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A COMPARATIVE ANALYSIS OF ROUTING

PROTOCOLS IN VANET ENVIROMNENT USING

REALISTIC VEHICULAR TRACES

A thesis submitted in partial fulfillment

of the requirements for the degree of

Master of Science (Computer and Information Science)

by

Junaid Mehboob Shaikh

Supervisor

Dr. Ivan Lee

School of Computer and Information Science

University of South Australia

JUNE 2010

ii

DECLARATION

I declare the following to be my own work, unless otherwise referenced, as

defined by the University‟s policy on plagiarism.

…………………………………..

Junaid Mehboob Shaikh

June 2010

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ABSTRACT

This research project is exploring the special application of infrastructure-less

wireless system called „Vehicular ad-hoc networks (VANET)‟ which is derived

from its parent network of „Mobile ad hoc networks (MANET).‟ The key focus of

this work is considering the investigative and analysis study of particular routing

protocols in VANET environment. The accumulated issues of these routing

protocols at network layer like: network formation, traffic movements, and route

hopping are examined thoroughly by involving the realistic traces of VANET

mobility patterns using network simulator ns-2.

The overall purpose of this study is also associated with the understanding of

available routing protocols along their effectiveness and underlying limitations

within certain density levels of city and highway scenarios. The careful

consideration of these ad hoc routing protocols: AODV, AOMDV, DSR, and

DSDV are separately incorporated by simulation models of low, medium, and

high concentration phases along with the precise parametric values of defined

scenes. The evaluative metrics for the examination of these protocols are

measured by packet delivery ratio (PDR) and average end-to-end delay

respectively.

The generated and analyzed outputs as a result of the specified inputs by mean

of extensive and rigorous simulations are reasonably rational for some and

irrational for others. AOMDV and AODV are on the rational side and originated

as the proper selection of ad hoc routing protocols for the given cases of city

and highway models of VANET with varying traffic concentration.

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ACKNOWLEDGEMENTS

First of all, I praise Allah Almighty for His favor to me in completing this study

work. Secondly, I would like to express thanks to my supervisor Dr. Ivan Lee for

his munificent support and encouragement throughout the research exercise. In

addition, I‟m also grateful to LST research group at ETH Zurich for providing the

generated vehicular traces for further incorporation by the investigative

community. Finally, my tremendous appreciations and gratitude are for my

parents and brothers whose blessings and moral support always remained with

me in every regard.

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ABBREVIATIONS & SYMBOLS

Symbol/Abbreviation Term

AODV -------------------------------------------------Ad Hoc on Demand Distance Vector

AOMDV ---------------------------------Ad Hoc on Demand Multipath Distance Vector

CBR ---------------------------------------------------------------------------Constant Bit Rate

DSDV--------------------------------------------Destination-Sequenced Distance-Vector

DSR ------------------------------------------------------------------Dynamic Source Routing

E2E ------------------------------------------------------------------------------------End-to-End

MANET ---------------------------------------------------------------Mobile Ad Hoc Network

NS-2 -----------------------------------------------------------------------Network Simulator 2

PDR ----------------------------------------------------------------------Packet Delivery Ratio

PERL---------------------------------------Practical Extraction and Reporting Language

QOS ---------------------------------------------------------------------------Quality of Service

TCL -----------------------------------------------------------------Tool Command Language

TCP -----------------------------------------------------------Transmission Control Protocol

UDP -------------------------------------------------------------------User Datagram Protocol

VANET ------------------------------------------------------------Vehicular Ad Hoc Network

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TABLE OF CONTENTS

----------------------------------------------------- ----------------------------------------------- Page

DECLARATION---- --------------------------------------------------------------------------------- ii

ABSTRACT---------------------- ------------------------------------------------------------------- iii

ACKNOWLEDGEMENTS ------------------------------------------------------------------ -----iv

ABBREVIATIONS & SYMBOLS ---------------------------------------------------------------- v

TABLE OF CONTENTS -------------------------------------------------------------------------- vi

LIST OF TABLES --------------------------- ----------------------------------------------------- viii

LIST OF FIGURES -------------------------------------------------------------------------------- ix

CHAPTER 1 – INTRODUCTION ------------------------------------------------------------- 01

1.1 General------------------ ----------------------------------------------------------------------- 01

1.2 Problem Definition --------------------------------------------------------------------------- 03

1.3 Study Objectives ----------------------------------------------------------------------------- 04

1.4 Scope of Work --- -------------------------------------------------------------------------05

1.5 Organization of Thesis --------------------------------------------------------------------- 05

CHAPTER 2 – LITERATURE REVIEW ----------------------------------------------------- 07

2.1 Vehicular Ad hoc Networks --------------------------------------------------------------- 08

2.1.1 Mobile Models and Patterns in VANET ------------------------------------------ 08

2.1.2 Reliability and Congestion Issues in VANET ----------------------------------- 11

2.2 Network Layer and Routing Protocols-------------------------------------------------- 13

2.2.1 Routing Protocols in VANET -------------------------------------------------------- 13

2.2.2 Selected Routing Protocols --------------------------------------------------------- 16

2.3 The Simulator---- ----------------------------------------------------------------------------- 17

2.3.1 Network Simulator 2 (NS-2) --------------------------------------------------------- 18

2.3.2 Simulaiton Components -------------------------------------------------------------- 18

2.3.3 Simulaiton Operations ---------------------------------------------------------------- 19

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CHAPTER 3 – RESEARCH METHODOLOGY ------------------------------------------- 20

