MANET Notes

INDOOR MOBILITY MODELLING FOR MANETS: AN ACTVITY APPROACH

by

Mbuyu Sumbwanyambe

DISSERTATION SUBMITTED IN FULLFILLMENT OF THE REQUIREMENTS FOR THE DEGREE

of

MAGISTER INGENERIAE

in

ELECTRICAL DEPARTMENT

in the

Faculty of Engineering and the Built Environment

at the

University of Johannesburg

Supervisor: Dr. W. Clarke University of Johannesburg © May 2008

i

Professor, E.W Dijkstra’s advice to a promising researcher, who asked how to select a topic for research, was: "Do only what only you can do"

ABSTRACT

INDOOR MOBILITY MODELLING FOR MANETS: A REALISTIC APPROACH

Sumbwanyambe Mbuyu

Master of Science Graduate Department of Electrical Engineering

University of Johannesburg

©2007

Mobile adhoc networks (MANETs) are multihop wireless topologies that have rapidly

changing node structure and limited connectivity. Since MANETs are not deployed

on a wide scale, the research community still depends on the simulators such as the

network simulator (Ns2) to evaluate MANET protocols.

The topic of how to accurately model an indoor environment in the MANET research

community is explored in this dissertation. We take an empirical and simulative

approach to model our mobility pattern. Our mobility model is based on activity

patterns drawn from the transport science.

A comparison with the random way point is made in order to understand the weighty

discrepancy between the two models. Our contribution in this research is three fold:

1. We argue that mobility modelling should be based on activities other than

stochastic process that have got no realistic backing;

2. We model our network using by putting up an algorithm and take an empirical

approach to model the radio frequency propagation. To show the difference of

the two mobility models, the behaviour of the signal strength on the two

mobility models is drawn; and

3. Finally an implementation of our mobility pattern and RF measurements in

ns2 is done.

Key words: Activity model, graph theory, ns2

ii

ACKNOWLEDGEMENTS

Sincere gratitude goes to my supervisor Dr .W. Clarke who guided and supported me

throughout my dissertation and my stay at the University of Johannesburg. Truthfully

I had a rough time with my research but with your help I am glad I can hold my head

high. Once again, thank you, for the guidance and for steering me in the right

direction. This has been a great time for me. Undoubtedly, my life at the university

could have been boring had not been for the support of my colleagues in the

Telecommunications Research Group (TRG). I sincerely thank Khmaies, Ling,

Marco, Dr Theo swart, Filip, Ali, Dr Benny Chisonga and Hailing. I will certainly

miss the great soccer days we shared on Fridays. I want to give a great thank you to

my family: Dr Silishebo Sumbwanyambe, Lubasi Masilokwa, Faith sumbwanyambe.

Your support was exceedingly valued.You modeled me to be the person I am proud to

be today and hence, I owe you this great opening I have in life. I want to thank all my

friends from Zambia who kept me with the nice zed music from back home. Lastly,

but certainly not the least, my greatest thanks to GOD almighty for granting me the

ability, perseverance and clarity to do this work.

iii

TABLE OF CONTENTS

ABSTRACT ................................................................................................................ I

ACKNOWLEDGEMENTS ....................................................................................... II

TABLE OF CONTENTS .......................................................................................... III

LIST OF FIGURES .................................................................................................. VI

LIST OF TABLES ................................................................................................. VIII

CHAPTER 1 ................................................................................................................. 1

1. OVERVIEW: INTRODUCTION AND OBJECTIVES ...................................... 1

1.1 INTRODUCTION...................................................................................................... 1

1.2 DEFINITION OF MOBILE ADHOC NETWORKS ............................................................ 1

1.3 MOTIVATION AND BACKGROUND ........................................................................... 1

1.4 PROBLEM STATEMENT ........................................................................................... 3

1.5 OBJECTIVE OF THE STUDY ...................................................................................... 5

1.6 DETAILED UNDERSTANDING OF OBJECTIVES .......................................................... 6

1.7 CONTRIBUTION ...................................................................................................... 7

1.8 DISSERTATION STRUCTURE .................................................................................... 8

CHAPTER 2 ............................................................................................................... 10

2. THE ROAD MAP: OVERVIEW OF MANETS AND RELATED

TECHNOLOGIES ..................................................................................................... 10

2.1 INTRODUCTION .................................................................................................... 10

2.2 CELLULAR NETWORKS ......................................................................................... 11

2.3 SATELLITE NETWORKS ......................................................................................... 11

2.3 WIRELESS LOCAL AREA NETWORKS (WLAN). ....................................................... 12

2.4 MOBILE ADHOC NETWORKS (MANETS) ................................................................. 13

2.5 SIMULATION TOOLS FOR MANETS - THE DE FACTO STANDARDS ........................... 30

2.6 CONCLUSION ....................................................................................................... 34

CHAPTER 3 ............................................................................................................... 35

3. UNDERSTANDING OF MOBILITY MODELS ............................................... 35

3.1 INTRODUCTION.................................................................................................... 35

3.2 RANDOM MOBILITY MODELS. .............................................................................. 36

3.3 MOBILITY MODELS WITH SPATIAL DEPENDENCY. ................................................ 38

iv

3.4 MOBILITY MODELS WITH TEMPORAL DEPENDENCY. ............................................ 39

3.6 OVERVIEW OF RELATED WORK ............................................................................ 40

3.7 CONCLUSION ....................................................................................................... 49

CHAPTER 4 ............................................................................................................... 50

4. RADIO FREQUENCY PROPAGATION MODELS IN WLANS OR

MANETS .................................................................................................................... 50

4.1 INTRODUCTION.................................................................................................... 50

4.2 SHADOWING ........................................................................................................ 51

4.3 RAY TRACING MODELS. ....................................................................................... 53

4.4 RF EMPIRICAL PATH- LOSS MODELS ................................................................... 54

4.5 RELATED WORK-PROPAGATION IN AN INDOOR ENVIRONMENT. ........................... 57

4.6 CONCLUSION ....................................................................................................... 61

CHAPTER 5 ............................................................................................................... 63

5. MOBILITY MODELLING AND IMPLEMENTATION ................................. 63

5.1 INTRODUCTION.................................................................................................... 63

5.2 TOPOLOGY – THE SPATIAL ENVIRONMENT ......................................................... 64

5.3 NODE DENSITY DISTRIBUTIONS PATTERNS .......................................................... 69

5.4 USER MOVEMENT DESCRIPTION-THE DYNAMICS. ................................................ 71

5.5 PATH CHOICE: SHORTEST PATH / ALL-OR-NOTHING .......................................... 72

5.6 IMPLEMENTATION OF THE MOBILITY MODEL IN NS2 ........................................... 77

5.7 CONCLUSION ....................................................................................................... 80

CHAPTER 6 ............................................................................................................... 81

6. METHODOLOGY OR EXPERIMENTAL PLANNING ................................. 81

6.1 INTRODUCTION.................................................................................................... 81

6.2 METHODOLOGY .................................................................................................. 81

6.3 ENVIRONMENTAL DESCRIPTION .......................................................................... 82

6.4 EXPERIMENT 1. UNDERSTANDING INDOOR TRAVEL PLANS AND ACTIVITY

PATTERNS. ................................................................................................................ 84

6.5 EXPERIMENT 2: MODELLING THE OBSERVED CHANNEL CHARACTERISTICS-

EMULATING THE MOBILITY PATTERNS. ...................................................................... 87

6.6 SECTION 3: MAIN EXPERIMENT, NS2 SIMULATIONS. ........................................... 96

v

6.7 CONCLUSION ....................................................................................................... 98

CHAPTER 7 ............................................................................................................. 100

7. EVALUATION AND DISCUSSION OF RESULTS........................................ 100

7.1 INTRODUCTION.................................................................................................. 100

7.2 RESULTS OF EXPERIMENT 1A: SPEED IN AN INDOOR LOCATION. ........................ 100

7.3 RESULTS FOR EXPERIMENT 1B: NODE DENSITY DISTRIBUTION AND ACTIVITY

DISTRIBUTION ......................................................................................................... 102

7.4 RESULTS OF EXPERIMENT C: SELECTION OF ROUTES IN AN INDOOR ENVIRONMENT

................................................................................................................................ 104

7.5 GENERAL DISCUSSIONS OF RESULTS: EFFECTS OF DISTANCE ON THE SIGNAL

STRENGTH. .............................................................................................................. 105

7.6 SECTION 2 MAIN EXPERIMENT: SIMULATION RESULTS AND ANALYSIS. ............. 116

7.7 CONCLUSIONS ................................................................................................... 120

CHAPTER 8 ............................................................................................................. 121

8. CONCLUSION AND FUTURE WORK ........................................................... 121

8.1 INTRODUCTION.................................................................................................. 121

8.2 CONTRIBUTIONS ................................................................................................ 121

8.3 OVERVIEW OF CHAPTERS .................................................................................. 121

8.4 LIMITATIONS OF OUR STUDY ............................................................................. 122

8.5 CONCLUSIONS OF RESULTS ................................................................................ 123

8.6 FUTURE WORK .................................................................................................. 123

8.7 CONCLUSION ..................................................................................................... 123

REFERENCE ........................................................................................................... 124

COLOPHON ............................................................................................................ 133

vi

LIST OF FIGURES

FIGURE 1: DIAGRAM SHOWING DIFFERENT TYPES OF TELECOMMUNICATION NETWORKS

.............................................................................................................................. 11

FIGURE 2: A BASIC SATELLITE COMMUNICATION SYSTEMS ........................................... 12

FIGURE 3: MANETS IN MILITARY OPERATIONS ............................................................ 14

FIGURE 4: NODE COMMUNICATION IN SENSOR NETWORKS ............................................ 15

FIGURE 5: EMERGENCY OPERATIONS IN MANETS ....................................................... 16

FIGURE 6: (A) A BAN NETWORK AND (B) A PAN NETWORK ........................................ 17

FIGURE 7: IEEE 802.11 LAYERED PROTOCOL STRUCTURE [4] ....................................... 20

FIGURE 8: PERFORMANCE OF THE 802.11 NETWORKS [71] ............................................ 21

FIGURE 9: THE HIDDEN TERMINAL PROBLEM ................................................................ 23

FIGURE 10: ROUTE REQUEST AND ROUTE REPLY IN AODV [15] ................................... 26

FIGURE 11: SCOPES IN THE FSR ROUTING PROTOCOL ................................................... 28

FIGURE 12: A SIMPLIFIED VIEW OF THE NS2 SIMULATION FLOW [40] ............................ 33

FIGURE 13: DIFFERENT TYPES OF MOBILITY MODELS [42] ............................................ 36

FIGURE 14: RANDOM MOVEMENT IN THE RANDOM WAY POINT MODEL ......................... 38

FIGURE 15: FREE WAY MODEL AND THE MANHATTAN MOBILITY MODELS [42] ............ 39

FIGURE 16: ACTIVITY PATTERNS IN AN INDOOR ENVIRONMENT .................................... 46

FIGURE 17: DRAWING SHOWING (A) SCATTERING (B) DIFFRACTION (C) REFLECTION .. 52

FIGURE 18: THE TWO RAY MODEL DIAGRAM [73] ........................................................ 53

FIGURE 20: SIMILARITIES IN GRAPHS PRESENTATION IN (A) AND (B) ............................. 65

FIGURE 21: NETWORK DIAGRAM .................................................................................. 67

FIGURE 22: NODE DENSITY DISTRIBUTION (A) RANDOMWAY POINT (B) ACTIVITY MODEL

.............................................................................................................................. 70

FIGURE 23: PICTURE OF NODE DENSITY DISTRIBUTION AT PARTICULAR TIMES OF THE

DAY (A) BREAK TIMES AND (B) DURING WORKING HOURS ..................................... 70

FIGURE 24 COMMON SCENARIOS IN AN INDOOR ENVIRONMENT; PICTURE SHOWING THE

CHOICE OF PATH BETWEEN THE STAIRS AND THE RAMP ......................................... 73

FIGURE 25: PICTURE SHOWING DIFFERENT ROUTE CHOICES IN AN INDOOR

ENVIRONMENT. ..................................................................................................... 73

FIGURE 26: GENERATING A MOBILITY TRACE IN NS2 USING ACTIVITY BASED MODEL ... 77

FIGURE 27: GENERATED CAD DRAWING OF AN INDOOR ENVIRONMENT ....................... 78

vii

FIGURE 28: PICTURE DESCRIPTION OF THE ENVIRONMENT WHERE EMPIRICAL

MEASUREMENTS WERE CONDUCTED ...................................................................... 83

FIGURE 29: NODE DENSITY MEASUREMENT AREA ........................................................ 85

FIGURE 30 FEATURES OF THE 802.11G PCI CARD ......................................................... 88

FIGURE 31: SCREENSHOT OF THE WIRELESSMON GUI ................................................... 89

FIGURE 32: SETUP CONNECTION OF THE EXPERIMENT ................................................... 90

FIGURE 33: LINE OF SIGHT MEASUREMENT AREA ......................................................... 91

FIGURE 34: OPEN PLAN OFFICE MEASUREMENTS AREA ................................................. 92

FIGURE 35: OPEN SPACE AREA WHERE EMULATION OF THE RANDOM WAY POINT WAS

CONDUCTED .......................................................................................................... 95

FIGURE 36: TWO TYPES OF RANDOM WAY POINT MOVEMENT WHICH WE EMULATED .... 96

FIGURE 37: PERCENT PROBABILITY OF NODE DENSITY IN CORRIDORS AT SPECIFIC TIMES

OF THE DAY ......................................................................................................... 103

FIGURE 38: PERCENTAGE USE OF TIME IN AN INDOOR ENVIRONMENT ......................... 104

FIGURE 39: VARIATIONS OF SIGNAL STRENGTH WITH DISTANCE ................................. 107

FIGURE 40: SIGNAL STRENGTH DECAY VERSUS DISTANCE IN AN OPEN PLAN OFFICE ... 108

FIGURE 41 VARIATIONS OF SIGNAL STRENGTH WITH DISTANCE IN A STAIR CORRIDOR 110

FIGURE 42: VARIATIONS OF SIGNAL STRENGTH WITH ONE NODE STATIONED IN AN

OFFICE AND ONE MOBILE ALONG THE CORRIDOR ................................................. 112

FIGURE 43: VARIATION OF SIGNAL STRENGTH VERSUS DISTANCE WHEN TWO NODES ARE

MOVING APART IN AN OBSTACLE FREE CORRIDOR ............................................... 113

FIGURE 44: SIGNAL STRENGTH VERSUS DISTANCE WITH BOTH NODES MOVING IN THE

SAME DIRECTION BUT IN A RANDOM MANNER FIGURE (A) ................................... 114

FIGURE 45 VARIATION OF SIGNAL STRENGTH VERSUS THE DISTANCE IN AN EMULATED

RANDOM MOVEMENT WITH TWO NODES MOVING OPPOSITELY FIG (B) ................ 115

FIGURE 46: SIGNAL STRENGTH DISPARITY VERSUS DISTANCE IN RANDOM MOVEMENT

KEEPING ONE NODE CONSTANT ........................................................................... 116

FIGURE 47 RESULTS FOR DSDV THROUGHPUT (A) AND DELAY (B) USING THE 1024

BYTES PACKET PAYLOAD .................................................................................... 118

FIGURE 48 THROUGHPUT (A) AND DELAY (B) RESULTS FOR 512 BYTES PACKET PAYLOAD

USING DSDV ...................................................................................................... 119

FIGURE 49 THROUGHPUT (A) AND DELAY (B) RESULTS FOR AODV WITH 1024 BYTES

PAYLOAD ............................................................................................................ 119

viii

LIST OF TABLES

TABLE 1: WIRELESS LAN THROUGHPUT BY IEEE STANDARD ..................................... 18

TABLE 2: SUMMARY OF THE DIFFERENT IEEE 802.11 STANDARDS AND MODULATION

TECHNIQUES .......................................................................................................... 22

TABLE 3: PATH-LOSS EXPONENT N FOR DIFFERENT ENVIRONMENTS [73] ...................... 56

TABLE 4: PARTITION LOSS OF DIFFERENT MATERIALS [73] ........................................... 59

TABLE 5: ACTIVITY TIME DISTRIBUTION IN AN INDOOR PLACE ...................................... 85

TABLE 6: SPEED VARIATION OF USERS IN AN INDOOR ENVIRONMENT ......................... 101

TABLE 7: AVERAGE SPEED MEASUREMENTS IN DIFFERENT INDOOR LOCATIONS ......... 101

TABLE 8: NODE DENSITY DISTRIBUTION IN DIFFERENT PLACES OF AN INDOOR

ENVIRONMENT .................................................................................................... 103

TABLE 9: THE STATISTICAL TABLE WAS TAKEN FROM A SMALL SURVEY OF 100 USERS AT

UNIVERSITY OF JOHANNESBURG. ........................................................................ 104

TABLE 10: VARIATIONS OF SIGNAL STRENGTH WITH DISTANCE .................................. 106

TABLE 11: SIGNAL STRENGTH DECAY VERSUS DISTANCE IN AN OPEN PLAN OFFICE .... 108

TABLE 12: VARIATIONS OF SIGNAL STRENGTH WITH DISTANCE IN A STAIR CORRIDOR

............................................................................................................................ 110

TABLE 13: VARIATIONS OF SIGNAL STRENGTH WITH ONE NODE STATIONED IN AN OFFICE

AND ONE MOBILE ALONG THE CORRIDOR ............................................................ 112

TABLE 14: VARIATION OF SIGNAL STRENGTH VERSUS DISTANCE WHEN TOW NODES ARE

MOVING APART IN AN OBSTACLE FREE CORRIDOR ............................................... 113

TABLE 15: SIGNAL STRENGTH VERSUS DISTANCE WITH BOTH NODES MOVING IN THE

SAME DIRECTION BUT IN A RANDOM MANNER FIGURE (A) ................................... 114

TABLE 16: VARIATION OF SIGNAL STRENGTH VERSUS THE DISTANCE IN AN EMULATED

RANDOM MOVEMENT WITH TWO NODES MOVING OPPOSITELY FIGURE (B) .......... 115

TABLE 17: SIGNAL STRENGTH DISPARITY VERSUS DISTANCE IN RANDOM MOVEMENT

KEEPING ONE NODE CONSTANT ........................................................................... 116

ix

Definitions and Terminology

AODV Ad-hoc on-demand Distance vector

APs Access points

ATIS Advanced Traveller Information Systems

AM Activity Model

BAN Body Area Network

CSMA/CA Carrier Sense Multiple Access with Collision Avoidance

CCK Complementary Code Keying

CTS Clear To Send

CRC Cyclic Redundancy Check

DARPA Defence Advanced Research Projects Agency

DCF Distributed Coordinated Function

DSR Dynamic source routing

DSDV Direct Sequence Destination Vector

DSSS Direct Sequence Spread Spectrum

FSR Fisheye State Routing Protocol

GPS Ground Position System

GTNets Georgia Tech Network simulator

HTC High-Tech Cellular

ISM-Band Industrial Scientific and Medical band

LAR Location aware routing protocol

LAN Local Area Network

LOS Line Of Sight

MAN Metropolitan Area Network

MAC Medium Acces Control

MATLAB MATrix LABoratory

MANETs Mobile Adhoc NETworks

MIRRORS Mobility Integration of Radio Requirements in Real-world

Simulations

NAV Network Allocation Vector

NS2 Network simulator 2

N-LOS Non Line Of Sight

x

OFDM Orthogonal Frequency Division Multiplexing OPNET

OPNET Optimized Network Engineering Tools

PDAs Personal Digital assistants

PDR Packet Delivery Ratio

PAN Personal Area Network

RTS Request To Send

RF Radio Frequency

RFID Radio Frequency IDentification

RPGM Reference Point Group Mobility

RWP Random Way Point

S-D Source to Destination

TTL Time To Live

TGn Task Group N

W-LANs Wireless Local area Network

Chapter 1 Overview of Dissertation: Introduction and Objectives

1

CHAPTER 1

1. OVERVIEW: INTRODUCTION AND OBJECTIVES

1.1 INTRODUCTION

Mark Wieseer the father of ubiquitous once said,” The most profound technologies

are those that disappear. They weave themselves into the fabric of everyday life until

they are indistinguishable from it

In this chapter we present an introduction to the topic of mobility modelling in Mobile

Adhoch Networks (MANET) simulations and its effects on radio frequency (RF)

physical (PHY) metrics. An overview of our research and the motivations behind our

research is also presented in this dissertation. Further more the dissertation outlines

the research problems we have developed in the area of MANETs and identifies the

contributions of our research. Finally, it provides an outline for the rest of the chapters

to follow.

1.2 DEFINITION OF MOBILE ADHOC NETWORKS

A mobile adhoc network (MANET) represents a system of communicating nodes that

can dynamically self- organise into temporal and arbitrary network topologies. Unlike

conventional wireless networks, MANETs have no fixed infrastructure or

administrative support such as in the cellular networks or other forms of wireless

networks. Sometimes MANETs are referred to as wireless LANs (WLANs) or just

wireless networks. In MANETs nodes act as routers, transmitters or receivers

depending on the transmission pattern of the network.

1.3 MOTIVATION AND BACKGROUND

In the next generation of wireless networks, mobile computing [9] is predicted to

become part of our lives. MANETs, which forms part of mobile computing, may find

increasingly use in situations where there is a need for the rapid deployment of

independent mobile users. Significant examples include:


2

• Establishing survivable, efficient and dynamic communication for

emergency/rescue operations (such as in the September 9/11);

• Disaster relief efforts such as in African countries where war and civil conflict

have brought down many telecommunication infrastructures; and

• Military networks where MANETs are deployed for effective war combat and

communication.

The research community is currently busy trying to understand the scalability of

MANETs in real world scenarios [44]. Recent efforts in research and development

have rapidly advanced the research in the wireless mobile computing. For example,

EUROtech has released the ZYPAD [5], a wearable computer which has a GPS

incorporated into it. The ZYPAD incorporates the dead reckoning system (Dead

reckoning (DR) is the method of approximating one's current spot based upon an

earlier determined spot, and advancing that spot based upon known speed, elapsed

time, and course). It can detect if the user has been motionless for a long period by

sending beaconing messages for quick location of the user.

