Wireless Networking Trends - Aalborg Universitetkom.aau.dk/~dsp/livslanglaering/JF-Location.pdfWireless Networking Trends ... disavantage Video Analysis. Page 13 João Figueiras Life-long

Page 1 João FigueirasLife-long learning, Aalborg University, Aug. 2005

Wireless Networking Trends –Architectures, Protocols & optimizations for future networking scenarios

H. Fathi, J. Figueiras, F. Fitzek, T. Madsen, R. Olsen, P. Popovski, HP Schwefel

• Session 1 Network Evolution & Mobility Support (HPS)

• Session 2 Ad-hoc networking (TKM/FF)

• Session 3 Enabling technologies for ad-hoc NWs (TKM/FF)

• Session 4 Wireless Sensor Networks (PP)

• Session 5 Performance aspects & optimizations (HF/TKM)

• Session 6 Context-sensitive Networking (RLO/JF)


Context-Sensitive Networking:Location Information

João [email protected]

http://kom.aau.dk/~jf


Content1. Motivation

– The use of Location Information2. Obtaining Location Information

– Location techniques– An example of triangulation in Bluetooth networks

3. Common problems and questions– Common problems in obtaining Location Information– Influencing factors on location information accuracy

4. Common solution: Filtering– The basics of a filter– Filters Implementations

5. Network Optimization based on Location Information– Bluetooth Basics– An example of topology formation in Bluetooth networks


Motivation

• Government laws persuading wireless companies to provide Location Information in their networks. – Ex: The FCC E911 service.

• Applications that use navigation or tracking of people, robots or objects.– Tracking people in football fields for game planning– Tracking animals in stables for health studies– Tracking robots in arenas to compare with human movements

• Context-sensitive networking:– Location Information has been used to optimize the wireless

networks in several aspects such as topology formation and routing.

The use of location Information









Obtaining Location InformationLocation Techniques

• Triangulation– Makes use of the

properties of the triangles

• Proximity– Uses the contact,

the proximity or the area of coverage

• Environment Analysis– Analyses the

scenario where the device is included



• Makes use of at least 3 Base Stations in a 2D scenario

• Measurements of Received Signal Strength (RSS) or (Time of Flight) ToF

• Propagation problems such as shadowing and multipath

Lateration



• 2 Base Stations in a 2D scenario

• Accuracy dependent on how narrow is the angle

• Propagation problems such as shadowing and multipath.

• Needed method to measure angles in the Base Stations.

Angulation



• Very accurate since the Base Station location in known

• Mandatory existence of physical contact

• No calculations needed

Physical Contact



• Low accuracy dependent on the AP range

• No calculations needed

Cell Identification



• Good accuracy under indoor scenarios

• Accuracy dependent on the amount of information stored in the database

• Update of the database may be a problem

Database Correlation



• The scenario where thedevice is included must be know.

• A big amount ofprocessing time

• The size of the video camera may be a disavantage

Video Analysis


Measured Power value PA, PB, PC to each AP

P1

d1

d1

PAP1

d2

d3

P3

d3

PCP3

AP A and BAP C

α1α3

α = α1 * α2 * α3 (triangulation)Normalize all the area

P2

d2

AP A

AP C

AP B

PBP2

α2

Obtaining Location InformationAn example of triangulation on Bluetooth networks

10log(d)10log(d)

dBmdBm

PDF PDF PDF

dBm dBm dBm









Common problems and questionsCommon problems in obtaining Location Information

• Channel Characterization

• Packet collision• Timing Aspects

– Network/Measurements delays in moving devices scenarios

• Hardware/Technology/Protocols Limitations

– Multipath – NLoS (Non Line of Sight) – Moving Objects


Common problems and questionsInfluencing factors on Location Information accuracy

2) Maximize triangulation conditions

3) Consider auto-calibration issues

1) Maximum proximity from the most probable areas to find

the devices

-20

-25

-30

-35

-40

-45

-50

-55

2dB

2dB

1.5m 7m Distance(m)

Power dBm









Common Solution: FilteringThe basics of a filter

• These techniques are used widely within several contexts, such as Signals & Systems, Image Processing or Mobile Tracking.

• Bayesian filtering techniques are techniques that estimate the state of a dynamic system based on noisy sensors measurements.

• To the specific case of location information estimation in scenarios with static or moving devices, the Bayesian filtering techniques are being used in order to optimize accuracy.

Time scale

X0 X1 X2 X3 X4 X5

X’1 X’2X’3

X’4 X’5

P1 P2 P3 P4 P5 Measurement

Correction

Prediction


Common Solution: FilteringThe basics of a filter

• The Bayes filter is an abstract concept that describes a probabilistic framework for recursive state estimation.