3.1 Simulations--------- --------------------------------------------------------------------------- 20

3.1.1 Tool Command Language ----------------------------------------------------------- 22

3.1.2 Network Animator and Trace Files ------------------------------------------------ 22

3.1.3 Text Analyzer --------------------------------------------------------------------------- 24

3.2 Observations---- ------------------------------------------------------------------------------ 24

3.2.1 Routing Metrics ----- ------------------------------------------------------------------- 24

CHAPTER 4 – SIMULATIONS AND RESULTS ------------------------------------------ 26

4.1 City Scene--------- ---------------------------------------------------------------------------- 26

4.1.1 Low Density Model -------------------------------------------------------------------- 29

4.1.2 Medium Density Model --------------------------------------------------------------- 32

4.1.3 High Density Model -------------------------------------------------------------------- 34

4.2 Highway Scene--------- ---------------------------------------------------------------------- 36

4.2.1 Low Density Model -------------------------------------------------------------------- 39

4.2.2 Medium Density Model --------------------------------------------------------------- 41

4.2.3 High Density Model -------------------------------------------------------------------- 43

CHAPTER 5 – ANALYSIS AND DISCUSSION ------------------------------------------- 46

5.1 City Results------------------------------------------------------------------------------------ 47

5.2 Highway Results ----------------------------------------------------------------------------- 49

5.3 Overall Results ------------------------------------------------------------------------------- 50

CHAPTER 6 – CONCLUSIONS AND FUTURE WORK-------------------------------- 54

REFERENCES----------- ------------------------------------------------------------------------- 57

APPENDIX A – TCL & AWK SCRIPTS WITH TRAFFIC PATTERN FILE -------- 61

APPENDIX B – ANALYZED SIMULATION RESULTS --------------------------------- 70

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LIST OF TABLES

Figure-------------------------------------------------------------------------------------------- Page

4.1 Common variables in city model ----------------------------------------------------- 28

4.2 City (low density) variables ------------------------------------------------------------ 29

4.3 Analyzed data of city low density ----------------------------------------------------- 30

4.4 City (medium density) variables ------------------------------------------------------ 32

4.5 Analyzed data of city medium density ---------------------------------------------- 32

4.6 City (high density) variables ----------------------------------------------------------- 34

4.7 Analyzed data of city high density --------------------------------------------------- 35 4.8 Common variables in highway model ----------------------------------------------- 38

4.9 Highway (low density) varialbles ----------------------------------------------------- 39

4.10 Analyzed data of highway low density --------------------------------------------- 39

4.11 Highway (medium density) variables ----------------------------------------------- 41

4.12 Analyzed data of highway medium density --------------------------------------- 42

4.13 Highway (high density) variables ----------------------------------------------------- 43

4.14 Analyzed data of highway high density --------------------------------------------- 44 5.1 Overall evaluation matrix --------------------------------------------------------------- 51

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LIST OF FIGURES

Figure-------------------------------------------------------------------------------------------- Page

1.1 Wireless infrastructure network ------------------------------------------------------- 01 1.2 Wireless ad hoc network --------------------------------------------------------------- 02

3.1 Methodology flow ------------------------------------------------------------------------- 21

3.2 NAM file output ---------------------------------------------------------------------------- 23 3.3 Trace file output --------------------------------------------------------------------------- 23 4.1 City movement traces on Google map---------------------------------------------- 27 4.2 City movement traces on network animator --------------------------------------- 27 4.3 PDR at city low density ----------------------------------------------------------------- 31 4,4 Average E2E at city low density ------------------------------------------------------ 31

4.5 PDR at city medium density ----------------------------------------------------------- 33 4.6 Average E2E at city medium density ------------------------------------------------ 34 4.7 PDR at city high density ----------------------------------------------------------------- 35 4.8 Average E2E at city high density ------------------------------------------------------ 36 4.9 Highway movement traces on Google map ---------------------------------------- 37 4.10 Highway movement traces on network animator --------------------------------- 37 4.11 PDR at highway low density ------------------------------------------------------------ 40 4.12 Average E2E at highway low density ------------------------------------------------ 41

4.13 PDR at highway medium density ----------------------------------------------------- 42 4.14 Average E2E at highway medium density ------------------------------------------ 43 4.15 PDR at highway high density ---------------------------------------------------------- 44 4.16 Average E2E at highway high density ---------------------------------------------- 45

