order to enable sensor device integration in systems where space is limited

Figure 15 SENTIO Platform [8]

The communication subsystem of the SENTIO sensor node was designed so that the correct operation of the radio chip is ensured The radio chip is powered by the on‐board voltage regulator and thus fluctuations in the battery voltage level do not affect the operation of the communication subsystem This platform uses Chipcon CC2420 IEEE 802154 radio transceiver operates in the 24GHz ISM band and includes a digital direct sequence spread spectrum (DSSS) modem providing a spreading gain of 9dB and an effective data rate of 250Kbps It was specifically designed for low power wireless applications and supports 8 discrete power levels 0dBm minus1dBm minus3dBm minus5dBm minus7dBm minus10dBm minus15dBm and minus25dBm at which its power consumption varies from 29mW to 52mW [21] A built‐in received signal strength indicator gives an 8‐bit digital value RSSIVAL The RSSIVAL is always averaged over 8 symbol periods (128μs) and a status bit indicates when the RSSIVAL is valid (meaning that the receiver was enabled for at least 8 symbol periods) The

Antenna

Communication Module

Processing Module

Sensing Module

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

32

power P at the RF pins can be obtained directly from RSSIVAL using the following equation

P = RSSIVAL + RSSIOFFSET [dBm] where the RSSIOFFSET is found empirically from the front‐end gain and it is approximately equal to minus45dBm In the next sections when we refer to the RSSI value we refer to the RSSIVAL and not the power P unless otherwise stated

43 Experimental Testbed The experimental test bed for object tracking application is as shown in figure 16 Experiment was conducted in Masterrsquos room at Mid Sweden University A DELL laptop is connected to SENTIO platform and software were primarily used to collect samples of RSS from APrsquos The software monitors IEEE 802154 radio frequency channels which operate in the 24GHz band

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

1030

BC - 1030

AP 3 AP 1

AP 2

AP 4

BS

HEWLETT

PACKARD

Figure 16 Layout of Masterʹs Room

A three dimensional scalable testbed is used for indoor sensor network deployments The testbed illustrated in figure 16 is a 3‐D structure measuring 16m (L) x 12m (W) x 3m (H) and it is designed to host a large number of static nodes as shown in figure 16 Arrow marks in the above figure represent the location where fingerprints were taken The centrepiece of our infrastructure

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

33

is the SENTIO sensor node [8] an open‐source general purpose sensing platform designed with AT Mega 128 microprocessor and the IEEE 802154 compliant CC2420 radio from Chipcon [19]

44 Obtaining data from the sensor network The next important step in the development of the object tracking application is to obtain the data from the sensor network and send it to the base station From there the data is transmitted through a serial port to the computer that runs the object tracking application When the data arrives at the serial port the application must capture it This paragraph gives a brief explanation of how and what kind data is transmitted by the static object fixed sensors and how this data is being used to estimate the position of the object First the static object transmits Beacon packet continuously which is received by all the fixed APrsquos The beacon packet format is as shown in table 6 Then the fixed APrsquos calculates the 8‐bit value of RSSI from the transmitted beacon packet and then sends the resultant RSSI value as a form of data packet (table 7) to the BS which calculates the position and sends back to the object

45 Object tracking application Testing and Evaluation The purpose of this section is to discuss and evaluate the object tracking application Finally we are ready with the experimental testbed as shown in figure 16 As shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors placed at the corners of the masterrsquos room BS is placed at the centre of the room connected to a PC All the nodes are power connected before an actual measurement takes place At first the object is placed at random position inside the room and transmits beacon packets continuously Then these packets are received by the fixed APrsquos which calculate the RSSI from the packet and then averages it until it receives 50 beacon packets and then transmits the data to the BS (figure 17) Then the BS waits until it receives the data from all the APrsquos Once it receives from all the APrsquos it calculates the position of the object (figure 18) The application was developed to demonstrate a real world use of a WSN in object tracking In evaluating the final position obtained with respect to the actual position we conclude that the object‐tracking was successful Although

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

34

it is a research prototype object tracking application is a simple stable stand‐alone system capable of demonstrating an important application of WSN It takes raw data from the WSN aggregates it evaluates it and based on the evaluation tracks the object The accuracy of estimating the position is less than 5m Table 11 below shows a log of readings taken during test

Start

Intialize micro‐controller and transeciever in receieve mode

If received_packet=

Beacon

Average RSSI values

Increase the counter

If countergt1000050

A

No

Yes

B

B

Yes

No

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

35

A

Reset counter

Set transceiever in transmit mode

Transmit averaged RSSI to Main sensor and set it back to receive mode

10000 samples ‐ Offline

50 samples ‐ Online

Figure 17 Flow chart representing the algorithm of fixed sensors

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

36

Start

Intialize micro‐controller and transeciever in receieve mode

Received RSSI values from all fixed Sensors

Open radio map

Calculate Euclidean distance between obtained RSSI values from fixed sensors and fingerprints

in radio map

No

Yes

Bubble sort for least error in signal strength values

A

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

37

A

Get the location of the object from the radiousing the least error value

Display location of object on PC

End

Figure 18 Flow chart representing the algorithm of main sensor

Table 11 Accuracy of positioning system

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

38

5 RESULTS This chapter presents how the system parameters such as number of samples mean or median etchellipare finalized All the tests are performed in masterrsquos room at Mid Sweden University This chapter is divided into three subsections First in Section 51 it reviews results obtained from samples test Section 52 discusses one dimensional test results and finally section 53 discusses results of two dimensional tests

51 Measurement setup A Dell laptop was connected to a SENTIO platform and is loaded with software (Appendix 2) This setup was used to determine number of samples required during object tracking mean or median position estimation algorithm and one dimensional test The setup is as follows

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

103

0

BC - 1030

AP 1AP 2

BS

HEWLETT

PACKARD

Figure 19 Test bed for one dimensional test in Masters Room

Two SENTIO nodes (AP1 and AP2 as shown above) were separated by a distance of 10 meters and these sensors are known as fixed sensors which are fixed to the ceiling such that there antenna is faced towards the ground They are linear is position A BS was connected to Dell laptop and it was placed in such a way that its antenna was faced towards the ceiling Another SENTIO

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

39

node was used as an object and its orientation was same as BS Figure 19 represents the complete setup for object tracking application Mobile node was moved between these fixed ones at regular intervals of one meter and RSSI values were logged at each interval

52 Number of samples test This test was performed to determine number of samples required for accurate positioning and either mean of the obtained RSSI values or median of them Figure 19 represents the setup for number of samples test Once the setup is ready the measurements are taken as follows

1 An object was tagged with SENTIO node and was placed stationary linear to the fixed sensors

2 10000 samples (RSSI) were taken at regular intervals of 1 meter up to 10 meters

3 The sample values were logged at every interval 4 Using these logged samples I have plotted graphs such as distribution

of RSS values (figure 20 21) meanmedian at intervals of 1 meter using MATLAB

5 The mean and median for various numbers of samples is as shown in figure 22

Figure 20 Histogram plot of RSSI values at 1m and 4m

Figure 21 Histogram plot of RSSI values at 6m and 8m

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

40

Figure 22 Distance vs RSSI

From the above graphs following conclusions were made 1 From the histogram plots we can observe that the distribution of RSSI

values follow Gaussians distribution law 2 As we can see from figure 22 that median curve has drastic changes

when compared to mean curve Hence mean is considered while estimating the position

3 From the observations we conclude that the number of samples does not matter since mean curve tends to be same for various numbers of samples

4 Hence 10000 samples are considered in order to get a proper location fingerprint during off‐line phase and during online phase 50 samples are enough to estimate the position of object

53 0ne dimensional test Measurement setup for one dimensional test is as shown in figure 19 This test was conducted to check the performance of algorithm and how accurate it was At first ie during offline phase fingerprints at each interval averaging 10000 samples were taken During online phase Euclidean algorithm was used estimate the position averaging 50 samples of RSSI From the results we

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

41

can realize that the error was less than 2 meters Table 12 shows the result of location estimation in one dimensional case

Table 12 One dimensional position estimation results

54 Two dimensional test This test is the final stage of my thesis work Experimental testbed for this stage is as shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors fixed at the corners of the room and a BS at the centre of the room There are 18 pre‐determined locations where the fingerprints (RSSI) were taken The fingerprints are as shown in Appendix 1 All the readings were taken in presence of userrsquos The algorithm for position estimation is shown in Appendix 2 Position estimation test is performed as follows

Tagged object is placed at random inside the masterrsquos room All the sensors including the base station and object are switched on Using the algorithm (Appendix 2) the BS estimates the position of the object and displays it on the PC

This test was performed using various numbers of samples to test which has a better accuracy Figure 23 represents the error rate using various numbers of samples From figure 23 we can conclude that 50 numbers of samples is enough to estimate the position of object with an accuracy of 3m

55 Causes of error in location determination The randomness of RSS is the major cause of error in any indoor

positioning system that uses the RSS for location inference If there is no randomness in the RSS and its location fingerprint every indoor positioning systems of this type will have no location detection error For example I have recorded RSS at one particular location during different times of the day From figures 24 and 25 you can observe that RSS values are different at different times of day

The performance of indoor positioning systems depends greatly on the separation of location fingerprints A location fingerprint

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

42

corresponding to a location can be identified correctly if it is difficult to classify it as another fingerprint by a pattern classifier

Path loss is one of the basic signal propagation properties of the RSS that best describes attenuation of transmit signal power over a distance between an access point and a mobile device

Presence of Userrsquos or userrsquos orientation affects RSS

Figure 23 Error Rate vs Number of samples

Figure 24 RSS distribution at 214 pm

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

43

Figure 25 RSS distribution at 100pm and 1100 am

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

6 DISCUSSIONS10102006

44

6 DISCUSSIONS 61 Summary and Conclusion While empirical results and performance studies of positioning systems based on location fingerprint have been presented in this report analytical model that can be used as a framework for efficiently designing the positioning systems are not available This thesis design work uses general principles of position estimation such as position estimation of sensors grid spacing etc This design work focuses on accuracy and precision The concept of location fingerprint technique based on RSS us explained in detail in chapter 2 It is found that RSS is random It is possible to approximate the RSS as being normally distributed While the RSS random process is non‐stationary the mean is more or less constant and could be used as the fingerprint of the location Model designed for position estimation is as shown in chapter 4 Accuracy and precision are the two major performance matrices in this study Uniform grid spacing has been used From the experimental analysis I have obtained an accuracy of 3 meters and 35 precision The sensitivity analysis among the system parameters and the performance matrices were done using the model as shown in chapter 4 For instance our study found that the minimum distance between two adjacent positions also affects the positioning performance This is due to the randomness of RSS patterns induced by the ever changing indoor environment

62 Future work The research in this thesis provides a ground work for the future study of efficient design of indoor positioning system The RSSI based position estimation still has limitations First the RSSI value is time dependent Thus the future study should address this issue Second the movement of object is not considered Third orientations of sensors affect the performance which should be taken care off Fourth to improve the performance of the system we need to find a solution to deal with those incomplete or missing data during the off‐line and the on‐line phase Fifth position estimation on multi‐floor and three‐dimensional coordinates is not available

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 110102006

45

APPENDIX 1

Location Fingerprints of Masters Room Time 1315 Observation Time 20 minutes Number of Samples 10000

People Presence SNo Co-ordinates AP1 (0x0001) AP2 (0x0002) AP3 (0x0004) AP4 (0x0008)

1 11 -24 -16 -27 -33 2 13 -26 -8 -34 -31 3 15 -43 -9 -39 -25 4 32 -21 -11 -27 -33 5 61 -6 -23 -32 -28 6 64 -20 -31 -25 -34 7 35 -23 -35 -29 -38 8 37 -29 -35 -22 -27 9 39 -38 -29 -21 -27 10 67 -27 -23 -20 -23 11 611 -26 -29 -23 -30 12 613 -35 -32 -2 -31 13 513 -34 -31 -20 -26 14 11 -39 -22 -29 -17 15 212 -37 -42 -28 -31 16 -212 -38 -33 -29 -6 17 -21 -42 -29 -33 -19 18 -38 -40 -23 -35 -22

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 210102006

46

APPENDIX 2

Begin

Open radio map and raw data files for write

Intialize radio map in memory

Check whether the

Packet is data or Beacon packet If data packet

Save the data to register

Check if data is

Received from all

sensors

Estimate the position of static

Display the position on PC

End Yes

No

A

A

Yes

No

Flow chart representing the main algorithm for position estimation

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

47

APPENDIX 3

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

48

Distribution of RSSI

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

49

REFERENCES [1] T S Rappaport J H Reed and B D Woerner ldquoPosition location using wireless communications on highways of the futureʺ IEEE Commun Mag vol 34 no 10 pp33‐41 Oct 1996 [2] A M Ladd K E Bekris G Marceau A Rudys L E Kavraki and D S Wallach rdquoRobotics‐based location sensing using wireless ethernetʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ02) 2002 pp 227‐238 [3] I Ramani R Bharadwaja and P V Rangan ldquoLocation tracking for media appliances in wireless home networksʺ in Proc International Conference on Multimedia and Expo (ICMEʹ03) July 2003 pp 769‐772 [4] K Pahlavan X Li and J P Makela ldquoIndoor geolocation science and technologyʺ IEEE Commun Mag vol 40 no 2 pp 112‐118 Feb 2002 [5] Konard Lorincz and Matt Welsh ldquoMote Track A Robust Decentralized Approach to RF‐Based Location Trackingrdquo [6] Paramvir Bahl and Venkata N Padmanabham ldquoRADAR An In‐Building RF‐based User Location and Tracking Systemrdquo [7] Kamol Kaemarungsi ldquoDesign of Indoor positioning system based on location fingerprint techiniquerdquo University of Pittsburgh 2005 [8] Fredrik Linnarsson Cheng Peng and Bengt Oelmann ldquoSENTIO A Hardware Platform for Rapid Prototyping of Wireless Sensor Networksrdquo [9] S Saha K Chaudhuri D Sanghi and P Bhagwat ldquoLocation determination of a mobile device using IEEE 80211b access point signalsʺ in Proc IEEE Wireless Communications and Networking Conference (WCNCʹ03) New Orleans LA Mar 2003 pp 1987‐1992 [10] T Roos P Myllymaki H Tirri P Misikangas and J Sievanen ldquoA probabilistic approach to WLAN user location estimationʺ International Journal of Wireless Information Networks vol 9 no 3 pp 155‐164 July 2002 [11] R Battiti M Brunato and A Villani ldquoStatistical learning theory for location fingerprinting in wireless LANrsquosʺ Technical Report Oct 2002 [Online] Available httprtmscienceunitnitraquobattitiarchive86pdf [12] P Prasithsangaree P Krishnamurthy and P K Chrysanthis ldquoAn indoor position location with wireless LANsʺ in Proc IEEE International Symposium on Personal Indoor and Mobile Radio Communications (PIMRCʹ02) Lisbon Portugal Sept 2002 [13] M A Youssef A Agrawala and A U Shankar ldquoWLAN location determination via clustering and probability distributionsʺ in Proc IEEE

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

50

International Conference on Pervasive Computing and Communications (PerComʹ03) Dallas‐Fort Worth TX Mar 23‐26 2003 pp 23‐26 [14] Z Xiang S Song J Chen H Wang J Huang and X Gao ldquoA wireless LAN‐based indoor positioning technologyʺ IBM Journal of Research and Development vol 48 no 56 pp 617‐626 SeptNov 2004 [15] R Want A Hopper V Falcao and J Gibbons ldquoThe active badge location systemʺACM Transactions on Information Systems vol 40 no 1 pp 91‐102 Jan 1992 [16] N B Priyantha A Chakraborty and H Balakrishnan ldquoThe cricket location‐support systemʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ00) Boston MA Aug 2000 pp 32‐43 [17] Holger K Willig A (2005) Protocols and Architectures for Wireless Sensor Networks London [18] IEEE (2003) Part 154 Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low‐Rate Wireless Personal Area Networks (LR‐WPANs) New York [19] Chipcon CC2420 802154 compliant radio httpwwwchipconcom [20] ZigBee Specification v10 ZigBee Alliance [21] Dimitrios Lymberopoulos Quentin Lindsey and Andreas Savvides ldquoAn Empirical Analysis of Radio Signal Strength Variability in IEEE 802154 Networks using Monopole Antennasrdquo [22] Kiran Yedavalli Bhaskar Krishnamachari Sharmila Ravula and Bhaskar Srinivasan ldquoEcolocation A sequence based technique for RF localization in wireless sensor networksrdquo Robert Bosch Research and Technology Centre Palo Alto CA

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

13

reduce the noise from the training data The process that creates the basis of location fingerprint discussed above could be considered as a part of pre‐processing

232 Location Estimation Algorithm Location estimation algorithms or positioning algorithms are procedures that exploit dependency between location information and location fingerprint basis in order to determine a position or location from samples of RSS signals Position algorithms can be classified into deterministic and probabilistic types based on the approaches that model the relationship between location fingerprints and location information The deterministic type of algorithms are those that based in the nearest neighbour classifiers and neural network classifiers The probabilistic type of algorithms is those that are based on the Bayesian inference and statistical learning theory such as support vector machines (SVR) The algorithm used in this project is discussed briefly

Euclidean distance method The Euclidean distance method is a deterministic approach of position estimation because they require only a set of constant location fingerprints which includes mean vectors In order to determine the location a form of discriminate function is used to classify a sample of RSS fingerprint into a position The mean or average RSS vector is a centre of a mass which represents each class of location fingerprint Suppose a set of l location fingerprints is denoted by F1 F2hellip Fl exist and each fingerprint has a one‐to‐one mapping to a set of positions L1 L2hellip Ll A sample of an RSS fingerprint measured during an on‐line phase is denoted as S which can be another mean or average RSS vector of small window of RSS samples Assuming that an indoor positioning system only considers the average RSS from N access points as a location fingerprint the sample of RSS vector is S = (s1 s2 helliphellip sN)T and each location fingerprint i in the database can be expressed as Fi = (β1 β2helliphellip βN)T The simplest closeness metric is a distance measurement in signal space denoted as the Dist () function [6] Thus the simple procedure of nearest neighbour algorithm is expressed as picking the fingerprint j that has the shortest signal distance

Dist(S Fj) le Dist(S Fk) for all k ne j A generalized weighted distance Lp summarized by [12] can be used to calculate different forms of distance in signal space as

Lp = 1N pN

i

pPiSiWi 1

1)||1(sum

=

minus

where N is the number of access points wi is a weighting factor (wi le 1) and p is the norm parameter starting from 1 The weighting factor wi is a bias

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

14

parameter that can demote or promote an important RSS component in the fingerprint

24 Protocols and Industry standards for WSN Although many applications for WSN are proposed the communication protocols supporting them remain mainly unexplored and diverged This is because the area of interest is new each implementation is application specific and the components of WSNrsquos have a lot of constraints Wireless sensor networks use the wireless medium to communicate However the traditional communication protocols that support wireless communications especially ad‐hoc mesh networks may not be well suited for them WSN communication is an active research area and many algorithms and protocols have already been proposed This section introduces two proposed standards the IEEE 802154 and the ZigBee The IEEE 802154 covers the physical layer and the MAC layer of low‐rate WPAN The ZigBee is ldquoan emerging standard that is based on the IEEE 802154 and adds network construction (star networks peer‐to‐peermesh networks and cluster‐tree networks) application services and morerdquo

241 IEEE 802154 Holger and Willig (2005) [17] mention home automation home networking and home security as possible applications for IEEE 802154 They add that ldquomost of these applications require only low‐to‐medium bitrates (up to some few hundreds of kbps) moderate average delays without too stringent delay guarantees and for certain nodes it is highly desirable to reduce the energy consumption to a minimumrdquo The IEEE 802154 standard (2003) defines the device types that can be used in a LR‐WPAN A device can be a FFD or a RFD The RFD can be used in simple applications in which they do not need to transmit large amounts of data and they have to communicate only with a specific FFD The FFD can work as a PAN coordinator as a coordinator or as a simple device It can communicate with either another FFD or a RFD In keeping with the application requirements the LR‐WPAN operates in a star or peer‐to‐peer topology (figure 4) In the star topology the RFD communicates with a single controller the PAN coordinator The PAN coordinator can perform the same function as the RFD but it is also responsible for controlling the PAN ldquoit initiates terminates or routes communication around the networkrdquo [18] The peer‐to‐peer topology

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

15

supports ad‐hoc mesh multi‐hop networking Any device in the peer‐to‐peer topology can communicate with any other device within its communication range however this topology also has a PAN coordinator All the devices in a LR‐WPAN have a unique 64‐bit address This or a short address allocated by the PAN coordinator can be used inside a PAN Additionally each PAN has a unique identifier The combination of the PAN identifier and the sort addresses allows communication across different PANs (IEEE 802154 Standard 2003)

Figure 4 Star and peer‐to‐peer topologies in LR‐WPAN [18]

The LR‐WPAN based on the OSI seven‐layer model has the layered architecture presented in figure 5 The application and the network layer are the upper layers in the LR‐WPAN architecture but are outside the scope of the IEEE 802154 standard Only the physical layer ldquowhich contains the RF transceiver along with its low‐level control mechanismrdquo and the MAC layer ldquothat provides access to the physical channelrdquo are included in the standard and will be introduced in the following sections Finally the Type I 8022 LLC and the SSCS are intermediate sub layers supporting communication with the above layers (IEEE 802154 Standard 2003)

Physical Layer The 802154 standard specifies two different services that the PHY provides The PHY data service controls the radio and thus the transmission and reception of the PPDUs In addition the management service performs ED in the channel The management service also performs CCA before sending the messages and provides LQI for the received packets

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

16

Figure 5 WPAN architecture [18]

Three different bands are defined by the standard The 868‐8686 MHz for Europe the 902‐928 MHz for North America and the 2400‐24835 MHz worldwide All of them belong in the ISM radio bands and they are using DSSS Each of the bands supports different a data rate and it uses a different modulation technique chip rate and number of channels Moreover if the system does not use the 2450 MHz frequency it operates in both the 802 MHz and 902 MHz frequencies Table 3 summarizes the supported bands and data rates (IEEE 802154 standard 2003)

PHY (MHz)

Frequency Band (MHz) Modulation

Bit Rate Number of Channels

868-8686 BPSK 20 1 868 915 902-928 BPSK 40 10

2450 2400-24835 O-QPSK 230 16 Table 3 Frequency bands and data rates for IEEE 802154 based on the IEEE

802154 standard [18] The physical layer uses PPDU packets to communicate Table 4 demonstrates its structure The LSB is always transmitted and received first The SHR contains the preamble and the SFD fields which helps receiver

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

17

synchronization The 8‐bytes preamble contains only zero and is used for synchronization The SFD contains a specific sequence of one and zeros and specifies the beginning of the frame The PHR contains the payload length Packets with a length of 9 or more bytes are MPDU as the next section further explains Packets with length 5 are used for MPDU acknowledgements The payload part of the PPDU encloses the MAC layer packet Finally the PPDU size can be up to 127 bytes [18]

4 bytes 1 byte 1 byte Variable

Preamble SFD Frame length

(7 bits) Reserved

(1 bit) Payload (PSU)

SHR PHR PHY Payload Table 4 Format based on the IEEE 802154 standard [18]

MAC Layer The MAC layer is the interface between the SSCS and the PHY layer Similar to the PHY layer the MAC layer supports two services The MAC data service is responsible for the transmission and reception of the MPDUs through the PHY data service The MAC management service if the device is a coordinator manages the network beacons It is also responsible for PAN association and disassociation frame validation and acknowledgment providing ldquoa reliable link between two peer MAC entitiesrdquo In addition it uses the CSMA‐CA for channel access and handles and maintains the GTS mechanism Finally it supports device security [18] The IEEE 802154 standard defines four different frame types the beacon data acknowledgment and MAC command frame All frame types are based on the general MAC frame format (table 5) The frame control field describes and specifies the above different frame types Every MAC frame comprises a MHR which consists of a frame control sequence number and the information field It also contains the MAC payload different frame types have different MAC payload fields The acknowledgment type does not have a payload Finally each frame includes a MFR which contains a FCS The data in the MPDU follows the same order as the PPDU the least significant bits are left in the frame and are transited first Octets2 1 02 028 02 028 Variable 2

Destination PAN Identifier

Destination Address

Source PAN

Identifier Source Address Frame

Control Sequence Number Addressing Field

Frame Payload FCS

MHR MAC

Payload MFRTable 5 General MAC frame format IEEE 802154 standard [18]

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

18

Table 6 7 8 and 9 present the four different MAC frame types The beacon frame is transmitted periodically by the PAN coordinator It provides information about the network management through the super frame and GTS fields which are analyzed later in the section It also synchronizes the network devices and indicates the proper communication period for them The data frame payload encapsulates data from the higher layers When a device receives a packet it is not obliged to response with an acknowledgement packet Finally the command frame identifier and command payload fields of the command frame are used for communication between the network devices The command identifier specifies actions like association disassociation and data GTS or beacon request Octets2 1 410 2 Variable Variable Variable 2

Frame Control

Sequence Number

Addressing Fields

Super frame Specification

GTS fields

Pending Addressing

Fields Beacon Payload FCS

MHR MAC Payload MFRTable 6 Beacon frame format [18]

Octets 2 1 Variable 2

Frame Control

Sequence Number

Addressing Field

Data Payload FCS

MHR MAC

Payload MFR Table 7 Data frame format [18]

Octets 2 1 2

Frame Control

Sequence Number FCS

MHR MFR Table 8 Acknowledgement frame format [18]

Octets2 1 1 Variable 2

Frame Control

Sequence Number

Addressing Fields

Command Frame Identifier

Command Payload FCS

MHR MAC Payload MFR Table 9 Command frame format [18]

In the LR‐WPAN every PAN has its own coordinator The PAN coordinator manages the communication in the local area it has two options to use or not use the super frame structure The super frame (figure 6) uses network beacons If the coordinator does not want to use a super frame structure it suspends the beacon transmission However the beacon is important for device association and disassociation If the coordinator wishes to maintain close communication control in the PAN and to support low‐latency devices it usually uses the super frame A super frame determines a specific time

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

19

period beacons bound it The beacon is transmitted in the first of the sixteen equal time slots that the super frame has It is used to ldquosynchronize the attached devices to identify the PAN and to describe the structure of the super framerdquo The super frame can have active and inactive periods All the communications have to be finished inside the super frame period In the inactive periods the devices can switch to the sleep mode but they have to be ready for the next beacon The active period is further divided into a CAP and a CFP During the CAP any device can communicate competing with the other devices in the PAN using slotted CSMA‐CA The CFP contains GTSs and follows the CAP A CFP may maintain up to seven GTSs and each GTS can reserve more than one time slot However the CAP should always be sufficient to allow new devices t join the PAN The GTSs are allocated to specific devices All the transactions must be completed inside the assigned time period (CAP GTS and CFP) (IEEE 802154 standard 2003)

Figure 6 Example of super frame structure [18]

In accordance with the 802154 standard (2003) three different types of data‐transfer exist In addition the types differ if the coordinator uses or does not beacons Data transfer from a device to the PAN coordinator is the first type (figure 7) For a ldquonon‐beacon‐enable networkrdquo it first senses the medium by using ldquoun‐slotted CSMA‐CArdquo and then a simple transmit to the data frame In a ldquobeacon‐enabled networkrdquo the sender waits for the beacon when it finds it ldquothe device synchronizes to the super fame structurerdquo In the specified time frame the sender again senses the medium by using ldquoslotted CSMA‐CArdquo and transmits the data to the coordinator In either case the coordinator has the option to acknowledge or not acknowledge the data reception after that the transaction is completed Data transfer from the coordinator is the next type described in the standard (figure 8) In a beacon‐enabled network the coordinator indicates a pending message through the beacon The messagersquos target device receives the beacon and if the message is pending it responses with a MAC command request

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

20

message using slotted CSMA‐CA the coordinator may or may not acknowledge the command message and through slotted CSMA‐CA sends the pending message The device acknowledges the received message After that the coordinator removes the message from the beaconrsquos pending list and completes the transaction In a non‐beacon enable network the device periodically sends a MAC command‐request frame to the coordinator The coordinator acknowledges the data request Then by using un‐slotted CSMA‐CA if it has a pending message it transmits it If it has not it respond with a data frame with a ldquozero‐length payloadrdquo To complete the transaction the device acknowledges the data reception

Figure 7 Communication from a device to a PAN coordinator in (a) a beacon‐enabled network and (b) a non‐beacon enabled network [18]

Peer‐to‐peer is the last type of data transfer In this situation the devices are free to communicate with any other device within their communication range In a peer‐to‐peer PAN the devices can ldquoeither receive constantly or synchronize with each otherrdquo If they are receiving constantly to transmit data they use un‐slotted CSAM‐CA In the second case synchronization must be achieved first (IEEE 802154 standard [IEEE 2003]) The IEEE 802154 (2003) standard establishes MAC and PHY standards for low‐cost low‐power and high‐density node deployments In addition to the above PHY and MAC characteristics IEEE 802154 provides a security baseline including ldquothe ability to maintain an ACL and use symmetric cryptographyrdquo for data encryption The algorithm that is used for encryption is the AES However the higher level layers decide when security is need The upper layers are in general responsible for device authentication and key management The next section introduces the ZigBee standard which

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

21

encapsulates the IEEE 802154 and provides additional standardization for the higher levels

Figure 8 Communication from a PAN coordinator to a device in (a) a beacon‐enabled network and (b) a non‐beacon enabled network [18]

242 ZigBee Heily (2004) defines ZigBee as ldquoa rapidly growing worldwide non‐profit industry consortiumrdquo whose mission is ldquoto define a reliable cost‐effective low‐power wirelessly networked monitoring and control product based on an open global standardrdquo Figure 9 illustrates the areas of interest for different wireless communication standards

Figure 9 Overview of the coverage for different wireless communication

standards [17]

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ZigBee a new standard which became publicly available in June 2005 is based on the IEEE 802154 standard It expands the IEEE 802154 by adding the framework for ldquothe network security and applicationrdquo (Craig 2005) Figure 10 presents the IEEE 802154ZigBee stack and the areas of responsibilities

Figure 10 IEEE 802154ZigBee stack [20]

Craig (2005) mentions three networking topologies that the standard covers the star mesh and cluster tree (figure 11) The ZigBee standard works on top of the IEEE 802154 addressing schema by using the standard 64‐bit and the short 16‐bit addressing Kinney (2005) summarizes the ZigBee network layer responsibilities the successful establishment of a new the network and successful new device configuration addressing assignment network synchronization frames security and message routing ZigBee further distinguishes the concept of the physical devices (RFD FFD) by using the notion of ldquological devicesrdquo ldquoZigBee Coordinatorrdquo is the first type of logical devices It is responsible for initializing maintaining and managing the network Under the coordinator in the network hierarchy is the ldquoZigBee routerrdquo which is responsible for controlling the message routing between the nodes Finally the ldquoZigBee End Devicerdquo acts as the end point of the network

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2 LITERATURE REVIEW10102006

23

structure The tasks that an end device can perform are specified in the ldquoApplication Profilesrdquo (Craig 2005)

Figure 11 IEEE 802154ZigBee stack [17]

The ZigBee specifications (2005) summarize the security services provided by ZigBee ldquokey establishment key transport frame protection and device managementrdquo ZigBee builds its security mechanism using symmetric key cryptography The security services also depend on the associated layer thus as the figure 10 shows the security mechanism covers the network and the application layer In addition if a MAC frame needs security protection the MAC layer is able to secure it Moreover the notion of end‐to‐end security is supported the source and destination devices have access and use the same share key In the MAC layer the 802154 AES mechanism provides the proper security The mechanism protects ldquothe confidentiality integrity and authenticity of the MAC framesrdquo (Kinney 2005) An auxiliary header field in front of the MAC payload indicates if the frame is encrypted or not The MAC framesrsquo integrity is supported by calculating and using a MIC at the end of the MAC payload In addition to the AES nonce is used to provide MAC confidentiality and authenticity Figure 12 illustrates a MAC frame with security For different security aspects the MAC layer uses different mode of the AES for the encryption it uses the AES in Counter (CTR) mode and for the integrity the

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

24

CBC‐MAC Finally the combination (CCM) of the above two modes is available providing both encryption and integrity (Kinney 2005)

Figure 12 ZigBee secure frame in MAC layer [17]

In the network layer the CCM (a modified MAC layer CCM mode) is used for encryption Because the network layer uses only the CCM mode a single key is used for all different security options The network layer security message format is similar to the MAC frame it is presented in the figure 13 Finally although the network layer is responsible for securing its layer messages the above layers specify the keys and the CCM option for each frame (Kinney 2005)

Figure 13 ZigBee secure frame in network layer [17]

Security in the application layer works similar to the network and MAC layers It uses the ldquolink keyrdquo or the ldquonetwork keyrdquo to secure the message and then encapsulate it inside a set of fields similar to the network format (figure 14) Other security responsibilities that the application layer has are to provide the ZDO and the applications with device management services key establishment and key transport (ZigBee specifications 2005)

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2 LITERATURE REVIEW10102006

25

Figure 14 ZigBee secure frame in application layer [17]

The ZigBee application layer contains the manufacturer‐defined application objects the ZDO and the application sub‐layer In addition to the security responsibilities the application sub‐layer binds devices based on their duties and needs maintains the binding tables and forwards messages between them The application sub‐layer also discovers the neighbour devices for a given device The ZDO is responsible for determining the devicersquos duty in the network for communicating using binding requests and for supporting security as was mentioned above The sub‐layer that implements the actual application is the manufacturer‐defined application object (Kinney 2005) The ZigBee specifications are a great step toward the wireless sensors networks standardization They cover all the OSI layers from the physical to the application providing guidance to the developers The specifications are the product of the ZigBee alliances a consortium of companies and academic institutes Thus they not only produce the specifications but also are the first use them The next section goes beyond the layer architecture and standardization and introduces the OS field another crucial element of WSNs

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

26

3 RECEIVED SIGNAL STRENGTH (RSS) Majority of the existing methods leverage the existence of IEEE 80211 base stations with powerful radio transmit powers of approximately 100mW per base station Such radios are in a different class from the low power IEEE 802154 compliant radios that typically transmit at low power levels ranging from 52mW to 29mW [19] The wide availability of larger number of IEEE 802154 radios in the research community has revived the interest for signal strength based localization in sensor network Despite of rapidly increasing popularity of IEEE 802154 radios and signal strength localization there is a lack of detailed characterization of the fundamental factors contributing to large signal strength variation This chapter investigates some of the properties of RSS observed during testing and analysis of research work collected during the period of thesis The analysis of RSS values is needed to understand the underlying features of location dependent RSS patterns and location fingerprints An understanding of the properties of the RSS values for location fingerprinting can assist in improving the design of positioning algorithms and in deployment of indoor positioning systems

31 Parameters that affect RSS Different factors that affect the indoor RSS are the userrsquos presenceabsence hisher orientation time of the day building type and material distance from transmitter and type of radio These factors are by no means exhaustive Other possible factors are antenna orientation directionality and type Table 10 classify the parameters which affect RSS [7]

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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10102006

27

Effect on Factors Options

1 Proximity of user Userrsquos presence or absence

2 Orientation of user and terminal

North East South West

Data Collection

3 Make of Radio 4 Time of measurement Time of day amp days of

week 5 Period of measurement

Second minute hour

7 Interference Co‐channel adjacent co‐channel

Statistics

8 Building environment Small offices or large hall

Table 10 Classification of factors the affect RSS [7]

Transmitter variability Different transmitters behave differently even when they are configured exactly in the same way In practice this means that when a transmitter is configured to send packets at a power level of d dBm then the transmitter will send these packets at a power level that is very close to d dBm but not necessarily exactly equal to d dBm This can alter the received signal strength indication and thus it can lead to inaccurate distance estimation

Receiver variability The sensitivity of the receivers across different radio chips is different In practice this means that the RSSI value recorded at different receivers can be different even when all the other parameters that affect the received signal strength are kept constant

Antenna orientation Each antenna has its own radiation pattern that is not uniform In practice this means that the RSSI value recorded at the receiver for a given pair of communicating nodes and for a given distance between them varies as the pair wise antenna orientations of the transmitter and the receiver are changed

Multi‐path fading and shadowing in the RF channel In indoor environments the transmitted signals get reflected after hitting on the walls andor on other objects in the room such as furniture Both the original signal and the reflected signal reach the receiver almost at the same time since they both travel at the speed of light As a result of this the receiver is not able to

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

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28

distinguish the two signals and it measures the received signal strength for both of them

32 Related work analysis A number of indoor location tracking systems have been proposed in the literature based on RF signals ultrasound infrared and some combinations of these As mentioned previously RF based positioning system are widely studied as shown in Table 2 Given a model of radio signal propagation in a building or other environment RSS can be used to estimate the distance between the transmitter and receiver Most of the algorithms (Table 2) have been evaluated in practice using 80211b compliant wireless radios and seem to produce a location error of 15ft to 20ft for an indoor network area of approximately 225ft x 144ft According to the results of this study all the localization techniques produce approximately the same location error over a range of environments Ecolocation [22] Mote Track [5] are two of the few localization algorithms that have been evaluated on a real sensor network that uses a low power wireless radio Ecolocation reports a location error of 10ft for a very small outdoor network deployment area (26ft x 49ft) Mote Track reports a location error of approximately 13ft for an indoor network deployment area of 18751ft2 In general the RSSI based localization algorithms can be divided into two broad categories the map based and the distance (or area) prediction based algorithms In the case of the map based algorithms the deployment area is divided into a large set of sample points that cover the whole area Most techniques collect one or more signal strength measurements from all visible beacon nodes and for each sample point Each sample point is then mapped to either a signal strength vector or a signal strength probability distribution This process generates an RSSI fingerprint map which is unique with respect to the beacon locations and the deployment area This fingerprint map is stored to a central base station A node that is not aware of its location collects one or more signal strength measurements from all the visible beacon nodes and creates its own RSSI fingerprint that is sent to the central base station The base station computes nodersquos location as the sample pointrsquos location whose RSSI fingerprint distance from the unknown nodersquos RSSI fingerprint is the minimum among all the sample points Despite the fact that the map based RSSI localization methods produce relatively small location errors they are not attractive mainly because they require an excessive amount of profiling data and because they are not fully distributed The detailed profiling of a building is a major holdback because it is complex time consuming and depends on beacon locations Therefore for different buildings or even for the

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

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same building and different beacon locations different RSSI maps have to be obtained The second category of RSSI based algorithms are the distance (or area) prediction algorithms which focus on minimizing the amount of profiling data In distance prediction based methods raw RSSI data are directly mapped to distances through a signal propagation model In some cases distances are not explicitly measured but they are taken into consideration through assumptions on the underlying distributions In the case of outdoor deployments where there are no obstacles a single signal propagation model is all the profiling data required In the case of indoor deployments the same applies but now the signal propagation model might be different across different buildings with different furniture and wall arrangements Given an underlying signal propagation model raw RSSI data can be mapped to specific distances These distances can be either used directly in order to perform localization or assigned a probability and used in a learning basedprobabilistic localization algorithm Obviously the accuracy of the distance prediction based methods depends heavily on the accuracy of the signal propagation model used for translating RSSI values to distances

33 Properties of RSSI Changes in signal due to propagation indoors are difficult to predict because of dense environment and propagation effects such as reflection diffraction and scattering The multi‐path fading effect which is the result of either constructive or destructive combination of multiple signal copies at the receiver causes the received signal to fluctuate around a mean value at a particular location The received signal is usually modelled by the combined effects of large‐scale fading and small‐scale fading The large‐scale fading component describes the signal attenuation as the signal travels over a distance and is absorbed by material such as walls and floors along the way to the receiver This component predicts the mean of the RSS and usually has a lognormal distribution On the other hand the small scale fading component explains the dramatic fluctuation of the signal due to multi‐path fading On the other hand the small scale‐fading component explains the dramatic fluctuation of the signal due to multi‐path fading If there is no LOS component the small‐scale‐fading is often modelled with a Rayleigh distribution Such scenarios are commonly called NLOS If there is a LOS component the small‐scale‐fading is typically modelled by a Rician distribution Note that when the RSS is measured the measurement averages out the small‐scale‐fading effects

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4 OBJECT TRACKING 41 Introduction This chapter presents the object‐tracking application the main objective of this thesis work The application is an algorithmic‐software implementation written in Embedded C which is capable of receiving data messages and generating object position estimation data The implementation of the object‐tracking application makes use of the SENTIO platform and uses the background knowledge introduced in the preceding chapters The motivation for the object‐tracking application is to determine the position of an object in indoor environments (Eg Home) The application acts as an interface which collects the raw data that the sensors generate (numerical values) It makes the proper comparisons and algorithmic processes to identify and track the object while it is static inside the area covered by the wireless sensor network Finally with the detection and tracking data it alerts the user or another system The following sections provide an overview of the object‐tracking application

42 Overview of hardware product Before describing the object tracking application it is important to know the characteristics of hardware product The SENTIO platform (figure 15) is built in modular fashion and has the following key features [8]

Flexibility This platform is very flexible in such a way that it is made easy to add or change the hardware for each sensor task depending on the requirement in the WSN For a specific platform this can be hard or sometimes even impossible to accomplish

Sensor resources Resources in a sensor module includes the ability of usage of controllerrsquos resources as much as possible such as ADC‐channels interrupts and digital Input‐Outputrsquos Local voltage regulation should also be possible since many sensors require higher supply voltages than digital Integrated Circuits do Further support of a shut‐down mechanism to power down the sensor modules when not needed

Computation and memory The processor module based on microcontroller should support many devices in a microcontroller family and support different power supply voltages enabling full range of clock frequencies All power‐down modes of the devices should be

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supported The platform should support different processor modules including FPGArsquos and very simple microcontrollers

Communication The focus should be on enabling an IEEE 802154 compliant communication interface With or without ZigBee stack this will provide a flexible platform for exploring different network topologies Support for power‐down of the entire communication module should be supported

Cost and form factor High flexibility is in conflict with low cost and small size The primary objective should be flexibility but the size should be considered in order to enable sensor device integration in systems where space is limited

Figure 15 SENTIO Platform [8]

The communication subsystem of the SENTIO sensor node was designed so that the correct operation of the radio chip is ensured The radio chip is powered by the on‐board voltage regulator and thus fluctuations in the battery voltage level do not affect the operation of the communication subsystem This platform uses Chipcon CC2420 IEEE 802154 radio transceiver operates in the 24GHz ISM band and includes a digital direct sequence spread spectrum (DSSS) modem providing a spreading gain of 9dB and an effective data rate of 250Kbps It was specifically designed for low power wireless applications and supports 8 discrete power levels 0dBm minus1dBm minus3dBm minus5dBm minus7dBm minus10dBm minus15dBm and minus25dBm at which its power consumption varies from 29mW to 52mW [21] A built‐in received signal strength indicator gives an 8‐bit digital value RSSIVAL The RSSIVAL is always averaged over 8 symbol periods (128μs) and a status bit indicates when the RSSIVAL is valid (meaning that the receiver was enabled for at least 8 symbol periods) The

Antenna

Communication Module

Processing Module

Sensing Module

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power P at the RF pins can be obtained directly from RSSIVAL using the following equation

P = RSSIVAL + RSSIOFFSET [dBm] where the RSSIOFFSET is found empirically from the front‐end gain and it is approximately equal to minus45dBm In the next sections when we refer to the RSSI value we refer to the RSSIVAL and not the power P unless otherwise stated

43 Experimental Testbed The experimental test bed for object tracking application is as shown in figure 16 Experiment was conducted in Masterrsquos room at Mid Sweden University A DELL laptop is connected to SENTIO platform and software were primarily used to collect samples of RSS from APrsquos The software monitors IEEE 802154 radio frequency channels which operate in the 24GHz band

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

1030

BC - 1030

AP 3 AP 1

AP 2

AP 4

BS

HEWLETT

PACKARD

Figure 16 Layout of Masterʹs Room

A three dimensional scalable testbed is used for indoor sensor network deployments The testbed illustrated in figure 16 is a 3‐D structure measuring 16m (L) x 12m (W) x 3m (H) and it is designed to host a large number of static nodes as shown in figure 16 Arrow marks in the above figure represent the location where fingerprints were taken The centrepiece of our infrastructure

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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is the SENTIO sensor node [8] an open‐source general purpose sensing platform designed with AT Mega 128 microprocessor and the IEEE 802154 compliant CC2420 radio from Chipcon [19]

44 Obtaining data from the sensor network The next important step in the development of the object tracking application is to obtain the data from the sensor network and send it to the base station From there the data is transmitted through a serial port to the computer that runs the object tracking application When the data arrives at the serial port the application must capture it This paragraph gives a brief explanation of how and what kind data is transmitted by the static object fixed sensors and how this data is being used to estimate the position of the object First the static object transmits Beacon packet continuously which is received by all the fixed APrsquos The beacon packet format is as shown in table 6 Then the fixed APrsquos calculates the 8‐bit value of RSSI from the transmitted beacon packet and then sends the resultant RSSI value as a form of data packet (table 7) to the BS which calculates the position and sends back to the object

45 Object tracking application Testing and Evaluation The purpose of this section is to discuss and evaluate the object tracking application Finally we are ready with the experimental testbed as shown in figure 16 As shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors placed at the corners of the masterrsquos room BS is placed at the centre of the room connected to a PC All the nodes are power connected before an actual measurement takes place At first the object is placed at random position inside the room and transmits beacon packets continuously Then these packets are received by the fixed APrsquos which calculate the RSSI from the packet and then averages it until it receives 50 beacon packets and then transmits the data to the BS (figure 17) Then the BS waits until it receives the data from all the APrsquos Once it receives from all the APrsquos it calculates the position of the object (figure 18) The application was developed to demonstrate a real world use of a WSN in object tracking In evaluating the final position obtained with respect to the actual position we conclude that the object‐tracking was successful Although

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

34

it is a research prototype object tracking application is a simple stable stand‐alone system capable of demonstrating an important application of WSN It takes raw data from the WSN aggregates it evaluates it and based on the evaluation tracks the object The accuracy of estimating the position is less than 5m Table 11 below shows a log of readings taken during test

Start

Intialize micro‐controller and transeciever in receieve mode

If received_packet=

Beacon

Average RSSI values

Increase the counter

If countergt1000050

A

No

Yes

B

B

Yes

No

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

35

A

Reset counter

Set transceiever in transmit mode

Transmit averaged RSSI to Main sensor and set it back to receive mode

10000 samples ‐ Offline

50 samples ‐ Online

Figure 17 Flow chart representing the algorithm of fixed sensors

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

36

Start

Intialize micro‐controller and transeciever in receieve mode

Received RSSI values from all fixed Sensors

Open radio map

Calculate Euclidean distance between obtained RSSI values from fixed sensors and fingerprints

in radio map

No

Yes

Bubble sort for least error in signal strength values

A

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

37

A

Get the location of the object from the radiousing the least error value

Display location of object on PC

End

Figure 18 Flow chart representing the algorithm of main sensor

Table 11 Accuracy of positioning system

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

38

5 RESULTS This chapter presents how the system parameters such as number of samples mean or median etchellipare finalized All the tests are performed in masterrsquos room at Mid Sweden University This chapter is divided into three subsections First in Section 51 it reviews results obtained from samples test Section 52 discusses one dimensional test results and finally section 53 discusses results of two dimensional tests

51 Measurement setup A Dell laptop was connected to a SENTIO platform and is loaded with software (Appendix 2) This setup was used to determine number of samples required during object tracking mean or median position estimation algorithm and one dimensional test The setup is as follows

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

103

0

BC - 1030

AP 1AP 2

BS

HEWLETT

PACKARD

Figure 19 Test bed for one dimensional test in Masters Room

Two SENTIO nodes (AP1 and AP2 as shown above) were separated by a distance of 10 meters and these sensors are known as fixed sensors which are fixed to the ceiling such that there antenna is faced towards the ground They are linear is position A BS was connected to Dell laptop and it was placed in such a way that its antenna was faced towards the ceiling Another SENTIO

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

39

node was used as an object and its orientation was same as BS Figure 19 represents the complete setup for object tracking application Mobile node was moved between these fixed ones at regular intervals of one meter and RSSI values were logged at each interval

52 Number of samples test This test was performed to determine number of samples required for accurate positioning and either mean of the obtained RSSI values or median of them Figure 19 represents the setup for number of samples test Once the setup is ready the measurements are taken as follows

1 An object was tagged with SENTIO node and was placed stationary linear to the fixed sensors

2 10000 samples (RSSI) were taken at regular intervals of 1 meter up to 10 meters

3 The sample values were logged at every interval 4 Using these logged samples I have plotted graphs such as distribution

of RSS values (figure 20 21) meanmedian at intervals of 1 meter using MATLAB

5 The mean and median for various numbers of samples is as shown in figure 22

Figure 20 Histogram plot of RSSI values at 1m and 4m

Figure 21 Histogram plot of RSSI values at 6m and 8m

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

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Figure 22 Distance vs RSSI

From the above graphs following conclusions were made 1 From the histogram plots we can observe that the distribution of RSSI

values follow Gaussians distribution law 2 As we can see from figure 22 that median curve has drastic changes

when compared to mean curve Hence mean is considered while estimating the position

3 From the observations we conclude that the number of samples does not matter since mean curve tends to be same for various numbers of samples

4 Hence 10000 samples are considered in order to get a proper location fingerprint during off‐line phase and during online phase 50 samples are enough to estimate the position of object

53 0ne dimensional test Measurement setup for one dimensional test is as shown in figure 19 This test was conducted to check the performance of algorithm and how accurate it was At first ie during offline phase fingerprints at each interval averaging 10000 samples were taken During online phase Euclidean algorithm was used estimate the position averaging 50 samples of RSSI From the results we

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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can realize that the error was less than 2 meters Table 12 shows the result of location estimation in one dimensional case

Table 12 One dimensional position estimation results

54 Two dimensional test This test is the final stage of my thesis work Experimental testbed for this stage is as shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors fixed at the corners of the room and a BS at the centre of the room There are 18 pre‐determined locations where the fingerprints (RSSI) were taken The fingerprints are as shown in Appendix 1 All the readings were taken in presence of userrsquos The algorithm for position estimation is shown in Appendix 2 Position estimation test is performed as follows

Tagged object is placed at random inside the masterrsquos room All the sensors including the base station and object are switched on Using the algorithm (Appendix 2) the BS estimates the position of the object and displays it on the PC

This test was performed using various numbers of samples to test which has a better accuracy Figure 23 represents the error rate using various numbers of samples From figure 23 we can conclude that 50 numbers of samples is enough to estimate the position of object with an accuracy of 3m

55 Causes of error in location determination The randomness of RSS is the major cause of error in any indoor

positioning system that uses the RSS for location inference If there is no randomness in the RSS and its location fingerprint every indoor positioning systems of this type will have no location detection error For example I have recorded RSS at one particular location during different times of the day From figures 24 and 25 you can observe that RSS values are different at different times of day

The performance of indoor positioning systems depends greatly on the separation of location fingerprints A location fingerprint

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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corresponding to a location can be identified correctly if it is difficult to classify it as another fingerprint by a pattern classifier

Path loss is one of the basic signal propagation properties of the RSS that best describes attenuation of transmit signal power over a distance between an access point and a mobile device

Presence of Userrsquos or userrsquos orientation affects RSS

Figure 23 Error Rate vs Number of samples

Figure 24 RSS distribution at 214 pm

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

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Figure 25 RSS distribution at 100pm and 1100 am

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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44

6 DISCUSSIONS 61 Summary and Conclusion While empirical results and performance studies of positioning systems based on location fingerprint have been presented in this report analytical model that can be used as a framework for efficiently designing the positioning systems are not available This thesis design work uses general principles of position estimation such as position estimation of sensors grid spacing etc This design work focuses on accuracy and precision The concept of location fingerprint technique based on RSS us explained in detail in chapter 2 It is found that RSS is random It is possible to approximate the RSS as being normally distributed While the RSS random process is non‐stationary the mean is more or less constant and could be used as the fingerprint of the location Model designed for position estimation is as shown in chapter 4 Accuracy and precision are the two major performance matrices in this study Uniform grid spacing has been used From the experimental analysis I have obtained an accuracy of 3 meters and 35 precision The sensitivity analysis among the system parameters and the performance matrices were done using the model as shown in chapter 4 For instance our study found that the minimum distance between two adjacent positions also affects the positioning performance This is due to the randomness of RSS patterns induced by the ever changing indoor environment

62 Future work The research in this thesis provides a ground work for the future study of efficient design of indoor positioning system The RSSI based position estimation still has limitations First the RSSI value is time dependent Thus the future study should address this issue Second the movement of object is not considered Third orientations of sensors affect the performance which should be taken care off Fourth to improve the performance of the system we need to find a solution to deal with those incomplete or missing data during the off‐line and the on‐line phase Fifth position estimation on multi‐floor and three‐dimensional coordinates is not available

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 110102006

45

APPENDIX 1

Location Fingerprints of Masters Room Time 1315 Observation Time 20 minutes Number of Samples 10000

People Presence SNo Co-ordinates AP1 (0x0001) AP2 (0x0002) AP3 (0x0004) AP4 (0x0008)

1 11 -24 -16 -27 -33 2 13 -26 -8 -34 -31 3 15 -43 -9 -39 -25 4 32 -21 -11 -27 -33 5 61 -6 -23 -32 -28 6 64 -20 -31 -25 -34 7 35 -23 -35 -29 -38 8 37 -29 -35 -22 -27 9 39 -38 -29 -21 -27 10 67 -27 -23 -20 -23 11 611 -26 -29 -23 -30 12 613 -35 -32 -2 -31 13 513 -34 -31 -20 -26 14 11 -39 -22 -29 -17 15 212 -37 -42 -28 -31 16 -212 -38 -33 -29 -6 17 -21 -42 -29 -33 -19 18 -38 -40 -23 -35 -22

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 210102006

46

APPENDIX 2

Begin

Open radio map and raw data files for write

Intialize radio map in memory

Check whether the

Packet is data or Beacon packet If data packet

Save the data to register

Check if data is

Received from all

sensors

Estimate the position of static

Display the position on PC

End Yes

No

A

A

Yes

No

Flow chart representing the main algorithm for position estimation

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

47

APPENDIX 3

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

48

Distribution of RSSI

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

49

REFERENCES [1] T S Rappaport J H Reed and B D Woerner ldquoPosition location using wireless communications on highways of the futureʺ IEEE Commun Mag vol 34 no 10 pp33‐41 Oct 1996 [2] A M Ladd K E Bekris G Marceau A Rudys L E Kavraki and D S Wallach rdquoRobotics‐based location sensing using wireless ethernetʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ02) 2002 pp 227‐238 [3] I Ramani R Bharadwaja and P V Rangan ldquoLocation tracking for media appliances in wireless home networksʺ in Proc International Conference on Multimedia and Expo (ICMEʹ03) July 2003 pp 769‐772 [4] K Pahlavan X Li and J P Makela ldquoIndoor geolocation science and technologyʺ IEEE Commun Mag vol 40 no 2 pp 112‐118 Feb 2002 [5] Konard Lorincz and Matt Welsh ldquoMote Track A Robust Decentralized Approach to RF‐Based Location Trackingrdquo [6] Paramvir Bahl and Venkata N Padmanabham ldquoRADAR An In‐Building RF‐based User Location and Tracking Systemrdquo [7] Kamol Kaemarungsi ldquoDesign of Indoor positioning system based on location fingerprint techiniquerdquo University of Pittsburgh 2005 [8] Fredrik Linnarsson Cheng Peng and Bengt Oelmann ldquoSENTIO A Hardware Platform for Rapid Prototyping of Wireless Sensor Networksrdquo [9] S Saha K Chaudhuri D Sanghi and P Bhagwat ldquoLocation determination of a mobile device using IEEE 80211b access point signalsʺ in Proc IEEE Wireless Communications and Networking Conference (WCNCʹ03) New Orleans LA Mar 2003 pp 1987‐1992 [10] T Roos P Myllymaki H Tirri P Misikangas and J Sievanen ldquoA probabilistic approach to WLAN user location estimationʺ International Journal of Wireless Information Networks vol 9 no 3 pp 155‐164 July 2002 [11] R Battiti M Brunato and A Villani ldquoStatistical learning theory for location fingerprinting in wireless LANrsquosʺ Technical Report Oct 2002 [Online] Available httprtmscienceunitnitraquobattitiarchive86pdf [12] P Prasithsangaree P Krishnamurthy and P K Chrysanthis ldquoAn indoor position location with wireless LANsʺ in Proc IEEE International Symposium on Personal Indoor and Mobile Radio Communications (PIMRCʹ02) Lisbon Portugal Sept 2002 [13] M A Youssef A Agrawala and A U Shankar ldquoWLAN location determination via clustering and probability distributionsʺ in Proc IEEE

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

50

International Conference on Pervasive Computing and Communications (PerComʹ03) Dallas‐Fort Worth TX Mar 23‐26 2003 pp 23‐26 [14] Z Xiang S Song J Chen H Wang J Huang and X Gao ldquoA wireless LAN‐based indoor positioning technologyʺ IBM Journal of Research and Development vol 48 no 56 pp 617‐626 SeptNov 2004 [15] R Want A Hopper V Falcao and J Gibbons ldquoThe active badge location systemʺACM Transactions on Information Systems vol 40 no 1 pp 91‐102 Jan 1992 [16] N B Priyantha A Chakraborty and H Balakrishnan ldquoThe cricket location‐support systemʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ00) Boston MA Aug 2000 pp 32‐43 [17] Holger K Willig A (2005) Protocols and Architectures for Wireless Sensor Networks London [18] IEEE (2003) Part 154 Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low‐Rate Wireless Personal Area Networks (LR‐WPANs) New York [19] Chipcon CC2420 802154 compliant radio httpwwwchipconcom [20] ZigBee Specification v10 ZigBee Alliance [21] Dimitrios Lymberopoulos Quentin Lindsey and Andreas Savvides ldquoAn Empirical Analysis of Radio Signal Strength Variability in IEEE 802154 Networks using Monopole Antennasrdquo [22] Kiran Yedavalli Bhaskar Krishnamachari Sharmila Ravula and Bhaskar Srinivasan ldquoEcolocation A sequence based technique for RF localization in wireless sensor networksrdquo Robert Bosch Research and Technology Centre Palo Alto CA

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

14

parameter that can demote or promote an important RSS component in the fingerprint

24 Protocols and Industry standards for WSN Although many applications for WSN are proposed the communication protocols supporting them remain mainly unexplored and diverged This is because the area of interest is new each implementation is application specific and the components of WSNrsquos have a lot of constraints Wireless sensor networks use the wireless medium to communicate However the traditional communication protocols that support wireless communications especially ad‐hoc mesh networks may not be well suited for them WSN communication is an active research area and many algorithms and protocols have already been proposed This section introduces two proposed standards the IEEE 802154 and the ZigBee The IEEE 802154 covers the physical layer and the MAC layer of low‐rate WPAN The ZigBee is ldquoan emerging standard that is based on the IEEE 802154 and adds network construction (star networks peer‐to‐peermesh networks and cluster‐tree networks) application services and morerdquo

241 IEEE 802154 Holger and Willig (2005) [17] mention home automation home networking and home security as possible applications for IEEE 802154 They add that ldquomost of these applications require only low‐to‐medium bitrates (up to some few hundreds of kbps) moderate average delays without too stringent delay guarantees and for certain nodes it is highly desirable to reduce the energy consumption to a minimumrdquo The IEEE 802154 standard (2003) defines the device types that can be used in a LR‐WPAN A device can be a FFD or a RFD The RFD can be used in simple applications in which they do not need to transmit large amounts of data and they have to communicate only with a specific FFD The FFD can work as a PAN coordinator as a coordinator or as a simple device It can communicate with either another FFD or a RFD In keeping with the application requirements the LR‐WPAN operates in a star or peer‐to‐peer topology (figure 4) In the star topology the RFD communicates with a single controller the PAN coordinator The PAN coordinator can perform the same function as the RFD but it is also responsible for controlling the PAN ldquoit initiates terminates or routes communication around the networkrdquo [18] The peer‐to‐peer topology

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

15

supports ad‐hoc mesh multi‐hop networking Any device in the peer‐to‐peer topology can communicate with any other device within its communication range however this topology also has a PAN coordinator All the devices in a LR‐WPAN have a unique 64‐bit address This or a short address allocated by the PAN coordinator can be used inside a PAN Additionally each PAN has a unique identifier The combination of the PAN identifier and the sort addresses allows communication across different PANs (IEEE 802154 Standard 2003)

Figure 4 Star and peer‐to‐peer topologies in LR‐WPAN [18]

The LR‐WPAN based on the OSI seven‐layer model has the layered architecture presented in figure 5 The application and the network layer are the upper layers in the LR‐WPAN architecture but are outside the scope of the IEEE 802154 standard Only the physical layer ldquowhich contains the RF transceiver along with its low‐level control mechanismrdquo and the MAC layer ldquothat provides access to the physical channelrdquo are included in the standard and will be introduced in the following sections Finally the Type I 8022 LLC and the SSCS are intermediate sub layers supporting communication with the above layers (IEEE 802154 Standard 2003)

Physical Layer The 802154 standard specifies two different services that the PHY provides The PHY data service controls the radio and thus the transmission and reception of the PPDUs In addition the management service performs ED in the channel The management service also performs CCA before sending the messages and provides LQI for the received packets

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

16

Figure 5 WPAN architecture [18]

Three different bands are defined by the standard The 868‐8686 MHz for Europe the 902‐928 MHz for North America and the 2400‐24835 MHz worldwide All of them belong in the ISM radio bands and they are using DSSS Each of the bands supports different a data rate and it uses a different modulation technique chip rate and number of channels Moreover if the system does not use the 2450 MHz frequency it operates in both the 802 MHz and 902 MHz frequencies Table 3 summarizes the supported bands and data rates (IEEE 802154 standard 2003)

PHY (MHz)

Frequency Band (MHz) Modulation

Bit Rate Number of Channels

868-8686 BPSK 20 1 868 915 902-928 BPSK 40 10

2450 2400-24835 O-QPSK 230 16 Table 3 Frequency bands and data rates for IEEE 802154 based on the IEEE

802154 standard [18] The physical layer uses PPDU packets to communicate Table 4 demonstrates its structure The LSB is always transmitted and received first The SHR contains the preamble and the SFD fields which helps receiver

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

17

synchronization The 8‐bytes preamble contains only zero and is used for synchronization The SFD contains a specific sequence of one and zeros and specifies the beginning of the frame The PHR contains the payload length Packets with a length of 9 or more bytes are MPDU as the next section further explains Packets with length 5 are used for MPDU acknowledgements The payload part of the PPDU encloses the MAC layer packet Finally the PPDU size can be up to 127 bytes [18]

4 bytes 1 byte 1 byte Variable

Preamble SFD Frame length

(7 bits) Reserved

(1 bit) Payload (PSU)

SHR PHR PHY Payload Table 4 Format based on the IEEE 802154 standard [18]

MAC Layer The MAC layer is the interface between the SSCS and the PHY layer Similar to the PHY layer the MAC layer supports two services The MAC data service is responsible for the transmission and reception of the MPDUs through the PHY data service The MAC management service if the device is a coordinator manages the network beacons It is also responsible for PAN association and disassociation frame validation and acknowledgment providing ldquoa reliable link between two peer MAC entitiesrdquo In addition it uses the CSMA‐CA for channel access and handles and maintains the GTS mechanism Finally it supports device security [18] The IEEE 802154 standard defines four different frame types the beacon data acknowledgment and MAC command frame All frame types are based on the general MAC frame format (table 5) The frame control field describes and specifies the above different frame types Every MAC frame comprises a MHR which consists of a frame control sequence number and the information field It also contains the MAC payload different frame types have different MAC payload fields The acknowledgment type does not have a payload Finally each frame includes a MFR which contains a FCS The data in the MPDU follows the same order as the PPDU the least significant bits are left in the frame and are transited first Octets2 1 02 028 02 028 Variable 2

Destination PAN Identifier

Destination Address

Source PAN

Identifier Source Address Frame

Control Sequence Number Addressing Field

Frame Payload FCS

MHR MAC

Payload MFRTable 5 General MAC frame format IEEE 802154 standard [18]

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

18

Table 6 7 8 and 9 present the four different MAC frame types The beacon frame is transmitted periodically by the PAN coordinator It provides information about the network management through the super frame and GTS fields which are analyzed later in the section It also synchronizes the network devices and indicates the proper communication period for them The data frame payload encapsulates data from the higher layers When a device receives a packet it is not obliged to response with an acknowledgement packet Finally the command frame identifier and command payload fields of the command frame are used for communication between the network devices The command identifier specifies actions like association disassociation and data GTS or beacon request Octets2 1 410 2 Variable Variable Variable 2

Frame Control

Sequence Number

Addressing Fields

Super frame Specification

GTS fields

Pending Addressing

Fields Beacon Payload FCS

MHR MAC Payload MFRTable 6 Beacon frame format [18]

Octets 2 1 Variable 2

Frame Control

Sequence Number

Addressing Field

Data Payload FCS

MHR MAC

Payload MFR Table 7 Data frame format [18]

Octets 2 1 2

Frame Control

Sequence Number FCS

MHR MFR Table 8 Acknowledgement frame format [18]

Octets2 1 1 Variable 2

Frame Control

Sequence Number

Addressing Fields

Command Frame Identifier

Command Payload FCS

MHR MAC Payload MFR Table 9 Command frame format [18]

In the LR‐WPAN every PAN has its own coordinator The PAN coordinator manages the communication in the local area it has two options to use or not use the super frame structure The super frame (figure 6) uses network beacons If the coordinator does not want to use a super frame structure it suspends the beacon transmission However the beacon is important for device association and disassociation If the coordinator wishes to maintain close communication control in the PAN and to support low‐latency devices it usually uses the super frame A super frame determines a specific time

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

19

period beacons bound it The beacon is transmitted in the first of the sixteen equal time slots that the super frame has It is used to ldquosynchronize the attached devices to identify the PAN and to describe the structure of the super framerdquo The super frame can have active and inactive periods All the communications have to be finished inside the super frame period In the inactive periods the devices can switch to the sleep mode but they have to be ready for the next beacon The active period is further divided into a CAP and a CFP During the CAP any device can communicate competing with the other devices in the PAN using slotted CSMA‐CA The CFP contains GTSs and follows the CAP A CFP may maintain up to seven GTSs and each GTS can reserve more than one time slot However the CAP should always be sufficient to allow new devices t join the PAN The GTSs are allocated to specific devices All the transactions must be completed inside the assigned time period (CAP GTS and CFP) (IEEE 802154 standard 2003)

Figure 6 Example of super frame structure [18]

In accordance with the 802154 standard (2003) three different types of data‐transfer exist In addition the types differ if the coordinator uses or does not beacons Data transfer from a device to the PAN coordinator is the first type (figure 7) For a ldquonon‐beacon‐enable networkrdquo it first senses the medium by using ldquoun‐slotted CSMA‐CArdquo and then a simple transmit to the data frame In a ldquobeacon‐enabled networkrdquo the sender waits for the beacon when it finds it ldquothe device synchronizes to the super fame structurerdquo In the specified time frame the sender again senses the medium by using ldquoslotted CSMA‐CArdquo and transmits the data to the coordinator In either case the coordinator has the option to acknowledge or not acknowledge the data reception after that the transaction is completed Data transfer from the coordinator is the next type described in the standard (figure 8) In a beacon‐enabled network the coordinator indicates a pending message through the beacon The messagersquos target device receives the beacon and if the message is pending it responses with a MAC command request

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

20

message using slotted CSMA‐CA the coordinator may or may not acknowledge the command message and through slotted CSMA‐CA sends the pending message The device acknowledges the received message After that the coordinator removes the message from the beaconrsquos pending list and completes the transaction In a non‐beacon enable network the device periodically sends a MAC command‐request frame to the coordinator The coordinator acknowledges the data request Then by using un‐slotted CSMA‐CA if it has a pending message it transmits it If it has not it respond with a data frame with a ldquozero‐length payloadrdquo To complete the transaction the device acknowledges the data reception

Figure 7 Communication from a device to a PAN coordinator in (a) a beacon‐enabled network and (b) a non‐beacon enabled network [18]

Peer‐to‐peer is the last type of data transfer In this situation the devices are free to communicate with any other device within their communication range In a peer‐to‐peer PAN the devices can ldquoeither receive constantly or synchronize with each otherrdquo If they are receiving constantly to transmit data they use un‐slotted CSAM‐CA In the second case synchronization must be achieved first (IEEE 802154 standard [IEEE 2003]) The IEEE 802154 (2003) standard establishes MAC and PHY standards for low‐cost low‐power and high‐density node deployments In addition to the above PHY and MAC characteristics IEEE 802154 provides a security baseline including ldquothe ability to maintain an ACL and use symmetric cryptographyrdquo for data encryption The algorithm that is used for encryption is the AES However the higher level layers decide when security is need The upper layers are in general responsible for device authentication and key management The next section introduces the ZigBee standard which

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

21

encapsulates the IEEE 802154 and provides additional standardization for the higher levels

Figure 8 Communication from a PAN coordinator to a device in (a) a beacon‐enabled network and (b) a non‐beacon enabled network [18]

242 ZigBee Heily (2004) defines ZigBee as ldquoa rapidly growing worldwide non‐profit industry consortiumrdquo whose mission is ldquoto define a reliable cost‐effective low‐power wirelessly networked monitoring and control product based on an open global standardrdquo Figure 9 illustrates the areas of interest for different wireless communication standards

Figure 9 Overview of the coverage for different wireless communication

standards [17]

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

22

ZigBee a new standard which became publicly available in June 2005 is based on the IEEE 802154 standard It expands the IEEE 802154 by adding the framework for ldquothe network security and applicationrdquo (Craig 2005) Figure 10 presents the IEEE 802154ZigBee stack and the areas of responsibilities

Figure 10 IEEE 802154ZigBee stack [20]

Craig (2005) mentions three networking topologies that the standard covers the star mesh and cluster tree (figure 11) The ZigBee standard works on top of the IEEE 802154 addressing schema by using the standard 64‐bit and the short 16‐bit addressing Kinney (2005) summarizes the ZigBee network layer responsibilities the successful establishment of a new the network and successful new device configuration addressing assignment network synchronization frames security and message routing ZigBee further distinguishes the concept of the physical devices (RFD FFD) by using the notion of ldquological devicesrdquo ldquoZigBee Coordinatorrdquo is the first type of logical devices It is responsible for initializing maintaining and managing the network Under the coordinator in the network hierarchy is the ldquoZigBee routerrdquo which is responsible for controlling the message routing between the nodes Finally the ldquoZigBee End Devicerdquo acts as the end point of the network

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

23

structure The tasks that an end device can perform are specified in the ldquoApplication Profilesrdquo (Craig 2005)

Figure 11 IEEE 802154ZigBee stack [17]

The ZigBee specifications (2005) summarize the security services provided by ZigBee ldquokey establishment key transport frame protection and device managementrdquo ZigBee builds its security mechanism using symmetric key cryptography The security services also depend on the associated layer thus as the figure 10 shows the security mechanism covers the network and the application layer In addition if a MAC frame needs security protection the MAC layer is able to secure it Moreover the notion of end‐to‐end security is supported the source and destination devices have access and use the same share key In the MAC layer the 802154 AES mechanism provides the proper security The mechanism protects ldquothe confidentiality integrity and authenticity of the MAC framesrdquo (Kinney 2005) An auxiliary header field in front of the MAC payload indicates if the frame is encrypted or not The MAC framesrsquo integrity is supported by calculating and using a MIC at the end of the MAC payload In addition to the AES nonce is used to provide MAC confidentiality and authenticity Figure 12 illustrates a MAC frame with security For different security aspects the MAC layer uses different mode of the AES for the encryption it uses the AES in Counter (CTR) mode and for the integrity the

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

24

CBC‐MAC Finally the combination (CCM) of the above two modes is available providing both encryption and integrity (Kinney 2005)

Figure 12 ZigBee secure frame in MAC layer [17]

In the network layer the CCM (a modified MAC layer CCM mode) is used for encryption Because the network layer uses only the CCM mode a single key is used for all different security options The network layer security message format is similar to the MAC frame it is presented in the figure 13 Finally although the network layer is responsible for securing its layer messages the above layers specify the keys and the CCM option for each frame (Kinney 2005)

Figure 13 ZigBee secure frame in network layer [17]

Security in the application layer works similar to the network and MAC layers It uses the ldquolink keyrdquo or the ldquonetwork keyrdquo to secure the message and then encapsulate it inside a set of fields similar to the network format (figure 14) Other security responsibilities that the application layer has are to provide the ZDO and the applications with device management services key establishment and key transport (ZigBee specifications 2005)

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

25

Figure 14 ZigBee secure frame in application layer [17]

The ZigBee application layer contains the manufacturer‐defined application objects the ZDO and the application sub‐layer In addition to the security responsibilities the application sub‐layer binds devices based on their duties and needs maintains the binding tables and forwards messages between them The application sub‐layer also discovers the neighbour devices for a given device The ZDO is responsible for determining the devicersquos duty in the network for communicating using binding requests and for supporting security as was mentioned above The sub‐layer that implements the actual application is the manufacturer‐defined application object (Kinney 2005) The ZigBee specifications are a great step toward the wireless sensors networks standardization They cover all the OSI layers from the physical to the application providing guidance to the developers The specifications are the product of the ZigBee alliances a consortium of companies and academic institutes Thus they not only produce the specifications but also are the first use them The next section goes beyond the layer architecture and standardization and introduces the OS field another crucial element of WSNs

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

26

3 RECEIVED SIGNAL STRENGTH (RSS) Majority of the existing methods leverage the existence of IEEE 80211 base stations with powerful radio transmit powers of approximately 100mW per base station Such radios are in a different class from the low power IEEE 802154 compliant radios that typically transmit at low power levels ranging from 52mW to 29mW [19] The wide availability of larger number of IEEE 802154 radios in the research community has revived the interest for signal strength based localization in sensor network Despite of rapidly increasing popularity of IEEE 802154 radios and signal strength localization there is a lack of detailed characterization of the fundamental factors contributing to large signal strength variation This chapter investigates some of the properties of RSS observed during testing and analysis of research work collected during the period of thesis The analysis of RSS values is needed to understand the underlying features of location dependent RSS patterns and location fingerprints An understanding of the properties of the RSS values for location fingerprinting can assist in improving the design of positioning algorithms and in deployment of indoor positioning systems

31 Parameters that affect RSS Different factors that affect the indoor RSS are the userrsquos presenceabsence hisher orientation time of the day building type and material distance from transmitter and type of radio These factors are by no means exhaustive Other possible factors are antenna orientation directionality and type Table 10 classify the parameters which affect RSS [7]

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

27

Effect on Factors Options

1 Proximity of user Userrsquos presence or absence

2 Orientation of user and terminal

North East South West

Data Collection

3 Make of Radio 4 Time of measurement Time of day amp days of

week 5 Period of measurement

Second minute hour

7 Interference Co‐channel adjacent co‐channel

Statistics

8 Building environment Small offices or large hall

Table 10 Classification of factors the affect RSS [7]

Transmitter variability Different transmitters behave differently even when they are configured exactly in the same way In practice this means that when a transmitter is configured to send packets at a power level of d dBm then the transmitter will send these packets at a power level that is very close to d dBm but not necessarily exactly equal to d dBm This can alter the received signal strength indication and thus it can lead to inaccurate distance estimation

Receiver variability The sensitivity of the receivers across different radio chips is different In practice this means that the RSSI value recorded at different receivers can be different even when all the other parameters that affect the received signal strength are kept constant

Antenna orientation Each antenna has its own radiation pattern that is not uniform In practice this means that the RSSI value recorded at the receiver for a given pair of communicating nodes and for a given distance between them varies as the pair wise antenna orientations of the transmitter and the receiver are changed

Multi‐path fading and shadowing in the RF channel In indoor environments the transmitted signals get reflected after hitting on the walls andor on other objects in the room such as furniture Both the original signal and the reflected signal reach the receiver almost at the same time since they both travel at the speed of light As a result of this the receiver is not able to

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

28

distinguish the two signals and it measures the received signal strength for both of them

32 Related work analysis A number of indoor location tracking systems have been proposed in the literature based on RF signals ultrasound infrared and some combinations of these As mentioned previously RF based positioning system are widely studied as shown in Table 2 Given a model of radio signal propagation in a building or other environment RSS can be used to estimate the distance between the transmitter and receiver Most of the algorithms (Table 2) have been evaluated in practice using 80211b compliant wireless radios and seem to produce a location error of 15ft to 20ft for an indoor network area of approximately 225ft x 144ft According to the results of this study all the localization techniques produce approximately the same location error over a range of environments Ecolocation [22] Mote Track [5] are two of the few localization algorithms that have been evaluated on a real sensor network that uses a low power wireless radio Ecolocation reports a location error of 10ft for a very small outdoor network deployment area (26ft x 49ft) Mote Track reports a location error of approximately 13ft for an indoor network deployment area of 18751ft2 In general the RSSI based localization algorithms can be divided into two broad categories the map based and the distance (or area) prediction based algorithms In the case of the map based algorithms the deployment area is divided into a large set of sample points that cover the whole area Most techniques collect one or more signal strength measurements from all visible beacon nodes and for each sample point Each sample point is then mapped to either a signal strength vector or a signal strength probability distribution This process generates an RSSI fingerprint map which is unique with respect to the beacon locations and the deployment area This fingerprint map is stored to a central base station A node that is not aware of its location collects one or more signal strength measurements from all the visible beacon nodes and creates its own RSSI fingerprint that is sent to the central base station The base station computes nodersquos location as the sample pointrsquos location whose RSSI fingerprint distance from the unknown nodersquos RSSI fingerprint is the minimum among all the sample points Despite the fact that the map based RSSI localization methods produce relatively small location errors they are not attractive mainly because they require an excessive amount of profiling data and because they are not fully distributed The detailed profiling of a building is a major holdback because it is complex time consuming and depends on beacon locations Therefore for different buildings or even for the

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

29

same building and different beacon locations different RSSI maps have to be obtained The second category of RSSI based algorithms are the distance (or area) prediction algorithms which focus on minimizing the amount of profiling data In distance prediction based methods raw RSSI data are directly mapped to distances through a signal propagation model In some cases distances are not explicitly measured but they are taken into consideration through assumptions on the underlying distributions In the case of outdoor deployments where there are no obstacles a single signal propagation model is all the profiling data required In the case of indoor deployments the same applies but now the signal propagation model might be different across different buildings with different furniture and wall arrangements Given an underlying signal propagation model raw RSSI data can be mapped to specific distances These distances can be either used directly in order to perform localization or assigned a probability and used in a learning basedprobabilistic localization algorithm Obviously the accuracy of the distance prediction based methods depends heavily on the accuracy of the signal propagation model used for translating RSSI values to distances

33 Properties of RSSI Changes in signal due to propagation indoors are difficult to predict because of dense environment and propagation effects such as reflection diffraction and scattering The multi‐path fading effect which is the result of either constructive or destructive combination of multiple signal copies at the receiver causes the received signal to fluctuate around a mean value at a particular location The received signal is usually modelled by the combined effects of large‐scale fading and small‐scale fading The large‐scale fading component describes the signal attenuation as the signal travels over a distance and is absorbed by material such as walls and floors along the way to the receiver This component predicts the mean of the RSS and usually has a lognormal distribution On the other hand the small scale fading component explains the dramatic fluctuation of the signal due to multi‐path fading On the other hand the small scale‐fading component explains the dramatic fluctuation of the signal due to multi‐path fading If there is no LOS component the small‐scale‐fading is often modelled with a Rayleigh distribution Such scenarios are commonly called NLOS If there is a LOS component the small‐scale‐fading is typically modelled by a Rician distribution Note that when the RSS is measured the measurement averages out the small‐scale‐fading effects

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

30

4 OBJECT TRACKING 41 Introduction This chapter presents the object‐tracking application the main objective of this thesis work The application is an algorithmic‐software implementation written in Embedded C which is capable of receiving data messages and generating object position estimation data The implementation of the object‐tracking application makes use of the SENTIO platform and uses the background knowledge introduced in the preceding chapters The motivation for the object‐tracking application is to determine the position of an object in indoor environments (Eg Home) The application acts as an interface which collects the raw data that the sensors generate (numerical values) It makes the proper comparisons and algorithmic processes to identify and track the object while it is static inside the area covered by the wireless sensor network Finally with the detection and tracking data it alerts the user or another system The following sections provide an overview of the object‐tracking application

42 Overview of hardware product Before describing the object tracking application it is important to know the characteristics of hardware product The SENTIO platform (figure 15) is built in modular fashion and has the following key features [8]

Flexibility This platform is very flexible in such a way that it is made easy to add or change the hardware for each sensor task depending on the requirement in the WSN For a specific platform this can be hard or sometimes even impossible to accomplish

Sensor resources Resources in a sensor module includes the ability of usage of controllerrsquos resources as much as possible such as ADC‐channels interrupts and digital Input‐Outputrsquos Local voltage regulation should also be possible since many sensors require higher supply voltages than digital Integrated Circuits do Further support of a shut‐down mechanism to power down the sensor modules when not needed

Computation and memory The processor module based on microcontroller should support many devices in a microcontroller family and support different power supply voltages enabling full range of clock frequencies All power‐down modes of the devices should be

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

31

supported The platform should support different processor modules including FPGArsquos and very simple microcontrollers

Communication The focus should be on enabling an IEEE 802154 compliant communication interface With or without ZigBee stack this will provide a flexible platform for exploring different network topologies Support for power‐down of the entire communication module should be supported

Cost and form factor High flexibility is in conflict with low cost and small size The primary objective should be flexibility but the size should be considered in order to enable sensor device integration in systems where space is limited

Figure 15 SENTIO Platform [8]

The communication subsystem of the SENTIO sensor node was designed so that the correct operation of the radio chip is ensured The radio chip is powered by the on‐board voltage regulator and thus fluctuations in the battery voltage level do not affect the operation of the communication subsystem This platform uses Chipcon CC2420 IEEE 802154 radio transceiver operates in the 24GHz ISM band and includes a digital direct sequence spread spectrum (DSSS) modem providing a spreading gain of 9dB and an effective data rate of 250Kbps It was specifically designed for low power wireless applications and supports 8 discrete power levels 0dBm minus1dBm minus3dBm minus5dBm minus7dBm minus10dBm minus15dBm and minus25dBm at which its power consumption varies from 29mW to 52mW [21] A built‐in received signal strength indicator gives an 8‐bit digital value RSSIVAL The RSSIVAL is always averaged over 8 symbol periods (128μs) and a status bit indicates when the RSSIVAL is valid (meaning that the receiver was enabled for at least 8 symbol periods) The

Antenna

Communication Module

Processing Module

Sensing Module

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

32

power P at the RF pins can be obtained directly from RSSIVAL using the following equation

P = RSSIVAL + RSSIOFFSET [dBm] where the RSSIOFFSET is found empirically from the front‐end gain and it is approximately equal to minus45dBm In the next sections when we refer to the RSSI value we refer to the RSSIVAL and not the power P unless otherwise stated

43 Experimental Testbed The experimental test bed for object tracking application is as shown in figure 16 Experiment was conducted in Masterrsquos room at Mid Sweden University A DELL laptop is connected to SENTIO platform and software were primarily used to collect samples of RSS from APrsquos The software monitors IEEE 802154 radio frequency channels which operate in the 24GHz band

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

1030

BC - 1030

AP 3 AP 1

AP 2

AP 4

BS

HEWLETT

PACKARD

Figure 16 Layout of Masterʹs Room

A three dimensional scalable testbed is used for indoor sensor network deployments The testbed illustrated in figure 16 is a 3‐D structure measuring 16m (L) x 12m (W) x 3m (H) and it is designed to host a large number of static nodes as shown in figure 16 Arrow marks in the above figure represent the location where fingerprints were taken The centrepiece of our infrastructure

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

33

is the SENTIO sensor node [8] an open‐source general purpose sensing platform designed with AT Mega 128 microprocessor and the IEEE 802154 compliant CC2420 radio from Chipcon [19]

44 Obtaining data from the sensor network The next important step in the development of the object tracking application is to obtain the data from the sensor network and send it to the base station From there the data is transmitted through a serial port to the computer that runs the object tracking application When the data arrives at the serial port the application must capture it This paragraph gives a brief explanation of how and what kind data is transmitted by the static object fixed sensors and how this data is being used to estimate the position of the object First the static object transmits Beacon packet continuously which is received by all the fixed APrsquos The beacon packet format is as shown in table 6 Then the fixed APrsquos calculates the 8‐bit value of RSSI from the transmitted beacon packet and then sends the resultant RSSI value as a form of data packet (table 7) to the BS which calculates the position and sends back to the object

45 Object tracking application Testing and Evaluation The purpose of this section is to discuss and evaluate the object tracking application Finally we are ready with the experimental testbed as shown in figure 16 As shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors placed at the corners of the masterrsquos room BS is placed at the centre of the room connected to a PC All the nodes are power connected before an actual measurement takes place At first the object is placed at random position inside the room and transmits beacon packets continuously Then these packets are received by the fixed APrsquos which calculate the RSSI from the packet and then averages it until it receives 50 beacon packets and then transmits the data to the BS (figure 17) Then the BS waits until it receives the data from all the APrsquos Once it receives from all the APrsquos it calculates the position of the object (figure 18) The application was developed to demonstrate a real world use of a WSN in object tracking In evaluating the final position obtained with respect to the actual position we conclude that the object‐tracking was successful Although

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

34

it is a research prototype object tracking application is a simple stable stand‐alone system capable of demonstrating an important application of WSN It takes raw data from the WSN aggregates it evaluates it and based on the evaluation tracks the object The accuracy of estimating the position is less than 5m Table 11 below shows a log of readings taken during test

Start

Intialize micro‐controller and transeciever in receieve mode

If received_packet=

Beacon

Average RSSI values

Increase the counter

If countergt1000050

A

No

Yes

B

B

Yes

No

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

35

A

Reset counter

Set transceiever in transmit mode

Transmit averaged RSSI to Main sensor and set it back to receive mode

10000 samples ‐ Offline

50 samples ‐ Online

Figure 17 Flow chart representing the algorithm of fixed sensors

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

36

Start

Intialize micro‐controller and transeciever in receieve mode

Received RSSI values from all fixed Sensors

Open radio map

Calculate Euclidean distance between obtained RSSI values from fixed sensors and fingerprints

in radio map

No

Yes

Bubble sort for least error in signal strength values

A

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

37

A

Get the location of the object from the radiousing the least error value

Display location of object on PC

End

Figure 18 Flow chart representing the algorithm of main sensor

Table 11 Accuracy of positioning system

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

38

5 RESULTS This chapter presents how the system parameters such as number of samples mean or median etchellipare finalized All the tests are performed in masterrsquos room at Mid Sweden University This chapter is divided into three subsections First in Section 51 it reviews results obtained from samples test Section 52 discusses one dimensional test results and finally section 53 discusses results of two dimensional tests

51 Measurement setup A Dell laptop was connected to a SENTIO platform and is loaded with software (Appendix 2) This setup was used to determine number of samples required during object tracking mean or median position estimation algorithm and one dimensional test The setup is as follows

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

103

0

BC - 1030

AP 1AP 2

BS

HEWLETT

PACKARD

Figure 19 Test bed for one dimensional test in Masters Room

Two SENTIO nodes (AP1 and AP2 as shown above) were separated by a distance of 10 meters and these sensors are known as fixed sensors which are fixed to the ceiling such that there antenna is faced towards the ground They are linear is position A BS was connected to Dell laptop and it was placed in such a way that its antenna was faced towards the ceiling Another SENTIO

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

39

node was used as an object and its orientation was same as BS Figure 19 represents the complete setup for object tracking application Mobile node was moved between these fixed ones at regular intervals of one meter and RSSI values were logged at each interval

52 Number of samples test This test was performed to determine number of samples required for accurate positioning and either mean of the obtained RSSI values or median of them Figure 19 represents the setup for number of samples test Once the setup is ready the measurements are taken as follows

1 An object was tagged with SENTIO node and was placed stationary linear to the fixed sensors

2 10000 samples (RSSI) were taken at regular intervals of 1 meter up to 10 meters

3 The sample values were logged at every interval 4 Using these logged samples I have plotted graphs such as distribution

of RSS values (figure 20 21) meanmedian at intervals of 1 meter using MATLAB

5 The mean and median for various numbers of samples is as shown in figure 22

Figure 20 Histogram plot of RSSI values at 1m and 4m

Figure 21 Histogram plot of RSSI values at 6m and 8m

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

40

Figure 22 Distance vs RSSI

From the above graphs following conclusions were made 1 From the histogram plots we can observe that the distribution of RSSI

values follow Gaussians distribution law 2 As we can see from figure 22 that median curve has drastic changes

when compared to mean curve Hence mean is considered while estimating the position

3 From the observations we conclude that the number of samples does not matter since mean curve tends to be same for various numbers of samples

4 Hence 10000 samples are considered in order to get a proper location fingerprint during off‐line phase and during online phase 50 samples are enough to estimate the position of object

53 0ne dimensional test Measurement setup for one dimensional test is as shown in figure 19 This test was conducted to check the performance of algorithm and how accurate it was At first ie during offline phase fingerprints at each interval averaging 10000 samples were taken During online phase Euclidean algorithm was used estimate the position averaging 50 samples of RSSI From the results we

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

41

can realize that the error was less than 2 meters Table 12 shows the result of location estimation in one dimensional case

Table 12 One dimensional position estimation results

54 Two dimensional test This test is the final stage of my thesis work Experimental testbed for this stage is as shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors fixed at the corners of the room and a BS at the centre of the room There are 18 pre‐determined locations where the fingerprints (RSSI) were taken The fingerprints are as shown in Appendix 1 All the readings were taken in presence of userrsquos The algorithm for position estimation is shown in Appendix 2 Position estimation test is performed as follows

Tagged object is placed at random inside the masterrsquos room All the sensors including the base station and object are switched on Using the algorithm (Appendix 2) the BS estimates the position of the object and displays it on the PC

This test was performed using various numbers of samples to test which has a better accuracy Figure 23 represents the error rate using various numbers of samples From figure 23 we can conclude that 50 numbers of samples is enough to estimate the position of object with an accuracy of 3m

55 Causes of error in location determination The randomness of RSS is the major cause of error in any indoor

positioning system that uses the RSS for location inference If there is no randomness in the RSS and its location fingerprint every indoor positioning systems of this type will have no location detection error For example I have recorded RSS at one particular location during different times of the day From figures 24 and 25 you can observe that RSS values are different at different times of day

The performance of indoor positioning systems depends greatly on the separation of location fingerprints A location fingerprint

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

42

corresponding to a location can be identified correctly if it is difficult to classify it as another fingerprint by a pattern classifier

Path loss is one of the basic signal propagation properties of the RSS that best describes attenuation of transmit signal power over a distance between an access point and a mobile device