Although research in MANETs has been a focus of many research institutions, a lot,

however, needs to be done especially in the area of modelling techniques and the

behaviour of MANETs in different environments.

Earlier techniques of MANET modelling were mostly dependent on analytical

approaches, but the trend changed with the introduction of stochastic principles to

model real world scenarios. Nevertheless, it has been difficult to model the

unpredictability of mobility and radio propagation inherent in real-world phenomena

with such models.

This led to the introduction of simulators in order to capture these scenarios in a more

realistic manner. Discrete event simulators such as the Ns2, [40] GloMosim [42],

OPNET [39], QUALNET and MATLAB have been extensively used in research

activities to model the characteristics of MANETs.


3

While it has been less complex to model wired networks, it has really been very

difficult to model wireless MANETs. Wireless networks simulations for indoor and

outdoor are being implemented by researchers, but most of the simulations are biased

towards evaluating the scalability of MANETs in outdoor scenarios [29, 32, 33, 34].

Modelling the real world underpinning the indoor environment is therefore difficult.

Even if modelling indoor environments might prove to be difficult, it is essential that

simulation models for adhoc networks must include sensible movement behaviour,

such as activity driven movement pattern, effects of obstructions on route choice and

signal variations due to channel characteristics [79, 84]. It is imperative that the

simulation should be realistic, in such a way that the parameter space and user

movement evaluated should reflect real world settings. Assumptions that do not

reflect the true nature of the problem domain edge the results of simulations to

academic importance.

Despite much research effort on the above aspects, the current state of the art is still

unsatisfactory and unrealistically implemented. Most simulations in wireless networks

ignore the effects of obstructions on path choice [44], radio propagation and tend to

ignore typical mobility patterns which are influenced by activity patterns.

1.4 PROBLEM STATEMENT

1.4.1 Unrealistic Mobility Patterns.

Mobility pattern is a major factor that affects the performance of a MANET and in

turn it will affect the results obtained in the simulation of mobile networks. Mobility

in most MANET simulations lack realism and do not really reflect the user’s

movement behaviour in such a place. Despite the fact that earlier models are credited

with ease of understanding and implementation, they are often based on theoretical

models rather than real world observations.

Mobility patterns such as the Random Way Point (RWP) [6, 42], Reference Point

Group model (RPGM) [42] and the more recent Down Town Mobility models [44]


4

such as Manhattan and freeway, are mobility patterns that are not based on realistic

movement behaviour motivated by activity and path choice.

Some of the aforementioned models assume scenarios devoid of obstacles with a

random user movement. Others are rather simplistic and do not really depict the

movement behaviour found in an indoor structure or channel.

Although, the movement pattern displayed by some of the above models may be a

reasonable assumption in certain outdoor situations, it is likely not applicable in many

indoor environments where the impact of indoor obstacles and pathways on both user

mobility, path choice and user density cannot be underestimated.

For example, students on campus will go to places where they want to perform an

activity, such as attending a lecture or going to the canteen. This choice influences

their movement pattern which in turn influences the node density in the path

traversed.

Selection of a path on the other hand, is determined by obstructions such as stairs,

ramps and lifts within such a path. It is common sense knowledge that people/users

tend to avoid paths which are congested with obstacles. For example, people and cars

tend to avoid paths with a lot of stairs and traffic lights respectively. Instead, they

select paths with fewer obstacles in corridors and with less or no traffic lights in

highways/freeways.

Transport science [36, 49, 51, 50] indicates that user movement in an environment is a

function of activity participation and route choice is a function of obstructions. This in

itself suggests that mobility patterns in any environment must be linked to path/route

choice and movement pattern generated by the users.

1.4.2 Indoor Propagation and Routing.

While wired links have been well understood in the research community, there has

been less understanding on the wireless links due the fluctuating nature of the channel

in which they operate. Space and channel parameter have a drastic effect on the

received signal strength, packet scheduling and link quality of wireless networks.


5

Since these effects have temporal as well as spatial properties, it is hard to model

them analytically or using the discrete event simulators.

Stochastic and deterministic modelling procedures [86] have been used to predict the

intrinsic behaviour of wireless phenomenon in an indoor environment but correct

ways are far from being reached. This is because indoor environments differ in their

geometry and construction, making transportability and applicability of such results to

other indoor environments questionable.

Also, the effect of materials has to be taken into consideration when modelling

wireless networks which is not included in discrete event simulators. It is for the

above reason that we take an empirical approach to model radio frequency (RF)

propagation in an indoor office environment.

In this dissertation the space is the office indoor environment in which we take

measurements and observe the received signal strength and the link quality of our

wireless transceivers.

1.5 OBJECTIVE OF THE STUDY

This dissertation has three major objectives, which are:

• To analyze activity patterns of both students and workers at the University of

Johannesburg. Analysis will include time allocation to activities, movement

pattern, route choice as a function of obstacles and node density. This in turn

will enable us to propose a more realistic mobility pattern for an indoor office

environment for MANETs based on activities and proper selection of routes

within a network;

• To emulate the observed mobility pattern as dictated to by the geometry and

obstructions of the building and compare it with the random waypoint, then

compare the signal strength (RF propagation) of the physical layer (PHY

layer) of the IEEE 802.11 standard with the two mobility patterns ( the random

way point and, activity and obstruction driven mobility pattern); and

• Implementation of this movement and RF propagation behaviour in Ns2.


6

1.6 DETAILED UNDERSTANDING OF OBJECTIVES

1.6.1 Mobility models in based on activities and route choice Notably, most mobility patterns in MANETs are a source- destination (S-D) type of

movement which may not be the case in real scenarios. Realistic mobility patterns are

dictated to by a number of factors such as activities and obstructions. Activities and

obstructions determine the destination of users and route choice respectively, for this

reason, these two factors also determine the node density within each particular link

traversed.

Despite much work or effort regarding mobility modelling, [8, 27, 30, 31,] the current

state of art is not to our satisfaction.

We argue that modelling mobility patterns should be based on activities and path

choice in space and time, rather than assumptions [6]

We propose a mobility model based on the activity and route choice [46, 47, 48, 49,

54, 52, 53] which focuses on mobility as a function of activities performed by

individuals and route choice as a function of obstructions in a particular environment.

Our mobility model is formulated from the data that we collected after a one week

observation of user movement in our chosen environments, which are the Electrical

Engineering office and the main administrative building.

Detailed observations about the user path choice, time management and mobility

patterns are observed. We use both mathematical approaches (graph theory) [56] and

the ns2 [40] to realistically implement our observations

We start by choosing our office space in which we carry out our movement patterns

based on observation of users or people. We evaluate how the structure of the space

we have chosen dictates the mobility pattern and the user choice of a path.

Furthermore, we observe the effects of any activities on time scheduling and the

restrictions such activities have on the movement behaviour of the users.


7

1.6.2 Indoor propagation - connectivity and radio range

Since obstructions and mobility govern the connectivity of nodes in wireless network,

we conduct experiments to see the effects of obstructions and mobility on radio

frequency propagation.

The challenge in this experiment is to evaluate how mobility patterns and obstructions

affect certain metrics such as the link quality and the received signal strength. These

results will lead us to a new understanding of the wireless routing problems and the

effect of such on the physical layer of 802.11 [2, 66, 65] in an indoor environment

We take an empirical approach (experimental analysis) to modelling the effects of

obstructions on mobility in the selected environments. We evaluate how different

mobility patterns in an indoor environment affect the Received Signal Strength

Indicator RSSI (RSSI is a measurement of the power present in a received radio

signal) and the link quality using wireless cards.

In addition we also take a compulsory evaluation of the effects of materials on the

signal strength indicator and the link quality. We perform our experiments in different

office buildings and record the effect of mobility coupled with building materials of

that particular office. We are optimistic that the above models will allow for more

efficient and scalable simulations parameters in event simulators.

1.7 CONTRIBUTION

In this dissertation, we develop a new mobility model based on the activities patterns

in an indoor environment. Our contribution in this dissertation is threefold:

Firstly, we present an algorithm which aims at selecting the most probable paths in an

indoor environment. We provide a mathematical descriptions, complexity analysis

and implementation of the algorithm;

Our second contribution, is the experimental analysis of the user mobility and radio

frequency behaviour in an indoor environment; and


8

Finally, we link these models together to obtain a new mobility model called the

Activity Model (AM). We show that when this mobility model is compared with the

Random way point model the results, in terms of performance, outweigh the Random

Way Point.

1.8 DISSERTATION STRUCTURE

Chapter 2 This chapter explains the background of wireless communication. Particular attention

is given to the Mobile Adhoc wireless networks. Furthermore, this chapter will also

focus on the necessary introduction to communication networks providing the reader

with necessary communications terminology and a brief introduction to evolution of

networks and the simulation tools.

Chapter 3 In chapter 3, we present some mobility patterns which are commonly used in the area

of wireless LANs (WLANs). We present the related research work on the field of

MANETs

Chapter 4 In chapter 4, we will introduce wireless propagation models and related work that

have been done so far to model the channel effects on the transmitted signal in

wireless LANs.

Chapter 5

In chapter 5, we propose a graph algorithm based on the activity patterns in an indoor

environment. We look at how building geometry dictate the mobility of the users in

question. We present our mobility pattern which includes graph abstraction, path

choice and speed dictators in this chapter.

Chapter 6

In chapter 6, we present the methodology and the procedures of the experiments.

Chapter 7


9

In this chapter we present the results of the work done in chapter 6.

Chapter 8

In chapter 8, we provide a discussion of our work, the conclusion and envisaged

future work in the area of indoor mobility modelling.

Chapter 2 The roadmap: overview of MANETs and related technologies

10

CHAPTER 2

2. THE ROAD MAP: OVERVIEW OF MANETS AND RELATED TECHNOLOGIES

2.1 INTRODUCTION

Telecommunications has been in existence from time memorial when man used to

communicate with each other in an ancient way. Old ways of communication

involved the use of smoke in the deep African continent to the use of whistle in the

Americas; other forms of communication in this area include the torch signalling,

flashing mirrors, signal flares and smoke. Such type of communication required a line

of sight (LOS) between the sender and the receiver. Observations towers were built on

hilltops and along the roads to relay these messages over a large distance.

Modern telecommunications involves the use of telephone or radio technology to

communicate over long distances through analogue or digital radio signals. This type

of communication uses microelectronic computer, and PC technologies to transmit,

receive, and switch voice, data, and video communications over different transmission

medium (copper, fibre, wireless and microwaves).

Various types of analogue and digital transmission technologies are employed in

telecommunications today [73]. Analogue communication technologies use a

continuously varying signal and are currently being phased out by digital

transmission. To the contrary, digital communication requires the transmission of data

to be done in discrete form or in bits comprising of ones and zeros. This transmission

in discrete form allows for, a faster signal processing, reduction in errors (errors can

be detected and corrected). In summary, digital communication works like a simple

light switch which is either on or off.

Telecommunication can be divided into two main streams, that is, wireless and wired.

The block diagram in figure1 shows the different blocks of telecommunication.


11

Figure 1: Diagram showing different types of telecommunication networks

2.2 CELLULAR NETWORKS

Cellular communications [7] also known as mobile communications has experienced

tremendous growth in the past two decades. In the world today more and more people

have seen themselves having a cell phone or a mobile gadget, making it the most

sought after type of communication. Cellular phones allow a person to make or

receive a call as long as the person is in the range of the tower. In a cellular network a

person is able to make a conversation while moving hence the name mobile

communication.

2.3 SATELLITE NETWORKS

Satellites such as the one shown in Figure 2 have been in use for a long time in the

history of wireless communication [1]. Satellites provide communication for long

distance communication across the continents of the world e.g. from Africa to Europe.

They have also found great use in space explorations e.g. National Aeronautics Space

Administration (NASA). Satellites can be divided into three major groups which are

mainly the Geosynchronous Orbit (GEO), Medium Earth Orbit (MEO) and Low Earth

Orbit (LEO) satellites.

Wired communication

Cellular communication

Satellite communications

MANETs W-LANS

Wireless communication

Telecommunications

Point topoint


12

Down link Up link Base station

Figure 2: A basic satellite communication systems

2.3 WIRELESS LOCAL AREA NETWORKS (WLAN).

Wireless LAN supports high speed data transmission in a small region and where the

mobility of users is within a limited area e.g. in a village community or a small

building. Wireless devices that support these LANs are typically stationary or moving

at pedestrians’ speeds.

The early form of wireless LANS were based primarily on proprietary and

incompatible protocols. These WLANs functioned within the 26 MHz spectrum of the

900 MHz industrial, scientific and medical band (ISM) with data rates of up to 1-2

Mbps [73]. Both star and peer to peer (P2P) network topologies are often used. The

non availability of standards for these products (Early WLANs) led to soaring

development costs, low capacity of production, and a small market for every

individual product.

The second generation of WLANs operated within 83.5 MHz of the spectrum in the

2.4 GHz ISM band. The IEEE 802.11b [2] was developed to address the troubles

which were faced in the earlier version of the WLANs. Versions of IEE 802.11 such

as the g and n were formed in order to address the problem of low data rates offered

by IEEE 802.11a and IEEE 802.11b. Unlike the earlier version of WLANs the 802.11

standards led to low development costs and high capacity of production.


13

In Europe, wireless LANs growth revolves around the HIPERLAN (high performance

radio LAN) standard. The HIPERLAN standard is very similar to the IEEE 802.11a

standard hence it has the same frequency operation of 5 GHz and data rate speeds of

54Mbps and approximately the same range of 30m. Although the HIPERLAN and the

IEEE 802.11a are similar they differ in their quality of service (QoS).

A more detailed discussion on the technology will be provided later in this chapter.

2.4 MOBILE ADHOC NETWORKS (MANETS)

2.4.1 Introduction

Mobile adhoc networks are networks without any fixed or centralized infrastructure

[4]. The networks can be deployed at any time when needed, especially in military

and emergency operations. As the world becomes more pervasive and ubiquitous,

telecommunications gadgets such as PDAs, Laptops, cell phones and intelligent

vehicle communication will all form a part of MANETs. The communication through

MANETs is adhoc, meaning that the signal is transmitted randomly. In mobile adhoc

networks each node can either be a router, sender or receiver.

MANETs find a lot of applications in our everyday life such as in military, disaster

and emergency operations, sensor networks, W-LANS, home networks and vehicular

communications, Personal Area Networks (PAN) and Body Area Networks (BAN)

2.4.2.1 Military

MANETs are often used in military battlefields for communications. Military

operations shown in Figure 3 always reflect high level of organised societies when it

comes to communication. In the digital age, the military effectiveness depends on the

information quality, availability, and on-reflex information sharing. The

characteristics of information are of great importance for the military. Since MANETs

do not need a structured system, they find great use in survivable military

communications as shown in Figure 3.


14

Figure 3: MANETs in military operations

2.4.2.2 Sensor Networks

Sensor networks shown in Figure 4 are composed of nodes that can either be

stationary or mobile. Such networks communicate with one another in order to

provide vital information about their surroundings to a centralized system. Sensor

networks can be networked together to share real time information such as in active

roads or in automation of systems (SCADA). Like MANETs, sensor networks also

have constrained or limited networks communication bandwidth and finite on-board

battery power. Due to this similarity, sensor networks are sometimes referred to as

MANETs as well.


15

D

Figure 4: Node communication in sensor networks

2.4.2.3 Home Networks

Home networks are envisioned [4] for future networks where ubiquitous or pervasive

home equipment will be deployed. This will support communication among PCs,

Laptops, cordless phones, smart appliances, security and monitoring systems

anywhere in and around the home. Home networks will also encompass sensor

networks and RFID networks for appropriate home management

2.4.2.4 Emergency Operations

MANETs have found practical applications in emergencies that need a quick network

to be established between members of emergency services. This type of MANET

makes use of the cars, laptops, PDAs and high-tech cellular phones (HTC) as routers

to direct information to the desired destination. In this age of critical emergency

operations as depicted in Figure 5, MANETs will find a very useful use in mitigating

disaster situations.

N1 s N2

N3

N4 N5

N6


16

Figure 5: Emergency operations in MANETs 2.4.2.5 Personal area network (PAN) and body area networks (BAN).

Short-range communication MANETs [4] as portrayed in Figure 6 can simplify the

intercommunication between various mobile devices (such as a PDA, a laptop, and a

cellular phone). In this communication network wired cables are replaced with

wireless connections such as Bluetooth or infrared. These adhoc networks can also

extend the access to the Internet or other networks by wireless mechanisms, e.g.

Wireless LAN (WLAN), GPRS, and UMTS.

A Body Area Network (BAN) is a network on the body of a person. The BAN is,

potentially, a promising application field of MANET in the future of pervasive

computing context, especially in identification and medical fields. The BAN

comprises of devices such as the wireless head screen, Bluetooth, wireless business

cards or other wireless devices such as the RFID. A combination of two or more

BANs becomes a personal area network (PAN)


17

Figure 6: (a) A BAN network and (b) a PAN network 2.4.3 The IEEE 802.11 and the Bluetooth standards for MANETs

MANETs currently use two networking standards, which are the IEEE 802.11

standards and the Bluetooth standard. Bluetooth is being developed by the Bluetooth

Special Interest Group (SIG) [3] which is made up of different telecommunications

company such as Nokia and Samsung [2]. The features offered by Bluetooth

technology are its low cost, robustness and low power [3].

Contrary to the IEEE802.11, Bluetooth is a short range wireless communication

platform currently being developed for portable devices such as personal digital

assistants (PDAs), cell phones and note books. Other than being used in wireless

mobile devices, Bluetooth is also being developed for motor vehicles in the area of

intelligent transportation.

The IEEE 802.11 group of protocols are currently being developed for long range

communication at much higher data rates and distance. The introduction of 802.11n as

implemented by Task Group N (TGn) stimulated a lot of research in the area of the

802.11 family of protocols. The 802.11n offers a lot of throughput and high

performance. Driven by modulation techniques such as the OFDM [71], the 802.11n

(a) (b)


18

is expected to shift the performance of the current wireless LANS to four times

higher. Table 1 shows different IEEE 802.11 standards with different transmission

rates.

Table 1: Wireless LAN throughput by IEEE standard

2.4.4 The IEEE 802.11 technology (overview)

The IEEE802.11 [2] was adopted in 1992 by the IEEE for Local area networks (LAN)

standards with rates up to 2Mbps. Since then SIGs/task groups (TGs) have been

formed to look into the affairs and extensions of standards with the IEEE 802.11

framework.

A lot of standards have been developed by the IEEE 802.11 task force to improve

communication in wireless networks [2]. IEEE 802.11 standards have different

characteristics in terms of speed, throughput and compatibility of chipsets among

different vendors.

The IEEE 802.11b was formulated by the IEEE to have higher data rates when

operational, with 2.4 GHz assigned as the frequency band of operation [2]. However,

802.11b came with its own deficiencies in its operation. The working group decided

to rectify these deficiencies by developing the IEEE 802.11 b-cort.

Continuous development of standards in IEEE by various task groups continued

leading to the introduction of other standards such as the IEEE 802.11 c, d, e, g, f and

more recently 802.11n etc.

IEEE WLAN Standard Over-the-Air (OTA) Estimates

Media Access Control Layer, Service Access

Point (MAC SAP) Estimates

802.11b 11 Mbps 5 Mbps 802.11g 54 Mbps 25 Mbps (when .11b is not

present) 802.11a 54 Mbps 25 Mbps 802.11n 200+ Mbps 100 Mbps


19

At present the most common one on the shelf is the 802.11g standard. The 802.11g

operates in the frequency range of 2.4 -2.5 GHz, has a clear signal and less

interference. Though, the 802.11g frequency works fine in penetrating walls or other

types of building obstructions, it can, in some situations be interfered by other devices

which operate in the same frequency spectrum. Considerably, high data rates are

enabled by the IEEE 802.11g physical-layer extension making the 802.11g better than

the 802.11b in terms of data rates.

One of the most imperative aspects of the IEEE 802.11g standard is its backward

compatibility with IEEE 802.11b. In this way engineers implementing 802.11g are

able to persuade a widespread and international adoption of IEEE 802.11b products

such as laptops, PDAs and other 802.11 gadgets. Additionally, backward

compatibility also prevents market confusion and allows for easy decisions by

engineers in the IT environment and network professionals as they look to upgrade

their networks to higher performance [2]. Backward compatibility with 802.11a is

still not possible because of the same modulation techniques and the identical nature

of the two standards

The IEEE 802.11 standard specifies a MAC and physical layer for wireless LANs.

The physical layer uses different technologies such as the Frequency Hoping Spread

Spectrum (FHSS) and Direct Sequence Spread Spectrum (DSSS). The MAC protocol

is the distributed coordinated function which has a carrier sense multiple access with

collision avoidance (CSMA/CA).

While the 11-Mbit/s modes of IEEE 802.11b attain peer-to-peer throughputs at the

MAC layer of about 7.1 Mbits/s for 1,500-byte packets, the 54-Mbit/s OFDM mode

of 802.11g will enable throughputs in excess of 24.3 Mbits/s. The new throughput

rates in 802.11g have brought with them excellent streaming of DVD video in the

world of multimedia and new applications, which are marginal with existing IEEE

802.11b rates. These figures assume use of the distributed coordination function

(DCF) channel-access mechanism of the 802.11 MAC layer. Figure 7 shows the

layered protocols that are common to W-LANs or MANETs.

.


20

Contention free services Contention services MAC layer Figure 7: IEEE 802.11 layered protocol structure [4]

2.4.4.1 The link layer (MAC Layer)

When two nodes are in communication, access to the wireless medium is controlled

by Medium Access Control Layer (MAC layer) with a distributed coordination

function (DCF) known as carrier sense multiple access with collision avoidance

(CSMA/CA) (Figure 8).