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5.1 Generic review ----------------------------------------------------------------------------- 46

5.2 PDR of routing protocols in city ------------------------------------------------------- 47

5.3 Average end-to-end delay of routing protocols in city -------------------------- 48

5.4 PDR of routing protocols on highway ----------------------------------------------- 49

5.5 Average end-to-end delay of routing protocols on highway ------------------- 50 5.6 Graphical representation of overall evaluation matrix --------------------------- 53

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CHAPTER 1 INTRODUCTION

1.1 General

The advent of wireless networking is responsible for the entire drift of the

communication paradigm we observe today. This is because of its easy

deployment and setup phases. Devices are simply required to be powered by

some source of energy and having their availability within the specified ranges

to form a network and start communicating with each others with no wires or

ducts. Furthermore, these wireless networks are classified depending on their

deployment modes of fixed and flexible mobile scenarios, and therefore termed

as wireless infrastructure and wireless ad-hoc networks respectively.

The fixed mode in wireless requires a firm infrastructure to be arranged before

devices can start communication with each other. These devices (commonly

known as nodes) are then come in contact with those centrally installed bridges

or routers to forward and receive the data. This depending feature of nodes on

fixed hardware devices is an example of an infrastructure mode of wireless

network (Figure 1.1).

Figure 1.1: Wireless infrastructure network

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On the other hand, there are circumstances where nodes do not require any

preinstalled setup due to various reasons and can directly establish their

communication by using the services of other co-joined nodes as a router for

forwarding and receiving of data in between nodes. This is an example of

wireless ad-hoc network (Figure 1.2). Moreover, ad-hoc networks with the

manipulation of WLAN (802.11) standard and its built-in support in various

devices of daily usage has introduced the notion of inexpensive and cheap

communication models. It is the most obvious reason for wireless popularity

nowadays.

Figure 1.2: Wireless ad hoc network

The communication area which is related with the scope of this thesis is an

emerging and exciting application of an ad-hoc network where vehicles are

serving as nodes. This area has certain promised aspects and activities to be

offered, which are broadly related with the safety, convenience, entertainment,

and various other topics of interest. It is an ad hoc network of vehicles, known

as ‘Vehicular Ad Hoc Network (VANET).’

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1.2 Problem Definition

Movement of vehicles on roads is constrained by different conditions, suggested

in James (2009). These conditions are related with speed zones, traffic

congestions, weather conditions, road works, etc. These kinds of limitations

allow vehicles to form a group of clusters among them to manage traffic flow in

all directions fairly and smoothly. Another condition discussed in Victor (2009)

regarding the varying velocities of vehicles and abrupt move of paths without

any notification. With these conditions and limitations it is sometimes not

possible for vehicles to establish direct link between one another with the help

of single hop, which is related with the specified area of coverage. Hence,

internetworking among different clusters needs to be considered. To manage

the communication link between out of ranged vehicles (nodes), different routing

protocols are involved. Through relevant studies in Victor (2009), Tarik (2006),

Chung (2006), and many other associated works, it is found that the available

routing protocols for ad-hoc networks already proposed and implemented are

not majorly compatible within VANET scenario due to above conditions. Hence,

certain adaption and improvements being made with respect to available

conditions of the said network and are still in focus of many researchers for

revision.

This research work is therefore (an effort to) highlighting the importance of

routing protocols in VANET environments under different conditions (especially

through pragmatic scenarios) and to observe and analyze their effects

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accordingly by mean of rigorous simulation test cases and comparative

analyses.

1.3 Study Objectives

The objectives of this research are devoted with the analyses of routing

protocols in vehicular networking environment. This is done by considering the

performance metrics of routes within various mobility models and densities of

vehicles; also involving their communication paradigm and hoping techniques.

The mobility models are actually the movement patterns for vehicular network

which are replicating the physical roads for simulation prototypes. Many of them

are already available and could be converged according to their particular

VANET scenarios. So this work will be emphasizing the study of most viable

routing protocols which are self converged and flexible enough within such

network situations. It also analyzes and examines the selected proposed

protocols with their mentioned future deployments on top of existing routing

protocols to observe their results. The major prominence for these analyses

would be varying according to the conditions of routing metrics like: packet

delivery ratios, average end-to-end delay, number of hop counts, and likewise.

Since many of the VANET research works are still simulation based but there

are some quite exciting and upcoming projects expected to be available before

long in the real world situations. Therefore, the study objectives highlighted here

are also depending upon various rigorous simulation scenarios. In outcome, the

analyzed results will show and differentiate the appropriate selection(s) of

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network layer protocols i.e. routing protocols within the given circumstances of

feasible path selection in the vehicular traffic scenarios.

1.4 Scope of Work

The scope of this work is associated with the coverage of complete

communication and routing paradigm in vehicular ad hoc networks.