Presence of Userrsquos or userrsquos orientation affects RSS

Figure 23 Error Rate vs Number of samples

Figure 24 RSS distribution at 214 pm

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

43

Figure 25 RSS distribution at 100pm and 1100 am

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

6 DISCUSSIONS10102006

44

6 DISCUSSIONS 61 Summary and Conclusion While empirical results and performance studies of positioning systems based on location fingerprint have been presented in this report analytical model that can be used as a framework for efficiently designing the positioning systems are not available This thesis design work uses general principles of position estimation such as position estimation of sensors grid spacing etc This design work focuses on accuracy and precision The concept of location fingerprint technique based on RSS us explained in detail in chapter 2 It is found that RSS is random It is possible to approximate the RSS as being normally distributed While the RSS random process is non‐stationary the mean is more or less constant and could be used as the fingerprint of the location Model designed for position estimation is as shown in chapter 4 Accuracy and precision are the two major performance matrices in this study Uniform grid spacing has been used From the experimental analysis I have obtained an accuracy of 3 meters and 35 precision The sensitivity analysis among the system parameters and the performance matrices were done using the model as shown in chapter 4 For instance our study found that the minimum distance between two adjacent positions also affects the positioning performance This is due to the randomness of RSS patterns induced by the ever changing indoor environment

62 Future work The research in this thesis provides a ground work for the future study of efficient design of indoor positioning system The RSSI based position estimation still has limitations First the RSSI value is time dependent Thus the future study should address this issue Second the movement of object is not considered Third orientations of sensors affect the performance which should be taken care off Fourth to improve the performance of the system we need to find a solution to deal with those incomplete or missing data during the off‐line and the on‐line phase Fifth position estimation on multi‐floor and three‐dimensional coordinates is not available

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 110102006

45

APPENDIX 1

Location Fingerprints of Masters Room Time 1315 Observation Time 20 minutes Number of Samples 10000

People Presence SNo Co-ordinates AP1 (0x0001) AP2 (0x0002) AP3 (0x0004) AP4 (0x0008)

1 11 -24 -16 -27 -33 2 13 -26 -8 -34 -31 3 15 -43 -9 -39 -25 4 32 -21 -11 -27 -33 5 61 -6 -23 -32 -28 6 64 -20 -31 -25 -34 7 35 -23 -35 -29 -38 8 37 -29 -35 -22 -27 9 39 -38 -29 -21 -27 10 67 -27 -23 -20 -23 11 611 -26 -29 -23 -30 12 613 -35 -32 -2 -31 13 513 -34 -31 -20 -26 14 11 -39 -22 -29 -17 15 212 -37 -42 -28 -31 16 -212 -38 -33 -29 -6 17 -21 -42 -29 -33 -19 18 -38 -40 -23 -35 -22

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 210102006

46

APPENDIX 2

Begin

Open radio map and raw data files for write

Intialize radio map in memory

Check whether the

Packet is data or Beacon packet If data packet

Save the data to register

Check if data is

Received from all

sensors

Estimate the position of static

Display the position on PC

End Yes

No

A

A

Yes

No

Flow chart representing the main algorithm for position estimation

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

47

APPENDIX 3

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

48

Distribution of RSSI

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

49

REFERENCES [1] T S Rappaport J H Reed and B D Woerner ldquoPosition location using wireless communications on highways of the futureʺ IEEE Commun Mag vol 34 no 10 pp33‐41 Oct 1996 [2] A M Ladd K E Bekris G Marceau A Rudys L E Kavraki and D S Wallach rdquoRobotics‐based location sensing using wireless ethernetʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ02) 2002 pp 227‐238 [3] I Ramani R Bharadwaja and P V Rangan ldquoLocation tracking for media appliances in wireless home networksʺ in Proc International Conference on Multimedia and Expo (ICMEʹ03) July 2003 pp 769‐772 [4] K Pahlavan X Li and J P Makela ldquoIndoor geolocation science and technologyʺ IEEE Commun Mag vol 40 no 2 pp 112‐118 Feb 2002 [5] Konard Lorincz and Matt Welsh ldquoMote Track A Robust Decentralized Approach to RF‐Based Location Trackingrdquo [6] Paramvir Bahl and Venkata N Padmanabham ldquoRADAR An In‐Building RF‐based User Location and Tracking Systemrdquo [7] Kamol Kaemarungsi ldquoDesign of Indoor positioning system based on location fingerprint techiniquerdquo University of Pittsburgh 2005 [8] Fredrik Linnarsson Cheng Peng and Bengt Oelmann ldquoSENTIO A Hardware Platform for Rapid Prototyping of Wireless Sensor Networksrdquo [9] S Saha K Chaudhuri D Sanghi and P Bhagwat ldquoLocation determination of a mobile device using IEEE 80211b access point signalsʺ in Proc IEEE Wireless Communications and Networking Conference (WCNCʹ03) New Orleans LA Mar 2003 pp 1987‐1992 [10] T Roos P Myllymaki H Tirri P Misikangas and J Sievanen ldquoA probabilistic approach to WLAN user location estimationʺ International Journal of Wireless Information Networks vol 9 no 3 pp 155‐164 July 2002 [11] R Battiti M Brunato and A Villani ldquoStatistical learning theory for location fingerprinting in wireless LANrsquosʺ Technical Report Oct 2002 [Online] Available httprtmscienceunitnitraquobattitiarchive86pdf [12] P Prasithsangaree P Krishnamurthy and P K Chrysanthis ldquoAn indoor position location with wireless LANsʺ in Proc IEEE International Symposium on Personal Indoor and Mobile Radio Communications (PIMRCʹ02) Lisbon Portugal Sept 2002 [13] M A Youssef A Agrawala and A U Shankar ldquoWLAN location determination via clustering and probability distributionsʺ in Proc IEEE

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

50

International Conference on Pervasive Computing and Communications (PerComʹ03) Dallas‐Fort Worth TX Mar 23‐26 2003 pp 23‐26 [14] Z Xiang S Song J Chen H Wang J Huang and X Gao ldquoA wireless LAN‐based indoor positioning technologyʺ IBM Journal of Research and Development vol 48 no 56 pp 617‐626 SeptNov 2004 [15] R Want A Hopper V Falcao and J Gibbons ldquoThe active badge location systemʺACM Transactions on Information Systems vol 40 no 1 pp 91‐102 Jan 1992 [16] N B Priyantha A Chakraborty and H Balakrishnan ldquoThe cricket location‐support systemʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ00) Boston MA Aug 2000 pp 32‐43 [17] Holger K Willig A (2005) Protocols and Architectures for Wireless Sensor Networks London [18] IEEE (2003) Part 154 Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low‐Rate Wireless Personal Area Networks (LR‐WPANs) New York [19] Chipcon CC2420 802154 compliant radio httpwwwchipconcom [20] ZigBee Specification v10 ZigBee Alliance [21] Dimitrios Lymberopoulos Quentin Lindsey and Andreas Savvides ldquoAn Empirical Analysis of Radio Signal Strength Variability in IEEE 802154 Networks using Monopole Antennasrdquo [22] Kiran Yedavalli Bhaskar Krishnamachari Sharmila Ravula and Bhaskar Srinivasan ldquoEcolocation A sequence based technique for RF localization in wireless sensor networksrdquo Robert Bosch Research and Technology Centre Palo Alto CA

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

15

supports ad‐hoc mesh multi‐hop networking Any device in the peer‐to‐peer topology can communicate with any other device within its communication range however this topology also has a PAN coordinator All the devices in a LR‐WPAN have a unique 64‐bit address This or a short address allocated by the PAN coordinator can be used inside a PAN Additionally each PAN has a unique identifier The combination of the PAN identifier and the sort addresses allows communication across different PANs (IEEE 802154 Standard 2003)

Figure 4 Star and peer‐to‐peer topologies in LR‐WPAN [18]

The LR‐WPAN based on the OSI seven‐layer model has the layered architecture presented in figure 5 The application and the network layer are the upper layers in the LR‐WPAN architecture but are outside the scope of the IEEE 802154 standard Only the physical layer ldquowhich contains the RF transceiver along with its low‐level control mechanismrdquo and the MAC layer ldquothat provides access to the physical channelrdquo are included in the standard and will be introduced in the following sections Finally the Type I 8022 LLC and the SSCS are intermediate sub layers supporting communication with the above layers (IEEE 802154 Standard 2003)

Physical Layer The 802154 standard specifies two different services that the PHY provides The PHY data service controls the radio and thus the transmission and reception of the PPDUs In addition the management service performs ED in the channel The management service also performs CCA before sending the messages and provides LQI for the received packets

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

16

Figure 5 WPAN architecture [18]

Three different bands are defined by the standard The 868‐8686 MHz for Europe the 902‐928 MHz for North America and the 2400‐24835 MHz worldwide All of them belong in the ISM radio bands and they are using DSSS Each of the bands supports different a data rate and it uses a different modulation technique chip rate and number of channels Moreover if the system does not use the 2450 MHz frequency it operates in both the 802 MHz and 902 MHz frequencies Table 3 summarizes the supported bands and data rates (IEEE 802154 standard 2003)

PHY (MHz)

Frequency Band (MHz) Modulation

Bit Rate Number of Channels

868-8686 BPSK 20 1 868 915 902-928 BPSK 40 10

2450 2400-24835 O-QPSK 230 16 Table 3 Frequency bands and data rates for IEEE 802154 based on the IEEE

802154 standard [18] The physical layer uses PPDU packets to communicate Table 4 demonstrates its structure The LSB is always transmitted and received first The SHR contains the preamble and the SFD fields which helps receiver

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

17

synchronization The 8‐bytes preamble contains only zero and is used for synchronization The SFD contains a specific sequence of one and zeros and specifies the beginning of the frame The PHR contains the payload length Packets with a length of 9 or more bytes are MPDU as the next section further explains Packets with length 5 are used for MPDU acknowledgements The payload part of the PPDU encloses the MAC layer packet Finally the PPDU size can be up to 127 bytes [18]

4 bytes 1 byte 1 byte Variable

Preamble SFD Frame length

(7 bits) Reserved

(1 bit) Payload (PSU)

SHR PHR PHY Payload Table 4 Format based on the IEEE 802154 standard [18]

MAC Layer The MAC layer is the interface between the SSCS and the PHY layer Similar to the PHY layer the MAC layer supports two services The MAC data service is responsible for the transmission and reception of the MPDUs through the PHY data service The MAC management service if the device is a coordinator manages the network beacons It is also responsible for PAN association and disassociation frame validation and acknowledgment providing ldquoa reliable link between two peer MAC entitiesrdquo In addition it uses the CSMA‐CA for channel access and handles and maintains the GTS mechanism Finally it supports device security [18] The IEEE 802154 standard defines four different frame types the beacon data acknowledgment and MAC command frame All frame types are based on the general MAC frame format (table 5) The frame control field describes and specifies the above different frame types Every MAC frame comprises a MHR which consists of a frame control sequence number and the information field It also contains the MAC payload different frame types have different MAC payload fields The acknowledgment type does not have a payload Finally each frame includes a MFR which contains a FCS The data in the MPDU follows the same order as the PPDU the least significant bits are left in the frame and are transited first Octets2 1 02 028 02 028 Variable 2

Destination PAN Identifier

Destination Address

Source PAN

Identifier Source Address Frame

Control Sequence Number Addressing Field

Frame Payload FCS

MHR MAC

Payload MFRTable 5 General MAC frame format IEEE 802154 standard [18]

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

18

Table 6 7 8 and 9 present the four different MAC frame types The beacon frame is transmitted periodically by the PAN coordinator It provides information about the network management through the super frame and GTS fields which are analyzed later in the section It also synchronizes the network devices and indicates the proper communication period for them The data frame payload encapsulates data from the higher layers When a device receives a packet it is not obliged to response with an acknowledgement packet Finally the command frame identifier and command payload fields of the command frame are used for communication between the network devices The command identifier specifies actions like association disassociation and data GTS or beacon request Octets2 1 410 2 Variable Variable Variable 2

Frame Control

Sequence Number

Addressing Fields

Super frame Specification

GTS fields

Pending Addressing

Fields Beacon Payload FCS

MHR MAC Payload MFRTable 6 Beacon frame format [18]

Octets 2 1 Variable 2

Frame Control

Sequence Number

Addressing Field

Data Payload FCS

MHR MAC

Payload MFR Table 7 Data frame format [18]

Octets 2 1 2

Frame Control

Sequence Number FCS

MHR MFR Table 8 Acknowledgement frame format [18]

Octets2 1 1 Variable 2

Frame Control

Sequence Number

Addressing Fields

Command Frame Identifier

Command Payload FCS

MHR MAC Payload MFR Table 9 Command frame format [18]

In the LR‐WPAN every PAN has its own coordinator The PAN coordinator manages the communication in the local area it has two options to use or not use the super frame structure The super frame (figure 6) uses network beacons If the coordinator does not want to use a super frame structure it suspends the beacon transmission However the beacon is important for device association and disassociation If the coordinator wishes to maintain close communication control in the PAN and to support low‐latency devices it usually uses the super frame A super frame determines a specific time

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

19

period beacons bound it The beacon is transmitted in the first of the sixteen equal time slots that the super frame has It is used to ldquosynchronize the attached devices to identify the PAN and to describe the structure of the super framerdquo The super frame can have active and inactive periods All the communications have to be finished inside the super frame period In the inactive periods the devices can switch to the sleep mode but they have to be ready for the next beacon The active period is further divided into a CAP and a CFP During the CAP any device can communicate competing with the other devices in the PAN using slotted CSMA‐CA The CFP contains GTSs and follows the CAP A CFP may maintain up to seven GTSs and each GTS can reserve more than one time slot However the CAP should always be sufficient to allow new devices t join the PAN The GTSs are allocated to specific devices All the transactions must be completed inside the assigned time period (CAP GTS and CFP) (IEEE 802154 standard 2003)

Figure 6 Example of super frame structure [18]

In accordance with the 802154 standard (2003) three different types of data‐transfer exist In addition the types differ if the coordinator uses or does not beacons Data transfer from a device to the PAN coordinator is the first type (figure 7) For a ldquonon‐beacon‐enable networkrdquo it first senses the medium by using ldquoun‐slotted CSMA‐CArdquo and then a simple transmit to the data frame In a ldquobeacon‐enabled networkrdquo the sender waits for the beacon when it finds it ldquothe device synchronizes to the super fame structurerdquo In the specified time frame the sender again senses the medium by using ldquoslotted CSMA‐CArdquo and transmits the data to the coordinator In either case the coordinator has the option to acknowledge or not acknowledge the data reception after that the transaction is completed Data transfer from the coordinator is the next type described in the standard (figure 8) In a beacon‐enabled network the coordinator indicates a pending message through the beacon The messagersquos target device receives the beacon and if the message is pending it responses with a MAC command request

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

20

message using slotted CSMA‐CA the coordinator may or may not acknowledge the command message and through slotted CSMA‐CA sends the pending message The device acknowledges the received message After that the coordinator removes the message from the beaconrsquos pending list and completes the transaction In a non‐beacon enable network the device periodically sends a MAC command‐request frame to the coordinator The coordinator acknowledges the data request Then by using un‐slotted CSMA‐CA if it has a pending message it transmits it If it has not it respond with a data frame with a ldquozero‐length payloadrdquo To complete the transaction the device acknowledges the data reception

Figure 7 Communication from a device to a PAN coordinator in (a) a beacon‐enabled network and (b) a non‐beacon enabled network [18]

Peer‐to‐peer is the last type of data transfer In this situation the devices are free to communicate with any other device within their communication range In a peer‐to‐peer PAN the devices can ldquoeither receive constantly or synchronize with each otherrdquo If they are receiving constantly to transmit data they use un‐slotted CSAM‐CA In the second case synchronization must be achieved first (IEEE 802154 standard [IEEE 2003]) The IEEE 802154 (2003) standard establishes MAC and PHY standards for low‐cost low‐power and high‐density node deployments In addition to the above PHY and MAC characteristics IEEE 802154 provides a security baseline including ldquothe ability to maintain an ACL and use symmetric cryptographyrdquo for data encryption The algorithm that is used for encryption is the AES However the higher level layers decide when security is need The upper layers are in general responsible for device authentication and key management The next section introduces the ZigBee standard which

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

21

encapsulates the IEEE 802154 and provides additional standardization for the higher levels

Figure 8 Communication from a PAN coordinator to a device in (a) a beacon‐enabled network and (b) a non‐beacon enabled network [18]

242 ZigBee Heily (2004) defines ZigBee as ldquoa rapidly growing worldwide non‐profit industry consortiumrdquo whose mission is ldquoto define a reliable cost‐effective low‐power wirelessly networked monitoring and control product based on an open global standardrdquo Figure 9 illustrates the areas of interest for different wireless communication standards

Figure 9 Overview of the coverage for different wireless communication

standards [17]

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ZigBee a new standard which became publicly available in June 2005 is based on the IEEE 802154 standard It expands the IEEE 802154 by adding the framework for ldquothe network security and applicationrdquo (Craig 2005) Figure 10 presents the IEEE 802154ZigBee stack and the areas of responsibilities

Figure 10 IEEE 802154ZigBee stack [20]

Craig (2005) mentions three networking topologies that the standard covers the star mesh and cluster tree (figure 11) The ZigBee standard works on top of the IEEE 802154 addressing schema by using the standard 64‐bit and the short 16‐bit addressing Kinney (2005) summarizes the ZigBee network layer responsibilities the successful establishment of a new the network and successful new device configuration addressing assignment network synchronization frames security and message routing ZigBee further distinguishes the concept of the physical devices (RFD FFD) by using the notion of ldquological devicesrdquo ldquoZigBee Coordinatorrdquo is the first type of logical devices It is responsible for initializing maintaining and managing the network Under the coordinator in the network hierarchy is the ldquoZigBee routerrdquo which is responsible for controlling the message routing between the nodes Finally the ldquoZigBee End Devicerdquo acts as the end point of the network

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2 LITERATURE REVIEW10102006

23

structure The tasks that an end device can perform are specified in the ldquoApplication Profilesrdquo (Craig 2005)

Figure 11 IEEE 802154ZigBee stack [17]

The ZigBee specifications (2005) summarize the security services provided by ZigBee ldquokey establishment key transport frame protection and device managementrdquo ZigBee builds its security mechanism using symmetric key cryptography The security services also depend on the associated layer thus as the figure 10 shows the security mechanism covers the network and the application layer In addition if a MAC frame needs security protection the MAC layer is able to secure it Moreover the notion of end‐to‐end security is supported the source and destination devices have access and use the same share key In the MAC layer the 802154 AES mechanism provides the proper security The mechanism protects ldquothe confidentiality integrity and authenticity of the MAC framesrdquo (Kinney 2005) An auxiliary header field in front of the MAC payload indicates if the frame is encrypted or not The MAC framesrsquo integrity is supported by calculating and using a MIC at the end of the MAC payload In addition to the AES nonce is used to provide MAC confidentiality and authenticity Figure 12 illustrates a MAC frame with security For different security aspects the MAC layer uses different mode of the AES for the encryption it uses the AES in Counter (CTR) mode and for the integrity the

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

24

CBC‐MAC Finally the combination (CCM) of the above two modes is available providing both encryption and integrity (Kinney 2005)

Figure 12 ZigBee secure frame in MAC layer [17]

In the network layer the CCM (a modified MAC layer CCM mode) is used for encryption Because the network layer uses only the CCM mode a single key is used for all different security options The network layer security message format is similar to the MAC frame it is presented in the figure 13 Finally although the network layer is responsible for securing its layer messages the above layers specify the keys and the CCM option for each frame (Kinney 2005)

Figure 13 ZigBee secure frame in network layer [17]

Security in the application layer works similar to the network and MAC layers It uses the ldquolink keyrdquo or the ldquonetwork keyrdquo to secure the message and then encapsulate it inside a set of fields similar to the network format (figure 14) Other security responsibilities that the application layer has are to provide the ZDO and the applications with device management services key establishment and key transport (ZigBee specifications 2005)

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2 LITERATURE REVIEW10102006

25

Figure 14 ZigBee secure frame in application layer [17]

The ZigBee application layer contains the manufacturer‐defined application objects the ZDO and the application sub‐layer In addition to the security responsibilities the application sub‐layer binds devices based on their duties and needs maintains the binding tables and forwards messages between them The application sub‐layer also discovers the neighbour devices for a given device The ZDO is responsible for determining the devicersquos duty in the network for communicating using binding requests and for supporting security as was mentioned above The sub‐layer that implements the actual application is the manufacturer‐defined application object (Kinney 2005) The ZigBee specifications are a great step toward the wireless sensors networks standardization They cover all the OSI layers from the physical to the application providing guidance to the developers The specifications are the product of the ZigBee alliances a consortium of companies and academic institutes Thus they not only produce the specifications but also are the first use them The next section goes beyond the layer architecture and standardization and introduces the OS field another crucial element of WSNs

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

26

3 RECEIVED SIGNAL STRENGTH (RSS) Majority of the existing methods leverage the existence of IEEE 80211 base stations with powerful radio transmit powers of approximately 100mW per base station Such radios are in a different class from the low power IEEE 802154 compliant radios that typically transmit at low power levels ranging from 52mW to 29mW [19] The wide availability of larger number of IEEE 802154 radios in the research community has revived the interest for signal strength based localization in sensor network Despite of rapidly increasing popularity of IEEE 802154 radios and signal strength localization there is a lack of detailed characterization of the fundamental factors contributing to large signal strength variation This chapter investigates some of the properties of RSS observed during testing and analysis of research work collected during the period of thesis The analysis of RSS values is needed to understand the underlying features of location dependent RSS patterns and location fingerprints An understanding of the properties of the RSS values for location fingerprinting can assist in improving the design of positioning algorithms and in deployment of indoor positioning systems

31 Parameters that affect RSS Different factors that affect the indoor RSS are the userrsquos presenceabsence hisher orientation time of the day building type and material distance from transmitter and type of radio These factors are by no means exhaustive Other possible factors are antenna orientation directionality and type Table 10 classify the parameters which affect RSS [7]

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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10102006

27

Effect on Factors Options

1 Proximity of user Userrsquos presence or absence

2 Orientation of user and terminal

North East South West

Data Collection

3 Make of Radio 4 Time of measurement Time of day amp days of

week 5 Period of measurement

Second minute hour

7 Interference Co‐channel adjacent co‐channel

Statistics

8 Building environment Small offices or large hall

Table 10 Classification of factors the affect RSS [7]

Transmitter variability Different transmitters behave differently even when they are configured exactly in the same way In practice this means that when a transmitter is configured to send packets at a power level of d dBm then the transmitter will send these packets at a power level that is very close to d dBm but not necessarily exactly equal to d dBm This can alter the received signal strength indication and thus it can lead to inaccurate distance estimation

Receiver variability The sensitivity of the receivers across different radio chips is different In practice this means that the RSSI value recorded at different receivers can be different even when all the other parameters that affect the received signal strength are kept constant

Antenna orientation Each antenna has its own radiation pattern that is not uniform In practice this means that the RSSI value recorded at the receiver for a given pair of communicating nodes and for a given distance between them varies as the pair wise antenna orientations of the transmitter and the receiver are changed

Multi‐path fading and shadowing in the RF channel In indoor environments the transmitted signals get reflected after hitting on the walls andor on other objects in the room such as furniture Both the original signal and the reflected signal reach the receiver almost at the same time since they both travel at the speed of light As a result of this the receiver is not able to

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

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28

distinguish the two signals and it measures the received signal strength for both of them

32 Related work analysis A number of indoor location tracking systems have been proposed in the literature based on RF signals ultrasound infrared and some combinations of these As mentioned previously RF based positioning system are widely studied as shown in Table 2 Given a model of radio signal propagation in a building or other environment RSS can be used to estimate the distance between the transmitter and receiver Most of the algorithms (Table 2) have been evaluated in practice using 80211b compliant wireless radios and seem to produce a location error of 15ft to 20ft for an indoor network area of approximately 225ft x 144ft According to the results of this study all the localization techniques produce approximately the same location error over a range of environments Ecolocation [22] Mote Track [5] are two of the few localization algorithms that have been evaluated on a real sensor network that uses a low power wireless radio Ecolocation reports a location error of 10ft for a very small outdoor network deployment area (26ft x 49ft) Mote Track reports a location error of approximately 13ft for an indoor network deployment area of 18751ft2 In general the RSSI based localization algorithms can be divided into two broad categories the map based and the distance (or area) prediction based algorithms In the case of the map based algorithms the deployment area is divided into a large set of sample points that cover the whole area Most techniques collect one or more signal strength measurements from all visible beacon nodes and for each sample point Each sample point is then mapped to either a signal strength vector or a signal strength probability distribution This process generates an RSSI fingerprint map which is unique with respect to the beacon locations and the deployment area This fingerprint map is stored to a central base station A node that is not aware of its location collects one or more signal strength measurements from all the visible beacon nodes and creates its own RSSI fingerprint that is sent to the central base station The base station computes nodersquos location as the sample pointrsquos location whose RSSI fingerprint distance from the unknown nodersquos RSSI fingerprint is the minimum among all the sample points Despite the fact that the map based RSSI localization methods produce relatively small location errors they are not attractive mainly because they require an excessive amount of profiling data and because they are not fully distributed The detailed profiling of a building is a major holdback because it is complex time consuming and depends on beacon locations Therefore for different buildings or even for the

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

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same building and different beacon locations different RSSI maps have to be obtained The second category of RSSI based algorithms are the distance (or area) prediction algorithms which focus on minimizing the amount of profiling data In distance prediction based methods raw RSSI data are directly mapped to distances through a signal propagation model In some cases distances are not explicitly measured but they are taken into consideration through assumptions on the underlying distributions In the case of outdoor deployments where there are no obstacles a single signal propagation model is all the profiling data required In the case of indoor deployments the same applies but now the signal propagation model might be different across different buildings with different furniture and wall arrangements Given an underlying signal propagation model raw RSSI data can be mapped to specific distances These distances can be either used directly in order to perform localization or assigned a probability and used in a learning basedprobabilistic localization algorithm Obviously the accuracy of the distance prediction based methods depends heavily on the accuracy of the signal propagation model used for translating RSSI values to distances

33 Properties of RSSI Changes in signal due to propagation indoors are difficult to predict because of dense environment and propagation effects such as reflection diffraction and scattering The multi‐path fading effect which is the result of either constructive or destructive combination of multiple signal copies at the receiver causes the received signal to fluctuate around a mean value at a particular location The received signal is usually modelled by the combined effects of large‐scale fading and small‐scale fading The large‐scale fading component describes the signal attenuation as the signal travels over a distance and is absorbed by material such as walls and floors along the way to the receiver This component predicts the mean of the RSS and usually has a lognormal distribution On the other hand the small scale fading component explains the dramatic fluctuation of the signal due to multi‐path fading On the other hand the small scale‐fading component explains the dramatic fluctuation of the signal due to multi‐path fading If there is no LOS component the small‐scale‐fading is often modelled with a Rayleigh distribution Such scenarios are commonly called NLOS If there is a LOS component the small‐scale‐fading is typically modelled by a Rician distribution Note that when the RSS is measured the measurement averages out the small‐scale‐fading effects

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4 OBJECT TRACKING 41 Introduction This chapter presents the object‐tracking application the main objective of this thesis work The application is an algorithmic‐software implementation written in Embedded C which is capable of receiving data messages and generating object position estimation data The implementation of the object‐tracking application makes use of the SENTIO platform and uses the background knowledge introduced in the preceding chapters The motivation for the object‐tracking application is to determine the position of an object in indoor environments (Eg Home) The application acts as an interface which collects the raw data that the sensors generate (numerical values) It makes the proper comparisons and algorithmic processes to identify and track the object while it is static inside the area covered by the wireless sensor network Finally with the detection and tracking data it alerts the user or another system The following sections provide an overview of the object‐tracking application

42 Overview of hardware product Before describing the object tracking application it is important to know the characteristics of hardware product The SENTIO platform (figure 15) is built in modular fashion and has the following key features [8]

Flexibility This platform is very flexible in such a way that it is made easy to add or change the hardware for each sensor task depending on the requirement in the WSN For a specific platform this can be hard or sometimes even impossible to accomplish

Sensor resources Resources in a sensor module includes the ability of usage of controllerrsquos resources as much as possible such as ADC‐channels interrupts and digital Input‐Outputrsquos Local voltage regulation should also be possible since many sensors require higher supply voltages than digital Integrated Circuits do Further support of a shut‐down mechanism to power down the sensor modules when not needed

Computation and memory The processor module based on microcontroller should support many devices in a microcontroller family and support different power supply voltages enabling full range of clock frequencies All power‐down modes of the devices should be

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supported The platform should support different processor modules including FPGArsquos and very simple microcontrollers

Communication The focus should be on enabling an IEEE 802154 compliant communication interface With or without ZigBee stack this will provide a flexible platform for exploring different network topologies Support for power‐down of the entire communication module should be supported

Cost and form factor High flexibility is in conflict with low cost and small size The primary objective should be flexibility but the size should be considered in order to enable sensor device integration in systems where space is limited

Figure 15 SENTIO Platform [8]

The communication subsystem of the SENTIO sensor node was designed so that the correct operation of the radio chip is ensured The radio chip is powered by the on‐board voltage regulator and thus fluctuations in the battery voltage level do not affect the operation of the communication subsystem This platform uses Chipcon CC2420 IEEE 802154 radio transceiver operates in the 24GHz ISM band and includes a digital direct sequence spread spectrum (DSSS) modem providing a spreading gain of 9dB and an effective data rate of 250Kbps It was specifically designed for low power wireless applications and supports 8 discrete power levels 0dBm minus1dBm minus3dBm minus5dBm minus7dBm minus10dBm minus15dBm and minus25dBm at which its power consumption varies from 29mW to 52mW [21] A built‐in received signal strength indicator gives an 8‐bit digital value RSSIVAL The RSSIVAL is always averaged over 8 symbol periods (128μs) and a status bit indicates when the RSSIVAL is valid (meaning that the receiver was enabled for at least 8 symbol periods) The

Antenna

Communication Module

Processing Module

Sensing Module

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power P at the RF pins can be obtained directly from RSSIVAL using the following equation

P = RSSIVAL + RSSIOFFSET [dBm] where the RSSIOFFSET is found empirically from the front‐end gain and it is approximately equal to minus45dBm In the next sections when we refer to the RSSI value we refer to the RSSIVAL and not the power P unless otherwise stated

43 Experimental Testbed The experimental test bed for object tracking application is as shown in figure 16 Experiment was conducted in Masterrsquos room at Mid Sweden University A DELL laptop is connected to SENTIO platform and software were primarily used to collect samples of RSS from APrsquos The software monitors IEEE 802154 radio frequency channels which operate in the 24GHz band

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

1030

BC - 1030

AP 3 AP 1

AP 2

AP 4

BS

HEWLETT

PACKARD

Figure 16 Layout of Masterʹs Room

A three dimensional scalable testbed is used for indoor sensor network deployments The testbed illustrated in figure 16 is a 3‐D structure measuring 16m (L) x 12m (W) x 3m (H) and it is designed to host a large number of static nodes as shown in figure 16 Arrow marks in the above figure represent the location where fingerprints were taken The centrepiece of our infrastructure

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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is the SENTIO sensor node [8] an open‐source general purpose sensing platform designed with AT Mega 128 microprocessor and the IEEE 802154 compliant CC2420 radio from Chipcon [19]

44 Obtaining data from the sensor network The next important step in the development of the object tracking application is to obtain the data from the sensor network and send it to the base station From there the data is transmitted through a serial port to the computer that runs the object tracking application When the data arrives at the serial port the application must capture it This paragraph gives a brief explanation of how and what kind data is transmitted by the static object fixed sensors and how this data is being used to estimate the position of the object First the static object transmits Beacon packet continuously which is received by all the fixed APrsquos The beacon packet format is as shown in table 6 Then the fixed APrsquos calculates the 8‐bit value of RSSI from the transmitted beacon packet and then sends the resultant RSSI value as a form of data packet (table 7) to the BS which calculates the position and sends back to the object

45 Object tracking application Testing and Evaluation The purpose of this section is to discuss and evaluate the object tracking application Finally we are ready with the experimental testbed as shown in figure 16 As shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors placed at the corners of the masterrsquos room BS is placed at the centre of the room connected to a PC All the nodes are power connected before an actual measurement takes place At first the object is placed at random position inside the room and transmits beacon packets continuously Then these packets are received by the fixed APrsquos which calculate the RSSI from the packet and then averages it until it receives 50 beacon packets and then transmits the data to the BS (figure 17) Then the BS waits until it receives the data from all the APrsquos Once it receives from all the APrsquos it calculates the position of the object (figure 18) The application was developed to demonstrate a real world use of a WSN in object tracking In evaluating the final position obtained with respect to the actual position we conclude that the object‐tracking was successful Although

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

34

it is a research prototype object tracking application is a simple stable stand‐alone system capable of demonstrating an important application of WSN It takes raw data from the WSN aggregates it evaluates it and based on the evaluation tracks the object The accuracy of estimating the position is less than 5m Table 11 below shows a log of readings taken during test

Start

Intialize micro‐controller and transeciever in receieve mode

If received_packet=

Beacon

Average RSSI values

Increase the counter

If countergt1000050

A

No

Yes

B

B

Yes

No

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

35

A

Reset counter

Set transceiever in transmit mode

Transmit averaged RSSI to Main sensor and set it back to receive mode

10000 samples ‐ Offline

50 samples ‐ Online

Figure 17 Flow chart representing the algorithm of fixed sensors

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

36

Start

Intialize micro‐controller and transeciever in receieve mode

Received RSSI values from all fixed Sensors

Open radio map

Calculate Euclidean distance between obtained RSSI values from fixed sensors and fingerprints

in radio map

No

Yes

Bubble sort for least error in signal strength values

A

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

37

A

Get the location of the object from the radiousing the least error value

Display location of object on PC

End

Figure 18 Flow chart representing the algorithm of main sensor

Table 11 Accuracy of positioning system

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

38

5 RESULTS This chapter presents how the system parameters such as number of samples mean or median etchellipare finalized All the tests are performed in masterrsquos room at Mid Sweden University This chapter is divided into three subsections First in Section 51 it reviews results obtained from samples test Section 52 discusses one dimensional test results and finally section 53 discusses results of two dimensional tests

51 Measurement setup A Dell laptop was connected to a SENTIO platform and is loaded with software (Appendix 2) This setup was used to determine number of samples required during object tracking mean or median position estimation algorithm and one dimensional test The setup is as follows

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

103

0

BC - 1030

AP 1AP 2

BS

HEWLETT

PACKARD

Figure 19 Test bed for one dimensional test in Masters Room

Two SENTIO nodes (AP1 and AP2 as shown above) were separated by a distance of 10 meters and these sensors are known as fixed sensors which are fixed to the ceiling such that there antenna is faced towards the ground They are linear is position A BS was connected to Dell laptop and it was placed in such a way that its antenna was faced towards the ceiling Another SENTIO

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

39

node was used as an object and its orientation was same as BS Figure 19 represents the complete setup for object tracking application Mobile node was moved between these fixed ones at regular intervals of one meter and RSSI values were logged at each interval

52 Number of samples test This test was performed to determine number of samples required for accurate positioning and either mean of the obtained RSSI values or median of them Figure 19 represents the setup for number of samples test Once the setup is ready the measurements are taken as follows

1 An object was tagged with SENTIO node and was placed stationary linear to the fixed sensors

2 10000 samples (RSSI) were taken at regular intervals of 1 meter up to 10 meters

3 The sample values were logged at every interval 4 Using these logged samples I have plotted graphs such as distribution

of RSS values (figure 20 21) meanmedian at intervals of 1 meter using MATLAB

5 The mean and median for various numbers of samples is as shown in figure 22

Figure 20 Histogram plot of RSSI values at 1m and 4m

Figure 21 Histogram plot of RSSI values at 6m and 8m

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

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Figure 22 Distance vs RSSI

From the above graphs following conclusions were made 1 From the histogram plots we can observe that the distribution of RSSI

values follow Gaussians distribution law 2 As we can see from figure 22 that median curve has drastic changes

when compared to mean curve Hence mean is considered while estimating the position

3 From the observations we conclude that the number of samples does not matter since mean curve tends to be same for various numbers of samples

4 Hence 10000 samples are considered in order to get a proper location fingerprint during off‐line phase and during online phase 50 samples are enough to estimate the position of object

53 0ne dimensional test Measurement setup for one dimensional test is as shown in figure 19 This test was conducted to check the performance of algorithm and how accurate it was At first ie during offline phase fingerprints at each interval averaging 10000 samples were taken During online phase Euclidean algorithm was used estimate the position averaging 50 samples of RSSI From the results we

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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can realize that the error was less than 2 meters Table 12 shows the result of location estimation in one dimensional case

Table 12 One dimensional position estimation results

54 Two dimensional test This test is the final stage of my thesis work Experimental testbed for this stage is as shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors fixed at the corners of the room and a BS at the centre of the room There are 18 pre‐determined locations where the fingerprints (RSSI) were taken The fingerprints are as shown in Appendix 1 All the readings were taken in presence of userrsquos The algorithm for position estimation is shown in Appendix 2 Position estimation test is performed as follows

Tagged object is placed at random inside the masterrsquos room All the sensors including the base station and object are switched on Using the algorithm (Appendix 2) the BS estimates the position of the object and displays it on the PC

This test was performed using various numbers of samples to test which has a better accuracy Figure 23 represents the error rate using various numbers of samples From figure 23 we can conclude that 50 numbers of samples is enough to estimate the position of object with an accuracy of 3m

55 Causes of error in location determination The randomness of RSS is the major cause of error in any indoor

positioning system that uses the RSS for location inference If there is no randomness in the RSS and its location fingerprint every indoor positioning systems of this type will have no location detection error For example I have recorded RSS at one particular location during different times of the day From figures 24 and 25 you can observe that RSS values are different at different times of day

The performance of indoor positioning systems depends greatly on the separation of location fingerprints A location fingerprint

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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corresponding to a location can be identified correctly if it is difficult to classify it as another fingerprint by a pattern classifier

Path loss is one of the basic signal propagation properties of the RSS that best describes attenuation of transmit signal power over a distance between an access point and a mobile device

Presence of Userrsquos or userrsquos orientation affects RSS

Figure 23 Error Rate vs Number of samples

Figure 24 RSS distribution at 214 pm

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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Figure 25 RSS distribution at 100pm and 1100 am

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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44

6 DISCUSSIONS 61 Summary and Conclusion While empirical results and performance studies of positioning systems based on location fingerprint have been presented in this report analytical model that can be used as a framework for efficiently designing the positioning systems are not available This thesis design work uses general principles of position estimation such as position estimation of sensors grid spacing etc This design work focuses on accuracy and precision The concept of location fingerprint technique based on RSS us explained in detail in chapter 2 It is found that RSS is random It is possible to approximate the RSS as being normally distributed While the RSS random process is non‐stationary the mean is more or less constant and could be used as the fingerprint of the location Model designed for position estimation is as shown in chapter 4 Accuracy and precision are the two major performance matrices in this study Uniform grid spacing has been used From the experimental analysis I have obtained an accuracy of 3 meters and 35 precision The sensitivity analysis among the system parameters and the performance matrices were done using the model as shown in chapter 4 For instance our study found that the minimum distance between two adjacent positions also affects the positioning performance This is due to the randomness of RSS patterns induced by the ever changing indoor environment

62 Future work The research in this thesis provides a ground work for the future study of efficient design of indoor positioning system The RSSI based position estimation still has limitations First the RSSI value is time dependent Thus the future study should address this issue Second the movement of object is not considered Third orientations of sensors affect the performance which should be taken care off Fourth to improve the performance of the system we need to find a solution to deal with those incomplete or missing data during the off‐line and the on‐line phase Fifth position estimation on multi‐floor and three‐dimensional coordinates is not available

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 110102006

45

APPENDIX 1

Location Fingerprints of Masters Room Time 1315 Observation Time 20 minutes Number of Samples 10000

People Presence SNo Co-ordinates AP1 (0x0001) AP2 (0x0002) AP3 (0x0004) AP4 (0x0008)

1 11 -24 -16 -27 -33 2 13 -26 -8 -34 -31 3 15 -43 -9 -39 -25 4 32 -21 -11 -27 -33 5 61 -6 -23 -32 -28 6 64 -20 -31 -25 -34 7 35 -23 -35 -29 -38 8 37 -29 -35 -22 -27 9 39 -38 -29 -21 -27 10 67 -27 -23 -20 -23 11 611 -26 -29 -23 -30 12 613 -35 -32 -2 -31 13 513 -34 -31 -20 -26 14 11 -39 -22 -29 -17 15 212 -37 -42 -28 -31 16 -212 -38 -33 -29 -6 17 -21 -42 -29 -33 -19 18 -38 -40 -23 -35 -22

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 210102006

46

APPENDIX 2

Begin

Open radio map and raw data files for write

Intialize radio map in memory

Check whether the

Packet is data or Beacon packet If data packet

Save the data to register

Check if data is

Received from all

sensors

Estimate the position of static

Display the position on PC

End Yes

No

A

A

Yes

No

Flow chart representing the main algorithm for position estimation

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

47

APPENDIX 3

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

48

Distribution of RSSI

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

49

REFERENCES [1] T S Rappaport J H Reed and B D Woerner ldquoPosition location using wireless communications on highways of the futureʺ IEEE Commun Mag vol 34 no 10 pp33‐41 Oct 1996 [2] A M Ladd K E Bekris G Marceau A Rudys L E Kavraki and D S Wallach rdquoRobotics‐based location sensing using wireless ethernetʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ02) 2002 pp 227‐238 [3] I Ramani R Bharadwaja and P V Rangan ldquoLocation tracking for media appliances in wireless home networksʺ in Proc International Conference on Multimedia and Expo (ICMEʹ03) July 2003 pp 769‐772 [4] K Pahlavan X Li and J P Makela ldquoIndoor geolocation science and technologyʺ IEEE Commun Mag vol 40 no 2 pp 112‐118 Feb 2002 [5] Konard Lorincz and Matt Welsh ldquoMote Track A Robust Decentralized Approach to RF‐Based Location Trackingrdquo [6] Paramvir Bahl and Venkata N Padmanabham ldquoRADAR An In‐Building RF‐based User Location and Tracking Systemrdquo [7] Kamol Kaemarungsi ldquoDesign of Indoor positioning system based on location fingerprint techiniquerdquo University of Pittsburgh 2005 [8] Fredrik Linnarsson Cheng Peng and Bengt Oelmann ldquoSENTIO A Hardware Platform for Rapid Prototyping of Wireless Sensor Networksrdquo [9] S Saha K Chaudhuri D Sanghi and P Bhagwat ldquoLocation determination of a mobile device using IEEE 80211b access point signalsʺ in Proc IEEE Wireless Communications and Networking Conference (WCNCʹ03) New Orleans LA Mar 2003 pp 1987‐1992 [10] T Roos P Myllymaki H Tirri P Misikangas and J Sievanen ldquoA probabilistic approach to WLAN user location estimationʺ International Journal of Wireless Information Networks vol 9 no 3 pp 155‐164 July 2002 [11] R Battiti M Brunato and A Villani ldquoStatistical learning theory for location fingerprinting in wireless LANrsquosʺ Technical Report Oct 2002 [Online] Available httprtmscienceunitnitraquobattitiarchive86pdf [12] P Prasithsangaree P Krishnamurthy and P K Chrysanthis ldquoAn indoor position location with wireless LANsʺ in Proc IEEE International Symposium on Personal Indoor and Mobile Radio Communications (PIMRCʹ02) Lisbon Portugal Sept 2002 [13] M A Youssef A Agrawala and A U Shankar ldquoWLAN location determination via clustering and probability distributionsʺ in Proc IEEE

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

50

International Conference on Pervasive Computing and Communications (PerComʹ03) Dallas‐Fort Worth TX Mar 23‐26 2003 pp 23‐26 [14] Z Xiang S Song J Chen H Wang J Huang and X Gao ldquoA wireless LAN‐based indoor positioning technologyʺ IBM Journal of Research and Development vol 48 no 56 pp 617‐626 SeptNov 2004 [15] R Want A Hopper V Falcao and J Gibbons ldquoThe active badge location systemʺACM Transactions on Information Systems vol 40 no 1 pp 91‐102 Jan 1992 [16] N B Priyantha A Chakraborty and H Balakrishnan ldquoThe cricket location‐support systemʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ00) Boston MA Aug 2000 pp 32‐43 [17] Holger K Willig A (2005) Protocols and Architectures for Wireless Sensor Networks London [18] IEEE (2003) Part 154 Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low‐Rate Wireless Personal Area Networks (LR‐WPANs) New York [19] Chipcon CC2420 802154 compliant radio httpwwwchipconcom [20] ZigBee Specification v10 ZigBee Alliance [21] Dimitrios Lymberopoulos Quentin Lindsey and Andreas Savvides ldquoAn Empirical Analysis of Radio Signal Strength Variability in IEEE 802154 Networks using Monopole Antennasrdquo [22] Kiran Yedavalli Bhaskar Krishnamachari Sharmila Ravula and Bhaskar Srinivasan ldquoEcolocation A sequence based technique for RF localization in wireless sensor networksrdquo Robert Bosch Research and Technology Centre Palo Alto CA

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

16

Figure 5 WPAN architecture [18]

Three different bands are defined by the standard The 868‐8686 MHz for Europe the 902‐928 MHz for North America and the 2400‐24835 MHz worldwide All of them belong in the ISM radio bands and they are using DSSS Each of the bands supports different a data rate and it uses a different modulation technique chip rate and number of channels Moreover if the system does not use the 2450 MHz frequency it operates in both the 802 MHz and 902 MHz frequencies Table 3 summarizes the supported bands and data rates (IEEE 802154 standard 2003)

PHY (MHz)

Frequency Band (MHz) Modulation

Bit Rate Number of Channels

868-8686 BPSK 20 1 868 915 902-928 BPSK 40 10

2450 2400-24835 O-QPSK 230 16 Table 3 Frequency bands and data rates for IEEE 802154 based on the IEEE

802154 standard [18] The physical layer uses PPDU packets to communicate Table 4 demonstrates its structure The LSB is always transmitted and received first The SHR contains the preamble and the SFD fields which helps receiver

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

17

synchronization The 8‐bytes preamble contains only zero and is used for synchronization The SFD contains a specific sequence of one and zeros and specifies the beginning of the frame The PHR contains the payload length Packets with a length of 9 or more bytes are MPDU as the next section further explains Packets with length 5 are used for MPDU acknowledgements The payload part of the PPDU encloses the MAC layer packet Finally the PPDU size can be up to 127 bytes [18]

4 bytes 1 byte 1 byte Variable

Preamble SFD Frame length

(7 bits) Reserved

(1 bit) Payload (PSU)

SHR PHR PHY Payload Table 4 Format based on the IEEE 802154 standard [18]

MAC Layer The MAC layer is the interface between the SSCS and the PHY layer Similar to the PHY layer the MAC layer supports two services The MAC data service is responsible for the transmission and reception of the MPDUs through the PHY data service The MAC management service if the device is a coordinator manages the network beacons It is also responsible for PAN association and disassociation frame validation and acknowledgment providing ldquoa reliable link between two peer MAC entitiesrdquo In addition it uses the CSMA‐CA for channel access and handles and maintains the GTS mechanism Finally it supports device security [18] The IEEE 802154 standard defines four different frame types the beacon data acknowledgment and MAC command frame All frame types are based on the general MAC frame format (table 5) The frame control field describes and specifies the above different frame types Every MAC frame comprises a MHR which consists of a frame control sequence number and the information field It also contains the MAC payload different frame types have different MAC payload fields The acknowledgment type does not have a payload Finally each frame includes a MFR which contains a FCS The data in the MPDU follows the same order as the PPDU the least significant bits are left in the frame and are transited first Octets2 1 02 028 02 028 Variable 2

Destination PAN Identifier

Destination Address

Source PAN

Identifier Source Address Frame

Control Sequence Number Addressing Field

Frame Payload FCS

MHR MAC

Payload MFRTable 5 General MAC frame format IEEE 802154 standard [18]

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

18

Table 6 7 8 and 9 present the four different MAC frame types The beacon frame is transmitted periodically by the PAN coordinator It provides information about the network management through the super frame and GTS fields which are analyzed later in the section It also synchronizes the network devices and indicates the proper communication period for them The data frame payload encapsulates data from the higher layers When a device receives a packet it is not obliged to response with an acknowledgement packet Finally the command frame identifier and command payload fields of the command frame are used for communication between the network devices The command identifier specifies actions like association disassociation and data GTS or beacon request Octets2 1 410 2 Variable Variable Variable 2

Frame Control

Sequence Number

Addressing Fields

Super frame Specification

GTS fields

Pending Addressing

Fields Beacon Payload FCS

MHR MAC Payload MFRTable 6 Beacon frame format [18]

Octets 2 1 Variable 2

Frame Control

Sequence Number

Addressing Field

Data Payload FCS

MHR MAC

Payload MFR Table 7 Data frame format [18]

Octets 2 1 2

Frame Control

Sequence Number FCS

MHR MFR Table 8 Acknowledgement frame format [18]

Octets2 1 1 Variable 2

Frame Control

Sequence Number

Addressing Fields

Command Frame Identifier

Command Payload FCS

MHR MAC Payload MFR Table 9 Command frame format [18]

In the LR‐WPAN every PAN has its own coordinator The PAN coordinator manages the communication in the local area it has two options to use or not use the super frame structure The super frame (figure 6) uses network beacons If the coordinator does not want to use a super frame structure it suspends the beacon transmission However the beacon is important for device association and disassociation If the coordinator wishes to maintain close communication control in the PAN and to support low‐latency devices it usually uses the super frame A super frame determines a specific time

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

19

period beacons bound it The beacon is transmitted in the first of the sixteen equal time slots that the super frame has It is used to ldquosynchronize the attached devices to identify the PAN and to describe the structure of the super framerdquo The super frame can have active and inactive periods All the communications have to be finished inside the super frame period In the inactive periods the devices can switch to the sleep mode but they have to be ready for the next beacon The active period is further divided into a CAP and a CFP During the CAP any device can communicate competing with the other devices in the PAN using slotted CSMA‐CA The CFP contains GTSs and follows the CAP A CFP may maintain up to seven GTSs and each GTS can reserve more than one time slot However the CAP should always be sufficient to allow new devices t join the PAN The GTSs are allocated to specific devices All the transactions must be completed inside the assigned time period (CAP GTS and CFP) (IEEE 802154 standard 2003)

Figure 6 Example of super frame structure [18]

In accordance with the 802154 standard (2003) three different types of data‐transfer exist In addition the types differ if the coordinator uses or does not beacons Data transfer from a device to the PAN coordinator is the first type (figure 7) For a ldquonon‐beacon‐enable networkrdquo it first senses the medium by using ldquoun‐slotted CSMA‐CArdquo and then a simple transmit to the data frame In a ldquobeacon‐enabled networkrdquo the sender waits for the beacon when it finds it ldquothe device synchronizes to the super fame structurerdquo In the specified time frame the sender again senses the medium by using ldquoslotted CSMA‐CArdquo and transmits the data to the coordinator In either case the coordinator has the option to acknowledge or not acknowledge the data reception after that the transaction is completed Data transfer from the coordinator is the next type described in the standard (figure 8) In a beacon‐enabled network the coordinator indicates a pending message through the beacon The messagersquos target device receives the beacon and if the message is pending it responses with a MAC command request

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

20

message using slotted CSMA‐CA the coordinator may or may not acknowledge the command message and through slotted CSMA‐CA sends the pending message The device acknowledges the received message After that the coordinator removes the message from the beaconrsquos pending list and completes the transaction In a non‐beacon enable network the device periodically sends a MAC command‐request frame to the coordinator The coordinator acknowledges the data request Then by using un‐slotted CSMA‐CA if it has a pending message it transmits it If it has not it respond with a data frame with a ldquozero‐length payloadrdquo To complete the transaction the device acknowledges the data reception

Figure 7 Communication from a device to a PAN coordinator in (a) a beacon‐enabled network and (b) a non‐beacon enabled network [18]

Peer‐to‐peer is the last type of data transfer In this situation the devices are free to communicate with any other device within their communication range In a peer‐to‐peer PAN the devices can ldquoeither receive constantly or synchronize with each otherrdquo If they are receiving constantly to transmit data they use un‐slotted CSAM‐CA In the second case synchronization must be achieved first (IEEE 802154 standard [IEEE 2003]) The IEEE 802154 (2003) standard establishes MAC and PHY standards for low‐cost low‐power and high‐density node deployments In addition to the above PHY and MAC characteristics IEEE 802154 provides a security baseline including ldquothe ability to maintain an ACL and use symmetric cryptographyrdquo for data encryption The algorithm that is used for encryption is the AES However the higher level layers decide when security is need The upper layers are in general responsible for device authentication and key management The next section introduces the ZigBee standard which

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

21

encapsulates the IEEE 802154 and provides additional standardization for the higher levels

Figure 8 Communication from a PAN coordinator to a device in (a) a beacon‐enabled network and (b) a non‐beacon enabled network [18]

242 ZigBee Heily (2004) defines ZigBee as ldquoa rapidly growing worldwide non‐profit industry consortiumrdquo whose mission is ldquoto define a reliable cost‐effective low‐power wirelessly networked monitoring and control product based on an open global standardrdquo Figure 9 illustrates the areas of interest for different wireless communication standards

Figure 9 Overview of the coverage for different wireless communication

standards [17]

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

22

ZigBee a new standard which became publicly available in June 2005 is based on the IEEE 802154 standard It expands the IEEE 802154 by adding the framework for ldquothe network security and applicationrdquo (Craig 2005) Figure 10 presents the IEEE 802154ZigBee stack and the areas of responsibilities

Figure 10 IEEE 802154ZigBee stack [20]

Craig (2005) mentions three networking topologies that the standard covers the star mesh and cluster tree (figure 11) The ZigBee standard works on top of the IEEE 802154 addressing schema by using the standard 64‐bit and the short 16‐bit addressing Kinney (2005) summarizes the ZigBee network layer responsibilities the successful establishment of a new the network and successful new device configuration addressing assignment network synchronization frames security and message routing ZigBee further distinguishes the concept of the physical devices (RFD FFD) by using the notion of ldquological devicesrdquo ldquoZigBee Coordinatorrdquo is the first type of logical devices It is responsible for initializing maintaining and managing the network Under the coordinator in the network hierarchy is the ldquoZigBee routerrdquo which is responsible for controlling the message routing between the nodes Finally the ldquoZigBee End Devicerdquo acts as the end point of the network

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

23

structure The tasks that an end device can perform are specified in the ldquoApplication Profilesrdquo (Craig 2005)

Figure 11 IEEE 802154ZigBee stack [17]

The ZigBee specifications (2005) summarize the security services provided by ZigBee ldquokey establishment key transport frame protection and device managementrdquo ZigBee builds its security mechanism using symmetric key cryptography The security services also depend on the associated layer thus as the figure 10 shows the security mechanism covers the network and the application layer In addition if a MAC frame needs security protection the MAC layer is able to secure it Moreover the notion of end‐to‐end security is supported the source and destination devices have access and use the same share key In the MAC layer the 802154 AES mechanism provides the proper security The mechanism protects ldquothe confidentiality integrity and authenticity of the MAC framesrdquo (Kinney 2005) An auxiliary header field in front of the MAC payload indicates if the frame is encrypted or not The MAC framesrsquo integrity is supported by calculating and using a MIC at the end of the MAC payload In addition to the AES nonce is used to provide MAC confidentiality and authenticity Figure 12 illustrates a MAC frame with security For different security aspects the MAC layer uses different mode of the AES for the encryption it uses the AES in Counter (CTR) mode and for the integrity the

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

24

CBC‐MAC Finally the combination (CCM) of the above two modes is available providing both encryption and integrity (Kinney 2005)

Figure 12 ZigBee secure frame in MAC layer [17]

In the network layer the CCM (a modified MAC layer CCM mode) is used for encryption Because the network layer uses only the CCM mode a single key is used for all different security options The network layer security message format is similar to the MAC frame it is presented in the figure 13 Finally although the network layer is responsible for securing its layer messages the above layers specify the keys and the CCM option for each frame (Kinney 2005)

Figure 13 ZigBee secure frame in network layer [17]

Security in the application layer works similar to the network and MAC layers It uses the ldquolink keyrdquo or the ldquonetwork keyrdquo to secure the message and then encapsulate it inside a set of fields similar to the network format (figure 14) Other security responsibilities that the application layer has are to provide the ZDO and the applications with device management services key establishment and key transport (ZigBee specifications 2005)

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

25

Figure 14 ZigBee secure frame in application layer [17]

The ZigBee application layer contains the manufacturer‐defined application objects the ZDO and the application sub‐layer In addition to the security responsibilities the application sub‐layer binds devices based on their duties and needs maintains the binding tables and forwards messages between them The application sub‐layer also discovers the neighbour devices for a given device The ZDO is responsible for determining the devicersquos duty in the network for communicating using binding requests and for supporting security as was mentioned above The sub‐layer that implements the actual application is the manufacturer‐defined application object (Kinney 2005) The ZigBee specifications are a great step toward the wireless sensors networks standardization They cover all the OSI layers from the physical to the application providing guidance to the developers The specifications are the product of the ZigBee alliances a consortium of companies and academic institutes Thus they not only produce the specifications but also are the first use them The next section goes beyond the layer architecture and standardization and introduces the OS field another crucial element of WSNs

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

26

3 RECEIVED SIGNAL STRENGTH (RSS) Majority of the existing methods leverage the existence of IEEE 80211 base stations with powerful radio transmit powers of approximately 100mW per base station Such radios are in a different class from the low power IEEE 802154 compliant radios that typically transmit at low power levels ranging from 52mW to 29mW [19] The wide availability of larger number of IEEE 802154 radios in the research community has revived the interest for signal strength based localization in sensor network Despite of rapidly increasing popularity of IEEE 802154 radios and signal strength localization there is a lack of detailed characterization of the fundamental factors contributing to large signal strength variation This chapter investigates some of the properties of RSS observed during testing and analysis of research work collected during the period of thesis The analysis of RSS values is needed to understand the underlying features of location dependent RSS patterns and location fingerprints An understanding of the properties of the RSS values for location fingerprinting can assist in improving the design of positioning algorithms and in deployment of indoor positioning systems

31 Parameters that affect RSS Different factors that affect the indoor RSS are the userrsquos presenceabsence hisher orientation time of the day building type and material distance from transmitter and type of radio These factors are by no means exhaustive Other possible factors are antenna orientation directionality and type Table 10 classify the parameters which affect RSS [7]

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

27

Effect on Factors Options

1 Proximity of user Userrsquos presence or absence

2 Orientation of user and terminal

North East South West

Data Collection

3 Make of Radio 4 Time of measurement Time of day amp days of

week 5 Period of measurement

Second minute hour

7 Interference Co‐channel adjacent co‐channel

Statistics

8 Building environment Small offices or large hall

Table 10 Classification of factors the affect RSS [7]

Transmitter variability Different transmitters behave differently even when they are configured exactly in the same way In practice this means that when a transmitter is configured to send packets at a power level of d dBm then the transmitter will send these packets at a power level that is very close to d dBm but not necessarily exactly equal to d dBm This can alter the received signal strength indication and thus it can lead to inaccurate distance estimation

Receiver variability The sensitivity of the receivers across different radio chips is different In practice this means that the RSSI value recorded at different receivers can be different even when all the other parameters that affect the received signal strength are kept constant

Antenna orientation Each antenna has its own radiation pattern that is not uniform In practice this means that the RSSI value recorded at the receiver for a given pair of communicating nodes and for a given distance between them varies as the pair wise antenna orientations of the transmitter and the receiver are changed

Multi‐path fading and shadowing in the RF channel In indoor environments the transmitted signals get reflected after hitting on the walls andor on other objects in the room such as furniture Both the original signal and the reflected signal reach the receiver almost at the same time since they both travel at the speed of light As a result of this the receiver is not able to

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

28

distinguish the two signals and it measures the received signal strength for both of them

32 Related work analysis A number of indoor location tracking systems have been proposed in the literature based on RF signals ultrasound infrared and some combinations of these As mentioned previously RF based positioning system are widely studied as shown in Table 2 Given a model of radio signal propagation in a building or other environment RSS can be used to estimate the distance between the transmitter and receiver Most of the algorithms (Table 2) have been evaluated in practice using 80211b compliant wireless radios and seem to produce a location error of 15ft to 20ft for an indoor network area of approximately 225ft x 144ft According to the results of this study all the localization techniques produce approximately the same location error over a range of environments Ecolocation [22] Mote Track [5] are two of the few localization algorithms that have been evaluated on a real sensor network that uses a low power wireless radio Ecolocation reports a location error of 10ft for a very small outdoor network deployment area (26ft x 49ft) Mote Track reports a location error of approximately 13ft for an indoor network deployment area of 18751ft2 In general the RSSI based localization algorithms can be divided into two broad categories the map based and the distance (or area) prediction based algorithms In the case of the map based algorithms the deployment area is divided into a large set of sample points that cover the whole area Most techniques collect one or more signal strength measurements from all visible beacon nodes and for each sample point Each sample point is then mapped to either a signal strength vector or a signal strength probability distribution This process generates an RSSI fingerprint map which is unique with respect to the beacon locations and the deployment area This fingerprint map is stored to a central base station A node that is not aware of its location collects one or more signal strength measurements from all the visible beacon nodes and creates its own RSSI fingerprint that is sent to the central base station The base station computes nodersquos location as the sample pointrsquos location whose RSSI fingerprint distance from the unknown nodersquos RSSI fingerprint is the minimum among all the sample points Despite the fact that the map based RSSI localization methods produce relatively small location errors they are not attractive mainly because they require an excessive amount of profiling data and because they are not fully distributed The detailed profiling of a building is a major holdback because it is complex time consuming and depends on beacon locations Therefore for different buildings or even for the

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

29

same building and different beacon locations different RSSI maps have to be obtained The second category of RSSI based algorithms are the distance (or area) prediction algorithms which focus on minimizing the amount of profiling data In distance prediction based methods raw RSSI data are directly mapped to distances through a signal propagation model In some cases distances are not explicitly measured but they are taken into consideration through assumptions on the underlying distributions In the case of outdoor deployments where there are no obstacles a single signal propagation model is all the profiling data required In the case of indoor deployments the same applies but now the signal propagation model might be different across different buildings with different furniture and wall arrangements Given an underlying signal propagation model raw RSSI data can be mapped to specific distances These distances can be either used directly in order to perform localization or assigned a probability and used in a learning basedprobabilistic localization algorithm Obviously the accuracy of the distance prediction based methods depends heavily on the accuracy of the signal propagation model used for translating RSSI values to distances

33 Properties of RSSI Changes in signal due to propagation indoors are difficult to predict because of dense environment and propagation effects such as reflection diffraction and scattering The multi‐path fading effect which is the result of either constructive or destructive combination of multiple signal copies at the receiver causes the received signal to fluctuate around a mean value at a particular location The received signal is usually modelled by the combined effects of large‐scale fading and small‐scale fading The large‐scale fading component describes the signal attenuation as the signal travels over a distance and is absorbed by material such as walls and floors along the way to the receiver This component predicts the mean of the RSS and usually has a lognormal distribution On the other hand the small scale fading component explains the dramatic fluctuation of the signal due to multi‐path fading On the other hand the small scale‐fading component explains the dramatic fluctuation of the signal due to multi‐path fading If there is no LOS component the small‐scale‐fading is often modelled with a Rayleigh distribution Such scenarios are commonly called NLOS If there is a LOS component the small‐scale‐fading is typically modelled by a Rician distribution Note that when the RSS is measured the measurement averages out the small‐scale‐fading effects

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

30

4 OBJECT TRACKING 41 Introduction This chapter presents the object‐tracking application the main objective of this thesis work The application is an algorithmic‐software implementation written in Embedded C which is capable of receiving data messages and generating object position estimation data The implementation of the object‐tracking application makes use of the SENTIO platform and uses the background knowledge introduced in the preceding chapters The motivation for the object‐tracking application is to determine the position of an object in indoor environments (Eg Home) The application acts as an interface which collects the raw data that the sensors generate (numerical values) It makes the proper comparisons and algorithmic processes to identify and track the object while it is static inside the area covered by the wireless sensor network Finally with the detection and tracking data it alerts the user or another system The following sections provide an overview of the object‐tracking application

42 Overview of hardware product Before describing the object tracking application it is important to know the characteristics of hardware product The SENTIO platform (figure 15) is built in modular fashion and has the following key features [8]

Flexibility This platform is very flexible in such a way that it is made easy to add or change the hardware for each sensor task depending on the requirement in the WSN For a specific platform this can be hard or sometimes even impossible to accomplish

Sensor resources Resources in a sensor module includes the ability of usage of controllerrsquos resources as much as possible such as ADC‐channels interrupts and digital Input‐Outputrsquos Local voltage regulation should also be possible since many sensors require higher supply voltages than digital Integrated Circuits do Further support of a shut‐down mechanism to power down the sensor modules when not needed

Computation and memory The processor module based on microcontroller should support many devices in a microcontroller family and support different power supply voltages enabling full range of clock frequencies All power‐down modes of the devices should be

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

31

supported The platform should support different processor modules including FPGArsquos and very simple microcontrollers

Communication The focus should be on enabling an IEEE 802154 compliant communication interface With or without ZigBee stack this will provide a flexible platform for exploring different network topologies Support for power‐down of the entire communication module should be supported

Cost and form factor High flexibility is in conflict with low cost and small size The primary objective should be flexibility but the size should be considered in order to enable sensor device integration in systems where space is limited

Figure 15 SENTIO Platform [8]

The communication subsystem of the SENTIO sensor node was designed so that the correct operation of the radio chip is ensured The radio chip is powered by the on‐board voltage regulator and thus fluctuations in the battery voltage level do not affect the operation of the communication subsystem This platform uses Chipcon CC2420 IEEE 802154 radio transceiver operates in the 24GHz ISM band and includes a digital direct sequence spread spectrum (DSSS) modem providing a spreading gain of 9dB and an effective data rate of 250Kbps It was specifically designed for low power wireless applications and supports 8 discrete power levels 0dBm minus1dBm minus3dBm minus5dBm minus7dBm minus10dBm minus15dBm and minus25dBm at which its power consumption varies from 29mW to 52mW [21] A built‐in received signal strength indicator gives an 8‐bit digital value RSSIVAL The RSSIVAL is always averaged over 8 symbol periods (128μs) and a status bit indicates when the RSSIVAL is valid (meaning that the receiver was enabled for at least 8 symbol periods) The

Antenna

Communication Module

Processing Module

Sensing Module

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

32

power P at the RF pins can be obtained directly from RSSIVAL using the following equation

P = RSSIVAL + RSSIOFFSET [dBm] where the RSSIOFFSET is found empirically from the front‐end gain and it is approximately equal to minus45dBm In the next sections when we refer to the RSSI value we refer to the RSSIVAL and not the power P unless otherwise stated

43 Experimental Testbed The experimental test bed for object tracking application is as shown in figure 16 Experiment was conducted in Masterrsquos room at Mid Sweden University A DELL laptop is connected to SENTIO platform and software were primarily used to collect samples of RSS from APrsquos The software monitors IEEE 802154 radio frequency channels which operate in the 24GHz band

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

1030

BC - 1030

AP 3 AP 1

AP 2

AP 4

BS

HEWLETT

PACKARD

Figure 16 Layout of Masterʹs Room

A three dimensional scalable testbed is used for indoor sensor network deployments The testbed illustrated in figure 16 is a 3‐D structure measuring 16m (L) x 12m (W) x 3m (H) and it is designed to host a large number of static nodes as shown in figure 16 Arrow marks in the above figure represent the location where fingerprints were taken The centrepiece of our infrastructure

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

33

is the SENTIO sensor node [8] an open‐source general purpose sensing platform designed with AT Mega 128 microprocessor and the IEEE 802154 compliant CC2420 radio from Chipcon [19]

44 Obtaining data from the sensor network The next important step in the development of the object tracking application is to obtain the data from the sensor network and send it to the base station From there the data is transmitted through a serial port to the computer that runs the object tracking application When the data arrives at the serial port the application must capture it This paragraph gives a brief explanation of how and what kind data is transmitted by the static object fixed sensors and how this data is being used to estimate the position of the object First the static object transmits Beacon packet continuously which is received by all the fixed APrsquos The beacon packet format is as shown in table 6 Then the fixed APrsquos calculates the 8‐bit value of RSSI from the transmitted beacon packet and then sends the resultant RSSI value as a form of data packet (table 7) to the BS which calculates the position and sends back to the object

45 Object tracking application Testing and Evaluation The purpose of this section is to discuss and evaluate the object tracking application Finally we are ready with the experimental testbed as shown in figure 16 As shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors placed at the corners of the masterrsquos room BS is placed at the centre of the room connected to a PC All the nodes are power connected before an actual measurement takes place At first the object is placed at random position inside the room and transmits beacon packets continuously Then these packets are received by the fixed APrsquos which calculate the RSSI from the packet and then averages it until it receives 50 beacon packets and then transmits the data to the BS (figure 17) Then the BS waits until it receives the data from all the APrsquos Once it receives from all the APrsquos it calculates the position of the object (figure 18) The application was developed to demonstrate a real world use of a WSN in object tracking In evaluating the final position obtained with respect to the actual position we conclude that the object‐tracking was successful Although

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

34

it is a research prototype object tracking application is a simple stable stand‐alone system capable of demonstrating an important application of WSN It takes raw data from the WSN aggregates it evaluates it and based on the evaluation tracks the object The accuracy of estimating the position is less than 5m Table 11 below shows a log of readings taken during test

Start

Intialize micro‐controller and transeciever in receieve mode

If received_packet=

Beacon

Average RSSI values

Increase the counter

If countergt1000050

A

No

Yes

B

B

Yes

No

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

35

A

Reset counter

Set transceiever in transmit mode

Transmit averaged RSSI to Main sensor and set it back to receive mode

10000 samples ‐ Offline

50 samples ‐ Online

Figure 17 Flow chart representing the algorithm of fixed sensors

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

36

Start

Intialize micro‐controller and transeciever in receieve mode

Received RSSI values from all fixed Sensors

Open radio map

Calculate Euclidean distance between obtained RSSI values from fixed sensors and fingerprints

in radio map

No

Yes

Bubble sort for least error in signal strength values

A

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

37

A

Get the location of the object from the radiousing the least error value

Display location of object on PC

End

Figure 18 Flow chart representing the algorithm of main sensor

Table 11 Accuracy of positioning system

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

38

5 RESULTS This chapter presents how the system parameters such as number of samples mean or median etchellipare finalized All the tests are performed in masterrsquos room at Mid Sweden University This chapter is divided into three subsections First in Section 51 it reviews results obtained from samples test Section 52 discusses one dimensional test results and finally section 53 discusses results of two dimensional tests

51 Measurement setup A Dell laptop was connected to a SENTIO platform and is loaded with software (Appendix 2) This setup was used to determine number of samples required during object tracking mean or median position estimation algorithm and one dimensional test The setup is as follows

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

103

0

BC - 1030

AP 1AP 2

BS

HEWLETT

PACKARD

Figure 19 Test bed for one dimensional test in Masters Room

Two SENTIO nodes (AP1 and AP2 as shown above) were separated by a distance of 10 meters and these sensors are known as fixed sensors which are fixed to the ceiling such that there antenna is faced towards the ground They are linear is position A BS was connected to Dell laptop and it was placed in such a way that its antenna was faced towards the ceiling Another SENTIO

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

39

node was used as an object and its orientation was same as BS Figure 19 represents the complete setup for object tracking application Mobile node was moved between these fixed ones at regular intervals of one meter and RSSI values were logged at each interval

52 Number of samples test This test was performed to determine number of samples required for accurate positioning and either mean of the obtained RSSI values or median of them Figure 19 represents the setup for number of samples test Once the setup is ready the measurements are taken as follows

1 An object was tagged with SENTIO node and was placed stationary linear to the fixed sensors

2 10000 samples (RSSI) were taken at regular intervals of 1 meter up to 10 meters

3 The sample values were logged at every interval 4 Using these logged samples I have plotted graphs such as distribution

of RSS values (figure 20 21) meanmedian at intervals of 1 meter using MATLAB

5 The mean and median for various numbers of samples is as shown in figure 22

Figure 20 Histogram plot of RSSI values at 1m and 4m

Figure 21 Histogram plot of RSSI values at 6m and 8m

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

40

Figure 22 Distance vs RSSI

From the above graphs following conclusions were made 1 From the histogram plots we can observe that the distribution of RSSI

values follow Gaussians distribution law 2 As we can see from figure 22 that median curve has drastic changes

when compared to mean curve Hence mean is considered while estimating the position

3 From the observations we conclude that the number of samples does not matter since mean curve tends to be same for various numbers of samples

4 Hence 10000 samples are considered in order to get a proper location fingerprint during off‐line phase and during online phase 50 samples are enough to estimate the position of object

53 0ne dimensional test Measurement setup for one dimensional test is as shown in figure 19 This test was conducted to check the performance of algorithm and how accurate it was At first ie during offline phase fingerprints at each interval averaging 10000 samples were taken During online phase Euclidean algorithm was used estimate the position averaging 50 samples of RSSI From the results we

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

41

can realize that the error was less than 2 meters Table 12 shows the result of location estimation in one dimensional case

Table 12 One dimensional position estimation results

54 Two dimensional test This test is the final stage of my thesis work Experimental testbed for this stage is as shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors fixed at the corners of the room and a BS at the centre of the room There are 18 pre‐determined locations where the fingerprints (RSSI) were taken The fingerprints are as shown in Appendix 1 All the readings were taken in presence of userrsquos The algorithm for position estimation is shown in Appendix 2 Position estimation test is performed as follows

Tagged object is placed at random inside the masterrsquos room All the sensors including the base station and object are switched on Using the algorithm (Appendix 2) the BS estimates the position of the object and displays it on the PC

This test was performed using various numbers of samples to test which has a better accuracy Figure 23 represents the error rate using various numbers of samples From figure 23 we can conclude that 50 numbers of samples is enough to estimate the position of object with an accuracy of 3m

55 Causes of error in location determination The randomness of RSS is the major cause of error in any indoor

positioning system that uses the RSS for location inference If there is no randomness in the RSS and its location fingerprint every indoor positioning systems of this type will have no location detection error For example I have recorded RSS at one particular location during different times of the day From figures 24 and 25 you can observe that RSS values are different at different times of day

The performance of indoor positioning systems depends greatly on the separation of location fingerprints A location fingerprint

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

42

corresponding to a location can be identified correctly if it is difficult to classify it as another fingerprint by a pattern classifier

Path loss is one of the basic signal propagation properties of the RSS that best describes attenuation of transmit signal power over a distance between an access point and a mobile device

Presence of Userrsquos or userrsquos orientation affects RSS

Figure 23 Error Rate vs Number of samples

Figure 24 RSS distribution at 214 pm

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

43

Figure 25 RSS distribution at 100pm and 1100 am

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

6 DISCUSSIONS10102006

44

6 DISCUSSIONS 61 Summary and Conclusion While empirical results and performance studies of positioning systems based on location fingerprint have been presented in this report analytical model that can be used as a framework for efficiently designing the positioning systems are not available This thesis design work uses general principles of position estimation such as position estimation of sensors grid spacing etc This design work focuses on accuracy and precision The concept of location fingerprint technique based on RSS us explained in detail in chapter 2 It is found that RSS is random It is possible to approximate the RSS as being normally distributed While the RSS random process is non‐stationary the mean is more or less constant and could be used as the fingerprint of the location Model designed for position estimation is as shown in chapter 4 Accuracy and precision are the two major performance matrices in this study Uniform grid spacing has been used From the experimental analysis I have obtained an accuracy of 3 meters and 35 precision The sensitivity analysis among the system parameters and the performance matrices were done using the model as shown in chapter 4 For instance our study found that the minimum distance between two adjacent positions also affects the positioning performance This is due to the randomness of RSS patterns induced by the ever changing indoor environment

62 Future work The research in this thesis provides a ground work for the future study of efficient design of indoor positioning system The RSSI based position estimation still has limitations First the RSSI value is time dependent Thus the future study should address this issue Second the movement of object is not considered Third orientations of sensors affect the performance which should be taken care off Fourth to improve the performance of the system we need to find a solution to deal with those incomplete or missing data during the off‐line and the on‐line phase Fifth position estimation on multi‐floor and three‐dimensional coordinates is not available

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 110102006

45

APPENDIX 1

Location Fingerprints of Masters Room Time 1315 Observation Time 20 minutes Number of Samples 10000

People Presence SNo Co-ordinates AP1 (0x0001) AP2 (0x0002) AP3 (0x0004) AP4 (0x0008)

1 11 -24 -16 -27 -33 2 13 -26 -8 -34 -31 3 15 -43 -9 -39 -25 4 32 -21 -11 -27 -33 5 61 -6 -23 -32 -28 6 64 -20 -31 -25 -34 7 35 -23 -35 -29 -38 8 37 -29 -35 -22 -27 9 39 -38 -29 -21 -27 10 67 -27 -23 -20 -23 11 611 -26 -29 -23 -30 12 613 -35 -32 -2 -31 13 513 -34 -31 -20 -26 14 11 -39 -22 -29 -17 15 212 -37 -42 -28 -31 16 -212 -38 -33 -29 -6 17 -21 -42 -29 -33 -19 18 -38 -40 -23 -35 -22

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 210102006

46

APPENDIX 2

Begin

Open radio map and raw data files for write

Intialize radio map in memory

Check whether the

Packet is data or Beacon packet If data packet

Save the data to register

Check if data is

Received from all

sensors

Estimate the position of static

Display the position on PC

End Yes

No

A

A

Yes

No

Flow chart representing the main algorithm for position estimation

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

47

APPENDIX 3

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

48

Distribution of RSSI

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

49

REFERENCES [1] T S Rappaport J H Reed and B D Woerner ldquoPosition location using wireless communications on highways of the futureʺ IEEE Commun Mag vol 34 no 10 pp33‐41 Oct 1996 [2] A M Ladd K E Bekris G Marceau A Rudys L E Kavraki and D S Wallach rdquoRobotics‐based location sensing using wireless ethernetʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ02) 2002 pp 227‐238 [3] I Ramani R Bharadwaja and P V Rangan ldquoLocation tracking for media appliances in wireless home networksʺ in Proc International Conference on Multimedia and Expo (ICMEʹ03) July 2003 pp 769‐772 [4] K Pahlavan X Li and J P Makela ldquoIndoor geolocation science and technologyʺ IEEE Commun Mag vol 40 no 2 pp 112‐118 Feb 2002 [5] Konard Lorincz and Matt Welsh ldquoMote Track A Robust Decentralized Approach to RF‐Based Location Trackingrdquo [6] Paramvir Bahl and Venkata N Padmanabham ldquoRADAR An In‐Building RF‐based User Location and Tracking Systemrdquo [7] Kamol Kaemarungsi ldquoDesign of Indoor positioning system based on location fingerprint techiniquerdquo University of Pittsburgh 2005 [8] Fredrik Linnarsson Cheng Peng and Bengt Oelmann ldquoSENTIO A Hardware Platform for Rapid Prototyping of Wireless Sensor Networksrdquo [9] S Saha K Chaudhuri D Sanghi and P Bhagwat ldquoLocation determination of a mobile device using IEEE 80211b access point signalsʺ in Proc IEEE Wireless Communications and Networking Conference (WCNCʹ03) New Orleans LA Mar 2003 pp 1987‐1992 [10] T Roos P Myllymaki H Tirri P Misikangas and J Sievanen ldquoA probabilistic approach to WLAN user location estimationʺ International Journal of Wireless Information Networks vol 9 no 3 pp 155‐164 July 2002 [11] R Battiti M Brunato and A Villani ldquoStatistical learning theory for location fingerprinting in wireless LANrsquosʺ Technical Report Oct 2002 [Online] Available httprtmscienceunitnitraquobattitiarchive86pdf [12] P Prasithsangaree P Krishnamurthy and P K Chrysanthis ldquoAn indoor position location with wireless LANsʺ in Proc IEEE International Symposium on Personal Indoor and Mobile Radio Communications (PIMRCʹ02) Lisbon Portugal Sept 2002 [13] M A Youssef A Agrawala and A U Shankar ldquoWLAN location determination via clustering and probability distributionsʺ in Proc IEEE

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

50

International Conference on Pervasive Computing and Communications (PerComʹ03) Dallas‐Fort Worth TX Mar 23‐26 2003 pp 23‐26 [14] Z Xiang S Song J Chen H Wang J Huang and X Gao ldquoA wireless LAN‐based indoor positioning technologyʺ IBM Journal of Research and Development vol 48 no 56 pp 617‐626 SeptNov 2004 [15] R Want A Hopper V Falcao and J Gibbons ldquoThe active badge location systemʺACM Transactions on Information Systems vol 40 no 1 pp 91‐102 Jan 1992 [16] N B Priyantha A Chakraborty and H Balakrishnan ldquoThe cricket location‐support systemʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ00) Boston MA Aug 2000 pp 32‐43 [17] Holger K Willig A (2005) Protocols and Architectures for Wireless Sensor Networks London [18] IEEE (2003) Part 154 Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low‐Rate Wireless Personal Area Networks (LR‐WPANs) New York [19] Chipcon CC2420 802154 compliant radio httpwwwchipconcom [20] ZigBee Specification v10 ZigBee Alliance [21] Dimitrios Lymberopoulos Quentin Lindsey and Andreas Savvides ldquoAn Empirical Analysis of Radio Signal Strength Variability in IEEE 802154 Networks using Monopole Antennasrdquo [22] Kiran Yedavalli Bhaskar Krishnamachari Sharmila Ravula and Bhaskar Srinivasan ldquoEcolocation A sequence based technique for RF localization in wireless sensor networksrdquo Robert Bosch Research and Technology Centre Palo Alto CA

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

17

synchronization The 8‐bytes preamble contains only zero and is used for synchronization The SFD contains a specific sequence of one and zeros and specifies the beginning of the frame The PHR contains the payload length Packets with a length of 9 or more bytes are MPDU as the next section further explains Packets with length 5 are used for MPDU acknowledgements The payload part of the PPDU encloses the MAC layer packet Finally the PPDU size can be up to 127 bytes [18]

4 bytes 1 byte 1 byte Variable

Preamble SFD Frame length

(7 bits) Reserved

(1 bit) Payload (PSU)

SHR PHR PHY Payload Table 4 Format based on the IEEE 802154 standard [18]

MAC Layer The MAC layer is the interface between the SSCS and the PHY layer Similar to the PHY layer the MAC layer supports two services The MAC data service is responsible for the transmission and reception of the MPDUs through the PHY data service The MAC management service if the device is a coordinator manages the network beacons It is also responsible for PAN association and disassociation frame validation and acknowledgment providing ldquoa reliable link between two peer MAC entitiesrdquo In addition it uses the CSMA‐CA for channel access and handles and maintains the GTS mechanism Finally it supports device security [18] The IEEE 802154 standard defines four different frame types the beacon data acknowledgment and MAC command frame All frame types are based on the general MAC frame format (table 5) The frame control field describes and specifies the above different frame types Every MAC frame comprises a MHR which consists of a frame control sequence number and the information field It also contains the MAC payload different frame types have different MAC payload fields The acknowledgment type does not have a payload Finally each frame includes a MFR which contains a FCS The data in the MPDU follows the same order as the PPDU the least significant bits are left in the frame and are transited first Octets2 1 02 028 02 028 Variable 2

Destination PAN Identifier

Destination Address

Source PAN

Identifier Source Address Frame

Control Sequence Number Addressing Field

Frame Payload FCS

MHR MAC

Payload MFRTable 5 General MAC frame format IEEE 802154 standard [18]

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

18

Table 6 7 8 and 9 present the four different MAC frame types The beacon frame is transmitted periodically by the PAN coordinator It provides information about the network management through the super frame and GTS fields which are analyzed later in the section It also synchronizes the network devices and indicates the proper communication period for them The data frame payload encapsulates data from the higher layers When a device receives a packet it is not obliged to response with an acknowledgement packet Finally the command frame identifier and command payload fields of the command frame are used for communication between the network devices The command identifier specifies actions like association disassociation and data GTS or beacon request Octets2 1 410 2 Variable Variable Variable 2

Frame Control

Sequence Number

Addressing Fields

Super frame Specification

GTS fields

Pending Addressing

Fields Beacon Payload FCS

MHR MAC Payload MFRTable 6 Beacon frame format [18]

Octets 2 1 Variable 2

Frame Control

Sequence Number

Addressing Field

Data Payload FCS

MHR MAC

Payload MFR Table 7 Data frame format [18]

Octets 2 1 2

Frame Control

Sequence Number FCS

MHR MFR Table 8 Acknowledgement frame format [18]

Octets2 1 1 Variable 2

Frame Control

Sequence Number

Addressing Fields

Command Frame Identifier

Command Payload FCS

MHR MAC Payload MFR Table 9 Command frame format [18]

In the LR‐WPAN every PAN has its own coordinator The PAN coordinator manages the communication in the local area it has two options to use or not use the super frame structure The super frame (figure 6) uses network beacons If the coordinator does not want to use a super frame structure it suspends the beacon transmission However the beacon is important for device association and disassociation If the coordinator wishes to maintain close communication control in the PAN and to support low‐latency devices it usually uses the super frame A super frame determines a specific time

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

19

period beacons bound it The beacon is transmitted in the first of the sixteen equal time slots that the super frame has It is used to ldquosynchronize the attached devices to identify the PAN and to describe the structure of the super framerdquo The super frame can have active and inactive periods All the communications have to be finished inside the super frame period In the inactive periods the devices can switch to the sleep mode but they have to be ready for the next beacon The active period is further divided into a CAP and a CFP During the CAP any device can communicate competing with the other devices in the PAN using slotted CSMA‐CA The CFP contains GTSs and follows the CAP A CFP may maintain up to seven GTSs and each GTS can reserve more than one time slot However the CAP should always be sufficient to allow new devices t join the PAN The GTSs are allocated to specific devices All the transactions must be completed inside the assigned time period (CAP GTS and CFP) (IEEE 802154 standard 2003)

Figure 6 Example of super frame structure [18]

In accordance with the 802154 standard (2003) three different types of data‐transfer exist In addition the types differ if the coordinator uses or does not beacons Data transfer from a device to the PAN coordinator is the first type (figure 7) For a ldquonon‐beacon‐enable networkrdquo it first senses the medium by using ldquoun‐slotted CSMA‐CArdquo and then a simple transmit to the data frame In a ldquobeacon‐enabled networkrdquo the sender waits for the beacon when it finds it ldquothe device synchronizes to the super fame structurerdquo In the specified time frame the sender again senses the medium by using ldquoslotted CSMA‐CArdquo and transmits the data to the coordinator In either case the coordinator has the option to acknowledge or not acknowledge the data reception after that the transaction is completed Data transfer from the coordinator is the next type described in the standard (figure 8) In a beacon‐enabled network the coordinator indicates a pending message through the beacon The messagersquos target device receives the beacon and if the message is pending it responses with a MAC command request

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

20

message using slotted CSMA‐CA the coordinator may or may not acknowledge the command message and through slotted CSMA‐CA sends the pending message The device acknowledges the received message After that the coordinator removes the message from the beaconrsquos pending list and completes the transaction In a non‐beacon enable network the device periodically sends a MAC command‐request frame to the coordinator The coordinator acknowledges the data request Then by using un‐slotted CSMA‐CA if it has a pending message it transmits it If it has not it respond with a data frame with a ldquozero‐length payloadrdquo To complete the transaction the device acknowledges the data reception

Figure 7 Communication from a device to a PAN coordinator in (a) a beacon‐enabled network and (b) a non‐beacon enabled network [18]

Peer‐to‐peer is the last type of data transfer In this situation the devices are free to communicate with any other device within their communication range In a peer‐to‐peer PAN the devices can ldquoeither receive constantly or synchronize with each otherrdquo If they are receiving constantly to transmit data they use un‐slotted CSAM‐CA In the second case synchronization must be achieved first (IEEE 802154 standard [IEEE 2003]) The IEEE 802154 (2003) standard establishes MAC and PHY standards for low‐cost low‐power and high‐density node deployments In addition to the above PHY and MAC characteristics IEEE 802154 provides a security baseline including ldquothe ability to maintain an ACL and use symmetric cryptographyrdquo for data encryption The algorithm that is used for encryption is the AES However the higher level layers decide when security is need The upper layers are in general responsible for device authentication and key management The next section introduces the ZigBee standard which

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

21

encapsulates the IEEE 802154 and provides additional standardization for the higher levels

Figure 8 Communication from a PAN coordinator to a device in (a) a beacon‐enabled network and (b) a non‐beacon enabled network [18]

242 ZigBee Heily (2004) defines ZigBee as ldquoa rapidly growing worldwide non‐profit industry consortiumrdquo whose mission is ldquoto define a reliable cost‐effective low‐power wirelessly networked monitoring and control product based on an open global standardrdquo Figure 9 illustrates the areas of interest for different wireless communication standards

Figure 9 Overview of the coverage for different wireless communication

standards [17]

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

22

ZigBee a new standard which became publicly available in June 2005 is based on the IEEE 802154 standard It expands the IEEE 802154 by adding the framework for ldquothe network security and applicationrdquo (Craig 2005) Figure 10 presents the IEEE 802154ZigBee stack and the areas of responsibilities

Figure 10 IEEE 802154ZigBee stack [20]

Craig (2005) mentions three networking topologies that the standard covers the star mesh and cluster tree (figure 11) The ZigBee standard works on top of the IEEE 802154 addressing schema by using the standard 64‐bit and the short 16‐bit addressing Kinney (2005) summarizes the ZigBee network layer responsibilities the successful establishment of a new the network and successful new device configuration addressing assignment network synchronization frames security and message routing ZigBee further distinguishes the concept of the physical devices (RFD FFD) by using the notion of ldquological devicesrdquo ldquoZigBee Coordinatorrdquo is the first type of logical devices It is responsible for initializing maintaining and managing the network Under the coordinator in the network hierarchy is the ldquoZigBee routerrdquo which is responsible for controlling the message routing between the nodes Finally the ldquoZigBee End Devicerdquo acts as the end point of the network

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

23

structure The tasks that an end device can perform are specified in the ldquoApplication Profilesrdquo (Craig 2005)

Figure 11 IEEE 802154ZigBee stack [17]

The ZigBee specifications (2005) summarize the security services provided by ZigBee ldquokey establishment key transport frame protection and device managementrdquo ZigBee builds its security mechanism using symmetric key cryptography The security services also depend on the associated layer thus as the figure 10 shows the security mechanism covers the network and the application layer In addition if a MAC frame needs security protection the MAC layer is able to secure it Moreover the notion of end‐to‐end security is supported the source and destination devices have access and use the same share key In the MAC layer the 802154 AES mechanism provides the proper security The mechanism protects ldquothe confidentiality integrity and authenticity of the MAC framesrdquo (Kinney 2005) An auxiliary header field in front of the MAC payload indicates if the frame is encrypted or not The MAC framesrsquo integrity is supported by calculating and using a MIC at the end of the MAC payload In addition to the AES nonce is used to provide MAC confidentiality and authenticity Figure 12 illustrates a MAC frame with security For different security aspects the MAC layer uses different mode of the AES for the encryption it uses the AES in Counter (CTR) mode and for the integrity the

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

24

CBC‐MAC Finally the combination (CCM) of the above two modes is available providing both encryption and integrity (Kinney 2005)