When using the contention-based CSMA/CA access method, nodes making up the

mobile ad hoc networks or MANETs must first listen to one of the nodes on the W-

LAN about to transmit a message on the appropriate frequency and ensure that no

other node is transmitting. After the node detects that there is no other device which is

about to transmit, the device can start to transmit provided the channel is clear. If the

channel is busy, the device or the node transmitting initiates a random back-off

message, which must expire before another attempt to transmit can be made.

2.4 GHz frequency hoping or direct sequence spread spectrum with data rates of 1Mbps and 2Mbps

Distributed coordination function (DCF)

Point coordination function (PCF)

Logical control link


21

Figure 8: Performance of the 802.11 networks [71]

2.4.4.2 The physical layer (PHY Layer)

The physical layer controls the communication or the transmission of data among or

between the nodes in a network. The 802.11b offers data rates of about 11Mbps at the

PHY layer whilst the The IEEE 802.11g standard offers 54 Mbps data throughput

values at PHY level [2].

However, different factors cause data throughput degradation. The main degradation

factor of data transmission in MANETs is the CSMA/CA, a mechanism used to detect

collision and prevent it. Other factors are the distance between the wireless node and

the access point (AP), and propagation conditions which may include line of sight or

non line of sight ((LOS or NLOS), as well as any materials that may be present in

the propagation path [81].

2.4.4.3 Modulation techniques in mobile Adhoc networks (MANETs)

Literature of 71 and 70 documents types of modulation techniques that are used in

MANETs, with each modulation offering a different kind of throughput. The most

popular ones are the DSSS, the orthogonal frequency division multiplexing (OFDM),

and the Wideband Frequency Hoping (WBFH) which is primarily used for the home

RF environments.


22

The DSSS has higher rate of data transmission and prevents a lot of online attacks. It

supports 11Mb/s and the 5.5 Mb/s in the 2.4 GHz unlicensed ISM band using an 8

chip complementary code keying (CCK) modulation scheme. Conversely, when

DSSS signalling of each bit in the DATA header is multiplied by Pseudo noise (PN)

code sequences, the result is a chipping code which is normally an 11bit number.

The OFDM supports up to 54 Mb/s of data transmission in the 802.11g standards.

OFDM is a digital multi-carrier modulation scheme, which uses a large number of

closely-spaced orthogonal sub-carriers. Each sub-carrier is modulated with a

conventional modulation scheme at a low symbol rate maintaining data rates similar

to conventional single-carrier modulation schemes.

The modulation techniques, frequency and the maximum Data rates of the 802.11

standards are summarized in Table 2.

Table 2: Summary of the different IEEE 802.11 standards and modulation techniques

Standard Max Data rate Frequency modulation 802.11 2 Mb/s 2.4 GHz FHSS and

DSSS 802.11a 54 Mb/s 5 GHz OFDM Home RF 2.0 10 Mb/s 2.4 GHz WBFH 802.11b 11 Mb/s 2.4 GHz DSSS 802.11g 54 Mb/s 2.4 GHz OFDM

2.4.4.4 IEEE 802.11 RTS/CTS

MANETs are usually faced with the problem of hidden terminal (Figure 9) when

transmitting data in adhoc manner. The hidden terminal problem [4] occurs when

two stations try to transmit to a single station simultaneously. The station wishing to

transmit senses that one station is not transmitting and therefore initiates the

transmission. This contention to transmit can result in interference between the

transmitting stations.

In this case, designing wireless LANS requires incorporation of the Carrier Sensing

Random Access protocol. This protocol avoids the confusing which is prevalent in the


23

hidden terminal problem. As an example consider two transmitting stations T1 and T2

in Figure 9 wishing to send to the receiving station R1. Since T1 and T2 are not in the

transmitting range of one another, it is impossible for each station to detect that

another station is transmitting.

To overcome this problem, the IEEE [2] has adopted the RTS/CTS virtual carrier

sensing protocol in which short beaconing packets called request to send (RTS) are

send to the receiving station to announce the request to transmit data. If the channel is

found idle for a period exceeding the Distributed Interframe Space (DIFS) the

transmitting station will continue with its transmission after receiving a clear to send

(CTS) short message from the receiving station. From our Figure in 9, station R2 will

in this case send a short beaconing message called clear to send (CTS) to indicate its

readiness to receive data or to reject data. The RTS and the CTS usually contain the

projected length of transmission between the receiver and the transmitter. This

information is stored in the Network Allocation Vector (NAV) which determines the

complete time schedule for the transmission of data between the two stations. After

the transmission of the data, the receiving station which in his case is R2 sends the

acknowledgement message (ACK), acknowledging the reception of data without any

errors. If at any point, some errors are encountered during the transmission process

then the receiving station will send a cyclic redundancy check algorithm (CRC). This

algorithm is implemented to discover errors during transmission.

Figure 9: The hidden terminal problem 2.4.5 Routing Protocols for MANETs

T1

R2 T2


24

In the past few years MANETs have seen a precedented growth in research activities,

especially in the area of routing protocols [11, 13, 14, 15, 17, 18, 20]. Most research

activities in MANETs were centred on finding the best routing protocol for MANETs

to optimize quality of service, power, throughput and bandwidth in MANETs.

Notable routing protocols in this area will include the publications of [10, 12, 11, 15,]

in which they present different routing protocols. Vaidya and Ko et al, [17] through

the use of Global Position System (GPS) introduces a Location Aware Routing

protocol (LAR) in which the position of the destination node is known before the data

is sent to that node. In their work the routing of data through the network is solely

based on the position of the node.

Other notable work regarding routing protocols in MANETs can be found in [18, 19,

20, 21, 22 and 23]. Routing protocols can be divided into either reactive or proactive

routing protocols.

2.4.5.1 Reactive routing protocol

Reactive routing is an on-demand routing protocol that calculates the path before

transmission of data occurs. Routing protocols such as these depend on data

transmission to be active. If no data is transmitted the routing session will not occur.

Reactive routing protocols are characterized by:

• Being bandwidth efficient when routing, since routing is done on demand;

• Elimination of conventional routing tables at the nodes, hence reduction in

routing overhead;

• The elimination of the routing tables at the nodes and the ability of the routing

protocol to do their updates to track topology changes.

Path discovery is done on-demand when data is supposed to be sent. Maintenance of

such a path is on as long as transmission is on and deletion of such, when the path is

no longer necessary.


25

Reactive routing protocols forward their data through two ways: Source routing and

hop by hop routing. Some few examples of reactive routing protocols include:

(a) Dynamic Source Routing (DSR) Dynamic Source Routing (DSR) [18] is a reactive routing protocol. Like the AODV

[15], it forms a route on-demand request for the transmission of messages and it uses

source routing instead of relying on the routing table at each intermediate device.

Determining source routes requires accumulating the address of each device between

the source and destination during route discovery. The accumulated path information

is cached by nodes processing the route discovery packets. The learned paths are then

used to route packets. To accomplish source routing, the routed packets contain the

address of each device the packet will traverse. It allows nodes to cache route

information by overhearing data packets.

(b) Adhoc On-demand Distance Vector (AODV) Like the DSR [18], the AODV [15] is a routing protocol that operates on demand i.e.

it builds routes only when need to transmit massages arise. It uses sequence numbers

to maintain the freshness of routes. It uses both multicast and unicast routing.

AODV builds routes based on route request and route reply cycle as shown in figure

10. Firstly a message is broadcast for the route request. When a node which has a

route to the destination receives the message it broadcasts the route reply message to

the sender node. However the route request is kept at the node of the intermediate

node. If a need arises to send a route request a node with the route request will send

the route request with much more time to live (TTL). Once the destination has been

reached a route reply (RREP) is sent to the source. As the RREP message propagates

back to the source, nodes set up forward pointers to the destination. Once the source

node receives the RREP, it immediately begins to forward data packets to the

destination. If the source later receives a RREP containing a greater sequence number

or contains the same sequence number with a smaller hop count, it may update its

routing information for that destination and begin using the better route. AODV

maintains the route discovered as long as the route is active.


26

Figure 10: Route request and route reply in AODV [15]

Other reactive routing protocols which have not been included in this brief summary

include but not limited to the ADV TORA and the ABR

2.4.5.2 Proactive Routing Protocols

Proactive routing protocols take advantage of the idea of flooding the network

constantly with route request and increasing the amount of topology information

stored at the header packets of each node. These types of routing protocols combine

both the Distance Vector (DV) and Link State (LS) features. In this way a source node

wishing to transfer data to the destination node experiences no delays in doing so as

the route is always available. Examples of proactive routing protocols include the

DSDV [12], OLSR and the FSR.

(a) Destination Sequenced Distance Vector (DSDV)

Destination sequenced distance vector, developed by Perkins et al [12] in 1994, is a

table driven routing scheme for MANETs. It is based on the classical distributed

Bellman-Ford algorithm.

In the DSDV routing protocol, each node maintains a set of distance entries in the

routing table. The sequence numbers are even, if a link or route is detected; else, an

odd number is used for no route. This number is more often than not generated by the

destination or the receiver, and the transmitter needs to send out the next update with

this number. In order to sustain the distance estimates up to date, each node monitors


27

the cost of its outgoing links and intermittently broadcasts to each one of its

neighbours; its current estimate of the shortest distance of every other node in the

network. Routing information is distributed between nodes by sending full packets

infrequently and smaller incremental updates more.

The DSDV [12] eliminates the looping dilemma by transmitting the packets between

the stations of the network using routing tables which are available at each station of

the network

(b) Fisheye State Routing protocol (FSR)

Fisheye State Protocol (FSR) [13] is the improvement of the greedy state routing

(GSR), and both of them are link state protocols. In this protocol the transmission of

packets to neighbours is through a one hop count instead of flooding the messages.

Normally, the FSR will maintain at its node; a route table, a neighbour list and a

topology of the table of the network. This type of routing protocol does not flood the

messages into the network like other link state routing protocols, but messages are

exchanged with neighbours only. It also stores vital information about the distance

and its neighbour.

The FSR in Figure 11 introduces what is known as the scope, which is basically a

number of neighbours that can be reached by a transmitting node in one hop count.

This kind of transmission makes the packet reaching destination node to have precise

knowledge of the transmission route.


28

Figure 11: Scopes in the FSR routing protocol

2.4.6 Protocol Performance Analysis

Protocol performance analysis is used in MANETs as a gauging tool for measuring

two complementary aspects of the network. Firstly, the performance analysis is used

to measure the cost indexes of routing protocols. This measure is compared against

factors such as the utilization of bandwidth in MANETs and battery optimization.

Since bandwidth is a valuable asset in MANETs, proper utilization of such is vital for

optimal operation of MANETs.

MANETs rely on on-board battery power and as such evaluating the performance of

MANETs against the battery consumption is of greater importance. At all costs power

must be conserved in MANETs so as to make sure that the life span of MANETs is

prolonged.

Extra aspects are based on/or concerns application oriented metrics such as the

throughput of the networks, packet delivery ratio, end-to-end data packet delay,

routing overhead, route discovery time, route optimally, number of out-of-order

packets, and power consumption. Whilst the first three metrics are the ones significant

for an application, the others provide insights into the efficiency of the routing

service.


29

• Throughput: It is defined as total number of packets received by the

destination or the amount of digital data per time unit (hour, minutes, seconds

etc.) that is delivered to a certain node or destination. It is a measure of

effectiveness of a routing protocol. Throughput is usually measured in bits per

second (bit/s or bps).

• Packet delivery ratio (PDR): The ratio between the number of packets

received by the TCP sink at the final destination and the number of packets

originated by the “application layer” sources. It is a measure of efficiency of

the protocol. It measured as packets delivered over packets generated.

• End to end data packet delay: This is the time interval measured from the

time when a packet is ready for transmission at the source node until when it

reaches the destination node.

• Routing overhead: The routing overhead measures the algorithm's internal

efficiency and is calculated as the total number of control packets sent divided

by the number of data packets delivered successfully. (Or in number of bytes).

Since end-to-end Network throughput (data routing performance) is defined as

the external measure of effectiveness, efficiency is considered to be the

internal measure. To achieve a given level of data routing performance, two

different protocols can use differing amounts of overhead packets, depending

on their internal efficiency, and thus protocol efficiency may or may not

directly affect data routing performance. If control and data traffic share the

same channel, and the channels capacity is limited, then excessive control

packets often impacts data routing performance

• Route discovery time: This is the time taken for a node to compute a new

route after a breakage of the other link. Alternatively it can be described as the

time taken for the establishment of the new route.

• Route optimality: This is the measure of the cost of the path taken to the cost

of the most optimal one.

• Power consumption: This is the energy required or consumed for each

delivered bit or packet.


30

Note that the above metrics are influenced by two factors:

1. The topological movement: Depending on the movement of the topology, the

above metrics will be influenced drastically under different protocols. And

depending on the speed of the nodes, each routing protocol will perform

differently. For example the DSDV is suitable for low mobility situations, under

high mobility conditions the DSDV fails to converge. The TORA protocol will

perform better for low mobility simulations.

The AODV and the DSR are well recommended for the high mobility and are

suitable when traffic diversity increases. However DSR is unable to cope when

traffic diversity increases.

2. The rate of packet transmission for each offered load: For the low values of

offered load, the DSDV periodic route updates results in high value for normalized

routing overhead.

The AODV and the DSR will perform well for a high offered load. However, the

combination of reactive and proactive routing protocols such as ADV [14], break the

AODV and the DSR in terms of high (50% or more) peak throughput, lower packet

delays and control overhead packets. Furthermore, ADV uses fewer routing and

control overhead packets than that of AODV and DSR, especially at moderate to high

loads.

2.5 SIMULATION TOOLS FOR MANETS - THE DE FACTO STANDARDS

Deployment of wireless Mobile adhoc networks is slow. Different research groups are

trying to implement the reality of mobile adhoc networks by means of simulations

[31, 32, 33, and 34]. Few of the many simulators that are available to the research

community include the ns2, Glomosim and Opnet.

2.5.1 The Georgia tech network simulator (GTNets)


31

Developed by the Georgia Institute of Technology under the leadership of Dr. George

F. Riley, GTNets is a full featured discrete network simulator which allows

researchers in wireless and computer networks to evaluate the behaviour of these

networks under a variety of conditions.

GTNets [41] creates a simulation environment which is more close to being realistic.

For instance, GTNets provides a clear separation of protocol stack layers and consist

of Protocol Data Units (PDUs) which are appended and removed from the packet as it

moves up and down the protocol stack. Each node in the network simulator is

associated with an IP address and an associated link.

Protocol object connections at the transport layer are specified using a source IP,

source port, destination IP and destination port.

Like the ns2 simulator [40], the GTNets uses a graphical user interface (GUI) to

observe the movement pattern of different nodes in the simulation environment. The

GUI can be adjusted to observe the behaviour of nodes under different conditions.

2.5.2 MATrix LABoratory (MATLAB) simulator

Matlab [38] developed by MathsWorks is a commercially available, high performance

language for technical computing used by engineers and researchers world wide.

Matlab integrates visualization, computation and programming in an easy to use

environment. Matlab simulator has been used extensively in research, the most

notable use being that of the Graphical representation which has been available to

other network simulators such as the ns2. Characteristically Matlab’s tools are capable

of:

1. Computation and Mathematical modelling;

2. Algorithm development and analysis;

3. Modelling and simulation data analysis; and

4. Engineering and scientific graphics, application development including the

graphical user interface.


32

2.5.3 Optimized Network Engineering Tools (OPNET)

Optimized Network Engineering Tools (OPNET) [39] is a commercially available

network simulator that is used to simulate a variety of computer networks and other

linked networks. OPNET Modeller offers a wide range of tools for network

simulation. These tools can be used for data mining and analysis, model design

simulation and network cost diagnosis.

OPNET offers a graphical user interface with which one can observe the flow of

packets, control and routing, packet loss link failures and bit errors at a visible speed.

2.5.4 The Network Simulator (Ns2)

The Ns2 [40] is a discrete event simulator which was written by the Defence

Advanced Research Projects Agency (DARPA) and supported by the VINT project at

UCB, Xerox and other organisations [40]. Currently ns2 is being supported by

DARPA with SARMAN. Contributions from other researchers to ns2 have always

been welcome. Particularly, work from the University of California at Berkely (UCB)

Daedelus, Carnegie Mellon University (CMU) Monarch and Sun Microsystems had a

significant role in the wireless part of this simulator. Ns2 is a commonly used

simulation tool for the MANET environment [84]. It has been used by different

researchers to predict the performance of mobile adhoc networks in different

environments.

Ns2 is open source software that supports an array of network protocols providing

viable resource for the simulation of both wired and wireless networks. The simulator

is event-driven and runs in a non-realtime fashion. It covers a wide range of

simulations such as adhoc networks, wired LANs, satellites and wireless simulations.

It provides substantial support for simulation of TCP and protocol support such as

single and multicast routing, reservation, transport and session protocols.

Ns2 is written in C++ as the main core language and the object tool command

language (Otcl) or tool command language (tcl) shell as an interface, allowing the

input file to be implemented and manipulated in the network simulation. Controlling


33

configuring and manipulation of the Ns2 to achieve the desired results is provided

through the tool command language. Usually the OTcl script will do the following:

• start or initiates an event scheduler;

• build and modify up the topology using network objects through the tcl; and

• to configure packet sources when to start/stop transmitting packets through the

event scheduler.

Users can use the class in order to define arbitrary network topologies composed of

nodes, routers, links and shared media. After the creation of networks, users can then

attach a rich set of protocol objects called agents to the nodes. The visualization of the

network topologies can be done through graphical interface called the network

animator (nam), which assists the users in getting more insights about their simulation

by visualising packet trace data.

Figure 12: A simplified view of the ns2 simulation flow [40]

Ns2 simulator is capable of implementing several features that are available within the

world of wireless networks. The following characteristics can be implemented in ns2:

• Router queue Management Techniques such as the Queue/DropTail object;

which uses or implements FIFO scheduling and drop-on-overflow buffer

management, other queuing methods include the RED and CBQ,

• Multicasting routing protocols among network nodes;

• Ns2 can simulate traffic source behaviour such as constant bit rate (CBR)

and variable bit rate (VBR)

• Applications –telnet, FTP, ping, and

• Tracing of packets on all links within the network structure.


34

2.6 CONCLUSION

In this chapter we have presented a background of our study giving the reader a wide

understanding of different types of wireless network. We have presented a brief

overview of the characteristics eminent to the wireless world and more particularly to

the mobile adhoc networks.

We have also looked at different simulations tools that are used in the simulation of

wireless networks

In the next chapter we present a detailed description of different mobility models. We

look at different types of mobility models that are commonly used in Mobile Adhoc

Networks. These include but not limited to the stochastic, non stochastic mobility

models and the more realistic activity based models.

Finally, work related to both the stochastic and the activity based models will be

presented in the next chapter. However, our related work we will focus primarily on

the activity based mobility model as this is the main mobility model to be used in this

dissertation.

Chapter 3 Understanding of Mobility Models

35

CHAPTER 3

3. UNDERSTANDING OF MOBILITY MODELS

3.1 INTRODUCTION

Mobility models are normally used to depict movement in MANETs, and are

designed to describe the movement pattern generated by mobile users in space and

time. Mobility models show how the location, velocity and acceleration of users

change with time.

Over time, various researchers have proposed different mobility models with a view

of capturing the realism of movement [28, 29, 30, 31]. This race to capture the

realism of movement trajectories took mobility models through a number of stages;

from models with random movement to mobility models with temporal dependency,

mobility models with spatial dependency, mobility models with geographic restriction

and finally to mobility models based on activity patterns.

Mobility patterns have played a crucial role in evaluating protocol performance [25]

of the WLANS or mobile adhoc networks. It therefore imperative that such models

must in any situation try to emulate the movement pattern of the targeted real life in

more realistic way.

In order to model mobility in MANETs one has to understand the scenario in which

he /she wants to evaluate the effects of such mobility on MANETs. It is critical that

mobility models must reflect the movement of targeted real life applications in a

reasonable way [31, 32]. If this is not the case the results drawn from these

simulations may be inaccurate and misleading and may be useful for academic

purposes only. Typically, the initial point will be to look deep into the nature of what

you intend to simulate.

One perceptive method used to create realistic mobility patterns would be to propose

some trace-based mobility models (in space and time), in which accurate information

about the mobility traces of users could be provided. This can only be possible with


36

the use of activity based mobility models drawn from transport science

[45,46,47,8,49]. Activity based mobility models tend to capture the travel patterns of

the user based on how the user runs his/her activities during the day.

However, since MANETs have not been implemented and deployed on a wide scale,

obtaining real mobility traces becomes an important challenge. In the next sections we

layout an introduction to mobility models and give you an idea about any related work

on mobility modelling.

Mobility models can be separated into different categories depending on the

complexity or the underlying pattern with which that mobility model follows. Figure

13 shows different mobility models that have been used and proposed in literature.

Figure 13: Different types of mobility models [42]

3.2 RANDOM MOBILITY MODELS.

Random mobility models are used to predict the effects of clear or unobstructed areas

on MANET performance. The movement of mobile users or nodes, in this type of

mobility pattern, is without any restrictions (random movement). The choice of speed,

acceleration and destination is random and independent of other nodes in the

simulation area. Being the earliest type of models, they have been used in a lot of

simulations which have been presented at Mobicom and Mobihoc conferences [84].


37

The random movement group of models includes but are not limited to the random

walk, or the random way point.

3.2.1 Random way point Proposed by Johnson and Maltz [25], the Random Way Point has been a cornerstone

for many simulations in adhoc networks. The random way point as shown in Figure

14 is used to evaluate a lot of metrics which are pertinent to the performance of the

MANETs. The random way point is generated during ns2 simulations by using the

setdest tool from the CMU Monarch group [40] which is available with the ns2

simulation package. The speed that is commonly used in random way point is

distributed between 0 and Vmax. The node chooses a speed, direction and destination

which are independent of other nodes. When the node reaches the intended

destination it pauses for a time pauseT seconds before continuing on its trajectory. In

this way the node’s parameters depend heavily on the Vmax and Tpause. Adjusting these

parameters will alter velocity and the movement pattern of the node. This undesirable

scenario led to Johansson, Larsson and Hedman [82] to suggest a mobility

metric, ( , , )S i j t , which quantifies the nodal speed between node i and j at a time t as

shown in equation 3.1

( )( , , ) ( )i j

tS i j t V t V

M⎡ ⎤

= −⎢ ⎥⎣ ⎦ , (3.1)

where Vi and Vj are the speeds of node i and node j, the mobility trace M is the

measured speed averaged over all the users and over all the time. It can

mathematically be described as shown in equation 3.2 as follows:

0

1 1

1 1 ( , , ),

N N T

i i i

M S i j t dti j T= = +

= ∑∑ ∫ . (3.2)

where ,i j is the number of the node pairs involved (i,j) and N is the total number of

nodes in the simulation terrain and T is the simulation time.