Initially, the simulation schemes would be considering the generic mobility

patterns of the road networks. These patterns will then be extended for the

specified cases of traffic scenarios, like city movements and highway flows

depending on the saturation capacity of the provided scenes. These aspects will

then be broadly dealing with message broadcasting, multicasting, and

unicasting requirements, depending on the nature of communication.

Additionally, the routing investigations at variable densities of these mobility

models would be simulated and formulated at their certain generated test-beds

setup in the tool called Network Simulator (NS2).

1.5 Organization of Thesis

This report is written in chapter wise format with the total of six chapters. The

first chapter gives the general idea regarding the motif and theme of the work. It

also discusses the brief and concise overview of problems involved, study

objectives and scope of work. The second chapter is of literature review which

will be covering all the related topics in more detail for further understanding.

The reason of this chapter is to make the grounding of work in depth through all

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the searched and reviewed stuff so that everything should be clear beforehand.

The concerned topics for this chapter will be mainly related with finding of issues

in VANETs and their proposed, deployed, and corresponding routing schemes

along with the simulation tool. The third chapter is of research methodology

where the useful working way will be conferred in the step wise layout. This

chapter discusses the procedures and approaches to be adopted for achieving

the objectives. The fourth chapter is the practical representation of its previous

chapter in which the Simulations‟ outcome and their Results are highlighted in

both tabulated and graphical formats. This will then be followed by the

comprehensive analysis and discussion section of chapter five. By academic

tradition, the final chapter will be of conclusions and future work.

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CHAPTER 2 LITERATURE REVIEW

This chapter covers the initial literature survey done for VANETs and their

associated schemes including network layer issues in particular. It further

followed by narrowing down to the specific literature reviews toward the selected

topic and its achieving objectives.

As mentioned earlier, due to the inherited form of MANET, most of the

operational phases of VANET are derived as well as adapted from the previous

type of network in one way or the other. Not completely relying, there are some

characteristics and distinctiveness differences from its classical beginnings.

According to the explicit discussion in Abedi (2008), the infeasible routing

criteria of well-known MANET protocols not fully compatible within VANET‟s

scenario, could be due to its mobility differences. In fact, adaptability approach

remains there for some achievable results. Before discussing the routing

contemplation (the core scheme of this proposition), there are many other

interrelated areas (actually subareas) with their issues and proposed solutions

are explored during the phase of literature surveying. The major differentiation

of these sub areas are identified according to the study of mobility patterns and

their associated models, and reliability concerns with traffic flow and congestion

controls. This will then leads toward the actual progression of routing scope

from network layer.

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2.1 Vehicular Ad hoc Networks

This section covers the related reviews on fundamental concepts and issues of

VANET mobility and reliability. It will be covering the topological formations

including various mobility models, traces, and patterns along with their available

and proposed tools. Further, some reliability schemes and congestion concerns

will be discussed.

2.1.1 Mobility Models and Patterns in VANET

The study of mobility patterns in different scenarios are depending on the

movement of node with respect to their speed and velocity. The linked

parameters remained part of study in separation for any working ambiguity.

Most of these working patterns and models are highly regarded in the following

literatures.

According to Chia-Chen (2008), the architectural model for carrying reliable

vehicle-to-vehicle services in an unreliable VANET environment has a range of

factors. These variable factors of VANET are due to its multi-hop delivery

mechanism with different network involvements. Therefore, emphasizing on

single network, instead, concept of heterogeneous vehicular network (HVN) is

proposed. The clusters of VANET with WMAN (wireless metropolitan area

network) are incorporated with each other for observing different mobility

patterns. The induction of Mobility Pattern Aware Routing Protocol (MPARP)

and HVN gave progressive outcomes. The simulation results also show the

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credibility of enhancement in terms of packet delivery ratio (PDR), number of

links break, and instant throughput and delay performances of the

communication mediums. Similarly in Choffnes (2005), another mobility model

is proposed for dynamic mobile nodes movement with concentration on

metropolitan areas. It provided some achievable trials for C3 (car-to-car

cooperation) project. To include the level of realistic features, actual city maps

were used for real-time consequences in the metropolitan areas. The proposed

model named STRAW (STreet RAndom Waypoint) also evaluated the routing

performance in the ad-hoc networks. In comparison of two main routing

protocols DSR and AODV with respect to packet delivery ratio gives the clear

picture of vehicular diversification of on the road networks. There is also a

comparative study available in Djenouri (2008) discussing different VANET

mobility models like: Freeway, Manhattan, City Section Model (CSM), Stop Sign

Model (SSM), and STRAW for some positive mobility considerations with

different tools.