Figure 12 ZigBee secure frame in MAC layer [17]

In the network layer the CCM (a modified MAC layer CCM mode) is used for encryption Because the network layer uses only the CCM mode a single key is used for all different security options The network layer security message format is similar to the MAC frame it is presented in the figure 13 Finally although the network layer is responsible for securing its layer messages the above layers specify the keys and the CCM option for each frame (Kinney 2005)

Figure 13 ZigBee secure frame in network layer [17]

Security in the application layer works similar to the network and MAC layers It uses the ldquolink keyrdquo or the ldquonetwork keyrdquo to secure the message and then encapsulate it inside a set of fields similar to the network format (figure 14) Other security responsibilities that the application layer has are to provide the ZDO and the applications with device management services key establishment and key transport (ZigBee specifications 2005)

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

25

Figure 14 ZigBee secure frame in application layer [17]

The ZigBee application layer contains the manufacturer‐defined application objects the ZDO and the application sub‐layer In addition to the security responsibilities the application sub‐layer binds devices based on their duties and needs maintains the binding tables and forwards messages between them The application sub‐layer also discovers the neighbour devices for a given device The ZDO is responsible for determining the devicersquos duty in the network for communicating using binding requests and for supporting security as was mentioned above The sub‐layer that implements the actual application is the manufacturer‐defined application object (Kinney 2005) The ZigBee specifications are a great step toward the wireless sensors networks standardization They cover all the OSI layers from the physical to the application providing guidance to the developers The specifications are the product of the ZigBee alliances a consortium of companies and academic institutes Thus they not only produce the specifications but also are the first use them The next section goes beyond the layer architecture and standardization and introduces the OS field another crucial element of WSNs

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

26

3 RECEIVED SIGNAL STRENGTH (RSS) Majority of the existing methods leverage the existence of IEEE 80211 base stations with powerful radio transmit powers of approximately 100mW per base station Such radios are in a different class from the low power IEEE 802154 compliant radios that typically transmit at low power levels ranging from 52mW to 29mW [19] The wide availability of larger number of IEEE 802154 radios in the research community has revived the interest for signal strength based localization in sensor network Despite of rapidly increasing popularity of IEEE 802154 radios and signal strength localization there is a lack of detailed characterization of the fundamental factors contributing to large signal strength variation This chapter investigates some of the properties of RSS observed during testing and analysis of research work collected during the period of thesis The analysis of RSS values is needed to understand the underlying features of location dependent RSS patterns and location fingerprints An understanding of the properties of the RSS values for location fingerprinting can assist in improving the design of positioning algorithms and in deployment of indoor positioning systems

31 Parameters that affect RSS Different factors that affect the indoor RSS are the userrsquos presenceabsence hisher orientation time of the day building type and material distance from transmitter and type of radio These factors are by no means exhaustive Other possible factors are antenna orientation directionality and type Table 10 classify the parameters which affect RSS [7]

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

27

Effect on Factors Options

1 Proximity of user Userrsquos presence or absence

2 Orientation of user and terminal

North East South West

Data Collection

3 Make of Radio 4 Time of measurement Time of day amp days of

week 5 Period of measurement

Second minute hour

7 Interference Co‐channel adjacent co‐channel

Statistics

8 Building environment Small offices or large hall

Table 10 Classification of factors the affect RSS [7]

Transmitter variability Different transmitters behave differently even when they are configured exactly in the same way In practice this means that when a transmitter is configured to send packets at a power level of d dBm then the transmitter will send these packets at a power level that is very close to d dBm but not necessarily exactly equal to d dBm This can alter the received signal strength indication and thus it can lead to inaccurate distance estimation

Receiver variability The sensitivity of the receivers across different radio chips is different In practice this means that the RSSI value recorded at different receivers can be different even when all the other parameters that affect the received signal strength are kept constant

Antenna orientation Each antenna has its own radiation pattern that is not uniform In practice this means that the RSSI value recorded at the receiver for a given pair of communicating nodes and for a given distance between them varies as the pair wise antenna orientations of the transmitter and the receiver are changed

Multi‐path fading and shadowing in the RF channel In indoor environments the transmitted signals get reflected after hitting on the walls andor on other objects in the room such as furniture Both the original signal and the reflected signal reach the receiver almost at the same time since they both travel at the speed of light As a result of this the receiver is not able to

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

28

distinguish the two signals and it measures the received signal strength for both of them

32 Related work analysis A number of indoor location tracking systems have been proposed in the literature based on RF signals ultrasound infrared and some combinations of these As mentioned previously RF based positioning system are widely studied as shown in Table 2 Given a model of radio signal propagation in a building or other environment RSS can be used to estimate the distance between the transmitter and receiver Most of the algorithms (Table 2) have been evaluated in practice using 80211b compliant wireless radios and seem to produce a location error of 15ft to 20ft for an indoor network area of approximately 225ft x 144ft According to the results of this study all the localization techniques produce approximately the same location error over a range of environments Ecolocation [22] Mote Track [5] are two of the few localization algorithms that have been evaluated on a real sensor network that uses a low power wireless radio Ecolocation reports a location error of 10ft for a very small outdoor network deployment area (26ft x 49ft) Mote Track reports a location error of approximately 13ft for an indoor network deployment area of 18751ft2 In general the RSSI based localization algorithms can be divided into two broad categories the map based and the distance (or area) prediction based algorithms In the case of the map based algorithms the deployment area is divided into a large set of sample points that cover the whole area Most techniques collect one or more signal strength measurements from all visible beacon nodes and for each sample point Each sample point is then mapped to either a signal strength vector or a signal strength probability distribution This process generates an RSSI fingerprint map which is unique with respect to the beacon locations and the deployment area This fingerprint map is stored to a central base station A node that is not aware of its location collects one or more signal strength measurements from all the visible beacon nodes and creates its own RSSI fingerprint that is sent to the central base station The base station computes nodersquos location as the sample pointrsquos location whose RSSI fingerprint distance from the unknown nodersquos RSSI fingerprint is the minimum among all the sample points Despite the fact that the map based RSSI localization methods produce relatively small location errors they are not attractive mainly because they require an excessive amount of profiling data and because they are not fully distributed The detailed profiling of a building is a major holdback because it is complex time consuming and depends on beacon locations Therefore for different buildings or even for the

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

29

same building and different beacon locations different RSSI maps have to be obtained The second category of RSSI based algorithms are the distance (or area) prediction algorithms which focus on minimizing the amount of profiling data In distance prediction based methods raw RSSI data are directly mapped to distances through a signal propagation model In some cases distances are not explicitly measured but they are taken into consideration through assumptions on the underlying distributions In the case of outdoor deployments where there are no obstacles a single signal propagation model is all the profiling data required In the case of indoor deployments the same applies but now the signal propagation model might be different across different buildings with different furniture and wall arrangements Given an underlying signal propagation model raw RSSI data can be mapped to specific distances These distances can be either used directly in order to perform localization or assigned a probability and used in a learning basedprobabilistic localization algorithm Obviously the accuracy of the distance prediction based methods depends heavily on the accuracy of the signal propagation model used for translating RSSI values to distances

33 Properties of RSSI Changes in signal due to propagation indoors are difficult to predict because of dense environment and propagation effects such as reflection diffraction and scattering The multi‐path fading effect which is the result of either constructive or destructive combination of multiple signal copies at the receiver causes the received signal to fluctuate around a mean value at a particular location The received signal is usually modelled by the combined effects of large‐scale fading and small‐scale fading The large‐scale fading component describes the signal attenuation as the signal travels over a distance and is absorbed by material such as walls and floors along the way to the receiver This component predicts the mean of the RSS and usually has a lognormal distribution On the other hand the small scale fading component explains the dramatic fluctuation of the signal due to multi‐path fading On the other hand the small scale‐fading component explains the dramatic fluctuation of the signal due to multi‐path fading If there is no LOS component the small‐scale‐fading is often modelled with a Rayleigh distribution Such scenarios are commonly called NLOS If there is a LOS component the small‐scale‐fading is typically modelled by a Rician distribution Note that when the RSS is measured the measurement averages out the small‐scale‐fading effects

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

30

4 OBJECT TRACKING 41 Introduction This chapter presents the object‐tracking application the main objective of this thesis work The application is an algorithmic‐software implementation written in Embedded C which is capable of receiving data messages and generating object position estimation data The implementation of the object‐tracking application makes use of the SENTIO platform and uses the background knowledge introduced in the preceding chapters The motivation for the object‐tracking application is to determine the position of an object in indoor environments (Eg Home) The application acts as an interface which collects the raw data that the sensors generate (numerical values) It makes the proper comparisons and algorithmic processes to identify and track the object while it is static inside the area covered by the wireless sensor network Finally with the detection and tracking data it alerts the user or another system The following sections provide an overview of the object‐tracking application

42 Overview of hardware product Before describing the object tracking application it is important to know the characteristics of hardware product The SENTIO platform (figure 15) is built in modular fashion and has the following key features [8]

Flexibility This platform is very flexible in such a way that it is made easy to add or change the hardware for each sensor task depending on the requirement in the WSN For a specific platform this can be hard or sometimes even impossible to accomplish

Sensor resources Resources in a sensor module includes the ability of usage of controllerrsquos resources as much as possible such as ADC‐channels interrupts and digital Input‐Outputrsquos Local voltage regulation should also be possible since many sensors require higher supply voltages than digital Integrated Circuits do Further support of a shut‐down mechanism to power down the sensor modules when not needed

Computation and memory The processor module based on microcontroller should support many devices in a microcontroller family and support different power supply voltages enabling full range of clock frequencies All power‐down modes of the devices should be

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

31

supported The platform should support different processor modules including FPGArsquos and very simple microcontrollers

Communication The focus should be on enabling an IEEE 802154 compliant communication interface With or without ZigBee stack this will provide a flexible platform for exploring different network topologies Support for power‐down of the entire communication module should be supported

Cost and form factor High flexibility is in conflict with low cost and small size The primary objective should be flexibility but the size should be considered in order to enable sensor device integration in systems where space is limited

Figure 15 SENTIO Platform [8]

The communication subsystem of the SENTIO sensor node was designed so that the correct operation of the radio chip is ensured The radio chip is powered by the on‐board voltage regulator and thus fluctuations in the battery voltage level do not affect the operation of the communication subsystem This platform uses Chipcon CC2420 IEEE 802154 radio transceiver operates in the 24GHz ISM band and includes a digital direct sequence spread spectrum (DSSS) modem providing a spreading gain of 9dB and an effective data rate of 250Kbps It was specifically designed for low power wireless applications and supports 8 discrete power levels 0dBm minus1dBm minus3dBm minus5dBm minus7dBm minus10dBm minus15dBm and minus25dBm at which its power consumption varies from 29mW to 52mW [21] A built‐in received signal strength indicator gives an 8‐bit digital value RSSIVAL The RSSIVAL is always averaged over 8 symbol periods (128μs) and a status bit indicates when the RSSIVAL is valid (meaning that the receiver was enabled for at least 8 symbol periods) The

Antenna

Communication Module

Processing Module

Sensing Module

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

32

power P at the RF pins can be obtained directly from RSSIVAL using the following equation

P = RSSIVAL + RSSIOFFSET [dBm] where the RSSIOFFSET is found empirically from the front‐end gain and it is approximately equal to minus45dBm In the next sections when we refer to the RSSI value we refer to the RSSIVAL and not the power P unless otherwise stated

43 Experimental Testbed The experimental test bed for object tracking application is as shown in figure 16 Experiment was conducted in Masterrsquos room at Mid Sweden University A DELL laptop is connected to SENTIO platform and software were primarily used to collect samples of RSS from APrsquos The software monitors IEEE 802154 radio frequency channels which operate in the 24GHz band

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

1030

BC - 1030

AP 3 AP 1

AP 2

AP 4

BS

HEWLETT

PACKARD

Figure 16 Layout of Masterʹs Room

A three dimensional scalable testbed is used for indoor sensor network deployments The testbed illustrated in figure 16 is a 3‐D structure measuring 16m (L) x 12m (W) x 3m (H) and it is designed to host a large number of static nodes as shown in figure 16 Arrow marks in the above figure represent the location where fingerprints were taken The centrepiece of our infrastructure

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

33

is the SENTIO sensor node [8] an open‐source general purpose sensing platform designed with AT Mega 128 microprocessor and the IEEE 802154 compliant CC2420 radio from Chipcon [19]

44 Obtaining data from the sensor network The next important step in the development of the object tracking application is to obtain the data from the sensor network and send it to the base station From there the data is transmitted through a serial port to the computer that runs the object tracking application When the data arrives at the serial port the application must capture it This paragraph gives a brief explanation of how and what kind data is transmitted by the static object fixed sensors and how this data is being used to estimate the position of the object First the static object transmits Beacon packet continuously which is received by all the fixed APrsquos The beacon packet format is as shown in table 6 Then the fixed APrsquos calculates the 8‐bit value of RSSI from the transmitted beacon packet and then sends the resultant RSSI value as a form of data packet (table 7) to the BS which calculates the position and sends back to the object

45 Object tracking application Testing and Evaluation The purpose of this section is to discuss and evaluate the object tracking application Finally we are ready with the experimental testbed as shown in figure 16 As shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors placed at the corners of the masterrsquos room BS is placed at the centre of the room connected to a PC All the nodes are power connected before an actual measurement takes place At first the object is placed at random position inside the room and transmits beacon packets continuously Then these packets are received by the fixed APrsquos which calculate the RSSI from the packet and then averages it until it receives 50 beacon packets and then transmits the data to the BS (figure 17) Then the BS waits until it receives the data from all the APrsquos Once it receives from all the APrsquos it calculates the position of the object (figure 18) The application was developed to demonstrate a real world use of a WSN in object tracking In evaluating the final position obtained with respect to the actual position we conclude that the object‐tracking was successful Although

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

34

it is a research prototype object tracking application is a simple stable stand‐alone system capable of demonstrating an important application of WSN It takes raw data from the WSN aggregates it evaluates it and based on the evaluation tracks the object The accuracy of estimating the position is less than 5m Table 11 below shows a log of readings taken during test

Start

Intialize micro‐controller and transeciever in receieve mode

If received_packet=

Beacon

Average RSSI values

Increase the counter

If countergt1000050

A

No

Yes

B

B

Yes

No

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

35

A

Reset counter

Set transceiever in transmit mode

Transmit averaged RSSI to Main sensor and set it back to receive mode

10000 samples ‐ Offline

50 samples ‐ Online

Figure 17 Flow chart representing the algorithm of fixed sensors

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

36

Start

Intialize micro‐controller and transeciever in receieve mode

Received RSSI values from all fixed Sensors

Open radio map

Calculate Euclidean distance between obtained RSSI values from fixed sensors and fingerprints

in radio map

No

Yes

Bubble sort for least error in signal strength values

A

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

37

A

Get the location of the object from the radiousing the least error value

Display location of object on PC

End

Figure 18 Flow chart representing the algorithm of main sensor

Table 11 Accuracy of positioning system

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

38

5 RESULTS This chapter presents how the system parameters such as number of samples mean or median etchellipare finalized All the tests are performed in masterrsquos room at Mid Sweden University This chapter is divided into three subsections First in Section 51 it reviews results obtained from samples test Section 52 discusses one dimensional test results and finally section 53 discusses results of two dimensional tests

51 Measurement setup A Dell laptop was connected to a SENTIO platform and is loaded with software (Appendix 2) This setup was used to determine number of samples required during object tracking mean or median position estimation algorithm and one dimensional test The setup is as follows

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

103

0

BC - 1030

AP 1AP 2

BS

HEWLETT

PACKARD

Figure 19 Test bed for one dimensional test in Masters Room

Two SENTIO nodes (AP1 and AP2 as shown above) were separated by a distance of 10 meters and these sensors are known as fixed sensors which are fixed to the ceiling such that there antenna is faced towards the ground They are linear is position A BS was connected to Dell laptop and it was placed in such a way that its antenna was faced towards the ceiling Another SENTIO

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

39

node was used as an object and its orientation was same as BS Figure 19 represents the complete setup for object tracking application Mobile node was moved between these fixed ones at regular intervals of one meter and RSSI values were logged at each interval

52 Number of samples test This test was performed to determine number of samples required for accurate positioning and either mean of the obtained RSSI values or median of them Figure 19 represents the setup for number of samples test Once the setup is ready the measurements are taken as follows

1 An object was tagged with SENTIO node and was placed stationary linear to the fixed sensors

2 10000 samples (RSSI) were taken at regular intervals of 1 meter up to 10 meters

3 The sample values were logged at every interval 4 Using these logged samples I have plotted graphs such as distribution

of RSS values (figure 20 21) meanmedian at intervals of 1 meter using MATLAB

5 The mean and median for various numbers of samples is as shown in figure 22

Figure 20 Histogram plot of RSSI values at 1m and 4m

Figure 21 Histogram plot of RSSI values at 6m and 8m

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

40

Figure 22 Distance vs RSSI

From the above graphs following conclusions were made 1 From the histogram plots we can observe that the distribution of RSSI

values follow Gaussians distribution law 2 As we can see from figure 22 that median curve has drastic changes

when compared to mean curve Hence mean is considered while estimating the position

3 From the observations we conclude that the number of samples does not matter since mean curve tends to be same for various numbers of samples

4 Hence 10000 samples are considered in order to get a proper location fingerprint during off‐line phase and during online phase 50 samples are enough to estimate the position of object

53 0ne dimensional test Measurement setup for one dimensional test is as shown in figure 19 This test was conducted to check the performance of algorithm and how accurate it was At first ie during offline phase fingerprints at each interval averaging 10000 samples were taken During online phase Euclidean algorithm was used estimate the position averaging 50 samples of RSSI From the results we

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

41

can realize that the error was less than 2 meters Table 12 shows the result of location estimation in one dimensional case

Table 12 One dimensional position estimation results

54 Two dimensional test This test is the final stage of my thesis work Experimental testbed for this stage is as shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors fixed at the corners of the room and a BS at the centre of the room There are 18 pre‐determined locations where the fingerprints (RSSI) were taken The fingerprints are as shown in Appendix 1 All the readings were taken in presence of userrsquos The algorithm for position estimation is shown in Appendix 2 Position estimation test is performed as follows

Tagged object is placed at random inside the masterrsquos room All the sensors including the base station and object are switched on Using the algorithm (Appendix 2) the BS estimates the position of the object and displays it on the PC

This test was performed using various numbers of samples to test which has a better accuracy Figure 23 represents the error rate using various numbers of samples From figure 23 we can conclude that 50 numbers of samples is enough to estimate the position of object with an accuracy of 3m

55 Causes of error in location determination The randomness of RSS is the major cause of error in any indoor

positioning system that uses the RSS for location inference If there is no randomness in the RSS and its location fingerprint every indoor positioning systems of this type will have no location detection error For example I have recorded RSS at one particular location during different times of the day From figures 24 and 25 you can observe that RSS values are different at different times of day

The performance of indoor positioning systems depends greatly on the separation of location fingerprints A location fingerprint

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

42

corresponding to a location can be identified correctly if it is difficult to classify it as another fingerprint by a pattern classifier

Path loss is one of the basic signal propagation properties of the RSS that best describes attenuation of transmit signal power over a distance between an access point and a mobile device

Presence of Userrsquos or userrsquos orientation affects RSS

Figure 23 Error Rate vs Number of samples

Figure 24 RSS distribution at 214 pm

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

43

Figure 25 RSS distribution at 100pm and 1100 am

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

6 DISCUSSIONS10102006

44

6 DISCUSSIONS 61 Summary and Conclusion While empirical results and performance studies of positioning systems based on location fingerprint have been presented in this report analytical model that can be used as a framework for efficiently designing the positioning systems are not available This thesis design work uses general principles of position estimation such as position estimation of sensors grid spacing etc This design work focuses on accuracy and precision The concept of location fingerprint technique based on RSS us explained in detail in chapter 2 It is found that RSS is random It is possible to approximate the RSS as being normally distributed While the RSS random process is non‐stationary the mean is more or less constant and could be used as the fingerprint of the location Model designed for position estimation is as shown in chapter 4 Accuracy and precision are the two major performance matrices in this study Uniform grid spacing has been used From the experimental analysis I have obtained an accuracy of 3 meters and 35 precision The sensitivity analysis among the system parameters and the performance matrices were done using the model as shown in chapter 4 For instance our study found that the minimum distance between two adjacent positions also affects the positioning performance This is due to the randomness of RSS patterns induced by the ever changing indoor environment

62 Future work The research in this thesis provides a ground work for the future study of efficient design of indoor positioning system The RSSI based position estimation still has limitations First the RSSI value is time dependent Thus the future study should address this issue Second the movement of object is not considered Third orientations of sensors affect the performance which should be taken care off Fourth to improve the performance of the system we need to find a solution to deal with those incomplete or missing data during the off‐line and the on‐line phase Fifth position estimation on multi‐floor and three‐dimensional coordinates is not available

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 110102006

45

APPENDIX 1

Location Fingerprints of Masters Room Time 1315 Observation Time 20 minutes Number of Samples 10000

People Presence SNo Co-ordinates AP1 (0x0001) AP2 (0x0002) AP3 (0x0004) AP4 (0x0008)

1 11 -24 -16 -27 -33 2 13 -26 -8 -34 -31 3 15 -43 -9 -39 -25 4 32 -21 -11 -27 -33 5 61 -6 -23 -32 -28 6 64 -20 -31 -25 -34 7 35 -23 -35 -29 -38 8 37 -29 -35 -22 -27 9 39 -38 -29 -21 -27 10 67 -27 -23 -20 -23 11 611 -26 -29 -23 -30 12 613 -35 -32 -2 -31 13 513 -34 -31 -20 -26 14 11 -39 -22 -29 -17 15 212 -37 -42 -28 -31 16 -212 -38 -33 -29 -6 17 -21 -42 -29 -33 -19 18 -38 -40 -23 -35 -22

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 210102006

46

APPENDIX 2

Begin

Open radio map and raw data files for write

Intialize radio map in memory

Check whether the

Packet is data or Beacon packet If data packet

Save the data to register

Check if data is

Received from all

sensors

Estimate the position of static

Display the position on PC

End Yes

No

A

A

Yes

No

Flow chart representing the main algorithm for position estimation

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

47

APPENDIX 3

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

48

Distribution of RSSI

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

49

REFERENCES [1] T S Rappaport J H Reed and B D Woerner ldquoPosition location using wireless communications on highways of the futureʺ IEEE Commun Mag vol 34 no 10 pp33‐41 Oct 1996 [2] A M Ladd K E Bekris G Marceau A Rudys L E Kavraki and D S Wallach rdquoRobotics‐based location sensing using wireless ethernetʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ02) 2002 pp 227‐238 [3] I Ramani R Bharadwaja and P V Rangan ldquoLocation tracking for media appliances in wireless home networksʺ in Proc International Conference on Multimedia and Expo (ICMEʹ03) July 2003 pp 769‐772 [4] K Pahlavan X Li and J P Makela ldquoIndoor geolocation science and technologyʺ IEEE Commun Mag vol 40 no 2 pp 112‐118 Feb 2002 [5] Konard Lorincz and Matt Welsh ldquoMote Track A Robust Decentralized Approach to RF‐Based Location Trackingrdquo [6] Paramvir Bahl and Venkata N Padmanabham ldquoRADAR An In‐Building RF‐based User Location and Tracking Systemrdquo [7] Kamol Kaemarungsi ldquoDesign of Indoor positioning system based on location fingerprint techiniquerdquo University of Pittsburgh 2005 [8] Fredrik Linnarsson Cheng Peng and Bengt Oelmann ldquoSENTIO A Hardware Platform for Rapid Prototyping of Wireless Sensor Networksrdquo [9] S Saha K Chaudhuri D Sanghi and P Bhagwat ldquoLocation determination of a mobile device using IEEE 80211b access point signalsʺ in Proc IEEE Wireless Communications and Networking Conference (WCNCʹ03) New Orleans LA Mar 2003 pp 1987‐1992 [10] T Roos P Myllymaki H Tirri P Misikangas and J Sievanen ldquoA probabilistic approach to WLAN user location estimationʺ International Journal of Wireless Information Networks vol 9 no 3 pp 155‐164 July 2002 [11] R Battiti M Brunato and A Villani ldquoStatistical learning theory for location fingerprinting in wireless LANrsquosʺ Technical Report Oct 2002 [Online] Available httprtmscienceunitnitraquobattitiarchive86pdf [12] P Prasithsangaree P Krishnamurthy and P K Chrysanthis ldquoAn indoor position location with wireless LANsʺ in Proc IEEE International Symposium on Personal Indoor and Mobile Radio Communications (PIMRCʹ02) Lisbon Portugal Sept 2002 [13] M A Youssef A Agrawala and A U Shankar ldquoWLAN location determination via clustering and probability distributionsʺ in Proc IEEE

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

50

International Conference on Pervasive Computing and Communications (PerComʹ03) Dallas‐Fort Worth TX Mar 23‐26 2003 pp 23‐26 [14] Z Xiang S Song J Chen H Wang J Huang and X Gao ldquoA wireless LAN‐based indoor positioning technologyʺ IBM Journal of Research and Development vol 48 no 56 pp 617‐626 SeptNov 2004 [15] R Want A Hopper V Falcao and J Gibbons ldquoThe active badge location systemʺACM Transactions on Information Systems vol 40 no 1 pp 91‐102 Jan 1992 [16] N B Priyantha A Chakraborty and H Balakrishnan ldquoThe cricket location‐support systemʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ00) Boston MA Aug 2000 pp 32‐43 [17] Holger K Willig A (2005) Protocols and Architectures for Wireless Sensor Networks London [18] IEEE (2003) Part 154 Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low‐Rate Wireless Personal Area Networks (LR‐WPANs) New York [19] Chipcon CC2420 802154 compliant radio httpwwwchipconcom [20] ZigBee Specification v10 ZigBee Alliance [21] Dimitrios Lymberopoulos Quentin Lindsey and Andreas Savvides ldquoAn Empirical Analysis of Radio Signal Strength Variability in IEEE 802154 Networks using Monopole Antennasrdquo [22] Kiran Yedavalli Bhaskar Krishnamachari Sharmila Ravula and Bhaskar Srinivasan ldquoEcolocation A sequence based technique for RF localization in wireless sensor networksrdquo Robert Bosch Research and Technology Centre Palo Alto CA

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

18

Table 6 7 8 and 9 present the four different MAC frame types The beacon frame is transmitted periodically by the PAN coordinator It provides information about the network management through the super frame and GTS fields which are analyzed later in the section It also synchronizes the network devices and indicates the proper communication period for them The data frame payload encapsulates data from the higher layers When a device receives a packet it is not obliged to response with an acknowledgement packet Finally the command frame identifier and command payload fields of the command frame are used for communication between the network devices The command identifier specifies actions like association disassociation and data GTS or beacon request Octets2 1 410 2 Variable Variable Variable 2

Frame Control

Sequence Number

Addressing Fields

Super frame Specification

GTS fields

Pending Addressing

Fields Beacon Payload FCS

MHR MAC Payload MFRTable 6 Beacon frame format [18]

Octets 2 1 Variable 2

Frame Control

Sequence Number

Addressing Field

Data Payload FCS

MHR MAC

Payload MFR Table 7 Data frame format [18]

Octets 2 1 2

Frame Control

Sequence Number FCS

MHR MFR Table 8 Acknowledgement frame format [18]

Octets2 1 1 Variable 2

Frame Control

Sequence Number

Addressing Fields

Command Frame Identifier

Command Payload FCS

MHR MAC Payload MFR Table 9 Command frame format [18]

In the LR‐WPAN every PAN has its own coordinator The PAN coordinator manages the communication in the local area it has two options to use or not use the super frame structure The super frame (figure 6) uses network beacons If the coordinator does not want to use a super frame structure it suspends the beacon transmission However the beacon is important for device association and disassociation If the coordinator wishes to maintain close communication control in the PAN and to support low‐latency devices it usually uses the super frame A super frame determines a specific time

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

19

period beacons bound it The beacon is transmitted in the first of the sixteen equal time slots that the super frame has It is used to ldquosynchronize the attached devices to identify the PAN and to describe the structure of the super framerdquo The super frame can have active and inactive periods All the communications have to be finished inside the super frame period In the inactive periods the devices can switch to the sleep mode but they have to be ready for the next beacon The active period is further divided into a CAP and a CFP During the CAP any device can communicate competing with the other devices in the PAN using slotted CSMA‐CA The CFP contains GTSs and follows the CAP A CFP may maintain up to seven GTSs and each GTS can reserve more than one time slot However the CAP should always be sufficient to allow new devices t join the PAN The GTSs are allocated to specific devices All the transactions must be completed inside the assigned time period (CAP GTS and CFP) (IEEE 802154 standard 2003)

Figure 6 Example of super frame structure [18]

In accordance with the 802154 standard (2003) three different types of data‐transfer exist In addition the types differ if the coordinator uses or does not beacons Data transfer from a device to the PAN coordinator is the first type (figure 7) For a ldquonon‐beacon‐enable networkrdquo it first senses the medium by using ldquoun‐slotted CSMA‐CArdquo and then a simple transmit to the data frame In a ldquobeacon‐enabled networkrdquo the sender waits for the beacon when it finds it ldquothe device synchronizes to the super fame structurerdquo In the specified time frame the sender again senses the medium by using ldquoslotted CSMA‐CArdquo and transmits the data to the coordinator In either case the coordinator has the option to acknowledge or not acknowledge the data reception after that the transaction is completed Data transfer from the coordinator is the next type described in the standard (figure 8) In a beacon‐enabled network the coordinator indicates a pending message through the beacon The messagersquos target device receives the beacon and if the message is pending it responses with a MAC command request

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

20

message using slotted CSMA‐CA the coordinator may or may not acknowledge the command message and through slotted CSMA‐CA sends the pending message The device acknowledges the received message After that the coordinator removes the message from the beaconrsquos pending list and completes the transaction In a non‐beacon enable network the device periodically sends a MAC command‐request frame to the coordinator The coordinator acknowledges the data request Then by using un‐slotted CSMA‐CA if it has a pending message it transmits it If it has not it respond with a data frame with a ldquozero‐length payloadrdquo To complete the transaction the device acknowledges the data reception

Figure 7 Communication from a device to a PAN coordinator in (a) a beacon‐enabled network and (b) a non‐beacon enabled network [18]

Peer‐to‐peer is the last type of data transfer In this situation the devices are free to communicate with any other device within their communication range In a peer‐to‐peer PAN the devices can ldquoeither receive constantly or synchronize with each otherrdquo If they are receiving constantly to transmit data they use un‐slotted CSAM‐CA In the second case synchronization must be achieved first (IEEE 802154 standard [IEEE 2003]) The IEEE 802154 (2003) standard establishes MAC and PHY standards for low‐cost low‐power and high‐density node deployments In addition to the above PHY and MAC characteristics IEEE 802154 provides a security baseline including ldquothe ability to maintain an ACL and use symmetric cryptographyrdquo for data encryption The algorithm that is used for encryption is the AES However the higher level layers decide when security is need The upper layers are in general responsible for device authentication and key management The next section introduces the ZigBee standard which

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

21

encapsulates the IEEE 802154 and provides additional standardization for the higher levels

Figure 8 Communication from a PAN coordinator to a device in (a) a beacon‐enabled network and (b) a non‐beacon enabled network [18]

242 ZigBee Heily (2004) defines ZigBee as ldquoa rapidly growing worldwide non‐profit industry consortiumrdquo whose mission is ldquoto define a reliable cost‐effective low‐power wirelessly networked monitoring and control product based on an open global standardrdquo Figure 9 illustrates the areas of interest for different wireless communication standards

Figure 9 Overview of the coverage for different wireless communication

standards [17]

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

22

ZigBee a new standard which became publicly available in June 2005 is based on the IEEE 802154 standard It expands the IEEE 802154 by adding the framework for ldquothe network security and applicationrdquo (Craig 2005) Figure 10 presents the IEEE 802154ZigBee stack and the areas of responsibilities

Figure 10 IEEE 802154ZigBee stack [20]

Craig (2005) mentions three networking topologies that the standard covers the star mesh and cluster tree (figure 11) The ZigBee standard works on top of the IEEE 802154 addressing schema by using the standard 64‐bit and the short 16‐bit addressing Kinney (2005) summarizes the ZigBee network layer responsibilities the successful establishment of a new the network and successful new device configuration addressing assignment network synchronization frames security and message routing ZigBee further distinguishes the concept of the physical devices (RFD FFD) by using the notion of ldquological devicesrdquo ldquoZigBee Coordinatorrdquo is the first type of logical devices It is responsible for initializing maintaining and managing the network Under the coordinator in the network hierarchy is the ldquoZigBee routerrdquo which is responsible for controlling the message routing between the nodes Finally the ldquoZigBee End Devicerdquo acts as the end point of the network

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

23

structure The tasks that an end device can perform are specified in the ldquoApplication Profilesrdquo (Craig 2005)

Figure 11 IEEE 802154ZigBee stack [17]

The ZigBee specifications (2005) summarize the security services provided by ZigBee ldquokey establishment key transport frame protection and device managementrdquo ZigBee builds its security mechanism using symmetric key cryptography The security services also depend on the associated layer thus as the figure 10 shows the security mechanism covers the network and the application layer In addition if a MAC frame needs security protection the MAC layer is able to secure it Moreover the notion of end‐to‐end security is supported the source and destination devices have access and use the same share key In the MAC layer the 802154 AES mechanism provides the proper security The mechanism protects ldquothe confidentiality integrity and authenticity of the MAC framesrdquo (Kinney 2005) An auxiliary header field in front of the MAC payload indicates if the frame is encrypted or not The MAC framesrsquo integrity is supported by calculating and using a MIC at the end of the MAC payload In addition to the AES nonce is used to provide MAC confidentiality and authenticity Figure 12 illustrates a MAC frame with security For different security aspects the MAC layer uses different mode of the AES for the encryption it uses the AES in Counter (CTR) mode and for the integrity the

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

24

CBC‐MAC Finally the combination (CCM) of the above two modes is available providing both encryption and integrity (Kinney 2005)

Figure 12 ZigBee secure frame in MAC layer [17]

In the network layer the CCM (a modified MAC layer CCM mode) is used for encryption Because the network layer uses only the CCM mode a single key is used for all different security options The network layer security message format is similar to the MAC frame it is presented in the figure 13 Finally although the network layer is responsible for securing its layer messages the above layers specify the keys and the CCM option for each frame (Kinney 2005)

Figure 13 ZigBee secure frame in network layer [17]

Security in the application layer works similar to the network and MAC layers It uses the ldquolink keyrdquo or the ldquonetwork keyrdquo to secure the message and then encapsulate it inside a set of fields similar to the network format (figure 14) Other security responsibilities that the application layer has are to provide the ZDO and the applications with device management services key establishment and key transport (ZigBee specifications 2005)

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

25

Figure 14 ZigBee secure frame in application layer [17]

The ZigBee application layer contains the manufacturer‐defined application objects the ZDO and the application sub‐layer In addition to the security responsibilities the application sub‐layer binds devices based on their duties and needs maintains the binding tables and forwards messages between them The application sub‐layer also discovers the neighbour devices for a given device The ZDO is responsible for determining the devicersquos duty in the network for communicating using binding requests and for supporting security as was mentioned above The sub‐layer that implements the actual application is the manufacturer‐defined application object (Kinney 2005) The ZigBee specifications are a great step toward the wireless sensors networks standardization They cover all the OSI layers from the physical to the application providing guidance to the developers The specifications are the product of the ZigBee alliances a consortium of companies and academic institutes Thus they not only produce the specifications but also are the first use them The next section goes beyond the layer architecture and standardization and introduces the OS field another crucial element of WSNs

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

26

3 RECEIVED SIGNAL STRENGTH (RSS) Majority of the existing methods leverage the existence of IEEE 80211 base stations with powerful radio transmit powers of approximately 100mW per base station Such radios are in a different class from the low power IEEE 802154 compliant radios that typically transmit at low power levels ranging from 52mW to 29mW [19] The wide availability of larger number of IEEE 802154 radios in the research community has revived the interest for signal strength based localization in sensor network Despite of rapidly increasing popularity of IEEE 802154 radios and signal strength localization there is a lack of detailed characterization of the fundamental factors contributing to large signal strength variation This chapter investigates some of the properties of RSS observed during testing and analysis of research work collected during the period of thesis The analysis of RSS values is needed to understand the underlying features of location dependent RSS patterns and location fingerprints An understanding of the properties of the RSS values for location fingerprinting can assist in improving the design of positioning algorithms and in deployment of indoor positioning systems

31 Parameters that affect RSS Different factors that affect the indoor RSS are the userrsquos presenceabsence hisher orientation time of the day building type and material distance from transmitter and type of radio These factors are by no means exhaustive Other possible factors are antenna orientation directionality and type Table 10 classify the parameters which affect RSS [7]

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

27

Effect on Factors Options

1 Proximity of user Userrsquos presence or absence

2 Orientation of user and terminal

North East South West

Data Collection

3 Make of Radio 4 Time of measurement Time of day amp days of

week 5 Period of measurement

Second minute hour

7 Interference Co‐channel adjacent co‐channel

Statistics

8 Building environment Small offices or large hall

Table 10 Classification of factors the affect RSS [7]

Transmitter variability Different transmitters behave differently even when they are configured exactly in the same way In practice this means that when a transmitter is configured to send packets at a power level of d dBm then the transmitter will send these packets at a power level that is very close to d dBm but not necessarily exactly equal to d dBm This can alter the received signal strength indication and thus it can lead to inaccurate distance estimation

Receiver variability The sensitivity of the receivers across different radio chips is different In practice this means that the RSSI value recorded at different receivers can be different even when all the other parameters that affect the received signal strength are kept constant

Antenna orientation Each antenna has its own radiation pattern that is not uniform In practice this means that the RSSI value recorded at the receiver for a given pair of communicating nodes and for a given distance between them varies as the pair wise antenna orientations of the transmitter and the receiver are changed

Multi‐path fading and shadowing in the RF channel In indoor environments the transmitted signals get reflected after hitting on the walls andor on other objects in the room such as furniture Both the original signal and the reflected signal reach the receiver almost at the same time since they both travel at the speed of light As a result of this the receiver is not able to

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

28

distinguish the two signals and it measures the received signal strength for both of them

32 Related work analysis A number of indoor location tracking systems have been proposed in the literature based on RF signals ultrasound infrared and some combinations of these As mentioned previously RF based positioning system are widely studied as shown in Table 2 Given a model of radio signal propagation in a building or other environment RSS can be used to estimate the distance between the transmitter and receiver Most of the algorithms (Table 2) have been evaluated in practice using 80211b compliant wireless radios and seem to produce a location error of 15ft to 20ft for an indoor network area of approximately 225ft x 144ft According to the results of this study all the localization techniques produce approximately the same location error over a range of environments Ecolocation [22] Mote Track [5] are two of the few localization algorithms that have been evaluated on a real sensor network that uses a low power wireless radio Ecolocation reports a location error of 10ft for a very small outdoor network deployment area (26ft x 49ft) Mote Track reports a location error of approximately 13ft for an indoor network deployment area of 18751ft2 In general the RSSI based localization algorithms can be divided into two broad categories the map based and the distance (or area) prediction based algorithms In the case of the map based algorithms the deployment area is divided into a large set of sample points that cover the whole area Most techniques collect one or more signal strength measurements from all visible beacon nodes and for each sample point Each sample point is then mapped to either a signal strength vector or a signal strength probability distribution This process generates an RSSI fingerprint map which is unique with respect to the beacon locations and the deployment area This fingerprint map is stored to a central base station A node that is not aware of its location collects one or more signal strength measurements from all the visible beacon nodes and creates its own RSSI fingerprint that is sent to the central base station The base station computes nodersquos location as the sample pointrsquos location whose RSSI fingerprint distance from the unknown nodersquos RSSI fingerprint is the minimum among all the sample points Despite the fact that the map based RSSI localization methods produce relatively small location errors they are not attractive mainly because they require an excessive amount of profiling data and because they are not fully distributed The detailed profiling of a building is a major holdback because it is complex time consuming and depends on beacon locations Therefore for different buildings or even for the

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

29

same building and different beacon locations different RSSI maps have to be obtained The second category of RSSI based algorithms are the distance (or area) prediction algorithms which focus on minimizing the amount of profiling data In distance prediction based methods raw RSSI data are directly mapped to distances through a signal propagation model In some cases distances are not explicitly measured but they are taken into consideration through assumptions on the underlying distributions In the case of outdoor deployments where there are no obstacles a single signal propagation model is all the profiling data required In the case of indoor deployments the same applies but now the signal propagation model might be different across different buildings with different furniture and wall arrangements Given an underlying signal propagation model raw RSSI data can be mapped to specific distances These distances can be either used directly in order to perform localization or assigned a probability and used in a learning basedprobabilistic localization algorithm Obviously the accuracy of the distance prediction based methods depends heavily on the accuracy of the signal propagation model used for translating RSSI values to distances

33 Properties of RSSI Changes in signal due to propagation indoors are difficult to predict because of dense environment and propagation effects such as reflection diffraction and scattering The multi‐path fading effect which is the result of either constructive or destructive combination of multiple signal copies at the receiver causes the received signal to fluctuate around a mean value at a particular location The received signal is usually modelled by the combined effects of large‐scale fading and small‐scale fading The large‐scale fading component describes the signal attenuation as the signal travels over a distance and is absorbed by material such as walls and floors along the way to the receiver This component predicts the mean of the RSS and usually has a lognormal distribution On the other hand the small scale fading component explains the dramatic fluctuation of the signal due to multi‐path fading On the other hand the small scale‐fading component explains the dramatic fluctuation of the signal due to multi‐path fading If there is no LOS component the small‐scale‐fading is often modelled with a Rayleigh distribution Such scenarios are commonly called NLOS If there is a LOS component the small‐scale‐fading is typically modelled by a Rician distribution Note that when the RSS is measured the measurement averages out the small‐scale‐fading effects

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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4 OBJECT TRACKING 41 Introduction This chapter presents the object‐tracking application the main objective of this thesis work The application is an algorithmic‐software implementation written in Embedded C which is capable of receiving data messages and generating object position estimation data The implementation of the object‐tracking application makes use of the SENTIO platform and uses the background knowledge introduced in the preceding chapters The motivation for the object‐tracking application is to determine the position of an object in indoor environments (Eg Home) The application acts as an interface which collects the raw data that the sensors generate (numerical values) It makes the proper comparisons and algorithmic processes to identify and track the object while it is static inside the area covered by the wireless sensor network Finally with the detection and tracking data it alerts the user or another system The following sections provide an overview of the object‐tracking application

42 Overview of hardware product Before describing the object tracking application it is important to know the characteristics of hardware product The SENTIO platform (figure 15) is built in modular fashion and has the following key features [8]

Flexibility This platform is very flexible in such a way that it is made easy to add or change the hardware for each sensor task depending on the requirement in the WSN For a specific platform this can be hard or sometimes even impossible to accomplish

Sensor resources Resources in a sensor module includes the ability of usage of controllerrsquos resources as much as possible such as ADC‐channels interrupts and digital Input‐Outputrsquos Local voltage regulation should also be possible since many sensors require higher supply voltages than digital Integrated Circuits do Further support of a shut‐down mechanism to power down the sensor modules when not needed

Computation and memory The processor module based on microcontroller should support many devices in a microcontroller family and support different power supply voltages enabling full range of clock frequencies All power‐down modes of the devices should be

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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31

supported The platform should support different processor modules including FPGArsquos and very simple microcontrollers

Communication The focus should be on enabling an IEEE 802154 compliant communication interface With or without ZigBee stack this will provide a flexible platform for exploring different network topologies Support for power‐down of the entire communication module should be supported

Cost and form factor High flexibility is in conflict with low cost and small size The primary objective should be flexibility but the size should be considered in order to enable sensor device integration in systems where space is limited

Figure 15 SENTIO Platform [8]

The communication subsystem of the SENTIO sensor node was designed so that the correct operation of the radio chip is ensured The radio chip is powered by the on‐board voltage regulator and thus fluctuations in the battery voltage level do not affect the operation of the communication subsystem This platform uses Chipcon CC2420 IEEE 802154 radio transceiver operates in the 24GHz ISM band and includes a digital direct sequence spread spectrum (DSSS) modem providing a spreading gain of 9dB and an effective data rate of 250Kbps It was specifically designed for low power wireless applications and supports 8 discrete power levels 0dBm minus1dBm minus3dBm minus5dBm minus7dBm minus10dBm minus15dBm and minus25dBm at which its power consumption varies from 29mW to 52mW [21] A built‐in received signal strength indicator gives an 8‐bit digital value RSSIVAL The RSSIVAL is always averaged over 8 symbol periods (128μs) and a status bit indicates when the RSSIVAL is valid (meaning that the receiver was enabled for at least 8 symbol periods) The

Antenna

Communication Module

Processing Module

Sensing Module

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

32

power P at the RF pins can be obtained directly from RSSIVAL using the following equation

P = RSSIVAL + RSSIOFFSET [dBm] where the RSSIOFFSET is found empirically from the front‐end gain and it is approximately equal to minus45dBm In the next sections when we refer to the RSSI value we refer to the RSSIVAL and not the power P unless otherwise stated

43 Experimental Testbed The experimental test bed for object tracking application is as shown in figure 16 Experiment was conducted in Masterrsquos room at Mid Sweden University A DELL laptop is connected to SENTIO platform and software were primarily used to collect samples of RSS from APrsquos The software monitors IEEE 802154 radio frequency channels which operate in the 24GHz band

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

1030

BC - 1030

AP 3 AP 1

AP 2

AP 4

BS

HEWLETT

PACKARD

Figure 16 Layout of Masterʹs Room

A three dimensional scalable testbed is used for indoor sensor network deployments The testbed illustrated in figure 16 is a 3‐D structure measuring 16m (L) x 12m (W) x 3m (H) and it is designed to host a large number of static nodes as shown in figure 16 Arrow marks in the above figure represent the location where fingerprints were taken The centrepiece of our infrastructure

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

33

is the SENTIO sensor node [8] an open‐source general purpose sensing platform designed with AT Mega 128 microprocessor and the IEEE 802154 compliant CC2420 radio from Chipcon [19]

44 Obtaining data from the sensor network The next important step in the development of the object tracking application is to obtain the data from the sensor network and send it to the base station From there the data is transmitted through a serial port to the computer that runs the object tracking application When the data arrives at the serial port the application must capture it This paragraph gives a brief explanation of how and what kind data is transmitted by the static object fixed sensors and how this data is being used to estimate the position of the object First the static object transmits Beacon packet continuously which is received by all the fixed APrsquos The beacon packet format is as shown in table 6 Then the fixed APrsquos calculates the 8‐bit value of RSSI from the transmitted beacon packet and then sends the resultant RSSI value as a form of data packet (table 7) to the BS which calculates the position and sends back to the object

45 Object tracking application Testing and Evaluation The purpose of this section is to discuss and evaluate the object tracking application Finally we are ready with the experimental testbed as shown in figure 16 As shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors placed at the corners of the masterrsquos room BS is placed at the centre of the room connected to a PC All the nodes are power connected before an actual measurement takes place At first the object is placed at random position inside the room and transmits beacon packets continuously Then these packets are received by the fixed APrsquos which calculate the RSSI from the packet and then averages it until it receives 50 beacon packets and then transmits the data to the BS (figure 17) Then the BS waits until it receives the data from all the APrsquos Once it receives from all the APrsquos it calculates the position of the object (figure 18) The application was developed to demonstrate a real world use of a WSN in object tracking In evaluating the final position obtained with respect to the actual position we conclude that the object‐tracking was successful Although

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

34

it is a research prototype object tracking application is a simple stable stand‐alone system capable of demonstrating an important application of WSN It takes raw data from the WSN aggregates it evaluates it and based on the evaluation tracks the object The accuracy of estimating the position is less than 5m Table 11 below shows a log of readings taken during test

Start

Intialize micro‐controller and transeciever in receieve mode

If received_packet=

Beacon

Average RSSI values

Increase the counter

If countergt1000050

A

No

Yes

B

B

Yes

No

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

35

A

Reset counter

Set transceiever in transmit mode

Transmit averaged RSSI to Main sensor and set it back to receive mode

10000 samples ‐ Offline

50 samples ‐ Online

Figure 17 Flow chart representing the algorithm of fixed sensors

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

36

Start

Intialize micro‐controller and transeciever in receieve mode

Received RSSI values from all fixed Sensors

Open radio map

Calculate Euclidean distance between obtained RSSI values from fixed sensors and fingerprints

in radio map

No

Yes

Bubble sort for least error in signal strength values

A

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

37

A

Get the location of the object from the radiousing the least error value

Display location of object on PC

End

Figure 18 Flow chart representing the algorithm of main sensor

Table 11 Accuracy of positioning system

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

38

5 RESULTS This chapter presents how the system parameters such as number of samples mean or median etchellipare finalized All the tests are performed in masterrsquos room at Mid Sweden University This chapter is divided into three subsections First in Section 51 it reviews results obtained from samples test Section 52 discusses one dimensional test results and finally section 53 discusses results of two dimensional tests

51 Measurement setup A Dell laptop was connected to a SENTIO platform and is loaded with software (Appendix 2) This setup was used to determine number of samples required during object tracking mean or median position estimation algorithm and one dimensional test The setup is as follows

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

103

0

BC - 1030

AP 1AP 2

BS

HEWLETT

PACKARD

Figure 19 Test bed for one dimensional test in Masters Room

Two SENTIO nodes (AP1 and AP2 as shown above) were separated by a distance of 10 meters and these sensors are known as fixed sensors which are fixed to the ceiling such that there antenna is faced towards the ground They are linear is position A BS was connected to Dell laptop and it was placed in such a way that its antenna was faced towards the ceiling Another SENTIO

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

39

node was used as an object and its orientation was same as BS Figure 19 represents the complete setup for object tracking application Mobile node was moved between these fixed ones at regular intervals of one meter and RSSI values were logged at each interval

52 Number of samples test This test was performed to determine number of samples required for accurate positioning and either mean of the obtained RSSI values or median of them Figure 19 represents the setup for number of samples test Once the setup is ready the measurements are taken as follows

1 An object was tagged with SENTIO node and was placed stationary linear to the fixed sensors

2 10000 samples (RSSI) were taken at regular intervals of 1 meter up to 10 meters

3 The sample values were logged at every interval 4 Using these logged samples I have plotted graphs such as distribution

of RSS values (figure 20 21) meanmedian at intervals of 1 meter using MATLAB

5 The mean and median for various numbers of samples is as shown in figure 22

Figure 20 Histogram plot of RSSI values at 1m and 4m

Figure 21 Histogram plot of RSSI values at 6m and 8m

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

40

Figure 22 Distance vs RSSI

From the above graphs following conclusions were made 1 From the histogram plots we can observe that the distribution of RSSI

values follow Gaussians distribution law 2 As we can see from figure 22 that median curve has drastic changes

when compared to mean curve Hence mean is considered while estimating the position

3 From the observations we conclude that the number of samples does not matter since mean curve tends to be same for various numbers of samples

4 Hence 10000 samples are considered in order to get a proper location fingerprint during off‐line phase and during online phase 50 samples are enough to estimate the position of object

53 0ne dimensional test Measurement setup for one dimensional test is as shown in figure 19 This test was conducted to check the performance of algorithm and how accurate it was At first ie during offline phase fingerprints at each interval averaging 10000 samples were taken During online phase Euclidean algorithm was used estimate the position averaging 50 samples of RSSI From the results we

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

41

can realize that the error was less than 2 meters Table 12 shows the result of location estimation in one dimensional case

Table 12 One dimensional position estimation results

54 Two dimensional test This test is the final stage of my thesis work Experimental testbed for this stage is as shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors fixed at the corners of the room and a BS at the centre of the room There are 18 pre‐determined locations where the fingerprints (RSSI) were taken The fingerprints are as shown in Appendix 1 All the readings were taken in presence of userrsquos The algorithm for position estimation is shown in Appendix 2 Position estimation test is performed as follows

Tagged object is placed at random inside the masterrsquos room All the sensors including the base station and object are switched on Using the algorithm (Appendix 2) the BS estimates the position of the object and displays it on the PC

This test was performed using various numbers of samples to test which has a better accuracy Figure 23 represents the error rate using various numbers of samples From figure 23 we can conclude that 50 numbers of samples is enough to estimate the position of object with an accuracy of 3m

55 Causes of error in location determination The randomness of RSS is the major cause of error in any indoor

positioning system that uses the RSS for location inference If there is no randomness in the RSS and its location fingerprint every indoor positioning systems of this type will have no location detection error For example I have recorded RSS at one particular location during different times of the day From figures 24 and 25 you can observe that RSS values are different at different times of day

The performance of indoor positioning systems depends greatly on the separation of location fingerprints A location fingerprint

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

42

corresponding to a location can be identified correctly if it is difficult to classify it as another fingerprint by a pattern classifier

Path loss is one of the basic signal propagation properties of the RSS that best describes attenuation of transmit signal power over a distance between an access point and a mobile device

Presence of Userrsquos or userrsquos orientation affects RSS

Figure 23 Error Rate vs Number of samples

Figure 24 RSS distribution at 214 pm

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

43

Figure 25 RSS distribution at 100pm and 1100 am

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

6 DISCUSSIONS10102006

44

6 DISCUSSIONS 61 Summary and Conclusion While empirical results and performance studies of positioning systems based on location fingerprint have been presented in this report analytical model that can be used as a framework for efficiently designing the positioning systems are not available This thesis design work uses general principles of position estimation such as position estimation of sensors grid spacing etc This design work focuses on accuracy and precision The concept of location fingerprint technique based on RSS us explained in detail in chapter 2 It is found that RSS is random It is possible to approximate the RSS as being normally distributed While the RSS random process is non‐stationary the mean is more or less constant and could be used as the fingerprint of the location Model designed for position estimation is as shown in chapter 4 Accuracy and precision are the two major performance matrices in this study Uniform grid spacing has been used From the experimental analysis I have obtained an accuracy of 3 meters and 35 precision The sensitivity analysis among the system parameters and the performance matrices were done using the model as shown in chapter 4 For instance our study found that the minimum distance between two adjacent positions also affects the positioning performance This is due to the randomness of RSS patterns induced by the ever changing indoor environment

62 Future work The research in this thesis provides a ground work for the future study of efficient design of indoor positioning system The RSSI based position estimation still has limitations First the RSSI value is time dependent Thus the future study should address this issue Second the movement of object is not considered Third orientations of sensors affect the performance which should be taken care off Fourth to improve the performance of the system we need to find a solution to deal with those incomplete or missing data during the off‐line and the on‐line phase Fifth position estimation on multi‐floor and three‐dimensional coordinates is not available

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 110102006

45

APPENDIX 1

Location Fingerprints of Masters Room Time 1315 Observation Time 20 minutes Number of Samples 10000

People Presence SNo Co-ordinates AP1 (0x0001) AP2 (0x0002) AP3 (0x0004) AP4 (0x0008)

1 11 -24 -16 -27 -33 2 13 -26 -8 -34 -31 3 15 -43 -9 -39 -25 4 32 -21 -11 -27 -33 5 61 -6 -23 -32 -28 6 64 -20 -31 -25 -34 7 35 -23 -35 -29 -38 8 37 -29 -35 -22 -27 9 39 -38 -29 -21 -27 10 67 -27 -23 -20 -23 11 611 -26 -29 -23 -30 12 613 -35 -32 -2 -31 13 513 -34 -31 -20 -26 14 11 -39 -22 -29 -17 15 212 -37 -42 -28 -31 16 -212 -38 -33 -29 -6 17 -21 -42 -29 -33 -19 18 -38 -40 -23 -35 -22

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 210102006

46

APPENDIX 2

Begin

Open radio map and raw data files for write

Intialize radio map in memory

Check whether the

Packet is data or Beacon packet If data packet

Save the data to register

Check if data is

Received from all

sensors

Estimate the position of static

Display the position on PC

End Yes

No

A

A

Yes

No

Flow chart representing the main algorithm for position estimation

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

47

APPENDIX 3

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

48

Distribution of RSSI

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

49

REFERENCES [1] T S Rappaport J H Reed and B D Woerner ldquoPosition location using wireless communications on highways of the futureʺ IEEE Commun Mag vol 34 no 10 pp33‐41 Oct 1996 [2] A M Ladd K E Bekris G Marceau A Rudys L E Kavraki and D S Wallach rdquoRobotics‐based location sensing using wireless ethernetʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ02) 2002 pp 227‐238 [3] I Ramani R Bharadwaja and P V Rangan ldquoLocation tracking for media appliances in wireless home networksʺ in Proc International Conference on Multimedia and Expo (ICMEʹ03) July 2003 pp 769‐772 [4] K Pahlavan X Li and J P Makela ldquoIndoor geolocation science and technologyʺ IEEE Commun Mag vol 40 no 2 pp 112‐118 Feb 2002 [5] Konard Lorincz and Matt Welsh ldquoMote Track A Robust Decentralized Approach to RF‐Based Location Trackingrdquo [6] Paramvir Bahl and Venkata N Padmanabham ldquoRADAR An In‐Building RF‐based User Location and Tracking Systemrdquo [7] Kamol Kaemarungsi ldquoDesign of Indoor positioning system based on location fingerprint techiniquerdquo University of Pittsburgh 2005 [8] Fredrik Linnarsson Cheng Peng and Bengt Oelmann ldquoSENTIO A Hardware Platform for Rapid Prototyping of Wireless Sensor Networksrdquo [9] S Saha K Chaudhuri D Sanghi and P Bhagwat ldquoLocation determination of a mobile device using IEEE 80211b access point signalsʺ in Proc IEEE Wireless Communications and Networking Conference (WCNCʹ03) New Orleans LA Mar 2003 pp 1987‐1992 [10] T Roos P Myllymaki H Tirri P Misikangas and J Sievanen ldquoA probabilistic approach to WLAN user location estimationʺ International Journal of Wireless Information Networks vol 9 no 3 pp 155‐164 July 2002 [11] R Battiti M Brunato and A Villani ldquoStatistical learning theory for location fingerprinting in wireless LANrsquosʺ Technical Report Oct 2002 [Online] Available httprtmscienceunitnitraquobattitiarchive86pdf [12] P Prasithsangaree P Krishnamurthy and P K Chrysanthis ldquoAn indoor position location with wireless LANsʺ in Proc IEEE International Symposium on Personal Indoor and Mobile Radio Communications (PIMRCʹ02) Lisbon Portugal Sept 2002 [13] M A Youssef A Agrawala and A U Shankar ldquoWLAN location determination via clustering and probability distributionsʺ in Proc IEEE

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

50

International Conference on Pervasive Computing and Communications (PerComʹ03) Dallas‐Fort Worth TX Mar 23‐26 2003 pp 23‐26 [14] Z Xiang S Song J Chen H Wang J Huang and X Gao ldquoA wireless LAN‐based indoor positioning technologyʺ IBM Journal of Research and Development vol 48 no 56 pp 617‐626 SeptNov 2004 [15] R Want A Hopper V Falcao and J Gibbons ldquoThe active badge location systemʺACM Transactions on Information Systems vol 40 no 1 pp 91‐102 Jan 1992 [16] N B Priyantha A Chakraborty and H Balakrishnan ldquoThe cricket location‐support systemʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ00) Boston MA Aug 2000 pp 32‐43 [17] Holger K Willig A (2005) Protocols and Architectures for Wireless Sensor Networks London [18] IEEE (2003) Part 154 Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low‐Rate Wireless Personal Area Networks (LR‐WPANs) New York [19] Chipcon CC2420 802154 compliant radio httpwwwchipconcom [20] ZigBee Specification v10 ZigBee Alliance [21] Dimitrios Lymberopoulos Quentin Lindsey and Andreas Savvides ldquoAn Empirical Analysis of Radio Signal Strength Variability in IEEE 802154 Networks using Monopole Antennasrdquo [22] Kiran Yedavalli Bhaskar Krishnamachari Sharmila Ravula and Bhaskar Srinivasan ldquoEcolocation A sequence based technique for RF localization in wireless sensor networksrdquo Robert Bosch Research and Technology Centre Palo Alto CA

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

19

period beacons bound it The beacon is transmitted in the first of the sixteen equal time slots that the super frame has It is used to ldquosynchronize the attached devices to identify the PAN and to describe the structure of the super framerdquo The super frame can have active and inactive periods All the communications have to be finished inside the super frame period In the inactive periods the devices can switch to the sleep mode but they have to be ready for the next beacon The active period is further divided into a CAP and a CFP During the CAP any device can communicate competing with the other devices in the PAN using slotted CSMA‐CA The CFP contains GTSs and follows the CAP A CFP may maintain up to seven GTSs and each GTS can reserve more than one time slot However the CAP should always be sufficient to allow new devices t join the PAN The GTSs are allocated to specific devices All the transactions must be completed inside the assigned time period (CAP GTS and CFP) (IEEE 802154 standard 2003)

Figure 6 Example of super frame structure [18]

In accordance with the 802154 standard (2003) three different types of data‐transfer exist In addition the types differ if the coordinator uses or does not beacons Data transfer from a device to the PAN coordinator is the first type (figure 7) For a ldquonon‐beacon‐enable networkrdquo it first senses the medium by using ldquoun‐slotted CSMA‐CArdquo and then a simple transmit to the data frame In a ldquobeacon‐enabled networkrdquo the sender waits for the beacon when it finds it ldquothe device synchronizes to the super fame structurerdquo In the specified time frame the sender again senses the medium by using ldquoslotted CSMA‐CArdquo and transmits the data to the coordinator In either case the coordinator has the option to acknowledge or not acknowledge the data reception after that the transaction is completed Data transfer from the coordinator is the next type described in the standard (figure 8) In a beacon‐enabled network the coordinator indicates a pending message through the beacon The messagersquos target device receives the beacon and if the message is pending it responses with a MAC command request

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

20

message using slotted CSMA‐CA the coordinator may or may not acknowledge the command message and through slotted CSMA‐CA sends the pending message The device acknowledges the received message After that the coordinator removes the message from the beaconrsquos pending list and completes the transaction In a non‐beacon enable network the device periodically sends a MAC command‐request frame to the coordinator The coordinator acknowledges the data request Then by using un‐slotted CSMA‐CA if it has a pending message it transmits it If it has not it respond with a data frame with a ldquozero‐length payloadrdquo To complete the transaction the device acknowledges the data reception

Figure 7 Communication from a device to a PAN coordinator in (a) a beacon‐enabled network and (b) a non‐beacon enabled network [18]

Peer‐to‐peer is the last type of data transfer In this situation the devices are free to communicate with any other device within their communication range In a peer‐to‐peer PAN the devices can ldquoeither receive constantly or synchronize with each otherrdquo If they are receiving constantly to transmit data they use un‐slotted CSAM‐CA In the second case synchronization must be achieved first (IEEE 802154 standard [IEEE 2003]) The IEEE 802154 (2003) standard establishes MAC and PHY standards for low‐cost low‐power and high‐density node deployments In addition to the above PHY and MAC characteristics IEEE 802154 provides a security baseline including ldquothe ability to maintain an ACL and use symmetric cryptographyrdquo for data encryption The algorithm that is used for encryption is the AES However the higher level layers decide when security is need The upper layers are in general responsible for device authentication and key management The next section introduces the ZigBee standard which

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

21

encapsulates the IEEE 802154 and provides additional standardization for the higher levels

Figure 8 Communication from a PAN coordinator to a device in (a) a beacon‐enabled network and (b) a non‐beacon enabled network [18]

242 ZigBee Heily (2004) defines ZigBee as ldquoa rapidly growing worldwide non‐profit industry consortiumrdquo whose mission is ldquoto define a reliable cost‐effective low‐power wirelessly networked monitoring and control product based on an open global standardrdquo Figure 9 illustrates the areas of interest for different wireless communication standards

Figure 9 Overview of the coverage for different wireless communication

standards [17]

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

22

ZigBee a new standard which became publicly available in June 2005 is based on the IEEE 802154 standard It expands the IEEE 802154 by adding the framework for ldquothe network security and applicationrdquo (Craig 2005) Figure 10 presents the IEEE 802154ZigBee stack and the areas of responsibilities

Figure 10 IEEE 802154ZigBee stack [20]

Craig (2005) mentions three networking topologies that the standard covers the star mesh and cluster tree (figure 11) The ZigBee standard works on top of the IEEE 802154 addressing schema by using the standard 64‐bit and the short 16‐bit addressing Kinney (2005) summarizes the ZigBee network layer responsibilities the successful establishment of a new the network and successful new device configuration addressing assignment network synchronization frames security and message routing ZigBee further distinguishes the concept of the physical devices (RFD FFD) by using the notion of ldquological devicesrdquo ldquoZigBee Coordinatorrdquo is the first type of logical devices It is responsible for initializing maintaining and managing the network Under the coordinator in the network hierarchy is the ldquoZigBee routerrdquo which is responsible for controlling the message routing between the nodes Finally the ldquoZigBee End Devicerdquo acts as the end point of the network

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

23

structure The tasks that an end device can perform are specified in the ldquoApplication Profilesrdquo (Craig 2005)

Figure 11 IEEE 802154ZigBee stack [17]

The ZigBee specifications (2005) summarize the security services provided by ZigBee ldquokey establishment key transport frame protection and device managementrdquo ZigBee builds its security mechanism using symmetric key cryptography The security services also depend on the associated layer thus as the figure 10 shows the security mechanism covers the network and the application layer In addition if a MAC frame needs security protection the MAC layer is able to secure it Moreover the notion of end‐to‐end security is supported the source and destination devices have access and use the same share key In the MAC layer the 802154 AES mechanism provides the proper security The mechanism protects ldquothe confidentiality integrity and authenticity of the MAC framesrdquo (Kinney 2005) An auxiliary header field in front of the MAC payload indicates if the frame is encrypted or not The MAC framesrsquo integrity is supported by calculating and using a MIC at the end of the MAC payload In addition to the AES nonce is used to provide MAC confidentiality and authenticity Figure 12 illustrates a MAC frame with security For different security aspects the MAC layer uses different mode of the AES for the encryption it uses the AES in Counter (CTR) mode and for the integrity the

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

24

CBC‐MAC Finally the combination (CCM) of the above two modes is available providing both encryption and integrity (Kinney 2005)

Figure 12 ZigBee secure frame in MAC layer [17]

In the network layer the CCM (a modified MAC layer CCM mode) is used for encryption Because the network layer uses only the CCM mode a single key is used for all different security options The network layer security message format is similar to the MAC frame it is presented in the figure 13 Finally although the network layer is responsible for securing its layer messages the above layers specify the keys and the CCM option for each frame (Kinney 2005)

Figure 13 ZigBee secure frame in network layer [17]

Security in the application layer works similar to the network and MAC layers It uses the ldquolink keyrdquo or the ldquonetwork keyrdquo to secure the message and then encapsulate it inside a set of fields similar to the network format (figure 14) Other security responsibilities that the application layer has are to provide the ZDO and the applications with device management services key establishment and key transport (ZigBee specifications 2005)

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

25

Figure 14 ZigBee secure frame in application layer [17]

The ZigBee application layer contains the manufacturer‐defined application objects the ZDO and the application sub‐layer In addition to the security responsibilities the application sub‐layer binds devices based on their duties and needs maintains the binding tables and forwards messages between them The application sub‐layer also discovers the neighbour devices for a given device The ZDO is responsible for determining the devicersquos duty in the network for communicating using binding requests and for supporting security as was mentioned above The sub‐layer that implements the actual application is the manufacturer‐defined application object (Kinney 2005) The ZigBee specifications are a great step toward the wireless sensors networks standardization They cover all the OSI layers from the physical to the application providing guidance to the developers The specifications are the product of the ZigBee alliances a consortium of companies and academic institutes Thus they not only produce the specifications but also are the first use them The next section goes beyond the layer architecture and standardization and introduces the OS field another crucial element of WSNs

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

26

3 RECEIVED SIGNAL STRENGTH (RSS) Majority of the existing methods leverage the existence of IEEE 80211 base stations with powerful radio transmit powers of approximately 100mW per base station Such radios are in a different class from the low power IEEE 802154 compliant radios that typically transmit at low power levels ranging from 52mW to 29mW [19] The wide availability of larger number of IEEE 802154 radios in the research community has revived the interest for signal strength based localization in sensor network Despite of rapidly increasing popularity of IEEE 802154 radios and signal strength localization there is a lack of detailed characterization of the fundamental factors contributing to large signal strength variation This chapter investigates some of the properties of RSS observed during testing and analysis of research work collected during the period of thesis The analysis of RSS values is needed to understand the underlying features of location dependent RSS patterns and location fingerprints An understanding of the properties of the RSS values for location fingerprinting can assist in improving the design of positioning algorithms and in deployment of indoor positioning systems

31 Parameters that affect RSS Different factors that affect the indoor RSS are the userrsquos presenceabsence hisher orientation time of the day building type and material distance from transmitter and type of radio These factors are by no means exhaustive Other possible factors are antenna orientation directionality and type Table 10 classify the parameters which affect RSS [7]

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

27

Effect on Factors Options

1 Proximity of user Userrsquos presence or absence

2 Orientation of user and terminal

North East South West

Data Collection

3 Make of Radio 4 Time of measurement Time of day amp days of

week 5 Period of measurement

Second minute hour

7 Interference Co‐channel adjacent co‐channel

Statistics

8 Building environment Small offices or large hall

Table 10 Classification of factors the affect RSS [7]

Transmitter variability Different transmitters behave differently even when they are configured exactly in the same way In practice this means that when a transmitter is configured to send packets at a power level of d dBm then the transmitter will send these packets at a power level that is very close to d dBm but not necessarily exactly equal to d dBm This can alter the received signal strength indication and thus it can lead to inaccurate distance estimation

Receiver variability The sensitivity of the receivers across different radio chips is different In practice this means that the RSSI value recorded at different receivers can be different even when all the other parameters that affect the received signal strength are kept constant

Antenna orientation Each antenna has its own radiation pattern that is not uniform In practice this means that the RSSI value recorded at the receiver for a given pair of communicating nodes and for a given distance between them varies as the pair wise antenna orientations of the transmitter and the receiver are changed

Multi‐path fading and shadowing in the RF channel In indoor environments the transmitted signals get reflected after hitting on the walls andor on other objects in the room such as furniture Both the original signal and the reflected signal reach the receiver almost at the same time since they both travel at the speed of light As a result of this the receiver is not able to

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

28

distinguish the two signals and it measures the received signal strength for both of them

32 Related work analysis A number of indoor location tracking systems have been proposed in the literature based on RF signals ultrasound infrared and some combinations of these As mentioned previously RF based positioning system are widely studied as shown in Table 2 Given a model of radio signal propagation in a building or other environment RSS can be used to estimate the distance between the transmitter and receiver Most of the algorithms (Table 2) have been evaluated in practice using 80211b compliant wireless radios and seem to produce a location error of 15ft to 20ft for an indoor network area of approximately 225ft x 144ft According to the results of this study all the localization techniques produce approximately the same location error over a range of environments Ecolocation [22] Mote Track [5] are two of the few localization algorithms that have been evaluated on a real sensor network that uses a low power wireless radio Ecolocation reports a location error of 10ft for a very small outdoor network deployment area (26ft x 49ft) Mote Track reports a location error of approximately 13ft for an indoor network deployment area of 18751ft2 In general the RSSI based localization algorithms can be divided into two broad categories the map based and the distance (or area) prediction based algorithms In the case of the map based algorithms the deployment area is divided into a large set of sample points that cover the whole area Most techniques collect one or more signal strength measurements from all visible beacon nodes and for each sample point Each sample point is then mapped to either a signal strength vector or a signal strength probability distribution This process generates an RSSI fingerprint map which is unique with respect to the beacon locations and the deployment area This fingerprint map is stored to a central base station A node that is not aware of its location collects one or more signal strength measurements from all the visible beacon nodes and creates its own RSSI fingerprint that is sent to the central base station The base station computes nodersquos location as the sample pointrsquos location whose RSSI fingerprint distance from the unknown nodersquos RSSI fingerprint is the minimum among all the sample points Despite the fact that the map based RSSI localization methods produce relatively small location errors they are not attractive mainly because they require an excessive amount of profiling data and because they are not fully distributed The detailed profiling of a building is a major holdback because it is complex time consuming and depends on beacon locations Therefore for different buildings or even for the

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

29

same building and different beacon locations different RSSI maps have to be obtained The second category of RSSI based algorithms are the distance (or area) prediction algorithms which focus on minimizing the amount of profiling data In distance prediction based methods raw RSSI data are directly mapped to distances through a signal propagation model In some cases distances are not explicitly measured but they are taken into consideration through assumptions on the underlying distributions In the case of outdoor deployments where there are no obstacles a single signal propagation model is all the profiling data required In the case of indoor deployments the same applies but now the signal propagation model might be different across different buildings with different furniture and wall arrangements Given an underlying signal propagation model raw RSSI data can be mapped to specific distances These distances can be either used directly in order to perform localization or assigned a probability and used in a learning basedprobabilistic localization algorithm Obviously the accuracy of the distance prediction based methods depends heavily on the accuracy of the signal propagation model used for translating RSSI values to distances

33 Properties of RSSI Changes in signal due to propagation indoors are difficult to predict because of dense environment and propagation effects such as reflection diffraction and scattering The multi‐path fading effect which is the result of either constructive or destructive combination of multiple signal copies at the receiver causes the received signal to fluctuate around a mean value at a particular location The received signal is usually modelled by the combined effects of large‐scale fading and small‐scale fading The large‐scale fading component describes the signal attenuation as the signal travels over a distance and is absorbed by material such as walls and floors along the way to the receiver This component predicts the mean of the RSS and usually has a lognormal distribution On the other hand the small scale fading component explains the dramatic fluctuation of the signal due to multi‐path fading On the other hand the small scale‐fading component explains the dramatic fluctuation of the signal due to multi‐path fading If there is no LOS component the small‐scale‐fading is often modelled with a Rayleigh distribution Such scenarios are commonly called NLOS If there is a LOS component the small‐scale‐fading is typically modelled by a Rician distribution Note that when the RSS is measured the measurement averages out the small‐scale‐fading effects

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

30

4 OBJECT TRACKING 41 Introduction This chapter presents the object‐tracking application the main objective of this thesis work The application is an algorithmic‐software implementation written in Embedded C which is capable of receiving data messages and generating object position estimation data The implementation of the object‐tracking application makes use of the SENTIO platform and uses the background knowledge introduced in the preceding chapters The motivation for the object‐tracking application is to determine the position of an object in indoor environments (Eg Home) The application acts as an interface which collects the raw data that the sensors generate (numerical values) It makes the proper comparisons and algorithmic processes to identify and track the object while it is static inside the area covered by the wireless sensor network Finally with the detection and tracking data it alerts the user or another system The following sections provide an overview of the object‐tracking application

42 Overview of hardware product Before describing the object tracking application it is important to know the characteristics of hardware product The SENTIO platform (figure 15) is built in modular fashion and has the following key features [8]

Flexibility This platform is very flexible in such a way that it is made easy to add or change the hardware for each sensor task depending on the requirement in the WSN For a specific platform this can be hard or sometimes even impossible to accomplish

Sensor resources Resources in a sensor module includes the ability of usage of controllerrsquos resources as much as possible such as ADC‐channels interrupts and digital Input‐Outputrsquos Local voltage regulation should also be possible since many sensors require higher supply voltages than digital Integrated Circuits do Further support of a shut‐down mechanism to power down the sensor modules when not needed

Computation and memory The processor module based on microcontroller should support many devices in a microcontroller family and support different power supply voltages enabling full range of clock frequencies All power‐down modes of the devices should be

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

31

supported The platform should support different processor modules including FPGArsquos and very simple microcontrollers

Communication The focus should be on enabling an IEEE 802154 compliant communication interface With or without ZigBee stack this will provide a flexible platform for exploring different network topologies Support for power‐down of the entire communication module should be supported

Cost and form factor High flexibility is in conflict with low cost and small size The primary objective should be flexibility but the size should be considered in order to enable sensor device integration in systems where space is limited

Figure 15 SENTIO Platform [8]

The communication subsystem of the SENTIO sensor node was designed so that the correct operation of the radio chip is ensured The radio chip is powered by the on‐board voltage regulator and thus fluctuations in the battery voltage level do not affect the operation of the communication subsystem This platform uses Chipcon CC2420 IEEE 802154 radio transceiver operates in the 24GHz ISM band and includes a digital direct sequence spread spectrum (DSSS) modem providing a spreading gain of 9dB and an effective data rate of 250Kbps It was specifically designed for low power wireless applications and supports 8 discrete power levels 0dBm minus1dBm minus3dBm minus5dBm minus7dBm minus10dBm minus15dBm and minus25dBm at which its power consumption varies from 29mW to 52mW [21] A built‐in received signal strength indicator gives an 8‐bit digital value RSSIVAL The RSSIVAL is always averaged over 8 symbol periods (128μs) and a status bit indicates when the RSSIVAL is valid (meaning that the receiver was enabled for at least 8 symbol periods) The

Antenna

Communication Module

Processing Module

Sensing Module

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

32

power P at the RF pins can be obtained directly from RSSIVAL using the following equation

P = RSSIVAL + RSSIOFFSET [dBm] where the RSSIOFFSET is found empirically from the front‐end gain and it is approximately equal to minus45dBm In the next sections when we refer to the RSSI value we refer to the RSSIVAL and not the power P unless otherwise stated

43 Experimental Testbed The experimental test bed for object tracking application is as shown in figure 16 Experiment was conducted in Masterrsquos room at Mid Sweden University A DELL laptop is connected to SENTIO platform and software were primarily used to collect samples of RSS from APrsquos The software monitors IEEE 802154 radio frequency channels which operate in the 24GHz band

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

1030

BC - 1030

AP 3 AP 1

AP 2

AP 4

BS

HEWLETT

PACKARD

Figure 16 Layout of Masterʹs Room

A three dimensional scalable testbed is used for indoor sensor network deployments The testbed illustrated in figure 16 is a 3‐D structure measuring 16m (L) x 12m (W) x 3m (H) and it is designed to host a large number of static nodes as shown in figure 16 Arrow marks in the above figure represent the location where fingerprints were taken The centrepiece of our infrastructure

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

33

is the SENTIO sensor node [8] an open‐source general purpose sensing platform designed with AT Mega 128 microprocessor and the IEEE 802154 compliant CC2420 radio from Chipcon [19]

44 Obtaining data from the sensor network The next important step in the development of the object tracking application is to obtain the data from the sensor network and send it to the base station From there the data is transmitted through a serial port to the computer that runs the object tracking application When the data arrives at the serial port the application must capture it This paragraph gives a brief explanation of how and what kind data is transmitted by the static object fixed sensors and how this data is being used to estimate the position of the object First the static object transmits Beacon packet continuously which is received by all the fixed APrsquos The beacon packet format is as shown in table 6 Then the fixed APrsquos calculates the 8‐bit value of RSSI from the transmitted beacon packet and then sends the resultant RSSI value as a form of data packet (table 7) to the BS which calculates the position and sends back to the object

45 Object tracking application Testing and Evaluation The purpose of this section is to discuss and evaluate the object tracking application Finally we are ready with the experimental testbed as shown in figure 16 As shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors placed at the corners of the masterrsquos room BS is placed at the centre of the room connected to a PC All the nodes are power connected before an actual measurement takes place At first the object is placed at random position inside the room and transmits beacon packets continuously Then these packets are received by the fixed APrsquos which calculate the RSSI from the packet and then averages it until it receives 50 beacon packets and then transmits the data to the BS (figure 17) Then the BS waits until it receives the data from all the APrsquos Once it receives from all the APrsquos it calculates the position of the object (figure 18) The application was developed to demonstrate a real world use of a WSN in object tracking In evaluating the final position obtained with respect to the actual position we conclude that the object‐tracking was successful Although

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

34

it is a research prototype object tracking application is a simple stable stand‐alone system capable of demonstrating an important application of WSN It takes raw data from the WSN aggregates it evaluates it and based on the evaluation tracks the object The accuracy of estimating the position is less than 5m Table 11 below shows a log of readings taken during test

Start

Intialize micro‐controller and transeciever in receieve mode

If received_packet=

Beacon

Average RSSI values

Increase the counter

If countergt1000050

A

No

Yes

B

B

Yes

No

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

35

A

Reset counter

Set transceiever in transmit mode

Transmit averaged RSSI to Main sensor and set it back to receive mode

10000 samples ‐ Offline

50 samples ‐ Online

Figure 17 Flow chart representing the algorithm of fixed sensors

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

36

Start

Intialize micro‐controller and transeciever in receieve mode

Received RSSI values from all fixed Sensors

Open radio map

Calculate Euclidean distance between obtained RSSI values from fixed sensors and fingerprints

in radio map

No

Yes

Bubble sort for least error in signal strength values

A

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

37

A

Get the location of the object from the radiousing the least error value

Display location of object on PC

End

Figure 18 Flow chart representing the algorithm of main sensor

Table 11 Accuracy of positioning system

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

38

5 RESULTS This chapter presents how the system parameters such as number of samples mean or median etchellipare finalized All the tests are performed in masterrsquos room at Mid Sweden University This chapter is divided into three subsections First in Section 51 it reviews results obtained from samples test Section 52 discusses one dimensional test results and finally section 53 discusses results of two dimensional tests

51 Measurement setup A Dell laptop was connected to a SENTIO platform and is loaded with software (Appendix 2) This setup was used to determine number of samples required during object tracking mean or median position estimation algorithm and one dimensional test The setup is as follows

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

103

0

BC - 1030

AP 1AP 2

BS

HEWLETT

PACKARD

Figure 19 Test bed for one dimensional test in Masters Room

Two SENTIO nodes (AP1 and AP2 as shown above) were separated by a distance of 10 meters and these sensors are known as fixed sensors which are fixed to the ceiling such that there antenna is faced towards the ground They are linear is position A BS was connected to Dell laptop and it was placed in such a way that its antenna was faced towards the ceiling Another SENTIO

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

39

node was used as an object and its orientation was same as BS Figure 19 represents the complete setup for object tracking application Mobile node was moved between these fixed ones at regular intervals of one meter and RSSI values were logged at each interval

52 Number of samples test This test was performed to determine number of samples required for accurate positioning and either mean of the obtained RSSI values or median of them Figure 19 represents the setup for number of samples test Once the setup is ready the measurements are taken as follows

1 An object was tagged with SENTIO node and was placed stationary linear to the fixed sensors

2 10000 samples (RSSI) were taken at regular intervals of 1 meter up to 10 meters

3 The sample values were logged at every interval 4 Using these logged samples I have plotted graphs such as distribution

of RSS values (figure 20 21) meanmedian at intervals of 1 meter using MATLAB

5 The mean and median for various numbers of samples is as shown in figure 22

Figure 20 Histogram plot of RSSI values at 1m and 4m

Figure 21 Histogram plot of RSSI values at 6m and 8m

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

40

Figure 22 Distance vs RSSI

From the above graphs following conclusions were made 1 From the histogram plots we can observe that the distribution of RSSI

values follow Gaussians distribution law 2 As we can see from figure 22 that median curve has drastic changes

when compared to mean curve Hence mean is considered while estimating the position

3 From the observations we conclude that the number of samples does not matter since mean curve tends to be same for various numbers of samples

4 Hence 10000 samples are considered in order to get a proper location fingerprint during off‐line phase and during online phase 50 samples are enough to estimate the position of object

53 0ne dimensional test Measurement setup for one dimensional test is as shown in figure 19 This test was conducted to check the performance of algorithm and how accurate it was At first ie during offline phase fingerprints at each interval averaging 10000 samples were taken During online phase Euclidean algorithm was used estimate the position averaging 50 samples of RSSI From the results we

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

41

can realize that the error was less than 2 meters Table 12 shows the result of location estimation in one dimensional case

Table 12 One dimensional position estimation results

54 Two dimensional test This test is the final stage of my thesis work Experimental testbed for this stage is as shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors fixed at the corners of the room and a BS at the centre of the room There are 18 pre‐determined locations where the fingerprints (RSSI) were taken The fingerprints are as shown in Appendix 1 All the readings were taken in presence of userrsquos The algorithm for position estimation is shown in Appendix 2 Position estimation test is performed as follows

Tagged object is placed at random inside the masterrsquos room All the sensors including the base station and object are switched on Using the algorithm (Appendix 2) the BS estimates the position of the object and displays it on the PC

This test was performed using various numbers of samples to test which has a better accuracy Figure 23 represents the error rate using various numbers of samples From figure 23 we can conclude that 50 numbers of samples is enough to estimate the position of object with an accuracy of 3m

55 Causes of error in location determination The randomness of RSS is the major cause of error in any indoor

positioning system that uses the RSS for location inference If there is no randomness in the RSS and its location fingerprint every indoor positioning systems of this type will have no location detection error For example I have recorded RSS at one particular location during different times of the day From figures 24 and 25 you can observe that RSS values are different at different times of day

The performance of indoor positioning systems depends greatly on the separation of location fingerprints A location fingerprint

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

42

corresponding to a location can be identified correctly if it is difficult to classify it as another fingerprint by a pattern classifier

Path loss is one of the basic signal propagation properties of the RSS that best describes attenuation of transmit signal power over a distance between an access point and a mobile device

Presence of Userrsquos or userrsquos orientation affects RSS

Figure 23 Error Rate vs Number of samples

Figure 24 RSS distribution at 214 pm

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

43

Figure 25 RSS distribution at 100pm and 1100 am

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

6 DISCUSSIONS10102006

44

6 DISCUSSIONS 61 Summary and Conclusion While empirical results and performance studies of positioning systems based on location fingerprint have been presented in this report analytical model that can be used as a framework for efficiently designing the positioning systems are not available This thesis design work uses general principles of position estimation such as position estimation of sensors grid spacing etc This design work focuses on accuracy and precision The concept of location fingerprint technique based on RSS us explained in detail in chapter 2 It is found that RSS is random It is possible to approximate the RSS as being normally distributed While the RSS random process is non‐stationary the mean is more or less constant and could be used as the fingerprint of the location Model designed for position estimation is as shown in chapter 4 Accuracy and precision are the two major performance matrices in this study Uniform grid spacing has been used From the experimental analysis I have obtained an accuracy of 3 meters and 35 precision The sensitivity analysis among the system parameters and the performance matrices were done using the model as shown in chapter 4 For instance our study found that the minimum distance between two adjacent positions also affects the positioning performance This is due to the randomness of RSS patterns induced by the ever changing indoor environment

62 Future work The research in this thesis provides a ground work for the future study of efficient design of indoor positioning system The RSSI based position estimation still has limitations First the RSSI value is time dependent Thus the future study should address this issue Second the movement of object is not considered Third orientations of sensors affect the performance which should be taken care off Fourth to improve the performance of the system we need to find a solution to deal with those incomplete or missing data during the off‐line and the on‐line phase Fifth position estimation on multi‐floor and three‐dimensional coordinates is not available

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 110102006

45

APPENDIX 1

Location Fingerprints of Masters Room Time 1315 Observation Time 20 minutes Number of Samples 10000

People Presence SNo Co-ordinates AP1 (0x0001) AP2 (0x0002) AP3 (0x0004) AP4 (0x0008)

1 11 -24 -16 -27 -33 2 13 -26 -8 -34 -31 3 15 -43 -9 -39 -25 4 32 -21 -11 -27 -33 5 61 -6 -23 -32 -28 6 64 -20 -31 -25 -34 7 35 -23 -35 -29 -38 8 37 -29 -35 -22 -27 9 39 -38 -29 -21 -27 10 67 -27 -23 -20 -23 11 611 -26 -29 -23 -30 12 613 -35 -32 -2 -31 13 513 -34 -31 -20 -26 14 11 -39 -22 -29 -17 15 212 -37 -42 -28 -31 16 -212 -38 -33 -29 -6 17 -21 -42 -29 -33 -19 18 -38 -40 -23 -35 -22

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 210102006

46

APPENDIX 2

Begin

Open radio map and raw data files for write

Intialize radio map in memory

Check whether the

Packet is data or Beacon packet If data packet

Save the data to register

Check if data is

Received from all

sensors

Estimate the position of static

Display the position on PC

End Yes

No

A

A

Yes

No

Flow chart representing the main algorithm for position estimation

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

47

APPENDIX 3

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

48

Distribution of RSSI

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

49

REFERENCES [1] T S Rappaport J H Reed and B D Woerner ldquoPosition location using wireless communications on highways of the futureʺ IEEE Commun Mag vol 34 no 10 pp33‐41 Oct 1996 [2] A M Ladd K E Bekris G Marceau A Rudys L E Kavraki and D S Wallach rdquoRobotics‐based location sensing using wireless ethernetʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ02) 2002 pp 227‐238 [3] I Ramani R Bharadwaja and P V Rangan ldquoLocation tracking for media appliances in wireless home networksʺ in Proc International Conference on Multimedia and Expo (ICMEʹ03) July 2003 pp 769‐772 [4] K Pahlavan X Li and J P Makela ldquoIndoor geolocation science and technologyʺ IEEE Commun Mag vol 40 no 2 pp 112‐118 Feb 2002 [5] Konard Lorincz and Matt Welsh ldquoMote Track A Robust Decentralized Approach to RF‐Based Location Trackingrdquo [6] Paramvir Bahl and Venkata N Padmanabham ldquoRADAR An In‐Building RF‐based User Location and Tracking Systemrdquo [7] Kamol Kaemarungsi ldquoDesign of Indoor positioning system based on location fingerprint techiniquerdquo University of Pittsburgh 2005 [8] Fredrik Linnarsson Cheng Peng and Bengt Oelmann ldquoSENTIO A Hardware Platform for Rapid Prototyping of Wireless Sensor Networksrdquo [9] S Saha K Chaudhuri D Sanghi and P Bhagwat ldquoLocation determination of a mobile device using IEEE 80211b access point signalsʺ in Proc IEEE Wireless Communications and Networking Conference (WCNCʹ03) New Orleans LA Mar 2003 pp 1987‐1992 [10] T Roos P Myllymaki H Tirri P Misikangas and J Sievanen ldquoA probabilistic approach to WLAN user location estimationʺ International Journal of Wireless Information Networks vol 9 no 3 pp 155‐164 July 2002 [11] R Battiti M Brunato and A Villani ldquoStatistical learning theory for location fingerprinting in wireless LANrsquosʺ Technical Report Oct 2002 [Online] Available httprtmscienceunitnitraquobattitiarchive86pdf [12] P Prasithsangaree P Krishnamurthy and P K Chrysanthis ldquoAn indoor position location with wireless LANsʺ in Proc IEEE International Symposium on Personal Indoor and Mobile Radio Communications (PIMRCʹ02) Lisbon Portugal Sept 2002 [13] M A Youssef A Agrawala and A U Shankar ldquoWLAN location determination via clustering and probability distributionsʺ in Proc IEEE