Because of its availability and simplicity in implementation, the performance of

different routing protocols has been investigated using the random way point.


38

However the realism of the random waypoint and its applicability in most simulations

has been considered and evaluated as presented in [6, 84]. Most criticisms about the

random way point have been its unrealistic movement, especially on the velocity and

the pause time that is used in simulations [6].

.

Figure 14: Random movement in the random way point model

3.2.2 The random walk model

Referred to as the Brownian motion, the random walk model was created to emulate

the unpredictable movement of particles in physics. In the random walk model, the

nodes in the simulation change their speed and direction at each time interval. It has

close similarities with the random way point in its movement pattern.

3.3 MOBILITY MODELS WITH SPATIAL DEPENDENCY.

When nodes move in space they are sometimes constrained by the movement of other

nodes in that particular environment. Their movement pattern is influenced by

neighbouring nodes and it is not as random as in the random way point or the random

walk models. This type of movement depends on the spatial velocity of other nodes.

Some common examples of spatial dependent models include the Reference Point

Group Model (RPGM) [31].

3.3.1 The reference point group model


39

The RPGM [31] group movement model depends mainly on the movement of the

main node or the leader node. This type of movement is characterized by a leader and

group members who follow the leading node. The spatial velocity of nodes is heavily

dependent on the velocity of the group leader. Typical examples of spatially

correlated mobility patterns would consist of the column mobility, nomadic mobility

and the pursue mobility model.

3.4 MOBILITY MODELS WITH TEMPORAL DEPENDENCY.

This is memory driven type of movement in which the node’s next movement is

dependent upon its previous state [35]. These scenarios exhibit a behaviour in which

the movement of the node is constrained by the physical laws of velocity, acceleration

and change of direction. These type of models can be modelled as Gauss Markov

models in which the node‘s previous velocity may have an effect on its current

velocity. In this situation, it is said that the node movement has a temporal

dependency on the velocity.

3.5 MOBILITY MODELS WITH SPATIAL RESTRICTION.

Recent mobility patterns developed for wireless LANs aim at depicting and modelling

the real mobility patterns that are presented in the spatial world. The mobility models

are constrained by geographic patterns that dictate the mobility of the nodes. It is

general knowledge that people’s movement in real life is constrained by corridors,

walkways and path ways, while those of vehicles are constrained to the freeways,

roads and sometimes to bush ways. Types of mobility models with spatial restriction

will include, but not limited to, the following: the path way mobility model, freeway

model, Manhattan model [31] (see Figure 15) and obstacle mobility models.

Figure 15: Free way model and the Manhattan mobility models [42]


40

3.6 OVERVIEW OF RELATED WORK

Much of research has been done regarding simulation of MANETs [84]. MANET

simulations can be divided into two types: indoor simulations and simulations of

outdoor scenarios/environments. It is worthwhile to say that most simulations have

been concentrating at achieving robustness and protocol performance of MANETs in

outdoor environments [12, 14, 29, 32] and very few simulations have been directed at

indoor environments. In the next section we present work related to different

movement trajectories (mobility models) in space and time occurring in different

environments.

3.6.1 Outdoor MANET simulations.

Outdoor simulations can be rated according to their level simplicity. By that we mean

the measure or the extent to which realism of geographical patterns have been

included in these simulations. Some models such as the Freeway Mobility Model and

Manhattan Mobility Model [31] are more or less alike. Obstacles are included in the

simulation area in order to govern the node movement in the simulation terrain.

Nodes in the simulation environment are directed to move within grid areas of

Manhattan Model or Freeway model. A probable consideration in both models is that

nodes are constrained within lanes, and by the nodes ahead in the same lane.

Even though, these types of mobility models added a milestone to the MANET

community they, however, do no reflect the real scenarios represented in the

user’s/node daily mobility patterns. They are memory-less models, whose next

segment of the movement has no dependency on the previous movement.

Moreover, both models would fail to account for users whose movement is in an open

space or in rural areas where lanes do not constrain their movement. For example

these models fail to represent a scenario where a user accelerates gradually or turns

smoothly on a sharp bend.

While these two models may be useful in other scenarios, they cannot be taken as the

representative of models for MANET simulations.


41

Some memory-models are however, proposed in [83]. In this paper a Gaussian-

Markov model which is a memory-dependent model is presented. In this model, the

velocity of the mobile node is correlated overtime and modelled as a Gauss-Markov

stochastic process.

Johansson et al [82] came up with three realistic mobility models depicting real life

scenarios. Their work models three different typical life scenarios which include:

• Conference scenario: in which some nodes are stationary while some move at

a very low speed;

• The event coverage scenario: Nodes are modelled as group of highly mobile

nodes which are constantly changing their position; and

• The disaster relief scenarios: In which node mobility differs considerably. In

this scenario some nodes move slowly while others move very fast;

In all of the above scenarios, rectangular shapes are randomly placed on the

simulation field. Mobility is done by the node choosing a proper movement path from

source to destination. The trajectory tries by all means to avoid obstacles placed on

the simulation field. In their work, RF propagation is assumed that the signal is fully

absorbed when propagated through the obstacles. However, they fail to account for

other effects such shadowing, scattering and reflection of signals common in such

places.

Jardosh, Belding-Royer and Almeroth [33] also investigate the effect of obstacles on

mobility modelling by using Voronoi diagrams. In their work, they place obstacles on

the simulation field to model the building within the University of California at Santa

Barbara (UCSB) campus environment. They realised that people do not reflect off the

building as depicted in the random waypoint or random walk mobility models but

walk in defined paths. The placement of paths is half way between the rectangular

objects which is consistent with common sense that paths tend to lie halfway between

the buildings. Nodes are allowed to enter and exit the rectangular models, depicting

students and workers entering and leaving the building.


42

The destination choice of the nodes is modelled by the Dijkstra algorithm [56, 57, 58]

which is based on the shortest route between the source and destination. Their work,

though a landmark in MANET simulations, needs to critically address other issues

such as selection of destination which is vague. Students on campus move because

they want to participate in some activities such as attend lectures or going to social

event The movement pattern of going in and through the building is based on a

narrow set of concepts.

The use of shortest path to a destination [56, 57] and the pause time at such a

destination does not depict the real life scenarios as shown by activity based models.

We will show in the next chapters that user mobility is governed by the activities that

induce travel patterns.

The use of Support Vector Graphics (SVG) in mobility modelling is introduced by

Gang Lu, Gordon and Demetrios [27]. In their paper, the present an environment

aware mobility model (EAM). Environment objects such as vehicular routes and

hotspots are introduced. The movement of the node is correlated with the sub area in

which it is located, and is allowed to be changed during the simulation.

They further go on to suggest the node heterogeneity, which describes the mobility of

nodes. Heterogeneity places nodes as either highly mobile node or low mobile nodes.

They also suggest that the EAM can integrate mobility models such as the random

way point, RPGM, with some modification to model complex environmental

scenarios.

In [32] the realistic graph based mobility model is implemented by J. Tian, et.al. The

graph can either be randomly created or carefully defined based on particular map of a

real city. The vertices of the graph symbolize the buildings of the city, and the edges

replicate the streets and freeways between those buildings. A comparison analysis of

results by using the random walk and their graph based mobility model (the path way

mobility model) using the three common routing protocols (DSDV, DSR, AODV)

showed that spatial constraints have a strong impact on the performance of adhoc

routing protocols.


43

Mobility Integration of Radio Requirements in Real-world Simulations (MIRRORS)

is a mobility model presented by Wenjun Hu, and Jon Crowcroft [29] as a technical

report from Cambridge University. It is an outdoor modelling of MANETs with a

specific target in mind. In their research they put forward a Taxi model which is

compared with other models. However their overall mobility model cannot be used

for indoor environments because it does not give the finer details of speed dynamics

constrained by the geometry and activities in particular place. Moreover their model is

environmental specific, making it a better option for outdoor scenarios than indoor

scenarios.

3.6.2 Indoor Simulations

MANETs for indoor simulations with obstacle enhancement have not been presented

on large scale; this may be due to a lot of factors such as the space mobility of nodes

in a 3D environment. Notable publications in this field will include the work of A. L.

Cavilla, et al [30] in which they present a constrained mobility (CM) model for indoor

environments. This is to the best of our knowledge the first paper which tries to model

mobility in an indoor environment.

They model the movement of nodes as source-destination type of movement, not

movement induced by activities. The destinations of nodes in the spatial graph are

offices.

In modelling the RF propagation the attenuation factor (AF) type of propagation

modelling is used.

3.6.3 Mobility models based on Travel plans and activity patterns.

In transport science or civil engineering [50, 52, 53, and 49] mobility is derived from

the desire to participate in some event or activity. People do not move from one place

to another because they want to create some form of motion or movement. People’s

movement is directly related to the desire of them performing an activity, which

would in turn meet their needs. Unlike in most analytical models, activity based

models are more realistic in modelling the behavioural movement of people. These

types of mobility models are based on the activity patterns in an indoor environment


44

[45, 46, 47, and 48], and captures travel pattern by understanding the nature of

activity participation that inspires it. Evidently, the demand for travel is derived from

the inherent demand to perform activities at specific location in an indoor

environment, which may either be offices, classrooms, kitchens and toilets.

Activity based models are sensitive to institutional changes such as work time,

duration and out of office or in office activities Most models proposed so far are

based on heuristic approach which does not take into account the user decisions or

behaviour when modelling the choice of the path and the dynamics of the built

environment.

McNally [45] describes the activity based model with the following characteristics:

• The desire to move is a function of activity participation;

• The activity and travel decisions in a particular place are direct delimited by

temporal and spatial constraints in such an environment, which may include

the time of operation;

• A connection exists between activities, locations times and individuals; and

• Participating in an activity will involve spatial choice, generation and

scheduling.

Even though human behaviour is usually constrained in time and space, humans are

capable of operating within a single time and space continuum in a specified

environment. This ability of humans to move in space and experience the time and

cost of movement in such a particular space, make activity modelling ideal to predict

the mobility pattern in MANETs.

Activity in this case is defined as an engagement into physical work that will

eventually satisfy the user/person’s needs. This enables the user to execute the

activities by connecting the locations of two consecutive activities. For example (refer

to figure 16) a person may move from an office to a tea room or from lecture hall to a

computer hall to perform an activity, depending on whether the activity is of higher

priority or not.


45

Obviously, individuals constantly perform some trade-off between enjoying activities

that have a high reward value (for instance working) and non reward activities such as

walking or eating. These types of activities are known as random utility models.

These models assume that users in a particular spatial environment try to maximize

their own utility. Referring to Figure 16, users would choose to go lectures-halls or

kitchen depending on the level of prioritisation of such an activity. To illustrate this,

let’s consider i to be the place of choice with high reward in an indoor environment, a

simple movement plan of (office-lecture halls -kitchen) can be presented by equation

(3.3):

;i i io ikV R C C iμ μ= − − + ∈ , (3.3)

where Ri is the reward associated with the place i and depends more on the benefits,

which may either be food reward or income bringing place. For students this could be

attending a lecture in a hall. ioC is the cost associated with travelling from office to

the intended destination (lecture hall) and ikC is the cost from lecture hall to kitchen.

The symbol μ represents scale factor, i is the random variable that is specific to the

individual and the value. Therefore taking equation 3.3, it can be shown that the

probability of choosing a particular place (kitchen) in an indoor environment can be

represented by equation (3.4):

1

exp( / ) ,exp( / )

kk k

ii

VPV

μ

μ=

−=

−∑ (3.4)


46

Figure 16: Activity patterns in an indoor environment

Chapin et al [54] was the first to suggest activity based approaches in human travel

patterns modelling. His work centred on evaluating the characteristics of human

activity patterns and their relationships with socio-psychological propensity factors.

The cognitive manifestation of human mind was put forward by Colledge et al in his

book [55] where he looked at the representation of choices such as:

1. The choice of the destination point ;

2. The mode or means of traversing that link or path;

3. The time of departure; and

4. Route contingent on choice of activity.

The choices presented above are collectively referred to this as the travel plan.

Notable examples in activity based modelling are found in [49]. This work primarily

focuses on understanding how activity affects the mobility pattern within a specified

environment. They all propose the mobility of people based on the activities people

do in relation to space and time.

It thus describes three key factors that impacts user movement in outdoor

environment. These three factors are:

Office activity Kitchen area

Movement from one office to another Lectures halls


47

• Outdoor environments movement constraints and points of interest which

are modelled by the spatial model;

• User travel model which is modelled by the user trip model; and

• User movement dynamics which tries to capture the speed and movement

of movement in the spatial model.

They further go on to consider the impact of road element such as the speed limits or

the number of traffic lanes. In this paper two dynamic models with reference to

pedestrians and vehicles are considered.

The use of activity based models in MANETS is presented in [28] where the authors

propose a comprehensive and extensible approach to model mobility of users in

outdoor scenarios. This model is based on user-oriented mobility Meta model. This

model tries to capture user behaviour and its influence on the mobility models of these

users. It reflects the main factors that influence user movement: spatial environments,

user travel decisions, and user movement dynamics and identifies model parameters.

A simulation environment based on GIS spatial information is used to implement their

approach.

Scourias and Kunz [49] present an outdoor stochastic mobility model based on daily

activity patterns of subscribers in a cellular network, providing a realistic balance

between completely deterministic and completely random mobility models.

A mobility model which simulates the daily movements of mobile subscribers,

incorporating realistic and individualized activity patterns and geographical focal

points is developed to model user mobility.

Kim and Bohacek [50] present a more realistic mobility model based on a US

Department of Bureau of Labour Statistics time-use study in an urban environment.

From this survey Kim and Bohacek derived a three hierarchical layer mobility pattern

different layers as described below:


48

• The highest layer is an activity model that determines the high level activity

that the node is performing;

• The second layer is a task model that determines the specific task within an

activity; and

• The third layer is an agent model that determines how the person moves from

one location to another (e.g., how a node navigates down a crowded hallway).

Bowman et al [47] presents a daily integrated activity model based on discrete choice

model system of an individual's daily activity and travel schedule, intended for use in

forecasting urban passenger travel demand. In his dissertation bowman divides the

model into three sections comprising of:

• The daily activity plan that includes the choice to travel and the day’s activity

plans;

• The primary tour; and

• And the secondary tour.

Both the secondary and the primary tour include the choice of time, destination and

mode of travel. The dissertation describes the tour models as being conditioned by the

choice of a daily activity pattern and the choice of a daily activity pattern is

influenced by the expected maximum utility derived from the available tour

alternatives.

Modelling of agents or users in spatial environments where users have limited,

accurate information about a small subset of the whole spatial environment is

presented by Marchal and Nagel [48]. In their work they present mobility as a

function of three systems which are the Geographical Information Systems (GIS) for

the spatial representation of the environment, the transportation system is presented

through a vector quantity and the traffic system is presented by the use of an external

traffic model.

In order to understand movement trajectory in a corridor Palmius and Silvergran [53]

compares the ideal path routes with the empirical route path in an indoor environment.


49

They perform a study in which students at a high school carried a camera-equipped

bag with a view of observing their path trajectory or movement in an indoor

environment. Their conclusion and observation was that, based on gender, women

tended to be more in the centre of the corridor when moving than their male

counterparts.

3.7 CONCLUSION

In this chapter we have revealed different kinds of mobility models presented by

different researchers. Mobility models with random mobility, temporal dependency,

spatial dependency or geographic restriction have been discussed and studied in this

chapter.

An introduction to activity based modelling of human movement and the related work

regarding mobility modelling and activity based modelling from transport science has

been presented. It is clear from the mobility models that mobility patterns have strong

impact on the routing performance of Mobile Adhoc networks. In the next chapter, we

present the radio frequency propagation modelling with the related work that has been

done in this area.

Chapter 4 Radio frequency propagation models in WLANS or MANETs

50

CHAPTER 4

4. RADIO FREQUENCY PROPAGATION MODELS IN WLANS OR MANETS

4.1 INTRODUCTION

Wireless networks differ from wired networks in the way the signal is transmitted

from one source to another. Whereas the wired medium depends on the physical path

to be established before the transfer of data, the wireless medium does not require the

establishment of the physical path. However due to path characteristics the wireless

channel suffers from heavy attenuation and multipath fading effects which are natural

to the area in which they are deployed.

Techniques to enumerate these characteristics natural to the indoor/outdoor channel

environment are required, and research activity to date has focussed on the

development of empirical/semi-empirical models derived from experimental

measurement. A difficulty in this approach is that the models derived are site-specific

and their transportability to other indoor environments (in which measurements have

not been made) is uncertain.

In radio wave propagation, two types of channel variations occur in a wireless

channel, i.e. path loss and shadowing (large scale fading) [61]. Shadowing is caused

by attenuation of the signal by obstacles that are within the path of the transmitter and

the receiver. Objects such as chairs, people and other obstacles may cause shadowing

in an indoor environment. The objects within the path attenuate the signal power

causing severe variations in the received signal. Common types of shadowing are

reflection, scattering, absorption and diffraction.

Path loss on the other hand is caused by dissipation or attenuation of the power which

falls off with the distance separation between the transmitter and the receiver. Path-

loss may either occur within the line of sight (LOS) or non line of sight (NLOS).


51

4.2 SHADOWING

Shadowing is caused by reflection, diffraction or refraction of signals as they

propagate through the channel environment. This causes attenuation on the received

signal, resulting in heavily distorted received signal.

4.2.1 Scattering

Scattering is when the RF signals propagate through medium which has objects with

dimension that are small compared to the wavelength. Scattering in an indoor

environment may also be induced if the number of obstacles in an indoor environment

is large per unit volume. In some cases it can also be produced by different objects

such as stairs, ramps, and other objects that may have irregular surface area. Such

kinds of objects are a common feature an in indoor environment.

4.2.3 Diffraction

Diffraction in an indoor environment occurs when the wave from transmitter to

receiver passes through sharp edged surfaces such as the junction places in a building.

Waves produced in this manner become present in the space and behind the obstacle

giving rise to bending of the wave around the obstacle or a corner. Although

diffraction happens when propagating waves comes across obstruction in their paths,

its effects are usually most prominent for waves where the wavelength is on the order

of the size of the diffracting objects.

4.2.4 Reflection

Reflection is the bouncing back of the signal from an obstacle placed in the path of

the signal. Normally reflection will occur on walls, furniture and other materials that

are part of the built environment. It happens when a propagating electromagnetic

wave impinges upon an object that has very large dimensions compared to the

wavelength of the propagating wave.


52

Figure 17: Drawing showing (A) scattering (B) Diffraction (C) reflection

In shadowing the received signal from the transmitter is represented as:

r t t r pS S G G L= + + − , (4.1)

In equation 4.1 above rS is the received signal in decibels (dB), and tS is the

transmitted signal in dB. tG and rG are the transmit and receive antenna gain

respectively. pL represents the propagation path loss which is very hard to predict

especially in an indoor environment due to the varying channel characteristics of this

environment [63, 79].

Different types of slope intercept equations have been proposed in order to predict the

general characteristics of this time varying channel. However the most common ones

will include (but not limited to) ray tracing methods and empirical models such as the

Hata models and Okumura models. In the next sections we will present different

slope-intercept models that have been presented by different academics.

A B C


53

4.3 RAY TRACING MODELS.

In a characteristic indoor environment, a signal transmitted from a fixed source to the

receiver will encounter a lot of obstacles resulting in multipath effects. These

multipath effects can be delayed in time, shifted in phase or otherwise attenuated in

power. When these signals arrive at the receiver they cause the received signal to have

some distortion in the form of noise relative to the transmitted signal.

These multipath effects can be resolved using the Maxwell equation or more

commonly know as the Ray tracing techniques. Ray tracing models represent the

wave fronts of these multipath effects as simple particles. Several ray tracing

examples exist in literature. The common ones being the Two Ray Tracing Model and

the Ten Ray Tracing Model.

The Two Ray Tracing model is used when a single ground reflection dominates and

the received signal is composed of two rays only. The LOS ray can be represented by

the path loss model as represented in equation 4.2.

( ) 10 lo g t

R

PP L d BP

= , (4.2)

In equation 4.2 Pt and PR represents the transmitting and receiving power

respectively.

Figure 18: The Two Ray Model diagram [73]

d l ht x hr Ø s


54

The diagram in Figure 18 depicts the two ray propagation model which has two

dominant rays. The l-ray is the LOS ray between the transmitter and the receiver. If

the transmitted signal from one antenna to another is of a narrow band (narrowband in

radio communications refers to a situation where the bandwidth of the data does not

significantly go beyond the channel's coherence bandwidth) type, then we can

approximate the power of the received signal as

22

4 4

jlG R GrePr Pt

x s

φλπ π

−⎡ ⎤= +⎢ ⎥ +⎣ ⎦, (4.3)

where φ = 2 ( ) /x s lπ λ+ − is the phase difference between the two received signals.

lG is the product of the transmitting antenna and the receiving antenna’s field

radiation patterns, assuming that a line of sight exist between the transceivers. From

geometry we can compute the value ( )x s l+ − as:

( )x s l+ − = ( ) ( )2 22 2ht hr d ht hr d+ + + − + , (4.4)

4.4 RF EMPIRICAL PATH- LOSS MODELS

Path loss, unlike shadowing, is the measure of the average RF attenuation which the

transmitted signal suffers when it reaches the receiver after traversing a path of

several wavelengths. Propagation channel types have different path loss effects on the

transmitted signal. Depending on the distance, the path loss effects may vary over a

wide range of different indoor channels. Path loss in telecommunication can be

defined as shown in equation 4.5

( ) 10 lo g t

R

PP L d BP

= , (4.5)

When propagation is in a free space (high above the ground) the power in the free

space model falls as a function of distance separating the receiving antenna and the

transmitting antenna. If we take for simplicity sake an isotropic omni-directional

(radiating equally in all directions) antenna radiating or transmitting at a power level

of Pt watts, the received power density of the receiver antenna which is located at a


55

distance of d meters will be defined by the Friis free space model equation [73] .The

free-space path-loss equation introduces a complex scale factor resulting in the signal

shown in equation 4.6 below:

2

2( ) Re ( )4

c

j d

l j f tG er t u t e

d

πλ

πλπ

−⎧ ⎫⎪ ⎪= ⎨ ⎬⎪ ⎪⎩ ⎭

, (4.6)

In equation 4.6 lG is the product of the transmitting antenna and the receiving

antenna’s field radiation patterns, assuming that a line of sight exist between the

transceivers. The value fc is the carrier frequency, r(t) is the received signal and u(t) is

the complex scale factor with in-phase component. Since the wave travels a distance

d, a phase shift represented as 2j d

eπλ

− is introduced in the transmitted signal.