A simulation tool is discussed in Karnadi (2007) for generating rapid mobility

pattern and experimenting real world mobility scenarios of VANET. The tool is

named as MOVE (MObility model generator for VEhicular networks) which

provides rapid realistic simulation environment for VANET cases. The

development of this tool (in Java) was made on top of open source micro traffic

simulator SUMO which includes the functionality of map and vehicle movements

editing. The GUI based approach fascinates the simulation environment with

encouraging thing for researcher is the public availability of MOVE with certain

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open issues need to be considered like node‟s random configuration and tight

coupling via proper interfaces with other advanced tools like ns-2 and Qualnet. It

provides the options to new researchers to study serious issues of location,

speed, and directions with dynamically adjustment of real time traffic.

Another scope of realistic mobility model for VANET is described in Saha (2004)

with multiple similarities of previous works on mobility issues. It is also focusing

on real map usage for realist results commented. The proposed model is

applicable in ns-2 and therefore contributes the open research communities in

specified domain. The presented model is identifying and evaluating the work

done by Karnadi (2007). This similar nature of work is already related with some

further clarification and having suggestions to consider the mobility scenarios of

residential and business area. It is also partially done by Rybicki (2007) in their

urban pedestrian flows models.

In Wenjing (2007), much of the focus was made on safety related problems

faced by vehicular ad hoc network (VANETs) with certain limitations observed

within and general traffic monitoring. In addition, another vehicular mobility

model is proposed that reflects real world vehicle movement on road and

performance of present network. The networking performance of VANET is

directly affected by traffic rules (physical) road layouts and traffic regulations.

Keeping this fact in mind, process of VANETs requires careful investigations.

The observation leads to the drawbacks of the MANET protocols which modified

with certain changes and an investigation made to large scale VANETs phases

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of routing protocols with the incorporations of map information and overlay road

graphs.

2.1.2 Reliability and Congestion Issues in VANET

The mode of communication paradigm related with reliability, like congestion

formation and their controlling mechanisms, are important considerations for

consistent messaging. They are greatly discussed in the following literatures.

In Chen (2007), the performance of Transmission Control Protocol (TCP) in

VANET environment with respect to transmission power of the network is

elaborated. It is highlighting, how increase in transmission power can boost the

poor performance of TCP in ad hoc networks to the level of improvement. In

VANET there are cases mostly within non-safety applications, where on-the-fly

internet connective, file sharing, message forwarding, and other likewise

communication facilities are desirable. In all of these enforcements, a reliable

connectivity via TCP is important. For this purpose, the authors have used

SWAN simulator to gather network statistics and then analyze the susceptible of

power loss. Their scope of maximum power transmission is limited to the vehicle

and the road side access point. According to similar studies, in VANETs, power

issues are more manageable via internal vehicle sources (Maeda, 2005).

According to Fahmy (2008), various interesting terms relating with specific

application oriented approach of vehicular networks are discussed. They have

proposed a mechanism for finding the relative traffic congestion and its specified

volume within the given scenario. The mode of beaconing is used to identify

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whether the congestion is occurring or not, depending on the reply of receiving

node within the certain interval of time. The proposed idea is fairly distributed in

three algorithm calls:

1. Discovering congestion, constructing tree, and counting nodes

2. Construct tree and counting when node receives beacon

3. Node loses its neighbor.

Depending on various simulation parameters, the justification of the scope with

respect to various involved complexities like message and time is readily shown

and described accordingly.

In Shie-Yuan (2008) and Wischhof (2005), emerging standard of IEEE 802.11

family for vehcles i.e. 802.11(p)/1609 along with the throttling effects are

discussed. It is fairly outlining the amendments of IEEE 802.11-2007 standard

and defines a new WAVE operational mode for vehicular environments.

The throttling issue in networks like congestion and its control always remains a

researchable topic under different circumstances. Here it is specifically focusing

vehicular networks. According to the authors in Wischhof (2005), congestion

control study within VANET is not fully identified. This is due to the high mobility

issues of node where connection loss is on high than proper connection

establishment. The proposed concept to deal with such scenario is termed as

utility-based congestion control and packet forwarding in VANETs. It is a utility

based control algorithm that encodes the quantitative information of the data

packets in a local environment. The after approach is of calling a decentralized

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algorithm which is responsible for calculating the average utility value of the

transmitted data. Its other propositions, decentralized Utility-Based Packet

Forwarding and Congestion Control (UBPFCC) are fairly research oriented.

2.2 Network Layer and Routing Protocols

The network layer plays a vital role in any communication scheme for organizing

and delivering certain packets from the source to the destination and vice versa,

if required. This is happened by mean of specified routing protocols which help

in calculating and identifying reasonable route(s) within the required set-ups of

networking and internetworking approaches.

This section will be discussing the reviews of some network layer protocols

suitable for routing in the environment of ad hoc networks specifically of

vehicles.

2.2.1 Routing Protocols in VANET

Routing is the crux of this formal thesis project. It manifests the working grounds

of complete forwarding and sharing approaches and is reviewed along with their

issues in formation, deployment, and implementations from major studies of the

following literatures.