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

50

International Conference on Pervasive Computing and Communications (PerComʹ03) Dallas‐Fort Worth TX Mar 23‐26 2003 pp 23‐26 [14] Z Xiang S Song J Chen H Wang J Huang and X Gao ldquoA wireless LAN‐based indoor positioning technologyʺ IBM Journal of Research and Development vol 48 no 56 pp 617‐626 SeptNov 2004 [15] R Want A Hopper V Falcao and J Gibbons ldquoThe active badge location systemʺACM Transactions on Information Systems vol 40 no 1 pp 91‐102 Jan 1992 [16] N B Priyantha A Chakraborty and H Balakrishnan ldquoThe cricket location‐support systemʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ00) Boston MA Aug 2000 pp 32‐43 [17] Holger K Willig A (2005) Protocols and Architectures for Wireless Sensor Networks London [18] IEEE (2003) Part 154 Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low‐Rate Wireless Personal Area Networks (LR‐WPANs) New York [19] Chipcon CC2420 802154 compliant radio httpwwwchipconcom [20] ZigBee Specification v10 ZigBee Alliance [21] Dimitrios Lymberopoulos Quentin Lindsey and Andreas Savvides ldquoAn Empirical Analysis of Radio Signal Strength Variability in IEEE 802154 Networks using Monopole Antennasrdquo [22] Kiran Yedavalli Bhaskar Krishnamachari Sharmila Ravula and Bhaskar Srinivasan ldquoEcolocation A sequence based technique for RF localization in wireless sensor networksrdquo Robert Bosch Research and Technology Centre Palo Alto CA

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

20

message using slotted CSMA‐CA the coordinator may or may not acknowledge the command message and through slotted CSMA‐CA sends the pending message The device acknowledges the received message After that the coordinator removes the message from the beaconrsquos pending list and completes the transaction In a non‐beacon enable network the device periodically sends a MAC command‐request frame to the coordinator The coordinator acknowledges the data request Then by using un‐slotted CSMA‐CA if it has a pending message it transmits it If it has not it respond with a data frame with a ldquozero‐length payloadrdquo To complete the transaction the device acknowledges the data reception

Figure 7 Communication from a device to a PAN coordinator in (a) a beacon‐enabled network and (b) a non‐beacon enabled network [18]

Peer‐to‐peer is the last type of data transfer In this situation the devices are free to communicate with any other device within their communication range In a peer‐to‐peer PAN the devices can ldquoeither receive constantly or synchronize with each otherrdquo If they are receiving constantly to transmit data they use un‐slotted CSAM‐CA In the second case synchronization must be achieved first (IEEE 802154 standard [IEEE 2003]) The IEEE 802154 (2003) standard establishes MAC and PHY standards for low‐cost low‐power and high‐density node deployments In addition to the above PHY and MAC characteristics IEEE 802154 provides a security baseline including ldquothe ability to maintain an ACL and use symmetric cryptographyrdquo for data encryption The algorithm that is used for encryption is the AES However the higher level layers decide when security is need The upper layers are in general responsible for device authentication and key management The next section introduces the ZigBee standard which

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

21

encapsulates the IEEE 802154 and provides additional standardization for the higher levels

Figure 8 Communication from a PAN coordinator to a device in (a) a beacon‐enabled network and (b) a non‐beacon enabled network [18]

242 ZigBee Heily (2004) defines ZigBee as ldquoa rapidly growing worldwide non‐profit industry consortiumrdquo whose mission is ldquoto define a reliable cost‐effective low‐power wirelessly networked monitoring and control product based on an open global standardrdquo Figure 9 illustrates the areas of interest for different wireless communication standards

Figure 9 Overview of the coverage for different wireless communication

standards [17]

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

22

ZigBee a new standard which became publicly available in June 2005 is based on the IEEE 802154 standard It expands the IEEE 802154 by adding the framework for ldquothe network security and applicationrdquo (Craig 2005) Figure 10 presents the IEEE 802154ZigBee stack and the areas of responsibilities

Figure 10 IEEE 802154ZigBee stack [20]

Craig (2005) mentions three networking topologies that the standard covers the star mesh and cluster tree (figure 11) The ZigBee standard works on top of the IEEE 802154 addressing schema by using the standard 64‐bit and the short 16‐bit addressing Kinney (2005) summarizes the ZigBee network layer responsibilities the successful establishment of a new the network and successful new device configuration addressing assignment network synchronization frames security and message routing ZigBee further distinguishes the concept of the physical devices (RFD FFD) by using the notion of ldquological devicesrdquo ldquoZigBee Coordinatorrdquo is the first type of logical devices It is responsible for initializing maintaining and managing the network Under the coordinator in the network hierarchy is the ldquoZigBee routerrdquo which is responsible for controlling the message routing between the nodes Finally the ldquoZigBee End Devicerdquo acts as the end point of the network

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

23

structure The tasks that an end device can perform are specified in the ldquoApplication Profilesrdquo (Craig 2005)

Figure 11 IEEE 802154ZigBee stack [17]

The ZigBee specifications (2005) summarize the security services provided by ZigBee ldquokey establishment key transport frame protection and device managementrdquo ZigBee builds its security mechanism using symmetric key cryptography The security services also depend on the associated layer thus as the figure 10 shows the security mechanism covers the network and the application layer In addition if a MAC frame needs security protection the MAC layer is able to secure it Moreover the notion of end‐to‐end security is supported the source and destination devices have access and use the same share key In the MAC layer the 802154 AES mechanism provides the proper security The mechanism protects ldquothe confidentiality integrity and authenticity of the MAC framesrdquo (Kinney 2005) An auxiliary header field in front of the MAC payload indicates if the frame is encrypted or not The MAC framesrsquo integrity is supported by calculating and using a MIC at the end of the MAC payload In addition to the AES nonce is used to provide MAC confidentiality and authenticity Figure 12 illustrates a MAC frame with security For different security aspects the MAC layer uses different mode of the AES for the encryption it uses the AES in Counter (CTR) mode and for the integrity the

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

24

CBC‐MAC Finally the combination (CCM) of the above two modes is available providing both encryption and integrity (Kinney 2005)

Figure 12 ZigBee secure frame in MAC layer [17]

In the network layer the CCM (a modified MAC layer CCM mode) is used for encryption Because the network layer uses only the CCM mode a single key is used for all different security options The network layer security message format is similar to the MAC frame it is presented in the figure 13 Finally although the network layer is responsible for securing its layer messages the above layers specify the keys and the CCM option for each frame (Kinney 2005)

Figure 13 ZigBee secure frame in network layer [17]

Security in the application layer works similar to the network and MAC layers It uses the ldquolink keyrdquo or the ldquonetwork keyrdquo to secure the message and then encapsulate it inside a set of fields similar to the network format (figure 14) Other security responsibilities that the application layer has are to provide the ZDO and the applications with device management services key establishment and key transport (ZigBee specifications 2005)

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

25

Figure 14 ZigBee secure frame in application layer [17]

The ZigBee application layer contains the manufacturer‐defined application objects the ZDO and the application sub‐layer In addition to the security responsibilities the application sub‐layer binds devices based on their duties and needs maintains the binding tables and forwards messages between them The application sub‐layer also discovers the neighbour devices for a given device The ZDO is responsible for determining the devicersquos duty in the network for communicating using binding requests and for supporting security as was mentioned above The sub‐layer that implements the actual application is the manufacturer‐defined application object (Kinney 2005) The ZigBee specifications are a great step toward the wireless sensors networks standardization They cover all the OSI layers from the physical to the application providing guidance to the developers The specifications are the product of the ZigBee alliances a consortium of companies and academic institutes Thus they not only produce the specifications but also are the first use them The next section goes beyond the layer architecture and standardization and introduces the OS field another crucial element of WSNs

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

26

3 RECEIVED SIGNAL STRENGTH (RSS) Majority of the existing methods leverage the existence of IEEE 80211 base stations with powerful radio transmit powers of approximately 100mW per base station Such radios are in a different class from the low power IEEE 802154 compliant radios that typically transmit at low power levels ranging from 52mW to 29mW [19] The wide availability of larger number of IEEE 802154 radios in the research community has revived the interest for signal strength based localization in sensor network Despite of rapidly increasing popularity of IEEE 802154 radios and signal strength localization there is a lack of detailed characterization of the fundamental factors contributing to large signal strength variation This chapter investigates some of the properties of RSS observed during testing and analysis of research work collected during the period of thesis The analysis of RSS values is needed to understand the underlying features of location dependent RSS patterns and location fingerprints An understanding of the properties of the RSS values for location fingerprinting can assist in improving the design of positioning algorithms and in deployment of indoor positioning systems

31 Parameters that affect RSS Different factors that affect the indoor RSS are the userrsquos presenceabsence hisher orientation time of the day building type and material distance from transmitter and type of radio These factors are by no means exhaustive Other possible factors are antenna orientation directionality and type Table 10 classify the parameters which affect RSS [7]

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

27

Effect on Factors Options

1 Proximity of user Userrsquos presence or absence

2 Orientation of user and terminal

North East South West

Data Collection

3 Make of Radio 4 Time of measurement Time of day amp days of

week 5 Period of measurement

Second minute hour

7 Interference Co‐channel adjacent co‐channel

Statistics

8 Building environment Small offices or large hall

Table 10 Classification of factors the affect RSS [7]

Transmitter variability Different transmitters behave differently even when they are configured exactly in the same way In practice this means that when a transmitter is configured to send packets at a power level of d dBm then the transmitter will send these packets at a power level that is very close to d dBm but not necessarily exactly equal to d dBm This can alter the received signal strength indication and thus it can lead to inaccurate distance estimation

Receiver variability The sensitivity of the receivers across different radio chips is different In practice this means that the RSSI value recorded at different receivers can be different even when all the other parameters that affect the received signal strength are kept constant

Antenna orientation Each antenna has its own radiation pattern that is not uniform In practice this means that the RSSI value recorded at the receiver for a given pair of communicating nodes and for a given distance between them varies as the pair wise antenna orientations of the transmitter and the receiver are changed

Multi‐path fading and shadowing in the RF channel In indoor environments the transmitted signals get reflected after hitting on the walls andor on other objects in the room such as furniture Both the original signal and the reflected signal reach the receiver almost at the same time since they both travel at the speed of light As a result of this the receiver is not able to

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

28

distinguish the two signals and it measures the received signal strength for both of them

32 Related work analysis A number of indoor location tracking systems have been proposed in the literature based on RF signals ultrasound infrared and some combinations of these As mentioned previously RF based positioning system are widely studied as shown in Table 2 Given a model of radio signal propagation in a building or other environment RSS can be used to estimate the distance between the transmitter and receiver Most of the algorithms (Table 2) have been evaluated in practice using 80211b compliant wireless radios and seem to produce a location error of 15ft to 20ft for an indoor network area of approximately 225ft x 144ft According to the results of this study all the localization techniques produce approximately the same location error over a range of environments Ecolocation [22] Mote Track [5] are two of the few localization algorithms that have been evaluated on a real sensor network that uses a low power wireless radio Ecolocation reports a location error of 10ft for a very small outdoor network deployment area (26ft x 49ft) Mote Track reports a location error of approximately 13ft for an indoor network deployment area of 18751ft2 In general the RSSI based localization algorithms can be divided into two broad categories the map based and the distance (or area) prediction based algorithms In the case of the map based algorithms the deployment area is divided into a large set of sample points that cover the whole area Most techniques collect one or more signal strength measurements from all visible beacon nodes and for each sample point Each sample point is then mapped to either a signal strength vector or a signal strength probability distribution This process generates an RSSI fingerprint map which is unique with respect to the beacon locations and the deployment area This fingerprint map is stored to a central base station A node that is not aware of its location collects one or more signal strength measurements from all the visible beacon nodes and creates its own RSSI fingerprint that is sent to the central base station The base station computes nodersquos location as the sample pointrsquos location whose RSSI fingerprint distance from the unknown nodersquos RSSI fingerprint is the minimum among all the sample points Despite the fact that the map based RSSI localization methods produce relatively small location errors they are not attractive mainly because they require an excessive amount of profiling data and because they are not fully distributed The detailed profiling of a building is a major holdback because it is complex time consuming and depends on beacon locations Therefore for different buildings or even for the

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

29

same building and different beacon locations different RSSI maps have to be obtained The second category of RSSI based algorithms are the distance (or area) prediction algorithms which focus on minimizing the amount of profiling data In distance prediction based methods raw RSSI data are directly mapped to distances through a signal propagation model In some cases distances are not explicitly measured but they are taken into consideration through assumptions on the underlying distributions In the case of outdoor deployments where there are no obstacles a single signal propagation model is all the profiling data required In the case of indoor deployments the same applies but now the signal propagation model might be different across different buildings with different furniture and wall arrangements Given an underlying signal propagation model raw RSSI data can be mapped to specific distances These distances can be either used directly in order to perform localization or assigned a probability and used in a learning basedprobabilistic localization algorithm Obviously the accuracy of the distance prediction based methods depends heavily on the accuracy of the signal propagation model used for translating RSSI values to distances

33 Properties of RSSI Changes in signal due to propagation indoors are difficult to predict because of dense environment and propagation effects such as reflection diffraction and scattering The multi‐path fading effect which is the result of either constructive or destructive combination of multiple signal copies at the receiver causes the received signal to fluctuate around a mean value at a particular location The received signal is usually modelled by the combined effects of large‐scale fading and small‐scale fading The large‐scale fading component describes the signal attenuation as the signal travels over a distance and is absorbed by material such as walls and floors along the way to the receiver This component predicts the mean of the RSS and usually has a lognormal distribution On the other hand the small scale fading component explains the dramatic fluctuation of the signal due to multi‐path fading On the other hand the small scale‐fading component explains the dramatic fluctuation of the signal due to multi‐path fading If there is no LOS component the small‐scale‐fading is often modelled with a Rayleigh distribution Such scenarios are commonly called NLOS If there is a LOS component the small‐scale‐fading is typically modelled by a Rician distribution Note that when the RSS is measured the measurement averages out the small‐scale‐fading effects

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

30

4 OBJECT TRACKING 41 Introduction This chapter presents the object‐tracking application the main objective of this thesis work The application is an algorithmic‐software implementation written in Embedded C which is capable of receiving data messages and generating object position estimation data The implementation of the object‐tracking application makes use of the SENTIO platform and uses the background knowledge introduced in the preceding chapters The motivation for the object‐tracking application is to determine the position of an object in indoor environments (Eg Home) The application acts as an interface which collects the raw data that the sensors generate (numerical values) It makes the proper comparisons and algorithmic processes to identify and track the object while it is static inside the area covered by the wireless sensor network Finally with the detection and tracking data it alerts the user or another system The following sections provide an overview of the object‐tracking application

42 Overview of hardware product Before describing the object tracking application it is important to know the characteristics of hardware product The SENTIO platform (figure 15) is built in modular fashion and has the following key features [8]

Flexibility This platform is very flexible in such a way that it is made easy to add or change the hardware for each sensor task depending on the requirement in the WSN For a specific platform this can be hard or sometimes even impossible to accomplish

Sensor resources Resources in a sensor module includes the ability of usage of controllerrsquos resources as much as possible such as ADC‐channels interrupts and digital Input‐Outputrsquos Local voltage regulation should also be possible since many sensors require higher supply voltages than digital Integrated Circuits do Further support of a shut‐down mechanism to power down the sensor modules when not needed

Computation and memory The processor module based on microcontroller should support many devices in a microcontroller family and support different power supply voltages enabling full range of clock frequencies All power‐down modes of the devices should be

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

31

supported The platform should support different processor modules including FPGArsquos and very simple microcontrollers

Communication The focus should be on enabling an IEEE 802154 compliant communication interface With or without ZigBee stack this will provide a flexible platform for exploring different network topologies Support for power‐down of the entire communication module should be supported

Cost and form factor High flexibility is in conflict with low cost and small size The primary objective should be flexibility but the size should be considered in order to enable sensor device integration in systems where space is limited

Figure 15 SENTIO Platform [8]

The communication subsystem of the SENTIO sensor node was designed so that the correct operation of the radio chip is ensured The radio chip is powered by the on‐board voltage regulator and thus fluctuations in the battery voltage level do not affect the operation of the communication subsystem This platform uses Chipcon CC2420 IEEE 802154 radio transceiver operates in the 24GHz ISM band and includes a digital direct sequence spread spectrum (DSSS) modem providing a spreading gain of 9dB and an effective data rate of 250Kbps It was specifically designed for low power wireless applications and supports 8 discrete power levels 0dBm minus1dBm minus3dBm minus5dBm minus7dBm minus10dBm minus15dBm and minus25dBm at which its power consumption varies from 29mW to 52mW [21] A built‐in received signal strength indicator gives an 8‐bit digital value RSSIVAL The RSSIVAL is always averaged over 8 symbol periods (128μs) and a status bit indicates when the RSSIVAL is valid (meaning that the receiver was enabled for at least 8 symbol periods) The

Antenna

Communication Module

Processing Module

Sensing Module

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

32

power P at the RF pins can be obtained directly from RSSIVAL using the following equation

P = RSSIVAL + RSSIOFFSET [dBm] where the RSSIOFFSET is found empirically from the front‐end gain and it is approximately equal to minus45dBm In the next sections when we refer to the RSSI value we refer to the RSSIVAL and not the power P unless otherwise stated

43 Experimental Testbed The experimental test bed for object tracking application is as shown in figure 16 Experiment was conducted in Masterrsquos room at Mid Sweden University A DELL laptop is connected to SENTIO platform and software were primarily used to collect samples of RSS from APrsquos The software monitors IEEE 802154 radio frequency channels which operate in the 24GHz band

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

1030

BC - 1030

AP 3 AP 1

AP 2

AP 4

BS

HEWLETT

PACKARD

Figure 16 Layout of Masterʹs Room

A three dimensional scalable testbed is used for indoor sensor network deployments The testbed illustrated in figure 16 is a 3‐D structure measuring 16m (L) x 12m (W) x 3m (H) and it is designed to host a large number of static nodes as shown in figure 16 Arrow marks in the above figure represent the location where fingerprints were taken The centrepiece of our infrastructure

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

33

is the SENTIO sensor node [8] an open‐source general purpose sensing platform designed with AT Mega 128 microprocessor and the IEEE 802154 compliant CC2420 radio from Chipcon [19]

44 Obtaining data from the sensor network The next important step in the development of the object tracking application is to obtain the data from the sensor network and send it to the base station From there the data is transmitted through a serial port to the computer that runs the object tracking application When the data arrives at the serial port the application must capture it This paragraph gives a brief explanation of how and what kind data is transmitted by the static object fixed sensors and how this data is being used to estimate the position of the object First the static object transmits Beacon packet continuously which is received by all the fixed APrsquos The beacon packet format is as shown in table 6 Then the fixed APrsquos calculates the 8‐bit value of RSSI from the transmitted beacon packet and then sends the resultant RSSI value as a form of data packet (table 7) to the BS which calculates the position and sends back to the object

45 Object tracking application Testing and Evaluation The purpose of this section is to discuss and evaluate the object tracking application Finally we are ready with the experimental testbed as shown in figure 16 As shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors placed at the corners of the masterrsquos room BS is placed at the centre of the room connected to a PC All the nodes are power connected before an actual measurement takes place At first the object is placed at random position inside the room and transmits beacon packets continuously Then these packets are received by the fixed APrsquos which calculate the RSSI from the packet and then averages it until it receives 50 beacon packets and then transmits the data to the BS (figure 17) Then the BS waits until it receives the data from all the APrsquos Once it receives from all the APrsquos it calculates the position of the object (figure 18) The application was developed to demonstrate a real world use of a WSN in object tracking In evaluating the final position obtained with respect to the actual position we conclude that the object‐tracking was successful Although

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

34

it is a research prototype object tracking application is a simple stable stand‐alone system capable of demonstrating an important application of WSN It takes raw data from the WSN aggregates it evaluates it and based on the evaluation tracks the object The accuracy of estimating the position is less than 5m Table 11 below shows a log of readings taken during test

Start

Intialize micro‐controller and transeciever in receieve mode

If received_packet=

Beacon

Average RSSI values

Increase the counter

If countergt1000050

A

No

Yes

B

B

Yes

No

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

35

A

Reset counter

Set transceiever in transmit mode

Transmit averaged RSSI to Main sensor and set it back to receive mode

10000 samples ‐ Offline

50 samples ‐ Online

Figure 17 Flow chart representing the algorithm of fixed sensors

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

36

Start

Intialize micro‐controller and transeciever in receieve mode

Received RSSI values from all fixed Sensors

Open radio map

Calculate Euclidean distance between obtained RSSI values from fixed sensors and fingerprints

in radio map

No

Yes

Bubble sort for least error in signal strength values

A

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

37

A

Get the location of the object from the radiousing the least error value

Display location of object on PC

End

Figure 18 Flow chart representing the algorithm of main sensor

Table 11 Accuracy of positioning system

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

38

5 RESULTS This chapter presents how the system parameters such as number of samples mean or median etchellipare finalized All the tests are performed in masterrsquos room at Mid Sweden University This chapter is divided into three subsections First in Section 51 it reviews results obtained from samples test Section 52 discusses one dimensional test results and finally section 53 discusses results of two dimensional tests

51 Measurement setup A Dell laptop was connected to a SENTIO platform and is loaded with software (Appendix 2) This setup was used to determine number of samples required during object tracking mean or median position estimation algorithm and one dimensional test The setup is as follows

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

103

0

BC - 1030

AP 1AP 2

BS

HEWLETT

PACKARD

Figure 19 Test bed for one dimensional test in Masters Room

Two SENTIO nodes (AP1 and AP2 as shown above) were separated by a distance of 10 meters and these sensors are known as fixed sensors which are fixed to the ceiling such that there antenna is faced towards the ground They are linear is position A BS was connected to Dell laptop and it was placed in such a way that its antenna was faced towards the ceiling Another SENTIO

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

39

node was used as an object and its orientation was same as BS Figure 19 represents the complete setup for object tracking application Mobile node was moved between these fixed ones at regular intervals of one meter and RSSI values were logged at each interval

52 Number of samples test This test was performed to determine number of samples required for accurate positioning and either mean of the obtained RSSI values or median of them Figure 19 represents the setup for number of samples test Once the setup is ready the measurements are taken as follows

1 An object was tagged with SENTIO node and was placed stationary linear to the fixed sensors

2 10000 samples (RSSI) were taken at regular intervals of 1 meter up to 10 meters

3 The sample values were logged at every interval 4 Using these logged samples I have plotted graphs such as distribution

of RSS values (figure 20 21) meanmedian at intervals of 1 meter using MATLAB

5 The mean and median for various numbers of samples is as shown in figure 22

Figure 20 Histogram plot of RSSI values at 1m and 4m

Figure 21 Histogram plot of RSSI values at 6m and 8m

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

40

Figure 22 Distance vs RSSI

From the above graphs following conclusions were made 1 From the histogram plots we can observe that the distribution of RSSI

values follow Gaussians distribution law 2 As we can see from figure 22 that median curve has drastic changes

when compared to mean curve Hence mean is considered while estimating the position

3 From the observations we conclude that the number of samples does not matter since mean curve tends to be same for various numbers of samples

4 Hence 10000 samples are considered in order to get a proper location fingerprint during off‐line phase and during online phase 50 samples are enough to estimate the position of object

53 0ne dimensional test Measurement setup for one dimensional test is as shown in figure 19 This test was conducted to check the performance of algorithm and how accurate it was At first ie during offline phase fingerprints at each interval averaging 10000 samples were taken During online phase Euclidean algorithm was used estimate the position averaging 50 samples of RSSI From the results we

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

41

can realize that the error was less than 2 meters Table 12 shows the result of location estimation in one dimensional case

Table 12 One dimensional position estimation results

54 Two dimensional test This test is the final stage of my thesis work Experimental testbed for this stage is as shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors fixed at the corners of the room and a BS at the centre of the room There are 18 pre‐determined locations where the fingerprints (RSSI) were taken The fingerprints are as shown in Appendix 1 All the readings were taken in presence of userrsquos The algorithm for position estimation is shown in Appendix 2 Position estimation test is performed as follows

Tagged object is placed at random inside the masterrsquos room All the sensors including the base station and object are switched on Using the algorithm (Appendix 2) the BS estimates the position of the object and displays it on the PC

This test was performed using various numbers of samples to test which has a better accuracy Figure 23 represents the error rate using various numbers of samples From figure 23 we can conclude that 50 numbers of samples is enough to estimate the position of object with an accuracy of 3m

55 Causes of error in location determination The randomness of RSS is the major cause of error in any indoor

positioning system that uses the RSS for location inference If there is no randomness in the RSS and its location fingerprint every indoor positioning systems of this type will have no location detection error For example I have recorded RSS at one particular location during different times of the day From figures 24 and 25 you can observe that RSS values are different at different times of day

The performance of indoor positioning systems depends greatly on the separation of location fingerprints A location fingerprint

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

42

corresponding to a location can be identified correctly if it is difficult to classify it as another fingerprint by a pattern classifier

Path loss is one of the basic signal propagation properties of the RSS that best describes attenuation of transmit signal power over a distance between an access point and a mobile device

Presence of Userrsquos or userrsquos orientation affects RSS

Figure 23 Error Rate vs Number of samples

Figure 24 RSS distribution at 214 pm

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

43

Figure 25 RSS distribution at 100pm and 1100 am

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

6 DISCUSSIONS10102006

44

6 DISCUSSIONS 61 Summary and Conclusion While empirical results and performance studies of positioning systems based on location fingerprint have been presented in this report analytical model that can be used as a framework for efficiently designing the positioning systems are not available This thesis design work uses general principles of position estimation such as position estimation of sensors grid spacing etc This design work focuses on accuracy and precision The concept of location fingerprint technique based on RSS us explained in detail in chapter 2 It is found that RSS is random It is possible to approximate the RSS as being normally distributed While the RSS random process is non‐stationary the mean is more or less constant and could be used as the fingerprint of the location Model designed for position estimation is as shown in chapter 4 Accuracy and precision are the two major performance matrices in this study Uniform grid spacing has been used From the experimental analysis I have obtained an accuracy of 3 meters and 35 precision The sensitivity analysis among the system parameters and the performance matrices were done using the model as shown in chapter 4 For instance our study found that the minimum distance between two adjacent positions also affects the positioning performance This is due to the randomness of RSS patterns induced by the ever changing indoor environment

62 Future work The research in this thesis provides a ground work for the future study of efficient design of indoor positioning system The RSSI based position estimation still has limitations First the RSSI value is time dependent Thus the future study should address this issue Second the movement of object is not considered Third orientations of sensors affect the performance which should be taken care off Fourth to improve the performance of the system we need to find a solution to deal with those incomplete or missing data during the off‐line and the on‐line phase Fifth position estimation on multi‐floor and three‐dimensional coordinates is not available

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 110102006

45

APPENDIX 1

Location Fingerprints of Masters Room Time 1315 Observation Time 20 minutes Number of Samples 10000

People Presence SNo Co-ordinates AP1 (0x0001) AP2 (0x0002) AP3 (0x0004) AP4 (0x0008)

1 11 -24 -16 -27 -33 2 13 -26 -8 -34 -31 3 15 -43 -9 -39 -25 4 32 -21 -11 -27 -33 5 61 -6 -23 -32 -28 6 64 -20 -31 -25 -34 7 35 -23 -35 -29 -38 8 37 -29 -35 -22 -27 9 39 -38 -29 -21 -27 10 67 -27 -23 -20 -23 11 611 -26 -29 -23 -30 12 613 -35 -32 -2 -31 13 513 -34 -31 -20 -26 14 11 -39 -22 -29 -17 15 212 -37 -42 -28 -31 16 -212 -38 -33 -29 -6 17 -21 -42 -29 -33 -19 18 -38 -40 -23 -35 -22

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 210102006

46

APPENDIX 2

Begin

Open radio map and raw data files for write

Intialize radio map in memory

Check whether the

Packet is data or Beacon packet If data packet

Save the data to register

Check if data is

Received from all

sensors

Estimate the position of static

Display the position on PC

End Yes

No

A

A

Yes

No

Flow chart representing the main algorithm for position estimation

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

47

APPENDIX 3

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

48

Distribution of RSSI

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

49

REFERENCES [1] T S Rappaport J H Reed and B D Woerner ldquoPosition location using wireless communications on highways of the futureʺ IEEE Commun Mag vol 34 no 10 pp33‐41 Oct 1996 [2] A M Ladd K E Bekris G Marceau A Rudys L E Kavraki and D S Wallach rdquoRobotics‐based location sensing using wireless ethernetʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ02) 2002 pp 227‐238 [3] I Ramani R Bharadwaja and P V Rangan ldquoLocation tracking for media appliances in wireless home networksʺ in Proc International Conference on Multimedia and Expo (ICMEʹ03) July 2003 pp 769‐772 [4] K Pahlavan X Li and J P Makela ldquoIndoor geolocation science and technologyʺ IEEE Commun Mag vol 40 no 2 pp 112‐118 Feb 2002 [5] Konard Lorincz and Matt Welsh ldquoMote Track A Robust Decentralized Approach to RF‐Based Location Trackingrdquo [6] Paramvir Bahl and Venkata N Padmanabham ldquoRADAR An In‐Building RF‐based User Location and Tracking Systemrdquo [7] Kamol Kaemarungsi ldquoDesign of Indoor positioning system based on location fingerprint techiniquerdquo University of Pittsburgh 2005 [8] Fredrik Linnarsson Cheng Peng and Bengt Oelmann ldquoSENTIO A Hardware Platform for Rapid Prototyping of Wireless Sensor Networksrdquo [9] S Saha K Chaudhuri D Sanghi and P Bhagwat ldquoLocation determination of a mobile device using IEEE 80211b access point signalsʺ in Proc IEEE Wireless Communications and Networking Conference (WCNCʹ03) New Orleans LA Mar 2003 pp 1987‐1992 [10] T Roos P Myllymaki H Tirri P Misikangas and J Sievanen ldquoA probabilistic approach to WLAN user location estimationʺ International Journal of Wireless Information Networks vol 9 no 3 pp 155‐164 July 2002 [11] R Battiti M Brunato and A Villani ldquoStatistical learning theory for location fingerprinting in wireless LANrsquosʺ Technical Report Oct 2002 [Online] Available httprtmscienceunitnitraquobattitiarchive86pdf [12] P Prasithsangaree P Krishnamurthy and P K Chrysanthis ldquoAn indoor position location with wireless LANsʺ in Proc IEEE International Symposium on Personal Indoor and Mobile Radio Communications (PIMRCʹ02) Lisbon Portugal Sept 2002 [13] M A Youssef A Agrawala and A U Shankar ldquoWLAN location determination via clustering and probability distributionsʺ in Proc IEEE

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

50

International Conference on Pervasive Computing and Communications (PerComʹ03) Dallas‐Fort Worth TX Mar 23‐26 2003 pp 23‐26 [14] Z Xiang S Song J Chen H Wang J Huang and X Gao ldquoA wireless LAN‐based indoor positioning technologyʺ IBM Journal of Research and Development vol 48 no 56 pp 617‐626 SeptNov 2004 [15] R Want A Hopper V Falcao and J Gibbons ldquoThe active badge location systemʺACM Transactions on Information Systems vol 40 no 1 pp 91‐102 Jan 1992 [16] N B Priyantha A Chakraborty and H Balakrishnan ldquoThe cricket location‐support systemʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ00) Boston MA Aug 2000 pp 32‐43 [17] Holger K Willig A (2005) Protocols and Architectures for Wireless Sensor Networks London [18] IEEE (2003) Part 154 Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low‐Rate Wireless Personal Area Networks (LR‐WPANs) New York [19] Chipcon CC2420 802154 compliant radio httpwwwchipconcom [20] ZigBee Specification v10 ZigBee Alliance [21] Dimitrios Lymberopoulos Quentin Lindsey and Andreas Savvides ldquoAn Empirical Analysis of Radio Signal Strength Variability in IEEE 802154 Networks using Monopole Antennasrdquo [22] Kiran Yedavalli Bhaskar Krishnamachari Sharmila Ravula and Bhaskar Srinivasan ldquoEcolocation A sequence based technique for RF localization in wireless sensor networksrdquo Robert Bosch Research and Technology Centre Palo Alto CA

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

21

encapsulates the IEEE 802154 and provides additional standardization for the higher levels

Figure 8 Communication from a PAN coordinator to a device in (a) a beacon‐enabled network and (b) a non‐beacon enabled network [18]

242 ZigBee Heily (2004) defines ZigBee as ldquoa rapidly growing worldwide non‐profit industry consortiumrdquo whose mission is ldquoto define a reliable cost‐effective low‐power wirelessly networked monitoring and control product based on an open global standardrdquo Figure 9 illustrates the areas of interest for different wireless communication standards

Figure 9 Overview of the coverage for different wireless communication

standards [17]

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

22

ZigBee a new standard which became publicly available in June 2005 is based on the IEEE 802154 standard It expands the IEEE 802154 by adding the framework for ldquothe network security and applicationrdquo (Craig 2005) Figure 10 presents the IEEE 802154ZigBee stack and the areas of responsibilities

Figure 10 IEEE 802154ZigBee stack [20]

Craig (2005) mentions three networking topologies that the standard covers the star mesh and cluster tree (figure 11) The ZigBee standard works on top of the IEEE 802154 addressing schema by using the standard 64‐bit and the short 16‐bit addressing Kinney (2005) summarizes the ZigBee network layer responsibilities the successful establishment of a new the network and successful new device configuration addressing assignment network synchronization frames security and message routing ZigBee further distinguishes the concept of the physical devices (RFD FFD) by using the notion of ldquological devicesrdquo ldquoZigBee Coordinatorrdquo is the first type of logical devices It is responsible for initializing maintaining and managing the network Under the coordinator in the network hierarchy is the ldquoZigBee routerrdquo which is responsible for controlling the message routing between the nodes Finally the ldquoZigBee End Devicerdquo acts as the end point of the network

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

23

structure The tasks that an end device can perform are specified in the ldquoApplication Profilesrdquo (Craig 2005)

Figure 11 IEEE 802154ZigBee stack [17]

The ZigBee specifications (2005) summarize the security services provided by ZigBee ldquokey establishment key transport frame protection and device managementrdquo ZigBee builds its security mechanism using symmetric key cryptography The security services also depend on the associated layer thus as the figure 10 shows the security mechanism covers the network and the application layer In addition if a MAC frame needs security protection the MAC layer is able to secure it Moreover the notion of end‐to‐end security is supported the source and destination devices have access and use the same share key In the MAC layer the 802154 AES mechanism provides the proper security The mechanism protects ldquothe confidentiality integrity and authenticity of the MAC framesrdquo (Kinney 2005) An auxiliary header field in front of the MAC payload indicates if the frame is encrypted or not The MAC framesrsquo integrity is supported by calculating and using a MIC at the end of the MAC payload In addition to the AES nonce is used to provide MAC confidentiality and authenticity Figure 12 illustrates a MAC frame with security For different security aspects the MAC layer uses different mode of the AES for the encryption it uses the AES in Counter (CTR) mode and for the integrity the

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

24

CBC‐MAC Finally the combination (CCM) of the above two modes is available providing both encryption and integrity (Kinney 2005)

Figure 12 ZigBee secure frame in MAC layer [17]

In the network layer the CCM (a modified MAC layer CCM mode) is used for encryption Because the network layer uses only the CCM mode a single key is used for all different security options The network layer security message format is similar to the MAC frame it is presented in the figure 13 Finally although the network layer is responsible for securing its layer messages the above layers specify the keys and the CCM option for each frame (Kinney 2005)

Figure 13 ZigBee secure frame in network layer [17]

Security in the application layer works similar to the network and MAC layers It uses the ldquolink keyrdquo or the ldquonetwork keyrdquo to secure the message and then encapsulate it inside a set of fields similar to the network format (figure 14) Other security responsibilities that the application layer has are to provide the ZDO and the applications with device management services key establishment and key transport (ZigBee specifications 2005)

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

25

Figure 14 ZigBee secure frame in application layer [17]

The ZigBee application layer contains the manufacturer‐defined application objects the ZDO and the application sub‐layer In addition to the security responsibilities the application sub‐layer binds devices based on their duties and needs maintains the binding tables and forwards messages between them The application sub‐layer also discovers the neighbour devices for a given device The ZDO is responsible for determining the devicersquos duty in the network for communicating using binding requests and for supporting security as was mentioned above The sub‐layer that implements the actual application is the manufacturer‐defined application object (Kinney 2005) The ZigBee specifications are a great step toward the wireless sensors networks standardization They cover all the OSI layers from the physical to the application providing guidance to the developers The specifications are the product of the ZigBee alliances a consortium of companies and academic institutes Thus they not only produce the specifications but also are the first use them The next section goes beyond the layer architecture and standardization and introduces the OS field another crucial element of WSNs

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

26

3 RECEIVED SIGNAL STRENGTH (RSS) Majority of the existing methods leverage the existence of IEEE 80211 base stations with powerful radio transmit powers of approximately 100mW per base station Such radios are in a different class from the low power IEEE 802154 compliant radios that typically transmit at low power levels ranging from 52mW to 29mW [19] The wide availability of larger number of IEEE 802154 radios in the research community has revived the interest for signal strength based localization in sensor network Despite of rapidly increasing popularity of IEEE 802154 radios and signal strength localization there is a lack of detailed characterization of the fundamental factors contributing to large signal strength variation This chapter investigates some of the properties of RSS observed during testing and analysis of research work collected during the period of thesis The analysis of RSS values is needed to understand the underlying features of location dependent RSS patterns and location fingerprints An understanding of the properties of the RSS values for location fingerprinting can assist in improving the design of positioning algorithms and in deployment of indoor positioning systems

31 Parameters that affect RSS Different factors that affect the indoor RSS are the userrsquos presenceabsence hisher orientation time of the day building type and material distance from transmitter and type of radio These factors are by no means exhaustive Other possible factors are antenna orientation directionality and type Table 10 classify the parameters which affect RSS [7]

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

27

Effect on Factors Options

1 Proximity of user Userrsquos presence or absence

2 Orientation of user and terminal

North East South West

Data Collection

3 Make of Radio 4 Time of measurement Time of day amp days of

week 5 Period of measurement

Second minute hour

7 Interference Co‐channel adjacent co‐channel

Statistics

8 Building environment Small offices or large hall

Table 10 Classification of factors the affect RSS [7]

Transmitter variability Different transmitters behave differently even when they are configured exactly in the same way In practice this means that when a transmitter is configured to send packets at a power level of d dBm then the transmitter will send these packets at a power level that is very close to d dBm but not necessarily exactly equal to d dBm This can alter the received signal strength indication and thus it can lead to inaccurate distance estimation

Receiver variability The sensitivity of the receivers across different radio chips is different In practice this means that the RSSI value recorded at different receivers can be different even when all the other parameters that affect the received signal strength are kept constant

Antenna orientation Each antenna has its own radiation pattern that is not uniform In practice this means that the RSSI value recorded at the receiver for a given pair of communicating nodes and for a given distance between them varies as the pair wise antenna orientations of the transmitter and the receiver are changed

Multi‐path fading and shadowing in the RF channel In indoor environments the transmitted signals get reflected after hitting on the walls andor on other objects in the room such as furniture Both the original signal and the reflected signal reach the receiver almost at the same time since they both travel at the speed of light As a result of this the receiver is not able to

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

28

distinguish the two signals and it measures the received signal strength for both of them

32 Related work analysis A number of indoor location tracking systems have been proposed in the literature based on RF signals ultrasound infrared and some combinations of these As mentioned previously RF based positioning system are widely studied as shown in Table 2 Given a model of radio signal propagation in a building or other environment RSS can be used to estimate the distance between the transmitter and receiver Most of the algorithms (Table 2) have been evaluated in practice using 80211b compliant wireless radios and seem to produce a location error of 15ft to 20ft for an indoor network area of approximately 225ft x 144ft According to the results of this study all the localization techniques produce approximately the same location error over a range of environments Ecolocation [22] Mote Track [5] are two of the few localization algorithms that have been evaluated on a real sensor network that uses a low power wireless radio Ecolocation reports a location error of 10ft for a very small outdoor network deployment area (26ft x 49ft) Mote Track reports a location error of approximately 13ft for an indoor network deployment area of 18751ft2 In general the RSSI based localization algorithms can be divided into two broad categories the map based and the distance (or area) prediction based algorithms In the case of the map based algorithms the deployment area is divided into a large set of sample points that cover the whole area Most techniques collect one or more signal strength measurements from all visible beacon nodes and for each sample point Each sample point is then mapped to either a signal strength vector or a signal strength probability distribution This process generates an RSSI fingerprint map which is unique with respect to the beacon locations and the deployment area This fingerprint map is stored to a central base station A node that is not aware of its location collects one or more signal strength measurements from all the visible beacon nodes and creates its own RSSI fingerprint that is sent to the central base station The base station computes nodersquos location as the sample pointrsquos location whose RSSI fingerprint distance from the unknown nodersquos RSSI fingerprint is the minimum among all the sample points Despite the fact that the map based RSSI localization methods produce relatively small location errors they are not attractive mainly because they require an excessive amount of profiling data and because they are not fully distributed The detailed profiling of a building is a major holdback because it is complex time consuming and depends on beacon locations Therefore for different buildings or even for the

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

29

same building and different beacon locations different RSSI maps have to be obtained The second category of RSSI based algorithms are the distance (or area) prediction algorithms which focus on minimizing the amount of profiling data In distance prediction based methods raw RSSI data are directly mapped to distances through a signal propagation model In some cases distances are not explicitly measured but they are taken into consideration through assumptions on the underlying distributions In the case of outdoor deployments where there are no obstacles a single signal propagation model is all the profiling data required In the case of indoor deployments the same applies but now the signal propagation model might be different across different buildings with different furniture and wall arrangements Given an underlying signal propagation model raw RSSI data can be mapped to specific distances These distances can be either used directly in order to perform localization or assigned a probability and used in a learning basedprobabilistic localization algorithm Obviously the accuracy of the distance prediction based methods depends heavily on the accuracy of the signal propagation model used for translating RSSI values to distances

33 Properties of RSSI Changes in signal due to propagation indoors are difficult to predict because of dense environment and propagation effects such as reflection diffraction and scattering The multi‐path fading effect which is the result of either constructive or destructive combination of multiple signal copies at the receiver causes the received signal to fluctuate around a mean value at a particular location The received signal is usually modelled by the combined effects of large‐scale fading and small‐scale fading The large‐scale fading component describes the signal attenuation as the signal travels over a distance and is absorbed by material such as walls and floors along the way to the receiver This component predicts the mean of the RSS and usually has a lognormal distribution On the other hand the small scale fading component explains the dramatic fluctuation of the signal due to multi‐path fading On the other hand the small scale‐fading component explains the dramatic fluctuation of the signal due to multi‐path fading If there is no LOS component the small‐scale‐fading is often modelled with a Rayleigh distribution Such scenarios are commonly called NLOS If there is a LOS component the small‐scale‐fading is typically modelled by a Rician distribution Note that when the RSS is measured the measurement averages out the small‐scale‐fading effects

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

30

4 OBJECT TRACKING 41 Introduction This chapter presents the object‐tracking application the main objective of this thesis work The application is an algorithmic‐software implementation written in Embedded C which is capable of receiving data messages and generating object position estimation data The implementation of the object‐tracking application makes use of the SENTIO platform and uses the background knowledge introduced in the preceding chapters The motivation for the object‐tracking application is to determine the position of an object in indoor environments (Eg Home) The application acts as an interface which collects the raw data that the sensors generate (numerical values) It makes the proper comparisons and algorithmic processes to identify and track the object while it is static inside the area covered by the wireless sensor network Finally with the detection and tracking data it alerts the user or another system The following sections provide an overview of the object‐tracking application

42 Overview of hardware product Before describing the object tracking application it is important to know the characteristics of hardware product The SENTIO platform (figure 15) is built in modular fashion and has the following key features [8]

Flexibility This platform is very flexible in such a way that it is made easy to add or change the hardware for each sensor task depending on the requirement in the WSN For a specific platform this can be hard or sometimes even impossible to accomplish

Sensor resources Resources in a sensor module includes the ability of usage of controllerrsquos resources as much as possible such as ADC‐channels interrupts and digital Input‐Outputrsquos Local voltage regulation should also be possible since many sensors require higher supply voltages than digital Integrated Circuits do Further support of a shut‐down mechanism to power down the sensor modules when not needed

Computation and memory The processor module based on microcontroller should support many devices in a microcontroller family and support different power supply voltages enabling full range of clock frequencies All power‐down modes of the devices should be

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

31

supported The platform should support different processor modules including FPGArsquos and very simple microcontrollers

Communication The focus should be on enabling an IEEE 802154 compliant communication interface With or without ZigBee stack this will provide a flexible platform for exploring different network topologies Support for power‐down of the entire communication module should be supported

Cost and form factor High flexibility is in conflict with low cost and small size The primary objective should be flexibility but the size should be considered in order to enable sensor device integration in systems where space is limited

Figure 15 SENTIO Platform [8]

The communication subsystem of the SENTIO sensor node was designed so that the correct operation of the radio chip is ensured The radio chip is powered by the on‐board voltage regulator and thus fluctuations in the battery voltage level do not affect the operation of the communication subsystem This platform uses Chipcon CC2420 IEEE 802154 radio transceiver operates in the 24GHz ISM band and includes a digital direct sequence spread spectrum (DSSS) modem providing a spreading gain of 9dB and an effective data rate of 250Kbps It was specifically designed for low power wireless applications and supports 8 discrete power levels 0dBm minus1dBm minus3dBm minus5dBm minus7dBm minus10dBm minus15dBm and minus25dBm at which its power consumption varies from 29mW to 52mW [21] A built‐in received signal strength indicator gives an 8‐bit digital value RSSIVAL The RSSIVAL is always averaged over 8 symbol periods (128μs) and a status bit indicates when the RSSIVAL is valid (meaning that the receiver was enabled for at least 8 symbol periods) The

Antenna

Communication Module

Processing Module

Sensing Module

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

32

power P at the RF pins can be obtained directly from RSSIVAL using the following equation

P = RSSIVAL + RSSIOFFSET [dBm] where the RSSIOFFSET is found empirically from the front‐end gain and it is approximately equal to minus45dBm In the next sections when we refer to the RSSI value we refer to the RSSIVAL and not the power P unless otherwise stated

43 Experimental Testbed The experimental test bed for object tracking application is as shown in figure 16 Experiment was conducted in Masterrsquos room at Mid Sweden University A DELL laptop is connected to SENTIO platform and software were primarily used to collect samples of RSS from APrsquos The software monitors IEEE 802154 radio frequency channels which operate in the 24GHz band

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

1030

BC - 1030

AP 3 AP 1

AP 2

AP 4

BS

HEWLETT

PACKARD

Figure 16 Layout of Masterʹs Room

A three dimensional scalable testbed is used for indoor sensor network deployments The testbed illustrated in figure 16 is a 3‐D structure measuring 16m (L) x 12m (W) x 3m (H) and it is designed to host a large number of static nodes as shown in figure 16 Arrow marks in the above figure represent the location where fingerprints were taken The centrepiece of our infrastructure

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

33

is the SENTIO sensor node [8] an open‐source general purpose sensing platform designed with AT Mega 128 microprocessor and the IEEE 802154 compliant CC2420 radio from Chipcon [19]

44 Obtaining data from the sensor network The next important step in the development of the object tracking application is to obtain the data from the sensor network and send it to the base station From there the data is transmitted through a serial port to the computer that runs the object tracking application When the data arrives at the serial port the application must capture it This paragraph gives a brief explanation of how and what kind data is transmitted by the static object fixed sensors and how this data is being used to estimate the position of the object First the static object transmits Beacon packet continuously which is received by all the fixed APrsquos The beacon packet format is as shown in table 6 Then the fixed APrsquos calculates the 8‐bit value of RSSI from the transmitted beacon packet and then sends the resultant RSSI value as a form of data packet (table 7) to the BS which calculates the position and sends back to the object

45 Object tracking application Testing and Evaluation The purpose of this section is to discuss and evaluate the object tracking application Finally we are ready with the experimental testbed as shown in figure 16 As shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors placed at the corners of the masterrsquos room BS is placed at the centre of the room connected to a PC All the nodes are power connected before an actual measurement takes place At first the object is placed at random position inside the room and transmits beacon packets continuously Then these packets are received by the fixed APrsquos which calculate the RSSI from the packet and then averages it until it receives 50 beacon packets and then transmits the data to the BS (figure 17) Then the BS waits until it receives the data from all the APrsquos Once it receives from all the APrsquos it calculates the position of the object (figure 18) The application was developed to demonstrate a real world use of a WSN in object tracking In evaluating the final position obtained with respect to the actual position we conclude that the object‐tracking was successful Although

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

34

it is a research prototype object tracking application is a simple stable stand‐alone system capable of demonstrating an important application of WSN It takes raw data from the WSN aggregates it evaluates it and based on the evaluation tracks the object The accuracy of estimating the position is less than 5m Table 11 below shows a log of readings taken during test

Start

Intialize micro‐controller and transeciever in receieve mode

If received_packet=

Beacon

Average RSSI values

Increase the counter

If countergt1000050

A

No

Yes

B

B

Yes

No

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

35

A

Reset counter

Set transceiever in transmit mode

Transmit averaged RSSI to Main sensor and set it back to receive mode

10000 samples ‐ Offline

50 samples ‐ Online

Figure 17 Flow chart representing the algorithm of fixed sensors

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

36

Start

Intialize micro‐controller and transeciever in receieve mode

Received RSSI values from all fixed Sensors

Open radio map

Calculate Euclidean distance between obtained RSSI values from fixed sensors and fingerprints

in radio map

No

Yes

Bubble sort for least error in signal strength values

A

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

37

A

Get the location of the object from the radiousing the least error value

Display location of object on PC

End

Figure 18 Flow chart representing the algorithm of main sensor

Table 11 Accuracy of positioning system

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

38

5 RESULTS This chapter presents how the system parameters such as number of samples mean or median etchellipare finalized All the tests are performed in masterrsquos room at Mid Sweden University This chapter is divided into three subsections First in Section 51 it reviews results obtained from samples test Section 52 discusses one dimensional test results and finally section 53 discusses results of two dimensional tests

51 Measurement setup A Dell laptop was connected to a SENTIO platform and is loaded with software (Appendix 2) This setup was used to determine number of samples required during object tracking mean or median position estimation algorithm and one dimensional test The setup is as follows

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

103

0

BC - 1030

AP 1AP 2

BS

HEWLETT

PACKARD

Figure 19 Test bed for one dimensional test in Masters Room

Two SENTIO nodes (AP1 and AP2 as shown above) were separated by a distance of 10 meters and these sensors are known as fixed sensors which are fixed to the ceiling such that there antenna is faced towards the ground They are linear is position A BS was connected to Dell laptop and it was placed in such a way that its antenna was faced towards the ceiling Another SENTIO

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

39

node was used as an object and its orientation was same as BS Figure 19 represents the complete setup for object tracking application Mobile node was moved between these fixed ones at regular intervals of one meter and RSSI values were logged at each interval

52 Number of samples test This test was performed to determine number of samples required for accurate positioning and either mean of the obtained RSSI values or median of them Figure 19 represents the setup for number of samples test Once the setup is ready the measurements are taken as follows

1 An object was tagged with SENTIO node and was placed stationary linear to the fixed sensors

2 10000 samples (RSSI) were taken at regular intervals of 1 meter up to 10 meters

3 The sample values were logged at every interval 4 Using these logged samples I have plotted graphs such as distribution

of RSS values (figure 20 21) meanmedian at intervals of 1 meter using MATLAB

5 The mean and median for various numbers of samples is as shown in figure 22

Figure 20 Histogram plot of RSSI values at 1m and 4m

Figure 21 Histogram plot of RSSI values at 6m and 8m

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

40

Figure 22 Distance vs RSSI

From the above graphs following conclusions were made 1 From the histogram plots we can observe that the distribution of RSSI

values follow Gaussians distribution law 2 As we can see from figure 22 that median curve has drastic changes

when compared to mean curve Hence mean is considered while estimating the position

3 From the observations we conclude that the number of samples does not matter since mean curve tends to be same for various numbers of samples

4 Hence 10000 samples are considered in order to get a proper location fingerprint during off‐line phase and during online phase 50 samples are enough to estimate the position of object

53 0ne dimensional test Measurement setup for one dimensional test is as shown in figure 19 This test was conducted to check the performance of algorithm and how accurate it was At first ie during offline phase fingerprints at each interval averaging 10000 samples were taken During online phase Euclidean algorithm was used estimate the position averaging 50 samples of RSSI From the results we

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

41

can realize that the error was less than 2 meters Table 12 shows the result of location estimation in one dimensional case

Table 12 One dimensional position estimation results

54 Two dimensional test This test is the final stage of my thesis work Experimental testbed for this stage is as shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors fixed at the corners of the room and a BS at the centre of the room There are 18 pre‐determined locations where the fingerprints (RSSI) were taken The fingerprints are as shown in Appendix 1 All the readings were taken in presence of userrsquos The algorithm for position estimation is shown in Appendix 2 Position estimation test is performed as follows

Tagged object is placed at random inside the masterrsquos room All the sensors including the base station and object are switched on Using the algorithm (Appendix 2) the BS estimates the position of the object and displays it on the PC

This test was performed using various numbers of samples to test which has a better accuracy Figure 23 represents the error rate using various numbers of samples From figure 23 we can conclude that 50 numbers of samples is enough to estimate the position of object with an accuracy of 3m

55 Causes of error in location determination The randomness of RSS is the major cause of error in any indoor

positioning system that uses the RSS for location inference If there is no randomness in the RSS and its location fingerprint every indoor positioning systems of this type will have no location detection error For example I have recorded RSS at one particular location during different times of the day From figures 24 and 25 you can observe that RSS values are different at different times of day

The performance of indoor positioning systems depends greatly on the separation of location fingerprints A location fingerprint

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

42

corresponding to a location can be identified correctly if it is difficult to classify it as another fingerprint by a pattern classifier

Path loss is one of the basic signal propagation properties of the RSS that best describes attenuation of transmit signal power over a distance between an access point and a mobile device

Presence of Userrsquos or userrsquos orientation affects RSS

Figure 23 Error Rate vs Number of samples

Figure 24 RSS distribution at 214 pm

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

43

Figure 25 RSS distribution at 100pm and 1100 am

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

6 DISCUSSIONS10102006

44

6 DISCUSSIONS 61 Summary and Conclusion While empirical results and performance studies of positioning systems based on location fingerprint have been presented in this report analytical model that can be used as a framework for efficiently designing the positioning systems are not available This thesis design work uses general principles of position estimation such as position estimation of sensors grid spacing etc This design work focuses on accuracy and precision The concept of location fingerprint technique based on RSS us explained in detail in chapter 2 It is found that RSS is random It is possible to approximate the RSS as being normally distributed While the RSS random process is non‐stationary the mean is more or less constant and could be used as the fingerprint of the location Model designed for position estimation is as shown in chapter 4 Accuracy and precision are the two major performance matrices in this study Uniform grid spacing has been used From the experimental analysis I have obtained an accuracy of 3 meters and 35 precision The sensitivity analysis among the system parameters and the performance matrices were done using the model as shown in chapter 4 For instance our study found that the minimum distance between two adjacent positions also affects the positioning performance This is due to the randomness of RSS patterns induced by the ever changing indoor environment

62 Future work The research in this thesis provides a ground work for the future study of efficient design of indoor positioning system The RSSI based position estimation still has limitations First the RSSI value is time dependent Thus the future study should address this issue Second the movement of object is not considered Third orientations of sensors affect the performance which should be taken care off Fourth to improve the performance of the system we need to find a solution to deal with those incomplete or missing data during the off‐line and the on‐line phase Fifth position estimation on multi‐floor and three‐dimensional coordinates is not available

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 110102006

45

APPENDIX 1

Location Fingerprints of Masters Room Time 1315 Observation Time 20 minutes Number of Samples 10000

People Presence SNo Co-ordinates AP1 (0x0001) AP2 (0x0002) AP3 (0x0004) AP4 (0x0008)

1 11 -24 -16 -27 -33 2 13 -26 -8 -34 -31 3 15 -43 -9 -39 -25 4 32 -21 -11 -27 -33 5 61 -6 -23 -32 -28 6 64 -20 -31 -25 -34 7 35 -23 -35 -29 -38 8 37 -29 -35 -22 -27 9 39 -38 -29 -21 -27 10 67 -27 -23 -20 -23 11 611 -26 -29 -23 -30 12 613 -35 -32 -2 -31 13 513 -34 -31 -20 -26 14 11 -39 -22 -29 -17 15 212 -37 -42 -28 -31 16 -212 -38 -33 -29 -6 17 -21 -42 -29 -33 -19 18 -38 -40 -23 -35 -22

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 210102006

46

APPENDIX 2

Begin

Open radio map and raw data files for write

Intialize radio map in memory

Check whether the

Packet is data or Beacon packet If data packet

Save the data to register

Check if data is

Received from all

sensors

Estimate the position of static

Display the position on PC

End Yes

No

A

A

Yes

No

Flow chart representing the main algorithm for position estimation

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

47

APPENDIX 3

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

48

Distribution of RSSI

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

49

REFERENCES [1] T S Rappaport J H Reed and B D Woerner ldquoPosition location using wireless communications on highways of the futureʺ IEEE Commun Mag vol 34 no 10 pp33‐41 Oct 1996 [2] A M Ladd K E Bekris G Marceau A Rudys L E Kavraki and D S Wallach rdquoRobotics‐based location sensing using wireless ethernetʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ02) 2002 pp 227‐238 [3] I Ramani R Bharadwaja and P V Rangan ldquoLocation tracking for media appliances in wireless home networksʺ in Proc International Conference on Multimedia and Expo (ICMEʹ03) July 2003 pp 769‐772 [4] K Pahlavan X Li and J P Makela ldquoIndoor geolocation science and technologyʺ IEEE Commun Mag vol 40 no 2 pp 112‐118 Feb 2002 [5] Konard Lorincz and Matt Welsh ldquoMote Track A Robust Decentralized Approach to RF‐Based Location Trackingrdquo [6] Paramvir Bahl and Venkata N Padmanabham ldquoRADAR An In‐Building RF‐based User Location and Tracking Systemrdquo [7] Kamol Kaemarungsi ldquoDesign of Indoor positioning system based on location fingerprint techiniquerdquo University of Pittsburgh 2005 [8] Fredrik Linnarsson Cheng Peng and Bengt Oelmann ldquoSENTIO A Hardware Platform for Rapid Prototyping of Wireless Sensor Networksrdquo [9] S Saha K Chaudhuri D Sanghi and P Bhagwat ldquoLocation determination of a mobile device using IEEE 80211b access point signalsʺ in Proc IEEE Wireless Communications and Networking Conference (WCNCʹ03) New Orleans LA Mar 2003 pp 1987‐1992 [10] T Roos P Myllymaki H Tirri P Misikangas and J Sievanen ldquoA probabilistic approach to WLAN user location estimationʺ International Journal of Wireless Information Networks vol 9 no 3 pp 155‐164 July 2002 [11] R Battiti M Brunato and A Villani ldquoStatistical learning theory for location fingerprinting in wireless LANrsquosʺ Technical Report Oct 2002 [Online] Available httprtmscienceunitnitraquobattitiarchive86pdf [12] P Prasithsangaree P Krishnamurthy and P K Chrysanthis ldquoAn indoor position location with wireless LANsʺ in Proc IEEE International Symposium on Personal Indoor and Mobile Radio Communications (PIMRCʹ02) Lisbon Portugal Sept 2002 [13] M A Youssef A Agrawala and A U Shankar ldquoWLAN location determination via clustering and probability distributionsʺ in Proc IEEE

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

50

International Conference on Pervasive Computing and Communications (PerComʹ03) Dallas‐Fort Worth TX Mar 23‐26 2003 pp 23‐26 [14] Z Xiang S Song J Chen H Wang J Huang and X Gao ldquoA wireless LAN‐based indoor positioning technologyʺ IBM Journal of Research and Development vol 48 no 56 pp 617‐626 SeptNov 2004 [15] R Want A Hopper V Falcao and J Gibbons ldquoThe active badge location systemʺACM Transactions on Information Systems vol 40 no 1 pp 91‐102 Jan 1992 [16] N B Priyantha A Chakraborty and H Balakrishnan ldquoThe cricket location‐support systemʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ00) Boston MA Aug 2000 pp 32‐43 [17] Holger K Willig A (2005) Protocols and Architectures for Wireless Sensor Networks London [18] IEEE (2003) Part 154 Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low‐Rate Wireless Personal Area Networks (LR‐WPANs) New York [19] Chipcon CC2420 802154 compliant radio httpwwwchipconcom [20] ZigBee Specification v10 ZigBee Alliance [21] Dimitrios Lymberopoulos Quentin Lindsey and Andreas Savvides ldquoAn Empirical Analysis of Radio Signal Strength Variability in IEEE 802154 Networks using Monopole Antennasrdquo [22] Kiran Yedavalli Bhaskar Krishnamachari Sharmila Ravula and Bhaskar Srinivasan ldquoEcolocation A sequence based technique for RF localization in wireless sensor networksrdquo Robert Bosch Research and Technology Centre Palo Alto CA

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

22

ZigBee a new standard which became publicly available in June 2005 is based on the IEEE 802154 standard It expands the IEEE 802154 by adding the framework for ldquothe network security and applicationrdquo (Craig 2005) Figure 10 presents the IEEE 802154ZigBee stack and the areas of responsibilities

Figure 10 IEEE 802154ZigBee stack [20]

Craig (2005) mentions three networking topologies that the standard covers the star mesh and cluster tree (figure 11) The ZigBee standard works on top of the IEEE 802154 addressing schema by using the standard 64‐bit and the short 16‐bit addressing Kinney (2005) summarizes the ZigBee network layer responsibilities the successful establishment of a new the network and successful new device configuration addressing assignment network synchronization frames security and message routing ZigBee further distinguishes the concept of the physical devices (RFD FFD) by using the notion of ldquological devicesrdquo ldquoZigBee Coordinatorrdquo is the first type of logical devices It is responsible for initializing maintaining and managing the network Under the coordinator in the network hierarchy is the ldquoZigBee routerrdquo which is responsible for controlling the message routing between the nodes Finally the ldquoZigBee End Devicerdquo acts as the end point of the network

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

23

structure The tasks that an end device can perform are specified in the ldquoApplication Profilesrdquo (Craig 2005)

Figure 11 IEEE 802154ZigBee stack [17]

The ZigBee specifications (2005) summarize the security services provided by ZigBee ldquokey establishment key transport frame protection and device managementrdquo ZigBee builds its security mechanism using symmetric key cryptography The security services also depend on the associated layer thus as the figure 10 shows the security mechanism covers the network and the application layer In addition if a MAC frame needs security protection the MAC layer is able to secure it Moreover the notion of end‐to‐end security is supported the source and destination devices have access and use the same share key In the MAC layer the 802154 AES mechanism provides the proper security The mechanism protects ldquothe confidentiality integrity and authenticity of the MAC framesrdquo (Kinney 2005) An auxiliary header field in front of the MAC payload indicates if the frame is encrypted or not The MAC framesrsquo integrity is supported by calculating and using a MIC at the end of the MAC payload In addition to the AES nonce is used to provide MAC confidentiality and authenticity Figure 12 illustrates a MAC frame with security For different security aspects the MAC layer uses different mode of the AES for the encryption it uses the AES in Counter (CTR) mode and for the integrity the

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

24

CBC‐MAC Finally the combination (CCM) of the above two modes is available providing both encryption and integrity (Kinney 2005)

Figure 12 ZigBee secure frame in MAC layer [17]

In the network layer the CCM (a modified MAC layer CCM mode) is used for encryption Because the network layer uses only the CCM mode a single key is used for all different security options The network layer security message format is similar to the MAC frame it is presented in the figure 13 Finally although the network layer is responsible for securing its layer messages the above layers specify the keys and the CCM option for each frame (Kinney 2005)

Figure 13 ZigBee secure frame in network layer [17]

Security in the application layer works similar to the network and MAC layers It uses the ldquolink keyrdquo or the ldquonetwork keyrdquo to secure the message and then encapsulate it inside a set of fields similar to the network format (figure 14) Other security responsibilities that the application layer has are to provide the ZDO and the applications with device management services key establishment and key transport (ZigBee specifications 2005)

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

25

Figure 14 ZigBee secure frame in application layer [17]

The ZigBee application layer contains the manufacturer‐defined application objects the ZDO and the application sub‐layer In addition to the security responsibilities the application sub‐layer binds devices based on their duties and needs maintains the binding tables and forwards messages between them The application sub‐layer also discovers the neighbour devices for a given device The ZDO is responsible for determining the devicersquos duty in the network for communicating using binding requests and for supporting security as was mentioned above The sub‐layer that implements the actual application is the manufacturer‐defined application object (Kinney 2005) The ZigBee specifications are a great step toward the wireless sensors networks standardization They cover all the OSI layers from the physical to the application providing guidance to the developers The specifications are the product of the ZigBee alliances a consortium of companies and academic institutes Thus they not only produce the specifications but also are the first use them The next section goes beyond the layer architecture and standardization and introduces the OS field another crucial element of WSNs

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

26

3 RECEIVED SIGNAL STRENGTH (RSS) Majority of the existing methods leverage the existence of IEEE 80211 base stations with powerful radio transmit powers of approximately 100mW per base station Such radios are in a different class from the low power IEEE 802154 compliant radios that typically transmit at low power levels ranging from 52mW to 29mW [19] The wide availability of larger number of IEEE 802154 radios in the research community has revived the interest for signal strength based localization in sensor network Despite of rapidly increasing popularity of IEEE 802154 radios and signal strength localization there is a lack of detailed characterization of the fundamental factors contributing to large signal strength variation This chapter investigates some of the properties of RSS observed during testing and analysis of research work collected during the period of thesis The analysis of RSS values is needed to understand the underlying features of location dependent RSS patterns and location fingerprints An understanding of the properties of the RSS values for location fingerprinting can assist in improving the design of positioning algorithms and in deployment of indoor positioning systems

31 Parameters that affect RSS Different factors that affect the indoor RSS are the userrsquos presenceabsence hisher orientation time of the day building type and material distance from transmitter and type of radio These factors are by no means exhaustive Other possible factors are antenna orientation directionality and type Table 10 classify the parameters which affect RSS [7]

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

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27

Effect on Factors Options

1 Proximity of user Userrsquos presence or absence

2 Orientation of user and terminal

North East South West

Data Collection

3 Make of Radio 4 Time of measurement Time of day amp days of

week 5 Period of measurement

Second minute hour

7 Interference Co‐channel adjacent co‐channel

Statistics

8 Building environment Small offices or large hall

Table 10 Classification of factors the affect RSS [7]

Transmitter variability Different transmitters behave differently even when they are configured exactly in the same way In practice this means that when a transmitter is configured to send packets at a power level of d dBm then the transmitter will send these packets at a power level that is very close to d dBm but not necessarily exactly equal to d dBm This can alter the received signal strength indication and thus it can lead to inaccurate distance estimation

Receiver variability The sensitivity of the receivers across different radio chips is different In practice this means that the RSSI value recorded at different receivers can be different even when all the other parameters that affect the received signal strength are kept constant

Antenna orientation Each antenna has its own radiation pattern that is not uniform In practice this means that the RSSI value recorded at the receiver for a given pair of communicating nodes and for a given distance between them varies as the pair wise antenna orientations of the transmitter and the receiver are changed

Multi‐path fading and shadowing in the RF channel In indoor environments the transmitted signals get reflected after hitting on the walls andor on other objects in the room such as furniture Both the original signal and the reflected signal reach the receiver almost at the same time since they both travel at the speed of light As a result of this the receiver is not able to

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

28

distinguish the two signals and it measures the received signal strength for both of them

32 Related work analysis A number of indoor location tracking systems have been proposed in the literature based on RF signals ultrasound infrared and some combinations of these As mentioned previously RF based positioning system are widely studied as shown in Table 2 Given a model of radio signal propagation in a building or other environment RSS can be used to estimate the distance between the transmitter and receiver Most of the algorithms (Table 2) have been evaluated in practice using 80211b compliant wireless radios and seem to produce a location error of 15ft to 20ft for an indoor network area of approximately 225ft x 144ft According to the results of this study all the localization techniques produce approximately the same location error over a range of environments Ecolocation [22] Mote Track [5] are two of the few localization algorithms that have been evaluated on a real sensor network that uses a low power wireless radio Ecolocation reports a location error of 10ft for a very small outdoor network deployment area (26ft x 49ft) Mote Track reports a location error of approximately 13ft for an indoor network deployment area of 18751ft2 In general the RSSI based localization algorithms can be divided into two broad categories the map based and the distance (or area) prediction based algorithms In the case of the map based algorithms the deployment area is divided into a large set of sample points that cover the whole area Most techniques collect one or more signal strength measurements from all visible beacon nodes and for each sample point Each sample point is then mapped to either a signal strength vector or a signal strength probability distribution This process generates an RSSI fingerprint map which is unique with respect to the beacon locations and the deployment area This fingerprint map is stored to a central base station A node that is not aware of its location collects one or more signal strength measurements from all the visible beacon nodes and creates its own RSSI fingerprint that is sent to the central base station The base station computes nodersquos location as the sample pointrsquos location whose RSSI fingerprint distance from the unknown nodersquos RSSI fingerprint is the minimum among all the sample points Despite the fact that the map based RSSI localization methods produce relatively small location errors they are not attractive mainly because they require an excessive amount of profiling data and because they are not fully distributed The detailed profiling of a building is a major holdback because it is complex time consuming and depends on beacon locations Therefore for different buildings or even for the

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

29

same building and different beacon locations different RSSI maps have to be obtained The second category of RSSI based algorithms are the distance (or area) prediction algorithms which focus on minimizing the amount of profiling data In distance prediction based methods raw RSSI data are directly mapped to distances through a signal propagation model In some cases distances are not explicitly measured but they are taken into consideration through assumptions on the underlying distributions In the case of outdoor deployments where there are no obstacles a single signal propagation model is all the profiling data required In the case of indoor deployments the same applies but now the signal propagation model might be different across different buildings with different furniture and wall arrangements Given an underlying signal propagation model raw RSSI data can be mapped to specific distances These distances can be either used directly in order to perform localization or assigned a probability and used in a learning basedprobabilistic localization algorithm Obviously the accuracy of the distance prediction based methods depends heavily on the accuracy of the signal propagation model used for translating RSSI values to distances

33 Properties of RSSI Changes in signal due to propagation indoors are difficult to predict because of dense environment and propagation effects such as reflection diffraction and scattering The multi‐path fading effect which is the result of either constructive or destructive combination of multiple signal copies at the receiver causes the received signal to fluctuate around a mean value at a particular location The received signal is usually modelled by the combined effects of large‐scale fading and small‐scale fading The large‐scale fading component describes the signal attenuation as the signal travels over a distance and is absorbed by material such as walls and floors along the way to the receiver This component predicts the mean of the RSS and usually has a lognormal distribution On the other hand the small scale fading component explains the dramatic fluctuation of the signal due to multi‐path fading On the other hand the small scale‐fading component explains the dramatic fluctuation of the signal due to multi‐path fading If there is no LOS component the small‐scale‐fading is often modelled with a Rayleigh distribution Such scenarios are commonly called NLOS If there is a LOS component the small‐scale‐fading is typically modelled by a Rician distribution Note that when the RSS is measured the measurement averages out the small‐scale‐fading effects

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

30

4 OBJECT TRACKING 41 Introduction This chapter presents the object‐tracking application the main objective of this thesis work The application is an algorithmic‐software implementation written in Embedded C which is capable of receiving data messages and generating object position estimation data The implementation of the object‐tracking application makes use of the SENTIO platform and uses the background knowledge introduced in the preceding chapters The motivation for the object‐tracking application is to determine the position of an object in indoor environments (Eg Home) The application acts as an interface which collects the raw data that the sensors generate (numerical values) It makes the proper comparisons and algorithmic processes to identify and track the object while it is static inside the area covered by the wireless sensor network Finally with the detection and tracking data it alerts the user or another system The following sections provide an overview of the object‐tracking application

42 Overview of hardware product Before describing the object tracking application it is important to know the characteristics of hardware product The SENTIO platform (figure 15) is built in modular fashion and has the following key features [8]

Flexibility This platform is very flexible in such a way that it is made easy to add or change the hardware for each sensor task depending on the requirement in the WSN For a specific platform this can be hard or sometimes even impossible to accomplish

Sensor resources Resources in a sensor module includes the ability of usage of controllerrsquos resources as much as possible such as ADC‐channels interrupts and digital Input‐Outputrsquos Local voltage regulation should also be possible since many sensors require higher supply voltages than digital Integrated Circuits do Further support of a shut‐down mechanism to power down the sensor modules when not needed

Computation and memory The processor module based on microcontroller should support many devices in a microcontroller family and support different power supply voltages enabling full range of clock frequencies All power‐down modes of the devices should be

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

31

supported The platform should support different processor modules including FPGArsquos and very simple microcontrollers

Communication The focus should be on enabling an IEEE 802154 compliant communication interface With or without ZigBee stack this will provide a flexible platform for exploring different network topologies Support for power‐down of the entire communication module should be supported

Cost and form factor High flexibility is in conflict with low cost and small size The primary objective should be flexibility but the size should be considered in order to enable sensor device integration in systems where space is limited

Figure 15 SENTIO Platform [8]

The communication subsystem of the SENTIO sensor node was designed so that the correct operation of the radio chip is ensured The radio chip is powered by the on‐board voltage regulator and thus fluctuations in the battery voltage level do not affect the operation of the communication subsystem This platform uses Chipcon CC2420 IEEE 802154 radio transceiver operates in the 24GHz ISM band and includes a digital direct sequence spread spectrum (DSSS) modem providing a spreading gain of 9dB and an effective data rate of 250Kbps It was specifically designed for low power wireless applications and supports 8 discrete power levels 0dBm minus1dBm minus3dBm minus5dBm minus7dBm minus10dBm minus15dBm and minus25dBm at which its power consumption varies from 29mW to 52mW [21] A built‐in received signal strength indicator gives an 8‐bit digital value RSSIVAL The RSSIVAL is always averaged over 8 symbol periods (128μs) and a status bit indicates when the RSSIVAL is valid (meaning that the receiver was enabled for at least 8 symbol periods) The

Antenna

Communication Module

Processing Module

Sensing Module

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

32

power P at the RF pins can be obtained directly from RSSIVAL using the following equation

P = RSSIVAL + RSSIOFFSET [dBm] where the RSSIOFFSET is found empirically from the front‐end gain and it is approximately equal to minus45dBm In the next sections when we refer to the RSSI value we refer to the RSSIVAL and not the power P unless otherwise stated

43 Experimental Testbed The experimental test bed for object tracking application is as shown in figure 16 Experiment was conducted in Masterrsquos room at Mid Sweden University A DELL laptop is connected to SENTIO platform and software were primarily used to collect samples of RSS from APrsquos The software monitors IEEE 802154 radio frequency channels which operate in the 24GHz band

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

1030

BC - 1030

AP 3 AP 1

AP 2

AP 4

BS

HEWLETT

PACKARD

Figure 16 Layout of Masterʹs Room

A three dimensional scalable testbed is used for indoor sensor network deployments The testbed illustrated in figure 16 is a 3‐D structure measuring 16m (L) x 12m (W) x 3m (H) and it is designed to host a large number of static nodes as shown in figure 16 Arrow marks in the above figure represent the location where fingerprints were taken The centrepiece of our infrastructure

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

33

is the SENTIO sensor node [8] an open‐source general purpose sensing platform designed with AT Mega 128 microprocessor and the IEEE 802154 compliant CC2420 radio from Chipcon [19]

44 Obtaining data from the sensor network The next important step in the development of the object tracking application is to obtain the data from the sensor network and send it to the base station From there the data is transmitted through a serial port to the computer that runs the object tracking application When the data arrives at the serial port the application must capture it This paragraph gives a brief explanation of how and what kind data is transmitted by the static object fixed sensors and how this data is being used to estimate the position of the object First the static object transmits Beacon packet continuously which is received by all the fixed APrsquos The beacon packet format is as shown in table 6 Then the fixed APrsquos calculates the 8‐bit value of RSSI from the transmitted beacon packet and then sends the resultant RSSI value as a form of data packet (table 7) to the BS which calculates the position and sends back to the object

45 Object tracking application Testing and Evaluation The purpose of this section is to discuss and evaluate the object tracking application Finally we are ready with the experimental testbed as shown in figure 16 As shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors placed at the corners of the masterrsquos room BS is placed at the centre of the room connected to a PC All the nodes are power connected before an actual measurement takes place At first the object is placed at random position inside the room and transmits beacon packets continuously Then these packets are received by the fixed APrsquos which calculate the RSSI from the packet and then averages it until it receives 50 beacon packets and then transmits the data to the BS (figure 17) Then the BS waits until it receives the data from all the APrsquos Once it receives from all the APrsquos it calculates the position of the object (figure 18) The application was developed to demonstrate a real world use of a WSN in object tracking In evaluating the final position obtained with respect to the actual position we conclude that the object‐tracking was successful Although

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

34

it is a research prototype object tracking application is a simple stable stand‐alone system capable of demonstrating an important application of WSN It takes raw data from the WSN aggregates it evaluates it and based on the evaluation tracks the object The accuracy of estimating the position is less than 5m Table 11 below shows a log of readings taken during test

Start

Intialize micro‐controller and transeciever in receieve mode

If received_packet=

Beacon

Average RSSI values

Increase the counter

If countergt1000050

A

No

Yes

B

B

Yes

No

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

35

A

Reset counter

Set transceiever in transmit mode

Transmit averaged RSSI to Main sensor and set it back to receive mode

10000 samples ‐ Offline

50 samples ‐ Online

Figure 17 Flow chart representing the algorithm of fixed sensors

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

36

Start

Intialize micro‐controller and transeciever in receieve mode

Received RSSI values from all fixed Sensors

Open radio map

Calculate Euclidean distance between obtained RSSI values from fixed sensors and fingerprints

in radio map

No

Yes

Bubble sort for least error in signal strength values

A

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

37

A

Get the location of the object from the radiousing the least error value

Display location of object on PC

End

Figure 18 Flow chart representing the algorithm of main sensor

Table 11 Accuracy of positioning system

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

38

5 RESULTS This chapter presents how the system parameters such as number of samples mean or median etchellipare finalized All the tests are performed in masterrsquos room at Mid Sweden University This chapter is divided into three subsections First in Section 51 it reviews results obtained from samples test Section 52 discusses one dimensional test results and finally section 53 discusses results of two dimensional tests

51 Measurement setup A Dell laptop was connected to a SENTIO platform and is loaded with software (Appendix 2) This setup was used to determine number of samples required during object tracking mean or median position estimation algorithm and one dimensional test The setup is as follows

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

103

0

BC - 1030

AP 1AP 2

BS

HEWLETT

PACKARD

Figure 19 Test bed for one dimensional test in Masters Room

Two SENTIO nodes (AP1 and AP2 as shown above) were separated by a distance of 10 meters and these sensors are known as fixed sensors which are fixed to the ceiling such that there antenna is faced towards the ground They are linear is position A BS was connected to Dell laptop and it was placed in such a way that its antenna was faced towards the ceiling Another SENTIO

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

39

node was used as an object and its orientation was same as BS Figure 19 represents the complete setup for object tracking application Mobile node was moved between these fixed ones at regular intervals of one meter and RSSI values were logged at each interval

52 Number of samples test This test was performed to determine number of samples required for accurate positioning and either mean of the obtained RSSI values or median of them Figure 19 represents the setup for number of samples test Once the setup is ready the measurements are taken as follows

1 An object was tagged with SENTIO node and was placed stationary linear to the fixed sensors

2 10000 samples (RSSI) were taken at regular intervals of 1 meter up to 10 meters

3 The sample values were logged at every interval 4 Using these logged samples I have plotted graphs such as distribution

of RSS values (figure 20 21) meanmedian at intervals of 1 meter using MATLAB

5 The mean and median for various numbers of samples is as shown in figure 22

Figure 20 Histogram plot of RSSI values at 1m and 4m

Figure 21 Histogram plot of RSSI values at 6m and 8m

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

40

Figure 22 Distance vs RSSI

From the above graphs following conclusions were made 1 From the histogram plots we can observe that the distribution of RSSI

values follow Gaussians distribution law 2 As we can see from figure 22 that median curve has drastic changes

when compared to mean curve Hence mean is considered while estimating the position

3 From the observations we conclude that the number of samples does not matter since mean curve tends to be same for various numbers of samples

4 Hence 10000 samples are considered in order to get a proper location fingerprint during off‐line phase and during online phase 50 samples are enough to estimate the position of object

53 0ne dimensional test Measurement setup for one dimensional test is as shown in figure 19 This test was conducted to check the performance of algorithm and how accurate it was At first ie during offline phase fingerprints at each interval averaging 10000 samples were taken During online phase Euclidean algorithm was used estimate the position averaging 50 samples of RSSI From the results we

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

41

can realize that the error was less than 2 meters Table 12 shows the result of location estimation in one dimensional case

Table 12 One dimensional position estimation results

54 Two dimensional test This test is the final stage of my thesis work Experimental testbed for this stage is as shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors fixed at the corners of the room and a BS at the centre of the room There are 18 pre‐determined locations where the fingerprints (RSSI) were taken The fingerprints are as shown in Appendix 1 All the readings were taken in presence of userrsquos The algorithm for position estimation is shown in Appendix 2 Position estimation test is performed as follows

Tagged object is placed at random inside the masterrsquos room All the sensors including the base station and object are switched on Using the algorithm (Appendix 2) the BS estimates the position of the object and displays it on the PC

This test was performed using various numbers of samples to test which has a better accuracy Figure 23 represents the error rate using various numbers of samples From figure 23 we can conclude that 50 numbers of samples is enough to estimate the position of object with an accuracy of 3m

55 Causes of error in location determination The randomness of RSS is the major cause of error in any indoor

positioning system that uses the RSS for location inference If there is no randomness in the RSS and its location fingerprint every indoor positioning systems of this type will have no location detection error For example I have recorded RSS at one particular location during different times of the day From figures 24 and 25 you can observe that RSS values are different at different times of day

The performance of indoor positioning systems depends greatly on the separation of location fingerprints A location fingerprint

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

42

corresponding to a location can be identified correctly if it is difficult to classify it as another fingerprint by a pattern classifier

Path loss is one of the basic signal propagation properties of the RSS that best describes attenuation of transmit signal power over a distance between an access point and a mobile device

Presence of Userrsquos or userrsquos orientation affects RSS

Figure 23 Error Rate vs Number of samples

Figure 24 RSS distribution at 214 pm

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

43

Figure 25 RSS distribution at 100pm and 1100 am

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

6 DISCUSSIONS10102006

44

6 DISCUSSIONS 61 Summary and Conclusion While empirical results and performance studies of positioning systems based on location fingerprint have been presented in this report analytical model that can be used as a framework for efficiently designing the positioning systems are not available This thesis design work uses general principles of position estimation such as position estimation of sensors grid spacing etc This design work focuses on accuracy and precision The concept of location fingerprint technique based on RSS us explained in detail in chapter 2 It is found that RSS is random It is possible to approximate the RSS as being normally distributed While the RSS random process is non‐stationary the mean is more or less constant and could be used as the fingerprint of the location Model designed for position estimation is as shown in chapter 4 Accuracy and precision are the two major performance matrices in this study Uniform grid spacing has been used From the experimental analysis I have obtained an accuracy of 3 meters and 35 precision The sensitivity analysis among the system parameters and the performance matrices were done using the model as shown in chapter 4 For instance our study found that the minimum distance between two adjacent positions also affects the positioning performance This is due to the randomness of RSS patterns induced by the ever changing indoor environment

62 Future work The research in this thesis provides a ground work for the future study of efficient design of indoor positioning system The RSSI based position estimation still has limitations First the RSSI value is time dependent Thus the future study should address this issue Second the movement of object is not considered Third orientations of sensors affect the performance which should be taken care off Fourth to improve the performance of the system we need to find a solution to deal with those incomplete or missing data during the off‐line and the on‐line phase Fifth position estimation on multi‐floor and three‐dimensional coordinates is not available

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 110102006

45

APPENDIX 1

Location Fingerprints of Masters Room Time 1315 Observation Time 20 minutes Number of Samples 10000

People Presence SNo Co-ordinates AP1 (0x0001) AP2 (0x0002) AP3 (0x0004) AP4 (0x0008)

1 11 -24 -16 -27 -33 2 13 -26 -8 -34 -31 3 15 -43 -9 -39 -25 4 32 -21 -11 -27 -33 5 61 -6 -23 -32 -28 6 64 -20 -31 -25 -34 7 35 -23 -35 -29 -38 8 37 -29 -35 -22 -27 9 39 -38 -29 -21 -27 10 67 -27 -23 -20 -23 11 611 -26 -29 -23 -30 12 613 -35 -32 -2 -31 13 513 -34 -31 -20 -26 14 11 -39 -22 -29 -17 15 212 -37 -42 -28 -31 16 -212 -38 -33 -29 -6 17 -21 -42 -29 -33 -19 18 -38 -40 -23 -35 -22

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 210102006

46

APPENDIX 2

Begin

Open radio map and raw data files for write

Intialize radio map in memory

Check whether the

Packet is data or Beacon packet If data packet

Save the data to register

Check if data is

Received from all

sensors

Estimate the position of static

Display the position on PC

End Yes

No

A

A

Yes

No

Flow chart representing the main algorithm for position estimation

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

47

APPENDIX 3

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

48

Distribution of RSSI

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

49

REFERENCES [1] T S Rappaport J H Reed and B D Woerner ldquoPosition location using wireless communications on highways of the futureʺ IEEE Commun Mag vol 34 no 10 pp33‐41 Oct 1996 [2] A M Ladd K E Bekris G Marceau A Rudys L E Kavraki and D S Wallach rdquoRobotics‐based location sensing using wireless ethernetʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ02) 2002 pp 227‐238 [3] I Ramani R Bharadwaja and P V Rangan ldquoLocation tracking for media appliances in wireless home networksʺ in Proc International Conference on Multimedia and Expo (ICMEʹ03) July 2003 pp 769‐772 [4] K Pahlavan X Li and J P Makela ldquoIndoor geolocation science and technologyʺ IEEE Commun Mag vol 40 no 2 pp 112‐118 Feb 2002 [5] Konard Lorincz and Matt Welsh ldquoMote Track A Robust Decentralized Approach to RF‐Based Location Trackingrdquo [6] Paramvir Bahl and Venkata N Padmanabham ldquoRADAR An In‐Building RF‐based User Location and Tracking Systemrdquo [7] Kamol Kaemarungsi ldquoDesign of Indoor positioning system based on location fingerprint techiniquerdquo University of Pittsburgh 2005 [8] Fredrik Linnarsson Cheng Peng and Bengt Oelmann ldquoSENTIO A Hardware Platform for Rapid Prototyping of Wireless Sensor Networksrdquo [9] S Saha K Chaudhuri D Sanghi and P Bhagwat ldquoLocation determination of a mobile device using IEEE 80211b access point signalsʺ in Proc IEEE Wireless Communications and Networking Conference (WCNCʹ03) New Orleans LA Mar 2003 pp 1987‐1992 [10] T Roos P Myllymaki H Tirri P Misikangas and J Sievanen ldquoA probabilistic approach to WLAN user location estimationʺ International Journal of Wireless Information Networks vol 9 no 3 pp 155‐164 July 2002 [11] R Battiti M Brunato and A Villani ldquoStatistical learning theory for location fingerprinting in wireless LANrsquosʺ Technical Report Oct 2002 [Online] Available httprtmscienceunitnitraquobattitiarchive86pdf [12] P Prasithsangaree P Krishnamurthy and P K Chrysanthis ldquoAn indoor position location with wireless LANsʺ in Proc IEEE International Symposium on Personal Indoor and Mobile Radio Communications (PIMRCʹ02) Lisbon Portugal Sept 2002 [13] M A Youssef A Agrawala and A U Shankar ldquoWLAN location determination via clustering and probability distributionsʺ in Proc IEEE

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

50

International Conference on Pervasive Computing and Communications (PerComʹ03) Dallas‐Fort Worth TX Mar 23‐26 2003 pp 23‐26 [14] Z Xiang S Song J Chen H Wang J Huang and X Gao ldquoA wireless LAN‐based indoor positioning technologyʺ IBM Journal of Research and Development vol 48 no 56 pp 617‐626 SeptNov 2004 [15] R Want A Hopper V Falcao and J Gibbons ldquoThe active badge location systemʺACM Transactions on Information Systems vol 40 no 1 pp 91‐102 Jan 1992 [16] N B Priyantha A Chakraborty and H Balakrishnan ldquoThe cricket location‐support systemʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ00) Boston MA Aug 2000 pp 32‐43 [17] Holger K Willig A (2005) Protocols and Architectures for Wireless Sensor Networks London [18] IEEE (2003) Part 154 Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low‐Rate Wireless Personal Area Networks (LR‐WPANs) New York [19] Chipcon CC2420 802154 compliant radio httpwwwchipconcom [20] ZigBee Specification v10 ZigBee Alliance [21] Dimitrios Lymberopoulos Quentin Lindsey and Andreas Savvides ldquoAn Empirical Analysis of Radio Signal Strength Variability in IEEE 802154 Networks using Monopole Antennasrdquo [22] Kiran Yedavalli Bhaskar Krishnamachari Sharmila Ravula and Bhaskar Srinivasan ldquoEcolocation A sequence based technique for RF localization in wireless sensor networksrdquo Robert Bosch Research and Technology Centre Palo Alto CA

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

23

structure The tasks that an end device can perform are specified in the ldquoApplication Profilesrdquo (Craig 2005)

Figure 11 IEEE 802154ZigBee stack [17]

The ZigBee specifications (2005) summarize the security services provided by ZigBee ldquokey establishment key transport frame protection and device managementrdquo ZigBee builds its security mechanism using symmetric key cryptography The security services also depend on the associated layer thus as the figure 10 shows the security mechanism covers the network and the application layer In addition if a MAC frame needs security protection the MAC layer is able to secure it Moreover the notion of end‐to‐end security is supported the source and destination devices have access and use the same share key In the MAC layer the 802154 AES mechanism provides the proper security The mechanism protects ldquothe confidentiality integrity and authenticity of the MAC framesrdquo (Kinney 2005) An auxiliary header field in front of the MAC payload indicates if the frame is encrypted or not The MAC framesrsquo integrity is supported by calculating and using a MIC at the end of the MAC payload In addition to the AES nonce is used to provide MAC confidentiality and authenticity Figure 12 illustrates a MAC frame with security For different security aspects the MAC layer uses different mode of the AES for the encryption it uses the AES in Counter (CTR) mode and for the integrity the

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

24

CBC‐MAC Finally the combination (CCM) of the above two modes is available providing both encryption and integrity (Kinney 2005)

Figure 12 ZigBee secure frame in MAC layer [17]

In the network layer the CCM (a modified MAC layer CCM mode) is used for encryption Because the network layer uses only the CCM mode a single key is used for all different security options The network layer security message format is similar to the MAC frame it is presented in the figure 13 Finally although the network layer is responsible for securing its layer messages the above layers specify the keys and the CCM option for each frame (Kinney 2005)

Figure 13 ZigBee secure frame in network layer [17]

Security in the application layer works similar to the network and MAC layers It uses the ldquolink keyrdquo or the ldquonetwork keyrdquo to secure the message and then encapsulate it inside a set of fields similar to the network format (figure 14) Other security responsibilities that the application layer has are to provide the ZDO and the applications with device management services key establishment and key transport (ZigBee specifications 2005)