The equation in 4.6 can further be simplified giving rise to equation in 4.7 below

( )( )

2

2 24t t r

rP G GP d

d Lλ

π= , (4.7)

where tG and rG are the gain of the transmitting antenna and the receiving antenna

respectively. The symbol L represents the system path loss factors, and this figure is

not connected to the propagation. The symbolλ represents the wavelength of the

signal propagated in meters and d represents the separation between the transmitting

antenna and the receiving antenna.

4.4.1 The log distance path loss model

Often the path loss in both outdoor and indoor environments is expressed by using the

path loss value n. The path loss n is an expression of the power loss across an average

distance between that of the transmitter and the receiver points.

For example suppose the distance between the receiver and the antenna is d then the

path-loss equation may be written as in equation 4.8 or 4.9.


56

( ) ( ) 10 logoo

dPL dB PL d nd

⎛ ⎞= + ⎜ ⎟

⎝ ⎠ , (4.8)

Or

n

oL

dPL P kd⎡ ⎤

= ⎢ ⎥⎣ ⎦

, (4.9)

the value of n is what is known as the path loss exponent and indicates the rate at

which path loss increases with distance d between the transmitter and the receiver. K

is the unitless constant which depends on the antenna characteristics and the average

attenuation, od is the reference point for the antenna far field. If plotted, the distance

d versus the PL (path loss) becomes a straight line [73] with a slope equal to 10n.

Table 3 below represents the path loss values in different environments

.

Table 3: Path-loss exponent n for different environments [73] Typical path-loss exponents Environment n range Urban macrocells 3.7-6.5 Urban microcells 2.7-3.5 Office building same floor 1.6-3.5 Office building multiple 2-6 Store 1.8-2.2 Factory 1.6-3.3 home 3 4.4.2 Okumura model [7]

Okumura, a Japanese scientist conducted a lot of measurements in urban city

macrocells. He used a transmitter and receiver which were separated by a distance of

1-100km and the antenna heights measuring 30-100m .When the power or signal

strength measurements were plotted against the distance, the slope intercept model

was produced, where the received signal followed the power law equation.

Okumura‘s empirical path-loss equation at a distance d parameterized by the carrier

frequency cf is presented as:


57

( ) ( ) ( ) ( )l c c r t areaPdB L f d A f d G h G h Gμ= + − − − , (4.10)

where L is the free space path loss at a distance d and carrier frequency fc represents

the median attenuation in addition to the free-space path-loss in all the macrocells.

( )tG h and ( )rG h are the base station antenna height and receiver height gain factor

respectively. The value areaG represents additional gain with regard to the specified

surroundings in which the measurements are carried.

4.4.3 Hata model [73]

The Hata model is based on the measurements which Okumura published. The

experimental measurements were taken using the frequency ranging between 150-

1500MHz.

4.4.4 COST 231 propagation model [73]

Operating in the 2GHz, the COST231 model is an extension to the Hata model. It was

introduced by the European cooperative for scientific and technical research (EURO-

COST). The COST 231 can be modelled and represented as shown in the equation

4.10:

1 2 1( ) 46.3 33.9log 13.82log ( ) (44.9 6.55log )logMHz kmL dB f h a h h d K= + − − + − − , (4.10)

where h1 is the height of the transmitting antenna,h2 height of receiving antenna and f

is the frequency of the propagating signal in a specified environment and K is the loss

constant to a particular environment.

4.5 RELATED WORK-PROPAGATION IN AN INDOOR ENVIRONMENT.

Literature documents a lot of empirical and analytical studies of indoor propagation

[60, 61, 62, 63, 64, 65, 67,]. A lot of work has been done to predict the effects of

obstacles on the transmitted signal in an indoor environment.


58

Propagation in an indoor environment is not influenced by terrain like the outdoor

environment but it is affected by the building material, obstacles and

architectural/geometrical layout of the building [60]. It is with reason that the signal in

an indoor environment will always reach the receiver through different paths due to

scattering, reflection and diffraction of signal by different obstacles present in the line

of transmission.

Obstacles have severe effects on the signal [73] that is reaching the receiver; and

therefore, it is imperative that statistical and analytical models predicting the

performance of the signal must capture these characteristics. Measurement of path

loss across a wide range of building floors has been performed, showing that

attenuation is different across a range of floor and wall demarcations [62, 63, 64].

In [75, 76] the researchers showed that at 900MHz, the attenuation of the signal when

the receiver and the transmitter is separated by single floors ranges between 10-20dB.

In order to capture this behaviour analytical (or empirically) an equation (equation

4.12) has been proposed to take into account the attenuation caused by the floors and

the wall/partitions.

Indoor environments have different architectural design and are made up of different

materials which together present challenges to the RF propagation. Materials of the

built environment do not have similar die-electric properties; this may cause the RF to

have losses through reflection, scattering, diffraction, penetration or absorption.

With reference to Table 4, it can be seen that the metals represents a set of materials

with the highest loss in dB, and soft materials such as the cloth with the least loss in

dB

The power received by the antenna as the signal propagates in a partitioned indoor

environment can be represented by the path loss equation as shown in 4.12:

r1 1

f pN N

t L i ii i

P P P FAF PAF= =

= − − −∑ ∑ (4.12)


59

where the FAF is the Floor Attenuation Factor for the ith floor, PAF represents the

partition attenuation factor for the ith partition, Pt is the transmit power, Pr is the

received power and Pl is the pathloss of the channel. Pt, Pl and Pr are all in decibels

meter (dBm). Nf and Np are the number of floors and partitions respectively. Table 4

shows different partition losses for different materials.

Table 4: Partition loss of different materials [73]

Typical partitions losses

Partition Type Partition loss in (dB) Cloth partition 1.4

Double plaster board wall 3.4 Foil insulation 3.9 concrete wall 13

Aluminium sliding 20.4 All Metal 26

Papers on signal propagation losses in different environments have been proposed

[63, 75] by different scholars and researchers.

M. Boulmalf et al [76] conducted a series of tests to characterize the performance of

the IEEE 802.11 g in a typical cubical office setting. They observed that the

throughput performance of UDP and TCP traffic behaves different from unrealistic

scenarios when evaluated under realistic circumstances. Their results showed that the

performance of IEEE 802.11g degrades very rapidly due to co-channel interferences

especially for TCP traffic. Additionally, they also proved that the video signal quality

is greatly affected when streamed over a WLAN in the presence of adjacent and co-

channel interferences. They went on to show that the PSNR of a video frame is

degraded by up to 13 dB in some cases due to ERP-OFDM modulation scheme.

Daniel.B.Faria et al [68] conducted a series of experiments in an indoor environment

using a Cisco Aironet 1200 at 2.4 GHz of the IEEE 802.11 standards. Their

measurements both from outside and inside environments indicated that the log

distance models used for indoor modelling closely approximates attenuation outside

the building. Despite the attenuation inside the building being higher than that from

outside, the path-loss exponent between the two environments were closely related.


60

Dinesh [66] conducted comprehensive measurements with view of predicting the

indoor propagation model at frequency of 2.4 GHz. The work involved a comparative

analysis of chipsets from different vendors. He conducted experiments in different

indoor places; involving a closed corridor, classroom and a lab area.

To predict the effects of indoor Dinesh used the log-distance path-loss model, the log

normal shadowing (empirical models) and the two ray models to determining path-

loss exponents from the described scenarios. The results from these experiments

revealed that:

• For an open corridor path-loss exponent n is 1.688 fro the D-Link and 1.63 for

the LinkSys;

• For the classroom the path –loss exponent was found to be 1.258 and 1.263 for

the D-link and the LinkSys respectively; and

• And for the Lab the path-loss exponent was found to be 1.447 for the D-link

and 1.48 for the LinkSys.

The impact of radio wave propagation models in MANETs is evaluated by Arne and

Martin [67]. They propose a model that uses a ray tracer to model radio propagation

in MANETs accurately. In their results they found that the physical layer simulation

of wireless systems has a great impact on the routing protocol efficiency.

Performance of the physical layer of WLANs 802.11g was evaluated by Boulmalf,

Sobh and Akhtar [77]. Their work involved the comparison of two modulation

techniques using simulink simulation block-set which is part of the Matlab. In

conclusion their results showed that simulation of 64 bit modulation of OFDM is

better than the 16 bit Quadrature amplitude modulation (QAM) of the OFDM in terms

of the bit error ratio (BER) versus the signal to noise ratio (SNR). In their evaluation

the noticed that when increasing the SNR the BER decreases. They were capable of

sending Data with small errors when the SNR was greater than 35dB.

Radio frequency in an indoor environment is affected by a lot of things which obstruct

the free propagation of the signal from the transmitter to the receiver. The effect of


61

Human obstruction on the transmitted signal is usually neglected in the modelling of

RF propagation in an indoor environment.

Ziri-Castro, Evans and Scanlon [85] carried a measurement campaign to determine

the effect of body interference on 802.11 standards with a 5.2 GHz propagation

frequency in an indoor environment. They compared the two results of the first and

the second order statistics of the empirical signals with a Gaussian derived

distribution used in wireless networks (802.11 standards). Their results from the

experiment showed that for some reason human obstruction also affects the

propagation of signal from the transmitter to the receiver. They derived a novel

statistical model that predicts the characteristics of the received envelope as a function

of indoor pedestrian activity.

A propagation model for short range wireless channels with predictable paths in

particular environments is presented by Domazetovic, Greenstein, Mandayam and

Seskar [86]. They consider a short range communication device which has low power;

low antenna heights such as the one deployed at toll gates where there is no

shadowing and where distances are short.

The experimental campaign involved the determination of both the deterministic part

and the stochastic component of the signal for their propagation model. In the end

they combined both the deterministic part of the signal and the stochastic for

modelling of dedicated short range communication gadgets in well-defined path or

geometry.

4.6 CONCLUSION

In this chapter we have shown different types of propagation models that are

frequently used in wireless networks. Different propagation models have been used to

model the behaviour of the wireless networks in different environments.

In the related work, it is clear from the measurements obtained by different

academics, scholars and researchers that different types of environments behave

differently to RF due to the characteristics of such an environment.


62

It is therefore sensible to say that RF propagation performed in an indoor environment

models presents a difficult challenge. The transportability of these models to different

environments is impossible because of the nature of the different in materials and

geometry of the place.

In the next chapter, we present the algorithm, graph abstraction and CAD drawings

which we will together linkup to create out Activity Model (AM) that we are to use in

our simulation in Ns2. We will show in the coming chapter on how to adjust the

setdest tool in Ns2 to get the desired mobility model.

Chapter 5 Mobility Modelling and Implementation

63

CHAPTER 5

5. MOBILITY MODELLING AND IMPLEMENTATION

5.1 INTRODUCTION

It is very vital for mobility models to reflect the movement of targeted real life

applications in a reasonable way. If this is not the case the results drawn from these

simulations may be inaccurate and misleading.

A more realistic mobility model which is based on activity patterns displayed in an

indoor place is proposed in this chapter. The inspiration of using activity based model

is drawn from the Advanced Traveller Information Systems (ATIS) [47, 48].

This chapter focuses on building a comprehensive, scalable and a flexible mobility

model for an indoor environment. In this model we aim to establish a relationship

between node mobility, node density and path choice behaviour in an indoor

environment. We derive an algorithm to show a relationship between node density

and path choice.

We also argue that movement in an indoor environment is not random as represented

by some models, but a well organised and a defined chain of sequences which are

driven by the desire to perform activity.

We are of a view that, Similar to the transportation planning, mobility modeling in an

indoor environment should include indoor characteristics such as:

• Graphical representation of the scenario being simulated;

• Node density distributed according to the activities being carried in specific

areas and along specific links; and

• Destination and route choice that determines the node density.

The model being presented in this chapter is based on activity patterns common in an

indoor environment. An interest is taken in the path choice, the architectural design of

indoor surroundings, and the point of interest to the user such the primary destination


64

areas (offices and classrooms) and the secondary areas which are places the users visit

later on. For example these places may include areas such as the kitchen, toilets, and

computer labs.

Our mobility model comprises of different sub models which when linked together

will form the mobility pattern which emulates the movement pattern in an indoor

environment.

Network graph model- The architectural floor plan of the area is transformed into a

network graph representing a simulation area in which corridors are represented as

edges and destination points as vertices

Route choice model- Additionally, the AM model incorporates route choice which is

very important in determining how node density distribution varies among the

routes/edges that exist within the network graph. It is this node density distribution

along the edges of graph which determines the performance/behaviour of the Physical

and the MAC layer of the network.

5.2 TOPOLOGY – THE SPATIAL ENVIRONMENT

In order to evaluate the mobility more efficiently we need to understand the

topography and the topology of the environment in which we are to carry out our

simulation. The topography in this scenario means how we set up our experiment to

obtain data readings. We will concentrate on this in the next chapter as we delve

through the methodology of our experiment. In this chapter our attention is more in

the topology scenario or the architectural drawings of the building.

5.2.1 Graphical Representation of the spatial environment To support our mobility patterns we build a topological graph from the architectural

drawings of the buildings. Figure 19 represents the Electrical Engineering building (a)

and the Canterbury building (b) from which we show how to abstract a network graph

model.


65

Electrical Engineering Dept (a)

Canterbury (b) [55]

Figure 19: Similarities in graphs presentation in (a) and (b)


66

Though the two drawings might be different in architectural representation, the

similarities in the graphs are distinct. In both the graphs, the vertices are joined to the

main link/edge of the graph. This point of connection represents an area that is

common to most indoor environments.

From our two buildings the point of interest such as the offices, lecture halls, kitchen

or toilets are represented as dots in a Cartesian coordinates system on the graph

(Figure 19).

Corridors and hallways on our graph are represented as edges/path in a directed graph,

such that there is always a connection between two points or vertices. It is in this

edge/path that nodes move to the offices (represented as vertices) to perform some

activity.

5.2.1.1 Coordinate system

Points in the spatial information map should be articulated in some type of coordinate

system. A coordinate system is defined by a representation (e.g., Cartesian, polar, or

long-lat coordinates) and a reference frame (i.e. the point of origin)

In our model the coordinates of the vertices in the spatial graph are represented in text

form by Cartesian coordinates as follows

[Floor 1] [Room six] [Space coordinates 645,234]


67

5.2.2 Deriving the network graph In MANETs, a network graph structure as shown in Figure 20 can be represented by a

graph [57]. Let’s consider a simple weighted directed pair of graph (digraph) G

represented by equation 5.1:

G = (V, tE ,W vID tvloc , ) , (5.1)

Figure 20: Network diagram

where V is a finite number of vertices or nodes and tE is a set of ordered pairs of time

varying edges called directed edges, arcs, or arrows, vID is the unique identification

number for node v and tvloc is the velocity of the of the node v at particular space and

time. If we assume that each path { }ijE ij V∈ in the graph G is associated with

link weight/obstruction w, then the link weight in path P, where P contains edges

1 2 3 1, , ... ke e e e − , can be represented by the summation of weights of edges in path P:

W(P)) = 1

0( )

k

ii

w e−

=∑ , (5.2)

where w(P) is the amount of obstruction associated with each link (path) or the

resistance which that path offers to the user‘s movement. (Assuming all the weights

in the graph are non-negative1 n k≤ ≤ ).


68

If we assume again that each link { }ijE ij V∈ in the graph G is associated with

link length l, then the link length in path P can be represented by:

1

0

( ) ( )k

ii

L P l e−

=

= ∑ , (5.3)

where l(P) is the link length in a particular path. From 5.3 and 5.2 above we can thus

represent the amount of obstruction on a particular path p ij∈ from i to j as a vector

dependent on the number of obstacles and length as shown in equation 5.4:

1

0

( ) ( ) ( )k

i ii

W L P l e w e−

=

= ∑ , (5.4)

where ( )W L P is an additive metric of path resistance ( ) ( )i il e w e (obstacles and

length) found on the path P .

Without loss of generality, nodes in a network will always prefer the shortest route

between two points. In order to represent the distance vector equation of particular

path choice that endeavours at minimizing the length and the obstacles within the path

we have to minimize the two parameters ( )iw e and ( )il e . The equation can be

represented as shown in equation 5.5

( )m i nm i n ( ) , ( )i iP w e l e= , (5.5)

where P is a minimal shortest distance between two nodes (i,j)

The traversing of the path or the choice of a particular path will depend on the sum of

the obstacles (ramps, doors, stairs shortest etc) path on that path. Other wise the user

chooses the other path.


69

5.3 NODE DENSITY DISTRIBUTIONS PATTERNS

In realistic mobility patterns, the end point/destination selection mechanism, as

opposed to the selection of an initial direction, gives rise to non-uniform spatial

distribution of nodes asymptotically [27]. Due to the activities being carried out in an

indoor place and defined movement in pathways, characteristics such as the average

node density, are likely to differ when compared with other mobility models. For

instance the common Random Way Point representation of node density as shown in

Figure 21(a) shows nodes randomly distributed within a simulation area. In our

mobility model the node distribution varied considerably with the corridors having

high node density whilst the offices and other places have low node density. This

entails that, our mobility model Figure 21(b) has areas which are highly congested

and blank-out regions in the area being simulated.

(a)


70

(b)

Figure 21: Node density distribution (a) Randomway point (b) Activity model

Node density in a particular area is the representation of how user distribution is

exhibited in a particular indoor area (location). The node movement in an indoor

environment differs from place to place hence giving rise to node density variations as

shown in Figure 22 below

Figure 22: Picture of node density distribution at particular times of the day (a) break times and (b) during working hours

For example, people may walk in groups or walk alone in corridors; this movement

pattern dictates the type of node density that is observed in such an environment. It is

fair to say that node distribution in an indoor environment is somehow aligned to the

activity that user wants to do at particular time.

For example at a certain times, passages may have high node density while at certain

times the will become literally empty. Consider our observation place, which is the

main building and the Electrical dept building, it is common to see little movement

during the time period from 7:30 to 12:55. At this time every body is busy with work

(a) (b)


71

and hence there less movement or no mobility at all along the corridors and office

places. After 12:55 a lot of movement (node density) is seen in the main corridors of

the two buildings.

5.3.1 Notation

We assume that a particular square meter area in a spatial environment (w×h) has a

number of nodes n the node density will represented as:

Node density =( )

nw h×

, (5.6)

If we take for simplicity sake that the node density is directly related to the number of

obstacles in a particular path we can then formulate the relationship between the node

density and the amount of obstacles as follows.

Node density = ( )

nw h×

, (5.7)

where suw is an additive value of obstacles in a particular path as shown in equation

5.4. By adjusting the value of suw , the node density in a particular path can be

modelled as function of the number of obstacles as shown in the equation 5.7.

5.4 USER MOVEMENT DESCRIPTION-THE DYNAMICS.

In order to model mobility correctly user movement in an indoor environment must be

modelled in a realistic fashion. The movement dynamics describes patterns in speed

and direction changes of mobile users during their movement between two locations.

We model the dynamics of pedestrians as a time varying speed motion. The speed is

randomly chosen at the start of the movement from a certain interval of speed that we


72

measured. For example, for the user walking freely in an indoor place the typical

values that we measured are between 0.5-2.5 m/s.

The movement speed at particular location, at a particular time and along the link

( , )e i j= can thus be represented by equation 5.8

0.5 2.50( , ) {t if actvity takes place

v otherwiseloc i j − →→= (5.8)

5.5 PATH CHOICE: SHORTEST PATH / ALL-OR-NOTHING

5.5.1 Overview

Path selection in an indoor environment is linked to what we would term as the

resistance to mobility w offered by the built environment. It is common for people to

select the path which offers the “least resistance” [ ( )minmin ( ), ( )i iP w e l e= ] in

terms of mobility. In generating different paths that may be of importance to those

moving from source to destination, users or nodes may take different views/functions

in route or path choice as shown in Figure 23. Many factors such as doors, ramps

stairs and elevators (see Figure 24) can dictate the choice of routes in an indoor

environment.

From our conducted surveys, university workers and students in an indoor

environment preferred the less resistant (least obstacles) paths in an indoor

environment. If a path had many obstacles like doors, stairs, and steps that path was

less likely to be traversed unless if there was no alternative to the destination. For

example, Figure 23 shows a group of people making a choice between the ramp and

the stairs in the main administrative building.


73

Figure 23 Common scenarios in an indoor environment; picture showing the choice of path between the stairs and the ramp

Figure 24: Picture showing different route choices in an indoor environment

On the whole, researchers opt to use the shortest path algorithm (Dijkstra algorithm)

based on heuristic approaches to model route choice in their simulations. The Dijkstra

algorithm has been a subject of criticism in the manner in which it determines the

shortest route among possible routes. Most researchers argue that in order to use the

Path 1 Path 2


74

Dijkstra algorithm [52, 53], users will be required to have advance knowledge of the

paths (links) and costs or constraints available in a particular link. If a user is not

aware of the route cost, the probability that they will select the shortest paths is not

feasible.