According to Victor (2009), movements of vehicles are described in the form of

distinct clusters, which is also an observable scene. Therefore, communication

within a single group of vehicles depends on the range of wireless coverage

being used; commonly deal with physical aspects of the networks. When it

comes to the internetworking, more generic - inter-cluster communication,

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where source vehicle from a group is unreachable in contact with destination

vehicle from other (could be same) group creates the communication

discontinuity. To overcome the problem like these terminations, various routing

protocols are employed for continuous communication manageability purposes.

Hence, the concept of hopping where destinations achieved by mean of relaying

on other intermediate node is used specifically.

The various experimentations done in Abedi (2008) have identified that the

popular Mobile ad-hoc network (MANET) routing protocol AODV is not much

suitable for Vehicular ad-hoc network (VANET) environment. Therefore they

have proposed its modified version considering its core parameter- direction - as

a focal point for route discovery and named it as DAODV. This proposed

algorithm via theoretical simulation results has shown the reduction of overall

overhead of route discovery than AODV in comparison of highly performance

measures. The results like recovery from broken links and route expiration time

are improved with respect to number of hops, nodes, and speed with the

projected version. The logical aspect of the intended protocol is only positive

while focusing on a particular mobility model for example, Manhattan, in this

case. Whereas, there are diversified models available depending on their

mobility constrains which need to be plotted for generic acceptance of DAODV.

An application oriented study in Baldessari (2007) done by NEC Europe

researchers for analyzing the networks mobility and their deployable ability

approaches in VANET environment. The convergence of IP mobility and

Network mobility within VANET is clearly highlighted with the term „VANEMO.‟ It

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differentiates the related applications of VANETs by mean of „safety‟ and „non-

safety‟ grouping with latter‟s classifications into economical and functional

performances, and deploy ability requirements. The two main approaches of

connectivity i.e. MANET-centric and NEMO-centric are discussed. With previous

approach (which agrees the scope), mobile nodes communicate with each other

by utilizing MANET layer for ad hoc routing protocols towards NEMO for high

end (infrastructure) connectivity. The latter approach is directly connecting the

physical layer of nodes with the NEMO layer. This difference is required to

maintain the connectivity tasks of safety (reliable) and non-safety (general ad

hoc) applications within different mobility scenarios. Similarly in Biswa (2006),

the application aspect of public safety issues in transportation system and how

to mitigate the cases of accident (through chain collision) cases by using vehicle

to vehicle communication paradigm are discussed. The major concepts

discussed are of Cooperative Collision Avoidance (CCA) with the help of

Wireless Collision Warning Messages (W-CWM) concept. After analyzing

various levels of connections establishment through MAC level and respective

scheduling protocols, a conclusive remark for broadcast oriented approach with

packet forwarding on geographical and temporal context being preferred. In

regard of safety application known doubts, such preferable measures are

required for possible connectivity but these may be exposed to security

concerns. The given pseudo code with plotted graphs show impressive outputs,

whether applicable for an unpredicted case is not identified. The performance

analysis of routing protocols in Garcia (2007) analyzes two location-based

routing protocols. The scope of this work is limited to SIFT (SImple Forwarding

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over Trajectory), and DREAM (Distance Routing Effect Algorithm for

Mobility) routing mechanisms. To make a realistic approach like most of the

VANET researchers prefer, a real mobility model already discussed in Djenouri

(2008) is used for carrying a comparative simulation study. According to similar

author‟s view point, routing schemes for actual ad hoc networks (MANET) are

not suitable for vehicular ad hoc networks (VANET). Hence the location-based

schemes like SIFT and DREAM are more appropriate for such scenarios for

enhanced efficiency. Further within these schemes certain issues like low-

connectivity zone, spatial-awareness, and other are discussed and studied

through simulations. At the end of conclusive remark, SIFT wins the game for

efficiently resolving these issues. In addition, the considerations of routing in

large scale are further studied in (Wenjing, 2007).

With the help of all these major interrelated sections of mobility and routing

explored in this literature review, a formal methodology be derived to consider

the overall aspects of mobility and reliability with different VANET routing

schemes in a pragmatic simulation environment with the help of NS2.

2.2.2 Selected Routing Protocols

Depending on these study reviews (above) and associated literatures, there are

various routing protocols which are proposed with their suitability in MANET and

VANET perspectives. After careful consideration, following are some chosen

ones for the exploration of city and highway‟s density levels of this study:

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DSDV: Destination-Sequence Distance-Vector (Perkins, 2007), is a table

driven routing protocol where every node maintains a table of information

(which updates periodically or when change occurred in the network) of

presence of every other node within the network. Any change in network

is broadcasted to every node of the network.

AODV: Ad hoc On-demand Distance Vector (Perkins, 2007), an

improved version of DSDV, as its name suggest, establishes the route

only when demanded or required for the transmission of data. By this

mean, it only updates the relevant neighboring node(s) instead of

broadcasting every node of the network i.e. it does not make source

routing to the entire node for the entire network.