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

25

Figure 14 ZigBee secure frame in application layer [17]

The ZigBee application layer contains the manufacturer‐defined application objects the ZDO and the application sub‐layer In addition to the security responsibilities the application sub‐layer binds devices based on their duties and needs maintains the binding tables and forwards messages between them The application sub‐layer also discovers the neighbour devices for a given device The ZDO is responsible for determining the devicersquos duty in the network for communicating using binding requests and for supporting security as was mentioned above The sub‐layer that implements the actual application is the manufacturer‐defined application object (Kinney 2005) The ZigBee specifications are a great step toward the wireless sensors networks standardization They cover all the OSI layers from the physical to the application providing guidance to the developers The specifications are the product of the ZigBee alliances a consortium of companies and academic institutes Thus they not only produce the specifications but also are the first use them The next section goes beyond the layer architecture and standardization and introduces the OS field another crucial element of WSNs

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

26

3 RECEIVED SIGNAL STRENGTH (RSS) Majority of the existing methods leverage the existence of IEEE 80211 base stations with powerful radio transmit powers of approximately 100mW per base station Such radios are in a different class from the low power IEEE 802154 compliant radios that typically transmit at low power levels ranging from 52mW to 29mW [19] The wide availability of larger number of IEEE 802154 radios in the research community has revived the interest for signal strength based localization in sensor network Despite of rapidly increasing popularity of IEEE 802154 radios and signal strength localization there is a lack of detailed characterization of the fundamental factors contributing to large signal strength variation This chapter investigates some of the properties of RSS observed during testing and analysis of research work collected during the period of thesis The analysis of RSS values is needed to understand the underlying features of location dependent RSS patterns and location fingerprints An understanding of the properties of the RSS values for location fingerprinting can assist in improving the design of positioning algorithms and in deployment of indoor positioning systems

31 Parameters that affect RSS Different factors that affect the indoor RSS are the userrsquos presenceabsence hisher orientation time of the day building type and material distance from transmitter and type of radio These factors are by no means exhaustive Other possible factors are antenna orientation directionality and type Table 10 classify the parameters which affect RSS [7]

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

27

Effect on Factors Options

1 Proximity of user Userrsquos presence or absence

2 Orientation of user and terminal

North East South West

Data Collection

3 Make of Radio 4 Time of measurement Time of day amp days of

week 5 Period of measurement

Second minute hour

7 Interference Co‐channel adjacent co‐channel

Statistics

8 Building environment Small offices or large hall

Table 10 Classification of factors the affect RSS [7]

Transmitter variability Different transmitters behave differently even when they are configured exactly in the same way In practice this means that when a transmitter is configured to send packets at a power level of d dBm then the transmitter will send these packets at a power level that is very close to d dBm but not necessarily exactly equal to d dBm This can alter the received signal strength indication and thus it can lead to inaccurate distance estimation

Receiver variability The sensitivity of the receivers across different radio chips is different In practice this means that the RSSI value recorded at different receivers can be different even when all the other parameters that affect the received signal strength are kept constant

Antenna orientation Each antenna has its own radiation pattern that is not uniform In practice this means that the RSSI value recorded at the receiver for a given pair of communicating nodes and for a given distance between them varies as the pair wise antenna orientations of the transmitter and the receiver are changed

Multi‐path fading and shadowing in the RF channel In indoor environments the transmitted signals get reflected after hitting on the walls andor on other objects in the room such as furniture Both the original signal and the reflected signal reach the receiver almost at the same time since they both travel at the speed of light As a result of this the receiver is not able to

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

28

distinguish the two signals and it measures the received signal strength for both of them

32 Related work analysis A number of indoor location tracking systems have been proposed in the literature based on RF signals ultrasound infrared and some combinations of these As mentioned previously RF based positioning system are widely studied as shown in Table 2 Given a model of radio signal propagation in a building or other environment RSS can be used to estimate the distance between the transmitter and receiver Most of the algorithms (Table 2) have been evaluated in practice using 80211b compliant wireless radios and seem to produce a location error of 15ft to 20ft for an indoor network area of approximately 225ft x 144ft According to the results of this study all the localization techniques produce approximately the same location error over a range of environments Ecolocation [22] Mote Track [5] are two of the few localization algorithms that have been evaluated on a real sensor network that uses a low power wireless radio Ecolocation reports a location error of 10ft for a very small outdoor network deployment area (26ft x 49ft) Mote Track reports a location error of approximately 13ft for an indoor network deployment area of 18751ft2 In general the RSSI based localization algorithms can be divided into two broad categories the map based and the distance (or area) prediction based algorithms In the case of the map based algorithms the deployment area is divided into a large set of sample points that cover the whole area Most techniques collect one or more signal strength measurements from all visible beacon nodes and for each sample point Each sample point is then mapped to either a signal strength vector or a signal strength probability distribution This process generates an RSSI fingerprint map which is unique with respect to the beacon locations and the deployment area This fingerprint map is stored to a central base station A node that is not aware of its location collects one or more signal strength measurements from all the visible beacon nodes and creates its own RSSI fingerprint that is sent to the central base station The base station computes nodersquos location as the sample pointrsquos location whose RSSI fingerprint distance from the unknown nodersquos RSSI fingerprint is the minimum among all the sample points Despite the fact that the map based RSSI localization methods produce relatively small location errors they are not attractive mainly because they require an excessive amount of profiling data and because they are not fully distributed The detailed profiling of a building is a major holdback because it is complex time consuming and depends on beacon locations Therefore for different buildings or even for the

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

29

same building and different beacon locations different RSSI maps have to be obtained The second category of RSSI based algorithms are the distance (or area) prediction algorithms which focus on minimizing the amount of profiling data In distance prediction based methods raw RSSI data are directly mapped to distances through a signal propagation model In some cases distances are not explicitly measured but they are taken into consideration through assumptions on the underlying distributions In the case of outdoor deployments where there are no obstacles a single signal propagation model is all the profiling data required In the case of indoor deployments the same applies but now the signal propagation model might be different across different buildings with different furniture and wall arrangements Given an underlying signal propagation model raw RSSI data can be mapped to specific distances These distances can be either used directly in order to perform localization or assigned a probability and used in a learning basedprobabilistic localization algorithm Obviously the accuracy of the distance prediction based methods depends heavily on the accuracy of the signal propagation model used for translating RSSI values to distances

33 Properties of RSSI Changes in signal due to propagation indoors are difficult to predict because of dense environment and propagation effects such as reflection diffraction and scattering The multi‐path fading effect which is the result of either constructive or destructive combination of multiple signal copies at the receiver causes the received signal to fluctuate around a mean value at a particular location The received signal is usually modelled by the combined effects of large‐scale fading and small‐scale fading The large‐scale fading component describes the signal attenuation as the signal travels over a distance and is absorbed by material such as walls and floors along the way to the receiver This component predicts the mean of the RSS and usually has a lognormal distribution On the other hand the small scale fading component explains the dramatic fluctuation of the signal due to multi‐path fading On the other hand the small scale‐fading component explains the dramatic fluctuation of the signal due to multi‐path fading If there is no LOS component the small‐scale‐fading is often modelled with a Rayleigh distribution Such scenarios are commonly called NLOS If there is a LOS component the small‐scale‐fading is typically modelled by a Rician distribution Note that when the RSS is measured the measurement averages out the small‐scale‐fading effects

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

30

4 OBJECT TRACKING 41 Introduction This chapter presents the object‐tracking application the main objective of this thesis work The application is an algorithmic‐software implementation written in Embedded C which is capable of receiving data messages and generating object position estimation data The implementation of the object‐tracking application makes use of the SENTIO platform and uses the background knowledge introduced in the preceding chapters The motivation for the object‐tracking application is to determine the position of an object in indoor environments (Eg Home) The application acts as an interface which collects the raw data that the sensors generate (numerical values) It makes the proper comparisons and algorithmic processes to identify and track the object while it is static inside the area covered by the wireless sensor network Finally with the detection and tracking data it alerts the user or another system The following sections provide an overview of the object‐tracking application

42 Overview of hardware product Before describing the object tracking application it is important to know the characteristics of hardware product The SENTIO platform (figure 15) is built in modular fashion and has the following key features [8]

Flexibility This platform is very flexible in such a way that it is made easy to add or change the hardware for each sensor task depending on the requirement in the WSN For a specific platform this can be hard or sometimes even impossible to accomplish

Sensor resources Resources in a sensor module includes the ability of usage of controllerrsquos resources as much as possible such as ADC‐channels interrupts and digital Input‐Outputrsquos Local voltage regulation should also be possible since many sensors require higher supply voltages than digital Integrated Circuits do Further support of a shut‐down mechanism to power down the sensor modules when not needed

Computation and memory The processor module based on microcontroller should support many devices in a microcontroller family and support different power supply voltages enabling full range of clock frequencies All power‐down modes of the devices should be

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

31

supported The platform should support different processor modules including FPGArsquos and very simple microcontrollers

Communication The focus should be on enabling an IEEE 802154 compliant communication interface With or without ZigBee stack this will provide a flexible platform for exploring different network topologies Support for power‐down of the entire communication module should be supported

Cost and form factor High flexibility is in conflict with low cost and small size The primary objective should be flexibility but the size should be considered in order to enable sensor device integration in systems where space is limited

Figure 15 SENTIO Platform [8]

The communication subsystem of the SENTIO sensor node was designed so that the correct operation of the radio chip is ensured The radio chip is powered by the on‐board voltage regulator and thus fluctuations in the battery voltage level do not affect the operation of the communication subsystem This platform uses Chipcon CC2420 IEEE 802154 radio transceiver operates in the 24GHz ISM band and includes a digital direct sequence spread spectrum (DSSS) modem providing a spreading gain of 9dB and an effective data rate of 250Kbps It was specifically designed for low power wireless applications and supports 8 discrete power levels 0dBm minus1dBm minus3dBm minus5dBm minus7dBm minus10dBm minus15dBm and minus25dBm at which its power consumption varies from 29mW to 52mW [21] A built‐in received signal strength indicator gives an 8‐bit digital value RSSIVAL The RSSIVAL is always averaged over 8 symbol periods (128μs) and a status bit indicates when the RSSIVAL is valid (meaning that the receiver was enabled for at least 8 symbol periods) The

Antenna

Communication Module

Processing Module

Sensing Module

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

32

power P at the RF pins can be obtained directly from RSSIVAL using the following equation

P = RSSIVAL + RSSIOFFSET [dBm] where the RSSIOFFSET is found empirically from the front‐end gain and it is approximately equal to minus45dBm In the next sections when we refer to the RSSI value we refer to the RSSIVAL and not the power P unless otherwise stated

43 Experimental Testbed The experimental test bed for object tracking application is as shown in figure 16 Experiment was conducted in Masterrsquos room at Mid Sweden University A DELL laptop is connected to SENTIO platform and software were primarily used to collect samples of RSS from APrsquos The software monitors IEEE 802154 radio frequency channels which operate in the 24GHz band

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

1030

BC - 1030

AP 3 AP 1

AP 2

AP 4

BS

HEWLETT

PACKARD

Figure 16 Layout of Masterʹs Room

A three dimensional scalable testbed is used for indoor sensor network deployments The testbed illustrated in figure 16 is a 3‐D structure measuring 16m (L) x 12m (W) x 3m (H) and it is designed to host a large number of static nodes as shown in figure 16 Arrow marks in the above figure represent the location where fingerprints were taken The centrepiece of our infrastructure

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

33

is the SENTIO sensor node [8] an open‐source general purpose sensing platform designed with AT Mega 128 microprocessor and the IEEE 802154 compliant CC2420 radio from Chipcon [19]

44 Obtaining data from the sensor network The next important step in the development of the object tracking application is to obtain the data from the sensor network and send it to the base station From there the data is transmitted through a serial port to the computer that runs the object tracking application When the data arrives at the serial port the application must capture it This paragraph gives a brief explanation of how and what kind data is transmitted by the static object fixed sensors and how this data is being used to estimate the position of the object First the static object transmits Beacon packet continuously which is received by all the fixed APrsquos The beacon packet format is as shown in table 6 Then the fixed APrsquos calculates the 8‐bit value of RSSI from the transmitted beacon packet and then sends the resultant RSSI value as a form of data packet (table 7) to the BS which calculates the position and sends back to the object

45 Object tracking application Testing and Evaluation The purpose of this section is to discuss and evaluate the object tracking application Finally we are ready with the experimental testbed as shown in figure 16 As shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors placed at the corners of the masterrsquos room BS is placed at the centre of the room connected to a PC All the nodes are power connected before an actual measurement takes place At first the object is placed at random position inside the room and transmits beacon packets continuously Then these packets are received by the fixed APrsquos which calculate the RSSI from the packet and then averages it until it receives 50 beacon packets and then transmits the data to the BS (figure 17) Then the BS waits until it receives the data from all the APrsquos Once it receives from all the APrsquos it calculates the position of the object (figure 18) The application was developed to demonstrate a real world use of a WSN in object tracking In evaluating the final position obtained with respect to the actual position we conclude that the object‐tracking was successful Although

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

34

it is a research prototype object tracking application is a simple stable stand‐alone system capable of demonstrating an important application of WSN It takes raw data from the WSN aggregates it evaluates it and based on the evaluation tracks the object The accuracy of estimating the position is less than 5m Table 11 below shows a log of readings taken during test

Start

Intialize micro‐controller and transeciever in receieve mode

If received_packet=

Beacon

Average RSSI values

Increase the counter

If countergt1000050

A

No

Yes

B

B

Yes

No

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

35

A

Reset counter

Set transceiever in transmit mode

Transmit averaged RSSI to Main sensor and set it back to receive mode

10000 samples ‐ Offline

50 samples ‐ Online

Figure 17 Flow chart representing the algorithm of fixed sensors

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

36

Start

Intialize micro‐controller and transeciever in receieve mode

Received RSSI values from all fixed Sensors

Open radio map

Calculate Euclidean distance between obtained RSSI values from fixed sensors and fingerprints

in radio map

No

Yes

Bubble sort for least error in signal strength values

A

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

37

A

Get the location of the object from the radiousing the least error value

Display location of object on PC

End

Figure 18 Flow chart representing the algorithm of main sensor

Table 11 Accuracy of positioning system

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

38

5 RESULTS This chapter presents how the system parameters such as number of samples mean or median etchellipare finalized All the tests are performed in masterrsquos room at Mid Sweden University This chapter is divided into three subsections First in Section 51 it reviews results obtained from samples test Section 52 discusses one dimensional test results and finally section 53 discusses results of two dimensional tests

51 Measurement setup A Dell laptop was connected to a SENTIO platform and is loaded with software (Appendix 2) This setup was used to determine number of samples required during object tracking mean or median position estimation algorithm and one dimensional test The setup is as follows

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

103

0

BC - 1030

AP 1AP 2

BS

HEWLETT

PACKARD

Figure 19 Test bed for one dimensional test in Masters Room

Two SENTIO nodes (AP1 and AP2 as shown above) were separated by a distance of 10 meters and these sensors are known as fixed sensors which are fixed to the ceiling such that there antenna is faced towards the ground They are linear is position A BS was connected to Dell laptop and it was placed in such a way that its antenna was faced towards the ceiling Another SENTIO

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

39

node was used as an object and its orientation was same as BS Figure 19 represents the complete setup for object tracking application Mobile node was moved between these fixed ones at regular intervals of one meter and RSSI values were logged at each interval

52 Number of samples test This test was performed to determine number of samples required for accurate positioning and either mean of the obtained RSSI values or median of them Figure 19 represents the setup for number of samples test Once the setup is ready the measurements are taken as follows

1 An object was tagged with SENTIO node and was placed stationary linear to the fixed sensors

2 10000 samples (RSSI) were taken at regular intervals of 1 meter up to 10 meters

3 The sample values were logged at every interval 4 Using these logged samples I have plotted graphs such as distribution

of RSS values (figure 20 21) meanmedian at intervals of 1 meter using MATLAB

5 The mean and median for various numbers of samples is as shown in figure 22

Figure 20 Histogram plot of RSSI values at 1m and 4m

Figure 21 Histogram plot of RSSI values at 6m and 8m

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

40

Figure 22 Distance vs RSSI

From the above graphs following conclusions were made 1 From the histogram plots we can observe that the distribution of RSSI

values follow Gaussians distribution law 2 As we can see from figure 22 that median curve has drastic changes

when compared to mean curve Hence mean is considered while estimating the position

3 From the observations we conclude that the number of samples does not matter since mean curve tends to be same for various numbers of samples

4 Hence 10000 samples are considered in order to get a proper location fingerprint during off‐line phase and during online phase 50 samples are enough to estimate the position of object

53 0ne dimensional test Measurement setup for one dimensional test is as shown in figure 19 This test was conducted to check the performance of algorithm and how accurate it was At first ie during offline phase fingerprints at each interval averaging 10000 samples were taken During online phase Euclidean algorithm was used estimate the position averaging 50 samples of RSSI From the results we

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

41

can realize that the error was less than 2 meters Table 12 shows the result of location estimation in one dimensional case

Table 12 One dimensional position estimation results

54 Two dimensional test This test is the final stage of my thesis work Experimental testbed for this stage is as shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors fixed at the corners of the room and a BS at the centre of the room There are 18 pre‐determined locations where the fingerprints (RSSI) were taken The fingerprints are as shown in Appendix 1 All the readings were taken in presence of userrsquos The algorithm for position estimation is shown in Appendix 2 Position estimation test is performed as follows

Tagged object is placed at random inside the masterrsquos room All the sensors including the base station and object are switched on Using the algorithm (Appendix 2) the BS estimates the position of the object and displays it on the PC

This test was performed using various numbers of samples to test which has a better accuracy Figure 23 represents the error rate using various numbers of samples From figure 23 we can conclude that 50 numbers of samples is enough to estimate the position of object with an accuracy of 3m

55 Causes of error in location determination The randomness of RSS is the major cause of error in any indoor

positioning system that uses the RSS for location inference If there is no randomness in the RSS and its location fingerprint every indoor positioning systems of this type will have no location detection error For example I have recorded RSS at one particular location during different times of the day From figures 24 and 25 you can observe that RSS values are different at different times of day

The performance of indoor positioning systems depends greatly on the separation of location fingerprints A location fingerprint

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

42

corresponding to a location can be identified correctly if it is difficult to classify it as another fingerprint by a pattern classifier

Path loss is one of the basic signal propagation properties of the RSS that best describes attenuation of transmit signal power over a distance between an access point and a mobile device

Presence of Userrsquos or userrsquos orientation affects RSS

Figure 23 Error Rate vs Number of samples

Figure 24 RSS distribution at 214 pm

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

43

Figure 25 RSS distribution at 100pm and 1100 am

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

6 DISCUSSIONS10102006

44

6 DISCUSSIONS 61 Summary and Conclusion While empirical results and performance studies of positioning systems based on location fingerprint have been presented in this report analytical model that can be used as a framework for efficiently designing the positioning systems are not available This thesis design work uses general principles of position estimation such as position estimation of sensors grid spacing etc This design work focuses on accuracy and precision The concept of location fingerprint technique based on RSS us explained in detail in chapter 2 It is found that RSS is random It is possible to approximate the RSS as being normally distributed While the RSS random process is non‐stationary the mean is more or less constant and could be used as the fingerprint of the location Model designed for position estimation is as shown in chapter 4 Accuracy and precision are the two major performance matrices in this study Uniform grid spacing has been used From the experimental analysis I have obtained an accuracy of 3 meters and 35 precision The sensitivity analysis among the system parameters and the performance matrices were done using the model as shown in chapter 4 For instance our study found that the minimum distance between two adjacent positions also affects the positioning performance This is due to the randomness of RSS patterns induced by the ever changing indoor environment

62 Future work The research in this thesis provides a ground work for the future study of efficient design of indoor positioning system The RSSI based position estimation still has limitations First the RSSI value is time dependent Thus the future study should address this issue Second the movement of object is not considered Third orientations of sensors affect the performance which should be taken care off Fourth to improve the performance of the system we need to find a solution to deal with those incomplete or missing data during the off‐line and the on‐line phase Fifth position estimation on multi‐floor and three‐dimensional coordinates is not available

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 110102006

45

APPENDIX 1

Location Fingerprints of Masters Room Time 1315 Observation Time 20 minutes Number of Samples 10000

People Presence SNo Co-ordinates AP1 (0x0001) AP2 (0x0002) AP3 (0x0004) AP4 (0x0008)

1 11 -24 -16 -27 -33 2 13 -26 -8 -34 -31 3 15 -43 -9 -39 -25 4 32 -21 -11 -27 -33 5 61 -6 -23 -32 -28 6 64 -20 -31 -25 -34 7 35 -23 -35 -29 -38 8 37 -29 -35 -22 -27 9 39 -38 -29 -21 -27 10 67 -27 -23 -20 -23 11 611 -26 -29 -23 -30 12 613 -35 -32 -2 -31 13 513 -34 -31 -20 -26 14 11 -39 -22 -29 -17 15 212 -37 -42 -28 -31 16 -212 -38 -33 -29 -6 17 -21 -42 -29 -33 -19 18 -38 -40 -23 -35 -22

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 210102006

46

APPENDIX 2

Begin

Open radio map and raw data files for write

Intialize radio map in memory

Check whether the

Packet is data or Beacon packet If data packet

Save the data to register

Check if data is

Received from all

sensors

Estimate the position of static

Display the position on PC

End Yes

No

A

A

Yes

No

Flow chart representing the main algorithm for position estimation

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

47

APPENDIX 3

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

48

Distribution of RSSI

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

49

REFERENCES [1] T S Rappaport J H Reed and B D Woerner ldquoPosition location using wireless communications on highways of the futureʺ IEEE Commun Mag vol 34 no 10 pp33‐41 Oct 1996 [2] A M Ladd K E Bekris G Marceau A Rudys L E Kavraki and D S Wallach rdquoRobotics‐based location sensing using wireless ethernetʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ02) 2002 pp 227‐238 [3] I Ramani R Bharadwaja and P V Rangan ldquoLocation tracking for media appliances in wireless home networksʺ in Proc International Conference on Multimedia and Expo (ICMEʹ03) July 2003 pp 769‐772 [4] K Pahlavan X Li and J P Makela ldquoIndoor geolocation science and technologyʺ IEEE Commun Mag vol 40 no 2 pp 112‐118 Feb 2002 [5] Konard Lorincz and Matt Welsh ldquoMote Track A Robust Decentralized Approach to RF‐Based Location Trackingrdquo [6] Paramvir Bahl and Venkata N Padmanabham ldquoRADAR An In‐Building RF‐based User Location and Tracking Systemrdquo [7] Kamol Kaemarungsi ldquoDesign of Indoor positioning system based on location fingerprint techiniquerdquo University of Pittsburgh 2005 [8] Fredrik Linnarsson Cheng Peng and Bengt Oelmann ldquoSENTIO A Hardware Platform for Rapid Prototyping of Wireless Sensor Networksrdquo [9] S Saha K Chaudhuri D Sanghi and P Bhagwat ldquoLocation determination of a mobile device using IEEE 80211b access point signalsʺ in Proc IEEE Wireless Communications and Networking Conference (WCNCʹ03) New Orleans LA Mar 2003 pp 1987‐1992 [10] T Roos P Myllymaki H Tirri P Misikangas and J Sievanen ldquoA probabilistic approach to WLAN user location estimationʺ International Journal of Wireless Information Networks vol 9 no 3 pp 155‐164 July 2002 [11] R Battiti M Brunato and A Villani ldquoStatistical learning theory for location fingerprinting in wireless LANrsquosʺ Technical Report Oct 2002 [Online] Available httprtmscienceunitnitraquobattitiarchive86pdf [12] P Prasithsangaree P Krishnamurthy and P K Chrysanthis ldquoAn indoor position location with wireless LANsʺ in Proc IEEE International Symposium on Personal Indoor and Mobile Radio Communications (PIMRCʹ02) Lisbon Portugal Sept 2002 [13] M A Youssef A Agrawala and A U Shankar ldquoWLAN location determination via clustering and probability distributionsʺ in Proc IEEE

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

50

International Conference on Pervasive Computing and Communications (PerComʹ03) Dallas‐Fort Worth TX Mar 23‐26 2003 pp 23‐26 [14] Z Xiang S Song J Chen H Wang J Huang and X Gao ldquoA wireless LAN‐based indoor positioning technologyʺ IBM Journal of Research and Development vol 48 no 56 pp 617‐626 SeptNov 2004 [15] R Want A Hopper V Falcao and J Gibbons ldquoThe active badge location systemʺACM Transactions on Information Systems vol 40 no 1 pp 91‐102 Jan 1992 [16] N B Priyantha A Chakraborty and H Balakrishnan ldquoThe cricket location‐support systemʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ00) Boston MA Aug 2000 pp 32‐43 [17] Holger K Willig A (2005) Protocols and Architectures for Wireless Sensor Networks London [18] IEEE (2003) Part 154 Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low‐Rate Wireless Personal Area Networks (LR‐WPANs) New York [19] Chipcon CC2420 802154 compliant radio httpwwwchipconcom [20] ZigBee Specification v10 ZigBee Alliance [21] Dimitrios Lymberopoulos Quentin Lindsey and Andreas Savvides ldquoAn Empirical Analysis of Radio Signal Strength Variability in IEEE 802154 Networks using Monopole Antennasrdquo [22] Kiran Yedavalli Bhaskar Krishnamachari Sharmila Ravula and Bhaskar Srinivasan ldquoEcolocation A sequence based technique for RF localization in wireless sensor networksrdquo Robert Bosch Research and Technology Centre Palo Alto CA

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

24

CBC‐MAC Finally the combination (CCM) of the above two modes is available providing both encryption and integrity (Kinney 2005)

Figure 12 ZigBee secure frame in MAC layer [17]

In the network layer the CCM (a modified MAC layer CCM mode) is used for encryption Because the network layer uses only the CCM mode a single key is used for all different security options The network layer security message format is similar to the MAC frame it is presented in the figure 13 Finally although the network layer is responsible for securing its layer messages the above layers specify the keys and the CCM option for each frame (Kinney 2005)

Figure 13 ZigBee secure frame in network layer [17]

Security in the application layer works similar to the network and MAC layers It uses the ldquolink keyrdquo or the ldquonetwork keyrdquo to secure the message and then encapsulate it inside a set of fields similar to the network format (figure 14) Other security responsibilities that the application layer has are to provide the ZDO and the applications with device management services key establishment and key transport (ZigBee specifications 2005)

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

25

Figure 14 ZigBee secure frame in application layer [17]

The ZigBee application layer contains the manufacturer‐defined application objects the ZDO and the application sub‐layer In addition to the security responsibilities the application sub‐layer binds devices based on their duties and needs maintains the binding tables and forwards messages between them The application sub‐layer also discovers the neighbour devices for a given device The ZDO is responsible for determining the devicersquos duty in the network for communicating using binding requests and for supporting security as was mentioned above The sub‐layer that implements the actual application is the manufacturer‐defined application object (Kinney 2005) The ZigBee specifications are a great step toward the wireless sensors networks standardization They cover all the OSI layers from the physical to the application providing guidance to the developers The specifications are the product of the ZigBee alliances a consortium of companies and academic institutes Thus they not only produce the specifications but also are the first use them The next section goes beyond the layer architecture and standardization and introduces the OS field another crucial element of WSNs

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

26

3 RECEIVED SIGNAL STRENGTH (RSS) Majority of the existing methods leverage the existence of IEEE 80211 base stations with powerful radio transmit powers of approximately 100mW per base station Such radios are in a different class from the low power IEEE 802154 compliant radios that typically transmit at low power levels ranging from 52mW to 29mW [19] The wide availability of larger number of IEEE 802154 radios in the research community has revived the interest for signal strength based localization in sensor network Despite of rapidly increasing popularity of IEEE 802154 radios and signal strength localization there is a lack of detailed characterization of the fundamental factors contributing to large signal strength variation This chapter investigates some of the properties of RSS observed during testing and analysis of research work collected during the period of thesis The analysis of RSS values is needed to understand the underlying features of location dependent RSS patterns and location fingerprints An understanding of the properties of the RSS values for location fingerprinting can assist in improving the design of positioning algorithms and in deployment of indoor positioning systems

31 Parameters that affect RSS Different factors that affect the indoor RSS are the userrsquos presenceabsence hisher orientation time of the day building type and material distance from transmitter and type of radio These factors are by no means exhaustive Other possible factors are antenna orientation directionality and type Table 10 classify the parameters which affect RSS [7]

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

27

Effect on Factors Options

1 Proximity of user Userrsquos presence or absence

2 Orientation of user and terminal

North East South West

Data Collection

3 Make of Radio 4 Time of measurement Time of day amp days of

week 5 Period of measurement

Second minute hour

7 Interference Co‐channel adjacent co‐channel

Statistics

8 Building environment Small offices or large hall

Table 10 Classification of factors the affect RSS [7]

Transmitter variability Different transmitters behave differently even when they are configured exactly in the same way In practice this means that when a transmitter is configured to send packets at a power level of d dBm then the transmitter will send these packets at a power level that is very close to d dBm but not necessarily exactly equal to d dBm This can alter the received signal strength indication and thus it can lead to inaccurate distance estimation

Receiver variability The sensitivity of the receivers across different radio chips is different In practice this means that the RSSI value recorded at different receivers can be different even when all the other parameters that affect the received signal strength are kept constant

Antenna orientation Each antenna has its own radiation pattern that is not uniform In practice this means that the RSSI value recorded at the receiver for a given pair of communicating nodes and for a given distance between them varies as the pair wise antenna orientations of the transmitter and the receiver are changed

Multi‐path fading and shadowing in the RF channel In indoor environments the transmitted signals get reflected after hitting on the walls andor on other objects in the room such as furniture Both the original signal and the reflected signal reach the receiver almost at the same time since they both travel at the speed of light As a result of this the receiver is not able to

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

28

distinguish the two signals and it measures the received signal strength for both of them

32 Related work analysis A number of indoor location tracking systems have been proposed in the literature based on RF signals ultrasound infrared and some combinations of these As mentioned previously RF based positioning system are widely studied as shown in Table 2 Given a model of radio signal propagation in a building or other environment RSS can be used to estimate the distance between the transmitter and receiver Most of the algorithms (Table 2) have been evaluated in practice using 80211b compliant wireless radios and seem to produce a location error of 15ft to 20ft for an indoor network area of approximately 225ft x 144ft According to the results of this study all the localization techniques produce approximately the same location error over a range of environments Ecolocation [22] Mote Track [5] are two of the few localization algorithms that have been evaluated on a real sensor network that uses a low power wireless radio Ecolocation reports a location error of 10ft for a very small outdoor network deployment area (26ft x 49ft) Mote Track reports a location error of approximately 13ft for an indoor network deployment area of 18751ft2 In general the RSSI based localization algorithms can be divided into two broad categories the map based and the distance (or area) prediction based algorithms In the case of the map based algorithms the deployment area is divided into a large set of sample points that cover the whole area Most techniques collect one or more signal strength measurements from all visible beacon nodes and for each sample point Each sample point is then mapped to either a signal strength vector or a signal strength probability distribution This process generates an RSSI fingerprint map which is unique with respect to the beacon locations and the deployment area This fingerprint map is stored to a central base station A node that is not aware of its location collects one or more signal strength measurements from all the visible beacon nodes and creates its own RSSI fingerprint that is sent to the central base station The base station computes nodersquos location as the sample pointrsquos location whose RSSI fingerprint distance from the unknown nodersquos RSSI fingerprint is the minimum among all the sample points Despite the fact that the map based RSSI localization methods produce relatively small location errors they are not attractive mainly because they require an excessive amount of profiling data and because they are not fully distributed The detailed profiling of a building is a major holdback because it is complex time consuming and depends on beacon locations Therefore for different buildings or even for the

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

29

same building and different beacon locations different RSSI maps have to be obtained The second category of RSSI based algorithms are the distance (or area) prediction algorithms which focus on minimizing the amount of profiling data In distance prediction based methods raw RSSI data are directly mapped to distances through a signal propagation model In some cases distances are not explicitly measured but they are taken into consideration through assumptions on the underlying distributions In the case of outdoor deployments where there are no obstacles a single signal propagation model is all the profiling data required In the case of indoor deployments the same applies but now the signal propagation model might be different across different buildings with different furniture and wall arrangements Given an underlying signal propagation model raw RSSI data can be mapped to specific distances These distances can be either used directly in order to perform localization or assigned a probability and used in a learning basedprobabilistic localization algorithm Obviously the accuracy of the distance prediction based methods depends heavily on the accuracy of the signal propagation model used for translating RSSI values to distances

33 Properties of RSSI Changes in signal due to propagation indoors are difficult to predict because of dense environment and propagation effects such as reflection diffraction and scattering The multi‐path fading effect which is the result of either constructive or destructive combination of multiple signal copies at the receiver causes the received signal to fluctuate around a mean value at a particular location The received signal is usually modelled by the combined effects of large‐scale fading and small‐scale fading The large‐scale fading component describes the signal attenuation as the signal travels over a distance and is absorbed by material such as walls and floors along the way to the receiver This component predicts the mean of the RSS and usually has a lognormal distribution On the other hand the small scale fading component explains the dramatic fluctuation of the signal due to multi‐path fading On the other hand the small scale‐fading component explains the dramatic fluctuation of the signal due to multi‐path fading If there is no LOS component the small‐scale‐fading is often modelled with a Rayleigh distribution Such scenarios are commonly called NLOS If there is a LOS component the small‐scale‐fading is typically modelled by a Rician distribution Note that when the RSS is measured the measurement averages out the small‐scale‐fading effects

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

30

4 OBJECT TRACKING 41 Introduction This chapter presents the object‐tracking application the main objective of this thesis work The application is an algorithmic‐software implementation written in Embedded C which is capable of receiving data messages and generating object position estimation data The implementation of the object‐tracking application makes use of the SENTIO platform and uses the background knowledge introduced in the preceding chapters The motivation for the object‐tracking application is to determine the position of an object in indoor environments (Eg Home) The application acts as an interface which collects the raw data that the sensors generate (numerical values) It makes the proper comparisons and algorithmic processes to identify and track the object while it is static inside the area covered by the wireless sensor network Finally with the detection and tracking data it alerts the user or another system The following sections provide an overview of the object‐tracking application

42 Overview of hardware product Before describing the object tracking application it is important to know the characteristics of hardware product The SENTIO platform (figure 15) is built in modular fashion and has the following key features [8]

Flexibility This platform is very flexible in such a way that it is made easy to add or change the hardware for each sensor task depending on the requirement in the WSN For a specific platform this can be hard or sometimes even impossible to accomplish

Sensor resources Resources in a sensor module includes the ability of usage of controllerrsquos resources as much as possible such as ADC‐channels interrupts and digital Input‐Outputrsquos Local voltage regulation should also be possible since many sensors require higher supply voltages than digital Integrated Circuits do Further support of a shut‐down mechanism to power down the sensor modules when not needed

Computation and memory The processor module based on microcontroller should support many devices in a microcontroller family and support different power supply voltages enabling full range of clock frequencies All power‐down modes of the devices should be

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

31

supported The platform should support different processor modules including FPGArsquos and very simple microcontrollers

Communication The focus should be on enabling an IEEE 802154 compliant communication interface With or without ZigBee stack this will provide a flexible platform for exploring different network topologies Support for power‐down of the entire communication module should be supported

Cost and form factor High flexibility is in conflict with low cost and small size The primary objective should be flexibility but the size should be considered in order to enable sensor device integration in systems where space is limited

Figure 15 SENTIO Platform [8]

The communication subsystem of the SENTIO sensor node was designed so that the correct operation of the radio chip is ensured The radio chip is powered by the on‐board voltage regulator and thus fluctuations in the battery voltage level do not affect the operation of the communication subsystem This platform uses Chipcon CC2420 IEEE 802154 radio transceiver operates in the 24GHz ISM band and includes a digital direct sequence spread spectrum (DSSS) modem providing a spreading gain of 9dB and an effective data rate of 250Kbps It was specifically designed for low power wireless applications and supports 8 discrete power levels 0dBm minus1dBm minus3dBm minus5dBm minus7dBm minus10dBm minus15dBm and minus25dBm at which its power consumption varies from 29mW to 52mW [21] A built‐in received signal strength indicator gives an 8‐bit digital value RSSIVAL The RSSIVAL is always averaged over 8 symbol periods (128μs) and a status bit indicates when the RSSIVAL is valid (meaning that the receiver was enabled for at least 8 symbol periods) The

Antenna

Communication Module

Processing Module

Sensing Module

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

32

power P at the RF pins can be obtained directly from RSSIVAL using the following equation

P = RSSIVAL + RSSIOFFSET [dBm] where the RSSIOFFSET is found empirically from the front‐end gain and it is approximately equal to minus45dBm In the next sections when we refer to the RSSI value we refer to the RSSIVAL and not the power P unless otherwise stated

43 Experimental Testbed The experimental test bed for object tracking application is as shown in figure 16 Experiment was conducted in Masterrsquos room at Mid Sweden University A DELL laptop is connected to SENTIO platform and software were primarily used to collect samples of RSS from APrsquos The software monitors IEEE 802154 radio frequency channels which operate in the 24GHz band

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

1030

BC - 1030

AP 3 AP 1

AP 2

AP 4

BS

HEWLETT

PACKARD

Figure 16 Layout of Masterʹs Room

A three dimensional scalable testbed is used for indoor sensor network deployments The testbed illustrated in figure 16 is a 3‐D structure measuring 16m (L) x 12m (W) x 3m (H) and it is designed to host a large number of static nodes as shown in figure 16 Arrow marks in the above figure represent the location where fingerprints were taken The centrepiece of our infrastructure

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

33

is the SENTIO sensor node [8] an open‐source general purpose sensing platform designed with AT Mega 128 microprocessor and the IEEE 802154 compliant CC2420 radio from Chipcon [19]

44 Obtaining data from the sensor network The next important step in the development of the object tracking application is to obtain the data from the sensor network and send it to the base station From there the data is transmitted through a serial port to the computer that runs the object tracking application When the data arrives at the serial port the application must capture it This paragraph gives a brief explanation of how and what kind data is transmitted by the static object fixed sensors and how this data is being used to estimate the position of the object First the static object transmits Beacon packet continuously which is received by all the fixed APrsquos The beacon packet format is as shown in table 6 Then the fixed APrsquos calculates the 8‐bit value of RSSI from the transmitted beacon packet and then sends the resultant RSSI value as a form of data packet (table 7) to the BS which calculates the position and sends back to the object

45 Object tracking application Testing and Evaluation The purpose of this section is to discuss and evaluate the object tracking application Finally we are ready with the experimental testbed as shown in figure 16 As shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors placed at the corners of the masterrsquos room BS is placed at the centre of the room connected to a PC All the nodes are power connected before an actual measurement takes place At first the object is placed at random position inside the room and transmits beacon packets continuously Then these packets are received by the fixed APrsquos which calculate the RSSI from the packet and then averages it until it receives 50 beacon packets and then transmits the data to the BS (figure 17) Then the BS waits until it receives the data from all the APrsquos Once it receives from all the APrsquos it calculates the position of the object (figure 18) The application was developed to demonstrate a real world use of a WSN in object tracking In evaluating the final position obtained with respect to the actual position we conclude that the object‐tracking was successful Although

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

34

it is a research prototype object tracking application is a simple stable stand‐alone system capable of demonstrating an important application of WSN It takes raw data from the WSN aggregates it evaluates it and based on the evaluation tracks the object The accuracy of estimating the position is less than 5m Table 11 below shows a log of readings taken during test

Start

Intialize micro‐controller and transeciever in receieve mode

If received_packet=

Beacon

Average RSSI values

Increase the counter

If countergt1000050

A

No

Yes

B

B

Yes

No

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

35

A

Reset counter

Set transceiever in transmit mode

Transmit averaged RSSI to Main sensor and set it back to receive mode

10000 samples ‐ Offline

50 samples ‐ Online

Figure 17 Flow chart representing the algorithm of fixed sensors

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

36

Start

Intialize micro‐controller and transeciever in receieve mode

Received RSSI values from all fixed Sensors

Open radio map

Calculate Euclidean distance between obtained RSSI values from fixed sensors and fingerprints

in radio map

No

Yes

Bubble sort for least error in signal strength values

A

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

37

A

Get the location of the object from the radiousing the least error value

Display location of object on PC

End

Figure 18 Flow chart representing the algorithm of main sensor

Table 11 Accuracy of positioning system

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

38

5 RESULTS This chapter presents how the system parameters such as number of samples mean or median etchellipare finalized All the tests are performed in masterrsquos room at Mid Sweden University This chapter is divided into three subsections First in Section 51 it reviews results obtained from samples test Section 52 discusses one dimensional test results and finally section 53 discusses results of two dimensional tests

51 Measurement setup A Dell laptop was connected to a SENTIO platform and is loaded with software (Appendix 2) This setup was used to determine number of samples required during object tracking mean or median position estimation algorithm and one dimensional test The setup is as follows

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

103

0

BC - 1030

AP 1AP 2

BS

HEWLETT

PACKARD

Figure 19 Test bed for one dimensional test in Masters Room

Two SENTIO nodes (AP1 and AP2 as shown above) were separated by a distance of 10 meters and these sensors are known as fixed sensors which are fixed to the ceiling such that there antenna is faced towards the ground They are linear is position A BS was connected to Dell laptop and it was placed in such a way that its antenna was faced towards the ceiling Another SENTIO

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

39

node was used as an object and its orientation was same as BS Figure 19 represents the complete setup for object tracking application Mobile node was moved between these fixed ones at regular intervals of one meter and RSSI values were logged at each interval

52 Number of samples test This test was performed to determine number of samples required for accurate positioning and either mean of the obtained RSSI values or median of them Figure 19 represents the setup for number of samples test Once the setup is ready the measurements are taken as follows

1 An object was tagged with SENTIO node and was placed stationary linear to the fixed sensors

2 10000 samples (RSSI) were taken at regular intervals of 1 meter up to 10 meters

3 The sample values were logged at every interval 4 Using these logged samples I have plotted graphs such as distribution

of RSS values (figure 20 21) meanmedian at intervals of 1 meter using MATLAB

5 The mean and median for various numbers of samples is as shown in figure 22

Figure 20 Histogram plot of RSSI values at 1m and 4m

Figure 21 Histogram plot of RSSI values at 6m and 8m

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

40

Figure 22 Distance vs RSSI

From the above graphs following conclusions were made 1 From the histogram plots we can observe that the distribution of RSSI

values follow Gaussians distribution law 2 As we can see from figure 22 that median curve has drastic changes

when compared to mean curve Hence mean is considered while estimating the position

3 From the observations we conclude that the number of samples does not matter since mean curve tends to be same for various numbers of samples

4 Hence 10000 samples are considered in order to get a proper location fingerprint during off‐line phase and during online phase 50 samples are enough to estimate the position of object

53 0ne dimensional test Measurement setup for one dimensional test is as shown in figure 19 This test was conducted to check the performance of algorithm and how accurate it was At first ie during offline phase fingerprints at each interval averaging 10000 samples were taken During online phase Euclidean algorithm was used estimate the position averaging 50 samples of RSSI From the results we

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

41

can realize that the error was less than 2 meters Table 12 shows the result of location estimation in one dimensional case

Table 12 One dimensional position estimation results

54 Two dimensional test This test is the final stage of my thesis work Experimental testbed for this stage is as shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors fixed at the corners of the room and a BS at the centre of the room There are 18 pre‐determined locations where the fingerprints (RSSI) were taken The fingerprints are as shown in Appendix 1 All the readings were taken in presence of userrsquos The algorithm for position estimation is shown in Appendix 2 Position estimation test is performed as follows

Tagged object is placed at random inside the masterrsquos room All the sensors including the base station and object are switched on Using the algorithm (Appendix 2) the BS estimates the position of the object and displays it on the PC

This test was performed using various numbers of samples to test which has a better accuracy Figure 23 represents the error rate using various numbers of samples From figure 23 we can conclude that 50 numbers of samples is enough to estimate the position of object with an accuracy of 3m

55 Causes of error in location determination The randomness of RSS is the major cause of error in any indoor

positioning system that uses the RSS for location inference If there is no randomness in the RSS and its location fingerprint every indoor positioning systems of this type will have no location detection error For example I have recorded RSS at one particular location during different times of the day From figures 24 and 25 you can observe that RSS values are different at different times of day

The performance of indoor positioning systems depends greatly on the separation of location fingerprints A location fingerprint

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

42

corresponding to a location can be identified correctly if it is difficult to classify it as another fingerprint by a pattern classifier

Path loss is one of the basic signal propagation properties of the RSS that best describes attenuation of transmit signal power over a distance between an access point and a mobile device

Presence of Userrsquos or userrsquos orientation affects RSS

Figure 23 Error Rate vs Number of samples

Figure 24 RSS distribution at 214 pm

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

43

Figure 25 RSS distribution at 100pm and 1100 am

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

6 DISCUSSIONS10102006

44

6 DISCUSSIONS 61 Summary and Conclusion While empirical results and performance studies of positioning systems based on location fingerprint have been presented in this report analytical model that can be used as a framework for efficiently designing the positioning systems are not available This thesis design work uses general principles of position estimation such as position estimation of sensors grid spacing etc This design work focuses on accuracy and precision The concept of location fingerprint technique based on RSS us explained in detail in chapter 2 It is found that RSS is random It is possible to approximate the RSS as being normally distributed While the RSS random process is non‐stationary the mean is more or less constant and could be used as the fingerprint of the location Model designed for position estimation is as shown in chapter 4 Accuracy and precision are the two major performance matrices in this study Uniform grid spacing has been used From the experimental analysis I have obtained an accuracy of 3 meters and 35 precision The sensitivity analysis among the system parameters and the performance matrices were done using the model as shown in chapter 4 For instance our study found that the minimum distance between two adjacent positions also affects the positioning performance This is due to the randomness of RSS patterns induced by the ever changing indoor environment

62 Future work The research in this thesis provides a ground work for the future study of efficient design of indoor positioning system The RSSI based position estimation still has limitations First the RSSI value is time dependent Thus the future study should address this issue Second the movement of object is not considered Third orientations of sensors affect the performance which should be taken care off Fourth to improve the performance of the system we need to find a solution to deal with those incomplete or missing data during the off‐line and the on‐line phase Fifth position estimation on multi‐floor and three‐dimensional coordinates is not available

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 110102006

45

APPENDIX 1

Location Fingerprints of Masters Room Time 1315 Observation Time 20 minutes Number of Samples 10000

People Presence SNo Co-ordinates AP1 (0x0001) AP2 (0x0002) AP3 (0x0004) AP4 (0x0008)

1 11 -24 -16 -27 -33 2 13 -26 -8 -34 -31 3 15 -43 -9 -39 -25 4 32 -21 -11 -27 -33 5 61 -6 -23 -32 -28 6 64 -20 -31 -25 -34 7 35 -23 -35 -29 -38 8 37 -29 -35 -22 -27 9 39 -38 -29 -21 -27 10 67 -27 -23 -20 -23 11 611 -26 -29 -23 -30 12 613 -35 -32 -2 -31 13 513 -34 -31 -20 -26 14 11 -39 -22 -29 -17 15 212 -37 -42 -28 -31 16 -212 -38 -33 -29 -6 17 -21 -42 -29 -33 -19 18 -38 -40 -23 -35 -22

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 210102006

46

APPENDIX 2

Begin

Open radio map and raw data files for write

Intialize radio map in memory

Check whether the

Packet is data or Beacon packet If data packet

Save the data to register

Check if data is

Received from all

sensors

Estimate the position of static

Display the position on PC

End Yes

No

A

A

Yes

No

Flow chart representing the main algorithm for position estimation

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

47

APPENDIX 3

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

48

Distribution of RSSI

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

49

REFERENCES [1] T S Rappaport J H Reed and B D Woerner ldquoPosition location using wireless communications on highways of the futureʺ IEEE Commun Mag vol 34 no 10 pp33‐41 Oct 1996 [2] A M Ladd K E Bekris G Marceau A Rudys L E Kavraki and D S Wallach rdquoRobotics‐based location sensing using wireless ethernetʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ02) 2002 pp 227‐238 [3] I Ramani R Bharadwaja and P V Rangan ldquoLocation tracking for media appliances in wireless home networksʺ in Proc International Conference on Multimedia and Expo (ICMEʹ03) July 2003 pp 769‐772 [4] K Pahlavan X Li and J P Makela ldquoIndoor geolocation science and technologyʺ IEEE Commun Mag vol 40 no 2 pp 112‐118 Feb 2002 [5] Konard Lorincz and Matt Welsh ldquoMote Track A Robust Decentralized Approach to RF‐Based Location Trackingrdquo [6] Paramvir Bahl and Venkata N Padmanabham ldquoRADAR An In‐Building RF‐based User Location and Tracking Systemrdquo [7] Kamol Kaemarungsi ldquoDesign of Indoor positioning system based on location fingerprint techiniquerdquo University of Pittsburgh 2005 [8] Fredrik Linnarsson Cheng Peng and Bengt Oelmann ldquoSENTIO A Hardware Platform for Rapid Prototyping of Wireless Sensor Networksrdquo [9] S Saha K Chaudhuri D Sanghi and P Bhagwat ldquoLocation determination of a mobile device using IEEE 80211b access point signalsʺ in Proc IEEE Wireless Communications and Networking Conference (WCNCʹ03) New Orleans LA Mar 2003 pp 1987‐1992 [10] T Roos P Myllymaki H Tirri P Misikangas and J Sievanen ldquoA probabilistic approach to WLAN user location estimationʺ International Journal of Wireless Information Networks vol 9 no 3 pp 155‐164 July 2002 [11] R Battiti M Brunato and A Villani ldquoStatistical learning theory for location fingerprinting in wireless LANrsquosʺ Technical Report Oct 2002 [Online] Available httprtmscienceunitnitraquobattitiarchive86pdf [12] P Prasithsangaree P Krishnamurthy and P K Chrysanthis ldquoAn indoor position location with wireless LANsʺ in Proc IEEE International Symposium on Personal Indoor and Mobile Radio Communications (PIMRCʹ02) Lisbon Portugal Sept 2002 [13] M A Youssef A Agrawala and A U Shankar ldquoWLAN location determination via clustering and probability distributionsʺ in Proc IEEE

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

50

International Conference on Pervasive Computing and Communications (PerComʹ03) Dallas‐Fort Worth TX Mar 23‐26 2003 pp 23‐26 [14] Z Xiang S Song J Chen H Wang J Huang and X Gao ldquoA wireless LAN‐based indoor positioning technologyʺ IBM Journal of Research and Development vol 48 no 56 pp 617‐626 SeptNov 2004 [15] R Want A Hopper V Falcao and J Gibbons ldquoThe active badge location systemʺACM Transactions on Information Systems vol 40 no 1 pp 91‐102 Jan 1992 [16] N B Priyantha A Chakraborty and H Balakrishnan ldquoThe cricket location‐support systemʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ00) Boston MA Aug 2000 pp 32‐43 [17] Holger K Willig A (2005) Protocols and Architectures for Wireless Sensor Networks London [18] IEEE (2003) Part 154 Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low‐Rate Wireless Personal Area Networks (LR‐WPANs) New York [19] Chipcon CC2420 802154 compliant radio httpwwwchipconcom [20] ZigBee Specification v10 ZigBee Alliance [21] Dimitrios Lymberopoulos Quentin Lindsey and Andreas Savvides ldquoAn Empirical Analysis of Radio Signal Strength Variability in IEEE 802154 Networks using Monopole Antennasrdquo [22] Kiran Yedavalli Bhaskar Krishnamachari Sharmila Ravula and Bhaskar Srinivasan ldquoEcolocation A sequence based technique for RF localization in wireless sensor networksrdquo Robert Bosch Research and Technology Centre Palo Alto CA

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

2 LITERATURE REVIEW10102006

25

Figure 14 ZigBee secure frame in application layer [17]

The ZigBee application layer contains the manufacturer‐defined application objects the ZDO and the application sub‐layer In addition to the security responsibilities the application sub‐layer binds devices based on their duties and needs maintains the binding tables and forwards messages between them The application sub‐layer also discovers the neighbour devices for a given device The ZDO is responsible for determining the devicersquos duty in the network for communicating using binding requests and for supporting security as was mentioned above The sub‐layer that implements the actual application is the manufacturer‐defined application object (Kinney 2005) The ZigBee specifications are a great step toward the wireless sensors networks standardization They cover all the OSI layers from the physical to the application providing guidance to the developers The specifications are the product of the ZigBee alliances a consortium of companies and academic institutes Thus they not only produce the specifications but also are the first use them The next section goes beyond the layer architecture and standardization and introduces the OS field another crucial element of WSNs

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

26

3 RECEIVED SIGNAL STRENGTH (RSS) Majority of the existing methods leverage the existence of IEEE 80211 base stations with powerful radio transmit powers of approximately 100mW per base station Such radios are in a different class from the low power IEEE 802154 compliant radios that typically transmit at low power levels ranging from 52mW to 29mW [19] The wide availability of larger number of IEEE 802154 radios in the research community has revived the interest for signal strength based localization in sensor network Despite of rapidly increasing popularity of IEEE 802154 radios and signal strength localization there is a lack of detailed characterization of the fundamental factors contributing to large signal strength variation This chapter investigates some of the properties of RSS observed during testing and analysis of research work collected during the period of thesis The analysis of RSS values is needed to understand the underlying features of location dependent RSS patterns and location fingerprints An understanding of the properties of the RSS values for location fingerprinting can assist in improving the design of positioning algorithms and in deployment of indoor positioning systems

31 Parameters that affect RSS Different factors that affect the indoor RSS are the userrsquos presenceabsence hisher orientation time of the day building type and material distance from transmitter and type of radio These factors are by no means exhaustive Other possible factors are antenna orientation directionality and type Table 10 classify the parameters which affect RSS [7]

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

27

Effect on Factors Options

1 Proximity of user Userrsquos presence or absence

2 Orientation of user and terminal

North East South West

Data Collection

3 Make of Radio 4 Time of measurement Time of day amp days of

week 5 Period of measurement

Second minute hour

7 Interference Co‐channel adjacent co‐channel

Statistics

8 Building environment Small offices or large hall

Table 10 Classification of factors the affect RSS [7]

Transmitter variability Different transmitters behave differently even when they are configured exactly in the same way In practice this means that when a transmitter is configured to send packets at a power level of d dBm then the transmitter will send these packets at a power level that is very close to d dBm but not necessarily exactly equal to d dBm This can alter the received signal strength indication and thus it can lead to inaccurate distance estimation

Receiver variability The sensitivity of the receivers across different radio chips is different In practice this means that the RSSI value recorded at different receivers can be different even when all the other parameters that affect the received signal strength are kept constant

Antenna orientation Each antenna has its own radiation pattern that is not uniform In practice this means that the RSSI value recorded at the receiver for a given pair of communicating nodes and for a given distance between them varies as the pair wise antenna orientations of the transmitter and the receiver are changed

Multi‐path fading and shadowing in the RF channel In indoor environments the transmitted signals get reflected after hitting on the walls andor on other objects in the room such as furniture Both the original signal and the reflected signal reach the receiver almost at the same time since they both travel at the speed of light As a result of this the receiver is not able to

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

28

distinguish the two signals and it measures the received signal strength for both of them

32 Related work analysis A number of indoor location tracking systems have been proposed in the literature based on RF signals ultrasound infrared and some combinations of these As mentioned previously RF based positioning system are widely studied as shown in Table 2 Given a model of radio signal propagation in a building or other environment RSS can be used to estimate the distance between the transmitter and receiver Most of the algorithms (Table 2) have been evaluated in practice using 80211b compliant wireless radios and seem to produce a location error of 15ft to 20ft for an indoor network area of approximately 225ft x 144ft According to the results of this study all the localization techniques produce approximately the same location error over a range of environments Ecolocation [22] Mote Track [5] are two of the few localization algorithms that have been evaluated on a real sensor network that uses a low power wireless radio Ecolocation reports a location error of 10ft for a very small outdoor network deployment area (26ft x 49ft) Mote Track reports a location error of approximately 13ft for an indoor network deployment area of 18751ft2 In general the RSSI based localization algorithms can be divided into two broad categories the map based and the distance (or area) prediction based algorithms In the case of the map based algorithms the deployment area is divided into a large set of sample points that cover the whole area Most techniques collect one or more signal strength measurements from all visible beacon nodes and for each sample point Each sample point is then mapped to either a signal strength vector or a signal strength probability distribution This process generates an RSSI fingerprint map which is unique with respect to the beacon locations and the deployment area This fingerprint map is stored to a central base station A node that is not aware of its location collects one or more signal strength measurements from all the visible beacon nodes and creates its own RSSI fingerprint that is sent to the central base station The base station computes nodersquos location as the sample pointrsquos location whose RSSI fingerprint distance from the unknown nodersquos RSSI fingerprint is the minimum among all the sample points Despite the fact that the map based RSSI localization methods produce relatively small location errors they are not attractive mainly because they require an excessive amount of profiling data and because they are not fully distributed The detailed profiling of a building is a major holdback because it is complex time consuming and depends on beacon locations Therefore for different buildings or even for the

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

29

same building and different beacon locations different RSSI maps have to be obtained The second category of RSSI based algorithms are the distance (or area) prediction algorithms which focus on minimizing the amount of profiling data In distance prediction based methods raw RSSI data are directly mapped to distances through a signal propagation model In some cases distances are not explicitly measured but they are taken into consideration through assumptions on the underlying distributions In the case of outdoor deployments where there are no obstacles a single signal propagation model is all the profiling data required In the case of indoor deployments the same applies but now the signal propagation model might be different across different buildings with different furniture and wall arrangements Given an underlying signal propagation model raw RSSI data can be mapped to specific distances These distances can be either used directly in order to perform localization or assigned a probability and used in a learning basedprobabilistic localization algorithm Obviously the accuracy of the distance prediction based methods depends heavily on the accuracy of the signal propagation model used for translating RSSI values to distances

33 Properties of RSSI Changes in signal due to propagation indoors are difficult to predict because of dense environment and propagation effects such as reflection diffraction and scattering The multi‐path fading effect which is the result of either constructive or destructive combination of multiple signal copies at the receiver causes the received signal to fluctuate around a mean value at a particular location The received signal is usually modelled by the combined effects of large‐scale fading and small‐scale fading The large‐scale fading component describes the signal attenuation as the signal travels over a distance and is absorbed by material such as walls and floors along the way to the receiver This component predicts the mean of the RSS and usually has a lognormal distribution On the other hand the small scale fading component explains the dramatic fluctuation of the signal due to multi‐path fading On the other hand the small scale‐fading component explains the dramatic fluctuation of the signal due to multi‐path fading If there is no LOS component the small‐scale‐fading is often modelled with a Rayleigh distribution Such scenarios are commonly called NLOS If there is a LOS component the small‐scale‐fading is typically modelled by a Rician distribution Note that when the RSS is measured the measurement averages out the small‐scale‐fading effects

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

30

4 OBJECT TRACKING 41 Introduction This chapter presents the object‐tracking application the main objective of this thesis work The application is an algorithmic‐software implementation written in Embedded C which is capable of receiving data messages and generating object position estimation data The implementation of the object‐tracking application makes use of the SENTIO platform and uses the background knowledge introduced in the preceding chapters The motivation for the object‐tracking application is to determine the position of an object in indoor environments (Eg Home) The application acts as an interface which collects the raw data that the sensors generate (numerical values) It makes the proper comparisons and algorithmic processes to identify and track the object while it is static inside the area covered by the wireless sensor network Finally with the detection and tracking data it alerts the user or another system The following sections provide an overview of the object‐tracking application

42 Overview of hardware product Before describing the object tracking application it is important to know the characteristics of hardware product The SENTIO platform (figure 15) is built in modular fashion and has the following key features [8]

Flexibility This platform is very flexible in such a way that it is made easy to add or change the hardware for each sensor task depending on the requirement in the WSN For a specific platform this can be hard or sometimes even impossible to accomplish

Sensor resources Resources in a sensor module includes the ability of usage of controllerrsquos resources as much as possible such as ADC‐channels interrupts and digital Input‐Outputrsquos Local voltage regulation should also be possible since many sensors require higher supply voltages than digital Integrated Circuits do Further support of a shut‐down mechanism to power down the sensor modules when not needed

Computation and memory The processor module based on microcontroller should support many devices in a microcontroller family and support different power supply voltages enabling full range of clock frequencies All power‐down modes of the devices should be

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

31

supported The platform should support different processor modules including FPGArsquos and very simple microcontrollers

Communication The focus should be on enabling an IEEE 802154 compliant communication interface With or without ZigBee stack this will provide a flexible platform for exploring different network topologies Support for power‐down of the entire communication module should be supported

Cost and form factor High flexibility is in conflict with low cost and small size The primary objective should be flexibility but the size should be considered in order to enable sensor device integration in systems where space is limited

Figure 15 SENTIO Platform [8]

The communication subsystem of the SENTIO sensor node was designed so that the correct operation of the radio chip is ensured The radio chip is powered by the on‐board voltage regulator and thus fluctuations in the battery voltage level do not affect the operation of the communication subsystem This platform uses Chipcon CC2420 IEEE 802154 radio transceiver operates in the 24GHz ISM band and includes a digital direct sequence spread spectrum (DSSS) modem providing a spreading gain of 9dB and an effective data rate of 250Kbps It was specifically designed for low power wireless applications and supports 8 discrete power levels 0dBm minus1dBm minus3dBm minus5dBm minus7dBm minus10dBm minus15dBm and minus25dBm at which its power consumption varies from 29mW to 52mW [21] A built‐in received signal strength indicator gives an 8‐bit digital value RSSIVAL The RSSIVAL is always averaged over 8 symbol periods (128μs) and a status bit indicates when the RSSIVAL is valid (meaning that the receiver was enabled for at least 8 symbol periods) The

Antenna

Communication Module

Processing Module

Sensing Module

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

32

power P at the RF pins can be obtained directly from RSSIVAL using the following equation

P = RSSIVAL + RSSIOFFSET [dBm] where the RSSIOFFSET is found empirically from the front‐end gain and it is approximately equal to minus45dBm In the next sections when we refer to the RSSI value we refer to the RSSIVAL and not the power P unless otherwise stated

43 Experimental Testbed The experimental test bed for object tracking application is as shown in figure 16 Experiment was conducted in Masterrsquos room at Mid Sweden University A DELL laptop is connected to SENTIO platform and software were primarily used to collect samples of RSS from APrsquos The software monitors IEEE 802154 radio frequency channels which operate in the 24GHz band

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

1030

BC - 1030

AP 3 AP 1

AP 2

AP 4

BS

HEWLETT

PACKARD

Figure 16 Layout of Masterʹs Room

A three dimensional scalable testbed is used for indoor sensor network deployments The testbed illustrated in figure 16 is a 3‐D structure measuring 16m (L) x 12m (W) x 3m (H) and it is designed to host a large number of static nodes as shown in figure 16 Arrow marks in the above figure represent the location where fingerprints were taken The centrepiece of our infrastructure

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

33

is the SENTIO sensor node [8] an open‐source general purpose sensing platform designed with AT Mega 128 microprocessor and the IEEE 802154 compliant CC2420 radio from Chipcon [19]

44 Obtaining data from the sensor network The next important step in the development of the object tracking application is to obtain the data from the sensor network and send it to the base station From there the data is transmitted through a serial port to the computer that runs the object tracking application When the data arrives at the serial port the application must capture it This paragraph gives a brief explanation of how and what kind data is transmitted by the static object fixed sensors and how this data is being used to estimate the position of the object First the static object transmits Beacon packet continuously which is received by all the fixed APrsquos The beacon packet format is as shown in table 6 Then the fixed APrsquos calculates the 8‐bit value of RSSI from the transmitted beacon packet and then sends the resultant RSSI value as a form of data packet (table 7) to the BS which calculates the position and sends back to the object

45 Object tracking application Testing and Evaluation The purpose of this section is to discuss and evaluate the object tracking application Finally we are ready with the experimental testbed as shown in figure 16 As shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors placed at the corners of the masterrsquos room BS is placed at the centre of the room connected to a PC All the nodes are power connected before an actual measurement takes place At first the object is placed at random position inside the room and transmits beacon packets continuously Then these packets are received by the fixed APrsquos which calculate the RSSI from the packet and then averages it until it receives 50 beacon packets and then transmits the data to the BS (figure 17) Then the BS waits until it receives the data from all the APrsquos Once it receives from all the APrsquos it calculates the position of the object (figure 18) The application was developed to demonstrate a real world use of a WSN in object tracking In evaluating the final position obtained with respect to the actual position we conclude that the object‐tracking was successful Although

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

34

it is a research prototype object tracking application is a simple stable stand‐alone system capable of demonstrating an important application of WSN It takes raw data from the WSN aggregates it evaluates it and based on the evaluation tracks the object The accuracy of estimating the position is less than 5m Table 11 below shows a log of readings taken during test

Start

Intialize micro‐controller and transeciever in receieve mode

If received_packet=

Beacon

Average RSSI values

Increase the counter

If countergt1000050

A

No

Yes

B

B

Yes

No

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

35

A

Reset counter

Set transceiever in transmit mode

Transmit averaged RSSI to Main sensor and set it back to receive mode

10000 samples ‐ Offline

50 samples ‐ Online

Figure 17 Flow chart representing the algorithm of fixed sensors

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

36

Start

Intialize micro‐controller and transeciever in receieve mode

Received RSSI values from all fixed Sensors

Open radio map

Calculate Euclidean distance between obtained RSSI values from fixed sensors and fingerprints

in radio map

No

Yes

Bubble sort for least error in signal strength values

A

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

37

A

Get the location of the object from the radiousing the least error value

Display location of object on PC

End

Figure 18 Flow chart representing the algorithm of main sensor

Table 11 Accuracy of positioning system

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

38

5 RESULTS This chapter presents how the system parameters such as number of samples mean or median etchellipare finalized All the tests are performed in masterrsquos room at Mid Sweden University This chapter is divided into three subsections First in Section 51 it reviews results obtained from samples test Section 52 discusses one dimensional test results and finally section 53 discusses results of two dimensional tests

51 Measurement setup A Dell laptop was connected to a SENTIO platform and is loaded with software (Appendix 2) This setup was used to determine number of samples required during object tracking mean or median position estimation algorithm and one dimensional test The setup is as follows

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

103

0

BC - 1030

AP 1AP 2

BS

HEWLETT

PACKARD

Figure 19 Test bed for one dimensional test in Masters Room

Two SENTIO nodes (AP1 and AP2 as shown above) were separated by a distance of 10 meters and these sensors are known as fixed sensors which are fixed to the ceiling such that there antenna is faced towards the ground They are linear is position A BS was connected to Dell laptop and it was placed in such a way that its antenna was faced towards the ceiling Another SENTIO

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

39

node was used as an object and its orientation was same as BS Figure 19 represents the complete setup for object tracking application Mobile node was moved between these fixed ones at regular intervals of one meter and RSSI values were logged at each interval

52 Number of samples test This test was performed to determine number of samples required for accurate positioning and either mean of the obtained RSSI values or median of them Figure 19 represents the setup for number of samples test Once the setup is ready the measurements are taken as follows

1 An object was tagged with SENTIO node and was placed stationary linear to the fixed sensors

2 10000 samples (RSSI) were taken at regular intervals of 1 meter up to 10 meters

3 The sample values were logged at every interval 4 Using these logged samples I have plotted graphs such as distribution

of RSS values (figure 20 21) meanmedian at intervals of 1 meter using MATLAB

5 The mean and median for various numbers of samples is as shown in figure 22

Figure 20 Histogram plot of RSSI values at 1m and 4m

Figure 21 Histogram plot of RSSI values at 6m and 8m

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

40

Figure 22 Distance vs RSSI

From the above graphs following conclusions were made 1 From the histogram plots we can observe that the distribution of RSSI

values follow Gaussians distribution law 2 As we can see from figure 22 that median curve has drastic changes

when compared to mean curve Hence mean is considered while estimating the position

3 From the observations we conclude that the number of samples does not matter since mean curve tends to be same for various numbers of samples

4 Hence 10000 samples are considered in order to get a proper location fingerprint during off‐line phase and during online phase 50 samples are enough to estimate the position of object

53 0ne dimensional test Measurement setup for one dimensional test is as shown in figure 19 This test was conducted to check the performance of algorithm and how accurate it was At first ie during offline phase fingerprints at each interval averaging 10000 samples were taken During online phase Euclidean algorithm was used estimate the position averaging 50 samples of RSSI From the results we

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

41

can realize that the error was less than 2 meters Table 12 shows the result of location estimation in one dimensional case

Table 12 One dimensional position estimation results

54 Two dimensional test This test is the final stage of my thesis work Experimental testbed for this stage is as shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors fixed at the corners of the room and a BS at the centre of the room There are 18 pre‐determined locations where the fingerprints (RSSI) were taken The fingerprints are as shown in Appendix 1 All the readings were taken in presence of userrsquos The algorithm for position estimation is shown in Appendix 2 Position estimation test is performed as follows

Tagged object is placed at random inside the masterrsquos room All the sensors including the base station and object are switched on Using the algorithm (Appendix 2) the BS estimates the position of the object and displays it on the PC

This test was performed using various numbers of samples to test which has a better accuracy Figure 23 represents the error rate using various numbers of samples From figure 23 we can conclude that 50 numbers of samples is enough to estimate the position of object with an accuracy of 3m

55 Causes of error in location determination The randomness of RSS is the major cause of error in any indoor

positioning system that uses the RSS for location inference If there is no randomness in the RSS and its location fingerprint every indoor positioning systems of this type will have no location detection error For example I have recorded RSS at one particular location during different times of the day From figures 24 and 25 you can observe that RSS values are different at different times of day

The performance of indoor positioning systems depends greatly on the separation of location fingerprints A location fingerprint

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

42

corresponding to a location can be identified correctly if it is difficult to classify it as another fingerprint by a pattern classifier

Path loss is one of the basic signal propagation properties of the RSS that best describes attenuation of transmit signal power over a distance between an access point and a mobile device

Presence of Userrsquos or userrsquos orientation affects RSS

Figure 23 Error Rate vs Number of samples

Figure 24 RSS distribution at 214 pm

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

43

Figure 25 RSS distribution at 100pm and 1100 am

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

6 DISCUSSIONS10102006

44

6 DISCUSSIONS 61 Summary and Conclusion While empirical results and performance studies of positioning systems based on location fingerprint have been presented in this report analytical model that can be used as a framework for efficiently designing the positioning systems are not available This thesis design work uses general principles of position estimation such as position estimation of sensors grid spacing etc This design work focuses on accuracy and precision The concept of location fingerprint technique based on RSS us explained in detail in chapter 2 It is found that RSS is random It is possible to approximate the RSS as being normally distributed While the RSS random process is non‐stationary the mean is more or less constant and could be used as the fingerprint of the location Model designed for position estimation is as shown in chapter 4 Accuracy and precision are the two major performance matrices in this study Uniform grid spacing has been used From the experimental analysis I have obtained an accuracy of 3 meters and 35 precision The sensitivity analysis among the system parameters and the performance matrices were done using the model as shown in chapter 4 For instance our study found that the minimum distance between two adjacent positions also affects the positioning performance This is due to the randomness of RSS patterns induced by the ever changing indoor environment

62 Future work The research in this thesis provides a ground work for the future study of efficient design of indoor positioning system The RSSI based position estimation still has limitations First the RSSI value is time dependent Thus the future study should address this issue Second the movement of object is not considered Third orientations of sensors affect the performance which should be taken care off Fourth to improve the performance of the system we need to find a solution to deal with those incomplete or missing data during the off‐line and the on‐line phase Fifth position estimation on multi‐floor and three‐dimensional coordinates is not available

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 110102006

45

APPENDIX 1

Location Fingerprints of Masters Room Time 1315 Observation Time 20 minutes Number of Samples 10000

People Presence SNo Co-ordinates AP1 (0x0001) AP2 (0x0002) AP3 (0x0004) AP4 (0x0008)

1 11 -24 -16 -27 -33 2 13 -26 -8 -34 -31 3 15 -43 -9 -39 -25 4 32 -21 -11 -27 -33 5 61 -6 -23 -32 -28 6 64 -20 -31 -25 -34 7 35 -23 -35 -29 -38 8 37 -29 -35 -22 -27 9 39 -38 -29 -21 -27 10 67 -27 -23 -20 -23 11 611 -26 -29 -23 -30 12 613 -35 -32 -2 -31 13 513 -34 -31 -20 -26 14 11 -39 -22 -29 -17 15 212 -37 -42 -28 -31 16 -212 -38 -33 -29 -6 17 -21 -42 -29 -33 -19 18 -38 -40 -23 -35 -22

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 210102006

46

APPENDIX 2

Begin

Open radio map and raw data files for write

Intialize radio map in memory

Check whether the

Packet is data or Beacon packet If data packet

Save the data to register

Check if data is

Received from all

sensors

Estimate the position of static

Display the position on PC

End Yes

No

A

A

Yes

No

Flow chart representing the main algorithm for position estimation

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

47

APPENDIX 3

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

48

Distribution of RSSI

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

49

REFERENCES [1] T S Rappaport J H Reed and B D Woerner ldquoPosition location using wireless communications on highways of the futureʺ IEEE Commun Mag vol 34 no 10 pp33‐41 Oct 1996 [2] A M Ladd K E Bekris G Marceau A Rudys L E Kavraki and D S Wallach rdquoRobotics‐based location sensing using wireless ethernetʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ02) 2002 pp 227‐238 [3] I Ramani R Bharadwaja and P V Rangan ldquoLocation tracking for media appliances in wireless home networksʺ in Proc International Conference on Multimedia and Expo (ICMEʹ03) July 2003 pp 769‐772 [4] K Pahlavan X Li and J P Makela ldquoIndoor geolocation science and technologyʺ IEEE Commun Mag vol 40 no 2 pp 112‐118 Feb 2002 [5] Konard Lorincz and Matt Welsh ldquoMote Track A Robust Decentralized Approach to RF‐Based Location Trackingrdquo [6] Paramvir Bahl and Venkata N Padmanabham ldquoRADAR An In‐Building RF‐based User Location and Tracking Systemrdquo [7] Kamol Kaemarungsi ldquoDesign of Indoor positioning system based on location fingerprint techiniquerdquo University of Pittsburgh 2005 [8] Fredrik Linnarsson Cheng Peng and Bengt Oelmann ldquoSENTIO A Hardware Platform for Rapid Prototyping of Wireless Sensor Networksrdquo [9] S Saha K Chaudhuri D Sanghi and P Bhagwat ldquoLocation determination of a mobile device using IEEE 80211b access point signalsʺ in Proc IEEE Wireless Communications and Networking Conference (WCNCʹ03) New Orleans LA Mar 2003 pp 1987‐1992 [10] T Roos P Myllymaki H Tirri P Misikangas and J Sievanen ldquoA probabilistic approach to WLAN user location estimationʺ International Journal of Wireless Information Networks vol 9 no 3 pp 155‐164 July 2002 [11] R Battiti M Brunato and A Villani ldquoStatistical learning theory for location fingerprinting in wireless LANrsquosʺ Technical Report Oct 2002 [Online] Available httprtmscienceunitnitraquobattitiarchive86pdf [12] P Prasithsangaree P Krishnamurthy and P K Chrysanthis ldquoAn indoor position location with wireless LANsʺ in Proc IEEE International Symposium on Personal Indoor and Mobile Radio Communications (PIMRCʹ02) Lisbon Portugal Sept 2002 [13] M A Youssef A Agrawala and A U Shankar ldquoWLAN location determination via clustering and probability distributionsʺ in Proc IEEE

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

50

International Conference on Pervasive Computing and Communications (PerComʹ03) Dallas‐Fort Worth TX Mar 23‐26 2003 pp 23‐26 [14] Z Xiang S Song J Chen H Wang J Huang and X Gao ldquoA wireless LAN‐based indoor positioning technologyʺ IBM Journal of Research and Development vol 48 no 56 pp 617‐626 SeptNov 2004 [15] R Want A Hopper V Falcao and J Gibbons ldquoThe active badge location systemʺACM Transactions on Information Systems vol 40 no 1 pp 91‐102 Jan 1992 [16] N B Priyantha A Chakraborty and H Balakrishnan ldquoThe cricket location‐support systemʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ00) Boston MA Aug 2000 pp 32‐43 [17] Holger K Willig A (2005) Protocols and Architectures for Wireless Sensor Networks London [18] IEEE (2003) Part 154 Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low‐Rate Wireless Personal Area Networks (LR‐WPANs) New York [19] Chipcon CC2420 802154 compliant radio httpwwwchipconcom [20] ZigBee Specification v10 ZigBee Alliance [21] Dimitrios Lymberopoulos Quentin Lindsey and Andreas Savvides ldquoAn Empirical Analysis of Radio Signal Strength Variability in IEEE 802154 Networks using Monopole Antennasrdquo [22] Kiran Yedavalli Bhaskar Krishnamachari Sharmila Ravula and Bhaskar Srinivasan ldquoEcolocation A sequence based technique for RF localization in wireless sensor networksrdquo Robert Bosch Research and Technology Centre Palo Alto CA

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

26

3 RECEIVED SIGNAL STRENGTH (RSS) Majority of the existing methods leverage the existence of IEEE 80211 base stations with powerful radio transmit powers of approximately 100mW per base station Such radios are in a different class from the low power IEEE 802154 compliant radios that typically transmit at low power levels ranging from 52mW to 29mW [19] The wide availability of larger number of IEEE 802154 radios in the research community has revived the interest for signal strength based localization in sensor network Despite of rapidly increasing popularity of IEEE 802154 radios and signal strength localization there is a lack of detailed characterization of the fundamental factors contributing to large signal strength variation This chapter investigates some of the properties of RSS observed during testing and analysis of research work collected during the period of thesis The analysis of RSS values is needed to understand the underlying features of location dependent RSS patterns and location fingerprints An understanding of the properties of the RSS values for location fingerprinting can assist in improving the design of positioning algorithms and in deployment of indoor positioning systems

31 Parameters that affect RSS Different factors that affect the indoor RSS are the userrsquos presenceabsence hisher orientation time of the day building type and material distance from transmitter and type of radio These factors are by no means exhaustive Other possible factors are antenna orientation directionality and type Table 10 classify the parameters which affect RSS [7]

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

27

Effect on Factors Options

1 Proximity of user Userrsquos presence or absence

2 Orientation of user and terminal

North East South West

Data Collection

3 Make of Radio 4 Time of measurement Time of day amp days of

week 5 Period of measurement

Second minute hour

7 Interference Co‐channel adjacent co‐channel

Statistics

8 Building environment Small offices or large hall

Table 10 Classification of factors the affect RSS [7]

Transmitter variability Different transmitters behave differently even when they are configured exactly in the same way In practice this means that when a transmitter is configured to send packets at a power level of d dBm then the transmitter will send these packets at a power level that is very close to d dBm but not necessarily exactly equal to d dBm This can alter the received signal strength indication and thus it can lead to inaccurate distance estimation

Receiver variability The sensitivity of the receivers across different radio chips is different In practice this means that the RSSI value recorded at different receivers can be different even when all the other parameters that affect the received signal strength are kept constant

Antenna orientation Each antenna has its own radiation pattern that is not uniform In practice this means that the RSSI value recorded at the receiver for a given pair of communicating nodes and for a given distance between them varies as the pair wise antenna orientations of the transmitter and the receiver are changed

Multi‐path fading and shadowing in the RF channel In indoor environments the transmitted signals get reflected after hitting on the walls andor on other objects in the room such as furniture Both the original signal and the reflected signal reach the receiver almost at the same time since they both travel at the speed of light As a result of this the receiver is not able to

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

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28

distinguish the two signals and it measures the received signal strength for both of them

32 Related work analysis A number of indoor location tracking systems have been proposed in the literature based on RF signals ultrasound infrared and some combinations of these As mentioned previously RF based positioning system are widely studied as shown in Table 2 Given a model of radio signal propagation in a building or other environment RSS can be used to estimate the distance between the transmitter and receiver Most of the algorithms (Table 2) have been evaluated in practice using 80211b compliant wireless radios and seem to produce a location error of 15ft to 20ft for an indoor network area of approximately 225ft x 144ft According to the results of this study all the localization techniques produce approximately the same location error over a range of environments Ecolocation [22] Mote Track [5] are two of the few localization algorithms that have been evaluated on a real sensor network that uses a low power wireless radio Ecolocation reports a location error of 10ft for a very small outdoor network deployment area (26ft x 49ft) Mote Track reports a location error of approximately 13ft for an indoor network deployment area of 18751ft2 In general the RSSI based localization algorithms can be divided into two broad categories the map based and the distance (or area) prediction based algorithms In the case of the map based algorithms the deployment area is divided into a large set of sample points that cover the whole area Most techniques collect one or more signal strength measurements from all visible beacon nodes and for each sample point Each sample point is then mapped to either a signal strength vector or a signal strength probability distribution This process generates an RSSI fingerprint map which is unique with respect to the beacon locations and the deployment area This fingerprint map is stored to a central base station A node that is not aware of its location collects one or more signal strength measurements from all the visible beacon nodes and creates its own RSSI fingerprint that is sent to the central base station The base station computes nodersquos location as the sample pointrsquos location whose RSSI fingerprint distance from the unknown nodersquos RSSI fingerprint is the minimum among all the sample points Despite the fact that the map based RSSI localization methods produce relatively small location errors they are not attractive mainly because they require an excessive amount of profiling data and because they are not fully distributed The detailed profiling of a building is a major holdback because it is complex time consuming and depends on beacon locations Therefore for different buildings or even for the

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

29

same building and different beacon locations different RSSI maps have to be obtained The second category of RSSI based algorithms are the distance (or area) prediction algorithms which focus on minimizing the amount of profiling data In distance prediction based methods raw RSSI data are directly mapped to distances through a signal propagation model In some cases distances are not explicitly measured but they are taken into consideration through assumptions on the underlying distributions In the case of outdoor deployments where there are no obstacles a single signal propagation model is all the profiling data required In the case of indoor deployments the same applies but now the signal propagation model might be different across different buildings with different furniture and wall arrangements Given an underlying signal propagation model raw RSSI data can be mapped to specific distances These distances can be either used directly in order to perform localization or assigned a probability and used in a learning basedprobabilistic localization algorithm Obviously the accuracy of the distance prediction based methods depends heavily on the accuracy of the signal propagation model used for translating RSSI values to distances

33 Properties of RSSI Changes in signal due to propagation indoors are difficult to predict because of dense environment and propagation effects such as reflection diffraction and scattering The multi‐path fading effect which is the result of either constructive or destructive combination of multiple signal copies at the receiver causes the received signal to fluctuate around a mean value at a particular location The received signal is usually modelled by the combined effects of large‐scale fading and small‐scale fading The large‐scale fading component describes the signal attenuation as the signal travels over a distance and is absorbed by material such as walls and floors along the way to the receiver This component predicts the mean of the RSS and usually has a lognormal distribution On the other hand the small scale fading component explains the dramatic fluctuation of the signal due to multi‐path fading On the other hand the small scale‐fading component explains the dramatic fluctuation of the signal due to multi‐path fading If there is no LOS component the small‐scale‐fading is often modelled with a Rayleigh distribution Such scenarios are commonly called NLOS If there is a LOS component the small‐scale‐fading is typically modelled by a Rician distribution Note that when the RSS is measured the measurement averages out the small‐scale‐fading effects

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

30

4 OBJECT TRACKING 41 Introduction This chapter presents the object‐tracking application the main objective of this thesis work The application is an algorithmic‐software implementation written in Embedded C which is capable of receiving data messages and generating object position estimation data The implementation of the object‐tracking application makes use of the SENTIO platform and uses the background knowledge introduced in the preceding chapters The motivation for the object‐tracking application is to determine the position of an object in indoor environments (Eg Home) The application acts as an interface which collects the raw data that the sensors generate (numerical values) It makes the proper comparisons and algorithmic processes to identify and track the object while it is static inside the area covered by the wireless sensor network Finally with the detection and tracking data it alerts the user or another system The following sections provide an overview of the object‐tracking application

42 Overview of hardware product Before describing the object tracking application it is important to know the characteristics of hardware product The SENTIO platform (figure 15) is built in modular fashion and has the following key features [8]

Flexibility This platform is very flexible in such a way that it is made easy to add or change the hardware for each sensor task depending on the requirement in the WSN For a specific platform this can be hard or sometimes even impossible to accomplish

Sensor resources Resources in a sensor module includes the ability of usage of controllerrsquos resources as much as possible such as ADC‐channels interrupts and digital Input‐Outputrsquos Local voltage regulation should also be possible since many sensors require higher supply voltages than digital Integrated Circuits do Further support of a shut‐down mechanism to power down the sensor modules when not needed

Computation and memory The processor module based on microcontroller should support many devices in a microcontroller family and support different power supply voltages enabling full range of clock frequencies All power‐down modes of the devices should be

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

31

supported The platform should support different processor modules including FPGArsquos and very simple microcontrollers

Communication The focus should be on enabling an IEEE 802154 compliant communication interface With or without ZigBee stack this will provide a flexible platform for exploring different network topologies Support for power‐down of the entire communication module should be supported

Cost and form factor High flexibility is in conflict with low cost and small size The primary objective should be flexibility but the size should be considered in order to enable sensor device integration in systems where space is limited

Figure 15 SENTIO Platform [8]

The communication subsystem of the SENTIO sensor node was designed so that the correct operation of the radio chip is ensured The radio chip is powered by the on‐board voltage regulator and thus fluctuations in the battery voltage level do not affect the operation of the communication subsystem This platform uses Chipcon CC2420 IEEE 802154 radio transceiver operates in the 24GHz ISM band and includes a digital direct sequence spread spectrum (DSSS) modem providing a spreading gain of 9dB and an effective data rate of 250Kbps It was specifically designed for low power wireless applications and supports 8 discrete power levels 0dBm minus1dBm minus3dBm minus5dBm minus7dBm minus10dBm minus15dBm and minus25dBm at which its power consumption varies from 29mW to 52mW [21] A built‐in received signal strength indicator gives an 8‐bit digital value RSSIVAL The RSSIVAL is always averaged over 8 symbol periods (128μs) and a status bit indicates when the RSSIVAL is valid (meaning that the receiver was enabled for at least 8 symbol periods) The

Antenna

Communication Module

Processing Module

Sensing Module

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

32

power P at the RF pins can be obtained directly from RSSIVAL using the following equation

P = RSSIVAL + RSSIOFFSET [dBm] where the RSSIOFFSET is found empirically from the front‐end gain and it is approximately equal to minus45dBm In the next sections when we refer to the RSSI value we refer to the RSSIVAL and not the power P unless otherwise stated

43 Experimental Testbed The experimental test bed for object tracking application is as shown in figure 16 Experiment was conducted in Masterrsquos room at Mid Sweden University A DELL laptop is connected to SENTIO platform and software were primarily used to collect samples of RSS from APrsquos The software monitors IEEE 802154 radio frequency channels which operate in the 24GHz band

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

1030

BC - 1030

AP 3 AP 1

AP 2

AP 4

BS

HEWLETT

PACKARD

Figure 16 Layout of Masterʹs Room

A three dimensional scalable testbed is used for indoor sensor network deployments The testbed illustrated in figure 16 is a 3‐D structure measuring 16m (L) x 12m (W) x 3m (H) and it is designed to host a large number of static nodes as shown in figure 16 Arrow marks in the above figure represent the location where fingerprints were taken The centrepiece of our infrastructure

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

33

is the SENTIO sensor node [8] an open‐source general purpose sensing platform designed with AT Mega 128 microprocessor and the IEEE 802154 compliant CC2420 radio from Chipcon [19]

44 Obtaining data from the sensor network The next important step in the development of the object tracking application is to obtain the data from the sensor network and send it to the base station From there the data is transmitted through a serial port to the computer that runs the object tracking application When the data arrives at the serial port the application must capture it This paragraph gives a brief explanation of how and what kind data is transmitted by the static object fixed sensors and how this data is being used to estimate the position of the object First the static object transmits Beacon packet continuously which is received by all the fixed APrsquos The beacon packet format is as shown in table 6 Then the fixed APrsquos calculates the 8‐bit value of RSSI from the transmitted beacon packet and then sends the resultant RSSI value as a form of data packet (table 7) to the BS which calculates the position and sends back to the object

45 Object tracking application Testing and Evaluation The purpose of this section is to discuss and evaluate the object tracking application Finally we are ready with the experimental testbed as shown in figure 16 As shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors placed at the corners of the masterrsquos room BS is placed at the centre of the room connected to a PC All the nodes are power connected before an actual measurement takes place At first the object is placed at random position inside the room and transmits beacon packets continuously Then these packets are received by the fixed APrsquos which calculate the RSSI from the packet and then averages it until it receives 50 beacon packets and then transmits the data to the BS (figure 17) Then the BS waits until it receives the data from all the APrsquos Once it receives from all the APrsquos it calculates the position of the object (figure 18) The application was developed to demonstrate a real world use of a WSN in object tracking In evaluating the final position obtained with respect to the actual position we conclude that the object‐tracking was successful Although

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

34

it is a research prototype object tracking application is a simple stable stand‐alone system capable of demonstrating an important application of WSN It takes raw data from the WSN aggregates it evaluates it and based on the evaluation tracks the object The accuracy of estimating the position is less than 5m Table 11 below shows a log of readings taken during test

Start

Intialize micro‐controller and transeciever in receieve mode

If received_packet=

Beacon

Average RSSI values

Increase the counter

If countergt1000050

A

No

Yes

B

B

Yes

No

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

35

A

Reset counter

Set transceiever in transmit mode

Transmit averaged RSSI to Main sensor and set it back to receive mode

10000 samples ‐ Offline

50 samples ‐ Online

Figure 17 Flow chart representing the algorithm of fixed sensors

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

36

Start

Intialize micro‐controller and transeciever in receieve mode

Received RSSI values from all fixed Sensors

Open radio map

Calculate Euclidean distance between obtained RSSI values from fixed sensors and fingerprints

in radio map

No

Yes

Bubble sort for least error in signal strength values

A

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

37

A

Get the location of the object from the radiousing the least error value

Display location of object on PC

End

Figure 18 Flow chart representing the algorithm of main sensor

Table 11 Accuracy of positioning system

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

38

5 RESULTS This chapter presents how the system parameters such as number of samples mean or median etchellipare finalized All the tests are performed in masterrsquos room at Mid Sweden University This chapter is divided into three subsections First in Section 51 it reviews results obtained from samples test Section 52 discusses one dimensional test results and finally section 53 discusses results of two dimensional tests

51 Measurement setup A Dell laptop was connected to a SENTIO platform and is loaded with software (Appendix 2) This setup was used to determine number of samples required during object tracking mean or median position estimation algorithm and one dimensional test The setup is as follows

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

103

0

BC - 1030

AP 1AP 2

BS

HEWLETT

PACKARD

Figure 19 Test bed for one dimensional test in Masters Room

Two SENTIO nodes (AP1 and AP2 as shown above) were separated by a distance of 10 meters and these sensors are known as fixed sensors which are fixed to the ceiling such that there antenna is faced towards the ground They are linear is position A BS was connected to Dell laptop and it was placed in such a way that its antenna was faced towards the ceiling Another SENTIO