The above paragraph is true to some extent when dealing with certain scenarios

especially the outdoor scenarios. For instance using Global Positioning System (GPS)

receivers, Jan, Horowitz and Peng [85] examined how drivers choose their route

choice in transit. It was observed that the drivers did not choose the shortest route.

The question to why they did not chose the shortest path; Jan, Horowitz and Peng did

not show the analysis or state why the drivers did not do so. Unlike Jan, Horowitz and

Peng our analysis states as to why people chose particular paths in an indoor

environment.

5.5.2 Modelling the path choice of users

To model the path choice of user we consider the following steps:

• We select the shortest path using the Dijkstra algorithm; and

• After selecting the shortest path using the Dijkstra algorithm, the user can then

weigh the number of obstructions within each path using the Stochastic User

Equilibrium or multinomial Logit (MNL) equation [52, 55].

For example, consider a user currently located at position s (which may either be an

office or classroom in an indoor environment) and he/she wants to perform an activity

at a destination d in a particular place. The user would choose the path that contains

the least obstacles so that he/she reaches the destination with the least effort in

traversing such a path. If we consider a time varying edge tE to be the path between

the source s and the destination d, with the cost of that link being w and l, where w is

the number of obstacles within a particular path and l the length of the path. By using

the Dijkstra algorithm, we can select the shortest probable paths among numerous

paths available as shown in the algorithm.


75

Our use of Dijkstra algorithm [56] is based upon the fact that users in an indoor

environment tend to be more familiar with their surroundings (cognitive

representation). For example, it is common knowledge that university workers and

students on a campus environment or any working environment tend to be more

familiar with the nature of their surroundings. It becomes undemanding for them to

remember different obstacles that are available within a particular route or path.

In biological sciences [55] it is usually assumed that users become more familiar with

their surroundings, the cognition process of path knowledge and path finding becomes

more prevalent. They become more aware of the length and the obstacles that lie

ahead or how much resistance does a certain path offer to movement. It is common

tradition that users will select the routes based on shortest path, shortest time, shortest

distance, least cost, turn minimization, longest leg first, minimizing obstacles (such as

stairs, ramps and doors), user congestion, hazardous areas, restriction to a known

corridor and minimizing the number of route segments. Users become more

probabilistic in the choice of their paths.

Shortest path Algorithm [56]

Graph G = <V, E, l> -- l[i] is the best (known) path cost -- From source to vertex i Calculate the distance Done: = {v1} For vertex i in V-{1} calculate the distance l[i] := E[1,i] --direct edges End for Loop |V|-1 times Find closest vertex to v1 in V - done Done +:= {closest} For vertex j in V - done l[j]:=min (l[j], --update knowledge on shortest paths, l [least] +E [closest]) --perhaps better? End for End loop


76

From 5.5 the relationship between the amount of obstacles w, distance l within a

particular path and the time varying edge ( , )tijE i j= can be represented as shown in

the equation of 5.12:

1 2 3 4 1min{ , , , ... }tij kE p p p p p −= , (5.12)

The multinomial logit (MNL) model can be consistently estimated on a subset of

alternatives. The probability that an individual n chooses an alternative path 1p over

2p among numerous paths is then conditional on the choice set Cn defined by the

modeller. This conditional probability is shown in equation 5.13 [51, 52]

1

1( 1 )

( / ) 1nk

n

p

p c kp

i c

ePe −

−

−−

=

=

∑, (5.13)

where 1( )P p is the probability of the node or the user using path (1)p and nC is the

choice set of feasible paths within an indoor environment. In the above equation

1p and 2p represents the obstruction or the cost of traversing the paths 1 1( )i J and 2 2( )i j

respectively. The value pe is a utility coefficient or “obstacle parameter” and can be

used to determine the node density within a particular path or link.


77

5.6 IMPLEMENTATION OF THE MOBILITY MODEL IN NS2

Figure 25: Generating a mobility trace in Ns2 using activity based model

The implementation of the mobility pattern is made by adjusting the setdest, a small

independent application provided by the Monarch Wireless Extensions. Setdest,

program which is bunched together with the Ns2 simulator, produces an OTcl script

which when adjusted properly will represent node movement in a typical indoor

scenario.

In executing the random way point the following parameters are taken into

consideration when simulating:

1. The total amount (T) simulation time;

2. The amount or the number of nodes specified by setdest;

3. The topological dimensions of the simulation field (rectangular); and

4. The pause and the speed of the nodes usually specified by P and Vmax (Vmin is

fixed at 0 m/s).

Generate mobility trace for an indoor environment

User activity based models

Spatial information systems

Auto CAD digital

Speed

Application server for mobility generation

Node density


78

5.6.1 Graphical representation

Figure 26: Generated CAD drawing of an indoor environment

To generate our mobility trace as in Figure 25 a few changes were seriously looked

into or taken into consideration:

• By using the coordinate system, our graphical representation of the building

was transported to the Ns2 simulator in a Drawing Exchange Format (DXF)

file with coordinates of offices and other places known using the CAD

drawing (Figure 26); and

• In our graph only the x-y coordinates are used in to model the graph. Even if it

is true to say that graph representation for indoor mobility must include the

use of lifts (represented by the z coordinate) we leave this part, because

modelling of these factors adds another layer of complexity to our model, we

leave the inclusion of these factors for future work. While the inclusion of the

z coordinate is a step towards accurate modelling of realistic indoor

environments, it is however at this time a herculean task which is reserved for

future work.


79

5.6.2 Ns2 movement description

We altered the setdest script for node movement in our graph. Each node was given

an initial coordinate point and its destination coordinates were also chosen. This

enabled the nodes to move within a specified link at a specified time.

The movement of the nodes between the vertices was modelled as a function of

activities. For example, at a 10 A.M at the University of Johannesburg users will

move from offices to kitchen or from classrooms to labs or student centres. At

particular times students will move from the lecture halls to other places in the same

place.

It is therefore correct for us to say that at particular times during our simulation,

certain links will exhibit high node density. This, in Ns2, is done by adjusting the

pause time of each and every node so that at a certain time instance, some nodes start

to move to a particular chosen destination in our graph (office vertex or Kitchen

vertex). It is worthwhile to say that not all nodes in our simulation are mobile. The

stationary nodes will represent the users who are immobile, such as those users who

would not want to take a break. This is comparable to the lecturers, students and

workers who are not willing to take a break.

The node density in the simulation environment is a function of movement at

particular time. Unlike in most simulations, node density in AM is simulated in an

incremental manner (that is starting with 10, 15, 20 and so on). The figures below

show the variation of node density in the Random Way Point and the Activity

Mobility model.

5.6.3 Speed descriptors

By using the setdest tool, the speed will be distributed according to Table 6 in the

range of 0.5 to 2.5 as from our measurements. The complete generation of the

mobility must take into account several mobility components as shown in Figure 25.


80

5.7 CONCLUSION

In this chapter, we presented a mathematical model of the Activity Model and

derived few parameters that we will be necessary to effectively and efficiently

understand the performance characteristics of Mobile Adhoc Networks (MANETs)

and in deciding which path a user will choose. Unlike in random way point, mobility

in an indoor area is not a random movement but a defined movement which is

governed by the activities in such an area.

Additionally, in this section, we presented a way in which to abstract a graph, assign

speed, pause time, choice of feasible paths and node density distribution when

simulating.

Whilst the random way point presented the movement in a hap-hazard fashion, our

results in this survey shows that movement in an indoor area is defined by the

corridors or paths available to the user.

We suggested the use of Dijkstra algorithm for the path choice and the MNL

equations from the transport science to predict the user choice of path based on the

geometry and obstructions available in such an environment. We further showed

mathematically on how to distribute the node density among feasible paths.

However, particular attention was placed on the obstacles or geometry of the area as it

dictates the movement pattern on the users in an indoor environment as shown from

our survey. It is this mobility pattern that we will use to emulate both the random way

point and the activity based mobility models/patterns.

Chapter 6 Methodology or Experimental planning

81

CHAPTER 6

6. METHODOLOGY OR EXPERIMENTAL PLANNING

6.1 INTRODUCTION

In this section, our key aim is to give answers to the questions or objectives that were

raised at the beginning of the dissertation (chapter 1). The objectives that need to be

answered are:

• Analysis of activity patterns of both students and workers at the University of

Johannesburg. This analysis will include time allocation to activities,

movement pattern and node density. This in turn will enable us to propose a

more realistic mobility pattern for an indoor office environment for MANETs

based on activity based models

• How to propose realistic wireless link models for an indoor office

environment and to see how the physical layer of 802.11 behaves when

mobility (comparison of random way point and our mobility pattern) is added

to the RF propagation

• Implementation of this activity mobility pattern in ns2 and to see the variation

of the mobility model and the random way point.

6.2 METHODOLOGY

In order to answer the problems which were proposed in the foregoing paragraph, we

divide our experiments into two sections and propose the following:

• First section: The first section our experiments involves determining the

parameters by conducting real time experiments on the radio frequency

behaviour and movement patterns in an indoor environment and;

• Second section: The second and our main experiment involve the application

of these parameters to Ns2. This will enable us to compare the activity model

and the random way point in a more realistic way.


82

6.2.1 Section 1 and 2: Parameter setting

Setting up of simulation parameters for the Ns2 was divided into parts as follows:

• The observation of people’s movement for some days on our campus

environment. We noted and recorded the mobility patterns at different times of

the day, the nodal density and how users choose their path in each

environment;

• Creating a two node network from which data was extracted using one of the

performance analysis software. Empirical readings of signal strength versus

the distance were documented. Two types of movement patterns were used in

this experiment which were the activity based which followed the geometry of

the place and the random way point in an open space;

• Performance data was recorded from various positions in the environment

using different metrics.

6.2.2 Section 3: Main experiment

In this experiment all the parameters obtained from section one were used in the Ns2

simulation for the comparison analysis of the random way point and the activity

Model (AM).

6.3 ENVIRONMENTAL DESCRIPTION

Observed and experimental measurements in this report were performed at the

University of Johannesburg Electrical Engineering Department and the main

administrative building.

The University of Johanesburg is made up of Different building materials. For

example the post graduate room is an open plan office made from wood. The

department walls are made from red bricks. Concrete bricks are a common feature in

the main administrative building which has similar architectural patterns to our own

department.


83

The department of Electrical Engineering building is comparatively rectangular in

shape. It is a two floor building with open stairs as illustrated in the pictures shown in

Figure 27. The corridors are a closed type, rectangular in shape. The offices for both

lectures and workers are close to being a square in shape. Some measurements were

performed in this area. While stairs, ramps and lifts are common in the main

administrative building, only stairs connect the two floors in the Department of

Electrical Engineering.

The two typical environments to the best of our knowledge, represents common

architectural design for most buildings. To the best of our knowledge both buildings

are a representative of most common indoor environments like malls and offices (a

corridor on the middle and offices or shops on the sides). The drawings in Figure 27

represent the corridors and the offices found in the Electrical Department building

where measurements were performed

Figure 27: Picture description of the environment


84

6.4 EXPERIMENT 1. UNDERSTANDING INDOOR TRAVEL PLANS AND ACTIVITY PATTERNS.

Observations for this experiment were performed in the main building at the

University of Johannesburg and our Electrical Engineering Department. We observed

and recorded movements of students at campus with a view of finding out how users

in an environment choose their path between source and destination and; how they

spent their time carrying out their daily activities. We divided this section into three

parts i.e. measuring user speed, node density measurement and route choice.

6.4.1 Experiment 1a: Measuring indoor speed.

This experiment was conducted without any difficulty as it required observing several

users’ movement. In this experiment we used the stop watch and a simple measuring

tape to calculate the variations of speed in different places of an indoor environment.

This will later on help us to adjust the nodal speed in the Ns2 simulator.

6.4.1.1 Procedure

We marked a distance of 5 meters in the corridor. We then selected at random any

individual who passed by that way. Our time measurement began when the individual

stepped on the first mark in the measurement arena up until when the person stepped

on the last mark. The time required to traverse the distance were recorded by an off

the shelf stop watch. In calculating the speed we used the simple formula depicted in

equation 6.1:

DsT

= , (6.1)

In the above equation s is the average speed in meters/second (m/s), D is the distance

in meters separating the two marks and T is the total time(in seconds) taken to

traverse the distance D.


85

The experiment was repeated with several users in different parts of the environment.

The variation of speed interval in different places was recorded

6.4.2 Experiment 1b: Node density distribution and activities time distribution in an

indoor environment

This experiment was quite challenging in that it needed some bit of reasoning unlike

the first one. In this experiment our aim was to measure users’ density in a particular

place at a specific time and also the activities time distribution. Our main concern was

the node density in the corridors and the offices and also how time activity is

distributed amongst different users in the university. (See Figure 28 and Table 5

respectively).

Figure 28: Node density measurement area

Table 5: Activity time distribution in an indoor place

Time range Time

percentage Total work time 10 hours 100% work

students

7 hours 70%

University workers

8 hours 80%

Break

workers 1 hours 10%

students 2 hours 20%

Time for movement

workers

1 hour 10 %

Students 1hour 10%


86

6.4.2.1 Procedure

In this experiment we recorded/counted a number of people traversing a particular

route (corridor or hallway) at certain times of the day for a week. We chose the

common corridors where user traffic is common. Our foremost aim was to determine

the number of people in a particular place at a particular time. We selected times like

lunch time, work time and break time. The variations in the user density at particular

times of the day were recorded.

Secondly, In order for us to calculate the amount of time users spend on work and

movement we used the time table which shows the allocation of time use for

university workers and students at large. Our time range was taken from 7:00hrs to

17:00hrs in the evening with short breaks of one hour each at 10:30 and 12:30. This

period is when there is a lot of movement in the corridors and other paths at the

university. Note however that student’s time of work is less than that of the workers

as them are not dictated to by the working conditions of the University working

policy, but to the university set class time table.

6.4.3 Experiment 1c: Selection of Routes in an indoor environment

Our preliminary point was the observations of student movement and their selection

of paths in the spatial environment, in this case our campus buildings. We took some

time to observe how users in the environment would select their paths given a choice

of two or more paths. In figure 23 (in the previous chapter) we see clearly the effects

of a path in the selection of routes in an environment as exhibited by a group of users

on the ramp than the stairs. Our main focus in this area was to compare route choice

among common indoor paths such as elevators, flat path, ramp path and stair path.

6.4.3.1 Procedure

This measurement setup was simple; we recorded how many users traversed a

particular path at a particular time. We recorded our measurements in different places

of the university. Our target was particularly the lifts, stairs and the ramp paths


87

(refer-to Figure 23 and 24). When we interviewed a few individuals as to why the

preferred the lift instead of the stairs, different responses such as tiring and longevity

were put forward. This showed us that individuals or users would prefer the shortest

and less obstructed path. In our results we expect that the choice of paths between the

lifts the stairs and the ramp will vary accordingly with the lifts being the first choice

followed by the ramp and finally the stairs.

6.5 EXPERIMENT 2: MODELLING THE OBSERVED CHANNEL CHARACTERISTICS-EMULATING THE MOBILITY PATTERNS.

In this experiment our main aim was to compare the effects of the random way point

and the indoor movement pattern on the signal strength and link quality. Our

measurement also looks at how certain office areas may affect the signal strength

quality. For example to determine the signal strength decay in an open plan office we

had to measure how the signal strength degrades over a certain distance in open plan

office.

We set up our experiment as shown in Figure 31. Our experiments which we

conducted were as follows:

1. Line of sight measurements

2. Open plan office measurements

3. Effects of human obstruction on signal strength

4. Two nodes moving apart in an indoor location

5. Effects of one node moving and the other stationary on the signal strength

6. Emulation of the random way point in an open area.

6.5.1 Equipment description and connectivity

We ran our test experiments on two Dell computers each fitted with the Ralink PCI

cards. The two computers were each running on the Windows Operating system with

Pentium 4 [69]. We equipped our two computers with Ralink 802.11g turbo wireless

PCI card (Figure 29) which is a product of the Canyon tech.


88

6.5.1.1 Configuration

We used the Ralink drivers which are provided with the cards. The cards were set to

adhoc mode which was the proper selection for non infrastructure (without wired

APs) network.

We set the transmissions rate to 54 Mbps and operated in channel 1 (note that any

channel can selected, however, in some countries there may be some restrictions on

channel use) of the industrial scientific and medical (ISM) band. The 802.11g of the

IEEE standards was selected and set the card power to 200mW. The 802.11g card can

be configured using multiples of 6; from 12 to 54 Mbps. We enabled the radio

measurement and turned on the Cisco centralized key management - fast roaming

(CCKM) on, for our measurements. It enabled us to connect between the two points

or antennas that we were using without necessary going through the server process.

The Lightweight Extensible Authentication Protocol (LEAP) was used to provide

mutual authentication between two wireless stations that we had. LEAP has a

dynamic wired equivalent privacy (WEP) that provides a secure scheme in Wi-Fi

networks.

Figure 29: Features of the 802.11g PCI card


89

6.5.1.2 The performance analysis software for the link dynamics.

The software used to measure link quality and the received signal strength in this

research was a free download from Wirelessmon [78] and the Ralink utility graphical

user interface (GUI) which is provided with the Ralink wireless drivers. Below is the

screen shot of the Wirelessmon graphical user interface (Figure 30).

Figure 30: Screenshot of the wirelessmon GUI

Wirelessmon is wireless LAN performance indication software which we used to

observe the signal strength and link quality in our experiment. The choice of

wirelessmon, which is the product of Passmark™ [78], software, does not indicate its

superiority over many performance analysis software, but its easy-to-use, cost and

easy of understanding made us choose it.

Wirelessmon is software that is capable of monitoring the status of WLANS in terms

of signal strength, data rates and the positions of the antennas. Moreover the

Wirelessmon is able to incorporate Global Position System (GPS) when dealing with

antenna location. But since we were dealing with an indoor environment the GPS use

was not so significant, hence it was not used in our measurement.

In all our experiments we took great care in the orientation of the antenna. We made

sure that the two antennas were facing one another all the time or had line of sight

between them. We positioned them such a way that the antennas had no obstructions

from the PCs that we were using. In order to reduce the effects of human obstructions

[79] we avoided being in the way of the antenna and, we positioned the computers, in

such a way that the screen was facing our side for readings.


90

Figure 31: Setup connection of the experiment

6.5.2 Measurements

The experiment was set up as shown in Figure 31 to measure the line of sight

measurements in the corridor (Figure 32). For every experiment we conducted three

sets of measurement were taken just to make sure that our measurements were

accurate. Our antenna tripods heights were set to height of 1.5 meters throughout our

measurements.

6.5.2.1 Experiment 2a: line of sight measurements

In this experiment we conducted measurements to understand the effects of a corridor

without and with human obstruction on the signal strength.

(a) Procedure

We started with a source node sending to a receiver at a given distance in a corridor

without stairs in the EE department. The transmitter was then moved in an

incremental step whilst maintaining the receiver sight. The spacing between the first

and the next measurement was of regular increments of about 5 meters until up to a

distance of 70 meters

PC PC

Ralink PCI card Ralink

PCI card

Antenna


91

Measurements of signal strength were taken using the Wirelessmon software. Care

was taken that the orientation of the two antennas was the same all the time. In the

first round of this experiment we avoided any human obstruction in the path of the

signal. We recorded the signal strength versus the distance in line of sight corridor

(Figure 32). In the second round of our experiment we conducted the measurements

with the human obstruction following the same procedure as before. All the

experiments were repeated twice just to verify our results.

.

Figure 32: Line of sight measurement area 6.5.2.2 Experiment 2b: Open plan office measurement The configuration pattern in this experiment was similar to the one in procedure one.

The only difference was that in this experiment, we measured the effects of the

received signal strength in an office which is made from wood partitioned offices as

shown in Figure 33.

(a) Procedure

We maintained one transmitter in one office whilst moving the receiver in different

offices or partitions. Our main aim was to find out how far the signal strength would


92

degrade in such an environment. These measurements in turn will enable the

modellers to accurate model the transmission rate in an indoor office plan.

Figure 33: Open plan office measurements area

6.5.2.3 Experiment 2c: Stair corridor versus signal strength.

The objective of this experiment was to determine the effects of stairs and mobility,

on signal strength and link quality (Physical layer of 802.11g) in the built

environment. In many cases, stairs and walls in the built environment can drastically

affect the quality of links, throughput and received signal strength between the

transmitter and the receiver.


93

(a) Procedure

We used the corridor with stairs and carried our measurement campaign as in the line

of sight (LOS) measurement. The antenna orientation and the experimental setup from

above procedure was maintained, the only difference was that in this experiment the

corridor terrain was not flat as in the first one. We observed and recorded the readings

of signal strength versus distance in a stair obstructed corridor.

6.5.2.4 Experiment 2d: Two nodes moving apart

In this experiment our main objective was to emulate a typical indoor environment

and find out how the signal strength versus distance behaves in an indoor environment

when two users move in opposite direction.

(a) Procedure

The transmitter and the receiver were placed at a centre mark of 35 meters and the

two nodes were moved apart at our normal walking speed. Our speed selection was

random; it was entirely on the user’s speed. This is in line with common sense that

users speed is purely a choice of the person concerned. The path traversed along the

corridor did not represent a straight line movement. We did this in order to get as

close as possible to the real movement of people in a corridor. We recorded our

results of distance and the signal strength decay.

However, we did not carry out the experiment of two nodes moving towards one

another because this type of experiment will be a replica of the two nodes moving

apart.

6.5.2.5Experiment 2e: One node stationary and the other mobile.

In this experimental setup, our main objective was to analyse how the signal strength

would behave when one node is located in the office and the other one mobile. This


94

scenario demonstrates some of the typical movement patterns of transmitter-receiver

communication in an indoor environment

(a) Procedure

We placed one node in one office and we kept another mobile in the corridor. Like

before we walked at our own pace without any adherence to a known speed. The

results of the experiment were recorded.