Time Update

(”predict”)

Measurement Update

(”correct”)

Start Up Parameters

• The main idea consists in estimate the distribution of the uncertainty, called belief, over the possible state space given the sensor measurements obtained until the time of the estimation .


Common Solution: FilteringThe basics of a filter• Prediction

At each time, the current state is updated based on previous states .

• Correction

describes the system dynamics (motion model).The belief for state space at time t0 is usually defined as an uniform distribution in case no a-priori knowledge exists.

represents the perceptual model (depends on the technology).normalization factor


Common Solution: FilteringFilters implementations

Example: Object moving with quasi-constant speed v. Measured RTT (Round Trip Time).

system dynamics (motion model): xt=v∆t+xt-1

observation model : xt=cT/2Where c – the light speed

T – the RRTxt – the state in time t

• The Kalman Filter– The most widely used– Approximate beliefs by Gaussians, making use or their first (mean) and

second moment (covariance matrix)– The observation model and system dynamics are linear functions of the

state

Time tT/2

Time t+1T/2

v


Common Solution: FilteringFilters implementations

• The Extended Kalman Filter– The observation model and system dynamics do not need anymore to be

a linear functions of the state

• The Particles Filter– Represents beliefs by sets of samples or particles. Each samples is

composed by a state and a non-negative weight called importance factors.

– Beliefs do not need to be Gaussian









Network optimizationBluetooth Basics

• Bluetooth is a short range wireless technology.• The type of network is designated as Scatternet. A Scatternet is composed by severalpiconets, where each piconet may be composedup to 8 devices• In a piconet one of the devices plays the roll of master, controlling all the

communication in the network, while the other devices play the roll of a slave.

• The nodes that belong to more than one piconet are called bridges are responsible to establish connection among the several piconets.

• The range of a piconet may be controlled by a power control system included in the Bluetooth specifications V1.2. This reduces the power consumption and may reduce the collision rate among piconets.


Network optimizationAn example of topology formation on Bluetooth networks

Without Location Information With Location Information

Source: Master Thesis of Satya Krishna Venkata

Master arbitrarily connected to slaves when there is more than 7 devices in its range

Master connected to closest slaves when there is more than 7 devices in its range



• Average Farthest Slave Distance (AFSD) is optimized with location information.

• For small number of Devices, the AFSD does not depend on having or not location information

• As larger the number of Devices, better the AFSD value with location information


Aver

age

Farth

est S

lave

Dis

tanc

e (m

eter

s)

Total number of nodes

Room 20m*20m || 7 slaves/piconet



The number of piconets with location information (non-red colors) is most of the cases higher than if no information (red line) was use. However the number of collisions may be decreased, depending on the algorithm.


Col

lisio

n R

ate

with

tran

smis

sion

in e

very

slo

tTotal number of nodes

Num

ber o

f Pic

onet

sfo

rmed

Total number of nodes


Summary• Location Information can be used as a context-sensitive

networking factor• Filtering techniques can give a big help on correcting

several of the common problems in obtaining Location Information

• Location Information gives spatial organization in the network. Topology formation in Bluetooth networks using Location Information increases the number of piconets in the network, but may reduce the collisions rate.


References[1] – João Figueiras. Location Information. On going technical report for WANDA project.[2] – João Figueiras, Hans-Peter Schwefel, and Istvan Kovacs. Accuracy and timing aspects of location information based on signal-strength measurements in bluetooth. In Proceedings of the 16th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC. IEEE, September 2005.[3] – Satya Krishna Mohan. Optimization of Topology Formation using Location Information in WPAN. Master’s project, Aalborg University, Mobile Communications Department, Spring 2005[4] – Guillaume Monghal, Yann Malidor, João Figueiras, Hans-Peter Schwefel, István Z. Kovács. Enhanced triangulation method for positioning of moving devices. In Proceedings of the 8th International Symposium on Wireless Personal Multimedia Communications, WMPC. September 2005.[5] – Hans-Peter Schwefel, Tatiana K. Madsen. Lecture Notes in Wireless Networks III: Advanced IP-based Networking Concepts. http://kom.aau.dk/%7Etatiana/WirelessNetworksIII_Fall04/MM2.pdf


Thank you!Questions?

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Wireless Networking Trends - Aalborg Universitetkom.aau.dk/~dsp/livslanglaering/JF-Location.pdfWireless Networking Trends ... disavantage Video Analysis. Page 13 João Figueiras Life-long