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

39

node was used as an object and its orientation was same as BS Figure 19 represents the complete setup for object tracking application Mobile node was moved between these fixed ones at regular intervals of one meter and RSSI values were logged at each interval

52 Number of samples test This test was performed to determine number of samples required for accurate positioning and either mean of the obtained RSSI values or median of them Figure 19 represents the setup for number of samples test Once the setup is ready the measurements are taken as follows

1 An object was tagged with SENTIO node and was placed stationary linear to the fixed sensors

2 10000 samples (RSSI) were taken at regular intervals of 1 meter up to 10 meters

3 The sample values were logged at every interval 4 Using these logged samples I have plotted graphs such as distribution

of RSS values (figure 20 21) meanmedian at intervals of 1 meter using MATLAB

5 The mean and median for various numbers of samples is as shown in figure 22

Figure 20 Histogram plot of RSSI values at 1m and 4m

Figure 21 Histogram plot of RSSI values at 6m and 8m

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

40

Figure 22 Distance vs RSSI

From the above graphs following conclusions were made 1 From the histogram plots we can observe that the distribution of RSSI

values follow Gaussians distribution law 2 As we can see from figure 22 that median curve has drastic changes

when compared to mean curve Hence mean is considered while estimating the position

3 From the observations we conclude that the number of samples does not matter since mean curve tends to be same for various numbers of samples

4 Hence 10000 samples are considered in order to get a proper location fingerprint during off‐line phase and during online phase 50 samples are enough to estimate the position of object

53 0ne dimensional test Measurement setup for one dimensional test is as shown in figure 19 This test was conducted to check the performance of algorithm and how accurate it was At first ie during offline phase fingerprints at each interval averaging 10000 samples were taken During online phase Euclidean algorithm was used estimate the position averaging 50 samples of RSSI From the results we

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

41

can realize that the error was less than 2 meters Table 12 shows the result of location estimation in one dimensional case

Table 12 One dimensional position estimation results

54 Two dimensional test This test is the final stage of my thesis work Experimental testbed for this stage is as shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors fixed at the corners of the room and a BS at the centre of the room There are 18 pre‐determined locations where the fingerprints (RSSI) were taken The fingerprints are as shown in Appendix 1 All the readings were taken in presence of userrsquos The algorithm for position estimation is shown in Appendix 2 Position estimation test is performed as follows

Tagged object is placed at random inside the masterrsquos room All the sensors including the base station and object are switched on Using the algorithm (Appendix 2) the BS estimates the position of the object and displays it on the PC

This test was performed using various numbers of samples to test which has a better accuracy Figure 23 represents the error rate using various numbers of samples From figure 23 we can conclude that 50 numbers of samples is enough to estimate the position of object with an accuracy of 3m

55 Causes of error in location determination The randomness of RSS is the major cause of error in any indoor

positioning system that uses the RSS for location inference If there is no randomness in the RSS and its location fingerprint every indoor positioning systems of this type will have no location detection error For example I have recorded RSS at one particular location during different times of the day From figures 24 and 25 you can observe that RSS values are different at different times of day

The performance of indoor positioning systems depends greatly on the separation of location fingerprints A location fingerprint

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

42

corresponding to a location can be identified correctly if it is difficult to classify it as another fingerprint by a pattern classifier

Path loss is one of the basic signal propagation properties of the RSS that best describes attenuation of transmit signal power over a distance between an access point and a mobile device

Presence of Userrsquos or userrsquos orientation affects RSS

Figure 23 Error Rate vs Number of samples

Figure 24 RSS distribution at 214 pm

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

43

Figure 25 RSS distribution at 100pm and 1100 am

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

6 DISCUSSIONS10102006

44

6 DISCUSSIONS 61 Summary and Conclusion While empirical results and performance studies of positioning systems based on location fingerprint have been presented in this report analytical model that can be used as a framework for efficiently designing the positioning systems are not available This thesis design work uses general principles of position estimation such as position estimation of sensors grid spacing etc This design work focuses on accuracy and precision The concept of location fingerprint technique based on RSS us explained in detail in chapter 2 It is found that RSS is random It is possible to approximate the RSS as being normally distributed While the RSS random process is non‐stationary the mean is more or less constant and could be used as the fingerprint of the location Model designed for position estimation is as shown in chapter 4 Accuracy and precision are the two major performance matrices in this study Uniform grid spacing has been used From the experimental analysis I have obtained an accuracy of 3 meters and 35 precision The sensitivity analysis among the system parameters and the performance matrices were done using the model as shown in chapter 4 For instance our study found that the minimum distance between two adjacent positions also affects the positioning performance This is due to the randomness of RSS patterns induced by the ever changing indoor environment

62 Future work The research in this thesis provides a ground work for the future study of efficient design of indoor positioning system The RSSI based position estimation still has limitations First the RSSI value is time dependent Thus the future study should address this issue Second the movement of object is not considered Third orientations of sensors affect the performance which should be taken care off Fourth to improve the performance of the system we need to find a solution to deal with those incomplete or missing data during the off‐line and the on‐line phase Fifth position estimation on multi‐floor and three‐dimensional coordinates is not available

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 110102006

45

APPENDIX 1

Location Fingerprints of Masters Room Time 1315 Observation Time 20 minutes Number of Samples 10000

People Presence SNo Co-ordinates AP1 (0x0001) AP2 (0x0002) AP3 (0x0004) AP4 (0x0008)

1 11 -24 -16 -27 -33 2 13 -26 -8 -34 -31 3 15 -43 -9 -39 -25 4 32 -21 -11 -27 -33 5 61 -6 -23 -32 -28 6 64 -20 -31 -25 -34 7 35 -23 -35 -29 -38 8 37 -29 -35 -22 -27 9 39 -38 -29 -21 -27 10 67 -27 -23 -20 -23 11 611 -26 -29 -23 -30 12 613 -35 -32 -2 -31 13 513 -34 -31 -20 -26 14 11 -39 -22 -29 -17 15 212 -37 -42 -28 -31 16 -212 -38 -33 -29 -6 17 -21 -42 -29 -33 -19 18 -38 -40 -23 -35 -22

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 210102006

46

APPENDIX 2

Begin

Open radio map and raw data files for write

Intialize radio map in memory

Check whether the

Packet is data or Beacon packet If data packet

Save the data to register

Check if data is

Received from all

sensors

Estimate the position of static

Display the position on PC

End Yes

No

A

A

Yes

No

Flow chart representing the main algorithm for position estimation

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

47

APPENDIX 3

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

48

Distribution of RSSI

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

49

REFERENCES [1] T S Rappaport J H Reed and B D Woerner ldquoPosition location using wireless communications on highways of the futureʺ IEEE Commun Mag vol 34 no 10 pp33‐41 Oct 1996 [2] A M Ladd K E Bekris G Marceau A Rudys L E Kavraki and D S Wallach rdquoRobotics‐based location sensing using wireless ethernetʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ02) 2002 pp 227‐238 [3] I Ramani R Bharadwaja and P V Rangan ldquoLocation tracking for media appliances in wireless home networksʺ in Proc International Conference on Multimedia and Expo (ICMEʹ03) July 2003 pp 769‐772 [4] K Pahlavan X Li and J P Makela ldquoIndoor geolocation science and technologyʺ IEEE Commun Mag vol 40 no 2 pp 112‐118 Feb 2002 [5] Konard Lorincz and Matt Welsh ldquoMote Track A Robust Decentralized Approach to RF‐Based Location Trackingrdquo [6] Paramvir Bahl and Venkata N Padmanabham ldquoRADAR An In‐Building RF‐based User Location and Tracking Systemrdquo [7] Kamol Kaemarungsi ldquoDesign of Indoor positioning system based on location fingerprint techiniquerdquo University of Pittsburgh 2005 [8] Fredrik Linnarsson Cheng Peng and Bengt Oelmann ldquoSENTIO A Hardware Platform for Rapid Prototyping of Wireless Sensor Networksrdquo [9] S Saha K Chaudhuri D Sanghi and P Bhagwat ldquoLocation determination of a mobile device using IEEE 80211b access point signalsʺ in Proc IEEE Wireless Communications and Networking Conference (WCNCʹ03) New Orleans LA Mar 2003 pp 1987‐1992 [10] T Roos P Myllymaki H Tirri P Misikangas and J Sievanen ldquoA probabilistic approach to WLAN user location estimationʺ International Journal of Wireless Information Networks vol 9 no 3 pp 155‐164 July 2002 [11] R Battiti M Brunato and A Villani ldquoStatistical learning theory for location fingerprinting in wireless LANrsquosʺ Technical Report Oct 2002 [Online] Available httprtmscienceunitnitraquobattitiarchive86pdf [12] P Prasithsangaree P Krishnamurthy and P K Chrysanthis ldquoAn indoor position location with wireless LANsʺ in Proc IEEE International Symposium on Personal Indoor and Mobile Radio Communications (PIMRCʹ02) Lisbon Portugal Sept 2002 [13] M A Youssef A Agrawala and A U Shankar ldquoWLAN location determination via clustering and probability distributionsʺ in Proc IEEE

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

50

International Conference on Pervasive Computing and Communications (PerComʹ03) Dallas‐Fort Worth TX Mar 23‐26 2003 pp 23‐26 [14] Z Xiang S Song J Chen H Wang J Huang and X Gao ldquoA wireless LAN‐based indoor positioning technologyʺ IBM Journal of Research and Development vol 48 no 56 pp 617‐626 SeptNov 2004 [15] R Want A Hopper V Falcao and J Gibbons ldquoThe active badge location systemʺACM Transactions on Information Systems vol 40 no 1 pp 91‐102 Jan 1992 [16] N B Priyantha A Chakraborty and H Balakrishnan ldquoThe cricket location‐support systemʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ00) Boston MA Aug 2000 pp 32‐43 [17] Holger K Willig A (2005) Protocols and Architectures for Wireless Sensor Networks London [18] IEEE (2003) Part 154 Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low‐Rate Wireless Personal Area Networks (LR‐WPANs) New York [19] Chipcon CC2420 802154 compliant radio httpwwwchipconcom [20] ZigBee Specification v10 ZigBee Alliance [21] Dimitrios Lymberopoulos Quentin Lindsey and Andreas Savvides ldquoAn Empirical Analysis of Radio Signal Strength Variability in IEEE 802154 Networks using Monopole Antennasrdquo [22] Kiran Yedavalli Bhaskar Krishnamachari Sharmila Ravula and Bhaskar Srinivasan ldquoEcolocation A sequence based technique for RF localization in wireless sensor networksrdquo Robert Bosch Research and Technology Centre Palo Alto CA

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

27

Effect on Factors Options

1 Proximity of user Userrsquos presence or absence

2 Orientation of user and terminal

North East South West

Data Collection

3 Make of Radio 4 Time of measurement Time of day amp days of

week 5 Period of measurement

Second minute hour

7 Interference Co‐channel adjacent co‐channel

Statistics

8 Building environment Small offices or large hall

Table 10 Classification of factors the affect RSS [7]

Transmitter variability Different transmitters behave differently even when they are configured exactly in the same way In practice this means that when a transmitter is configured to send packets at a power level of d dBm then the transmitter will send these packets at a power level that is very close to d dBm but not necessarily exactly equal to d dBm This can alter the received signal strength indication and thus it can lead to inaccurate distance estimation

Receiver variability The sensitivity of the receivers across different radio chips is different In practice this means that the RSSI value recorded at different receivers can be different even when all the other parameters that affect the received signal strength are kept constant

Antenna orientation Each antenna has its own radiation pattern that is not uniform In practice this means that the RSSI value recorded at the receiver for a given pair of communicating nodes and for a given distance between them varies as the pair wise antenna orientations of the transmitter and the receiver are changed

Multi‐path fading and shadowing in the RF channel In indoor environments the transmitted signals get reflected after hitting on the walls andor on other objects in the room such as furniture Both the original signal and the reflected signal reach the receiver almost at the same time since they both travel at the speed of light As a result of this the receiver is not able to

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

28

distinguish the two signals and it measures the received signal strength for both of them

32 Related work analysis A number of indoor location tracking systems have been proposed in the literature based on RF signals ultrasound infrared and some combinations of these As mentioned previously RF based positioning system are widely studied as shown in Table 2 Given a model of radio signal propagation in a building or other environment RSS can be used to estimate the distance between the transmitter and receiver Most of the algorithms (Table 2) have been evaluated in practice using 80211b compliant wireless radios and seem to produce a location error of 15ft to 20ft for an indoor network area of approximately 225ft x 144ft According to the results of this study all the localization techniques produce approximately the same location error over a range of environments Ecolocation [22] Mote Track [5] are two of the few localization algorithms that have been evaluated on a real sensor network that uses a low power wireless radio Ecolocation reports a location error of 10ft for a very small outdoor network deployment area (26ft x 49ft) Mote Track reports a location error of approximately 13ft for an indoor network deployment area of 18751ft2 In general the RSSI based localization algorithms can be divided into two broad categories the map based and the distance (or area) prediction based algorithms In the case of the map based algorithms the deployment area is divided into a large set of sample points that cover the whole area Most techniques collect one or more signal strength measurements from all visible beacon nodes and for each sample point Each sample point is then mapped to either a signal strength vector or a signal strength probability distribution This process generates an RSSI fingerprint map which is unique with respect to the beacon locations and the deployment area This fingerprint map is stored to a central base station A node that is not aware of its location collects one or more signal strength measurements from all the visible beacon nodes and creates its own RSSI fingerprint that is sent to the central base station The base station computes nodersquos location as the sample pointrsquos location whose RSSI fingerprint distance from the unknown nodersquos RSSI fingerprint is the minimum among all the sample points Despite the fact that the map based RSSI localization methods produce relatively small location errors they are not attractive mainly because they require an excessive amount of profiling data and because they are not fully distributed The detailed profiling of a building is a major holdback because it is complex time consuming and depends on beacon locations Therefore for different buildings or even for the

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

29

same building and different beacon locations different RSSI maps have to be obtained The second category of RSSI based algorithms are the distance (or area) prediction algorithms which focus on minimizing the amount of profiling data In distance prediction based methods raw RSSI data are directly mapped to distances through a signal propagation model In some cases distances are not explicitly measured but they are taken into consideration through assumptions on the underlying distributions In the case of outdoor deployments where there are no obstacles a single signal propagation model is all the profiling data required In the case of indoor deployments the same applies but now the signal propagation model might be different across different buildings with different furniture and wall arrangements Given an underlying signal propagation model raw RSSI data can be mapped to specific distances These distances can be either used directly in order to perform localization or assigned a probability and used in a learning basedprobabilistic localization algorithm Obviously the accuracy of the distance prediction based methods depends heavily on the accuracy of the signal propagation model used for translating RSSI values to distances

33 Properties of RSSI Changes in signal due to propagation indoors are difficult to predict because of dense environment and propagation effects such as reflection diffraction and scattering The multi‐path fading effect which is the result of either constructive or destructive combination of multiple signal copies at the receiver causes the received signal to fluctuate around a mean value at a particular location The received signal is usually modelled by the combined effects of large‐scale fading and small‐scale fading The large‐scale fading component describes the signal attenuation as the signal travels over a distance and is absorbed by material such as walls and floors along the way to the receiver This component predicts the mean of the RSS and usually has a lognormal distribution On the other hand the small scale fading component explains the dramatic fluctuation of the signal due to multi‐path fading On the other hand the small scale‐fading component explains the dramatic fluctuation of the signal due to multi‐path fading If there is no LOS component the small‐scale‐fading is often modelled with a Rayleigh distribution Such scenarios are commonly called NLOS If there is a LOS component the small‐scale‐fading is typically modelled by a Rician distribution Note that when the RSS is measured the measurement averages out the small‐scale‐fading effects

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

30

4 OBJECT TRACKING 41 Introduction This chapter presents the object‐tracking application the main objective of this thesis work The application is an algorithmic‐software implementation written in Embedded C which is capable of receiving data messages and generating object position estimation data The implementation of the object‐tracking application makes use of the SENTIO platform and uses the background knowledge introduced in the preceding chapters The motivation for the object‐tracking application is to determine the position of an object in indoor environments (Eg Home) The application acts as an interface which collects the raw data that the sensors generate (numerical values) It makes the proper comparisons and algorithmic processes to identify and track the object while it is static inside the area covered by the wireless sensor network Finally with the detection and tracking data it alerts the user or another system The following sections provide an overview of the object‐tracking application

42 Overview of hardware product Before describing the object tracking application it is important to know the characteristics of hardware product The SENTIO platform (figure 15) is built in modular fashion and has the following key features [8]

Flexibility This platform is very flexible in such a way that it is made easy to add or change the hardware for each sensor task depending on the requirement in the WSN For a specific platform this can be hard or sometimes even impossible to accomplish

Sensor resources Resources in a sensor module includes the ability of usage of controllerrsquos resources as much as possible such as ADC‐channels interrupts and digital Input‐Outputrsquos Local voltage regulation should also be possible since many sensors require higher supply voltages than digital Integrated Circuits do Further support of a shut‐down mechanism to power down the sensor modules when not needed

Computation and memory The processor module based on microcontroller should support many devices in a microcontroller family and support different power supply voltages enabling full range of clock frequencies All power‐down modes of the devices should be

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

31

supported The platform should support different processor modules including FPGArsquos and very simple microcontrollers

Communication The focus should be on enabling an IEEE 802154 compliant communication interface With or without ZigBee stack this will provide a flexible platform for exploring different network topologies Support for power‐down of the entire communication module should be supported

Cost and form factor High flexibility is in conflict with low cost and small size The primary objective should be flexibility but the size should be considered in order to enable sensor device integration in systems where space is limited

Figure 15 SENTIO Platform [8]

The communication subsystem of the SENTIO sensor node was designed so that the correct operation of the radio chip is ensured The radio chip is powered by the on‐board voltage regulator and thus fluctuations in the battery voltage level do not affect the operation of the communication subsystem This platform uses Chipcon CC2420 IEEE 802154 radio transceiver operates in the 24GHz ISM band and includes a digital direct sequence spread spectrum (DSSS) modem providing a spreading gain of 9dB and an effective data rate of 250Kbps It was specifically designed for low power wireless applications and supports 8 discrete power levels 0dBm minus1dBm minus3dBm minus5dBm minus7dBm minus10dBm minus15dBm and minus25dBm at which its power consumption varies from 29mW to 52mW [21] A built‐in received signal strength indicator gives an 8‐bit digital value RSSIVAL The RSSIVAL is always averaged over 8 symbol periods (128μs) and a status bit indicates when the RSSIVAL is valid (meaning that the receiver was enabled for at least 8 symbol periods) The

Antenna

Communication Module

Processing Module

Sensing Module

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

32

power P at the RF pins can be obtained directly from RSSIVAL using the following equation

P = RSSIVAL + RSSIOFFSET [dBm] where the RSSIOFFSET is found empirically from the front‐end gain and it is approximately equal to minus45dBm In the next sections when we refer to the RSSI value we refer to the RSSIVAL and not the power P unless otherwise stated

43 Experimental Testbed The experimental test bed for object tracking application is as shown in figure 16 Experiment was conducted in Masterrsquos room at Mid Sweden University A DELL laptop is connected to SENTIO platform and software were primarily used to collect samples of RSS from APrsquos The software monitors IEEE 802154 radio frequency channels which operate in the 24GHz band

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

1030

BC - 1030

AP 3 AP 1

AP 2

AP 4

BS

HEWLETT

PACKARD

Figure 16 Layout of Masterʹs Room

A three dimensional scalable testbed is used for indoor sensor network deployments The testbed illustrated in figure 16 is a 3‐D structure measuring 16m (L) x 12m (W) x 3m (H) and it is designed to host a large number of static nodes as shown in figure 16 Arrow marks in the above figure represent the location where fingerprints were taken The centrepiece of our infrastructure

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

33

is the SENTIO sensor node [8] an open‐source general purpose sensing platform designed with AT Mega 128 microprocessor and the IEEE 802154 compliant CC2420 radio from Chipcon [19]

44 Obtaining data from the sensor network The next important step in the development of the object tracking application is to obtain the data from the sensor network and send it to the base station From there the data is transmitted through a serial port to the computer that runs the object tracking application When the data arrives at the serial port the application must capture it This paragraph gives a brief explanation of how and what kind data is transmitted by the static object fixed sensors and how this data is being used to estimate the position of the object First the static object transmits Beacon packet continuously which is received by all the fixed APrsquos The beacon packet format is as shown in table 6 Then the fixed APrsquos calculates the 8‐bit value of RSSI from the transmitted beacon packet and then sends the resultant RSSI value as a form of data packet (table 7) to the BS which calculates the position and sends back to the object

45 Object tracking application Testing and Evaluation The purpose of this section is to discuss and evaluate the object tracking application Finally we are ready with the experimental testbed as shown in figure 16 As shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors placed at the corners of the masterrsquos room BS is placed at the centre of the room connected to a PC All the nodes are power connected before an actual measurement takes place At first the object is placed at random position inside the room and transmits beacon packets continuously Then these packets are received by the fixed APrsquos which calculate the RSSI from the packet and then averages it until it receives 50 beacon packets and then transmits the data to the BS (figure 17) Then the BS waits until it receives the data from all the APrsquos Once it receives from all the APrsquos it calculates the position of the object (figure 18) The application was developed to demonstrate a real world use of a WSN in object tracking In evaluating the final position obtained with respect to the actual position we conclude that the object‐tracking was successful Although

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

34

it is a research prototype object tracking application is a simple stable stand‐alone system capable of demonstrating an important application of WSN It takes raw data from the WSN aggregates it evaluates it and based on the evaluation tracks the object The accuracy of estimating the position is less than 5m Table 11 below shows a log of readings taken during test

Start

Intialize micro‐controller and transeciever in receieve mode

If received_packet=

Beacon

Average RSSI values

Increase the counter

If countergt1000050

A

No

Yes

B

B

Yes

No

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

35

A

Reset counter

Set transceiever in transmit mode

Transmit averaged RSSI to Main sensor and set it back to receive mode

10000 samples ‐ Offline

50 samples ‐ Online

Figure 17 Flow chart representing the algorithm of fixed sensors

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

36

Start

Intialize micro‐controller and transeciever in receieve mode

Received RSSI values from all fixed Sensors

Open radio map

Calculate Euclidean distance between obtained RSSI values from fixed sensors and fingerprints

in radio map

No

Yes

Bubble sort for least error in signal strength values

A

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

37

A

Get the location of the object from the radiousing the least error value

Display location of object on PC

End

Figure 18 Flow chart representing the algorithm of main sensor

Table 11 Accuracy of positioning system

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

38

5 RESULTS This chapter presents how the system parameters such as number of samples mean or median etchellipare finalized All the tests are performed in masterrsquos room at Mid Sweden University This chapter is divided into three subsections First in Section 51 it reviews results obtained from samples test Section 52 discusses one dimensional test results and finally section 53 discusses results of two dimensional tests

51 Measurement setup A Dell laptop was connected to a SENTIO platform and is loaded with software (Appendix 2) This setup was used to determine number of samples required during object tracking mean or median position estimation algorithm and one dimensional test The setup is as follows

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

103

0

BC - 1030

AP 1AP 2

BS

HEWLETT

PACKARD

Figure 19 Test bed for one dimensional test in Masters Room

Two SENTIO nodes (AP1 and AP2 as shown above) were separated by a distance of 10 meters and these sensors are known as fixed sensors which are fixed to the ceiling such that there antenna is faced towards the ground They are linear is position A BS was connected to Dell laptop and it was placed in such a way that its antenna was faced towards the ceiling Another SENTIO

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

39

node was used as an object and its orientation was same as BS Figure 19 represents the complete setup for object tracking application Mobile node was moved between these fixed ones at regular intervals of one meter and RSSI values were logged at each interval

52 Number of samples test This test was performed to determine number of samples required for accurate positioning and either mean of the obtained RSSI values or median of them Figure 19 represents the setup for number of samples test Once the setup is ready the measurements are taken as follows

1 An object was tagged with SENTIO node and was placed stationary linear to the fixed sensors

2 10000 samples (RSSI) were taken at regular intervals of 1 meter up to 10 meters

3 The sample values were logged at every interval 4 Using these logged samples I have plotted graphs such as distribution

of RSS values (figure 20 21) meanmedian at intervals of 1 meter using MATLAB

5 The mean and median for various numbers of samples is as shown in figure 22

Figure 20 Histogram plot of RSSI values at 1m and 4m

Figure 21 Histogram plot of RSSI values at 6m and 8m

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

40

Figure 22 Distance vs RSSI

From the above graphs following conclusions were made 1 From the histogram plots we can observe that the distribution of RSSI

values follow Gaussians distribution law 2 As we can see from figure 22 that median curve has drastic changes

when compared to mean curve Hence mean is considered while estimating the position

3 From the observations we conclude that the number of samples does not matter since mean curve tends to be same for various numbers of samples

4 Hence 10000 samples are considered in order to get a proper location fingerprint during off‐line phase and during online phase 50 samples are enough to estimate the position of object

53 0ne dimensional test Measurement setup for one dimensional test is as shown in figure 19 This test was conducted to check the performance of algorithm and how accurate it was At first ie during offline phase fingerprints at each interval averaging 10000 samples were taken During online phase Euclidean algorithm was used estimate the position averaging 50 samples of RSSI From the results we

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

41

can realize that the error was less than 2 meters Table 12 shows the result of location estimation in one dimensional case

Table 12 One dimensional position estimation results

54 Two dimensional test This test is the final stage of my thesis work Experimental testbed for this stage is as shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors fixed at the corners of the room and a BS at the centre of the room There are 18 pre‐determined locations where the fingerprints (RSSI) were taken The fingerprints are as shown in Appendix 1 All the readings were taken in presence of userrsquos The algorithm for position estimation is shown in Appendix 2 Position estimation test is performed as follows

Tagged object is placed at random inside the masterrsquos room All the sensors including the base station and object are switched on Using the algorithm (Appendix 2) the BS estimates the position of the object and displays it on the PC

This test was performed using various numbers of samples to test which has a better accuracy Figure 23 represents the error rate using various numbers of samples From figure 23 we can conclude that 50 numbers of samples is enough to estimate the position of object with an accuracy of 3m

55 Causes of error in location determination The randomness of RSS is the major cause of error in any indoor

positioning system that uses the RSS for location inference If there is no randomness in the RSS and its location fingerprint every indoor positioning systems of this type will have no location detection error For example I have recorded RSS at one particular location during different times of the day From figures 24 and 25 you can observe that RSS values are different at different times of day

The performance of indoor positioning systems depends greatly on the separation of location fingerprints A location fingerprint

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

42

corresponding to a location can be identified correctly if it is difficult to classify it as another fingerprint by a pattern classifier

Path loss is one of the basic signal propagation properties of the RSS that best describes attenuation of transmit signal power over a distance between an access point and a mobile device

Presence of Userrsquos or userrsquos orientation affects RSS

Figure 23 Error Rate vs Number of samples

Figure 24 RSS distribution at 214 pm

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

43

Figure 25 RSS distribution at 100pm and 1100 am

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

6 DISCUSSIONS10102006

44

6 DISCUSSIONS 61 Summary and Conclusion While empirical results and performance studies of positioning systems based on location fingerprint have been presented in this report analytical model that can be used as a framework for efficiently designing the positioning systems are not available This thesis design work uses general principles of position estimation such as position estimation of sensors grid spacing etc This design work focuses on accuracy and precision The concept of location fingerprint technique based on RSS us explained in detail in chapter 2 It is found that RSS is random It is possible to approximate the RSS as being normally distributed While the RSS random process is non‐stationary the mean is more or less constant and could be used as the fingerprint of the location Model designed for position estimation is as shown in chapter 4 Accuracy and precision are the two major performance matrices in this study Uniform grid spacing has been used From the experimental analysis I have obtained an accuracy of 3 meters and 35 precision The sensitivity analysis among the system parameters and the performance matrices were done using the model as shown in chapter 4 For instance our study found that the minimum distance between two adjacent positions also affects the positioning performance This is due to the randomness of RSS patterns induced by the ever changing indoor environment

62 Future work The research in this thesis provides a ground work for the future study of efficient design of indoor positioning system The RSSI based position estimation still has limitations First the RSSI value is time dependent Thus the future study should address this issue Second the movement of object is not considered Third orientations of sensors affect the performance which should be taken care off Fourth to improve the performance of the system we need to find a solution to deal with those incomplete or missing data during the off‐line and the on‐line phase Fifth position estimation on multi‐floor and three‐dimensional coordinates is not available

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 110102006

45

APPENDIX 1

Location Fingerprints of Masters Room Time 1315 Observation Time 20 minutes Number of Samples 10000

People Presence SNo Co-ordinates AP1 (0x0001) AP2 (0x0002) AP3 (0x0004) AP4 (0x0008)

1 11 -24 -16 -27 -33 2 13 -26 -8 -34 -31 3 15 -43 -9 -39 -25 4 32 -21 -11 -27 -33 5 61 -6 -23 -32 -28 6 64 -20 -31 -25 -34 7 35 -23 -35 -29 -38 8 37 -29 -35 -22 -27 9 39 -38 -29 -21 -27 10 67 -27 -23 -20 -23 11 611 -26 -29 -23 -30 12 613 -35 -32 -2 -31 13 513 -34 -31 -20 -26 14 11 -39 -22 -29 -17 15 212 -37 -42 -28 -31 16 -212 -38 -33 -29 -6 17 -21 -42 -29 -33 -19 18 -38 -40 -23 -35 -22

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 210102006

46

APPENDIX 2

Begin

Open radio map and raw data files for write

Intialize radio map in memory

Check whether the

Packet is data or Beacon packet If data packet

Save the data to register

Check if data is

Received from all

sensors

Estimate the position of static

Display the position on PC

End Yes

No

A

A

Yes

No

Flow chart representing the main algorithm for position estimation

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

47

APPENDIX 3

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

48

Distribution of RSSI

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

49

REFERENCES [1] T S Rappaport J H Reed and B D Woerner ldquoPosition location using wireless communications on highways of the futureʺ IEEE Commun Mag vol 34 no 10 pp33‐41 Oct 1996 [2] A M Ladd K E Bekris G Marceau A Rudys L E Kavraki and D S Wallach rdquoRobotics‐based location sensing using wireless ethernetʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ02) 2002 pp 227‐238 [3] I Ramani R Bharadwaja and P V Rangan ldquoLocation tracking for media appliances in wireless home networksʺ in Proc International Conference on Multimedia and Expo (ICMEʹ03) July 2003 pp 769‐772 [4] K Pahlavan X Li and J P Makela ldquoIndoor geolocation science and technologyʺ IEEE Commun Mag vol 40 no 2 pp 112‐118 Feb 2002 [5] Konard Lorincz and Matt Welsh ldquoMote Track A Robust Decentralized Approach to RF‐Based Location Trackingrdquo [6] Paramvir Bahl and Venkata N Padmanabham ldquoRADAR An In‐Building RF‐based User Location and Tracking Systemrdquo [7] Kamol Kaemarungsi ldquoDesign of Indoor positioning system based on location fingerprint techiniquerdquo University of Pittsburgh 2005 [8] Fredrik Linnarsson Cheng Peng and Bengt Oelmann ldquoSENTIO A Hardware Platform for Rapid Prototyping of Wireless Sensor Networksrdquo [9] S Saha K Chaudhuri D Sanghi and P Bhagwat ldquoLocation determination of a mobile device using IEEE 80211b access point signalsʺ in Proc IEEE Wireless Communications and Networking Conference (WCNCʹ03) New Orleans LA Mar 2003 pp 1987‐1992 [10] T Roos P Myllymaki H Tirri P Misikangas and J Sievanen ldquoA probabilistic approach to WLAN user location estimationʺ International Journal of Wireless Information Networks vol 9 no 3 pp 155‐164 July 2002 [11] R Battiti M Brunato and A Villani ldquoStatistical learning theory for location fingerprinting in wireless LANrsquosʺ Technical Report Oct 2002 [Online] Available httprtmscienceunitnitraquobattitiarchive86pdf [12] P Prasithsangaree P Krishnamurthy and P K Chrysanthis ldquoAn indoor position location with wireless LANsʺ in Proc IEEE International Symposium on Personal Indoor and Mobile Radio Communications (PIMRCʹ02) Lisbon Portugal Sept 2002 [13] M A Youssef A Agrawala and A U Shankar ldquoWLAN location determination via clustering and probability distributionsʺ in Proc IEEE

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

50

International Conference on Pervasive Computing and Communications (PerComʹ03) Dallas‐Fort Worth TX Mar 23‐26 2003 pp 23‐26 [14] Z Xiang S Song J Chen H Wang J Huang and X Gao ldquoA wireless LAN‐based indoor positioning technologyʺ IBM Journal of Research and Development vol 48 no 56 pp 617‐626 SeptNov 2004 [15] R Want A Hopper V Falcao and J Gibbons ldquoThe active badge location systemʺACM Transactions on Information Systems vol 40 no 1 pp 91‐102 Jan 1992 [16] N B Priyantha A Chakraborty and H Balakrishnan ldquoThe cricket location‐support systemʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ00) Boston MA Aug 2000 pp 32‐43 [17] Holger K Willig A (2005) Protocols and Architectures for Wireless Sensor Networks London [18] IEEE (2003) Part 154 Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low‐Rate Wireless Personal Area Networks (LR‐WPANs) New York [19] Chipcon CC2420 802154 compliant radio httpwwwchipconcom [20] ZigBee Specification v10 ZigBee Alliance [21] Dimitrios Lymberopoulos Quentin Lindsey and Andreas Savvides ldquoAn Empirical Analysis of Radio Signal Strength Variability in IEEE 802154 Networks using Monopole Antennasrdquo [22] Kiran Yedavalli Bhaskar Krishnamachari Sharmila Ravula and Bhaskar Srinivasan ldquoEcolocation A sequence based technique for RF localization in wireless sensor networksrdquo Robert Bosch Research and Technology Centre Palo Alto CA

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

28

distinguish the two signals and it measures the received signal strength for both of them

32 Related work analysis A number of indoor location tracking systems have been proposed in the literature based on RF signals ultrasound infrared and some combinations of these As mentioned previously RF based positioning system are widely studied as shown in Table 2 Given a model of radio signal propagation in a building or other environment RSS can be used to estimate the distance between the transmitter and receiver Most of the algorithms (Table 2) have been evaluated in practice using 80211b compliant wireless radios and seem to produce a location error of 15ft to 20ft for an indoor network area of approximately 225ft x 144ft According to the results of this study all the localization techniques produce approximately the same location error over a range of environments Ecolocation [22] Mote Track [5] are two of the few localization algorithms that have been evaluated on a real sensor network that uses a low power wireless radio Ecolocation reports a location error of 10ft for a very small outdoor network deployment area (26ft x 49ft) Mote Track reports a location error of approximately 13ft for an indoor network deployment area of 18751ft2 In general the RSSI based localization algorithms can be divided into two broad categories the map based and the distance (or area) prediction based algorithms In the case of the map based algorithms the deployment area is divided into a large set of sample points that cover the whole area Most techniques collect one or more signal strength measurements from all visible beacon nodes and for each sample point Each sample point is then mapped to either a signal strength vector or a signal strength probability distribution This process generates an RSSI fingerprint map which is unique with respect to the beacon locations and the deployment area This fingerprint map is stored to a central base station A node that is not aware of its location collects one or more signal strength measurements from all the visible beacon nodes and creates its own RSSI fingerprint that is sent to the central base station The base station computes nodersquos location as the sample pointrsquos location whose RSSI fingerprint distance from the unknown nodersquos RSSI fingerprint is the minimum among all the sample points Despite the fact that the map based RSSI localization methods produce relatively small location errors they are not attractive mainly because they require an excessive amount of profiling data and because they are not fully distributed The detailed profiling of a building is a major holdback because it is complex time consuming and depends on beacon locations Therefore for different buildings or even for the

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

29

same building and different beacon locations different RSSI maps have to be obtained The second category of RSSI based algorithms are the distance (or area) prediction algorithms which focus on minimizing the amount of profiling data In distance prediction based methods raw RSSI data are directly mapped to distances through a signal propagation model In some cases distances are not explicitly measured but they are taken into consideration through assumptions on the underlying distributions In the case of outdoor deployments where there are no obstacles a single signal propagation model is all the profiling data required In the case of indoor deployments the same applies but now the signal propagation model might be different across different buildings with different furniture and wall arrangements Given an underlying signal propagation model raw RSSI data can be mapped to specific distances These distances can be either used directly in order to perform localization or assigned a probability and used in a learning basedprobabilistic localization algorithm Obviously the accuracy of the distance prediction based methods depends heavily on the accuracy of the signal propagation model used for translating RSSI values to distances

33 Properties of RSSI Changes in signal due to propagation indoors are difficult to predict because of dense environment and propagation effects such as reflection diffraction and scattering The multi‐path fading effect which is the result of either constructive or destructive combination of multiple signal copies at the receiver causes the received signal to fluctuate around a mean value at a particular location The received signal is usually modelled by the combined effects of large‐scale fading and small‐scale fading The large‐scale fading component describes the signal attenuation as the signal travels over a distance and is absorbed by material such as walls and floors along the way to the receiver This component predicts the mean of the RSS and usually has a lognormal distribution On the other hand the small scale fading component explains the dramatic fluctuation of the signal due to multi‐path fading On the other hand the small scale‐fading component explains the dramatic fluctuation of the signal due to multi‐path fading If there is no LOS component the small‐scale‐fading is often modelled with a Rayleigh distribution Such scenarios are commonly called NLOS If there is a LOS component the small‐scale‐fading is typically modelled by a Rician distribution Note that when the RSS is measured the measurement averages out the small‐scale‐fading effects

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

30

4 OBJECT TRACKING 41 Introduction This chapter presents the object‐tracking application the main objective of this thesis work The application is an algorithmic‐software implementation written in Embedded C which is capable of receiving data messages and generating object position estimation data The implementation of the object‐tracking application makes use of the SENTIO platform and uses the background knowledge introduced in the preceding chapters The motivation for the object‐tracking application is to determine the position of an object in indoor environments (Eg Home) The application acts as an interface which collects the raw data that the sensors generate (numerical values) It makes the proper comparisons and algorithmic processes to identify and track the object while it is static inside the area covered by the wireless sensor network Finally with the detection and tracking data it alerts the user or another system The following sections provide an overview of the object‐tracking application

42 Overview of hardware product Before describing the object tracking application it is important to know the characteristics of hardware product The SENTIO platform (figure 15) is built in modular fashion and has the following key features [8]

Flexibility This platform is very flexible in such a way that it is made easy to add or change the hardware for each sensor task depending on the requirement in the WSN For a specific platform this can be hard or sometimes even impossible to accomplish

Sensor resources Resources in a sensor module includes the ability of usage of controllerrsquos resources as much as possible such as ADC‐channels interrupts and digital Input‐Outputrsquos Local voltage regulation should also be possible since many sensors require higher supply voltages than digital Integrated Circuits do Further support of a shut‐down mechanism to power down the sensor modules when not needed

Computation and memory The processor module based on microcontroller should support many devices in a microcontroller family and support different power supply voltages enabling full range of clock frequencies All power‐down modes of the devices should be

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

31

supported The platform should support different processor modules including FPGArsquos and very simple microcontrollers

Communication The focus should be on enabling an IEEE 802154 compliant communication interface With or without ZigBee stack this will provide a flexible platform for exploring different network topologies Support for power‐down of the entire communication module should be supported

Cost and form factor High flexibility is in conflict with low cost and small size The primary objective should be flexibility but the size should be considered in order to enable sensor device integration in systems where space is limited

Figure 15 SENTIO Platform [8]

The communication subsystem of the SENTIO sensor node was designed so that the correct operation of the radio chip is ensured The radio chip is powered by the on‐board voltage regulator and thus fluctuations in the battery voltage level do not affect the operation of the communication subsystem This platform uses Chipcon CC2420 IEEE 802154 radio transceiver operates in the 24GHz ISM band and includes a digital direct sequence spread spectrum (DSSS) modem providing a spreading gain of 9dB and an effective data rate of 250Kbps It was specifically designed for low power wireless applications and supports 8 discrete power levels 0dBm minus1dBm minus3dBm minus5dBm minus7dBm minus10dBm minus15dBm and minus25dBm at which its power consumption varies from 29mW to 52mW [21] A built‐in received signal strength indicator gives an 8‐bit digital value RSSIVAL The RSSIVAL is always averaged over 8 symbol periods (128μs) and a status bit indicates when the RSSIVAL is valid (meaning that the receiver was enabled for at least 8 symbol periods) The

Antenna

Communication Module

Processing Module

Sensing Module

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

32

power P at the RF pins can be obtained directly from RSSIVAL using the following equation

P = RSSIVAL + RSSIOFFSET [dBm] where the RSSIOFFSET is found empirically from the front‐end gain and it is approximately equal to minus45dBm In the next sections when we refer to the RSSI value we refer to the RSSIVAL and not the power P unless otherwise stated

43 Experimental Testbed The experimental test bed for object tracking application is as shown in figure 16 Experiment was conducted in Masterrsquos room at Mid Sweden University A DELL laptop is connected to SENTIO platform and software were primarily used to collect samples of RSS from APrsquos The software monitors IEEE 802154 radio frequency channels which operate in the 24GHz band

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

1030

BC - 1030

AP 3 AP 1

AP 2

AP 4

BS

HEWLETT

PACKARD

Figure 16 Layout of Masterʹs Room

A three dimensional scalable testbed is used for indoor sensor network deployments The testbed illustrated in figure 16 is a 3‐D structure measuring 16m (L) x 12m (W) x 3m (H) and it is designed to host a large number of static nodes as shown in figure 16 Arrow marks in the above figure represent the location where fingerprints were taken The centrepiece of our infrastructure

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

33

is the SENTIO sensor node [8] an open‐source general purpose sensing platform designed with AT Mega 128 microprocessor and the IEEE 802154 compliant CC2420 radio from Chipcon [19]

44 Obtaining data from the sensor network The next important step in the development of the object tracking application is to obtain the data from the sensor network and send it to the base station From there the data is transmitted through a serial port to the computer that runs the object tracking application When the data arrives at the serial port the application must capture it This paragraph gives a brief explanation of how and what kind data is transmitted by the static object fixed sensors and how this data is being used to estimate the position of the object First the static object transmits Beacon packet continuously which is received by all the fixed APrsquos The beacon packet format is as shown in table 6 Then the fixed APrsquos calculates the 8‐bit value of RSSI from the transmitted beacon packet and then sends the resultant RSSI value as a form of data packet (table 7) to the BS which calculates the position and sends back to the object

45 Object tracking application Testing and Evaluation The purpose of this section is to discuss and evaluate the object tracking application Finally we are ready with the experimental testbed as shown in figure 16 As shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors placed at the corners of the masterrsquos room BS is placed at the centre of the room connected to a PC All the nodes are power connected before an actual measurement takes place At first the object is placed at random position inside the room and transmits beacon packets continuously Then these packets are received by the fixed APrsquos which calculate the RSSI from the packet and then averages it until it receives 50 beacon packets and then transmits the data to the BS (figure 17) Then the BS waits until it receives the data from all the APrsquos Once it receives from all the APrsquos it calculates the position of the object (figure 18) The application was developed to demonstrate a real world use of a WSN in object tracking In evaluating the final position obtained with respect to the actual position we conclude that the object‐tracking was successful Although

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

34

it is a research prototype object tracking application is a simple stable stand‐alone system capable of demonstrating an important application of WSN It takes raw data from the WSN aggregates it evaluates it and based on the evaluation tracks the object The accuracy of estimating the position is less than 5m Table 11 below shows a log of readings taken during test

Start

Intialize micro‐controller and transeciever in receieve mode

If received_packet=

Beacon

Average RSSI values

Increase the counter

If countergt1000050

A

No

Yes

B

B

Yes

No

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

35

A

Reset counter

Set transceiever in transmit mode

Transmit averaged RSSI to Main sensor and set it back to receive mode

10000 samples ‐ Offline

50 samples ‐ Online

Figure 17 Flow chart representing the algorithm of fixed sensors

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

36

Start

Intialize micro‐controller and transeciever in receieve mode

Received RSSI values from all fixed Sensors

Open radio map

Calculate Euclidean distance between obtained RSSI values from fixed sensors and fingerprints

in radio map

No

Yes

Bubble sort for least error in signal strength values

A

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

37

A

Get the location of the object from the radiousing the least error value

Display location of object on PC

End

Figure 18 Flow chart representing the algorithm of main sensor

Table 11 Accuracy of positioning system

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

38

5 RESULTS This chapter presents how the system parameters such as number of samples mean or median etchellipare finalized All the tests are performed in masterrsquos room at Mid Sweden University This chapter is divided into three subsections First in Section 51 it reviews results obtained from samples test Section 52 discusses one dimensional test results and finally section 53 discusses results of two dimensional tests

51 Measurement setup A Dell laptop was connected to a SENTIO platform and is loaded with software (Appendix 2) This setup was used to determine number of samples required during object tracking mean or median position estimation algorithm and one dimensional test The setup is as follows

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

103

0

BC - 1030

AP 1AP 2

BS

HEWLETT

PACKARD

Figure 19 Test bed for one dimensional test in Masters Room

Two SENTIO nodes (AP1 and AP2 as shown above) were separated by a distance of 10 meters and these sensors are known as fixed sensors which are fixed to the ceiling such that there antenna is faced towards the ground They are linear is position A BS was connected to Dell laptop and it was placed in such a way that its antenna was faced towards the ceiling Another SENTIO

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

39

node was used as an object and its orientation was same as BS Figure 19 represents the complete setup for object tracking application Mobile node was moved between these fixed ones at regular intervals of one meter and RSSI values were logged at each interval

52 Number of samples test This test was performed to determine number of samples required for accurate positioning and either mean of the obtained RSSI values or median of them Figure 19 represents the setup for number of samples test Once the setup is ready the measurements are taken as follows

1 An object was tagged with SENTIO node and was placed stationary linear to the fixed sensors

2 10000 samples (RSSI) were taken at regular intervals of 1 meter up to 10 meters

3 The sample values were logged at every interval 4 Using these logged samples I have plotted graphs such as distribution

of RSS values (figure 20 21) meanmedian at intervals of 1 meter using MATLAB

5 The mean and median for various numbers of samples is as shown in figure 22

Figure 20 Histogram plot of RSSI values at 1m and 4m

Figure 21 Histogram plot of RSSI values at 6m and 8m

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

40

Figure 22 Distance vs RSSI

From the above graphs following conclusions were made 1 From the histogram plots we can observe that the distribution of RSSI

values follow Gaussians distribution law 2 As we can see from figure 22 that median curve has drastic changes

when compared to mean curve Hence mean is considered while estimating the position

3 From the observations we conclude that the number of samples does not matter since mean curve tends to be same for various numbers of samples

4 Hence 10000 samples are considered in order to get a proper location fingerprint during off‐line phase and during online phase 50 samples are enough to estimate the position of object

53 0ne dimensional test Measurement setup for one dimensional test is as shown in figure 19 This test was conducted to check the performance of algorithm and how accurate it was At first ie during offline phase fingerprints at each interval averaging 10000 samples were taken During online phase Euclidean algorithm was used estimate the position averaging 50 samples of RSSI From the results we

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

41

can realize that the error was less than 2 meters Table 12 shows the result of location estimation in one dimensional case

Table 12 One dimensional position estimation results

54 Two dimensional test This test is the final stage of my thesis work Experimental testbed for this stage is as shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors fixed at the corners of the room and a BS at the centre of the room There are 18 pre‐determined locations where the fingerprints (RSSI) were taken The fingerprints are as shown in Appendix 1 All the readings were taken in presence of userrsquos The algorithm for position estimation is shown in Appendix 2 Position estimation test is performed as follows

Tagged object is placed at random inside the masterrsquos room All the sensors including the base station and object are switched on Using the algorithm (Appendix 2) the BS estimates the position of the object and displays it on the PC

This test was performed using various numbers of samples to test which has a better accuracy Figure 23 represents the error rate using various numbers of samples From figure 23 we can conclude that 50 numbers of samples is enough to estimate the position of object with an accuracy of 3m

55 Causes of error in location determination The randomness of RSS is the major cause of error in any indoor

positioning system that uses the RSS for location inference If there is no randomness in the RSS and its location fingerprint every indoor positioning systems of this type will have no location detection error For example I have recorded RSS at one particular location during different times of the day From figures 24 and 25 you can observe that RSS values are different at different times of day

The performance of indoor positioning systems depends greatly on the separation of location fingerprints A location fingerprint

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

42

corresponding to a location can be identified correctly if it is difficult to classify it as another fingerprint by a pattern classifier

Path loss is one of the basic signal propagation properties of the RSS that best describes attenuation of transmit signal power over a distance between an access point and a mobile device

Presence of Userrsquos or userrsquos orientation affects RSS

Figure 23 Error Rate vs Number of samples

Figure 24 RSS distribution at 214 pm

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

43

Figure 25 RSS distribution at 100pm and 1100 am

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

6 DISCUSSIONS10102006

44

6 DISCUSSIONS 61 Summary and Conclusion While empirical results and performance studies of positioning systems based on location fingerprint have been presented in this report analytical model that can be used as a framework for efficiently designing the positioning systems are not available This thesis design work uses general principles of position estimation such as position estimation of sensors grid spacing etc This design work focuses on accuracy and precision The concept of location fingerprint technique based on RSS us explained in detail in chapter 2 It is found that RSS is random It is possible to approximate the RSS as being normally distributed While the RSS random process is non‐stationary the mean is more or less constant and could be used as the fingerprint of the location Model designed for position estimation is as shown in chapter 4 Accuracy and precision are the two major performance matrices in this study Uniform grid spacing has been used From the experimental analysis I have obtained an accuracy of 3 meters and 35 precision The sensitivity analysis among the system parameters and the performance matrices were done using the model as shown in chapter 4 For instance our study found that the minimum distance between two adjacent positions also affects the positioning performance This is due to the randomness of RSS patterns induced by the ever changing indoor environment

62 Future work The research in this thesis provides a ground work for the future study of efficient design of indoor positioning system The RSSI based position estimation still has limitations First the RSSI value is time dependent Thus the future study should address this issue Second the movement of object is not considered Third orientations of sensors affect the performance which should be taken care off Fourth to improve the performance of the system we need to find a solution to deal with those incomplete or missing data during the off‐line and the on‐line phase Fifth position estimation on multi‐floor and three‐dimensional coordinates is not available

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 110102006

45

APPENDIX 1

Location Fingerprints of Masters Room Time 1315 Observation Time 20 minutes Number of Samples 10000

People Presence SNo Co-ordinates AP1 (0x0001) AP2 (0x0002) AP3 (0x0004) AP4 (0x0008)

1 11 -24 -16 -27 -33 2 13 -26 -8 -34 -31 3 15 -43 -9 -39 -25 4 32 -21 -11 -27 -33 5 61 -6 -23 -32 -28 6 64 -20 -31 -25 -34 7 35 -23 -35 -29 -38 8 37 -29 -35 -22 -27 9 39 -38 -29 -21 -27 10 67 -27 -23 -20 -23 11 611 -26 -29 -23 -30 12 613 -35 -32 -2 -31 13 513 -34 -31 -20 -26 14 11 -39 -22 -29 -17 15 212 -37 -42 -28 -31 16 -212 -38 -33 -29 -6 17 -21 -42 -29 -33 -19 18 -38 -40 -23 -35 -22

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 210102006

46

APPENDIX 2

Begin

Open radio map and raw data files for write

Intialize radio map in memory

Check whether the

Packet is data or Beacon packet If data packet

Save the data to register

Check if data is

Received from all

sensors

Estimate the position of static

Display the position on PC

End Yes

No

A

A

Yes

No

Flow chart representing the main algorithm for position estimation

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

47

APPENDIX 3

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

48

Distribution of RSSI

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

49

REFERENCES [1] T S Rappaport J H Reed and B D Woerner ldquoPosition location using wireless communications on highways of the futureʺ IEEE Commun Mag vol 34 no 10 pp33‐41 Oct 1996 [2] A M Ladd K E Bekris G Marceau A Rudys L E Kavraki and D S Wallach rdquoRobotics‐based location sensing using wireless ethernetʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ02) 2002 pp 227‐238 [3] I Ramani R Bharadwaja and P V Rangan ldquoLocation tracking for media appliances in wireless home networksʺ in Proc International Conference on Multimedia and Expo (ICMEʹ03) July 2003 pp 769‐772 [4] K Pahlavan X Li and J P Makela ldquoIndoor geolocation science and technologyʺ IEEE Commun Mag vol 40 no 2 pp 112‐118 Feb 2002 [5] Konard Lorincz and Matt Welsh ldquoMote Track A Robust Decentralized Approach to RF‐Based Location Trackingrdquo [6] Paramvir Bahl and Venkata N Padmanabham ldquoRADAR An In‐Building RF‐based User Location and Tracking Systemrdquo [7] Kamol Kaemarungsi ldquoDesign of Indoor positioning system based on location fingerprint techiniquerdquo University of Pittsburgh 2005 [8] Fredrik Linnarsson Cheng Peng and Bengt Oelmann ldquoSENTIO A Hardware Platform for Rapid Prototyping of Wireless Sensor Networksrdquo [9] S Saha K Chaudhuri D Sanghi and P Bhagwat ldquoLocation determination of a mobile device using IEEE 80211b access point signalsʺ in Proc IEEE Wireless Communications and Networking Conference (WCNCʹ03) New Orleans LA Mar 2003 pp 1987‐1992 [10] T Roos P Myllymaki H Tirri P Misikangas and J Sievanen ldquoA probabilistic approach to WLAN user location estimationʺ International Journal of Wireless Information Networks vol 9 no 3 pp 155‐164 July 2002 [11] R Battiti M Brunato and A Villani ldquoStatistical learning theory for location fingerprinting in wireless LANrsquosʺ Technical Report Oct 2002 [Online] Available httprtmscienceunitnitraquobattitiarchive86pdf [12] P Prasithsangaree P Krishnamurthy and P K Chrysanthis ldquoAn indoor position location with wireless LANsʺ in Proc IEEE International Symposium on Personal Indoor and Mobile Radio Communications (PIMRCʹ02) Lisbon Portugal Sept 2002 [13] M A Youssef A Agrawala and A U Shankar ldquoWLAN location determination via clustering and probability distributionsʺ in Proc IEEE

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

50

International Conference on Pervasive Computing and Communications (PerComʹ03) Dallas‐Fort Worth TX Mar 23‐26 2003 pp 23‐26 [14] Z Xiang S Song J Chen H Wang J Huang and X Gao ldquoA wireless LAN‐based indoor positioning technologyʺ IBM Journal of Research and Development vol 48 no 56 pp 617‐626 SeptNov 2004 [15] R Want A Hopper V Falcao and J Gibbons ldquoThe active badge location systemʺACM Transactions on Information Systems vol 40 no 1 pp 91‐102 Jan 1992 [16] N B Priyantha A Chakraborty and H Balakrishnan ldquoThe cricket location‐support systemʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ00) Boston MA Aug 2000 pp 32‐43 [17] Holger K Willig A (2005) Protocols and Architectures for Wireless Sensor Networks London [18] IEEE (2003) Part 154 Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low‐Rate Wireless Personal Area Networks (LR‐WPANs) New York [19] Chipcon CC2420 802154 compliant radio httpwwwchipconcom [20] ZigBee Specification v10 ZigBee Alliance [21] Dimitrios Lymberopoulos Quentin Lindsey and Andreas Savvides ldquoAn Empirical Analysis of Radio Signal Strength Variability in IEEE 802154 Networks using Monopole Antennasrdquo [22] Kiran Yedavalli Bhaskar Krishnamachari Sharmila Ravula and Bhaskar Srinivasan ldquoEcolocation A sequence based technique for RF localization in wireless sensor networksrdquo Robert Bosch Research and Technology Centre Palo Alto CA

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

3 RECEIVED SIGNAL STRENGTH (RSS)

10102006

29

same building and different beacon locations different RSSI maps have to be obtained The second category of RSSI based algorithms are the distance (or area) prediction algorithms which focus on minimizing the amount of profiling data In distance prediction based methods raw RSSI data are directly mapped to distances through a signal propagation model In some cases distances are not explicitly measured but they are taken into consideration through assumptions on the underlying distributions In the case of outdoor deployments where there are no obstacles a single signal propagation model is all the profiling data required In the case of indoor deployments the same applies but now the signal propagation model might be different across different buildings with different furniture and wall arrangements Given an underlying signal propagation model raw RSSI data can be mapped to specific distances These distances can be either used directly in order to perform localization or assigned a probability and used in a learning basedprobabilistic localization algorithm Obviously the accuracy of the distance prediction based methods depends heavily on the accuracy of the signal propagation model used for translating RSSI values to distances

33 Properties of RSSI Changes in signal due to propagation indoors are difficult to predict because of dense environment and propagation effects such as reflection diffraction and scattering The multi‐path fading effect which is the result of either constructive or destructive combination of multiple signal copies at the receiver causes the received signal to fluctuate around a mean value at a particular location The received signal is usually modelled by the combined effects of large‐scale fading and small‐scale fading The large‐scale fading component describes the signal attenuation as the signal travels over a distance and is absorbed by material such as walls and floors along the way to the receiver This component predicts the mean of the RSS and usually has a lognormal distribution On the other hand the small scale fading component explains the dramatic fluctuation of the signal due to multi‐path fading On the other hand the small scale‐fading component explains the dramatic fluctuation of the signal due to multi‐path fading If there is no LOS component the small‐scale‐fading is often modelled with a Rayleigh distribution Such scenarios are commonly called NLOS If there is a LOS component the small‐scale‐fading is typically modelled by a Rician distribution Note that when the RSS is measured the measurement averages out the small‐scale‐fading effects

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

30

4 OBJECT TRACKING 41 Introduction This chapter presents the object‐tracking application the main objective of this thesis work The application is an algorithmic‐software implementation written in Embedded C which is capable of receiving data messages and generating object position estimation data The implementation of the object‐tracking application makes use of the SENTIO platform and uses the background knowledge introduced in the preceding chapters The motivation for the object‐tracking application is to determine the position of an object in indoor environments (Eg Home) The application acts as an interface which collects the raw data that the sensors generate (numerical values) It makes the proper comparisons and algorithmic processes to identify and track the object while it is static inside the area covered by the wireless sensor network Finally with the detection and tracking data it alerts the user or another system The following sections provide an overview of the object‐tracking application

42 Overview of hardware product Before describing the object tracking application it is important to know the characteristics of hardware product The SENTIO platform (figure 15) is built in modular fashion and has the following key features [8]

Flexibility This platform is very flexible in such a way that it is made easy to add or change the hardware for each sensor task depending on the requirement in the WSN For a specific platform this can be hard or sometimes even impossible to accomplish

Sensor resources Resources in a sensor module includes the ability of usage of controllerrsquos resources as much as possible such as ADC‐channels interrupts and digital Input‐Outputrsquos Local voltage regulation should also be possible since many sensors require higher supply voltages than digital Integrated Circuits do Further support of a shut‐down mechanism to power down the sensor modules when not needed

Computation and memory The processor module based on microcontroller should support many devices in a microcontroller family and support different power supply voltages enabling full range of clock frequencies All power‐down modes of the devices should be

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

31

supported The platform should support different processor modules including FPGArsquos and very simple microcontrollers

Communication The focus should be on enabling an IEEE 802154 compliant communication interface With or without ZigBee stack this will provide a flexible platform for exploring different network topologies Support for power‐down of the entire communication module should be supported

Cost and form factor High flexibility is in conflict with low cost and small size The primary objective should be flexibility but the size should be considered in order to enable sensor device integration in systems where space is limited

Figure 15 SENTIO Platform [8]

The communication subsystem of the SENTIO sensor node was designed so that the correct operation of the radio chip is ensured The radio chip is powered by the on‐board voltage regulator and thus fluctuations in the battery voltage level do not affect the operation of the communication subsystem This platform uses Chipcon CC2420 IEEE 802154 radio transceiver operates in the 24GHz ISM band and includes a digital direct sequence spread spectrum (DSSS) modem providing a spreading gain of 9dB and an effective data rate of 250Kbps It was specifically designed for low power wireless applications and supports 8 discrete power levels 0dBm minus1dBm minus3dBm minus5dBm minus7dBm minus10dBm minus15dBm and minus25dBm at which its power consumption varies from 29mW to 52mW [21] A built‐in received signal strength indicator gives an 8‐bit digital value RSSIVAL The RSSIVAL is always averaged over 8 symbol periods (128μs) and a status bit indicates when the RSSIVAL is valid (meaning that the receiver was enabled for at least 8 symbol periods) The

Antenna

Communication Module

Processing Module

Sensing Module

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

32

power P at the RF pins can be obtained directly from RSSIVAL using the following equation

P = RSSIVAL + RSSIOFFSET [dBm] where the RSSIOFFSET is found empirically from the front‐end gain and it is approximately equal to minus45dBm In the next sections when we refer to the RSSI value we refer to the RSSIVAL and not the power P unless otherwise stated

43 Experimental Testbed The experimental test bed for object tracking application is as shown in figure 16 Experiment was conducted in Masterrsquos room at Mid Sweden University A DELL laptop is connected to SENTIO platform and software were primarily used to collect samples of RSS from APrsquos The software monitors IEEE 802154 radio frequency channels which operate in the 24GHz band

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

1030

BC - 1030

AP 3 AP 1

AP 2

AP 4

BS

HEWLETT

PACKARD

Figure 16 Layout of Masterʹs Room

A three dimensional scalable testbed is used for indoor sensor network deployments The testbed illustrated in figure 16 is a 3‐D structure measuring 16m (L) x 12m (W) x 3m (H) and it is designed to host a large number of static nodes as shown in figure 16 Arrow marks in the above figure represent the location where fingerprints were taken The centrepiece of our infrastructure

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

33

is the SENTIO sensor node [8] an open‐source general purpose sensing platform designed with AT Mega 128 microprocessor and the IEEE 802154 compliant CC2420 radio from Chipcon [19]

44 Obtaining data from the sensor network The next important step in the development of the object tracking application is to obtain the data from the sensor network and send it to the base station From there the data is transmitted through a serial port to the computer that runs the object tracking application When the data arrives at the serial port the application must capture it This paragraph gives a brief explanation of how and what kind data is transmitted by the static object fixed sensors and how this data is being used to estimate the position of the object First the static object transmits Beacon packet continuously which is received by all the fixed APrsquos The beacon packet format is as shown in table 6 Then the fixed APrsquos calculates the 8‐bit value of RSSI from the transmitted beacon packet and then sends the resultant RSSI value as a form of data packet (table 7) to the BS which calculates the position and sends back to the object

45 Object tracking application Testing and Evaluation The purpose of this section is to discuss and evaluate the object tracking application Finally we are ready with the experimental testbed as shown in figure 16 As shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors placed at the corners of the masterrsquos room BS is placed at the centre of the room connected to a PC All the nodes are power connected before an actual measurement takes place At first the object is placed at random position inside the room and transmits beacon packets continuously Then these packets are received by the fixed APrsquos which calculate the RSSI from the packet and then averages it until it receives 50 beacon packets and then transmits the data to the BS (figure 17) Then the BS waits until it receives the data from all the APrsquos Once it receives from all the APrsquos it calculates the position of the object (figure 18) The application was developed to demonstrate a real world use of a WSN in object tracking In evaluating the final position obtained with respect to the actual position we conclude that the object‐tracking was successful Although

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

34

it is a research prototype object tracking application is a simple stable stand‐alone system capable of demonstrating an important application of WSN It takes raw data from the WSN aggregates it evaluates it and based on the evaluation tracks the object The accuracy of estimating the position is less than 5m Table 11 below shows a log of readings taken during test

Start

Intialize micro‐controller and transeciever in receieve mode

If received_packet=

Beacon

Average RSSI values

Increase the counter

If countergt1000050

A

No

Yes

B

B

Yes

No

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

35

A

Reset counter

Set transceiever in transmit mode

Transmit averaged RSSI to Main sensor and set it back to receive mode

10000 samples ‐ Offline

50 samples ‐ Online

Figure 17 Flow chart representing the algorithm of fixed sensors

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

36

Start

Intialize micro‐controller and transeciever in receieve mode

Received RSSI values from all fixed Sensors

Open radio map

Calculate Euclidean distance between obtained RSSI values from fixed sensors and fingerprints

in radio map

No

Yes

Bubble sort for least error in signal strength values

A

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

37

A

Get the location of the object from the radiousing the least error value

Display location of object on PC

End

Figure 18 Flow chart representing the algorithm of main sensor

Table 11 Accuracy of positioning system

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

38

5 RESULTS This chapter presents how the system parameters such as number of samples mean or median etchellipare finalized All the tests are performed in masterrsquos room at Mid Sweden University This chapter is divided into three subsections First in Section 51 it reviews results obtained from samples test Section 52 discusses one dimensional test results and finally section 53 discusses results of two dimensional tests

51 Measurement setup A Dell laptop was connected to a SENTIO platform and is loaded with software (Appendix 2) This setup was used to determine number of samples required during object tracking mean or median position estimation algorithm and one dimensional test The setup is as follows

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

103

0

BC - 1030

AP 1AP 2

BS

HEWLETT

PACKARD

Figure 19 Test bed for one dimensional test in Masters Room

Two SENTIO nodes (AP1 and AP2 as shown above) were separated by a distance of 10 meters and these sensors are known as fixed sensors which are fixed to the ceiling such that there antenna is faced towards the ground They are linear is position A BS was connected to Dell laptop and it was placed in such a way that its antenna was faced towards the ceiling Another SENTIO

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

39

node was used as an object and its orientation was same as BS Figure 19 represents the complete setup for object tracking application Mobile node was moved between these fixed ones at regular intervals of one meter and RSSI values were logged at each interval

52 Number of samples test This test was performed to determine number of samples required for accurate positioning and either mean of the obtained RSSI values or median of them Figure 19 represents the setup for number of samples test Once the setup is ready the measurements are taken as follows

1 An object was tagged with SENTIO node and was placed stationary linear to the fixed sensors

2 10000 samples (RSSI) were taken at regular intervals of 1 meter up to 10 meters

3 The sample values were logged at every interval 4 Using these logged samples I have plotted graphs such as distribution

of RSS values (figure 20 21) meanmedian at intervals of 1 meter using MATLAB

5 The mean and median for various numbers of samples is as shown in figure 22

Figure 20 Histogram plot of RSSI values at 1m and 4m

Figure 21 Histogram plot of RSSI values at 6m and 8m

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

40

Figure 22 Distance vs RSSI

From the above graphs following conclusions were made 1 From the histogram plots we can observe that the distribution of RSSI

values follow Gaussians distribution law 2 As we can see from figure 22 that median curve has drastic changes

when compared to mean curve Hence mean is considered while estimating the position

3 From the observations we conclude that the number of samples does not matter since mean curve tends to be same for various numbers of samples

4 Hence 10000 samples are considered in order to get a proper location fingerprint during off‐line phase and during online phase 50 samples are enough to estimate the position of object

53 0ne dimensional test Measurement setup for one dimensional test is as shown in figure 19 This test was conducted to check the performance of algorithm and how accurate it was At first ie during offline phase fingerprints at each interval averaging 10000 samples were taken During online phase Euclidean algorithm was used estimate the position averaging 50 samples of RSSI From the results we

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

41

can realize that the error was less than 2 meters Table 12 shows the result of location estimation in one dimensional case

Table 12 One dimensional position estimation results

54 Two dimensional test This test is the final stage of my thesis work Experimental testbed for this stage is as shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors fixed at the corners of the room and a BS at the centre of the room There are 18 pre‐determined locations where the fingerprints (RSSI) were taken The fingerprints are as shown in Appendix 1 All the readings were taken in presence of userrsquos The algorithm for position estimation is shown in Appendix 2 Position estimation test is performed as follows

Tagged object is placed at random inside the masterrsquos room All the sensors including the base station and object are switched on Using the algorithm (Appendix 2) the BS estimates the position of the object and displays it on the PC

This test was performed using various numbers of samples to test which has a better accuracy Figure 23 represents the error rate using various numbers of samples From figure 23 we can conclude that 50 numbers of samples is enough to estimate the position of object with an accuracy of 3m

55 Causes of error in location determination The randomness of RSS is the major cause of error in any indoor

positioning system that uses the RSS for location inference If there is no randomness in the RSS and its location fingerprint every indoor positioning systems of this type will have no location detection error For example I have recorded RSS at one particular location during different times of the day From figures 24 and 25 you can observe that RSS values are different at different times of day

The performance of indoor positioning systems depends greatly on the separation of location fingerprints A location fingerprint

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

42

corresponding to a location can be identified correctly if it is difficult to classify it as another fingerprint by a pattern classifier

Path loss is one of the basic signal propagation properties of the RSS that best describes attenuation of transmit signal power over a distance between an access point and a mobile device

Presence of Userrsquos or userrsquos orientation affects RSS

Figure 23 Error Rate vs Number of samples

Figure 24 RSS distribution at 214 pm

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

43

Figure 25 RSS distribution at 100pm and 1100 am

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

6 DISCUSSIONS10102006

44

6 DISCUSSIONS 61 Summary and Conclusion While empirical results and performance studies of positioning systems based on location fingerprint have been presented in this report analytical model that can be used as a framework for efficiently designing the positioning systems are not available This thesis design work uses general principles of position estimation such as position estimation of sensors grid spacing etc This design work focuses on accuracy and precision The concept of location fingerprint technique based on RSS us explained in detail in chapter 2 It is found that RSS is random It is possible to approximate the RSS as being normally distributed While the RSS random process is non‐stationary the mean is more or less constant and could be used as the fingerprint of the location Model designed for position estimation is as shown in chapter 4 Accuracy and precision are the two major performance matrices in this study Uniform grid spacing has been used From the experimental analysis I have obtained an accuracy of 3 meters and 35 precision The sensitivity analysis among the system parameters and the performance matrices were done using the model as shown in chapter 4 For instance our study found that the minimum distance between two adjacent positions also affects the positioning performance This is due to the randomness of RSS patterns induced by the ever changing indoor environment

62 Future work The research in this thesis provides a ground work for the future study of efficient design of indoor positioning system The RSSI based position estimation still has limitations First the RSSI value is time dependent Thus the future study should address this issue Second the movement of object is not considered Third orientations of sensors affect the performance which should be taken care off Fourth to improve the performance of the system we need to find a solution to deal with those incomplete or missing data during the off‐line and the on‐line phase Fifth position estimation on multi‐floor and three‐dimensional coordinates is not available

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 110102006

45

APPENDIX 1

Location Fingerprints of Masters Room Time 1315 Observation Time 20 minutes Number of Samples 10000

People Presence SNo Co-ordinates AP1 (0x0001) AP2 (0x0002) AP3 (0x0004) AP4 (0x0008)

1 11 -24 -16 -27 -33 2 13 -26 -8 -34 -31 3 15 -43 -9 -39 -25 4 32 -21 -11 -27 -33 5 61 -6 -23 -32 -28 6 64 -20 -31 -25 -34 7 35 -23 -35 -29 -38 8 37 -29 -35 -22 -27 9 39 -38 -29 -21 -27 10 67 -27 -23 -20 -23 11 611 -26 -29 -23 -30 12 613 -35 -32 -2 -31 13 513 -34 -31 -20 -26 14 11 -39 -22 -29 -17 15 212 -37 -42 -28 -31 16 -212 -38 -33 -29 -6 17 -21 -42 -29 -33 -19 18 -38 -40 -23 -35 -22

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 210102006

46

APPENDIX 2

Begin

Open radio map and raw data files for write

Intialize radio map in memory

Check whether the

Packet is data or Beacon packet If data packet

Save the data to register

Check if data is

Received from all

sensors

Estimate the position of static

Display the position on PC

End Yes

No

A

A

Yes

No

Flow chart representing the main algorithm for position estimation

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

47

APPENDIX 3

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

48

Distribution of RSSI

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

49

REFERENCES [1] T S Rappaport J H Reed and B D Woerner ldquoPosition location using wireless communications on highways of the futureʺ IEEE Commun Mag vol 34 no 10 pp33‐41 Oct 1996 [2] A M Ladd K E Bekris G Marceau A Rudys L E Kavraki and D S Wallach rdquoRobotics‐based location sensing using wireless ethernetʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ02) 2002 pp 227‐238 [3] I Ramani R Bharadwaja and P V Rangan ldquoLocation tracking for media appliances in wireless home networksʺ in Proc International Conference on Multimedia and Expo (ICMEʹ03) July 2003 pp 769‐772 [4] K Pahlavan X Li and J P Makela ldquoIndoor geolocation science and technologyʺ IEEE Commun Mag vol 40 no 2 pp 112‐118 Feb 2002 [5] Konard Lorincz and Matt Welsh ldquoMote Track A Robust Decentralized Approach to RF‐Based Location Trackingrdquo [6] Paramvir Bahl and Venkata N Padmanabham ldquoRADAR An In‐Building RF‐based User Location and Tracking Systemrdquo [7] Kamol Kaemarungsi ldquoDesign of Indoor positioning system based on location fingerprint techiniquerdquo University of Pittsburgh 2005 [8] Fredrik Linnarsson Cheng Peng and Bengt Oelmann ldquoSENTIO A Hardware Platform for Rapid Prototyping of Wireless Sensor Networksrdquo [9] S Saha K Chaudhuri D Sanghi and P Bhagwat ldquoLocation determination of a mobile device using IEEE 80211b access point signalsʺ in Proc IEEE Wireless Communications and Networking Conference (WCNCʹ03) New Orleans LA Mar 2003 pp 1987‐1992 [10] T Roos P Myllymaki H Tirri P Misikangas and J Sievanen ldquoA probabilistic approach to WLAN user location estimationʺ International Journal of Wireless Information Networks vol 9 no 3 pp 155‐164 July 2002 [11] R Battiti M Brunato and A Villani ldquoStatistical learning theory for location fingerprinting in wireless LANrsquosʺ Technical Report Oct 2002 [Online] Available httprtmscienceunitnitraquobattitiarchive86pdf [12] P Prasithsangaree P Krishnamurthy and P K Chrysanthis ldquoAn indoor position location with wireless LANsʺ in Proc IEEE International Symposium on Personal Indoor and Mobile Radio Communications (PIMRCʹ02) Lisbon Portugal Sept 2002 [13] M A Youssef A Agrawala and A U Shankar ldquoWLAN location determination via clustering and probability distributionsʺ in Proc IEEE

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

50

International Conference on Pervasive Computing and Communications (PerComʹ03) Dallas‐Fort Worth TX Mar 23‐26 2003 pp 23‐26 [14] Z Xiang S Song J Chen H Wang J Huang and X Gao ldquoA wireless LAN‐based indoor positioning technologyʺ IBM Journal of Research and Development vol 48 no 56 pp 617‐626 SeptNov 2004 [15] R Want A Hopper V Falcao and J Gibbons ldquoThe active badge location systemʺACM Transactions on Information Systems vol 40 no 1 pp 91‐102 Jan 1992 [16] N B Priyantha A Chakraborty and H Balakrishnan ldquoThe cricket location‐support systemʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ00) Boston MA Aug 2000 pp 32‐43 [17] Holger K Willig A (2005) Protocols and Architectures for Wireless Sensor Networks London [18] IEEE (2003) Part 154 Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low‐Rate Wireless Personal Area Networks (LR‐WPANs) New York [19] Chipcon CC2420 802154 compliant radio httpwwwchipconcom [20] ZigBee Specification v10 ZigBee Alliance [21] Dimitrios Lymberopoulos Quentin Lindsey and Andreas Savvides ldquoAn Empirical Analysis of Radio Signal Strength Variability in IEEE 802154 Networks using Monopole Antennasrdquo [22] Kiran Yedavalli Bhaskar Krishnamachari Sharmila Ravula and Bhaskar Srinivasan ldquoEcolocation A sequence based technique for RF localization in wireless sensor networksrdquo Robert Bosch Research and Technology Centre Palo Alto CA

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

30

4 OBJECT TRACKING 41 Introduction This chapter presents the object‐tracking application the main objective of this thesis work The application is an algorithmic‐software implementation written in Embedded C which is capable of receiving data messages and generating object position estimation data The implementation of the object‐tracking application makes use of the SENTIO platform and uses the background knowledge introduced in the preceding chapters The motivation for the object‐tracking application is to determine the position of an object in indoor environments (Eg Home) The application acts as an interface which collects the raw data that the sensors generate (numerical values) It makes the proper comparisons and algorithmic processes to identify and track the object while it is static inside the area covered by the wireless sensor network Finally with the detection and tracking data it alerts the user or another system The following sections provide an overview of the object‐tracking application

42 Overview of hardware product Before describing the object tracking application it is important to know the characteristics of hardware product The SENTIO platform (figure 15) is built in modular fashion and has the following key features [8]

Flexibility This platform is very flexible in such a way that it is made easy to add or change the hardware for each sensor task depending on the requirement in the WSN For a specific platform this can be hard or sometimes even impossible to accomplish

Sensor resources Resources in a sensor module includes the ability of usage of controllerrsquos resources as much as possible such as ADC‐channels interrupts and digital Input‐Outputrsquos Local voltage regulation should also be possible since many sensors require higher supply voltages than digital Integrated Circuits do Further support of a shut‐down mechanism to power down the sensor modules when not needed

Computation and memory The processor module based on microcontroller should support many devices in a microcontroller family and support different power supply voltages enabling full range of clock frequencies All power‐down modes of the devices should be

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

31

supported The platform should support different processor modules including FPGArsquos and very simple microcontrollers

Communication The focus should be on enabling an IEEE 802154 compliant communication interface With or without ZigBee stack this will provide a flexible platform for exploring different network topologies Support for power‐down of the entire communication module should be supported

Cost and form factor High flexibility is in conflict with low cost and small size The primary objective should be flexibility but the size should be considered in order to enable sensor device integration in systems where space is limited

Figure 15 SENTIO Platform [8]

The communication subsystem of the SENTIO sensor node was designed so that the correct operation of the radio chip is ensured The radio chip is powered by the on‐board voltage regulator and thus fluctuations in the battery voltage level do not affect the operation of the communication subsystem This platform uses Chipcon CC2420 IEEE 802154 radio transceiver operates in the 24GHz ISM band and includes a digital direct sequence spread spectrum (DSSS) modem providing a spreading gain of 9dB and an effective data rate of 250Kbps It was specifically designed for low power wireless applications and supports 8 discrete power levels 0dBm minus1dBm minus3dBm minus5dBm minus7dBm minus10dBm minus15dBm and minus25dBm at which its power consumption varies from 29mW to 52mW [21] A built‐in received signal strength indicator gives an 8‐bit digital value RSSIVAL The RSSIVAL is always averaged over 8 symbol periods (128μs) and a status bit indicates when the RSSIVAL is valid (meaning that the receiver was enabled for at least 8 symbol periods) The

Antenna

Communication Module

Processing Module

Sensing Module

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

32

power P at the RF pins can be obtained directly from RSSIVAL using the following equation

P = RSSIVAL + RSSIOFFSET [dBm] where the RSSIOFFSET is found empirically from the front‐end gain and it is approximately equal to minus45dBm In the next sections when we refer to the RSSI value we refer to the RSSIVAL and not the power P unless otherwise stated

43 Experimental Testbed The experimental test bed for object tracking application is as shown in figure 16 Experiment was conducted in Masterrsquos room at Mid Sweden University A DELL laptop is connected to SENTIO platform and software were primarily used to collect samples of RSS from APrsquos The software monitors IEEE 802154 radio frequency channels which operate in the 24GHz band

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

1030

BC - 1030

AP 3 AP 1

AP 2

AP 4

BS

HEWLETT

PACKARD

Figure 16 Layout of Masterʹs Room

A three dimensional scalable testbed is used for indoor sensor network deployments The testbed illustrated in figure 16 is a 3‐D structure measuring 16m (L) x 12m (W) x 3m (H) and it is designed to host a large number of static nodes as shown in figure 16 Arrow marks in the above figure represent the location where fingerprints were taken The centrepiece of our infrastructure

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

33

is the SENTIO sensor node [8] an open‐source general purpose sensing platform designed with AT Mega 128 microprocessor and the IEEE 802154 compliant CC2420 radio from Chipcon [19]

44 Obtaining data from the sensor network The next important step in the development of the object tracking application is to obtain the data from the sensor network and send it to the base station From there the data is transmitted through a serial port to the computer that runs the object tracking application When the data arrives at the serial port the application must capture it This paragraph gives a brief explanation of how and what kind data is transmitted by the static object fixed sensors and how this data is being used to estimate the position of the object First the static object transmits Beacon packet continuously which is received by all the fixed APrsquos The beacon packet format is as shown in table 6 Then the fixed APrsquos calculates the 8‐bit value of RSSI from the transmitted beacon packet and then sends the resultant RSSI value as a form of data packet (table 7) to the BS which calculates the position and sends back to the object

45 Object tracking application Testing and Evaluation The purpose of this section is to discuss and evaluate the object tracking application Finally we are ready with the experimental testbed as shown in figure 16 As shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors placed at the corners of the masterrsquos room BS is placed at the centre of the room connected to a PC All the nodes are power connected before an actual measurement takes place At first the object is placed at random position inside the room and transmits beacon packets continuously Then these packets are received by the fixed APrsquos which calculate the RSSI from the packet and then averages it until it receives 50 beacon packets and then transmits the data to the BS (figure 17) Then the BS waits until it receives the data from all the APrsquos Once it receives from all the APrsquos it calculates the position of the object (figure 18) The application was developed to demonstrate a real world use of a WSN in object tracking In evaluating the final position obtained with respect to the actual position we conclude that the object‐tracking was successful Although

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

34

it is a research prototype object tracking application is a simple stable stand‐alone system capable of demonstrating an important application of WSN It takes raw data from the WSN aggregates it evaluates it and based on the evaluation tracks the object The accuracy of estimating the position is less than 5m Table 11 below shows a log of readings taken during test

Start

Intialize micro‐controller and transeciever in receieve mode

If received_packet=

Beacon

Average RSSI values

Increase the counter

If countergt1000050

A

No

Yes

B

B

Yes

No

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

35

A

Reset counter

Set transceiever in transmit mode

Transmit averaged RSSI to Main sensor and set it back to receive mode

10000 samples ‐ Offline

50 samples ‐ Online

Figure 17 Flow chart representing the algorithm of fixed sensors

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

36

Start

Intialize micro‐controller and transeciever in receieve mode

Received RSSI values from all fixed Sensors

Open radio map

Calculate Euclidean distance between obtained RSSI values from fixed sensors and fingerprints

in radio map

No

Yes

Bubble sort for least error in signal strength values

A

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

37

A

Get the location of the object from the radiousing the least error value

Display location of object on PC

End

Figure 18 Flow chart representing the algorithm of main sensor

Table 11 Accuracy of positioning system

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

38

5 RESULTS This chapter presents how the system parameters such as number of samples mean or median etchellipare finalized All the tests are performed in masterrsquos room at Mid Sweden University This chapter is divided into three subsections First in Section 51 it reviews results obtained from samples test Section 52 discusses one dimensional test results and finally section 53 discusses results of two dimensional tests

51 Measurement setup A Dell laptop was connected to a SENTIO platform and is loaded with software (Appendix 2) This setup was used to determine number of samples required during object tracking mean or median position estimation algorithm and one dimensional test The setup is as follows

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

103

0

BC - 1030

AP 1AP 2

BS

HEWLETT

PACKARD

Figure 19 Test bed for one dimensional test in Masters Room

Two SENTIO nodes (AP1 and AP2 as shown above) were separated by a distance of 10 meters and these sensors are known as fixed sensors which are fixed to the ceiling such that there antenna is faced towards the ground They are linear is position A BS was connected to Dell laptop and it was placed in such a way that its antenna was faced towards the ceiling Another SENTIO

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

39

node was used as an object and its orientation was same as BS Figure 19 represents the complete setup for object tracking application Mobile node was moved between these fixed ones at regular intervals of one meter and RSSI values were logged at each interval

52 Number of samples test This test was performed to determine number of samples required for accurate positioning and either mean of the obtained RSSI values or median of them Figure 19 represents the setup for number of samples test Once the setup is ready the measurements are taken as follows

1 An object was tagged with SENTIO node and was placed stationary linear to the fixed sensors

2 10000 samples (RSSI) were taken at regular intervals of 1 meter up to 10 meters

3 The sample values were logged at every interval 4 Using these logged samples I have plotted graphs such as distribution

of RSS values (figure 20 21) meanmedian at intervals of 1 meter using MATLAB

5 The mean and median for various numbers of samples is as shown in figure 22

Figure 20 Histogram plot of RSSI values at 1m and 4m

Figure 21 Histogram plot of RSSI values at 6m and 8m

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

40

Figure 22 Distance vs RSSI

From the above graphs following conclusions were made 1 From the histogram plots we can observe that the distribution of RSSI

values follow Gaussians distribution law 2 As we can see from figure 22 that median curve has drastic changes

when compared to mean curve Hence mean is considered while estimating the position

3 From the observations we conclude that the number of samples does not matter since mean curve tends to be same for various numbers of samples

4 Hence 10000 samples are considered in order to get a proper location fingerprint during off‐line phase and during online phase 50 samples are enough to estimate the position of object

53 0ne dimensional test Measurement setup for one dimensional test is as shown in figure 19 This test was conducted to check the performance of algorithm and how accurate it was At first ie during offline phase fingerprints at each interval averaging 10000 samples were taken During online phase Euclidean algorithm was used estimate the position averaging 50 samples of RSSI From the results we

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

41

can realize that the error was less than 2 meters Table 12 shows the result of location estimation in one dimensional case

Table 12 One dimensional position estimation results

54 Two dimensional test This test is the final stage of my thesis work Experimental testbed for this stage is as shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors fixed at the corners of the room and a BS at the centre of the room There are 18 pre‐determined locations where the fingerprints (RSSI) were taken The fingerprints are as shown in Appendix 1 All the readings were taken in presence of userrsquos The algorithm for position estimation is shown in Appendix 2 Position estimation test is performed as follows

Tagged object is placed at random inside the masterrsquos room All the sensors including the base station and object are switched on Using the algorithm (Appendix 2) the BS estimates the position of the object and displays it on the PC

This test was performed using various numbers of samples to test which has a better accuracy Figure 23 represents the error rate using various numbers of samples From figure 23 we can conclude that 50 numbers of samples is enough to estimate the position of object with an accuracy of 3m

55 Causes of error in location determination The randomness of RSS is the major cause of error in any indoor

positioning system that uses the RSS for location inference If there is no randomness in the RSS and its location fingerprint every indoor positioning systems of this type will have no location detection error For example I have recorded RSS at one particular location during different times of the day From figures 24 and 25 you can observe that RSS values are different at different times of day

The performance of indoor positioning systems depends greatly on the separation of location fingerprints A location fingerprint

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

42

corresponding to a location can be identified correctly if it is difficult to classify it as another fingerprint by a pattern classifier

Path loss is one of the basic signal propagation properties of the RSS that best describes attenuation of transmit signal power over a distance between an access point and a mobile device

Presence of Userrsquos or userrsquos orientation affects RSS

Figure 23 Error Rate vs Number of samples

Figure 24 RSS distribution at 214 pm

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

43

Figure 25 RSS distribution at 100pm and 1100 am

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

6 DISCUSSIONS10102006

44

6 DISCUSSIONS 61 Summary and Conclusion While empirical results and performance studies of positioning systems based on location fingerprint have been presented in this report analytical model that can be used as a framework for efficiently designing the positioning systems are not available This thesis design work uses general principles of position estimation such as position estimation of sensors grid spacing etc This design work focuses on accuracy and precision The concept of location fingerprint technique based on RSS us explained in detail in chapter 2 It is found that RSS is random It is possible to approximate the RSS as being normally distributed While the RSS random process is non‐stationary the mean is more or less constant and could be used as the fingerprint of the location Model designed for position estimation is as shown in chapter 4 Accuracy and precision are the two major performance matrices in this study Uniform grid spacing has been used From the experimental analysis I have obtained an accuracy of 3 meters and 35 precision The sensitivity analysis among the system parameters and the performance matrices were done using the model as shown in chapter 4 For instance our study found that the minimum distance between two adjacent positions also affects the positioning performance This is due to the randomness of RSS patterns induced by the ever changing indoor environment

62 Future work The research in this thesis provides a ground work for the future study of efficient design of indoor positioning system The RSSI based position estimation still has limitations First the RSSI value is time dependent Thus the future study should address this issue Second the movement of object is not considered Third orientations of sensors affect the performance which should be taken care off Fourth to improve the performance of the system we need to find a solution to deal with those incomplete or missing data during the off‐line and the on‐line phase Fifth position estimation on multi‐floor and three‐dimensional coordinates is not available

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 110102006

45

APPENDIX 1

Location Fingerprints of Masters Room Time 1315 Observation Time 20 minutes Number of Samples 10000

People Presence SNo Co-ordinates AP1 (0x0001) AP2 (0x0002) AP3 (0x0004) AP4 (0x0008)

1 11 -24 -16 -27 -33 2 13 -26 -8 -34 -31 3 15 -43 -9 -39 -25 4 32 -21 -11 -27 -33 5 61 -6 -23 -32 -28 6 64 -20 -31 -25 -34 7 35 -23 -35 -29 -38 8 37 -29 -35 -22 -27 9 39 -38 -29 -21 -27 10 67 -27 -23 -20 -23 11 611 -26 -29 -23 -30 12 613 -35 -32 -2 -31 13 513 -34 -31 -20 -26 14 11 -39 -22 -29 -17 15 212 -37 -42 -28 -31 16 -212 -38 -33 -29 -6 17 -21 -42 -29 -33 -19 18 -38 -40 -23 -35 -22

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 210102006

46

APPENDIX 2

Begin

Open radio map and raw data files for write

Intialize radio map in memory

Check whether the

Packet is data or Beacon packet If data packet

Save the data to register

Check if data is

Received from all

sensors

Estimate the position of static

Display the position on PC

End Yes

No

A

A

Yes

No

Flow chart representing the main algorithm for position estimation

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

47

APPENDIX 3

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

48

Distribution of RSSI

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

49

REFERENCES [1] T S Rappaport J H Reed and B D Woerner ldquoPosition location using wireless communications on highways of the futureʺ IEEE Commun Mag vol 34 no 10 pp33‐41 Oct 1996 [2] A M Ladd K E Bekris G Marceau A Rudys L E Kavraki and D S Wallach rdquoRobotics‐based location sensing using wireless ethernetʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ02) 2002 pp 227‐238 [3] I Ramani R Bharadwaja and P V Rangan ldquoLocation tracking for media appliances in wireless home networksʺ in Proc International Conference on Multimedia and Expo (ICMEʹ03) July 2003 pp 769‐772 [4] K Pahlavan X Li and J P Makela ldquoIndoor geolocation science and technologyʺ IEEE Commun Mag vol 40 no 2 pp 112‐118 Feb 2002 [5] Konard Lorincz and Matt Welsh ldquoMote Track A Robust Decentralized Approach to RF‐Based Location Trackingrdquo [6] Paramvir Bahl and Venkata N Padmanabham ldquoRADAR An In‐Building RF‐based User Location and Tracking Systemrdquo [7] Kamol Kaemarungsi ldquoDesign of Indoor positioning system based on location fingerprint techiniquerdquo University of Pittsburgh 2005 [8] Fredrik Linnarsson Cheng Peng and Bengt Oelmann ldquoSENTIO A Hardware Platform for Rapid Prototyping of Wireless Sensor Networksrdquo [9] S Saha K Chaudhuri D Sanghi and P Bhagwat ldquoLocation determination of a mobile device using IEEE 80211b access point signalsʺ in Proc IEEE Wireless Communications and Networking Conference (WCNCʹ03) New Orleans LA Mar 2003 pp 1987‐1992 [10] T Roos P Myllymaki H Tirri P Misikangas and J Sievanen ldquoA probabilistic approach to WLAN user location estimationʺ International Journal of Wireless Information Networks vol 9 no 3 pp 155‐164 July 2002 [11] R Battiti M Brunato and A Villani ldquoStatistical learning theory for location fingerprinting in wireless LANrsquosʺ Technical Report Oct 2002 [Online] Available httprtmscienceunitnitraquobattitiarchive86pdf [12] P Prasithsangaree P Krishnamurthy and P K Chrysanthis ldquoAn indoor position location with wireless LANsʺ in Proc IEEE International Symposium on Personal Indoor and Mobile Radio Communications (PIMRCʹ02) Lisbon Portugal Sept 2002 [13] M A Youssef A Agrawala and A U Shankar ldquoWLAN location determination via clustering and probability distributionsʺ in Proc IEEE