6.5.2.6 Experiment 2f: Emulation of the random way point

In this experiment our aim was to emulate the random way point and observe the

variation of signal strength when the user moves in a zigzag (random) mode. The

results from this experiment will be compared with the indoor results with a view of

observing how the signal strength varies in different places, under different types of

mobility patterns. Capturing these realistic behaviours is an important step towards

making simulations close to reality within the Ns2 simulation package.

(a) Procedure

In order to be close to the random way point movement as possible (with no

obstructions and no wall reflections), we opted to carry our experiment in an open

area .The movement emulated was the random way movement in a wide and non

obstructed parking lot (see Figure 34). This space was at least close to size of our

office building.

Despite the fact that we tried very much to emulate the random movement in the

department, the Random movement in the corridor was impossible because of the

wall restrictions or rather the geometry of the building which dictated the movement.

From [46] it is clear that the environmental layout and the activities patterns carried in

an indoor environment dictates the movement pattern of the users. Our experiment

was done on Saturday when there were no cars and therefore less obstruction. Our


95

movement trajectory was in a zigzag fashion or pattern as illustrated in Figure 35 of

(a) and (b) of our methodology.

In order to avoid interference from our own bodies, in an obstacle free area, we made

sure that the transmitter and the receiver are in sight of one another every time we

perform our measurement.

In this experiment three types of measurements were taken to emulate the random

way point in real life. Our movements were as follows:

1. Two nodes starting from the same age of the field moved in a zigzag pattern

until when the met at the centre. We recorded the observed signal strength

indicators for our evaluation.

2. Two nodes moved in opposite directions to one another but still repeating the

same pattern of a zigzag movement.

3. One node was stationary and the other node traversed a zigzag movement

pattern across the field.

Figure 34: Open space area where emulation of the random way point was conducted


96

(a) (b)

Figure 35: Two types of random way point movement which we emulated

6.5.3 Conclusions

The parameters set in the preceding chapters will be used in Ns2 to configure the

radio frequency settings and the mobility parameters will be used to set up the node

mobility in Ns2.

6.6 SECTION 3: MAIN EXPERIMENT, NS2 SIMULATIONS.

In this section our aim is to perform a comparative analysis of our activity mobility

model and the random way point. To establish the impact of the mobility and

pathways on the performance of routing, we employ the DSDV and AODV routing

protocols for route finding and path set up. In these simulations, we also compare the

results with the performance of AODV using the random waypoint model. The

metrics used are the:

1. The throughput; and

2. Delay under the two mobility patterns.


97

6.6.1 Definition of the metrics

(a) Throughput- is the amount of digital bits that traverses a particular path link or

per unit time or the amount of data that is delivered at a certain terminal.

(b) End–End Delay- is the difference time between when the node source initiates a

sending of the packet over the link to the point when the packet is received by the

receiving node.

Where T is the time taken for receiving and sending packets over the link.

6.6.2 Procedure

We loaded the Ns2 with our mobility pattern as described in chapter 5 of this

dissertation. We adjusted the setdest tool to produce the mobility pattern that we

desired as demonstrated in chapter 5. All of the simulations were run using the

network simulator (Ns2) [1]. The simulation area is 760 × 760m, and the maximum

node transmission range is 70m. However, in the presence of different building

materials and indoor geometry, the actual transmission range of each individual node

is likely to vary greatly. The propagation model is the two-ray path-loss model. At the

MAC layer, the IEEE 802.11 DCF protocol is used, and the bandwidth is 2Mbps.

Because we are modeling an indoor environment, the movement speed of the

users/nodes, unless otherwise stated, is arbitrarily selected between 0.5 and 2.5 m/s to

represent walking speeds. The pause time in our simulations is also randomly

selected. Hence, when a node reaches its intended destination, it pauses for a certain

period of time and then selects a new destination and speed and continues movement.

Each data point is an average of five simulation runs with the nodes distributed in

different initial positions. To evaluate the characteristics of the network topologies

created by the two mobility models, we randomly distribute the nodes at the

beginning of the simulation. The data packet sending rate is set to 1mbps throughout

our simulations and in all our simulation we compare the random-way point and our

activity model as described in section 5.

destination receives packets source sents packetsEnd End delay T T− = −


98

In the first scenario we calculate the behaviour of throughput under DSDV protocol

with the 1024 bytes payload. We compare the throughput versus time. The nodes are

distributed according to our mobility model as described in Chapter five. Paths that

are more likely to be traversed by users are loaded with different node densities.

In the second scenario, still with the payload of 1024 and using the DSDV protocol

we evaluate the delay of packets with time in our simulation environment. The

number of nodes is kept constant like in the preceding paragraph. Like above we

compare the random way point and our activity model (AM)

In the third scenario, we alter the payload to 512 bytes still maintaining the same

mobility patterns and routing protocol as in scenario one. In a likewise manner we

compare the throughput and the delay for both our mobility patterns (random way

point and our activity model).

The fourth scenario, repeats exactly the third scenario setup but instead of measuring

the throughput we measure the delay of packets among the nodes. Likewise we

compare our two mobility models using the same metrics.

The fifth scenario uses AODV as a routing protocol. The mobility pattern is set as

described in section 5 of this dissertation. A packet payload or data packet size of

1024 bytes is used. Using the metrics above we compare how the two mobility

patterns behave.

In the sixth scenario we repeat the scenario described in scenario number five but in

this we measure the delay of the packets between the nodes.

6.7 CONCLUSION

In this chapter we have presented the methodology of our experiments and how we

have carried out the experiments for us to achieve our desired results. We have

presented how we have emulated the two mobility patterns, with one being carried out

in an open field and the other indoors.


99

Simulation procedure using the Ns2 has also been presented and how we used our

metrics to compare the two mobility patterns.

Chapter 7 Evaluation and Discussion of Results

100

CHAPTER 7

7. EVALUATION AND DISCUSSION OF RESULTS

7.1 INTRODUCTION

In this chapter we discuss and evaluate the results of our study. Different types of

measurements were conducted in order to achieve our desired objective (realistic

Mobility Modelling in an indoor environment). Results from different experiments

(section 1 and Section 2) that were conducted are presented.

7.2 RESULTS OF EXPERIMENT 1A: SPEED IN AN INDOOR LOCATION.

In this section results from experiment one are presented. A variation of speed among

different users was observed in an indoor building (See Table 6). Depending on the

location within an environment users had different measured speeds. For example the

offices represented the speed of zero because minimal movement, which we

neglected, was observed. On the other hand corridors and the hallways represented

varying speeds. In the corridors and hallways speed kept varying between 0.5 m/s to

2.5 m/s in conformity with a typical measured speed [28, 27, 30]. Higher speeds of

3m/s -3.5m/s were rarely measured, so these were not taken into consideration. Table

7 represents the average of different speeds exhibited by user in an indoor

environment between the two buildings.


101

Table 6: Speed variation of users in an indoor environment

Experiment 1a speed variation in an indoor place

Amount of people Speed 1 0.5 2 1.4 3 2 4 1.6 5 2.1 6 2.2 7 2.5 8 0.9 9 1.5 10 1.9 11 1.4 12 1 13 2.34 14 2.1 15 2.2 16 2.17 17 0.9 18 2.2 19 2.7 20 3.2 21 1.2 22 2.7 23 2.1 24 2.3 25 1.9 26 2.4 27 2.45 28 2.3 29 0.5 30 0.4 31 0.3 32 2.61 33 2.3 34 1.98 35 0.3 36 0.7 37 2.5 38 2.41 39 2.02 40 1.87

Standard deviation 0.760648 average 1.80125 median 2.06

Range is 0.5-2.5

Table 7: Average speed measurements in different indoor locations


102

Place Speed Movement direction Office/working place 0m/s stationary Normal mobility (corridor) 0.5 – 2.5 m/s In different ways

7.3 RESULTS FOR EXPERIMENT 1B: NODE DENSITY DISTRIBUTION AND ACTIVITY DISTRIBUTION

People may walk in groups or walk alone in corridors; this movement pattern dictates

the type of node density that is observed in such an environment. As a result it is

reasonable to say that node distribution in an indoor location is somehow aligned to

the activity that is taking place.

In this section we present the results of how the node density is distributed in an

indoor environment at certain times of the day. Our results show that node density is

not uniform throughout the day as shown in Figure 36. The graph clearly shows that

the variation of the node density is controlled by the activities of the day and the

amount of obstruction within a particular route. From the graph of Figure 37 we

observe clearly how the activities of workers and students are displayed in a campus

area. Evidently it can be seen that movement in an indoor environment occupies a

smaller amount of the total activity time during the day.

From our results, routes that are commonly used have high node density per square

meter. Of particular interest also is that, at particular times of the day the node density

will be high at specific times of the day especially at break times as shown in Table 8.

This scenario represents a situation where certain routes in an indoor area will have

high node density and others will have none or less depending on the activity that is

taking place. For example at 10:00hrs the route to the kitchen in the Electrical

Department has high node density.


103

Node density distribution of users in an indoor enviroment (main building and EE dept)

01020304050607080

07:00

08:00

09:00

10:00

11:00

12:00

13:00

14:00

15:00

16:00

17:00

Time

perc

enta

ge

node density dstribution

Figure 36: Probability of node density in corridors at specific times of the day Table 8: Node density distribution in different places of an indoor environment

Node density distribution at particular times of the day

Venue place Morning Mid morning

Afternoon Mid afternoon

Evening

Main building

Passage high low High low high

offices Low low low low low

Campus indoor EE Dept

Offices or classrooms

High Moderate Low High Low

Passages or corridors

low Moderate Low Low High


104

Activity patterns in an indoor enviroment

0

20

40

60

80

100

1 2 3

1: working 2: movement 3: miscelenous

Perc

ent t

ime

studentslecturers and wokers

Figure 37: Percentage use of time in an indoor environment

7.4 RESULTS OF EXPERIMENT C: SELECTION OF ROUTES IN AN INDOOR ENVIRONMENT

It was clear from the observation that people opted for the lifts than the ramp and the

ramp for the stairs. The observation can be seen in Figure 23 where we see a group of

users opting for the ramp than the stair way.

Although some users would use the stair ways, we observed that it was not due to

their natural choice. This phenomenon was because users tried to avoid the congestion

that was choking the ramp route. The choice of the elevators was clear in the amount

of people opting for the elevators than the stairs as represented in Table 9.

Table 9: Choice preference of routes from the University of Johannesburg

Route type

Preference of routes from the survey

Frequency of usage

Why the preference of such a route

LIFTS 1st choice Very often Short quick and not tiring

STAIRS 3rd choice Not very often

Long and tiring

RAMP PATH

2nd choice Often Not tiring but long


105

7.5 GENERAL DISCUSSIONS OF RESULTS: EFFECTS OF DISTANCE ON THE SIGNAL STRENGTH.

In this section, discussions and results regarding the effects of building geometry and

mobility, on the signal strength in an indoor environment is presented.

It is common assumption that a relationship between the transmitter and the receiver

must follow the power law equation [73]. This equation holds if we take into

consideration ideal conditions that the science behind the relation holds true. In actual

fact a real environment is a poor match to the ideal conditions assumed. Variations

due to obstruction, reflection, refraction, scattering, fading and shadowing in practice

cause considerable variation from ideal behavior. This is more commonly observed

when the transmitter and the antenna are separated at a distance d from one another

and are obstructed by different materials of varying thickness.

The noise level observed on our reading was high. This could have been due to

additive white Gaussian noise (AWGN) and the interference caused by reflected and

refracted waves, which was high when the separation between the transmitter and the

receiver was small and low when the separation was large. This phenomenon was

caused by cards which were internally generating some noise and was transmitted

with the frames. Another observation was that the link quality remained constant

throughout the measurements. The only drop in the link quality from 100 percent to

91 percent was when the LOS measurement was close to 70 meters mark in the

Electrical Department corridor. It was clear that the link quality was also dependent

on the separation of the transmitter and the receiver. Exceptional link quality was

observed to be proportional to the distance.


106

7.5.1 Results for Experiment 2a: Comparison variation of received signal strength

with and without human obstruction (corridor)

Figure 38 shows the variations of signal strength with distance across a corridor in our

Department. From the graph, it is clear that the signal strength decaying with

increasing distance is very evident in this graph. This phenomenon in some way also

follows the power law and is true in accordance with the signal-distance experiments

conducted by a lot of researchers [79, 80, and 81].

Table 10: Variations of signal strength with distance

Transmitter receiver

separation

No human obstruction With human obstruction

0 -20 -30

5 -30 -35

10 -31 -37

15 -37 -38

20 -39 -39

25 -41 -42

30 -45 -48

35 -50 -53

40 -52 -58

45 -57 -61

50 -60 -61

55 -61 -67

60 -69 -69

65 -70 -71


107

Comparison of human obstruction with non obstructed LOS measurement

-80

-70

-60

-50

-40

-30

-20

-10

0

0 5 10 15 20 25 30 35 40 45 50 55 60 65Distance Meters

sign

al d

Bs

1 2

1: No human obstruction (LOS)

2: Human Obstruction

Figure 38: Variations of signal strength with distance

7.5.2 Results for experiment 2b: Analysis of signal strength versus distance in open

plan office experiment.

Figure 39 shows how the signal strength behaves with distance in an open plan office.

The variations of signal strength versus distance in an open plan office differ very

much from the LOS corridor measurements. The signal strength in an open plan office

tends to fail after a few meters mainly due to the obstructions involved in such an

area. Modelling such an area, from our study/results requires the scrupulously

understanding of obstacles within such an environment.

The link quality and the noise level remained almost similar to the LOS corridor

measurements observed earlier, only after beyond a certain point of more than 8


108

meters did the signal strength begun to fluctuate heavily. A divergent sharp drop was

observed in an open plan office, where the link between the transmitter and the

receiver was out of range after traversing 3 cubicles each measuring 1.74 meters in

length, made out of plywood with thickness of 2cm.

Table 11: Signal strength decay versus distance in an open plan office


separation

Signal strength

0 -29

2 -36

4 -40

6 -45

8 -63

signal

-80

-70

-60

-50

-40

-30

-20

-10

0

0 2 4 6 8meters (T-R) separation

dBs

sign

al

signal

Figure 39: Signal strength decay versus distance in an open plan office


109

7.5.3 Experiment 2c: Disparity of signal strength with distance in corridor with stairs

The graph of Figure 40 was conducted in an indoor environment with stairs. Like in

our previous experiments, we kept the antenna orientation and moved the computers

at our walking pace, which is the similar to the normal walking pace of users in such

an environment. The stair height measured 80cm in height.

In order to get accurate results the computers were moved at a distance interval of 5

meters and periodically paused for measurement. This was done in stepwise manner

until when the 60 meters distance was reached.

The link quality and the noise level were at 100% and 97dB respectively. However

the link quality was affected at distance of more than 60 meters. At a distance beyond

60 meters the link quality started to fluctuate between 96% and 99%. Nonetheless the

signal strength varied as before, except after the stairs when there was a great signal

strength variation due to a change in the corridor elevation caused by stairs. This type

of layout, where a corridor has a change in elevation, is more common in some

buildings and it is imperative that effects of such an environment are taken into

consideration.

The comparison of Figure 40 and Figure 38 shows a great incongruity in the values of

the signal strength versus distance. Figure 40 shows a sharp curve after the 30 meters

mark, which shows the effect of stairs on the signal strength in a stair corridor. A

consistency fall-off of signal strength versus distance is displayed in Figure 38 line 2

of the graph.


110

Table 12: Variations of signal strength with distance in a stair corridor

Transmitter receiver separation Signal strength

0 -21

10 -31

20 -32

30 -35

40 -55

50 -60

60 -60

Figure 40: Variations of signal strength with distance in a stair corridor

7.5.4 Interpretation of results experiment as observed in experiment 2d, 2e and 2f:

Emulation of random way point and the activity model

From the experiments and observation that we carried it is clear that the mobility

patterns or the movement exhibited by users in an indoor environment is not as

random as presented by the random way point mobility model.


111

In this section we compare the results as shown Figures 40, 41, 42, 43 and 44.

In Figure 40, 41, 42, the experimental results show great variation in the signal

strength versus the distance when movement is carried out in an indoor environment.

A sloping graph which somehow follows the power law is observed. In all the figures

the graph starts at a very high signal strength and gradually decreases as the distance

increases.

Similarly we emulated two types of movement for the random waypoint in an open

area (see Figure 34). The movement that was followed was a zigzag mobility as

shown in Figure 35. Figure 43 displays the results of both nodes starting from the

centre and moving in zigzag manner away from the centre. The signal strength

decreased as shown in the graph of Figure 43. Figure 44 shows two nodes moving

apart from the centre. The crest on the graph represents the times when the nodes are

vertically opposite one another and the troughs represents the times when the nodes

were diagonally opposite to one another. Figure 45 displays more or less the type of

characteristics similar to the Figure 44 one. The discrepancy in results of signal

strength decay versus distance in both areas is astonishing as displayed by the graphs.

From the graphs it is clear that the signal strength variations in the random way point

is not similar to an indoor one. From the graph of Figures 40, 41, 42 indoor

environments will present the power law type of signal strength vs. distance graph

whereas the random way point movement presents a completely different type of

graph as displayed in the results as in Figures 43, 44 and 45

This is a clear indication that the random way point cannot be used for modelling

indoor mobility patterns because of its improper mobility patterns which are not

suitable for an indoor environment. The use of the random way point in performance

analysis is certainly not viable for indoor environments.


112

Table 13: Variations of signal strength with one node stationary and one mobile


separation

Signal strength

0 -31

10 -45

20 -51

30 -57

40 -61

50 -64

60 -67

Figure 41: Variations of signal strength with one node stationed in an office and one mobile along the corridor


113

Table 14: Variation of signal strength versus distance when tow nodes are moving apart in an obstacle free corridor

Transmitter-receiver separation

Signal strength

0 -21 10 -29 20 -32 30 -39 40 -41 50 -45 60 -53

Two nodes moving apart LOS

-60

-50

-40

-30

-20

-10

0

0 10 20 30 40 50 60Distance in meters

sign

al s

treng

th (d

Bs)

signal strength decay

Figure 42: Variation of signal strength versus distance when two nodes are moving apart in an obstacle free corridor


114

Table 15: Signal strength versus distance with both nodes moving in the same direction but in a random manner figure (a)

Transmitter receiver separation

Signal strength in decibels

0 -21 20 -39 40 -41 60 -61

Figure 43: Signal strength versus distance with both nodes moving in the same direction but in a random manner figure (a)


115

Table 16: Variation of signal strength versus the distance in an emulated random movement with two nodes moving oppositely Figure (b)


separation Signal strength in

dBs 0 -26

10 -43 20 -50 30 -45 40 -61 50 -50 60 0

Figure 44: Variation of signal strength versus the distance in an emulated random movement with two nodes moving oppositely Fig (b)


116

Table 17: Signal strength disparity versus distance in random movement keeping one node constant

Figure 45: Signal strength disparity versus distance in random movement keeping one node constant

7.6 SECTION 2 MAIN EXPERIMENT: SIMULATION RESULTS AND ANALYSIS.

The main objective of our simulation was to comprehend the impact of our mobility

model and the random waypoint movement pattern on the mobility distribution of an

ad-hoc network which in turn has an impact on the network performance. To acquire

quantitative information concerning the proposed mobility model and contrast it with

the random way point model, we have simulated our algorithm for an ad-hoc

environment under certain movement constraints as proposed in chapter 5.


separation

Signal strength in dBm

0 -21 10 -47 20 -41 30 -50 40 -43 50 -65

60 -59


117

The results portrayed in Figure 46, 47 and 48 shows that the overall protocol

performance depends on the movement pattern of the nodes in the simulation area,

and the node density within the simulation area. The Figures of 46 and 47 show the

throughput and the delay of the DSDV under a different payload. Results in Figure 48

shows protocol performance under AODV under a different packet payloads. The

results show that the throughput of our proposed mobility model is high compared to

the random way pointing in all the results. On the other hand the end-end delay of

packets is lower with our mobility model than with the random way point. The

explanation of this is linked to the fact that the nodes in our proposed mobility model

are close to one another and therefore the connectivity between different nodes is

high.

Figure 46 shows the end-to-end data packet delivery delay. These results are a

contrast between defined connectivity and non defined connectivity in the random

way point. The Figure shows that the data delivery delay for the activity based model

(AM) is considerably lower than in the random waypoint model. Because of defined

connectivity among nodes and fewer data sessions that are able to be completed, there

is a reduced amount of data traffic in the overall network. Accordingly data packets in

this network experience a lesser amount of contention for transmission and are able to

be delivered more quickly to their destinations.

The throughput in our results as shown in Figures 46, 47, 48 is higher with our

mobility model than with the random way point. It is evident from the graph that,

unlike RWP model, our proposed model is able to capture the movement pattern

which is the replica of activity driven movement in a particular place. Additionally

our mobility model does not assume random pause time like in RWP but a scaled

down pause time. Nodes reaching the primary areas such as the office take on a high

node pause time and secondary points take on shorter pause time. Because nodes in

our simulations are moving in particular defined path ways it generates very high

node connectivity, this established connectivity can only be maintained for a short

time because later they will head for a particular destination.


118

The results point out that throughput and delay performance degrades when the

random way point is used in an indoor environment. The grounds that clarifies this

claim is that, the network graph experiences more link breakages due random

movement of the random way point.

(a)

(b) Figure 46: results for DSDV throughput (a) and delay (b) using the 1024 bytes packet payload

(a)


119

(b) Figure 47: Throughput (a) and delay (b) results for 512 bytes packet payload using DSDV .

(a)

(b)

Figure 48: Throughput (a) and delay (b) results for AODV with 1024 bytes payload


120

7.7 CONCLUSIONS

In this section we present our finale of chapter 7. From our study it is clear that the

mobility modelling serious affects the RF propagation in an indoor environment.

While it is common for most simulators to use the random way point movement

pattern, it is certainly not applicable for an indoor environment. The signal strength

heavily attenuates in an open plan office and less in corridors where the LOS is a

predominant case. Less attenuation is observed in the corridors with stairs.