AOMDV: Ad hoc On-demand Multipath Distance Vector, an extension of

AODV with an additional feature of multipath route discovery which

prevents this on-demand routing protocol to form any loop or alternative

paths (Padmani, 2008).

DSR: Dynamic Source Routing (Perkins, 2007), an on demand routing

protocol like AOD(M)V. It maintains the source routing, in which, every

neighbor maintains the entire network route from source to the

destination.

2.3 The Simulator

This section will be reviewing the important aspect of this research including the

parts concerned with the simulation tool.

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2.3.1 Network Simulator – 2 (NS-2)

The network simulator (NS2), as its name suggests, is a simulation tool for

replicating real life networking environment and their working and adjoining

standards respectively. It works with the combinations of different development

tools and languages because of its environment of open source

possessiveness. Mainly by default, the backend object oriented and scripting

languages used by this simulator are the „C++‟ and „TCL.‟ The previous is used

for the development and implementation of low level operations and algorithms,

whereas, the latter is used for the actual scripting codes for the simulations

output scenarios. There are some associated tools with NS, like Network

Animator (NAM) and Ad-Hockey; these associates are majorly used for

visualization purposes.

2.3.2 Simulation Components

There are some very basic and generic components used by NS to establish

various special and diverse simulation scenarios. The most common (but not

limited) are the Nodes, Agents, and Links.

The nodes are the participating objects within the simulation environment.

Vehicles are the appropriate example in case of simulation scene for VANET.

These nodes can further be classified with the attributes of source and sink

depending on their traffic generator and/or receptor functions respectively.

Agents on the other hand are the dependent elements. They rely on nodes for

specifying the traffic type between their communication processes. And finally,

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links are used to specify the medium of connection i.e. wired or wireless

between the participating nodes.

2.3.3 Simulation Operations

The simulation operations performed by the NS-2 after employing the

components (mentioned above) can be broadly categorized as follow:

Creating the event scheduler: in this operation different event related

activities being done. For example: create scheduler, schedule event(s)

and start scheduler.

Creating network: in this operation the required nodes with their linkage

and queuing operations are created.

Creating connection: in this operation the actual connection scheme e.g.

TCP or UDP is given (this work deals with TCP connection).

Creating traffic: in this operation traffic flow is being mentioned i.e. how

much traffic is needed for the simulated network. The common traffic

creation criterion is Constant Bit Rate (CBR) where constantly bits of

traffic are supplied to the network.

Tracing: this is the crucial operation which reads the NS-2 simulation

generated output file and shows different output results in the form of text

or graph.

20

CHAPTER 3 RESEARCH METHODOLOGY

The scope of Vehicular Ad Hoc Network (VANET) and its related research

studies are still in progression phases to a major extend. The limited practical

deployable options under different projects are purely simulation based before

their actual implementations in the real scenarios. The list of all major projects

along with some related developments could be found in (VANET Projects). The

collaboration of imminent research objectives and its related scope in this study

are also collapsed into same influence of simulation environment for generating

some authenticated outcomes. For this purpose, the adopted methodology for

the results of this research work (specifically comparative routing analyses) is

based on simulations near to the real time packages before any actual

implementation.

3.1 Simulations

The most reliable and authenticated tools used and preferred by most of the

researchers for these kinds of simulations are: NS-2 (Kevin, 2009) and/or

OPNET for real looking simulations according to their parameter precisions. For

vehicular movements on roads, as discussed in previous chapter, another

particular tool and its extendable variant „SUMO,‟ the helping tool for traffic

mobility patterns generation for network simulator is used.

The generic and experimental simulation runs of this methodology adoption are

illustrated in Figure 3.1 below:

21

Figure 3.1: Methodology flow

TCL File with support of Mobility Patterns, Comm. Paradigms, Reliability constraints,

and Related Parameters

NS-2

Simulator

Mobility and Traffic Generator

Compile

Multiple Trace & NAM Files

(According to various parameters)

Trace File Analysis

(Preferably AWK Script)

City

Scenario

Highway

Scenario

AODV

AOMDV

DSR

DSDV

22

According to the objectives of this research, the major emphasis of this study

depends on the analysis of VANET routing protocols. These protocols are

needed to be defined individually within their specified TCL file along with their

supporting components. The movement and traffic files are generated and

compiled separately before associating with NS-2 simulation, which would then

be in the receiving format for NS2 to amalgamate with the body of actual TCL.

3.1.1 Tool Command Language

The Tool Command Language (TCL) file is the scripting representation for

coding and developing the desired networking scenarios (wired/wireless) – in

this particular case, ad hoc vehicular network flow on the road is scripted for

generating and associating relevant file. These scenarios are based on various

parameters and their settings of generated traffics along with their mobility,

reliability, and likewise constraints as discussed in previous chapter.

Initializing the routing protocol within a TCL file as inputs in association of

particular traffic and movement files, the NS-2 simulates accordingly. Ultimately,

as a result, it generates two files i.e. Network Animator File (*.nam) and a Trace

files (*.tr) as the outputs.