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

50

International Conference on Pervasive Computing and Communications (PerComʹ03) Dallas‐Fort Worth TX Mar 23‐26 2003 pp 23‐26 [14] Z Xiang S Song J Chen H Wang J Huang and X Gao ldquoA wireless LAN‐based indoor positioning technologyʺ IBM Journal of Research and Development vol 48 no 56 pp 617‐626 SeptNov 2004 [15] R Want A Hopper V Falcao and J Gibbons ldquoThe active badge location systemʺACM Transactions on Information Systems vol 40 no 1 pp 91‐102 Jan 1992 [16] N B Priyantha A Chakraborty and H Balakrishnan ldquoThe cricket location‐support systemʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ00) Boston MA Aug 2000 pp 32‐43 [17] Holger K Willig A (2005) Protocols and Architectures for Wireless Sensor Networks London [18] IEEE (2003) Part 154 Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low‐Rate Wireless Personal Area Networks (LR‐WPANs) New York [19] Chipcon CC2420 802154 compliant radio httpwwwchipconcom [20] ZigBee Specification v10 ZigBee Alliance [21] Dimitrios Lymberopoulos Quentin Lindsey and Andreas Savvides ldquoAn Empirical Analysis of Radio Signal Strength Variability in IEEE 802154 Networks using Monopole Antennasrdquo [22] Kiran Yedavalli Bhaskar Krishnamachari Sharmila Ravula and Bhaskar Srinivasan ldquoEcolocation A sequence based technique for RF localization in wireless sensor networksrdquo Robert Bosch Research and Technology Centre Palo Alto CA

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

31

supported The platform should support different processor modules including FPGArsquos and very simple microcontrollers

Communication The focus should be on enabling an IEEE 802154 compliant communication interface With or without ZigBee stack this will provide a flexible platform for exploring different network topologies Support for power‐down of the entire communication module should be supported

Cost and form factor High flexibility is in conflict with low cost and small size The primary objective should be flexibility but the size should be considered in order to enable sensor device integration in systems where space is limited

Figure 15 SENTIO Platform [8]

The communication subsystem of the SENTIO sensor node was designed so that the correct operation of the radio chip is ensured The radio chip is powered by the on‐board voltage regulator and thus fluctuations in the battery voltage level do not affect the operation of the communication subsystem This platform uses Chipcon CC2420 IEEE 802154 radio transceiver operates in the 24GHz ISM band and includes a digital direct sequence spread spectrum (DSSS) modem providing a spreading gain of 9dB and an effective data rate of 250Kbps It was specifically designed for low power wireless applications and supports 8 discrete power levels 0dBm minus1dBm minus3dBm minus5dBm minus7dBm minus10dBm minus15dBm and minus25dBm at which its power consumption varies from 29mW to 52mW [21] A built‐in received signal strength indicator gives an 8‐bit digital value RSSIVAL The RSSIVAL is always averaged over 8 symbol periods (128μs) and a status bit indicates when the RSSIVAL is valid (meaning that the receiver was enabled for at least 8 symbol periods) The

Antenna

Communication Module

Processing Module

Sensing Module

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

32

power P at the RF pins can be obtained directly from RSSIVAL using the following equation

P = RSSIVAL + RSSIOFFSET [dBm] where the RSSIOFFSET is found empirically from the front‐end gain and it is approximately equal to minus45dBm In the next sections when we refer to the RSSI value we refer to the RSSIVAL and not the power P unless otherwise stated

43 Experimental Testbed The experimental test bed for object tracking application is as shown in figure 16 Experiment was conducted in Masterrsquos room at Mid Sweden University A DELL laptop is connected to SENTIO platform and software were primarily used to collect samples of RSS from APrsquos The software monitors IEEE 802154 radio frequency channels which operate in the 24GHz band

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

1030

BC - 1030

AP 3 AP 1

AP 2

AP 4

BS

HEWLETT

PACKARD

Figure 16 Layout of Masterʹs Room

A three dimensional scalable testbed is used for indoor sensor network deployments The testbed illustrated in figure 16 is a 3‐D structure measuring 16m (L) x 12m (W) x 3m (H) and it is designed to host a large number of static nodes as shown in figure 16 Arrow marks in the above figure represent the location where fingerprints were taken The centrepiece of our infrastructure

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

33

is the SENTIO sensor node [8] an open‐source general purpose sensing platform designed with AT Mega 128 microprocessor and the IEEE 802154 compliant CC2420 radio from Chipcon [19]

44 Obtaining data from the sensor network The next important step in the development of the object tracking application is to obtain the data from the sensor network and send it to the base station From there the data is transmitted through a serial port to the computer that runs the object tracking application When the data arrives at the serial port the application must capture it This paragraph gives a brief explanation of how and what kind data is transmitted by the static object fixed sensors and how this data is being used to estimate the position of the object First the static object transmits Beacon packet continuously which is received by all the fixed APrsquos The beacon packet format is as shown in table 6 Then the fixed APrsquos calculates the 8‐bit value of RSSI from the transmitted beacon packet and then sends the resultant RSSI value as a form of data packet (table 7) to the BS which calculates the position and sends back to the object

45 Object tracking application Testing and Evaluation The purpose of this section is to discuss and evaluate the object tracking application Finally we are ready with the experimental testbed as shown in figure 16 As shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors placed at the corners of the masterrsquos room BS is placed at the centre of the room connected to a PC All the nodes are power connected before an actual measurement takes place At first the object is placed at random position inside the room and transmits beacon packets continuously Then these packets are received by the fixed APrsquos which calculate the RSSI from the packet and then averages it until it receives 50 beacon packets and then transmits the data to the BS (figure 17) Then the BS waits until it receives the data from all the APrsquos Once it receives from all the APrsquos it calculates the position of the object (figure 18) The application was developed to demonstrate a real world use of a WSN in object tracking In evaluating the final position obtained with respect to the actual position we conclude that the object‐tracking was successful Although

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

34

it is a research prototype object tracking application is a simple stable stand‐alone system capable of demonstrating an important application of WSN It takes raw data from the WSN aggregates it evaluates it and based on the evaluation tracks the object The accuracy of estimating the position is less than 5m Table 11 below shows a log of readings taken during test

Start

Intialize micro‐controller and transeciever in receieve mode

If received_packet=

Beacon

Average RSSI values

Increase the counter

If countergt1000050

A

No

Yes

B

B

Yes

No

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

35

A

Reset counter

Set transceiever in transmit mode

Transmit averaged RSSI to Main sensor and set it back to receive mode

10000 samples ‐ Offline

50 samples ‐ Online

Figure 17 Flow chart representing the algorithm of fixed sensors

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

36

Start

Intialize micro‐controller and transeciever in receieve mode

Received RSSI values from all fixed Sensors

Open radio map

Calculate Euclidean distance between obtained RSSI values from fixed sensors and fingerprints

in radio map

No

Yes

Bubble sort for least error in signal strength values

A

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

37

A

Get the location of the object from the radiousing the least error value

Display location of object on PC

End

Figure 18 Flow chart representing the algorithm of main sensor

Table 11 Accuracy of positioning system

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

38

5 RESULTS This chapter presents how the system parameters such as number of samples mean or median etchellipare finalized All the tests are performed in masterrsquos room at Mid Sweden University This chapter is divided into three subsections First in Section 51 it reviews results obtained from samples test Section 52 discusses one dimensional test results and finally section 53 discusses results of two dimensional tests

51 Measurement setup A Dell laptop was connected to a SENTIO platform and is loaded with software (Appendix 2) This setup was used to determine number of samples required during object tracking mean or median position estimation algorithm and one dimensional test The setup is as follows

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

103

0

BC - 1030

AP 1AP 2

BS

HEWLETT

PACKARD

Figure 19 Test bed for one dimensional test in Masters Room

Two SENTIO nodes (AP1 and AP2 as shown above) were separated by a distance of 10 meters and these sensors are known as fixed sensors which are fixed to the ceiling such that there antenna is faced towards the ground They are linear is position A BS was connected to Dell laptop and it was placed in such a way that its antenna was faced towards the ceiling Another SENTIO

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

39

node was used as an object and its orientation was same as BS Figure 19 represents the complete setup for object tracking application Mobile node was moved between these fixed ones at regular intervals of one meter and RSSI values were logged at each interval

52 Number of samples test This test was performed to determine number of samples required for accurate positioning and either mean of the obtained RSSI values or median of them Figure 19 represents the setup for number of samples test Once the setup is ready the measurements are taken as follows

1 An object was tagged with SENTIO node and was placed stationary linear to the fixed sensors

2 10000 samples (RSSI) were taken at regular intervals of 1 meter up to 10 meters

3 The sample values were logged at every interval 4 Using these logged samples I have plotted graphs such as distribution

of RSS values (figure 20 21) meanmedian at intervals of 1 meter using MATLAB

5 The mean and median for various numbers of samples is as shown in figure 22

Figure 20 Histogram plot of RSSI values at 1m and 4m

Figure 21 Histogram plot of RSSI values at 6m and 8m

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

40

Figure 22 Distance vs RSSI

From the above graphs following conclusions were made 1 From the histogram plots we can observe that the distribution of RSSI

values follow Gaussians distribution law 2 As we can see from figure 22 that median curve has drastic changes

when compared to mean curve Hence mean is considered while estimating the position

3 From the observations we conclude that the number of samples does not matter since mean curve tends to be same for various numbers of samples

4 Hence 10000 samples are considered in order to get a proper location fingerprint during off‐line phase and during online phase 50 samples are enough to estimate the position of object

53 0ne dimensional test Measurement setup for one dimensional test is as shown in figure 19 This test was conducted to check the performance of algorithm and how accurate it was At first ie during offline phase fingerprints at each interval averaging 10000 samples were taken During online phase Euclidean algorithm was used estimate the position averaging 50 samples of RSSI From the results we

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

41

can realize that the error was less than 2 meters Table 12 shows the result of location estimation in one dimensional case

Table 12 One dimensional position estimation results

54 Two dimensional test This test is the final stage of my thesis work Experimental testbed for this stage is as shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors fixed at the corners of the room and a BS at the centre of the room There are 18 pre‐determined locations where the fingerprints (RSSI) were taken The fingerprints are as shown in Appendix 1 All the readings were taken in presence of userrsquos The algorithm for position estimation is shown in Appendix 2 Position estimation test is performed as follows

Tagged object is placed at random inside the masterrsquos room All the sensors including the base station and object are switched on Using the algorithm (Appendix 2) the BS estimates the position of the object and displays it on the PC

This test was performed using various numbers of samples to test which has a better accuracy Figure 23 represents the error rate using various numbers of samples From figure 23 we can conclude that 50 numbers of samples is enough to estimate the position of object with an accuracy of 3m

55 Causes of error in location determination The randomness of RSS is the major cause of error in any indoor

positioning system that uses the RSS for location inference If there is no randomness in the RSS and its location fingerprint every indoor positioning systems of this type will have no location detection error For example I have recorded RSS at one particular location during different times of the day From figures 24 and 25 you can observe that RSS values are different at different times of day

The performance of indoor positioning systems depends greatly on the separation of location fingerprints A location fingerprint

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

42

corresponding to a location can be identified correctly if it is difficult to classify it as another fingerprint by a pattern classifier

Path loss is one of the basic signal propagation properties of the RSS that best describes attenuation of transmit signal power over a distance between an access point and a mobile device

Presence of Userrsquos or userrsquos orientation affects RSS

Figure 23 Error Rate vs Number of samples

Figure 24 RSS distribution at 214 pm

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

43

Figure 25 RSS distribution at 100pm and 1100 am

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

6 DISCUSSIONS10102006

44

6 DISCUSSIONS 61 Summary and Conclusion While empirical results and performance studies of positioning systems based on location fingerprint have been presented in this report analytical model that can be used as a framework for efficiently designing the positioning systems are not available This thesis design work uses general principles of position estimation such as position estimation of sensors grid spacing etc This design work focuses on accuracy and precision The concept of location fingerprint technique based on RSS us explained in detail in chapter 2 It is found that RSS is random It is possible to approximate the RSS as being normally distributed While the RSS random process is non‐stationary the mean is more or less constant and could be used as the fingerprint of the location Model designed for position estimation is as shown in chapter 4 Accuracy and precision are the two major performance matrices in this study Uniform grid spacing has been used From the experimental analysis I have obtained an accuracy of 3 meters and 35 precision The sensitivity analysis among the system parameters and the performance matrices were done using the model as shown in chapter 4 For instance our study found that the minimum distance between two adjacent positions also affects the positioning performance This is due to the randomness of RSS patterns induced by the ever changing indoor environment

62 Future work The research in this thesis provides a ground work for the future study of efficient design of indoor positioning system The RSSI based position estimation still has limitations First the RSSI value is time dependent Thus the future study should address this issue Second the movement of object is not considered Third orientations of sensors affect the performance which should be taken care off Fourth to improve the performance of the system we need to find a solution to deal with those incomplete or missing data during the off‐line and the on‐line phase Fifth position estimation on multi‐floor and three‐dimensional coordinates is not available

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 110102006

45

APPENDIX 1

Location Fingerprints of Masters Room Time 1315 Observation Time 20 minutes Number of Samples 10000

People Presence SNo Co-ordinates AP1 (0x0001) AP2 (0x0002) AP3 (0x0004) AP4 (0x0008)

1 11 -24 -16 -27 -33 2 13 -26 -8 -34 -31 3 15 -43 -9 -39 -25 4 32 -21 -11 -27 -33 5 61 -6 -23 -32 -28 6 64 -20 -31 -25 -34 7 35 -23 -35 -29 -38 8 37 -29 -35 -22 -27 9 39 -38 -29 -21 -27 10 67 -27 -23 -20 -23 11 611 -26 -29 -23 -30 12 613 -35 -32 -2 -31 13 513 -34 -31 -20 -26 14 11 -39 -22 -29 -17 15 212 -37 -42 -28 -31 16 -212 -38 -33 -29 -6 17 -21 -42 -29 -33 -19 18 -38 -40 -23 -35 -22

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 210102006

46

APPENDIX 2

Begin

Open radio map and raw data files for write

Intialize radio map in memory

Check whether the

Packet is data or Beacon packet If data packet

Save the data to register

Check if data is

Received from all

sensors

Estimate the position of static

Display the position on PC

End Yes

No

A

A

Yes

No

Flow chart representing the main algorithm for position estimation

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

47

APPENDIX 3

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

48

Distribution of RSSI

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

49

REFERENCES [1] T S Rappaport J H Reed and B D Woerner ldquoPosition location using wireless communications on highways of the futureʺ IEEE Commun Mag vol 34 no 10 pp33‐41 Oct 1996 [2] A M Ladd K E Bekris G Marceau A Rudys L E Kavraki and D S Wallach rdquoRobotics‐based location sensing using wireless ethernetʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ02) 2002 pp 227‐238 [3] I Ramani R Bharadwaja and P V Rangan ldquoLocation tracking for media appliances in wireless home networksʺ in Proc International Conference on Multimedia and Expo (ICMEʹ03) July 2003 pp 769‐772 [4] K Pahlavan X Li and J P Makela ldquoIndoor geolocation science and technologyʺ IEEE Commun Mag vol 40 no 2 pp 112‐118 Feb 2002 [5] Konard Lorincz and Matt Welsh ldquoMote Track A Robust Decentralized Approach to RF‐Based Location Trackingrdquo [6] Paramvir Bahl and Venkata N Padmanabham ldquoRADAR An In‐Building RF‐based User Location and Tracking Systemrdquo [7] Kamol Kaemarungsi ldquoDesign of Indoor positioning system based on location fingerprint techiniquerdquo University of Pittsburgh 2005 [8] Fredrik Linnarsson Cheng Peng and Bengt Oelmann ldquoSENTIO A Hardware Platform for Rapid Prototyping of Wireless Sensor Networksrdquo [9] S Saha K Chaudhuri D Sanghi and P Bhagwat ldquoLocation determination of a mobile device using IEEE 80211b access point signalsʺ in Proc IEEE Wireless Communications and Networking Conference (WCNCʹ03) New Orleans LA Mar 2003 pp 1987‐1992 [10] T Roos P Myllymaki H Tirri P Misikangas and J Sievanen ldquoA probabilistic approach to WLAN user location estimationʺ International Journal of Wireless Information Networks vol 9 no 3 pp 155‐164 July 2002 [11] R Battiti M Brunato and A Villani ldquoStatistical learning theory for location fingerprinting in wireless LANrsquosʺ Technical Report Oct 2002 [Online] Available httprtmscienceunitnitraquobattitiarchive86pdf [12] P Prasithsangaree P Krishnamurthy and P K Chrysanthis ldquoAn indoor position location with wireless LANsʺ in Proc IEEE International Symposium on Personal Indoor and Mobile Radio Communications (PIMRCʹ02) Lisbon Portugal Sept 2002 [13] M A Youssef A Agrawala and A U Shankar ldquoWLAN location determination via clustering and probability distributionsʺ in Proc IEEE

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

50

International Conference on Pervasive Computing and Communications (PerComʹ03) Dallas‐Fort Worth TX Mar 23‐26 2003 pp 23‐26 [14] Z Xiang S Song J Chen H Wang J Huang and X Gao ldquoA wireless LAN‐based indoor positioning technologyʺ IBM Journal of Research and Development vol 48 no 56 pp 617‐626 SeptNov 2004 [15] R Want A Hopper V Falcao and J Gibbons ldquoThe active badge location systemʺACM Transactions on Information Systems vol 40 no 1 pp 91‐102 Jan 1992 [16] N B Priyantha A Chakraborty and H Balakrishnan ldquoThe cricket location‐support systemʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ00) Boston MA Aug 2000 pp 32‐43 [17] Holger K Willig A (2005) Protocols and Architectures for Wireless Sensor Networks London [18] IEEE (2003) Part 154 Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low‐Rate Wireless Personal Area Networks (LR‐WPANs) New York [19] Chipcon CC2420 802154 compliant radio httpwwwchipconcom [20] ZigBee Specification v10 ZigBee Alliance [21] Dimitrios Lymberopoulos Quentin Lindsey and Andreas Savvides ldquoAn Empirical Analysis of Radio Signal Strength Variability in IEEE 802154 Networks using Monopole Antennasrdquo [22] Kiran Yedavalli Bhaskar Krishnamachari Sharmila Ravula and Bhaskar Srinivasan ldquoEcolocation A sequence based technique for RF localization in wireless sensor networksrdquo Robert Bosch Research and Technology Centre Palo Alto CA

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

32

power P at the RF pins can be obtained directly from RSSIVAL using the following equation

P = RSSIVAL + RSSIOFFSET [dBm] where the RSSIOFFSET is found empirically from the front‐end gain and it is approximately equal to minus45dBm In the next sections when we refer to the RSSI value we refer to the RSSIVAL and not the power P unless otherwise stated

43 Experimental Testbed The experimental test bed for object tracking application is as shown in figure 16 Experiment was conducted in Masterrsquos room at Mid Sweden University A DELL laptop is connected to SENTIO platform and software were primarily used to collect samples of RSS from APrsquos The software monitors IEEE 802154 radio frequency channels which operate in the 24GHz band

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

1030

BC - 1030

AP 3 AP 1

AP 2

AP 4

BS

HEWLETT

PACKARD

Figure 16 Layout of Masterʹs Room

A three dimensional scalable testbed is used for indoor sensor network deployments The testbed illustrated in figure 16 is a 3‐D structure measuring 16m (L) x 12m (W) x 3m (H) and it is designed to host a large number of static nodes as shown in figure 16 Arrow marks in the above figure represent the location where fingerprints were taken The centrepiece of our infrastructure

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

33

is the SENTIO sensor node [8] an open‐source general purpose sensing platform designed with AT Mega 128 microprocessor and the IEEE 802154 compliant CC2420 radio from Chipcon [19]

44 Obtaining data from the sensor network The next important step in the development of the object tracking application is to obtain the data from the sensor network and send it to the base station From there the data is transmitted through a serial port to the computer that runs the object tracking application When the data arrives at the serial port the application must capture it This paragraph gives a brief explanation of how and what kind data is transmitted by the static object fixed sensors and how this data is being used to estimate the position of the object First the static object transmits Beacon packet continuously which is received by all the fixed APrsquos The beacon packet format is as shown in table 6 Then the fixed APrsquos calculates the 8‐bit value of RSSI from the transmitted beacon packet and then sends the resultant RSSI value as a form of data packet (table 7) to the BS which calculates the position and sends back to the object

45 Object tracking application Testing and Evaluation The purpose of this section is to discuss and evaluate the object tracking application Finally we are ready with the experimental testbed as shown in figure 16 As shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors placed at the corners of the masterrsquos room BS is placed at the centre of the room connected to a PC All the nodes are power connected before an actual measurement takes place At first the object is placed at random position inside the room and transmits beacon packets continuously Then these packets are received by the fixed APrsquos which calculate the RSSI from the packet and then averages it until it receives 50 beacon packets and then transmits the data to the BS (figure 17) Then the BS waits until it receives the data from all the APrsquos Once it receives from all the APrsquos it calculates the position of the object (figure 18) The application was developed to demonstrate a real world use of a WSN in object tracking In evaluating the final position obtained with respect to the actual position we conclude that the object‐tracking was successful Although

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

34

it is a research prototype object tracking application is a simple stable stand‐alone system capable of demonstrating an important application of WSN It takes raw data from the WSN aggregates it evaluates it and based on the evaluation tracks the object The accuracy of estimating the position is less than 5m Table 11 below shows a log of readings taken during test

Start

Intialize micro‐controller and transeciever in receieve mode

If received_packet=

Beacon

Average RSSI values

Increase the counter

If countergt1000050

A

No

Yes

B

B

Yes

No

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

35

A

Reset counter

Set transceiever in transmit mode

Transmit averaged RSSI to Main sensor and set it back to receive mode

10000 samples ‐ Offline

50 samples ‐ Online

Figure 17 Flow chart representing the algorithm of fixed sensors

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

36

Start

Intialize micro‐controller and transeciever in receieve mode

Received RSSI values from all fixed Sensors

Open radio map

Calculate Euclidean distance between obtained RSSI values from fixed sensors and fingerprints

in radio map

No

Yes

Bubble sort for least error in signal strength values

A

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

37

A

Get the location of the object from the radiousing the least error value

Display location of object on PC

End

Figure 18 Flow chart representing the algorithm of main sensor

Table 11 Accuracy of positioning system

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

38

5 RESULTS This chapter presents how the system parameters such as number of samples mean or median etchellipare finalized All the tests are performed in masterrsquos room at Mid Sweden University This chapter is divided into three subsections First in Section 51 it reviews results obtained from samples test Section 52 discusses one dimensional test results and finally section 53 discusses results of two dimensional tests

51 Measurement setup A Dell laptop was connected to a SENTIO platform and is loaded with software (Appendix 2) This setup was used to determine number of samples required during object tracking mean or median position estimation algorithm and one dimensional test The setup is as follows

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

103

0

BC - 1030

AP 1AP 2

BS

HEWLETT

PACKARD

Figure 19 Test bed for one dimensional test in Masters Room

Two SENTIO nodes (AP1 and AP2 as shown above) were separated by a distance of 10 meters and these sensors are known as fixed sensors which are fixed to the ceiling such that there antenna is faced towards the ground They are linear is position A BS was connected to Dell laptop and it was placed in such a way that its antenna was faced towards the ceiling Another SENTIO

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

39

node was used as an object and its orientation was same as BS Figure 19 represents the complete setup for object tracking application Mobile node was moved between these fixed ones at regular intervals of one meter and RSSI values were logged at each interval

52 Number of samples test This test was performed to determine number of samples required for accurate positioning and either mean of the obtained RSSI values or median of them Figure 19 represents the setup for number of samples test Once the setup is ready the measurements are taken as follows

1 An object was tagged with SENTIO node and was placed stationary linear to the fixed sensors

2 10000 samples (RSSI) were taken at regular intervals of 1 meter up to 10 meters

3 The sample values were logged at every interval 4 Using these logged samples I have plotted graphs such as distribution

of RSS values (figure 20 21) meanmedian at intervals of 1 meter using MATLAB

5 The mean and median for various numbers of samples is as shown in figure 22

Figure 20 Histogram plot of RSSI values at 1m and 4m

Figure 21 Histogram plot of RSSI values at 6m and 8m

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

40

Figure 22 Distance vs RSSI

From the above graphs following conclusions were made 1 From the histogram plots we can observe that the distribution of RSSI

values follow Gaussians distribution law 2 As we can see from figure 22 that median curve has drastic changes

when compared to mean curve Hence mean is considered while estimating the position

3 From the observations we conclude that the number of samples does not matter since mean curve tends to be same for various numbers of samples

4 Hence 10000 samples are considered in order to get a proper location fingerprint during off‐line phase and during online phase 50 samples are enough to estimate the position of object

53 0ne dimensional test Measurement setup for one dimensional test is as shown in figure 19 This test was conducted to check the performance of algorithm and how accurate it was At first ie during offline phase fingerprints at each interval averaging 10000 samples were taken During online phase Euclidean algorithm was used estimate the position averaging 50 samples of RSSI From the results we

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

41

can realize that the error was less than 2 meters Table 12 shows the result of location estimation in one dimensional case

Table 12 One dimensional position estimation results

54 Two dimensional test This test is the final stage of my thesis work Experimental testbed for this stage is as shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors fixed at the corners of the room and a BS at the centre of the room There are 18 pre‐determined locations where the fingerprints (RSSI) were taken The fingerprints are as shown in Appendix 1 All the readings were taken in presence of userrsquos The algorithm for position estimation is shown in Appendix 2 Position estimation test is performed as follows

Tagged object is placed at random inside the masterrsquos room All the sensors including the base station and object are switched on Using the algorithm (Appendix 2) the BS estimates the position of the object and displays it on the PC

This test was performed using various numbers of samples to test which has a better accuracy Figure 23 represents the error rate using various numbers of samples From figure 23 we can conclude that 50 numbers of samples is enough to estimate the position of object with an accuracy of 3m

55 Causes of error in location determination The randomness of RSS is the major cause of error in any indoor

positioning system that uses the RSS for location inference If there is no randomness in the RSS and its location fingerprint every indoor positioning systems of this type will have no location detection error For example I have recorded RSS at one particular location during different times of the day From figures 24 and 25 you can observe that RSS values are different at different times of day

The performance of indoor positioning systems depends greatly on the separation of location fingerprints A location fingerprint

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

42

corresponding to a location can be identified correctly if it is difficult to classify it as another fingerprint by a pattern classifier

Path loss is one of the basic signal propagation properties of the RSS that best describes attenuation of transmit signal power over a distance between an access point and a mobile device

Presence of Userrsquos or userrsquos orientation affects RSS

Figure 23 Error Rate vs Number of samples

Figure 24 RSS distribution at 214 pm

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

43

Figure 25 RSS distribution at 100pm and 1100 am

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

6 DISCUSSIONS10102006

44

6 DISCUSSIONS 61 Summary and Conclusion While empirical results and performance studies of positioning systems based on location fingerprint have been presented in this report analytical model that can be used as a framework for efficiently designing the positioning systems are not available This thesis design work uses general principles of position estimation such as position estimation of sensors grid spacing etc This design work focuses on accuracy and precision The concept of location fingerprint technique based on RSS us explained in detail in chapter 2 It is found that RSS is random It is possible to approximate the RSS as being normally distributed While the RSS random process is non‐stationary the mean is more or less constant and could be used as the fingerprint of the location Model designed for position estimation is as shown in chapter 4 Accuracy and precision are the two major performance matrices in this study Uniform grid spacing has been used From the experimental analysis I have obtained an accuracy of 3 meters and 35 precision The sensitivity analysis among the system parameters and the performance matrices were done using the model as shown in chapter 4 For instance our study found that the minimum distance between two adjacent positions also affects the positioning performance This is due to the randomness of RSS patterns induced by the ever changing indoor environment

62 Future work The research in this thesis provides a ground work for the future study of efficient design of indoor positioning system The RSSI based position estimation still has limitations First the RSSI value is time dependent Thus the future study should address this issue Second the movement of object is not considered Third orientations of sensors affect the performance which should be taken care off Fourth to improve the performance of the system we need to find a solution to deal with those incomplete or missing data during the off‐line and the on‐line phase Fifth position estimation on multi‐floor and three‐dimensional coordinates is not available

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 110102006

45

APPENDIX 1

Location Fingerprints of Masters Room Time 1315 Observation Time 20 minutes Number of Samples 10000

People Presence SNo Co-ordinates AP1 (0x0001) AP2 (0x0002) AP3 (0x0004) AP4 (0x0008)

1 11 -24 -16 -27 -33 2 13 -26 -8 -34 -31 3 15 -43 -9 -39 -25 4 32 -21 -11 -27 -33 5 61 -6 -23 -32 -28 6 64 -20 -31 -25 -34 7 35 -23 -35 -29 -38 8 37 -29 -35 -22 -27 9 39 -38 -29 -21 -27 10 67 -27 -23 -20 -23 11 611 -26 -29 -23 -30 12 613 -35 -32 -2 -31 13 513 -34 -31 -20 -26 14 11 -39 -22 -29 -17 15 212 -37 -42 -28 -31 16 -212 -38 -33 -29 -6 17 -21 -42 -29 -33 -19 18 -38 -40 -23 -35 -22

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 210102006

46

APPENDIX 2

Begin

Open radio map and raw data files for write

Intialize radio map in memory

Check whether the

Packet is data or Beacon packet If data packet

Save the data to register

Check if data is

Received from all

sensors

Estimate the position of static

Display the position on PC

End Yes

No

A

A

Yes

No

Flow chart representing the main algorithm for position estimation

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

47

APPENDIX 3

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

48

Distribution of RSSI

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

49

REFERENCES [1] T S Rappaport J H Reed and B D Woerner ldquoPosition location using wireless communications on highways of the futureʺ IEEE Commun Mag vol 34 no 10 pp33‐41 Oct 1996 [2] A M Ladd K E Bekris G Marceau A Rudys L E Kavraki and D S Wallach rdquoRobotics‐based location sensing using wireless ethernetʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ02) 2002 pp 227‐238 [3] I Ramani R Bharadwaja and P V Rangan ldquoLocation tracking for media appliances in wireless home networksʺ in Proc International Conference on Multimedia and Expo (ICMEʹ03) July 2003 pp 769‐772 [4] K Pahlavan X Li and J P Makela ldquoIndoor geolocation science and technologyʺ IEEE Commun Mag vol 40 no 2 pp 112‐118 Feb 2002 [5] Konard Lorincz and Matt Welsh ldquoMote Track A Robust Decentralized Approach to RF‐Based Location Trackingrdquo [6] Paramvir Bahl and Venkata N Padmanabham ldquoRADAR An In‐Building RF‐based User Location and Tracking Systemrdquo [7] Kamol Kaemarungsi ldquoDesign of Indoor positioning system based on location fingerprint techiniquerdquo University of Pittsburgh 2005 [8] Fredrik Linnarsson Cheng Peng and Bengt Oelmann ldquoSENTIO A Hardware Platform for Rapid Prototyping of Wireless Sensor Networksrdquo [9] S Saha K Chaudhuri D Sanghi and P Bhagwat ldquoLocation determination of a mobile device using IEEE 80211b access point signalsʺ in Proc IEEE Wireless Communications and Networking Conference (WCNCʹ03) New Orleans LA Mar 2003 pp 1987‐1992 [10] T Roos P Myllymaki H Tirri P Misikangas and J Sievanen ldquoA probabilistic approach to WLAN user location estimationʺ International Journal of Wireless Information Networks vol 9 no 3 pp 155‐164 July 2002 [11] R Battiti M Brunato and A Villani ldquoStatistical learning theory for location fingerprinting in wireless LANrsquosʺ Technical Report Oct 2002 [Online] Available httprtmscienceunitnitraquobattitiarchive86pdf [12] P Prasithsangaree P Krishnamurthy and P K Chrysanthis ldquoAn indoor position location with wireless LANsʺ in Proc IEEE International Symposium on Personal Indoor and Mobile Radio Communications (PIMRCʹ02) Lisbon Portugal Sept 2002 [13] M A Youssef A Agrawala and A U Shankar ldquoWLAN location determination via clustering and probability distributionsʺ in Proc IEEE

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

50

International Conference on Pervasive Computing and Communications (PerComʹ03) Dallas‐Fort Worth TX Mar 23‐26 2003 pp 23‐26 [14] Z Xiang S Song J Chen H Wang J Huang and X Gao ldquoA wireless LAN‐based indoor positioning technologyʺ IBM Journal of Research and Development vol 48 no 56 pp 617‐626 SeptNov 2004 [15] R Want A Hopper V Falcao and J Gibbons ldquoThe active badge location systemʺACM Transactions on Information Systems vol 40 no 1 pp 91‐102 Jan 1992 [16] N B Priyantha A Chakraborty and H Balakrishnan ldquoThe cricket location‐support systemʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ00) Boston MA Aug 2000 pp 32‐43 [17] Holger K Willig A (2005) Protocols and Architectures for Wireless Sensor Networks London [18] IEEE (2003) Part 154 Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low‐Rate Wireless Personal Area Networks (LR‐WPANs) New York [19] Chipcon CC2420 802154 compliant radio httpwwwchipconcom [20] ZigBee Specification v10 ZigBee Alliance [21] Dimitrios Lymberopoulos Quentin Lindsey and Andreas Savvides ldquoAn Empirical Analysis of Radio Signal Strength Variability in IEEE 802154 Networks using Monopole Antennasrdquo [22] Kiran Yedavalli Bhaskar Krishnamachari Sharmila Ravula and Bhaskar Srinivasan ldquoEcolocation A sequence based technique for RF localization in wireless sensor networksrdquo Robert Bosch Research and Technology Centre Palo Alto CA

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

33

is the SENTIO sensor node [8] an open‐source general purpose sensing platform designed with AT Mega 128 microprocessor and the IEEE 802154 compliant CC2420 radio from Chipcon [19]

44 Obtaining data from the sensor network The next important step in the development of the object tracking application is to obtain the data from the sensor network and send it to the base station From there the data is transmitted through a serial port to the computer that runs the object tracking application When the data arrives at the serial port the application must capture it This paragraph gives a brief explanation of how and what kind data is transmitted by the static object fixed sensors and how this data is being used to estimate the position of the object First the static object transmits Beacon packet continuously which is received by all the fixed APrsquos The beacon packet format is as shown in table 6 Then the fixed APrsquos calculates the 8‐bit value of RSSI from the transmitted beacon packet and then sends the resultant RSSI value as a form of data packet (table 7) to the BS which calculates the position and sends back to the object

45 Object tracking application Testing and Evaluation The purpose of this section is to discuss and evaluate the object tracking application Finally we are ready with the experimental testbed as shown in figure 16 As shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors placed at the corners of the masterrsquos room BS is placed at the centre of the room connected to a PC All the nodes are power connected before an actual measurement takes place At first the object is placed at random position inside the room and transmits beacon packets continuously Then these packets are received by the fixed APrsquos which calculate the RSSI from the packet and then averages it until it receives 50 beacon packets and then transmits the data to the BS (figure 17) Then the BS waits until it receives the data from all the APrsquos Once it receives from all the APrsquos it calculates the position of the object (figure 18) The application was developed to demonstrate a real world use of a WSN in object tracking In evaluating the final position obtained with respect to the actual position we conclude that the object‐tracking was successful Although

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

34

it is a research prototype object tracking application is a simple stable stand‐alone system capable of demonstrating an important application of WSN It takes raw data from the WSN aggregates it evaluates it and based on the evaluation tracks the object The accuracy of estimating the position is less than 5m Table 11 below shows a log of readings taken during test

Start

Intialize micro‐controller and transeciever in receieve mode

If received_packet=

Beacon

Average RSSI values

Increase the counter

If countergt1000050

A

No

Yes

B

B

Yes

No

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

35

A

Reset counter

Set transceiever in transmit mode

Transmit averaged RSSI to Main sensor and set it back to receive mode

10000 samples ‐ Offline

50 samples ‐ Online

Figure 17 Flow chart representing the algorithm of fixed sensors

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

36

Start

Intialize micro‐controller and transeciever in receieve mode

Received RSSI values from all fixed Sensors

Open radio map

Calculate Euclidean distance between obtained RSSI values from fixed sensors and fingerprints

in radio map

No

Yes

Bubble sort for least error in signal strength values

A

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

37

A

Get the location of the object from the radiousing the least error value

Display location of object on PC

End

Figure 18 Flow chart representing the algorithm of main sensor

Table 11 Accuracy of positioning system

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

38

5 RESULTS This chapter presents how the system parameters such as number of samples mean or median etchellipare finalized All the tests are performed in masterrsquos room at Mid Sweden University This chapter is divided into three subsections First in Section 51 it reviews results obtained from samples test Section 52 discusses one dimensional test results and finally section 53 discusses results of two dimensional tests

51 Measurement setup A Dell laptop was connected to a SENTIO platform and is loaded with software (Appendix 2) This setup was used to determine number of samples required during object tracking mean or median position estimation algorithm and one dimensional test The setup is as follows

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

103

0

BC - 1030

AP 1AP 2

BS

HEWLETT

PACKARD

Figure 19 Test bed for one dimensional test in Masters Room

Two SENTIO nodes (AP1 and AP2 as shown above) were separated by a distance of 10 meters and these sensors are known as fixed sensors which are fixed to the ceiling such that there antenna is faced towards the ground They are linear is position A BS was connected to Dell laptop and it was placed in such a way that its antenna was faced towards the ceiling Another SENTIO

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

39

node was used as an object and its orientation was same as BS Figure 19 represents the complete setup for object tracking application Mobile node was moved between these fixed ones at regular intervals of one meter and RSSI values were logged at each interval

52 Number of samples test This test was performed to determine number of samples required for accurate positioning and either mean of the obtained RSSI values or median of them Figure 19 represents the setup for number of samples test Once the setup is ready the measurements are taken as follows

1 An object was tagged with SENTIO node and was placed stationary linear to the fixed sensors

2 10000 samples (RSSI) were taken at regular intervals of 1 meter up to 10 meters

3 The sample values were logged at every interval 4 Using these logged samples I have plotted graphs such as distribution

of RSS values (figure 20 21) meanmedian at intervals of 1 meter using MATLAB

5 The mean and median for various numbers of samples is as shown in figure 22

Figure 20 Histogram plot of RSSI values at 1m and 4m

Figure 21 Histogram plot of RSSI values at 6m and 8m

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

40

Figure 22 Distance vs RSSI

From the above graphs following conclusions were made 1 From the histogram plots we can observe that the distribution of RSSI

values follow Gaussians distribution law 2 As we can see from figure 22 that median curve has drastic changes

when compared to mean curve Hence mean is considered while estimating the position

3 From the observations we conclude that the number of samples does not matter since mean curve tends to be same for various numbers of samples

4 Hence 10000 samples are considered in order to get a proper location fingerprint during off‐line phase and during online phase 50 samples are enough to estimate the position of object

53 0ne dimensional test Measurement setup for one dimensional test is as shown in figure 19 This test was conducted to check the performance of algorithm and how accurate it was At first ie during offline phase fingerprints at each interval averaging 10000 samples were taken During online phase Euclidean algorithm was used estimate the position averaging 50 samples of RSSI From the results we

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

41

can realize that the error was less than 2 meters Table 12 shows the result of location estimation in one dimensional case

Table 12 One dimensional position estimation results

54 Two dimensional test This test is the final stage of my thesis work Experimental testbed for this stage is as shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors fixed at the corners of the room and a BS at the centre of the room There are 18 pre‐determined locations where the fingerprints (RSSI) were taken The fingerprints are as shown in Appendix 1 All the readings were taken in presence of userrsquos The algorithm for position estimation is shown in Appendix 2 Position estimation test is performed as follows

Tagged object is placed at random inside the masterrsquos room All the sensors including the base station and object are switched on Using the algorithm (Appendix 2) the BS estimates the position of the object and displays it on the PC

This test was performed using various numbers of samples to test which has a better accuracy Figure 23 represents the error rate using various numbers of samples From figure 23 we can conclude that 50 numbers of samples is enough to estimate the position of object with an accuracy of 3m

55 Causes of error in location determination The randomness of RSS is the major cause of error in any indoor

positioning system that uses the RSS for location inference If there is no randomness in the RSS and its location fingerprint every indoor positioning systems of this type will have no location detection error For example I have recorded RSS at one particular location during different times of the day From figures 24 and 25 you can observe that RSS values are different at different times of day

The performance of indoor positioning systems depends greatly on the separation of location fingerprints A location fingerprint

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

42

corresponding to a location can be identified correctly if it is difficult to classify it as another fingerprint by a pattern classifier

Path loss is one of the basic signal propagation properties of the RSS that best describes attenuation of transmit signal power over a distance between an access point and a mobile device

Presence of Userrsquos or userrsquos orientation affects RSS

Figure 23 Error Rate vs Number of samples

Figure 24 RSS distribution at 214 pm

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

43

Figure 25 RSS distribution at 100pm and 1100 am

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

6 DISCUSSIONS10102006

44

6 DISCUSSIONS 61 Summary and Conclusion While empirical results and performance studies of positioning systems based on location fingerprint have been presented in this report analytical model that can be used as a framework for efficiently designing the positioning systems are not available This thesis design work uses general principles of position estimation such as position estimation of sensors grid spacing etc This design work focuses on accuracy and precision The concept of location fingerprint technique based on RSS us explained in detail in chapter 2 It is found that RSS is random It is possible to approximate the RSS as being normally distributed While the RSS random process is non‐stationary the mean is more or less constant and could be used as the fingerprint of the location Model designed for position estimation is as shown in chapter 4 Accuracy and precision are the two major performance matrices in this study Uniform grid spacing has been used From the experimental analysis I have obtained an accuracy of 3 meters and 35 precision The sensitivity analysis among the system parameters and the performance matrices were done using the model as shown in chapter 4 For instance our study found that the minimum distance between two adjacent positions also affects the positioning performance This is due to the randomness of RSS patterns induced by the ever changing indoor environment

62 Future work The research in this thesis provides a ground work for the future study of efficient design of indoor positioning system The RSSI based position estimation still has limitations First the RSSI value is time dependent Thus the future study should address this issue Second the movement of object is not considered Third orientations of sensors affect the performance which should be taken care off Fourth to improve the performance of the system we need to find a solution to deal with those incomplete or missing data during the off‐line and the on‐line phase Fifth position estimation on multi‐floor and three‐dimensional coordinates is not available

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 110102006

45

APPENDIX 1

Location Fingerprints of Masters Room Time 1315 Observation Time 20 minutes Number of Samples 10000

People Presence SNo Co-ordinates AP1 (0x0001) AP2 (0x0002) AP3 (0x0004) AP4 (0x0008)

1 11 -24 -16 -27 -33 2 13 -26 -8 -34 -31 3 15 -43 -9 -39 -25 4 32 -21 -11 -27 -33 5 61 -6 -23 -32 -28 6 64 -20 -31 -25 -34 7 35 -23 -35 -29 -38 8 37 -29 -35 -22 -27 9 39 -38 -29 -21 -27 10 67 -27 -23 -20 -23 11 611 -26 -29 -23 -30 12 613 -35 -32 -2 -31 13 513 -34 -31 -20 -26 14 11 -39 -22 -29 -17 15 212 -37 -42 -28 -31 16 -212 -38 -33 -29 -6 17 -21 -42 -29 -33 -19 18 -38 -40 -23 -35 -22

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 210102006

46

APPENDIX 2

Begin

Open radio map and raw data files for write

Intialize radio map in memory

Check whether the

Packet is data or Beacon packet If data packet

Save the data to register

Check if data is

Received from all

sensors

Estimate the position of static

Display the position on PC

End Yes

No

A

A

Yes

No

Flow chart representing the main algorithm for position estimation

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

47

APPENDIX 3

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

48

Distribution of RSSI

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

49

REFERENCES [1] T S Rappaport J H Reed and B D Woerner ldquoPosition location using wireless communications on highways of the futureʺ IEEE Commun Mag vol 34 no 10 pp33‐41 Oct 1996 [2] A M Ladd K E Bekris G Marceau A Rudys L E Kavraki and D S Wallach rdquoRobotics‐based location sensing using wireless ethernetʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ02) 2002 pp 227‐238 [3] I Ramani R Bharadwaja and P V Rangan ldquoLocation tracking for media appliances in wireless home networksʺ in Proc International Conference on Multimedia and Expo (ICMEʹ03) July 2003 pp 769‐772 [4] K Pahlavan X Li and J P Makela ldquoIndoor geolocation science and technologyʺ IEEE Commun Mag vol 40 no 2 pp 112‐118 Feb 2002 [5] Konard Lorincz and Matt Welsh ldquoMote Track A Robust Decentralized Approach to RF‐Based Location Trackingrdquo [6] Paramvir Bahl and Venkata N Padmanabham ldquoRADAR An In‐Building RF‐based User Location and Tracking Systemrdquo [7] Kamol Kaemarungsi ldquoDesign of Indoor positioning system based on location fingerprint techiniquerdquo University of Pittsburgh 2005 [8] Fredrik Linnarsson Cheng Peng and Bengt Oelmann ldquoSENTIO A Hardware Platform for Rapid Prototyping of Wireless Sensor Networksrdquo [9] S Saha K Chaudhuri D Sanghi and P Bhagwat ldquoLocation determination of a mobile device using IEEE 80211b access point signalsʺ in Proc IEEE Wireless Communications and Networking Conference (WCNCʹ03) New Orleans LA Mar 2003 pp 1987‐1992 [10] T Roos P Myllymaki H Tirri P Misikangas and J Sievanen ldquoA probabilistic approach to WLAN user location estimationʺ International Journal of Wireless Information Networks vol 9 no 3 pp 155‐164 July 2002 [11] R Battiti M Brunato and A Villani ldquoStatistical learning theory for location fingerprinting in wireless LANrsquosʺ Technical Report Oct 2002 [Online] Available httprtmscienceunitnitraquobattitiarchive86pdf [12] P Prasithsangaree P Krishnamurthy and P K Chrysanthis ldquoAn indoor position location with wireless LANsʺ in Proc IEEE International Symposium on Personal Indoor and Mobile Radio Communications (PIMRCʹ02) Lisbon Portugal Sept 2002 [13] M A Youssef A Agrawala and A U Shankar ldquoWLAN location determination via clustering and probability distributionsʺ in Proc IEEE

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

50

International Conference on Pervasive Computing and Communications (PerComʹ03) Dallas‐Fort Worth TX Mar 23‐26 2003 pp 23‐26 [14] Z Xiang S Song J Chen H Wang J Huang and X Gao ldquoA wireless LAN‐based indoor positioning technologyʺ IBM Journal of Research and Development vol 48 no 56 pp 617‐626 SeptNov 2004 [15] R Want A Hopper V Falcao and J Gibbons ldquoThe active badge location systemʺACM Transactions on Information Systems vol 40 no 1 pp 91‐102 Jan 1992 [16] N B Priyantha A Chakraborty and H Balakrishnan ldquoThe cricket location‐support systemʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ00) Boston MA Aug 2000 pp 32‐43 [17] Holger K Willig A (2005) Protocols and Architectures for Wireless Sensor Networks London [18] IEEE (2003) Part 154 Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low‐Rate Wireless Personal Area Networks (LR‐WPANs) New York [19] Chipcon CC2420 802154 compliant radio httpwwwchipconcom [20] ZigBee Specification v10 ZigBee Alliance [21] Dimitrios Lymberopoulos Quentin Lindsey and Andreas Savvides ldquoAn Empirical Analysis of Radio Signal Strength Variability in IEEE 802154 Networks using Monopole Antennasrdquo [22] Kiran Yedavalli Bhaskar Krishnamachari Sharmila Ravula and Bhaskar Srinivasan ldquoEcolocation A sequence based technique for RF localization in wireless sensor networksrdquo Robert Bosch Research and Technology Centre Palo Alto CA

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

34

it is a research prototype object tracking application is a simple stable stand‐alone system capable of demonstrating an important application of WSN It takes raw data from the WSN aggregates it evaluates it and based on the evaluation tracks the object The accuracy of estimating the position is less than 5m Table 11 below shows a log of readings taken during test

Start

Intialize micro‐controller and transeciever in receieve mode

If received_packet=

Beacon

Average RSSI values

Increase the counter

If countergt1000050

A

No

Yes

B

B

Yes

No

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

35

A

Reset counter

Set transceiever in transmit mode

Transmit averaged RSSI to Main sensor and set it back to receive mode

10000 samples ‐ Offline

50 samples ‐ Online

Figure 17 Flow chart representing the algorithm of fixed sensors

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

36

Start

Intialize micro‐controller and transeciever in receieve mode

Received RSSI values from all fixed Sensors

Open radio map

Calculate Euclidean distance between obtained RSSI values from fixed sensors and fingerprints

in radio map

No

Yes

Bubble sort for least error in signal strength values

A

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

37

A

Get the location of the object from the radiousing the least error value

Display location of object on PC

End

Figure 18 Flow chart representing the algorithm of main sensor

Table 11 Accuracy of positioning system

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

38

5 RESULTS This chapter presents how the system parameters such as number of samples mean or median etchellipare finalized All the tests are performed in masterrsquos room at Mid Sweden University This chapter is divided into three subsections First in Section 51 it reviews results obtained from samples test Section 52 discusses one dimensional test results and finally section 53 discusses results of two dimensional tests

51 Measurement setup A Dell laptop was connected to a SENTIO platform and is loaded with software (Appendix 2) This setup was used to determine number of samples required during object tracking mean or median position estimation algorithm and one dimensional test The setup is as follows

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

103

0

BC - 1030

AP 1AP 2

BS

HEWLETT

PACKARD

Figure 19 Test bed for one dimensional test in Masters Room

Two SENTIO nodes (AP1 and AP2 as shown above) were separated by a distance of 10 meters and these sensors are known as fixed sensors which are fixed to the ceiling such that there antenna is faced towards the ground They are linear is position A BS was connected to Dell laptop and it was placed in such a way that its antenna was faced towards the ceiling Another SENTIO

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

39

node was used as an object and its orientation was same as BS Figure 19 represents the complete setup for object tracking application Mobile node was moved between these fixed ones at regular intervals of one meter and RSSI values were logged at each interval

52 Number of samples test This test was performed to determine number of samples required for accurate positioning and either mean of the obtained RSSI values or median of them Figure 19 represents the setup for number of samples test Once the setup is ready the measurements are taken as follows

1 An object was tagged with SENTIO node and was placed stationary linear to the fixed sensors

2 10000 samples (RSSI) were taken at regular intervals of 1 meter up to 10 meters

3 The sample values were logged at every interval 4 Using these logged samples I have plotted graphs such as distribution

of RSS values (figure 20 21) meanmedian at intervals of 1 meter using MATLAB

5 The mean and median for various numbers of samples is as shown in figure 22

Figure 20 Histogram plot of RSSI values at 1m and 4m

Figure 21 Histogram plot of RSSI values at 6m and 8m

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

40

Figure 22 Distance vs RSSI

From the above graphs following conclusions were made 1 From the histogram plots we can observe that the distribution of RSSI

values follow Gaussians distribution law 2 As we can see from figure 22 that median curve has drastic changes

when compared to mean curve Hence mean is considered while estimating the position

3 From the observations we conclude that the number of samples does not matter since mean curve tends to be same for various numbers of samples

4 Hence 10000 samples are considered in order to get a proper location fingerprint during off‐line phase and during online phase 50 samples are enough to estimate the position of object

53 0ne dimensional test Measurement setup for one dimensional test is as shown in figure 19 This test was conducted to check the performance of algorithm and how accurate it was At first ie during offline phase fingerprints at each interval averaging 10000 samples were taken During online phase Euclidean algorithm was used estimate the position averaging 50 samples of RSSI From the results we

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

41

can realize that the error was less than 2 meters Table 12 shows the result of location estimation in one dimensional case

Table 12 One dimensional position estimation results

54 Two dimensional test This test is the final stage of my thesis work Experimental testbed for this stage is as shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors fixed at the corners of the room and a BS at the centre of the room There are 18 pre‐determined locations where the fingerprints (RSSI) were taken The fingerprints are as shown in Appendix 1 All the readings were taken in presence of userrsquos The algorithm for position estimation is shown in Appendix 2 Position estimation test is performed as follows

Tagged object is placed at random inside the masterrsquos room All the sensors including the base station and object are switched on Using the algorithm (Appendix 2) the BS estimates the position of the object and displays it on the PC

This test was performed using various numbers of samples to test which has a better accuracy Figure 23 represents the error rate using various numbers of samples From figure 23 we can conclude that 50 numbers of samples is enough to estimate the position of object with an accuracy of 3m

55 Causes of error in location determination The randomness of RSS is the major cause of error in any indoor

positioning system that uses the RSS for location inference If there is no randomness in the RSS and its location fingerprint every indoor positioning systems of this type will have no location detection error For example I have recorded RSS at one particular location during different times of the day From figures 24 and 25 you can observe that RSS values are different at different times of day

The performance of indoor positioning systems depends greatly on the separation of location fingerprints A location fingerprint

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

42

corresponding to a location can be identified correctly if it is difficult to classify it as another fingerprint by a pattern classifier

Path loss is one of the basic signal propagation properties of the RSS that best describes attenuation of transmit signal power over a distance between an access point and a mobile device

Presence of Userrsquos or userrsquos orientation affects RSS

Figure 23 Error Rate vs Number of samples

Figure 24 RSS distribution at 214 pm

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

43

Figure 25 RSS distribution at 100pm and 1100 am

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

6 DISCUSSIONS10102006

44

6 DISCUSSIONS 61 Summary and Conclusion While empirical results and performance studies of positioning systems based on location fingerprint have been presented in this report analytical model that can be used as a framework for efficiently designing the positioning systems are not available This thesis design work uses general principles of position estimation such as position estimation of sensors grid spacing etc This design work focuses on accuracy and precision The concept of location fingerprint technique based on RSS us explained in detail in chapter 2 It is found that RSS is random It is possible to approximate the RSS as being normally distributed While the RSS random process is non‐stationary the mean is more or less constant and could be used as the fingerprint of the location Model designed for position estimation is as shown in chapter 4 Accuracy and precision are the two major performance matrices in this study Uniform grid spacing has been used From the experimental analysis I have obtained an accuracy of 3 meters and 35 precision The sensitivity analysis among the system parameters and the performance matrices were done using the model as shown in chapter 4 For instance our study found that the minimum distance between two adjacent positions also affects the positioning performance This is due to the randomness of RSS patterns induced by the ever changing indoor environment

62 Future work The research in this thesis provides a ground work for the future study of efficient design of indoor positioning system The RSSI based position estimation still has limitations First the RSSI value is time dependent Thus the future study should address this issue Second the movement of object is not considered Third orientations of sensors affect the performance which should be taken care off Fourth to improve the performance of the system we need to find a solution to deal with those incomplete or missing data during the off‐line and the on‐line phase Fifth position estimation on multi‐floor and three‐dimensional coordinates is not available

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 110102006

45

APPENDIX 1

Location Fingerprints of Masters Room Time 1315 Observation Time 20 minutes Number of Samples 10000

People Presence SNo Co-ordinates AP1 (0x0001) AP2 (0x0002) AP3 (0x0004) AP4 (0x0008)

1 11 -24 -16 -27 -33 2 13 -26 -8 -34 -31 3 15 -43 -9 -39 -25 4 32 -21 -11 -27 -33 5 61 -6 -23 -32 -28 6 64 -20 -31 -25 -34 7 35 -23 -35 -29 -38 8 37 -29 -35 -22 -27 9 39 -38 -29 -21 -27 10 67 -27 -23 -20 -23 11 611 -26 -29 -23 -30 12 613 -35 -32 -2 -31 13 513 -34 -31 -20 -26 14 11 -39 -22 -29 -17 15 212 -37 -42 -28 -31 16 -212 -38 -33 -29 -6 17 -21 -42 -29 -33 -19 18 -38 -40 -23 -35 -22

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 210102006

46

APPENDIX 2

Begin

Open radio map and raw data files for write

Intialize radio map in memory

Check whether the

Packet is data or Beacon packet If data packet

Save the data to register

Check if data is

Received from all

sensors

Estimate the position of static

Display the position on PC

End Yes

No

A

A

Yes

No

Flow chart representing the main algorithm for position estimation

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

47

APPENDIX 3

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

48

Distribution of RSSI

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

49

REFERENCES [1] T S Rappaport J H Reed and B D Woerner ldquoPosition location using wireless communications on highways of the futureʺ IEEE Commun Mag vol 34 no 10 pp33‐41 Oct 1996 [2] A M Ladd K E Bekris G Marceau A Rudys L E Kavraki and D S Wallach rdquoRobotics‐based location sensing using wireless ethernetʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ02) 2002 pp 227‐238 [3] I Ramani R Bharadwaja and P V Rangan ldquoLocation tracking for media appliances in wireless home networksʺ in Proc International Conference on Multimedia and Expo (ICMEʹ03) July 2003 pp 769‐772 [4] K Pahlavan X Li and J P Makela ldquoIndoor geolocation science and technologyʺ IEEE Commun Mag vol 40 no 2 pp 112‐118 Feb 2002 [5] Konard Lorincz and Matt Welsh ldquoMote Track A Robust Decentralized Approach to RF‐Based Location Trackingrdquo [6] Paramvir Bahl and Venkata N Padmanabham ldquoRADAR An In‐Building RF‐based User Location and Tracking Systemrdquo [7] Kamol Kaemarungsi ldquoDesign of Indoor positioning system based on location fingerprint techiniquerdquo University of Pittsburgh 2005 [8] Fredrik Linnarsson Cheng Peng and Bengt Oelmann ldquoSENTIO A Hardware Platform for Rapid Prototyping of Wireless Sensor Networksrdquo [9] S Saha K Chaudhuri D Sanghi and P Bhagwat ldquoLocation determination of a mobile device using IEEE 80211b access point signalsʺ in Proc IEEE Wireless Communications and Networking Conference (WCNCʹ03) New Orleans LA Mar 2003 pp 1987‐1992 [10] T Roos P Myllymaki H Tirri P Misikangas and J Sievanen ldquoA probabilistic approach to WLAN user location estimationʺ International Journal of Wireless Information Networks vol 9 no 3 pp 155‐164 July 2002 [11] R Battiti M Brunato and A Villani ldquoStatistical learning theory for location fingerprinting in wireless LANrsquosʺ Technical Report Oct 2002 [Online] Available httprtmscienceunitnitraquobattitiarchive86pdf [12] P Prasithsangaree P Krishnamurthy and P K Chrysanthis ldquoAn indoor position location with wireless LANsʺ in Proc IEEE International Symposium on Personal Indoor and Mobile Radio Communications (PIMRCʹ02) Lisbon Portugal Sept 2002 [13] M A Youssef A Agrawala and A U Shankar ldquoWLAN location determination via clustering and probability distributionsʺ in Proc IEEE

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

50

International Conference on Pervasive Computing and Communications (PerComʹ03) Dallas‐Fort Worth TX Mar 23‐26 2003 pp 23‐26 [14] Z Xiang S Song J Chen H Wang J Huang and X Gao ldquoA wireless LAN‐based indoor positioning technologyʺ IBM Journal of Research and Development vol 48 no 56 pp 617‐626 SeptNov 2004 [15] R Want A Hopper V Falcao and J Gibbons ldquoThe active badge location systemʺACM Transactions on Information Systems vol 40 no 1 pp 91‐102 Jan 1992 [16] N B Priyantha A Chakraborty and H Balakrishnan ldquoThe cricket location‐support systemʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ00) Boston MA Aug 2000 pp 32‐43 [17] Holger K Willig A (2005) Protocols and Architectures for Wireless Sensor Networks London [18] IEEE (2003) Part 154 Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low‐Rate Wireless Personal Area Networks (LR‐WPANs) New York [19] Chipcon CC2420 802154 compliant radio httpwwwchipconcom [20] ZigBee Specification v10 ZigBee Alliance [21] Dimitrios Lymberopoulos Quentin Lindsey and Andreas Savvides ldquoAn Empirical Analysis of Radio Signal Strength Variability in IEEE 802154 Networks using Monopole Antennasrdquo [22] Kiran Yedavalli Bhaskar Krishnamachari Sharmila Ravula and Bhaskar Srinivasan ldquoEcolocation A sequence based technique for RF localization in wireless sensor networksrdquo Robert Bosch Research and Technology Centre Palo Alto CA

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

35

A

Reset counter

Set transceiever in transmit mode

Transmit averaged RSSI to Main sensor and set it back to receive mode

10000 samples ‐ Offline

50 samples ‐ Online

Figure 17 Flow chart representing the algorithm of fixed sensors

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

36

Start

Intialize micro‐controller and transeciever in receieve mode

Received RSSI values from all fixed Sensors

Open radio map

Calculate Euclidean distance between obtained RSSI values from fixed sensors and fingerprints

in radio map

No

Yes

Bubble sort for least error in signal strength values

A

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

37

A

Get the location of the object from the radiousing the least error value

Display location of object on PC

End

Figure 18 Flow chart representing the algorithm of main sensor

Table 11 Accuracy of positioning system

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

38

5 RESULTS This chapter presents how the system parameters such as number of samples mean or median etchellipare finalized All the tests are performed in masterrsquos room at Mid Sweden University This chapter is divided into three subsections First in Section 51 it reviews results obtained from samples test Section 52 discusses one dimensional test results and finally section 53 discusses results of two dimensional tests

51 Measurement setup A Dell laptop was connected to a SENTIO platform and is loaded with software (Appendix 2) This setup was used to determine number of samples required during object tracking mean or median position estimation algorithm and one dimensional test The setup is as follows

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

103

0

BC - 1030

AP 1AP 2

BS

HEWLETT

PACKARD

Figure 19 Test bed for one dimensional test in Masters Room

Two SENTIO nodes (AP1 and AP2 as shown above) were separated by a distance of 10 meters and these sensors are known as fixed sensors which are fixed to the ceiling such that there antenna is faced towards the ground They are linear is position A BS was connected to Dell laptop and it was placed in such a way that its antenna was faced towards the ceiling Another SENTIO

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

39

node was used as an object and its orientation was same as BS Figure 19 represents the complete setup for object tracking application Mobile node was moved between these fixed ones at regular intervals of one meter and RSSI values were logged at each interval

52 Number of samples test This test was performed to determine number of samples required for accurate positioning and either mean of the obtained RSSI values or median of them Figure 19 represents the setup for number of samples test Once the setup is ready the measurements are taken as follows

1 An object was tagged with SENTIO node and was placed stationary linear to the fixed sensors

2 10000 samples (RSSI) were taken at regular intervals of 1 meter up to 10 meters

3 The sample values were logged at every interval 4 Using these logged samples I have plotted graphs such as distribution

of RSS values (figure 20 21) meanmedian at intervals of 1 meter using MATLAB

5 The mean and median for various numbers of samples is as shown in figure 22

Figure 20 Histogram plot of RSSI values at 1m and 4m

Figure 21 Histogram plot of RSSI values at 6m and 8m

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

40

Figure 22 Distance vs RSSI

From the above graphs following conclusions were made 1 From the histogram plots we can observe that the distribution of RSSI

values follow Gaussians distribution law 2 As we can see from figure 22 that median curve has drastic changes

when compared to mean curve Hence mean is considered while estimating the position

3 From the observations we conclude that the number of samples does not matter since mean curve tends to be same for various numbers of samples

4 Hence 10000 samples are considered in order to get a proper location fingerprint during off‐line phase and during online phase 50 samples are enough to estimate the position of object

53 0ne dimensional test Measurement setup for one dimensional test is as shown in figure 19 This test was conducted to check the performance of algorithm and how accurate it was At first ie during offline phase fingerprints at each interval averaging 10000 samples were taken During online phase Euclidean algorithm was used estimate the position averaging 50 samples of RSSI From the results we

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

41

can realize that the error was less than 2 meters Table 12 shows the result of location estimation in one dimensional case

Table 12 One dimensional position estimation results

54 Two dimensional test This test is the final stage of my thesis work Experimental testbed for this stage is as shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors fixed at the corners of the room and a BS at the centre of the room There are 18 pre‐determined locations where the fingerprints (RSSI) were taken The fingerprints are as shown in Appendix 1 All the readings were taken in presence of userrsquos The algorithm for position estimation is shown in Appendix 2 Position estimation test is performed as follows

Tagged object is placed at random inside the masterrsquos room All the sensors including the base station and object are switched on Using the algorithm (Appendix 2) the BS estimates the position of the object and displays it on the PC

This test was performed using various numbers of samples to test which has a better accuracy Figure 23 represents the error rate using various numbers of samples From figure 23 we can conclude that 50 numbers of samples is enough to estimate the position of object with an accuracy of 3m

55 Causes of error in location determination The randomness of RSS is the major cause of error in any indoor

positioning system that uses the RSS for location inference If there is no randomness in the RSS and its location fingerprint every indoor positioning systems of this type will have no location detection error For example I have recorded RSS at one particular location during different times of the day From figures 24 and 25 you can observe that RSS values are different at different times of day

The performance of indoor positioning systems depends greatly on the separation of location fingerprints A location fingerprint

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

42

corresponding to a location can be identified correctly if it is difficult to classify it as another fingerprint by a pattern classifier

Path loss is one of the basic signal propagation properties of the RSS that best describes attenuation of transmit signal power over a distance between an access point and a mobile device

Presence of Userrsquos or userrsquos orientation affects RSS

Figure 23 Error Rate vs Number of samples

Figure 24 RSS distribution at 214 pm

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

43

Figure 25 RSS distribution at 100pm and 1100 am

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

6 DISCUSSIONS10102006

44

6 DISCUSSIONS 61 Summary and Conclusion While empirical results and performance studies of positioning systems based on location fingerprint have been presented in this report analytical model that can be used as a framework for efficiently designing the positioning systems are not available This thesis design work uses general principles of position estimation such as position estimation of sensors grid spacing etc This design work focuses on accuracy and precision The concept of location fingerprint technique based on RSS us explained in detail in chapter 2 It is found that RSS is random It is possible to approximate the RSS as being normally distributed While the RSS random process is non‐stationary the mean is more or less constant and could be used as the fingerprint of the location Model designed for position estimation is as shown in chapter 4 Accuracy and precision are the two major performance matrices in this study Uniform grid spacing has been used From the experimental analysis I have obtained an accuracy of 3 meters and 35 precision The sensitivity analysis among the system parameters and the performance matrices were done using the model as shown in chapter 4 For instance our study found that the minimum distance between two adjacent positions also affects the positioning performance This is due to the randomness of RSS patterns induced by the ever changing indoor environment

62 Future work The research in this thesis provides a ground work for the future study of efficient design of indoor positioning system The RSSI based position estimation still has limitations First the RSSI value is time dependent Thus the future study should address this issue Second the movement of object is not considered Third orientations of sensors affect the performance which should be taken care off Fourth to improve the performance of the system we need to find a solution to deal with those incomplete or missing data during the off‐line and the on‐line phase Fifth position estimation on multi‐floor and three‐dimensional coordinates is not available

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 110102006

45

APPENDIX 1

Location Fingerprints of Masters Room Time 1315 Observation Time 20 minutes Number of Samples 10000

People Presence SNo Co-ordinates AP1 (0x0001) AP2 (0x0002) AP3 (0x0004) AP4 (0x0008)

1 11 -24 -16 -27 -33 2 13 -26 -8 -34 -31 3 15 -43 -9 -39 -25 4 32 -21 -11 -27 -33 5 61 -6 -23 -32 -28 6 64 -20 -31 -25 -34 7 35 -23 -35 -29 -38 8 37 -29 -35 -22 -27 9 39 -38 -29 -21 -27 10 67 -27 -23 -20 -23 11 611 -26 -29 -23 -30 12 613 -35 -32 -2 -31 13 513 -34 -31 -20 -26 14 11 -39 -22 -29 -17 15 212 -37 -42 -28 -31 16 -212 -38 -33 -29 -6 17 -21 -42 -29 -33 -19 18 -38 -40 -23 -35 -22

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 210102006

46

APPENDIX 2

Begin

Open radio map and raw data files for write

Intialize radio map in memory

Check whether the

Packet is data or Beacon packet If data packet

Save the data to register

Check if data is

Received from all

sensors

Estimate the position of static

Display the position on PC

End Yes

No

A

A

Yes

No

Flow chart representing the main algorithm for position estimation

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

47

APPENDIX 3

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

48

Distribution of RSSI

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

49

REFERENCES [1] T S Rappaport J H Reed and B D Woerner ldquoPosition location using wireless communications on highways of the futureʺ IEEE Commun Mag vol 34 no 10 pp33‐41 Oct 1996 [2] A M Ladd K E Bekris G Marceau A Rudys L E Kavraki and D S Wallach rdquoRobotics‐based location sensing using wireless ethernetʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ02) 2002 pp 227‐238 [3] I Ramani R Bharadwaja and P V Rangan ldquoLocation tracking for media appliances in wireless home networksʺ in Proc International Conference on Multimedia and Expo (ICMEʹ03) July 2003 pp 769‐772 [4] K Pahlavan X Li and J P Makela ldquoIndoor geolocation science and technologyʺ IEEE Commun Mag vol 40 no 2 pp 112‐118 Feb 2002 [5] Konard Lorincz and Matt Welsh ldquoMote Track A Robust Decentralized Approach to RF‐Based Location Trackingrdquo [6] Paramvir Bahl and Venkata N Padmanabham ldquoRADAR An In‐Building RF‐based User Location and Tracking Systemrdquo [7] Kamol Kaemarungsi ldquoDesign of Indoor positioning system based on location fingerprint techiniquerdquo University of Pittsburgh 2005 [8] Fredrik Linnarsson Cheng Peng and Bengt Oelmann ldquoSENTIO A Hardware Platform for Rapid Prototyping of Wireless Sensor Networksrdquo [9] S Saha K Chaudhuri D Sanghi and P Bhagwat ldquoLocation determination of a mobile device using IEEE 80211b access point signalsʺ in Proc IEEE Wireless Communications and Networking Conference (WCNCʹ03) New Orleans LA Mar 2003 pp 1987‐1992 [10] T Roos P Myllymaki H Tirri P Misikangas and J Sievanen ldquoA probabilistic approach to WLAN user location estimationʺ International Journal of Wireless Information Networks vol 9 no 3 pp 155‐164 July 2002 [11] R Battiti M Brunato and A Villani ldquoStatistical learning theory for location fingerprinting in wireless LANrsquosʺ Technical Report Oct 2002 [Online] Available httprtmscienceunitnitraquobattitiarchive86pdf [12] P Prasithsangaree P Krishnamurthy and P K Chrysanthis ldquoAn indoor position location with wireless LANsʺ in Proc IEEE International Symposium on Personal Indoor and Mobile Radio Communications (PIMRCʹ02) Lisbon Portugal Sept 2002 [13] M A Youssef A Agrawala and A U Shankar ldquoWLAN location determination via clustering and probability distributionsʺ in Proc IEEE

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

50

International Conference on Pervasive Computing and Communications (PerComʹ03) Dallas‐Fort Worth TX Mar 23‐26 2003 pp 23‐26 [14] Z Xiang S Song J Chen H Wang J Huang and X Gao ldquoA wireless LAN‐based indoor positioning technologyʺ IBM Journal of Research and Development vol 48 no 56 pp 617‐626 SeptNov 2004 [15] R Want A Hopper V Falcao and J Gibbons ldquoThe active badge location systemʺACM Transactions on Information Systems vol 40 no 1 pp 91‐102 Jan 1992 [16] N B Priyantha A Chakraborty and H Balakrishnan ldquoThe cricket location‐support systemʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ00) Boston MA Aug 2000 pp 32‐43 [17] Holger K Willig A (2005) Protocols and Architectures for Wireless Sensor Networks London [18] IEEE (2003) Part 154 Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low‐Rate Wireless Personal Area Networks (LR‐WPANs) New York [19] Chipcon CC2420 802154 compliant radio httpwwwchipconcom [20] ZigBee Specification v10 ZigBee Alliance [21] Dimitrios Lymberopoulos Quentin Lindsey and Andreas Savvides ldquoAn Empirical Analysis of Radio Signal Strength Variability in IEEE 802154 Networks using Monopole Antennasrdquo [22] Kiran Yedavalli Bhaskar Krishnamachari Sharmila Ravula and Bhaskar Srinivasan ldquoEcolocation A sequence based technique for RF localization in wireless sensor networksrdquo Robert Bosch Research and Technology Centre Palo Alto CA

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

36

Start

Intialize micro‐controller and transeciever in receieve mode

Received RSSI values from all fixed Sensors

Open radio map

Calculate Euclidean distance between obtained RSSI values from fixed sensors and fingerprints

in radio map

No

Yes

Bubble sort for least error in signal strength values

A

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

37

A

Get the location of the object from the radiousing the least error value

Display location of object on PC

End

Figure 18 Flow chart representing the algorithm of main sensor

Table 11 Accuracy of positioning system

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

38

5 RESULTS This chapter presents how the system parameters such as number of samples mean or median etchellipare finalized All the tests are performed in masterrsquos room at Mid Sweden University This chapter is divided into three subsections First in Section 51 it reviews results obtained from samples test Section 52 discusses one dimensional test results and finally section 53 discusses results of two dimensional tests

51 Measurement setup A Dell laptop was connected to a SENTIO platform and is loaded with software (Appendix 2) This setup was used to determine number of samples required during object tracking mean or median position estimation algorithm and one dimensional test The setup is as follows

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

103

0

BC - 1030

AP 1AP 2

BS

HEWLETT

PACKARD

Figure 19 Test bed for one dimensional test in Masters Room

Two SENTIO nodes (AP1 and AP2 as shown above) were separated by a distance of 10 meters and these sensors are known as fixed sensors which are fixed to the ceiling such that there antenna is faced towards the ground They are linear is position A BS was connected to Dell laptop and it was placed in such a way that its antenna was faced towards the ceiling Another SENTIO

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

39

node was used as an object and its orientation was same as BS Figure 19 represents the complete setup for object tracking application Mobile node was moved between these fixed ones at regular intervals of one meter and RSSI values were logged at each interval

52 Number of samples test This test was performed to determine number of samples required for accurate positioning and either mean of the obtained RSSI values or median of them Figure 19 represents the setup for number of samples test Once the setup is ready the measurements are taken as follows

1 An object was tagged with SENTIO node and was placed stationary linear to the fixed sensors

2 10000 samples (RSSI) were taken at regular intervals of 1 meter up to 10 meters

3 The sample values were logged at every interval 4 Using these logged samples I have plotted graphs such as distribution

of RSS values (figure 20 21) meanmedian at intervals of 1 meter using MATLAB

5 The mean and median for various numbers of samples is as shown in figure 22

Figure 20 Histogram plot of RSSI values at 1m and 4m

Figure 21 Histogram plot of RSSI values at 6m and 8m

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

40

Figure 22 Distance vs RSSI

From the above graphs following conclusions were made 1 From the histogram plots we can observe that the distribution of RSSI

values follow Gaussians distribution law 2 As we can see from figure 22 that median curve has drastic changes

when compared to mean curve Hence mean is considered while estimating the position

3 From the observations we conclude that the number of samples does not matter since mean curve tends to be same for various numbers of samples

4 Hence 10000 samples are considered in order to get a proper location fingerprint during off‐line phase and during online phase 50 samples are enough to estimate the position of object

53 0ne dimensional test Measurement setup for one dimensional test is as shown in figure 19 This test was conducted to check the performance of algorithm and how accurate it was At first ie during offline phase fingerprints at each interval averaging 10000 samples were taken During online phase Euclidean algorithm was used estimate the position averaging 50 samples of RSSI From the results we

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

41

can realize that the error was less than 2 meters Table 12 shows the result of location estimation in one dimensional case

Table 12 One dimensional position estimation results

54 Two dimensional test This test is the final stage of my thesis work Experimental testbed for this stage is as shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors fixed at the corners of the room and a BS at the centre of the room There are 18 pre‐determined locations where the fingerprints (RSSI) were taken The fingerprints are as shown in Appendix 1 All the readings were taken in presence of userrsquos The algorithm for position estimation is shown in Appendix 2 Position estimation test is performed as follows

Tagged object is placed at random inside the masterrsquos room All the sensors including the base station and object are switched on Using the algorithm (Appendix 2) the BS estimates the position of the object and displays it on the PC

This test was performed using various numbers of samples to test which has a better accuracy Figure 23 represents the error rate using various numbers of samples From figure 23 we can conclude that 50 numbers of samples is enough to estimate the position of object with an accuracy of 3m

55 Causes of error in location determination The randomness of RSS is the major cause of error in any indoor

positioning system that uses the RSS for location inference If there is no randomness in the RSS and its location fingerprint every indoor positioning systems of this type will have no location detection error For example I have recorded RSS at one particular location during different times of the day From figures 24 and 25 you can observe that RSS values are different at different times of day

The performance of indoor positioning systems depends greatly on the separation of location fingerprints A location fingerprint

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

42

corresponding to a location can be identified correctly if it is difficult to classify it as another fingerprint by a pattern classifier

Path loss is one of the basic signal propagation properties of the RSS that best describes attenuation of transmit signal power over a distance between an access point and a mobile device

Presence of Userrsquos or userrsquos orientation affects RSS

Figure 23 Error Rate vs Number of samples

Figure 24 RSS distribution at 214 pm

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

43

Figure 25 RSS distribution at 100pm and 1100 am

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

6 DISCUSSIONS10102006

44

6 DISCUSSIONS 61 Summary and Conclusion While empirical results and performance studies of positioning systems based on location fingerprint have been presented in this report analytical model that can be used as a framework for efficiently designing the positioning systems are not available This thesis design work uses general principles of position estimation such as position estimation of sensors grid spacing etc This design work focuses on accuracy and precision The concept of location fingerprint technique based on RSS us explained in detail in chapter 2 It is found that RSS is random It is possible to approximate the RSS as being normally distributed While the RSS random process is non‐stationary the mean is more or less constant and could be used as the fingerprint of the location Model designed for position estimation is as shown in chapter 4 Accuracy and precision are the two major performance matrices in this study Uniform grid spacing has been used From the experimental analysis I have obtained an accuracy of 3 meters and 35 precision The sensitivity analysis among the system parameters and the performance matrices were done using the model as shown in chapter 4 For instance our study found that the minimum distance between two adjacent positions also affects the positioning performance This is due to the randomness of RSS patterns induced by the ever changing indoor environment

62 Future work The research in this thesis provides a ground work for the future study of efficient design of indoor positioning system The RSSI based position estimation still has limitations First the RSSI value is time dependent Thus the future study should address this issue Second the movement of object is not considered Third orientations of sensors affect the performance which should be taken care off Fourth to improve the performance of the system we need to find a solution to deal with those incomplete or missing data during the off‐line and the on‐line phase Fifth position estimation on multi‐floor and three‐dimensional coordinates is not available

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 110102006

45

APPENDIX 1

Location Fingerprints of Masters Room Time 1315 Observation Time 20 minutes Number of Samples 10000

People Presence SNo Co-ordinates AP1 (0x0001) AP2 (0x0002) AP3 (0x0004) AP4 (0x0008)

1 11 -24 -16 -27 -33 2 13 -26 -8 -34 -31 3 15 -43 -9 -39 -25 4 32 -21 -11 -27 -33 5 61 -6 -23 -32 -28 6 64 -20 -31 -25 -34 7 35 -23 -35 -29 -38 8 37 -29 -35 -22 -27 9 39 -38 -29 -21 -27 10 67 -27 -23 -20 -23 11 611 -26 -29 -23 -30 12 613 -35 -32 -2 -31 13 513 -34 -31 -20 -26 14 11 -39 -22 -29 -17 15 212 -37 -42 -28 -31 16 -212 -38 -33 -29 -6 17 -21 -42 -29 -33 -19 18 -38 -40 -23 -35 -22

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 210102006

46

APPENDIX 2

Begin

Open radio map and raw data files for write

Intialize radio map in memory

Check whether the

Packet is data or Beacon packet If data packet

Save the data to register

Check if data is

Received from all

sensors

Estimate the position of static

Display the position on PC

End Yes

No

A

A

Yes

No

Flow chart representing the main algorithm for position estimation

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

47

APPENDIX 3

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

48

Distribution of RSSI

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

49

REFERENCES [1] T S Rappaport J H Reed and B D Woerner ldquoPosition location using wireless communications on highways of the futureʺ IEEE Commun Mag vol 34 no 10 pp33‐41 Oct 1996 [2] A M Ladd K E Bekris G Marceau A Rudys L E Kavraki and D S Wallach rdquoRobotics‐based location sensing using wireless ethernetʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ02) 2002 pp 227‐238 [3] I Ramani R Bharadwaja and P V Rangan ldquoLocation tracking for media appliances in wireless home networksʺ in Proc International Conference on Multimedia and Expo (ICMEʹ03) July 2003 pp 769‐772 [4] K Pahlavan X Li and J P Makela ldquoIndoor geolocation science and technologyʺ IEEE Commun Mag vol 40 no 2 pp 112‐118 Feb 2002 [5] Konard Lorincz and Matt Welsh ldquoMote Track A Robust Decentralized Approach to RF‐Based Location Trackingrdquo [6] Paramvir Bahl and Venkata N Padmanabham ldquoRADAR An In‐Building RF‐based User Location and Tracking Systemrdquo [7] Kamol Kaemarungsi ldquoDesign of Indoor positioning system based on location fingerprint techiniquerdquo University of Pittsburgh 2005 [8] Fredrik Linnarsson Cheng Peng and Bengt Oelmann ldquoSENTIO A Hardware Platform for Rapid Prototyping of Wireless Sensor Networksrdquo [9] S Saha K Chaudhuri D Sanghi and P Bhagwat ldquoLocation determination of a mobile device using IEEE 80211b access point signalsʺ in Proc IEEE Wireless Communications and Networking Conference (WCNCʹ03) New Orleans LA Mar 2003 pp 1987‐1992 [10] T Roos P Myllymaki H Tirri P Misikangas and J Sievanen ldquoA probabilistic approach to WLAN user location estimationʺ International Journal of Wireless Information Networks vol 9 no 3 pp 155‐164 July 2002 [11] R Battiti M Brunato and A Villani ldquoStatistical learning theory for location fingerprinting in wireless LANrsquosʺ Technical Report Oct 2002 [Online] Available httprtmscienceunitnitraquobattitiarchive86pdf [12] P Prasithsangaree P Krishnamurthy and P K Chrysanthis ldquoAn indoor position location with wireless LANsʺ in Proc IEEE International Symposium on Personal Indoor and Mobile Radio Communications (PIMRCʹ02) Lisbon Portugal Sept 2002 [13] M A Youssef A Agrawala and A U Shankar ldquoWLAN location determination via clustering and probability distributionsʺ in Proc IEEE

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

50

International Conference on Pervasive Computing and Communications (PerComʹ03) Dallas‐Fort Worth TX Mar 23‐26 2003 pp 23‐26 [14] Z Xiang S Song J Chen H Wang J Huang and X Gao ldquoA wireless LAN‐based indoor positioning technologyʺ IBM Journal of Research and Development vol 48 no 56 pp 617‐626 SeptNov 2004 [15] R Want A Hopper V Falcao and J Gibbons ldquoThe active badge location systemʺACM Transactions on Information Systems vol 40 no 1 pp 91‐102 Jan 1992 [16] N B Priyantha A Chakraborty and H Balakrishnan ldquoThe cricket location‐support systemʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ00) Boston MA Aug 2000 pp 32‐43 [17] Holger K Willig A (2005) Protocols and Architectures for Wireless Sensor Networks London [18] IEEE (2003) Part 154 Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low‐Rate Wireless Personal Area Networks (LR‐WPANs) New York [19] Chipcon CC2420 802154 compliant radio httpwwwchipconcom [20] ZigBee Specification v10 ZigBee Alliance [21] Dimitrios Lymberopoulos Quentin Lindsey and Andreas Savvides ldquoAn Empirical Analysis of Radio Signal Strength Variability in IEEE 802154 Networks using Monopole Antennasrdquo [22] Kiran Yedavalli Bhaskar Krishnamachari Sharmila Ravula and Bhaskar Srinivasan ldquoEcolocation A sequence based technique for RF localization in wireless sensor networksrdquo Robert Bosch Research and Technology Centre Palo Alto CA

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

4 OBJECT TRACKING10102006

37

A

Get the location of the object from the radiousing the least error value

Display location of object on PC

End

Figure 18 Flow chart representing the algorithm of main sensor

Table 11 Accuracy of positioning system

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

38

5 RESULTS This chapter presents how the system parameters such as number of samples mean or median etchellipare finalized All the tests are performed in masterrsquos room at Mid Sweden University This chapter is divided into three subsections First in Section 51 it reviews results obtained from samples test Section 52 discusses one dimensional test results and finally section 53 discusses results of two dimensional tests

51 Measurement setup A Dell laptop was connected to a SENTIO platform and is loaded with software (Appendix 2) This setup was used to determine number of samples required during object tracking mean or median position estimation algorithm and one dimensional test The setup is as follows

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

103

0

BC - 1030

AP 1AP 2

BS

HEWLETT

PACKARD

Figure 19 Test bed for one dimensional test in Masters Room

Two SENTIO nodes (AP1 and AP2 as shown above) were separated by a distance of 10 meters and these sensors are known as fixed sensors which are fixed to the ceiling such that there antenna is faced towards the ground They are linear is position A BS was connected to Dell laptop and it was placed in such a way that its antenna was faced towards the ceiling Another SENTIO

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

39

node was used as an object and its orientation was same as BS Figure 19 represents the complete setup for object tracking application Mobile node was moved between these fixed ones at regular intervals of one meter and RSSI values were logged at each interval

52 Number of samples test This test was performed to determine number of samples required for accurate positioning and either mean of the obtained RSSI values or median of them Figure 19 represents the setup for number of samples test Once the setup is ready the measurements are taken as follows

1 An object was tagged with SENTIO node and was placed stationary linear to the fixed sensors

2 10000 samples (RSSI) were taken at regular intervals of 1 meter up to 10 meters

3 The sample values were logged at every interval 4 Using these logged samples I have plotted graphs such as distribution

of RSS values (figure 20 21) meanmedian at intervals of 1 meter using MATLAB

5 The mean and median for various numbers of samples is as shown in figure 22

Figure 20 Histogram plot of RSSI values at 1m and 4m

Figure 21 Histogram plot of RSSI values at 6m and 8m

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

40

Figure 22 Distance vs RSSI

From the above graphs following conclusions were made 1 From the histogram plots we can observe that the distribution of RSSI

values follow Gaussians distribution law 2 As we can see from figure 22 that median curve has drastic changes

when compared to mean curve Hence mean is considered while estimating the position

3 From the observations we conclude that the number of samples does not matter since mean curve tends to be same for various numbers of samples

4 Hence 10000 samples are considered in order to get a proper location fingerprint during off‐line phase and during online phase 50 samples are enough to estimate the position of object

53 0ne dimensional test Measurement setup for one dimensional test is as shown in figure 19 This test was conducted to check the performance of algorithm and how accurate it was At first ie during offline phase fingerprints at each interval averaging 10000 samples were taken During online phase Euclidean algorithm was used estimate the position averaging 50 samples of RSSI From the results we

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

41

can realize that the error was less than 2 meters Table 12 shows the result of location estimation in one dimensional case

Table 12 One dimensional position estimation results

54 Two dimensional test This test is the final stage of my thesis work Experimental testbed for this stage is as shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors fixed at the corners of the room and a BS at the centre of the room There are 18 pre‐determined locations where the fingerprints (RSSI) were taken The fingerprints are as shown in Appendix 1 All the readings were taken in presence of userrsquos The algorithm for position estimation is shown in Appendix 2 Position estimation test is performed as follows

Tagged object is placed at random inside the masterrsquos room All the sensors including the base station and object are switched on Using the algorithm (Appendix 2) the BS estimates the position of the object and displays it on the PC

This test was performed using various numbers of samples to test which has a better accuracy Figure 23 represents the error rate using various numbers of samples From figure 23 we can conclude that 50 numbers of samples is enough to estimate the position of object with an accuracy of 3m

55 Causes of error in location determination The randomness of RSS is the major cause of error in any indoor

positioning system that uses the RSS for location inference If there is no randomness in the RSS and its location fingerprint every indoor positioning systems of this type will have no location detection error For example I have recorded RSS at one particular location during different times of the day From figures 24 and 25 you can observe that RSS values are different at different times of day

The performance of indoor positioning systems depends greatly on the separation of location fingerprints A location fingerprint

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

42

corresponding to a location can be identified correctly if it is difficult to classify it as another fingerprint by a pattern classifier

Path loss is one of the basic signal propagation properties of the RSS that best describes attenuation of transmit signal power over a distance between an access point and a mobile device

Presence of Userrsquos or userrsquos orientation affects RSS

Figure 23 Error Rate vs Number of samples

Figure 24 RSS distribution at 214 pm

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

43

Figure 25 RSS distribution at 100pm and 1100 am

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

6 DISCUSSIONS10102006

44

6 DISCUSSIONS 61 Summary and Conclusion While empirical results and performance studies of positioning systems based on location fingerprint have been presented in this report analytical model that can be used as a framework for efficiently designing the positioning systems are not available This thesis design work uses general principles of position estimation such as position estimation of sensors grid spacing etc This design work focuses on accuracy and precision The concept of location fingerprint technique based on RSS us explained in detail in chapter 2 It is found that RSS is random It is possible to approximate the RSS as being normally distributed While the RSS random process is non‐stationary the mean is more or less constant and could be used as the fingerprint of the location Model designed for position estimation is as shown in chapter 4 Accuracy and precision are the two major performance matrices in this study Uniform grid spacing has been used From the experimental analysis I have obtained an accuracy of 3 meters and 35 precision The sensitivity analysis among the system parameters and the performance matrices were done using the model as shown in chapter 4 For instance our study found that the minimum distance between two adjacent positions also affects the positioning performance This is due to the randomness of RSS patterns induced by the ever changing indoor environment