The comparison of the Random Way Point parameters and our indoor parameters

yielded different parameters for our two mobility models. When these the results

(parameters) of these two mobility models were compared the graphical results were

surprising as displayed in our chapter seven. These parameters also brought out

different results when loaded in the network simulator two as shown in our Ns2

simulation results

Chapter 8 Conclusion and future work

121

CHAPTER 8

8. CONCLUSION AND FUTURE WORK

8.1 INTRODUCTION

Protocol performance analysis in different environments requires that a person

performing the simulation is quite aware of the mobility pattern he/she will be using.

Failure to use the right mobility pattern for the simulation gives rise to incorrect

results.

A more reliable way of performing simulation in an indoor is to follow the mobility

model as dictated by the activities that are performed in such an area. The movement

pattern should follow the geometry and at all cost try to avoid the obstacles that are

found within a particular link or path.

8.2 CONTRIBUTIONS

The objective of this study was to investigate or formulate a more realistic indoor

movement pattern based on the activity models drawn from the transport science. This

methodology consisted of four components: (1) abstraction of a graph from the

architectural drawings, (2) evaluate reasonable node density in realistic paths, (3)

modelling movement as a function of activities, and (4) modelling route choice in a

way that reflects user perceptions of the network. A comparison analysis of the

indoor activity mobility model and the random-way point was done in order to

effectively see how the two behave in an emulation and simulation process.

8.3 OVERVIEW OF CHAPTERS

In chapter one, we gave an initial introduction to the MANET world and our

objectives of our study. Chapter one also outlined how the whole dissertation would

follow afterwards.

In chapter two, we presented our background of our study. We gave an overview on

wireless LANs (MANETs) and went on to outline different routing protocols that are

used in MANET simulations. We went on to give an overview of related technologies


122

and simulation tools that have been used within the wireless and the wired

communities.

In chapter three, an overview of different mobility models that are used in the

simulations of MANETs and any related work to mobility modelling was given.

Chapter four gave an overview on Radio Frequency propagation models that are used

in the wireless community and the Ns2 simulator.

Chapter five presented our mobility modelling based on activities that are done in an

indoor place. A mathematical approach using the graph theory was used. However in

our mobility modelling, we described movement as a function of activities. Nodes

only visited the vertices that in normal circumstances users or people would visit.

Chapter 6 presented a methodology of our study. A comprehensibly outline of

different procedures on how we carried out our study was presented in this chapter. A

comparison analysis of the random way point and the activity mobility model is

drawn

Chapter 7 presented the results of both our emulation and simulation of our studies.

Graphs and tables to different experiments are presented in this section.

8.4 LIMITATIONS OF OUR STUDY

Our study, though it was insightful venture into activity based modelling, it was

however, not without limitations. Our limitations were more particularly on the

simulation of an indoor environment. The unavailability of the z-coordinate in the

Ns2 made it impossible for us to simulate path choice through the lifts. In such an

eventuality our simulations were more limited to the simulation of the x-y coordinate

system. Paths such as the lifts were very difficult to simulate. We left these for future

research. Other limitation was the inability to obtain a more realistic antenna

propagation distance in our simulation because of great variation of propagation

distance in an indoor area.


123

8.5 CONCLUSIONS OF RESULTS

In our results, it is apparent that there is marked difference between the random way

point and our mobility model based on activities in an indoor environment.

Astonishingly the variation of the results in both the simulation and the emulation of

our mobility models, as presented from the graphs, gave a clear indication that the

random way point cannot be used for protocol performance in an indoor environment

8.6 FUTURE WORK

Future work on this work may include the inclusion of the z-coordinate in the ns2 if

proper simulations of the indoor environment are to be done. Most simulations in this

area (MANETs) only use the x-y coordinate system which may not really present a

true reality of an indoor environment. We have however, laid a foundation for

modelling path choice and node density in an indoor environment based on activity

patterns in indoor area.

8.7 CONCLUSION

This chapter gives a conclusion to our study. It has looks at the summary of the

chapters, results, limitations and future work on our study.

Colophon

124

REFERENCE

[1] B.R. Elbert, “Introduction to satellite communication” 1st edition Boston Hughes

communication Inc.

[2] IEEE 802.11 standards and amendments online from http://www.ieee802.org/11/

(accessed 5 May 2007)

[3] Bluetooth basics available online from http://www.bluetooth.com (accessed May

2004)

[4] M. llyas. “Adhoc wireless networks”. CRC press , 2003

[5] Wearable computers. Available online from http://www.zypad.com (Accessed 4

July 2006)

[6] J. Yoon, M. Liu, B. Noble “Random Waypoint Considered Harmful” www.ieee-

infocom.org/2003/papers

[7] R. Ganesh, K. Pahlavan “Wireless network deployment” Springer published 2000.

[8] P. Venkateswaran, R.Ghosh, A. Das, S.K. Sanyal, R. Nandi “An Obstacle Based

Realistic Ad-Hoc Mobility Model for Social Networks” journal of networks, vol. 1,

no. 2, June 2006

[9] Sigmobile news and updates. Available from http://www.sigmobile.org (accessed

4 August 2007)

[10] C.K. Toh “A Novel Distributed Routing Protocol To Support Ad hoc Mobile

Computing “IEEE 15th Annual International Phoenix Conference on Computers and

Communications

Colophon

125

[11] V. Park, M.S. Corson, “A Performance Comparison of the Temporally-Ordered

Routing Algorithm and Ideal Link State Routing,” Proceedings of ISCC conference

'98, Athens, Greece

[12] C. E. Perkins, P. Bhagwat “Highly Dynamic Destination Sequenced Distance

Vector Routing DSDV for Mobile Computers” ACM SIGCOM 94 Conference on

Communications Architectures, Protocols and Applications

[13] G. Pei, M. Gerla, Tsu-Wei Chen “Fisheye State Routing: A Routing Scheme for

Ad Hoc Wireless Networks” IEEE International Conference (2000)

[14] R.V Boppana, S.P Konduru, “An adaptive distance vector routing algorithm for

mobile, ad hocnetworks” INFOCOM 2001. Twentieth Annual Joint Conference of the

IEEE Computer and Communications Societies. Proceedings. IEEE Volume 3, Issue ,

2001 Page(s):1753 - 1762 vol.3

[15] I. D. Chakeres , E.M. Belding-Royer. “AODV routing protocol implementation

and design” Proceedings of the International Workshop on Wireless Ad Hoc

Networking (WWAN), Tokyo, Japan, March 2004

[16] B. Das, E. Sivakumar,V. Bhargavan, “Routing in ad-hoc networks using a virtual

backbone” Manuscript (1997).

[17] Y.B. Ko, N. H. Vaidya, Location-aided routing (LAR) in mobile ad hoc

networks, in: ACM/IEEE Int. Conf. on Mobile Computing and Networking

(MobiCom’98) (October 1998).

[18] D. Johnson, D.A. Maltz , J. Broch, “The dynamic source routing protocol for

mobile ad hoc networks, Internet Draft, Mobile Ad Hoc Network (MANET) Working

Group” IETF (March 1998).

[19] S. S. Kulkarni, G.R. Dattatreya “SMART: Statistically Multiplexed Adaptive

Routing Technique for adhoc networks” Wireless networks 2004

Colophon

126

[20] M.S. Corson, A. Ephremides, “A distributed routing algorithm for mobile

wireless networks” Wireless Networks 1(1) (1995) 61–81.

[21] S. Lee, M. Gerla “Dynamic Load-Aware Routing in Ad hoc Networks” IEEE

international conference volume 10 issue 2001

[22] Yunjung Yi, T. Jin Kwon, M. Gerla “A load aWare routing (LWR) based on

local information” proceeding of the IEEE published 2001

[23] R. Ramanujan, S. Takkella, J.Bonney, K. Thurber. “Source-initiated adaptive

routing algorithm (SARA) for autonomous wireless local area networks” proceding

23rd annual conference 1998

[24] I.T. Haque, C. Assi, J.W. Atwood “Randomized Energy Aware Routing

algorithms in Mobile Ad Hoc Networks” MSWiM’05, October 10–13, 2005,

[25] J.Broch, D. A. Maltz, D. B. Johnson, Y.-C. Hu, and J. Jetcheva, “A performance

comparison of multi-hop wireless ad hoc network routing protocols” in Proceedings

of the Fourth Annual ACM/IEEE International Conference on Mobile Computing and

Networking(Mobicom98), ACM, October 1998.

[26] F. Bai, N. Sadagopan, and A. Helmy, “IMPORTANT: A framework to

systematically analyze the impact of mobility on performance of routing protocols for

ad hoc networks” in Proceedings of IEEE Information Communications Conference

(INFOCOM 2003), San Francisco, Apr. 2003.

[27] G. lu, M. Gordon, B. Demetrios, “Mobility modelling in mobile adhoc networks

with environment –Aware”. May 2006

[28] I. Stepanov, P. J Marrón, K. Rothermel, “Mobility Modeling of Outdoor

Scenarios for MANETs” Simulation Symposium, 2005 Proceedings.

Colophon

127

[29] H. Wenjun, J. Crowcroft, “MIRRORS: An integrated framework for capturing

real world behaviour for models of ad hoc networks” available online from

www.cl.cam.ac.uk/techreports/UCAM-CL-TR-631 (n.d)

[30] A. L. Cavilla, G. Baron, T. E. Hart, L. Litty, “Simplified Simulation Models for

Indoor MANET Evaluation are not Robust” 2004. IEEE SECON 2004. 2004 First

Annual IEEE Communications Society Conference on wireless networks

[31] X. Hong, M. Gerla, G. Pei, C.C. Chiang, “A group mobility model for ad hoc

wireless networks”. Proceedings of ACM Intern. Workshop on Modeling, Analysis,

and Simulation of Wireless and Mobile Systems (MSWiM), August 1999.

[32] J. Tian, J. Hahner, C. Becker, I. Stepanov and K. Rothermel, “Graph-based

Mobility Model for Mobile Ad Hoc Network Simulation” Proceedings of 35th

Annual Simulation Symposium, in cooperation with the IEEE Computer Society

and ACM. San Diego, California. April 2002.

[33] A. Jardosh, E.M. Belding-Royer, K. C. Almeroth, and S. Suri, “Towards

Realistic Mobility Models for Mobile Ad hoc Networks” in Proceedings of Ninth

Annual International Conference on Mobile Computing and Networking (MobiCom

2003), San Diego, CA, pp. 217-229, September 2003.

[34] K.H. Souley, S. Cherkaoui, “Advanced mobility models for ad hoc network

simulations” systems Communications, 2005.Proceedings Volume, Issue, 14-17 Aug.

2005 Page(s): 50 - 55

[35] P. Venkateswaran; R. Ghosh, A. Das, S.K. Sanyal, and R. Nandi, “An Obstacle

Based Realistic Ad-Hoc Mobility Model for Social Networks” journal of networks,

vol. 1, no. 2, june 2006

[36] M. Ben-Akiva, J. L. Bowman, D. Gopinath, “Travel demand model system for

the information era” Transportation 23: 241-266, 1996. 1996 Kluwer Academic

Publishers.

Colophon

128

[37] G. Jovicic, “Activity based travel demand modelling” available online from:

www.dtu.dk/upload/dtf/notater/not0801.pdf (Accessed n.d)

[38] Mathworks website. (2007) available from http://www.mathworks.com/ (Accessed 4 september2007)

[39] OPNET website A guide to understanding OPNET available from

http://www.opnet.com/ (accessed 12 August 2007)

[40] The network simulator 2 available from http://www.isi.edu/nsnam/ns/ (accessed

3 September 2006)

[41] G. F. Riley, “Using the Georgia Tech Network Simulator” Department of

Electrical and Computer Engineering Georgia Institute of Technology

[42] F. Bai and A. Helmy, “A survey of mobility models in wireless adhoc networks”

University of Southern California,U.S.A

[43] E. Pelletta, H. Velayos, “Performance measurements of the saturation throughput

in IEEE 802.11 access points” Modelling and Optimization in Mobile, Ad Hoc, and

Wireless Networks, April 2005. Third International Symposium. Issue, 3-7 Page(s)

129 - 138

[44] V. Sridhara, J. Kim, S. Bohacek, “Models and Methodologies for Simulating

Mobile Ad- Hoc Networks” IEEE conference Volume: 1, on page(s) 34- 41 vol.1 June

2005

[45] M. G. McNally, “An Activity-Based Microsimulation Model for Travel Demand

Forecasting” Conference on Activity-based Approaches Eindhoven University of

Technology, Eindhoven, The Netherlands, May 25-28, 1995

[46] M. McNally, W. Recker, “On the Formation of Household Travel-Activity

Patterns: A Simulation Approach” Final Report, USDOT, Washington,DC (1996)

Colophon

129

[47] J. L. Bowman, “Activity based travel demand models system with daily activity

schedules” master of science thesis 1995

[48] F. Marchal, K. Nagel, “Modelling location choice in activity-based models with

cooperative agents” Conference paper STRC 2004.

[49] J. Scourias, T. Kunz, “An Activity-based Mobility Model and Location

Management Simulation frame work” International Workshop on Modelling Analysis

and Simulation of Wireless and Mobile Symposium Pages: 61 - 68 1999

[50] J. Kim, S. Bohacek, “A Survey-Based Mobility Model of People for Simulation

of Urban Mesh Networks” In Proc. of Mesh Nets 2005,

[51] M. E. Ben-Akiva, S. R. Lerman (1985), “Discrete Choice Analysis: Theory and

Application to Travel Demand”. MIT Press, Cambridge, MA.

[52] M. E. Ben-Akiva, M. J. Bergman, A. J. Daly, R. Ramaswamy, “Modelling Inter

Urban Route Choice Behavior.” Ninth International Symposium on transportation

and Traffic Theory. VNU Science Press. 299-330.

[53] J. Palmius, J. Silvergran, “An Evaluation of Ideal Route Models of Indoor

Navigation” Bachelor Thesis in Psychology presented at MIUN 2003

[54] Chapin, F. Stuart “Activity Systems and Urban Structure: A Working Schema”

Journal of the American Institute of Planners, 34 (January, 1968), pp. 11-18. 16.

[55] R.G. Colledge “Wayfinding Behavior: Cognitive Mapping and Other Spatial

Processes” Published 1999 JHU Press

[56] E. W. Dijkstra (1959) “Note on Two Problems in Connection with Graphs.”

Numerical Mathematics, 1 (1959), 269–271

Colophon

130

[57] R. K. Ahuja, T. L. Magnanti, J. B. Orlin (1993), “Network Flows: Theory,

Algorithms, and Applications.” Englewood Cliffs, NJ: Prentice Hall.

[58] The manuscripts of E.W. Dijkstra available online from http://www.cs.utexas.edu/users/EWD (accessed on 12 august 2007)

[59] Drawing available on line from www.canterbury.ac.uk/ (accessed on 6 June

2007))

[60] M. Iskander, Z. Yun, Z. Zhang, “Outdoor/indoor propagation modelling for

wireless communications systems.” IEEE Antennas and Propagation Society

International Symposium, 2:150–153, July 2001.

[61] Atheros communication “Methodology for testing wireless LAN performance”

2003 Atheros communication inc 2003

[62] J. Tarng, T. Liu, “Effective models in evaluating radio coverage on single floors

of multifloor building”. IEEE Veh. Technol. Conf., 48:782–789, 1999.

[63] W.K. Tam, V.N. Tran, “Multi-ray propagation model for indoor wireless

communications”. Electronics and Communication Engineering Journal, 32:135–137,

January 1996.

[64] A. Neskovic, N. Neskovic, G. Paunovic, “Modern approaches in modelling of

Mobile radio systems propagation environment”. IEEE Communications Surveys,

http://www.comsoc.org/pubs/surveys, 2000.

[65] S.P.T. Kumar, B. Farhang-Boroujeny, S. Uysal, C.S. Ng, “Microwave indoor

radio propagation measurements and modeling at 5 GHz for future wireless LAN

systems”. Microwave Conference, 1999 Asia Pacific, 3:606–609, 30 November-3

December. 1999.

[66] B.S. Dinesh Tummala, “Indoor propagation modeling at 2.4 GHz for IEEE

802.11 networks” MSc thesis December 2005

Colophon

131

[67] A. schmitz, M.Wenig, “The effects of the radio wave propagation model in

mobile Ad Hoc Networks” MSWiM 06 October 2-6 2006, orremolinos,Malaga,Spain

[68] D.B. Faria, “Modelling signal attenuation in IEEE 802.11 wireless LANS”

Volume 1 Communication July 3-5 2006, Banf, AB, Canada.

[69] Dell system support available online: http://support.dell.com/support/downloads

(n.d)

[70] Wi-Fi alliance website available online from http://www.wi-fi.org (accessed 24

July 2007)

[71] I. Pellejero, F. Andreu, A. Barbero, A. Lesta, “Compatibility between IEEE

802.11b and IEEE 802.11g networks: Impact on throughput” R&D and Technology

Department, Strategy and Business Development (n.d)

[72] H.J. Zepernick, T.A. Wysocki, “Multipath channel parameters for the indoor

radio at 2.4 GHz ISM band”. IEEE Veh. Technol. Conf., 1:190–193, May 1999.

[73] A. Gold smith, “wireless communication” published 2005 Cambridge university

press

[74] A.J. Motley, J.M. Keenan, “Personal communication radio coverage in buildings

at 900MHz and 1700MHz” electrical .lett Volume 24, Issue 12, 9 Jun 1988 Page(s)

763 - 764

[75] S.Y. Seidel, T.S. Rappaport, M.J. Feuerstein, K.L Blackard, L.Grindstaff, “ The

impact of surrounding buildings on propagation for wireless in-building personal

communication system design” proceedings of the IEEE conference May 1992.

[76] M. Boulmalf, T. Rabie, K. Shuaib, A. Lakas, H. Elsayed, “Performance

characterization of IEEE 802.11gin an office Environmentl” European and

Mediterranean Conference on Information Systems (EMCIS) July 6-7 2006

Colophon

132

[77] M. Boulmalf, A. Sobh, S. Akhtar, “Physical layer performance of 802.11g

WLANS” Applied telecommunications symposium (n.d)

[78] Free Wirelessmon download available online http://www.passmark.com. (n.d)

[79] J. C. Stein, “Indoor Radio WLAN Performance Part II: Range Performance in a

Dense Office Environment” Intersil Corporation, 2401 Palm Bay, Florida 32905

2003

[80] D. Aguayo, J. Bicket, S. Biswas, G. Judd, R. Morris, “Link-level Measurements

from an 802.11b Mesh Network” sigcomm’04, Aug. 30–Sept. 3, 2004,

[81] D. Gupta, “WLAN signal characteristics in an indoor Environment – an analytic

model and experiments” Master of Science dissertation August 2005

[82] P. Johansson, T. Larsson, N, Hedman, “Scenario-based Performance Analysis of Routing Protocols for Mobile Ad-hoc Networks” International Conference on Mobile Computing and Networking (MobiCom 1999)

[83] B. Liang, Z. J. Haas, “Predictive Distance-Based Mobility Management for PCS

Networks” Proceedings of IEEE Information Communications Conference (INFOCOM 1999), Apr. 1999.

. [84] S. Kurkowski, T. Camp, M. Colagrosso “MANET Simulation Studies: The Incredibles” Mobile Computing and Communications Review, Volume 9, Number 4 [85] O. Jan, A.J. Horowitz, Z.R. Peng, “Using GPS Data to Understand Variations in Path Choice” http://www.uwm.edu/~horowitz/pathchoice.pdf (n.d)

Colophon

133

Colophon

This thesis was written or typed with Microsoft Word 2003 and the running copy was

printed on a Hewlett-Packard LaserJet 6L. Body text is 12 point Times New Roman

spaced at one-and-a-half lines within lines, and with 3 points of leading between

paragraphs. Equations follow the same line spacing as paragraph. The number of

variables presented in the equation is defined after an equation in regular body text.

Only equations referenced in the text are numbered. Equation numbers have the form

of a chapter number, a full stop and an increasing equation number. Equation numbers

are set off in parentheses flush right from the equation they reference. Abstracts at the

beginning of page are written with the 11 point bold Times New Roman.

Chapter, appendix and other major titles are right-justified 14 point Times New

Roman bold followed by 12 points of leading. Chapter numbers and appendix letters

are left-justified 14 point Times New Roman. Section headings are 12 point Times

New Roman bold, and section subheadings (e.g., 2.1.3) are also 12 point Times New

Roman. Both section headings and subheadings are preceded by 3 points line spacing.

A four point system of numbering is observed and there after the bulleted type of

numbering is observed. Page headers and footers are 12 point Times New Roman.,

justified at the bottom and the top of the page. Headers are separated by a 1-point line,

while footers are also separated by a 1-point line. Chapter, appendix and other major

entries in the table of contents are 12 point Times New Roman.

Section heading, section subheading, and table and figure entries are 12 point Times

New Roman. Text of the references is set in 12 point Times New Roman, with

numbers justified right at 12 point lead. Numbers representing the referenced book,

paper or journal is enclosed in square brackets.

Appendix

134

APPENDIX 1

Questionnaire 1: choice of a path within an indoor

environment 1. Are you a student at the University of Johannesburg? yes ….no (tick ) 2. When moving around campus what type of paths/route would you prefer

1. A stairs 2. B ramp 3. C lifts

3. Why would you prefer such kind of the route?

1. A short 2. B quicker 3. C Not tiring

4. If you were given routes with following obstacles in it which one would prefer assuming the length of all the routes are the same?

1. lifts 2. stairs 3. Ramp

5. How often do you use the following routes with the named obstacles in it? List them in the order with which you would use them. (a) Stairs

• Very often • Often • Not very often

(b) Lifts


(c) Ramp


Thank you for cooperation and participation in this survey.

Appendix

135

APPENDIX 2

RESULTS TO THE SURVEY QUESTIONAIRE

Question 2: preference of the route: number of people Stairs 23 Lifts 98

Ramp 34

RANDOM RESULTS OF PREFERENCE OF ROUTES

A

M

O

U

N

T

STAIRS LIFTS RAMP

VERY

OFTEN

23 123 45

OFTEN 33 36 54

NOT

VERY

OFTEN

12 12 45

Documents

MANET Notes