62 Future work The research in this thesis provides a ground work for the future study of efficient design of indoor positioning system The RSSI based position estimation still has limitations First the RSSI value is time dependent Thus the future study should address this issue Second the movement of object is not considered Third orientations of sensors affect the performance which should be taken care off Fourth to improve the performance of the system we need to find a solution to deal with those incomplete or missing data during the off‐line and the on‐line phase Fifth position estimation on multi‐floor and three‐dimensional coordinates is not available

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 110102006

45

APPENDIX 1

Location Fingerprints of Masters Room Time 1315 Observation Time 20 minutes Number of Samples 10000

People Presence SNo Co-ordinates AP1 (0x0001) AP2 (0x0002) AP3 (0x0004) AP4 (0x0008)

1 11 -24 -16 -27 -33 2 13 -26 -8 -34 -31 3 15 -43 -9 -39 -25 4 32 -21 -11 -27 -33 5 61 -6 -23 -32 -28 6 64 -20 -31 -25 -34 7 35 -23 -35 -29 -38 8 37 -29 -35 -22 -27 9 39 -38 -29 -21 -27 10 67 -27 -23 -20 -23 11 611 -26 -29 -23 -30 12 613 -35 -32 -2 -31 13 513 -34 -31 -20 -26 14 11 -39 -22 -29 -17 15 212 -37 -42 -28 -31 16 -212 -38 -33 -29 -6 17 -21 -42 -29 -33 -19 18 -38 -40 -23 -35 -22

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 210102006

46

APPENDIX 2

Begin

Open radio map and raw data files for write

Intialize radio map in memory

Check whether the

Packet is data or Beacon packet If data packet

Save the data to register

Check if data is

Received from all

sensors

Estimate the position of static

Display the position on PC

End Yes

No

A

A

Yes

No

Flow chart representing the main algorithm for position estimation

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

47

APPENDIX 3

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

48

Distribution of RSSI

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

49

REFERENCES [1] T S Rappaport J H Reed and B D Woerner ldquoPosition location using wireless communications on highways of the futureʺ IEEE Commun Mag vol 34 no 10 pp33‐41 Oct 1996 [2] A M Ladd K E Bekris G Marceau A Rudys L E Kavraki and D S Wallach rdquoRobotics‐based location sensing using wireless ethernetʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ02) 2002 pp 227‐238 [3] I Ramani R Bharadwaja and P V Rangan ldquoLocation tracking for media appliances in wireless home networksʺ in Proc International Conference on Multimedia and Expo (ICMEʹ03) July 2003 pp 769‐772 [4] K Pahlavan X Li and J P Makela ldquoIndoor geolocation science and technologyʺ IEEE Commun Mag vol 40 no 2 pp 112‐118 Feb 2002 [5] Konard Lorincz and Matt Welsh ldquoMote Track A Robust Decentralized Approach to RF‐Based Location Trackingrdquo [6] Paramvir Bahl and Venkata N Padmanabham ldquoRADAR An In‐Building RF‐based User Location and Tracking Systemrdquo [7] Kamol Kaemarungsi ldquoDesign of Indoor positioning system based on location fingerprint techiniquerdquo University of Pittsburgh 2005 [8] Fredrik Linnarsson Cheng Peng and Bengt Oelmann ldquoSENTIO A Hardware Platform for Rapid Prototyping of Wireless Sensor Networksrdquo [9] S Saha K Chaudhuri D Sanghi and P Bhagwat ldquoLocation determination of a mobile device using IEEE 80211b access point signalsʺ in Proc IEEE Wireless Communications and Networking Conference (WCNCʹ03) New Orleans LA Mar 2003 pp 1987‐1992 [10] T Roos P Myllymaki H Tirri P Misikangas and J Sievanen ldquoA probabilistic approach to WLAN user location estimationʺ International Journal of Wireless Information Networks vol 9 no 3 pp 155‐164 July 2002 [11] R Battiti M Brunato and A Villani ldquoStatistical learning theory for location fingerprinting in wireless LANrsquosʺ Technical Report Oct 2002 [Online] Available httprtmscienceunitnitraquobattitiarchive86pdf [12] P Prasithsangaree P Krishnamurthy and P K Chrysanthis ldquoAn indoor position location with wireless LANsʺ in Proc IEEE International Symposium on Personal Indoor and Mobile Radio Communications (PIMRCʹ02) Lisbon Portugal Sept 2002 [13] M A Youssef A Agrawala and A U Shankar ldquoWLAN location determination via clustering and probability distributionsʺ in Proc IEEE

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

50

International Conference on Pervasive Computing and Communications (PerComʹ03) Dallas‐Fort Worth TX Mar 23‐26 2003 pp 23‐26 [14] Z Xiang S Song J Chen H Wang J Huang and X Gao ldquoA wireless LAN‐based indoor positioning technologyʺ IBM Journal of Research and Development vol 48 no 56 pp 617‐626 SeptNov 2004 [15] R Want A Hopper V Falcao and J Gibbons ldquoThe active badge location systemʺACM Transactions on Information Systems vol 40 no 1 pp 91‐102 Jan 1992 [16] N B Priyantha A Chakraborty and H Balakrishnan ldquoThe cricket location‐support systemʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ00) Boston MA Aug 2000 pp 32‐43 [17] Holger K Willig A (2005) Protocols and Architectures for Wireless Sensor Networks London [18] IEEE (2003) Part 154 Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low‐Rate Wireless Personal Area Networks (LR‐WPANs) New York [19] Chipcon CC2420 802154 compliant radio httpwwwchipconcom [20] ZigBee Specification v10 ZigBee Alliance [21] Dimitrios Lymberopoulos Quentin Lindsey and Andreas Savvides ldquoAn Empirical Analysis of Radio Signal Strength Variability in IEEE 802154 Networks using Monopole Antennasrdquo [22] Kiran Yedavalli Bhaskar Krishnamachari Sharmila Ravula and Bhaskar Srinivasan ldquoEcolocation A sequence based technique for RF localization in wireless sensor networksrdquo Robert Bosch Research and Technology Centre Palo Alto CA

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

38

5 RESULTS This chapter presents how the system parameters such as number of samples mean or median etchellipare finalized All the tests are performed in masterrsquos room at Mid Sweden University This chapter is divided into three subsections First in Section 51 it reviews results obtained from samples test Section 52 discusses one dimensional test results and finally section 53 discusses results of two dimensional tests

51 Measurement setup A Dell laptop was connected to a SENTIO platform and is loaded with software (Appendix 2) This setup was used to determine number of samples required during object tracking mean or median position estimation algorithm and one dimensional test The setup is as follows

BC - 1030BC - 1030

BC

- 1030

BC

- 1

03

0

BC

- 1

03

0

BC

- 1

03

0

BC -

103

0

BC - 1030BC - 1030

BC

- 10

30

BC -

103

0BC

- 103

0

BC

- 1030

BC - 1030 BC - 1030

BC - 1030BC - 1030

BC -

103

0

BC - 1030

AP 1AP 2

BS

HEWLETT

PACKARD

Figure 19 Test bed for one dimensional test in Masters Room

Two SENTIO nodes (AP1 and AP2 as shown above) were separated by a distance of 10 meters and these sensors are known as fixed sensors which are fixed to the ceiling such that there antenna is faced towards the ground They are linear is position A BS was connected to Dell laptop and it was placed in such a way that its antenna was faced towards the ceiling Another SENTIO

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

39

node was used as an object and its orientation was same as BS Figure 19 represents the complete setup for object tracking application Mobile node was moved between these fixed ones at regular intervals of one meter and RSSI values were logged at each interval

52 Number of samples test This test was performed to determine number of samples required for accurate positioning and either mean of the obtained RSSI values or median of them Figure 19 represents the setup for number of samples test Once the setup is ready the measurements are taken as follows

1 An object was tagged with SENTIO node and was placed stationary linear to the fixed sensors

2 10000 samples (RSSI) were taken at regular intervals of 1 meter up to 10 meters

3 The sample values were logged at every interval 4 Using these logged samples I have plotted graphs such as distribution

of RSS values (figure 20 21) meanmedian at intervals of 1 meter using MATLAB

5 The mean and median for various numbers of samples is as shown in figure 22

Figure 20 Histogram plot of RSSI values at 1m and 4m

Figure 21 Histogram plot of RSSI values at 6m and 8m

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

40

Figure 22 Distance vs RSSI

From the above graphs following conclusions were made 1 From the histogram plots we can observe that the distribution of RSSI

values follow Gaussians distribution law 2 As we can see from figure 22 that median curve has drastic changes

when compared to mean curve Hence mean is considered while estimating the position

3 From the observations we conclude that the number of samples does not matter since mean curve tends to be same for various numbers of samples

4 Hence 10000 samples are considered in order to get a proper location fingerprint during off‐line phase and during online phase 50 samples are enough to estimate the position of object

53 0ne dimensional test Measurement setup for one dimensional test is as shown in figure 19 This test was conducted to check the performance of algorithm and how accurate it was At first ie during offline phase fingerprints at each interval averaging 10000 samples were taken During online phase Euclidean algorithm was used estimate the position averaging 50 samples of RSSI From the results we

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

41

can realize that the error was less than 2 meters Table 12 shows the result of location estimation in one dimensional case

Table 12 One dimensional position estimation results

54 Two dimensional test This test is the final stage of my thesis work Experimental testbed for this stage is as shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors fixed at the corners of the room and a BS at the centre of the room There are 18 pre‐determined locations where the fingerprints (RSSI) were taken The fingerprints are as shown in Appendix 1 All the readings were taken in presence of userrsquos The algorithm for position estimation is shown in Appendix 2 Position estimation test is performed as follows

Tagged object is placed at random inside the masterrsquos room All the sensors including the base station and object are switched on Using the algorithm (Appendix 2) the BS estimates the position of the object and displays it on the PC

This test was performed using various numbers of samples to test which has a better accuracy Figure 23 represents the error rate using various numbers of samples From figure 23 we can conclude that 50 numbers of samples is enough to estimate the position of object with an accuracy of 3m

55 Causes of error in location determination The randomness of RSS is the major cause of error in any indoor

positioning system that uses the RSS for location inference If there is no randomness in the RSS and its location fingerprint every indoor positioning systems of this type will have no location detection error For example I have recorded RSS at one particular location during different times of the day From figures 24 and 25 you can observe that RSS values are different at different times of day

The performance of indoor positioning systems depends greatly on the separation of location fingerprints A location fingerprint

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

42

corresponding to a location can be identified correctly if it is difficult to classify it as another fingerprint by a pattern classifier

Path loss is one of the basic signal propagation properties of the RSS that best describes attenuation of transmit signal power over a distance between an access point and a mobile device

Presence of Userrsquos or userrsquos orientation affects RSS

Figure 23 Error Rate vs Number of samples

Figure 24 RSS distribution at 214 pm

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

43

Figure 25 RSS distribution at 100pm and 1100 am

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

6 DISCUSSIONS10102006

44

6 DISCUSSIONS 61 Summary and Conclusion While empirical results and performance studies of positioning systems based on location fingerprint have been presented in this report analytical model that can be used as a framework for efficiently designing the positioning systems are not available This thesis design work uses general principles of position estimation such as position estimation of sensors grid spacing etc This design work focuses on accuracy and precision The concept of location fingerprint technique based on RSS us explained in detail in chapter 2 It is found that RSS is random It is possible to approximate the RSS as being normally distributed While the RSS random process is non‐stationary the mean is more or less constant and could be used as the fingerprint of the location Model designed for position estimation is as shown in chapter 4 Accuracy and precision are the two major performance matrices in this study Uniform grid spacing has been used From the experimental analysis I have obtained an accuracy of 3 meters and 35 precision The sensitivity analysis among the system parameters and the performance matrices were done using the model as shown in chapter 4 For instance our study found that the minimum distance between two adjacent positions also affects the positioning performance This is due to the randomness of RSS patterns induced by the ever changing indoor environment

62 Future work The research in this thesis provides a ground work for the future study of efficient design of indoor positioning system The RSSI based position estimation still has limitations First the RSSI value is time dependent Thus the future study should address this issue Second the movement of object is not considered Third orientations of sensors affect the performance which should be taken care off Fourth to improve the performance of the system we need to find a solution to deal with those incomplete or missing data during the off‐line and the on‐line phase Fifth position estimation on multi‐floor and three‐dimensional coordinates is not available

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 110102006

45

APPENDIX 1

Location Fingerprints of Masters Room Time 1315 Observation Time 20 minutes Number of Samples 10000

People Presence SNo Co-ordinates AP1 (0x0001) AP2 (0x0002) AP3 (0x0004) AP4 (0x0008)

1 11 -24 -16 -27 -33 2 13 -26 -8 -34 -31 3 15 -43 -9 -39 -25 4 32 -21 -11 -27 -33 5 61 -6 -23 -32 -28 6 64 -20 -31 -25 -34 7 35 -23 -35 -29 -38 8 37 -29 -35 -22 -27 9 39 -38 -29 -21 -27 10 67 -27 -23 -20 -23 11 611 -26 -29 -23 -30 12 613 -35 -32 -2 -31 13 513 -34 -31 -20 -26 14 11 -39 -22 -29 -17 15 212 -37 -42 -28 -31 16 -212 -38 -33 -29 -6 17 -21 -42 -29 -33 -19 18 -38 -40 -23 -35 -22

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 210102006

46

APPENDIX 2

Begin

Open radio map and raw data files for write

Intialize radio map in memory

Check whether the

Packet is data or Beacon packet If data packet

Save the data to register

Check if data is

Received from all

sensors

Estimate the position of static

Display the position on PC

End Yes

No

A

A

Yes

No

Flow chart representing the main algorithm for position estimation

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

47

APPENDIX 3

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

48

Distribution of RSSI

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

49

REFERENCES [1] T S Rappaport J H Reed and B D Woerner ldquoPosition location using wireless communications on highways of the futureʺ IEEE Commun Mag vol 34 no 10 pp33‐41 Oct 1996 [2] A M Ladd K E Bekris G Marceau A Rudys L E Kavraki and D S Wallach rdquoRobotics‐based location sensing using wireless ethernetʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ02) 2002 pp 227‐238 [3] I Ramani R Bharadwaja and P V Rangan ldquoLocation tracking for media appliances in wireless home networksʺ in Proc International Conference on Multimedia and Expo (ICMEʹ03) July 2003 pp 769‐772 [4] K Pahlavan X Li and J P Makela ldquoIndoor geolocation science and technologyʺ IEEE Commun Mag vol 40 no 2 pp 112‐118 Feb 2002 [5] Konard Lorincz and Matt Welsh ldquoMote Track A Robust Decentralized Approach to RF‐Based Location Trackingrdquo [6] Paramvir Bahl and Venkata N Padmanabham ldquoRADAR An In‐Building RF‐based User Location and Tracking Systemrdquo [7] Kamol Kaemarungsi ldquoDesign of Indoor positioning system based on location fingerprint techiniquerdquo University of Pittsburgh 2005 [8] Fredrik Linnarsson Cheng Peng and Bengt Oelmann ldquoSENTIO A Hardware Platform for Rapid Prototyping of Wireless Sensor Networksrdquo [9] S Saha K Chaudhuri D Sanghi and P Bhagwat ldquoLocation determination of a mobile device using IEEE 80211b access point signalsʺ in Proc IEEE Wireless Communications and Networking Conference (WCNCʹ03) New Orleans LA Mar 2003 pp 1987‐1992 [10] T Roos P Myllymaki H Tirri P Misikangas and J Sievanen ldquoA probabilistic approach to WLAN user location estimationʺ International Journal of Wireless Information Networks vol 9 no 3 pp 155‐164 July 2002 [11] R Battiti M Brunato and A Villani ldquoStatistical learning theory for location fingerprinting in wireless LANrsquosʺ Technical Report Oct 2002 [Online] Available httprtmscienceunitnitraquobattitiarchive86pdf [12] P Prasithsangaree P Krishnamurthy and P K Chrysanthis ldquoAn indoor position location with wireless LANsʺ in Proc IEEE International Symposium on Personal Indoor and Mobile Radio Communications (PIMRCʹ02) Lisbon Portugal Sept 2002 [13] M A Youssef A Agrawala and A U Shankar ldquoWLAN location determination via clustering and probability distributionsʺ in Proc IEEE

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

50

International Conference on Pervasive Computing and Communications (PerComʹ03) Dallas‐Fort Worth TX Mar 23‐26 2003 pp 23‐26 [14] Z Xiang S Song J Chen H Wang J Huang and X Gao ldquoA wireless LAN‐based indoor positioning technologyʺ IBM Journal of Research and Development vol 48 no 56 pp 617‐626 SeptNov 2004 [15] R Want A Hopper V Falcao and J Gibbons ldquoThe active badge location systemʺACM Transactions on Information Systems vol 40 no 1 pp 91‐102 Jan 1992 [16] N B Priyantha A Chakraborty and H Balakrishnan ldquoThe cricket location‐support systemʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ00) Boston MA Aug 2000 pp 32‐43 [17] Holger K Willig A (2005) Protocols and Architectures for Wireless Sensor Networks London [18] IEEE (2003) Part 154 Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low‐Rate Wireless Personal Area Networks (LR‐WPANs) New York [19] Chipcon CC2420 802154 compliant radio httpwwwchipconcom [20] ZigBee Specification v10 ZigBee Alliance [21] Dimitrios Lymberopoulos Quentin Lindsey and Andreas Savvides ldquoAn Empirical Analysis of Radio Signal Strength Variability in IEEE 802154 Networks using Monopole Antennasrdquo [22] Kiran Yedavalli Bhaskar Krishnamachari Sharmila Ravula and Bhaskar Srinivasan ldquoEcolocation A sequence based technique for RF localization in wireless sensor networksrdquo Robert Bosch Research and Technology Centre Palo Alto CA

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

39

node was used as an object and its orientation was same as BS Figure 19 represents the complete setup for object tracking application Mobile node was moved between these fixed ones at regular intervals of one meter and RSSI values were logged at each interval

52 Number of samples test This test was performed to determine number of samples required for accurate positioning and either mean of the obtained RSSI values or median of them Figure 19 represents the setup for number of samples test Once the setup is ready the measurements are taken as follows

1 An object was tagged with SENTIO node and was placed stationary linear to the fixed sensors

2 10000 samples (RSSI) were taken at regular intervals of 1 meter up to 10 meters

3 The sample values were logged at every interval 4 Using these logged samples I have plotted graphs such as distribution

of RSS values (figure 20 21) meanmedian at intervals of 1 meter using MATLAB

5 The mean and median for various numbers of samples is as shown in figure 22

Figure 20 Histogram plot of RSSI values at 1m and 4m

Figure 21 Histogram plot of RSSI values at 6m and 8m

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

40

Figure 22 Distance vs RSSI

From the above graphs following conclusions were made 1 From the histogram plots we can observe that the distribution of RSSI

values follow Gaussians distribution law 2 As we can see from figure 22 that median curve has drastic changes

when compared to mean curve Hence mean is considered while estimating the position

3 From the observations we conclude that the number of samples does not matter since mean curve tends to be same for various numbers of samples

4 Hence 10000 samples are considered in order to get a proper location fingerprint during off‐line phase and during online phase 50 samples are enough to estimate the position of object

53 0ne dimensional test Measurement setup for one dimensional test is as shown in figure 19 This test was conducted to check the performance of algorithm and how accurate it was At first ie during offline phase fingerprints at each interval averaging 10000 samples were taken During online phase Euclidean algorithm was used estimate the position averaging 50 samples of RSSI From the results we

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

41

can realize that the error was less than 2 meters Table 12 shows the result of location estimation in one dimensional case

Table 12 One dimensional position estimation results

54 Two dimensional test This test is the final stage of my thesis work Experimental testbed for this stage is as shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors fixed at the corners of the room and a BS at the centre of the room There are 18 pre‐determined locations where the fingerprints (RSSI) were taken The fingerprints are as shown in Appendix 1 All the readings were taken in presence of userrsquos The algorithm for position estimation is shown in Appendix 2 Position estimation test is performed as follows

Tagged object is placed at random inside the masterrsquos room All the sensors including the base station and object are switched on Using the algorithm (Appendix 2) the BS estimates the position of the object and displays it on the PC

This test was performed using various numbers of samples to test which has a better accuracy Figure 23 represents the error rate using various numbers of samples From figure 23 we can conclude that 50 numbers of samples is enough to estimate the position of object with an accuracy of 3m

55 Causes of error in location determination The randomness of RSS is the major cause of error in any indoor

positioning system that uses the RSS for location inference If there is no randomness in the RSS and its location fingerprint every indoor positioning systems of this type will have no location detection error For example I have recorded RSS at one particular location during different times of the day From figures 24 and 25 you can observe that RSS values are different at different times of day

The performance of indoor positioning systems depends greatly on the separation of location fingerprints A location fingerprint

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

42

corresponding to a location can be identified correctly if it is difficult to classify it as another fingerprint by a pattern classifier

Path loss is one of the basic signal propagation properties of the RSS that best describes attenuation of transmit signal power over a distance between an access point and a mobile device

Presence of Userrsquos or userrsquos orientation affects RSS

Figure 23 Error Rate vs Number of samples

Figure 24 RSS distribution at 214 pm

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

43

Figure 25 RSS distribution at 100pm and 1100 am

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

6 DISCUSSIONS10102006

44

6 DISCUSSIONS 61 Summary and Conclusion While empirical results and performance studies of positioning systems based on location fingerprint have been presented in this report analytical model that can be used as a framework for efficiently designing the positioning systems are not available This thesis design work uses general principles of position estimation such as position estimation of sensors grid spacing etc This design work focuses on accuracy and precision The concept of location fingerprint technique based on RSS us explained in detail in chapter 2 It is found that RSS is random It is possible to approximate the RSS as being normally distributed While the RSS random process is non‐stationary the mean is more or less constant and could be used as the fingerprint of the location Model designed for position estimation is as shown in chapter 4 Accuracy and precision are the two major performance matrices in this study Uniform grid spacing has been used From the experimental analysis I have obtained an accuracy of 3 meters and 35 precision The sensitivity analysis among the system parameters and the performance matrices were done using the model as shown in chapter 4 For instance our study found that the minimum distance between two adjacent positions also affects the positioning performance This is due to the randomness of RSS patterns induced by the ever changing indoor environment

62 Future work The research in this thesis provides a ground work for the future study of efficient design of indoor positioning system The RSSI based position estimation still has limitations First the RSSI value is time dependent Thus the future study should address this issue Second the movement of object is not considered Third orientations of sensors affect the performance which should be taken care off Fourth to improve the performance of the system we need to find a solution to deal with those incomplete or missing data during the off‐line and the on‐line phase Fifth position estimation on multi‐floor and three‐dimensional coordinates is not available

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 110102006

45

APPENDIX 1

Location Fingerprints of Masters Room Time 1315 Observation Time 20 minutes Number of Samples 10000

People Presence SNo Co-ordinates AP1 (0x0001) AP2 (0x0002) AP3 (0x0004) AP4 (0x0008)

1 11 -24 -16 -27 -33 2 13 -26 -8 -34 -31 3 15 -43 -9 -39 -25 4 32 -21 -11 -27 -33 5 61 -6 -23 -32 -28 6 64 -20 -31 -25 -34 7 35 -23 -35 -29 -38 8 37 -29 -35 -22 -27 9 39 -38 -29 -21 -27 10 67 -27 -23 -20 -23 11 611 -26 -29 -23 -30 12 613 -35 -32 -2 -31 13 513 -34 -31 -20 -26 14 11 -39 -22 -29 -17 15 212 -37 -42 -28 -31 16 -212 -38 -33 -29 -6 17 -21 -42 -29 -33 -19 18 -38 -40 -23 -35 -22

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 210102006

46

APPENDIX 2

Begin

Open radio map and raw data files for write

Intialize radio map in memory

Check whether the

Packet is data or Beacon packet If data packet

Save the data to register

Check if data is

Received from all

sensors

Estimate the position of static

Display the position on PC

End Yes

No

A

A

Yes

No

Flow chart representing the main algorithm for position estimation

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

47

APPENDIX 3

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

48

Distribution of RSSI

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

49

REFERENCES [1] T S Rappaport J H Reed and B D Woerner ldquoPosition location using wireless communications on highways of the futureʺ IEEE Commun Mag vol 34 no 10 pp33‐41 Oct 1996 [2] A M Ladd K E Bekris G Marceau A Rudys L E Kavraki and D S Wallach rdquoRobotics‐based location sensing using wireless ethernetʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ02) 2002 pp 227‐238 [3] I Ramani R Bharadwaja and P V Rangan ldquoLocation tracking for media appliances in wireless home networksʺ in Proc International Conference on Multimedia and Expo (ICMEʹ03) July 2003 pp 769‐772 [4] K Pahlavan X Li and J P Makela ldquoIndoor geolocation science and technologyʺ IEEE Commun Mag vol 40 no 2 pp 112‐118 Feb 2002 [5] Konard Lorincz and Matt Welsh ldquoMote Track A Robust Decentralized Approach to RF‐Based Location Trackingrdquo [6] Paramvir Bahl and Venkata N Padmanabham ldquoRADAR An In‐Building RF‐based User Location and Tracking Systemrdquo [7] Kamol Kaemarungsi ldquoDesign of Indoor positioning system based on location fingerprint techiniquerdquo University of Pittsburgh 2005 [8] Fredrik Linnarsson Cheng Peng and Bengt Oelmann ldquoSENTIO A Hardware Platform for Rapid Prototyping of Wireless Sensor Networksrdquo [9] S Saha K Chaudhuri D Sanghi and P Bhagwat ldquoLocation determination of a mobile device using IEEE 80211b access point signalsʺ in Proc IEEE Wireless Communications and Networking Conference (WCNCʹ03) New Orleans LA Mar 2003 pp 1987‐1992 [10] T Roos P Myllymaki H Tirri P Misikangas and J Sievanen ldquoA probabilistic approach to WLAN user location estimationʺ International Journal of Wireless Information Networks vol 9 no 3 pp 155‐164 July 2002 [11] R Battiti M Brunato and A Villani ldquoStatistical learning theory for location fingerprinting in wireless LANrsquosʺ Technical Report Oct 2002 [Online] Available httprtmscienceunitnitraquobattitiarchive86pdf [12] P Prasithsangaree P Krishnamurthy and P K Chrysanthis ldquoAn indoor position location with wireless LANsʺ in Proc IEEE International Symposium on Personal Indoor and Mobile Radio Communications (PIMRCʹ02) Lisbon Portugal Sept 2002 [13] M A Youssef A Agrawala and A U Shankar ldquoWLAN location determination via clustering and probability distributionsʺ in Proc IEEE

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

50

International Conference on Pervasive Computing and Communications (PerComʹ03) Dallas‐Fort Worth TX Mar 23‐26 2003 pp 23‐26 [14] Z Xiang S Song J Chen H Wang J Huang and X Gao ldquoA wireless LAN‐based indoor positioning technologyʺ IBM Journal of Research and Development vol 48 no 56 pp 617‐626 SeptNov 2004 [15] R Want A Hopper V Falcao and J Gibbons ldquoThe active badge location systemʺACM Transactions on Information Systems vol 40 no 1 pp 91‐102 Jan 1992 [16] N B Priyantha A Chakraborty and H Balakrishnan ldquoThe cricket location‐support systemʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ00) Boston MA Aug 2000 pp 32‐43 [17] Holger K Willig A (2005) Protocols and Architectures for Wireless Sensor Networks London [18] IEEE (2003) Part 154 Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low‐Rate Wireless Personal Area Networks (LR‐WPANs) New York [19] Chipcon CC2420 802154 compliant radio httpwwwchipconcom [20] ZigBee Specification v10 ZigBee Alliance [21] Dimitrios Lymberopoulos Quentin Lindsey and Andreas Savvides ldquoAn Empirical Analysis of Radio Signal Strength Variability in IEEE 802154 Networks using Monopole Antennasrdquo [22] Kiran Yedavalli Bhaskar Krishnamachari Sharmila Ravula and Bhaskar Srinivasan ldquoEcolocation A sequence based technique for RF localization in wireless sensor networksrdquo Robert Bosch Research and Technology Centre Palo Alto CA

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

40

Figure 22 Distance vs RSSI

From the above graphs following conclusions were made 1 From the histogram plots we can observe that the distribution of RSSI

values follow Gaussians distribution law 2 As we can see from figure 22 that median curve has drastic changes

when compared to mean curve Hence mean is considered while estimating the position

3 From the observations we conclude that the number of samples does not matter since mean curve tends to be same for various numbers of samples

4 Hence 10000 samples are considered in order to get a proper location fingerprint during off‐line phase and during online phase 50 samples are enough to estimate the position of object

53 0ne dimensional test Measurement setup for one dimensional test is as shown in figure 19 This test was conducted to check the performance of algorithm and how accurate it was At first ie during offline phase fingerprints at each interval averaging 10000 samples were taken During online phase Euclidean algorithm was used estimate the position averaging 50 samples of RSSI From the results we

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

41

can realize that the error was less than 2 meters Table 12 shows the result of location estimation in one dimensional case

Table 12 One dimensional position estimation results

54 Two dimensional test This test is the final stage of my thesis work Experimental testbed for this stage is as shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors fixed at the corners of the room and a BS at the centre of the room There are 18 pre‐determined locations where the fingerprints (RSSI) were taken The fingerprints are as shown in Appendix 1 All the readings were taken in presence of userrsquos The algorithm for position estimation is shown in Appendix 2 Position estimation test is performed as follows

Tagged object is placed at random inside the masterrsquos room All the sensors including the base station and object are switched on Using the algorithm (Appendix 2) the BS estimates the position of the object and displays it on the PC

This test was performed using various numbers of samples to test which has a better accuracy Figure 23 represents the error rate using various numbers of samples From figure 23 we can conclude that 50 numbers of samples is enough to estimate the position of object with an accuracy of 3m

55 Causes of error in location determination The randomness of RSS is the major cause of error in any indoor

positioning system that uses the RSS for location inference If there is no randomness in the RSS and its location fingerprint every indoor positioning systems of this type will have no location detection error For example I have recorded RSS at one particular location during different times of the day From figures 24 and 25 you can observe that RSS values are different at different times of day

The performance of indoor positioning systems depends greatly on the separation of location fingerprints A location fingerprint

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

42

corresponding to a location can be identified correctly if it is difficult to classify it as another fingerprint by a pattern classifier

Path loss is one of the basic signal propagation properties of the RSS that best describes attenuation of transmit signal power over a distance between an access point and a mobile device

Presence of Userrsquos or userrsquos orientation affects RSS

Figure 23 Error Rate vs Number of samples

Figure 24 RSS distribution at 214 pm

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

43

Figure 25 RSS distribution at 100pm and 1100 am

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

6 DISCUSSIONS10102006

44

6 DISCUSSIONS 61 Summary and Conclusion While empirical results and performance studies of positioning systems based on location fingerprint have been presented in this report analytical model that can be used as a framework for efficiently designing the positioning systems are not available This thesis design work uses general principles of position estimation such as position estimation of sensors grid spacing etc This design work focuses on accuracy and precision The concept of location fingerprint technique based on RSS us explained in detail in chapter 2 It is found that RSS is random It is possible to approximate the RSS as being normally distributed While the RSS random process is non‐stationary the mean is more or less constant and could be used as the fingerprint of the location Model designed for position estimation is as shown in chapter 4 Accuracy and precision are the two major performance matrices in this study Uniform grid spacing has been used From the experimental analysis I have obtained an accuracy of 3 meters and 35 precision The sensitivity analysis among the system parameters and the performance matrices were done using the model as shown in chapter 4 For instance our study found that the minimum distance between two adjacent positions also affects the positioning performance This is due to the randomness of RSS patterns induced by the ever changing indoor environment

62 Future work The research in this thesis provides a ground work for the future study of efficient design of indoor positioning system The RSSI based position estimation still has limitations First the RSSI value is time dependent Thus the future study should address this issue Second the movement of object is not considered Third orientations of sensors affect the performance which should be taken care off Fourth to improve the performance of the system we need to find a solution to deal with those incomplete or missing data during the off‐line and the on‐line phase Fifth position estimation on multi‐floor and three‐dimensional coordinates is not available

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 110102006

45

APPENDIX 1

Location Fingerprints of Masters Room Time 1315 Observation Time 20 minutes Number of Samples 10000

People Presence SNo Co-ordinates AP1 (0x0001) AP2 (0x0002) AP3 (0x0004) AP4 (0x0008)

1 11 -24 -16 -27 -33 2 13 -26 -8 -34 -31 3 15 -43 -9 -39 -25 4 32 -21 -11 -27 -33 5 61 -6 -23 -32 -28 6 64 -20 -31 -25 -34 7 35 -23 -35 -29 -38 8 37 -29 -35 -22 -27 9 39 -38 -29 -21 -27 10 67 -27 -23 -20 -23 11 611 -26 -29 -23 -30 12 613 -35 -32 -2 -31 13 513 -34 -31 -20 -26 14 11 -39 -22 -29 -17 15 212 -37 -42 -28 -31 16 -212 -38 -33 -29 -6 17 -21 -42 -29 -33 -19 18 -38 -40 -23 -35 -22

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 210102006

46

APPENDIX 2

Begin

Open radio map and raw data files for write

Intialize radio map in memory

Check whether the

Packet is data or Beacon packet If data packet

Save the data to register

Check if data is

Received from all

sensors

Estimate the position of static

Display the position on PC

End Yes

No

A

A

Yes

No

Flow chart representing the main algorithm for position estimation

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

47

APPENDIX 3

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

48

Distribution of RSSI

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

49

REFERENCES [1] T S Rappaport J H Reed and B D Woerner ldquoPosition location using wireless communications on highways of the futureʺ IEEE Commun Mag vol 34 no 10 pp33‐41 Oct 1996 [2] A M Ladd K E Bekris G Marceau A Rudys L E Kavraki and D S Wallach rdquoRobotics‐based location sensing using wireless ethernetʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ02) 2002 pp 227‐238 [3] I Ramani R Bharadwaja and P V Rangan ldquoLocation tracking for media appliances in wireless home networksʺ in Proc International Conference on Multimedia and Expo (ICMEʹ03) July 2003 pp 769‐772 [4] K Pahlavan X Li and J P Makela ldquoIndoor geolocation science and technologyʺ IEEE Commun Mag vol 40 no 2 pp 112‐118 Feb 2002 [5] Konard Lorincz and Matt Welsh ldquoMote Track A Robust Decentralized Approach to RF‐Based Location Trackingrdquo [6] Paramvir Bahl and Venkata N Padmanabham ldquoRADAR An In‐Building RF‐based User Location and Tracking Systemrdquo [7] Kamol Kaemarungsi ldquoDesign of Indoor positioning system based on location fingerprint techiniquerdquo University of Pittsburgh 2005 [8] Fredrik Linnarsson Cheng Peng and Bengt Oelmann ldquoSENTIO A Hardware Platform for Rapid Prototyping of Wireless Sensor Networksrdquo [9] S Saha K Chaudhuri D Sanghi and P Bhagwat ldquoLocation determination of a mobile device using IEEE 80211b access point signalsʺ in Proc IEEE Wireless Communications and Networking Conference (WCNCʹ03) New Orleans LA Mar 2003 pp 1987‐1992 [10] T Roos P Myllymaki H Tirri P Misikangas and J Sievanen ldquoA probabilistic approach to WLAN user location estimationʺ International Journal of Wireless Information Networks vol 9 no 3 pp 155‐164 July 2002 [11] R Battiti M Brunato and A Villani ldquoStatistical learning theory for location fingerprinting in wireless LANrsquosʺ Technical Report Oct 2002 [Online] Available httprtmscienceunitnitraquobattitiarchive86pdf [12] P Prasithsangaree P Krishnamurthy and P K Chrysanthis ldquoAn indoor position location with wireless LANsʺ in Proc IEEE International Symposium on Personal Indoor and Mobile Radio Communications (PIMRCʹ02) Lisbon Portugal Sept 2002 [13] M A Youssef A Agrawala and A U Shankar ldquoWLAN location determination via clustering and probability distributionsʺ in Proc IEEE

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

50

International Conference on Pervasive Computing and Communications (PerComʹ03) Dallas‐Fort Worth TX Mar 23‐26 2003 pp 23‐26 [14] Z Xiang S Song J Chen H Wang J Huang and X Gao ldquoA wireless LAN‐based indoor positioning technologyʺ IBM Journal of Research and Development vol 48 no 56 pp 617‐626 SeptNov 2004 [15] R Want A Hopper V Falcao and J Gibbons ldquoThe active badge location systemʺACM Transactions on Information Systems vol 40 no 1 pp 91‐102 Jan 1992 [16] N B Priyantha A Chakraborty and H Balakrishnan ldquoThe cricket location‐support systemʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ00) Boston MA Aug 2000 pp 32‐43 [17] Holger K Willig A (2005) Protocols and Architectures for Wireless Sensor Networks London [18] IEEE (2003) Part 154 Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low‐Rate Wireless Personal Area Networks (LR‐WPANs) New York [19] Chipcon CC2420 802154 compliant radio httpwwwchipconcom [20] ZigBee Specification v10 ZigBee Alliance [21] Dimitrios Lymberopoulos Quentin Lindsey and Andreas Savvides ldquoAn Empirical Analysis of Radio Signal Strength Variability in IEEE 802154 Networks using Monopole Antennasrdquo [22] Kiran Yedavalli Bhaskar Krishnamachari Sharmila Ravula and Bhaskar Srinivasan ldquoEcolocation A sequence based technique for RF localization in wireless sensor networksrdquo Robert Bosch Research and Technology Centre Palo Alto CA

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

41

can realize that the error was less than 2 meters Table 12 shows the result of location estimation in one dimensional case

Table 12 One dimensional position estimation results

54 Two dimensional test This test is the final stage of my thesis work Experimental testbed for this stage is as shown in figure 16 AP1 AP2 AP3 and AP4 are the fixed sensors fixed at the corners of the room and a BS at the centre of the room There are 18 pre‐determined locations where the fingerprints (RSSI) were taken The fingerprints are as shown in Appendix 1 All the readings were taken in presence of userrsquos The algorithm for position estimation is shown in Appendix 2 Position estimation test is performed as follows

Tagged object is placed at random inside the masterrsquos room All the sensors including the base station and object are switched on Using the algorithm (Appendix 2) the BS estimates the position of the object and displays it on the PC

This test was performed using various numbers of samples to test which has a better accuracy Figure 23 represents the error rate using various numbers of samples From figure 23 we can conclude that 50 numbers of samples is enough to estimate the position of object with an accuracy of 3m

55 Causes of error in location determination The randomness of RSS is the major cause of error in any indoor

positioning system that uses the RSS for location inference If there is no randomness in the RSS and its location fingerprint every indoor positioning systems of this type will have no location detection error For example I have recorded RSS at one particular location during different times of the day From figures 24 and 25 you can observe that RSS values are different at different times of day

The performance of indoor positioning systems depends greatly on the separation of location fingerprints A location fingerprint

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

42

corresponding to a location can be identified correctly if it is difficult to classify it as another fingerprint by a pattern classifier

Path loss is one of the basic signal propagation properties of the RSS that best describes attenuation of transmit signal power over a distance between an access point and a mobile device

Presence of Userrsquos or userrsquos orientation affects RSS

Figure 23 Error Rate vs Number of samples

Figure 24 RSS distribution at 214 pm

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

43

Figure 25 RSS distribution at 100pm and 1100 am

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

6 DISCUSSIONS10102006

44

6 DISCUSSIONS 61 Summary and Conclusion While empirical results and performance studies of positioning systems based on location fingerprint have been presented in this report analytical model that can be used as a framework for efficiently designing the positioning systems are not available This thesis design work uses general principles of position estimation such as position estimation of sensors grid spacing etc This design work focuses on accuracy and precision The concept of location fingerprint technique based on RSS us explained in detail in chapter 2 It is found that RSS is random It is possible to approximate the RSS as being normally distributed While the RSS random process is non‐stationary the mean is more or less constant and could be used as the fingerprint of the location Model designed for position estimation is as shown in chapter 4 Accuracy and precision are the two major performance matrices in this study Uniform grid spacing has been used From the experimental analysis I have obtained an accuracy of 3 meters and 35 precision The sensitivity analysis among the system parameters and the performance matrices were done using the model as shown in chapter 4 For instance our study found that the minimum distance between two adjacent positions also affects the positioning performance This is due to the randomness of RSS patterns induced by the ever changing indoor environment

62 Future work The research in this thesis provides a ground work for the future study of efficient design of indoor positioning system The RSSI based position estimation still has limitations First the RSSI value is time dependent Thus the future study should address this issue Second the movement of object is not considered Third orientations of sensors affect the performance which should be taken care off Fourth to improve the performance of the system we need to find a solution to deal with those incomplete or missing data during the off‐line and the on‐line phase Fifth position estimation on multi‐floor and three‐dimensional coordinates is not available

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 110102006

45

APPENDIX 1

Location Fingerprints of Masters Room Time 1315 Observation Time 20 minutes Number of Samples 10000

People Presence SNo Co-ordinates AP1 (0x0001) AP2 (0x0002) AP3 (0x0004) AP4 (0x0008)

1 11 -24 -16 -27 -33 2 13 -26 -8 -34 -31 3 15 -43 -9 -39 -25 4 32 -21 -11 -27 -33 5 61 -6 -23 -32 -28 6 64 -20 -31 -25 -34 7 35 -23 -35 -29 -38 8 37 -29 -35 -22 -27 9 39 -38 -29 -21 -27 10 67 -27 -23 -20 -23 11 611 -26 -29 -23 -30 12 613 -35 -32 -2 -31 13 513 -34 -31 -20 -26 14 11 -39 -22 -29 -17 15 212 -37 -42 -28 -31 16 -212 -38 -33 -29 -6 17 -21 -42 -29 -33 -19 18 -38 -40 -23 -35 -22

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 210102006

46

APPENDIX 2

Begin

Open radio map and raw data files for write

Intialize radio map in memory

Check whether the

Packet is data or Beacon packet If data packet

Save the data to register

Check if data is

Received from all

sensors

Estimate the position of static

Display the position on PC

End Yes

No

A

A

Yes

No

Flow chart representing the main algorithm for position estimation

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

47

APPENDIX 3

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

48

Distribution of RSSI

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

49

REFERENCES [1] T S Rappaport J H Reed and B D Woerner ldquoPosition location using wireless communications on highways of the futureʺ IEEE Commun Mag vol 34 no 10 pp33‐41 Oct 1996 [2] A M Ladd K E Bekris G Marceau A Rudys L E Kavraki and D S Wallach rdquoRobotics‐based location sensing using wireless ethernetʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ02) 2002 pp 227‐238 [3] I Ramani R Bharadwaja and P V Rangan ldquoLocation tracking for media appliances in wireless home networksʺ in Proc International Conference on Multimedia and Expo (ICMEʹ03) July 2003 pp 769‐772 [4] K Pahlavan X Li and J P Makela ldquoIndoor geolocation science and technologyʺ IEEE Commun Mag vol 40 no 2 pp 112‐118 Feb 2002 [5] Konard Lorincz and Matt Welsh ldquoMote Track A Robust Decentralized Approach to RF‐Based Location Trackingrdquo [6] Paramvir Bahl and Venkata N Padmanabham ldquoRADAR An In‐Building RF‐based User Location and Tracking Systemrdquo [7] Kamol Kaemarungsi ldquoDesign of Indoor positioning system based on location fingerprint techiniquerdquo University of Pittsburgh 2005 [8] Fredrik Linnarsson Cheng Peng and Bengt Oelmann ldquoSENTIO A Hardware Platform for Rapid Prototyping of Wireless Sensor Networksrdquo [9] S Saha K Chaudhuri D Sanghi and P Bhagwat ldquoLocation determination of a mobile device using IEEE 80211b access point signalsʺ in Proc IEEE Wireless Communications and Networking Conference (WCNCʹ03) New Orleans LA Mar 2003 pp 1987‐1992 [10] T Roos P Myllymaki H Tirri P Misikangas and J Sievanen ldquoA probabilistic approach to WLAN user location estimationʺ International Journal of Wireless Information Networks vol 9 no 3 pp 155‐164 July 2002 [11] R Battiti M Brunato and A Villani ldquoStatistical learning theory for location fingerprinting in wireless LANrsquosʺ Technical Report Oct 2002 [Online] Available httprtmscienceunitnitraquobattitiarchive86pdf [12] P Prasithsangaree P Krishnamurthy and P K Chrysanthis ldquoAn indoor position location with wireless LANsʺ in Proc IEEE International Symposium on Personal Indoor and Mobile Radio Communications (PIMRCʹ02) Lisbon Portugal Sept 2002 [13] M A Youssef A Agrawala and A U Shankar ldquoWLAN location determination via clustering and probability distributionsʺ in Proc IEEE

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

50

International Conference on Pervasive Computing and Communications (PerComʹ03) Dallas‐Fort Worth TX Mar 23‐26 2003 pp 23‐26 [14] Z Xiang S Song J Chen H Wang J Huang and X Gao ldquoA wireless LAN‐based indoor positioning technologyʺ IBM Journal of Research and Development vol 48 no 56 pp 617‐626 SeptNov 2004 [15] R Want A Hopper V Falcao and J Gibbons ldquoThe active badge location systemʺACM Transactions on Information Systems vol 40 no 1 pp 91‐102 Jan 1992 [16] N B Priyantha A Chakraborty and H Balakrishnan ldquoThe cricket location‐support systemʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ00) Boston MA Aug 2000 pp 32‐43 [17] Holger K Willig A (2005) Protocols and Architectures for Wireless Sensor Networks London [18] IEEE (2003) Part 154 Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low‐Rate Wireless Personal Area Networks (LR‐WPANs) New York [19] Chipcon CC2420 802154 compliant radio httpwwwchipconcom [20] ZigBee Specification v10 ZigBee Alliance [21] Dimitrios Lymberopoulos Quentin Lindsey and Andreas Savvides ldquoAn Empirical Analysis of Radio Signal Strength Variability in IEEE 802154 Networks using Monopole Antennasrdquo [22] Kiran Yedavalli Bhaskar Krishnamachari Sharmila Ravula and Bhaskar Srinivasan ldquoEcolocation A sequence based technique for RF localization in wireless sensor networksrdquo Robert Bosch Research and Technology Centre Palo Alto CA

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

42

corresponding to a location can be identified correctly if it is difficult to classify it as another fingerprint by a pattern classifier

Path loss is one of the basic signal propagation properties of the RSS that best describes attenuation of transmit signal power over a distance between an access point and a mobile device

Presence of Userrsquos or userrsquos orientation affects RSS

Figure 23 Error Rate vs Number of samples

Figure 24 RSS distribution at 214 pm

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

43

Figure 25 RSS distribution at 100pm and 1100 am

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

6 DISCUSSIONS10102006

44

6 DISCUSSIONS 61 Summary and Conclusion While empirical results and performance studies of positioning systems based on location fingerprint have been presented in this report analytical model that can be used as a framework for efficiently designing the positioning systems are not available This thesis design work uses general principles of position estimation such as position estimation of sensors grid spacing etc This design work focuses on accuracy and precision The concept of location fingerprint technique based on RSS us explained in detail in chapter 2 It is found that RSS is random It is possible to approximate the RSS as being normally distributed While the RSS random process is non‐stationary the mean is more or less constant and could be used as the fingerprint of the location Model designed for position estimation is as shown in chapter 4 Accuracy and precision are the two major performance matrices in this study Uniform grid spacing has been used From the experimental analysis I have obtained an accuracy of 3 meters and 35 precision The sensitivity analysis among the system parameters and the performance matrices were done using the model as shown in chapter 4 For instance our study found that the minimum distance between two adjacent positions also affects the positioning performance This is due to the randomness of RSS patterns induced by the ever changing indoor environment

62 Future work The research in this thesis provides a ground work for the future study of efficient design of indoor positioning system The RSSI based position estimation still has limitations First the RSSI value is time dependent Thus the future study should address this issue Second the movement of object is not considered Third orientations of sensors affect the performance which should be taken care off Fourth to improve the performance of the system we need to find a solution to deal with those incomplete or missing data during the off‐line and the on‐line phase Fifth position estimation on multi‐floor and three‐dimensional coordinates is not available

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 110102006

45

APPENDIX 1

Location Fingerprints of Masters Room Time 1315 Observation Time 20 minutes Number of Samples 10000

People Presence SNo Co-ordinates AP1 (0x0001) AP2 (0x0002) AP3 (0x0004) AP4 (0x0008)

1 11 -24 -16 -27 -33 2 13 -26 -8 -34 -31 3 15 -43 -9 -39 -25 4 32 -21 -11 -27 -33 5 61 -6 -23 -32 -28 6 64 -20 -31 -25 -34 7 35 -23 -35 -29 -38 8 37 -29 -35 -22 -27 9 39 -38 -29 -21 -27 10 67 -27 -23 -20 -23 11 611 -26 -29 -23 -30 12 613 -35 -32 -2 -31 13 513 -34 -31 -20 -26 14 11 -39 -22 -29 -17 15 212 -37 -42 -28 -31 16 -212 -38 -33 -29 -6 17 -21 -42 -29 -33 -19 18 -38 -40 -23 -35 -22

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 210102006

46

APPENDIX 2

Begin

Open radio map and raw data files for write

Intialize radio map in memory

Check whether the

Packet is data or Beacon packet If data packet

Save the data to register

Check if data is

Received from all

sensors

Estimate the position of static

Display the position on PC

End Yes

No

A

A

Yes

No

Flow chart representing the main algorithm for position estimation

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

47

APPENDIX 3

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

48

Distribution of RSSI

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

49

REFERENCES [1] T S Rappaport J H Reed and B D Woerner ldquoPosition location using wireless communications on highways of the futureʺ IEEE Commun Mag vol 34 no 10 pp33‐41 Oct 1996 [2] A M Ladd K E Bekris G Marceau A Rudys L E Kavraki and D S Wallach rdquoRobotics‐based location sensing using wireless ethernetʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ02) 2002 pp 227‐238 [3] I Ramani R Bharadwaja and P V Rangan ldquoLocation tracking for media appliances in wireless home networksʺ in Proc International Conference on Multimedia and Expo (ICMEʹ03) July 2003 pp 769‐772 [4] K Pahlavan X Li and J P Makela ldquoIndoor geolocation science and technologyʺ IEEE Commun Mag vol 40 no 2 pp 112‐118 Feb 2002 [5] Konard Lorincz and Matt Welsh ldquoMote Track A Robust Decentralized Approach to RF‐Based Location Trackingrdquo [6] Paramvir Bahl and Venkata N Padmanabham ldquoRADAR An In‐Building RF‐based User Location and Tracking Systemrdquo [7] Kamol Kaemarungsi ldquoDesign of Indoor positioning system based on location fingerprint techiniquerdquo University of Pittsburgh 2005 [8] Fredrik Linnarsson Cheng Peng and Bengt Oelmann ldquoSENTIO A Hardware Platform for Rapid Prototyping of Wireless Sensor Networksrdquo [9] S Saha K Chaudhuri D Sanghi and P Bhagwat ldquoLocation determination of a mobile device using IEEE 80211b access point signalsʺ in Proc IEEE Wireless Communications and Networking Conference (WCNCʹ03) New Orleans LA Mar 2003 pp 1987‐1992 [10] T Roos P Myllymaki H Tirri P Misikangas and J Sievanen ldquoA probabilistic approach to WLAN user location estimationʺ International Journal of Wireless Information Networks vol 9 no 3 pp 155‐164 July 2002 [11] R Battiti M Brunato and A Villani ldquoStatistical learning theory for location fingerprinting in wireless LANrsquosʺ Technical Report Oct 2002 [Online] Available httprtmscienceunitnitraquobattitiarchive86pdf [12] P Prasithsangaree P Krishnamurthy and P K Chrysanthis ldquoAn indoor position location with wireless LANsʺ in Proc IEEE International Symposium on Personal Indoor and Mobile Radio Communications (PIMRCʹ02) Lisbon Portugal Sept 2002 [13] M A Youssef A Agrawala and A U Shankar ldquoWLAN location determination via clustering and probability distributionsʺ in Proc IEEE

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

50

International Conference on Pervasive Computing and Communications (PerComʹ03) Dallas‐Fort Worth TX Mar 23‐26 2003 pp 23‐26 [14] Z Xiang S Song J Chen H Wang J Huang and X Gao ldquoA wireless LAN‐based indoor positioning technologyʺ IBM Journal of Research and Development vol 48 no 56 pp 617‐626 SeptNov 2004 [15] R Want A Hopper V Falcao and J Gibbons ldquoThe active badge location systemʺACM Transactions on Information Systems vol 40 no 1 pp 91‐102 Jan 1992 [16] N B Priyantha A Chakraborty and H Balakrishnan ldquoThe cricket location‐support systemʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ00) Boston MA Aug 2000 pp 32‐43 [17] Holger K Willig A (2005) Protocols and Architectures for Wireless Sensor Networks London [18] IEEE (2003) Part 154 Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low‐Rate Wireless Personal Area Networks (LR‐WPANs) New York [19] Chipcon CC2420 802154 compliant radio httpwwwchipconcom [20] ZigBee Specification v10 ZigBee Alliance [21] Dimitrios Lymberopoulos Quentin Lindsey and Andreas Savvides ldquoAn Empirical Analysis of Radio Signal Strength Variability in IEEE 802154 Networks using Monopole Antennasrdquo [22] Kiran Yedavalli Bhaskar Krishnamachari Sharmila Ravula and Bhaskar Srinivasan ldquoEcolocation A sequence based technique for RF localization in wireless sensor networksrdquo Robert Bosch Research and Technology Centre Palo Alto CA

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

5 RESULTS10102006

43

Figure 25 RSS distribution at 100pm and 1100 am

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

6 DISCUSSIONS10102006

44

6 DISCUSSIONS 61 Summary and Conclusion While empirical results and performance studies of positioning systems based on location fingerprint have been presented in this report analytical model that can be used as a framework for efficiently designing the positioning systems are not available This thesis design work uses general principles of position estimation such as position estimation of sensors grid spacing etc This design work focuses on accuracy and precision The concept of location fingerprint technique based on RSS us explained in detail in chapter 2 It is found that RSS is random It is possible to approximate the RSS as being normally distributed While the RSS random process is non‐stationary the mean is more or less constant and could be used as the fingerprint of the location Model designed for position estimation is as shown in chapter 4 Accuracy and precision are the two major performance matrices in this study Uniform grid spacing has been used From the experimental analysis I have obtained an accuracy of 3 meters and 35 precision The sensitivity analysis among the system parameters and the performance matrices were done using the model as shown in chapter 4 For instance our study found that the minimum distance between two adjacent positions also affects the positioning performance This is due to the randomness of RSS patterns induced by the ever changing indoor environment

62 Future work The research in this thesis provides a ground work for the future study of efficient design of indoor positioning system The RSSI based position estimation still has limitations First the RSSI value is time dependent Thus the future study should address this issue Second the movement of object is not considered Third orientations of sensors affect the performance which should be taken care off Fourth to improve the performance of the system we need to find a solution to deal with those incomplete or missing data during the off‐line and the on‐line phase Fifth position estimation on multi‐floor and three‐dimensional coordinates is not available

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 110102006

45

APPENDIX 1

Location Fingerprints of Masters Room Time 1315 Observation Time 20 minutes Number of Samples 10000

People Presence SNo Co-ordinates AP1 (0x0001) AP2 (0x0002) AP3 (0x0004) AP4 (0x0008)

1 11 -24 -16 -27 -33 2 13 -26 -8 -34 -31 3 15 -43 -9 -39 -25 4 32 -21 -11 -27 -33 5 61 -6 -23 -32 -28 6 64 -20 -31 -25 -34 7 35 -23 -35 -29 -38 8 37 -29 -35 -22 -27 9 39 -38 -29 -21 -27 10 67 -27 -23 -20 -23 11 611 -26 -29 -23 -30 12 613 -35 -32 -2 -31 13 513 -34 -31 -20 -26 14 11 -39 -22 -29 -17 15 212 -37 -42 -28 -31 16 -212 -38 -33 -29 -6 17 -21 -42 -29 -33 -19 18 -38 -40 -23 -35 -22

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 210102006

46

APPENDIX 2

Begin

Open radio map and raw data files for write

Intialize radio map in memory

Check whether the

Packet is data or Beacon packet If data packet

Save the data to register

Check if data is

Received from all

sensors

Estimate the position of static

Display the position on PC

End Yes

No

A

A

Yes

No

Flow chart representing the main algorithm for position estimation

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

47

APPENDIX 3

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

48

Distribution of RSSI

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

49

REFERENCES [1] T S Rappaport J H Reed and B D Woerner ldquoPosition location using wireless communications on highways of the futureʺ IEEE Commun Mag vol 34 no 10 pp33‐41 Oct 1996 [2] A M Ladd K E Bekris G Marceau A Rudys L E Kavraki and D S Wallach rdquoRobotics‐based location sensing using wireless ethernetʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ02) 2002 pp 227‐238 [3] I Ramani R Bharadwaja and P V Rangan ldquoLocation tracking for media appliances in wireless home networksʺ in Proc International Conference on Multimedia and Expo (ICMEʹ03) July 2003 pp 769‐772 [4] K Pahlavan X Li and J P Makela ldquoIndoor geolocation science and technologyʺ IEEE Commun Mag vol 40 no 2 pp 112‐118 Feb 2002 [5] Konard Lorincz and Matt Welsh ldquoMote Track A Robust Decentralized Approach to RF‐Based Location Trackingrdquo [6] Paramvir Bahl and Venkata N Padmanabham ldquoRADAR An In‐Building RF‐based User Location and Tracking Systemrdquo [7] Kamol Kaemarungsi ldquoDesign of Indoor positioning system based on location fingerprint techiniquerdquo University of Pittsburgh 2005 [8] Fredrik Linnarsson Cheng Peng and Bengt Oelmann ldquoSENTIO A Hardware Platform for Rapid Prototyping of Wireless Sensor Networksrdquo [9] S Saha K Chaudhuri D Sanghi and P Bhagwat ldquoLocation determination of a mobile device using IEEE 80211b access point signalsʺ in Proc IEEE Wireless Communications and Networking Conference (WCNCʹ03) New Orleans LA Mar 2003 pp 1987‐1992 [10] T Roos P Myllymaki H Tirri P Misikangas and J Sievanen ldquoA probabilistic approach to WLAN user location estimationʺ International Journal of Wireless Information Networks vol 9 no 3 pp 155‐164 July 2002 [11] R Battiti M Brunato and A Villani ldquoStatistical learning theory for location fingerprinting in wireless LANrsquosʺ Technical Report Oct 2002 [Online] Available httprtmscienceunitnitraquobattitiarchive86pdf [12] P Prasithsangaree P Krishnamurthy and P K Chrysanthis ldquoAn indoor position location with wireless LANsʺ in Proc IEEE International Symposium on Personal Indoor and Mobile Radio Communications (PIMRCʹ02) Lisbon Portugal Sept 2002 [13] M A Youssef A Agrawala and A U Shankar ldquoWLAN location determination via clustering and probability distributionsʺ in Proc IEEE

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

50

International Conference on Pervasive Computing and Communications (PerComʹ03) Dallas‐Fort Worth TX Mar 23‐26 2003 pp 23‐26 [14] Z Xiang S Song J Chen H Wang J Huang and X Gao ldquoA wireless LAN‐based indoor positioning technologyʺ IBM Journal of Research and Development vol 48 no 56 pp 617‐626 SeptNov 2004 [15] R Want A Hopper V Falcao and J Gibbons ldquoThe active badge location systemʺACM Transactions on Information Systems vol 40 no 1 pp 91‐102 Jan 1992 [16] N B Priyantha A Chakraborty and H Balakrishnan ldquoThe cricket location‐support systemʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ00) Boston MA Aug 2000 pp 32‐43 [17] Holger K Willig A (2005) Protocols and Architectures for Wireless Sensor Networks London [18] IEEE (2003) Part 154 Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low‐Rate Wireless Personal Area Networks (LR‐WPANs) New York [19] Chipcon CC2420 802154 compliant radio httpwwwchipconcom [20] ZigBee Specification v10 ZigBee Alliance [21] Dimitrios Lymberopoulos Quentin Lindsey and Andreas Savvides ldquoAn Empirical Analysis of Radio Signal Strength Variability in IEEE 802154 Networks using Monopole Antennasrdquo [22] Kiran Yedavalli Bhaskar Krishnamachari Sharmila Ravula and Bhaskar Srinivasan ldquoEcolocation A sequence based technique for RF localization in wireless sensor networksrdquo Robert Bosch Research and Technology Centre Palo Alto CA

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

6 DISCUSSIONS10102006

44

6 DISCUSSIONS 61 Summary and Conclusion While empirical results and performance studies of positioning systems based on location fingerprint have been presented in this report analytical model that can be used as a framework for efficiently designing the positioning systems are not available This thesis design work uses general principles of position estimation such as position estimation of sensors grid spacing etc This design work focuses on accuracy and precision The concept of location fingerprint technique based on RSS us explained in detail in chapter 2 It is found that RSS is random It is possible to approximate the RSS as being normally distributed While the RSS random process is non‐stationary the mean is more or less constant and could be used as the fingerprint of the location Model designed for position estimation is as shown in chapter 4 Accuracy and precision are the two major performance matrices in this study Uniform grid spacing has been used From the experimental analysis I have obtained an accuracy of 3 meters and 35 precision The sensitivity analysis among the system parameters and the performance matrices were done using the model as shown in chapter 4 For instance our study found that the minimum distance between two adjacent positions also affects the positioning performance This is due to the randomness of RSS patterns induced by the ever changing indoor environment

62 Future work The research in this thesis provides a ground work for the future study of efficient design of indoor positioning system The RSSI based position estimation still has limitations First the RSSI value is time dependent Thus the future study should address this issue Second the movement of object is not considered Third orientations of sensors affect the performance which should be taken care off Fourth to improve the performance of the system we need to find a solution to deal with those incomplete or missing data during the off‐line and the on‐line phase Fifth position estimation on multi‐floor and three‐dimensional coordinates is not available

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 110102006

45

APPENDIX 1

Location Fingerprints of Masters Room Time 1315 Observation Time 20 minutes Number of Samples 10000

People Presence SNo Co-ordinates AP1 (0x0001) AP2 (0x0002) AP3 (0x0004) AP4 (0x0008)

1 11 -24 -16 -27 -33 2 13 -26 -8 -34 -31 3 15 -43 -9 -39 -25 4 32 -21 -11 -27 -33 5 61 -6 -23 -32 -28 6 64 -20 -31 -25 -34 7 35 -23 -35 -29 -38 8 37 -29 -35 -22 -27 9 39 -38 -29 -21 -27 10 67 -27 -23 -20 -23 11 611 -26 -29 -23 -30 12 613 -35 -32 -2 -31 13 513 -34 -31 -20 -26 14 11 -39 -22 -29 -17 15 212 -37 -42 -28 -31 16 -212 -38 -33 -29 -6 17 -21 -42 -29 -33 -19 18 -38 -40 -23 -35 -22

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 210102006

46

APPENDIX 2

Begin

Open radio map and raw data files for write

Intialize radio map in memory

Check whether the

Packet is data or Beacon packet If data packet

Save the data to register

Check if data is

Received from all

sensors

Estimate the position of static

Display the position on PC

End Yes

No

A

A

Yes

No

Flow chart representing the main algorithm for position estimation

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

47

APPENDIX 3

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

48

Distribution of RSSI

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

49

REFERENCES [1] T S Rappaport J H Reed and B D Woerner ldquoPosition location using wireless communications on highways of the futureʺ IEEE Commun Mag vol 34 no 10 pp33‐41 Oct 1996 [2] A M Ladd K E Bekris G Marceau A Rudys L E Kavraki and D S Wallach rdquoRobotics‐based location sensing using wireless ethernetʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ02) 2002 pp 227‐238 [3] I Ramani R Bharadwaja and P V Rangan ldquoLocation tracking for media appliances in wireless home networksʺ in Proc International Conference on Multimedia and Expo (ICMEʹ03) July 2003 pp 769‐772 [4] K Pahlavan X Li and J P Makela ldquoIndoor geolocation science and technologyʺ IEEE Commun Mag vol 40 no 2 pp 112‐118 Feb 2002 [5] Konard Lorincz and Matt Welsh ldquoMote Track A Robust Decentralized Approach to RF‐Based Location Trackingrdquo [6] Paramvir Bahl and Venkata N Padmanabham ldquoRADAR An In‐Building RF‐based User Location and Tracking Systemrdquo [7] Kamol Kaemarungsi ldquoDesign of Indoor positioning system based on location fingerprint techiniquerdquo University of Pittsburgh 2005 [8] Fredrik Linnarsson Cheng Peng and Bengt Oelmann ldquoSENTIO A Hardware Platform for Rapid Prototyping of Wireless Sensor Networksrdquo [9] S Saha K Chaudhuri D Sanghi and P Bhagwat ldquoLocation determination of a mobile device using IEEE 80211b access point signalsʺ in Proc IEEE Wireless Communications and Networking Conference (WCNCʹ03) New Orleans LA Mar 2003 pp 1987‐1992 [10] T Roos P Myllymaki H Tirri P Misikangas and J Sievanen ldquoA probabilistic approach to WLAN user location estimationʺ International Journal of Wireless Information Networks vol 9 no 3 pp 155‐164 July 2002 [11] R Battiti M Brunato and A Villani ldquoStatistical learning theory for location fingerprinting in wireless LANrsquosʺ Technical Report Oct 2002 [Online] Available httprtmscienceunitnitraquobattitiarchive86pdf [12] P Prasithsangaree P Krishnamurthy and P K Chrysanthis ldquoAn indoor position location with wireless LANsʺ in Proc IEEE International Symposium on Personal Indoor and Mobile Radio Communications (PIMRCʹ02) Lisbon Portugal Sept 2002 [13] M A Youssef A Agrawala and A U Shankar ldquoWLAN location determination via clustering and probability distributionsʺ in Proc IEEE

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

50

International Conference on Pervasive Computing and Communications (PerComʹ03) Dallas‐Fort Worth TX Mar 23‐26 2003 pp 23‐26 [14] Z Xiang S Song J Chen H Wang J Huang and X Gao ldquoA wireless LAN‐based indoor positioning technologyʺ IBM Journal of Research and Development vol 48 no 56 pp 617‐626 SeptNov 2004 [15] R Want A Hopper V Falcao and J Gibbons ldquoThe active badge location systemʺACM Transactions on Information Systems vol 40 no 1 pp 91‐102 Jan 1992 [16] N B Priyantha A Chakraborty and H Balakrishnan ldquoThe cricket location‐support systemʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ00) Boston MA Aug 2000 pp 32‐43 [17] Holger K Willig A (2005) Protocols and Architectures for Wireless Sensor Networks London [18] IEEE (2003) Part 154 Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low‐Rate Wireless Personal Area Networks (LR‐WPANs) New York [19] Chipcon CC2420 802154 compliant radio httpwwwchipconcom [20] ZigBee Specification v10 ZigBee Alliance [21] Dimitrios Lymberopoulos Quentin Lindsey and Andreas Savvides ldquoAn Empirical Analysis of Radio Signal Strength Variability in IEEE 802154 Networks using Monopole Antennasrdquo [22] Kiran Yedavalli Bhaskar Krishnamachari Sharmila Ravula and Bhaskar Srinivasan ldquoEcolocation A sequence based technique for RF localization in wireless sensor networksrdquo Robert Bosch Research and Technology Centre Palo Alto CA

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 110102006

45

APPENDIX 1

Location Fingerprints of Masters Room Time 1315 Observation Time 20 minutes Number of Samples 10000

People Presence SNo Co-ordinates AP1 (0x0001) AP2 (0x0002) AP3 (0x0004) AP4 (0x0008)

1 11 -24 -16 -27 -33 2 13 -26 -8 -34 -31 3 15 -43 -9 -39 -25 4 32 -21 -11 -27 -33 5 61 -6 -23 -32 -28 6 64 -20 -31 -25 -34 7 35 -23 -35 -29 -38 8 37 -29 -35 -22 -27 9 39 -38 -29 -21 -27 10 67 -27 -23 -20 -23 11 611 -26 -29 -23 -30 12 613 -35 -32 -2 -31 13 513 -34 -31 -20 -26 14 11 -39 -22 -29 -17 15 212 -37 -42 -28 -31 16 -212 -38 -33 -29 -6 17 -21 -42 -29 -33 -19 18 -38 -40 -23 -35 -22

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 210102006

46

APPENDIX 2

Begin

Open radio map and raw data files for write

Intialize radio map in memory

Check whether the

Packet is data or Beacon packet If data packet

Save the data to register

Check if data is

Received from all

sensors

Estimate the position of static

Display the position on PC

End Yes

No

A

A

Yes

No

Flow chart representing the main algorithm for position estimation

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

47

APPENDIX 3

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

48

Distribution of RSSI

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

49

REFERENCES [1] T S Rappaport J H Reed and B D Woerner ldquoPosition location using wireless communications on highways of the futureʺ IEEE Commun Mag vol 34 no 10 pp33‐41 Oct 1996 [2] A M Ladd K E Bekris G Marceau A Rudys L E Kavraki and D S Wallach rdquoRobotics‐based location sensing using wireless ethernetʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ02) 2002 pp 227‐238 [3] I Ramani R Bharadwaja and P V Rangan ldquoLocation tracking for media appliances in wireless home networksʺ in Proc International Conference on Multimedia and Expo (ICMEʹ03) July 2003 pp 769‐772 [4] K Pahlavan X Li and J P Makela ldquoIndoor geolocation science and technologyʺ IEEE Commun Mag vol 40 no 2 pp 112‐118 Feb 2002 [5] Konard Lorincz and Matt Welsh ldquoMote Track A Robust Decentralized Approach to RF‐Based Location Trackingrdquo [6] Paramvir Bahl and Venkata N Padmanabham ldquoRADAR An In‐Building RF‐based User Location and Tracking Systemrdquo [7] Kamol Kaemarungsi ldquoDesign of Indoor positioning system based on location fingerprint techiniquerdquo University of Pittsburgh 2005 [8] Fredrik Linnarsson Cheng Peng and Bengt Oelmann ldquoSENTIO A Hardware Platform for Rapid Prototyping of Wireless Sensor Networksrdquo [9] S Saha K Chaudhuri D Sanghi and P Bhagwat ldquoLocation determination of a mobile device using IEEE 80211b access point signalsʺ in Proc IEEE Wireless Communications and Networking Conference (WCNCʹ03) New Orleans LA Mar 2003 pp 1987‐1992 [10] T Roos P Myllymaki H Tirri P Misikangas and J Sievanen ldquoA probabilistic approach to WLAN user location estimationʺ International Journal of Wireless Information Networks vol 9 no 3 pp 155‐164 July 2002 [11] R Battiti M Brunato and A Villani ldquoStatistical learning theory for location fingerprinting in wireless LANrsquosʺ Technical Report Oct 2002 [Online] Available httprtmscienceunitnitraquobattitiarchive86pdf [12] P Prasithsangaree P Krishnamurthy and P K Chrysanthis ldquoAn indoor position location with wireless LANsʺ in Proc IEEE International Symposium on Personal Indoor and Mobile Radio Communications (PIMRCʹ02) Lisbon Portugal Sept 2002 [13] M A Youssef A Agrawala and A U Shankar ldquoWLAN location determination via clustering and probability distributionsʺ in Proc IEEE

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

50

International Conference on Pervasive Computing and Communications (PerComʹ03) Dallas‐Fort Worth TX Mar 23‐26 2003 pp 23‐26 [14] Z Xiang S Song J Chen H Wang J Huang and X Gao ldquoA wireless LAN‐based indoor positioning technologyʺ IBM Journal of Research and Development vol 48 no 56 pp 617‐626 SeptNov 2004 [15] R Want A Hopper V Falcao and J Gibbons ldquoThe active badge location systemʺACM Transactions on Information Systems vol 40 no 1 pp 91‐102 Jan 1992 [16] N B Priyantha A Chakraborty and H Balakrishnan ldquoThe cricket location‐support systemʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ00) Boston MA Aug 2000 pp 32‐43 [17] Holger K Willig A (2005) Protocols and Architectures for Wireless Sensor Networks London [18] IEEE (2003) Part 154 Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low‐Rate Wireless Personal Area Networks (LR‐WPANs) New York [19] Chipcon CC2420 802154 compliant radio httpwwwchipconcom [20] ZigBee Specification v10 ZigBee Alliance [21] Dimitrios Lymberopoulos Quentin Lindsey and Andreas Savvides ldquoAn Empirical Analysis of Radio Signal Strength Variability in IEEE 802154 Networks using Monopole Antennasrdquo [22] Kiran Yedavalli Bhaskar Krishnamachari Sharmila Ravula and Bhaskar Srinivasan ldquoEcolocation A sequence based technique for RF localization in wireless sensor networksrdquo Robert Bosch Research and Technology Centre Palo Alto CA

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 210102006

46

APPENDIX 2

Begin

Open radio map and raw data files for write

Intialize radio map in memory

Check whether the

Packet is data or Beacon packet If data packet

Save the data to register

Check if data is

Received from all

sensors

Estimate the position of static

Display the position on PC

End Yes

No

A

A

Yes

No

Flow chart representing the main algorithm for position estimation

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

47

APPENDIX 3

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

48

Distribution of RSSI

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

49

REFERENCES [1] T S Rappaport J H Reed and B D Woerner ldquoPosition location using wireless communications on highways of the futureʺ IEEE Commun Mag vol 34 no 10 pp33‐41 Oct 1996 [2] A M Ladd K E Bekris G Marceau A Rudys L E Kavraki and D S Wallach rdquoRobotics‐based location sensing using wireless ethernetʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ02) 2002 pp 227‐238 [3] I Ramani R Bharadwaja and P V Rangan ldquoLocation tracking for media appliances in wireless home networksʺ in Proc International Conference on Multimedia and Expo (ICMEʹ03) July 2003 pp 769‐772 [4] K Pahlavan X Li and J P Makela ldquoIndoor geolocation science and technologyʺ IEEE Commun Mag vol 40 no 2 pp 112‐118 Feb 2002 [5] Konard Lorincz and Matt Welsh ldquoMote Track A Robust Decentralized Approach to RF‐Based Location Trackingrdquo [6] Paramvir Bahl and Venkata N Padmanabham ldquoRADAR An In‐Building RF‐based User Location and Tracking Systemrdquo [7] Kamol Kaemarungsi ldquoDesign of Indoor positioning system based on location fingerprint techiniquerdquo University of Pittsburgh 2005 [8] Fredrik Linnarsson Cheng Peng and Bengt Oelmann ldquoSENTIO A Hardware Platform for Rapid Prototyping of Wireless Sensor Networksrdquo [9] S Saha K Chaudhuri D Sanghi and P Bhagwat ldquoLocation determination of a mobile device using IEEE 80211b access point signalsʺ in Proc IEEE Wireless Communications and Networking Conference (WCNCʹ03) New Orleans LA Mar 2003 pp 1987‐1992 [10] T Roos P Myllymaki H Tirri P Misikangas and J Sievanen ldquoA probabilistic approach to WLAN user location estimationʺ International Journal of Wireless Information Networks vol 9 no 3 pp 155‐164 July 2002 [11] R Battiti M Brunato and A Villani ldquoStatistical learning theory for location fingerprinting in wireless LANrsquosʺ Technical Report Oct 2002 [Online] Available httprtmscienceunitnitraquobattitiarchive86pdf [12] P Prasithsangaree P Krishnamurthy and P K Chrysanthis ldquoAn indoor position location with wireless LANsʺ in Proc IEEE International Symposium on Personal Indoor and Mobile Radio Communications (PIMRCʹ02) Lisbon Portugal Sept 2002 [13] M A Youssef A Agrawala and A U Shankar ldquoWLAN location determination via clustering and probability distributionsʺ in Proc IEEE

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

50

International Conference on Pervasive Computing and Communications (PerComʹ03) Dallas‐Fort Worth TX Mar 23‐26 2003 pp 23‐26 [14] Z Xiang S Song J Chen H Wang J Huang and X Gao ldquoA wireless LAN‐based indoor positioning technologyʺ IBM Journal of Research and Development vol 48 no 56 pp 617‐626 SeptNov 2004 [15] R Want A Hopper V Falcao and J Gibbons ldquoThe active badge location systemʺACM Transactions on Information Systems vol 40 no 1 pp 91‐102 Jan 1992 [16] N B Priyantha A Chakraborty and H Balakrishnan ldquoThe cricket location‐support systemʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ00) Boston MA Aug 2000 pp 32‐43 [17] Holger K Willig A (2005) Protocols and Architectures for Wireless Sensor Networks London [18] IEEE (2003) Part 154 Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low‐Rate Wireless Personal Area Networks (LR‐WPANs) New York [19] Chipcon CC2420 802154 compliant radio httpwwwchipconcom [20] ZigBee Specification v10 ZigBee Alliance [21] Dimitrios Lymberopoulos Quentin Lindsey and Andreas Savvides ldquoAn Empirical Analysis of Radio Signal Strength Variability in IEEE 802154 Networks using Monopole Antennasrdquo [22] Kiran Yedavalli Bhaskar Krishnamachari Sharmila Ravula and Bhaskar Srinivasan ldquoEcolocation A sequence based technique for RF localization in wireless sensor networksrdquo Robert Bosch Research and Technology Centre Palo Alto CA

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

47

APPENDIX 3

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

48

Distribution of RSSI

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

49

REFERENCES [1] T S Rappaport J H Reed and B D Woerner ldquoPosition location using wireless communications on highways of the futureʺ IEEE Commun Mag vol 34 no 10 pp33‐41 Oct 1996 [2] A M Ladd K E Bekris G Marceau A Rudys L E Kavraki and D S Wallach rdquoRobotics‐based location sensing using wireless ethernetʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ02) 2002 pp 227‐238 [3] I Ramani R Bharadwaja and P V Rangan ldquoLocation tracking for media appliances in wireless home networksʺ in Proc International Conference on Multimedia and Expo (ICMEʹ03) July 2003 pp 769‐772 [4] K Pahlavan X Li and J P Makela ldquoIndoor geolocation science and technologyʺ IEEE Commun Mag vol 40 no 2 pp 112‐118 Feb 2002 [5] Konard Lorincz and Matt Welsh ldquoMote Track A Robust Decentralized Approach to RF‐Based Location Trackingrdquo [6] Paramvir Bahl and Venkata N Padmanabham ldquoRADAR An In‐Building RF‐based User Location and Tracking Systemrdquo [7] Kamol Kaemarungsi ldquoDesign of Indoor positioning system based on location fingerprint techiniquerdquo University of Pittsburgh 2005 [8] Fredrik Linnarsson Cheng Peng and Bengt Oelmann ldquoSENTIO A Hardware Platform for Rapid Prototyping of Wireless Sensor Networksrdquo [9] S Saha K Chaudhuri D Sanghi and P Bhagwat ldquoLocation determination of a mobile device using IEEE 80211b access point signalsʺ in Proc IEEE Wireless Communications and Networking Conference (WCNCʹ03) New Orleans LA Mar 2003 pp 1987‐1992 [10] T Roos P Myllymaki H Tirri P Misikangas and J Sievanen ldquoA probabilistic approach to WLAN user location estimationʺ International Journal of Wireless Information Networks vol 9 no 3 pp 155‐164 July 2002 [11] R Battiti M Brunato and A Villani ldquoStatistical learning theory for location fingerprinting in wireless LANrsquosʺ Technical Report Oct 2002 [Online] Available httprtmscienceunitnitraquobattitiarchive86pdf [12] P Prasithsangaree P Krishnamurthy and P K Chrysanthis ldquoAn indoor position location with wireless LANsʺ in Proc IEEE International Symposium on Personal Indoor and Mobile Radio Communications (PIMRCʹ02) Lisbon Portugal Sept 2002 [13] M A Youssef A Agrawala and A U Shankar ldquoWLAN location determination via clustering and probability distributionsʺ in Proc IEEE

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

50

International Conference on Pervasive Computing and Communications (PerComʹ03) Dallas‐Fort Worth TX Mar 23‐26 2003 pp 23‐26 [14] Z Xiang S Song J Chen H Wang J Huang and X Gao ldquoA wireless LAN‐based indoor positioning technologyʺ IBM Journal of Research and Development vol 48 no 56 pp 617‐626 SeptNov 2004 [15] R Want A Hopper V Falcao and J Gibbons ldquoThe active badge location systemʺACM Transactions on Information Systems vol 40 no 1 pp 91‐102 Jan 1992 [16] N B Priyantha A Chakraborty and H Balakrishnan ldquoThe cricket location‐support systemʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ00) Boston MA Aug 2000 pp 32‐43 [17] Holger K Willig A (2005) Protocols and Architectures for Wireless Sensor Networks London [18] IEEE (2003) Part 154 Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low‐Rate Wireless Personal Area Networks (LR‐WPANs) New York [19] Chipcon CC2420 802154 compliant radio httpwwwchipconcom [20] ZigBee Specification v10 ZigBee Alliance [21] Dimitrios Lymberopoulos Quentin Lindsey and Andreas Savvides ldquoAn Empirical Analysis of Radio Signal Strength Variability in IEEE 802154 Networks using Monopole Antennasrdquo [22] Kiran Yedavalli Bhaskar Krishnamachari Sharmila Ravula and Bhaskar Srinivasan ldquoEcolocation A sequence based technique for RF localization in wireless sensor networksrdquo Robert Bosch Research and Technology Centre Palo Alto CA

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

APPENDIX 310102006

48

Distribution of RSSI

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

49

REFERENCES [1] T S Rappaport J H Reed and B D Woerner ldquoPosition location using wireless communications on highways of the futureʺ IEEE Commun Mag vol 34 no 10 pp33‐41 Oct 1996 [2] A M Ladd K E Bekris G Marceau A Rudys L E Kavraki and D S Wallach rdquoRobotics‐based location sensing using wireless ethernetʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ02) 2002 pp 227‐238 [3] I Ramani R Bharadwaja and P V Rangan ldquoLocation tracking for media appliances in wireless home networksʺ in Proc International Conference on Multimedia and Expo (ICMEʹ03) July 2003 pp 769‐772 [4] K Pahlavan X Li and J P Makela ldquoIndoor geolocation science and technologyʺ IEEE Commun Mag vol 40 no 2 pp 112‐118 Feb 2002 [5] Konard Lorincz and Matt Welsh ldquoMote Track A Robust Decentralized Approach to RF‐Based Location Trackingrdquo [6] Paramvir Bahl and Venkata N Padmanabham ldquoRADAR An In‐Building RF‐based User Location and Tracking Systemrdquo [7] Kamol Kaemarungsi ldquoDesign of Indoor positioning system based on location fingerprint techiniquerdquo University of Pittsburgh 2005 [8] Fredrik Linnarsson Cheng Peng and Bengt Oelmann ldquoSENTIO A Hardware Platform for Rapid Prototyping of Wireless Sensor Networksrdquo [9] S Saha K Chaudhuri D Sanghi and P Bhagwat ldquoLocation determination of a mobile device using IEEE 80211b access point signalsʺ in Proc IEEE Wireless Communications and Networking Conference (WCNCʹ03) New Orleans LA Mar 2003 pp 1987‐1992 [10] T Roos P Myllymaki H Tirri P Misikangas and J Sievanen ldquoA probabilistic approach to WLAN user location estimationʺ International Journal of Wireless Information Networks vol 9 no 3 pp 155‐164 July 2002 [11] R Battiti M Brunato and A Villani ldquoStatistical learning theory for location fingerprinting in wireless LANrsquosʺ Technical Report Oct 2002 [Online] Available httprtmscienceunitnitraquobattitiarchive86pdf [12] P Prasithsangaree P Krishnamurthy and P K Chrysanthis ldquoAn indoor position location with wireless LANsʺ in Proc IEEE International Symposium on Personal Indoor and Mobile Radio Communications (PIMRCʹ02) Lisbon Portugal Sept 2002 [13] M A Youssef A Agrawala and A U Shankar ldquoWLAN location determination via clustering and probability distributionsʺ in Proc IEEE

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

50

International Conference on Pervasive Computing and Communications (PerComʹ03) Dallas‐Fort Worth TX Mar 23‐26 2003 pp 23‐26 [14] Z Xiang S Song J Chen H Wang J Huang and X Gao ldquoA wireless LAN‐based indoor positioning technologyʺ IBM Journal of Research and Development vol 48 no 56 pp 617‐626 SeptNov 2004 [15] R Want A Hopper V Falcao and J Gibbons ldquoThe active badge location systemʺACM Transactions on Information Systems vol 40 no 1 pp 91‐102 Jan 1992 [16] N B Priyantha A Chakraborty and H Balakrishnan ldquoThe cricket location‐support systemʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ00) Boston MA Aug 2000 pp 32‐43 [17] Holger K Willig A (2005) Protocols and Architectures for Wireless Sensor Networks London [18] IEEE (2003) Part 154 Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low‐Rate Wireless Personal Area Networks (LR‐WPANs) New York [19] Chipcon CC2420 802154 compliant radio httpwwwchipconcom [20] ZigBee Specification v10 ZigBee Alliance [21] Dimitrios Lymberopoulos Quentin Lindsey and Andreas Savvides ldquoAn Empirical Analysis of Radio Signal Strength Variability in IEEE 802154 Networks using Monopole Antennasrdquo [22] Kiran Yedavalli Bhaskar Krishnamachari Sharmila Ravula and Bhaskar Srinivasan ldquoEcolocation A sequence based technique for RF localization in wireless sensor networksrdquo Robert Bosch Research and Technology Centre Palo Alto CA

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

49

REFERENCES [1] T S Rappaport J H Reed and B D Woerner ldquoPosition location using wireless communications on highways of the futureʺ IEEE Commun Mag vol 34 no 10 pp33‐41 Oct 1996 [2] A M Ladd K E Bekris G Marceau A Rudys L E Kavraki and D S Wallach rdquoRobotics‐based location sensing using wireless ethernetʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ02) 2002 pp 227‐238 [3] I Ramani R Bharadwaja and P V Rangan ldquoLocation tracking for media appliances in wireless home networksʺ in Proc International Conference on Multimedia and Expo (ICMEʹ03) July 2003 pp 769‐772 [4] K Pahlavan X Li and J P Makela ldquoIndoor geolocation science and technologyʺ IEEE Commun Mag vol 40 no 2 pp 112‐118 Feb 2002 [5] Konard Lorincz and Matt Welsh ldquoMote Track A Robust Decentralized Approach to RF‐Based Location Trackingrdquo [6] Paramvir Bahl and Venkata N Padmanabham ldquoRADAR An In‐Building RF‐based User Location and Tracking Systemrdquo [7] Kamol Kaemarungsi ldquoDesign of Indoor positioning system based on location fingerprint techiniquerdquo University of Pittsburgh 2005 [8] Fredrik Linnarsson Cheng Peng and Bengt Oelmann ldquoSENTIO A Hardware Platform for Rapid Prototyping of Wireless Sensor Networksrdquo [9] S Saha K Chaudhuri D Sanghi and P Bhagwat ldquoLocation determination of a mobile device using IEEE 80211b access point signalsʺ in Proc IEEE Wireless Communications and Networking Conference (WCNCʹ03) New Orleans LA Mar 2003 pp 1987‐1992 [10] T Roos P Myllymaki H Tirri P Misikangas and J Sievanen ldquoA probabilistic approach to WLAN user location estimationʺ International Journal of Wireless Information Networks vol 9 no 3 pp 155‐164 July 2002 [11] R Battiti M Brunato and A Villani ldquoStatistical learning theory for location fingerprinting in wireless LANrsquosʺ Technical Report Oct 2002 [Online] Available httprtmscienceunitnitraquobattitiarchive86pdf [12] P Prasithsangaree P Krishnamurthy and P K Chrysanthis ldquoAn indoor position location with wireless LANsʺ in Proc IEEE International Symposium on Personal Indoor and Mobile Radio Communications (PIMRCʹ02) Lisbon Portugal Sept 2002 [13] M A Youssef A Agrawala and A U Shankar ldquoWLAN location determination via clustering and probability distributionsʺ in Proc IEEE

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

50

International Conference on Pervasive Computing and Communications (PerComʹ03) Dallas‐Fort Worth TX Mar 23‐26 2003 pp 23‐26 [14] Z Xiang S Song J Chen H Wang J Huang and X Gao ldquoA wireless LAN‐based indoor positioning technologyʺ IBM Journal of Research and Development vol 48 no 56 pp 617‐626 SeptNov 2004 [15] R Want A Hopper V Falcao and J Gibbons ldquoThe active badge location systemʺACM Transactions on Information Systems vol 40 no 1 pp 91‐102 Jan 1992 [16] N B Priyantha A Chakraborty and H Balakrishnan ldquoThe cricket location‐support systemʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ00) Boston MA Aug 2000 pp 32‐43 [17] Holger K Willig A (2005) Protocols and Architectures for Wireless Sensor Networks London [18] IEEE (2003) Part 154 Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low‐Rate Wireless Personal Area Networks (LR‐WPANs) New York [19] Chipcon CC2420 802154 compliant radio httpwwwchipconcom [20] ZigBee Specification v10 ZigBee Alliance [21] Dimitrios Lymberopoulos Quentin Lindsey and Andreas Savvides ldquoAn Empirical Analysis of Radio Signal Strength Variability in IEEE 802154 Networks using Monopole Antennasrdquo [22] Kiran Yedavalli Bhaskar Krishnamachari Sharmila Ravula and Bhaskar Srinivasan ldquoEcolocation A sequence based technique for RF localization in wireless sensor networksrdquo Robert Bosch Research and Technology Centre Palo Alto CA

Indoor Local Positioning System For ZigBee Based On RSSI Shashank Tadakamadla

REFERENCES10102006

50

International Conference on Pervasive Computing and Communications (PerComʹ03) Dallas‐Fort Worth TX Mar 23‐26 2003 pp 23‐26 [14] Z Xiang S Song J Chen H Wang J Huang and X Gao ldquoA wireless LAN‐based indoor positioning technologyʺ IBM Journal of Research and Development vol 48 no 56 pp 617‐626 SeptNov 2004 [15] R Want A Hopper V Falcao and J Gibbons ldquoThe active badge location systemʺACM Transactions on Information Systems vol 40 no 1 pp 91‐102 Jan 1992 [16] N B Priyantha A Chakraborty and H Balakrishnan ldquoThe cricket location‐support systemʺ in Proc ACM International Conference on Mobile Computing and Networking (MOBICOMʹ00) Boston MA Aug 2000 pp 32‐43 [17] Holger K Willig A (2005) Protocols and Architectures for Wireless Sensor Networks London [18] IEEE (2003) Part 154 Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low‐Rate Wireless Personal Area Networks (LR‐WPANs) New York [19] Chipcon CC2420 802154 compliant radio httpwwwchipconcom [20] ZigBee Specification v10 ZigBee Alliance [21] Dimitrios Lymberopoulos Quentin Lindsey and Andreas Savvides ldquoAn Empirical Analysis of Radio Signal Strength Variability in IEEE 802154 Networks using Monopole Antennasrdquo [22] Kiran Yedavalli Bhaskar Krishnamachari Sharmila Ravula and Bhaskar Srinivasan ldquoEcolocation A sequence based technique for RF localization in wireless sensor networksrdquo Robert Bosch Research and Technology Centre Palo Alto CA