3.1.2 Network Animator and Trace Files

The NAM file consists of all the operations to be performed at the time of

simulation with all the positioning and graphical information and their defined

parameters. This NAM file then can be called or executed by its built-in “nam”

23

command from the operation component of NS itself. The output example of this

file is shown in Figure 3.2.

Figure 3.2: NAM file output

On the other hand, the trace file contains all of the data e.g. how many packets

are sent, received, dropped and with what sequence number, type, size, etc.

The trace file is simply available in a text format and could be called as a log file

of the simulation with all the information logged in columns format (Figure 3.3).

Figure 3.3: Trace file output

M 0.01000 7 (3076.65, 4672.97, 0.00), (3198.59, 4629.61), 13.65

s 2.556838879 _1_ AGT --- 0 cbr 512 [0 0 0 0] ------- [1:0 2:0 32 0] [0] 0 0

r 2.556838879 _1_ RTR --- 0 cbr 512 [0 0 0 0] ------- [1:0 2:0 32 0] [0] 0 0

s 2.560742394 _1_ RTR --- 1 DSR 32 [0 0 0 0] ------- [1:255 2:255 32 0] 1 [1

1] [0 1 0 0->0] [0 0 0 0->0]

r 2.561962728 _4_ RTR --- 1 DSR 32 [0 ffffffff 1 800] ------- [1:255 2:255

32 0] 1 [1 1] [0 1 0 0->0] [0 0 0 0->0]

r 2.561963021 _6_ RTR --- 1 DSR 32 [0 ffffffff 1 800] ------- [1:255 2:255

32 0] 1 [1 1] [0 1 0 0->0] [0 0 0 0->0]

s 2.604736825 _1_ RTR --- 2 DSR 32 [0 0 0 0] ------- [1:255 2:255 32 0] 1 [1

2] [0 2 0 0->16] [0 0 0 0->0]

24

3.1.3 Text Analyzer

The next task is to analyze the trace file(s). This could be done by mean of

various analyzing methods and scripting codes, for example: PERL (Practical

Extraction and Reporting Language), AWK (named after their writers, Alfred

Aho, Peter Weinberger, and Brian Kernighan) and some other third parties text

search software. For this study, AWA is used to extract meaningful values from

the generated trace files.

The sample of coding scripts for TCL, Traffic, and AWK are provided in

Appendix A.

3.2 Observations

The main observation factors are related with the calculations of particular

routing metrics. They identify the accumulated results from the output trace files

which are generated by the simulator upon their specified inputs from mobility

and traffic files.

3.2.1 Routing Metrics

There are various routing metrics devised in different literatures to signify the

importance and measuring purposes of numerous routing protocols. In (Realistic

Traces), complete surveys along with the taxonomy of these metrics with their

particular classifications are discussed in detail. The two highly discussed

metrics which are very useful in differentiating the performing trends of routing;

and specially picked by similar assessments of such protocols while analyzing

25

ns-2 traces are used for results generation in this research project. They are

„packet delivery ratio‟ and „average end-to-end delay.‟

Packet Delivery Ratio (PDR): It is the fraction of generated packets by

received packets. That is, the ratios of packets received at the destination to

those of the packets generated by the source. As of relative amount, the usual

calculation of this system of measurement is in percentage (%) form. Higher the

percentage, more privileged is the routing protocol.

Average End-to-End Delay (E2E Delay): It is the calculation of typical time

taken by packet (in average packets) to cover its journey from the source end to

the destination end. In other words, it covers all of the potential delays such as

route discovery, buffering processes, various in-between queuing stays, etc,

during the entire trip of transmission of the packet. The classical unit of this

metric is millisecond (ms). For this metric, lower the time taken, more privileged

the routing protocol is considered.

26

CHAPTER 4 SIMULAITONS AND RESULTS

This chapter comprises of complete simulation criteria for considering the

resolution of specified objectives and their problem reports simultaneously, that

is, the behavior of routing protocols in VANETs by considering the realistic

vehicular traces. The outlining traffic mobility traces associated with simulations

and results in this chapter are the manageable efforts done by the respective

researchers of VANET domain. They are available as an attempt of contribution

and are also made public for open research communities to couple them within

their scope of study. The downloadable contents could be found at Rainer et al.

(2007) along with their relevant descriptive formation approaches and major

focus on real maps in Saha (2004). On the basis of these traces, the simulation

models are developed and used here for VANET scenarios which are classified

into two pragmatic scenes:

1. City Scene

2. Highway Scene

4.1 City Scene

The city model is considering the road patterns of the bona fide city settings of

Switzerland expanse. The specified regions within the movement files and their

masking outlines from the Google Maps to Network Animator are shown in

Figure 4.1 and Figure 4.2 respectively.

27

Figure 4.1: City movement traces on Google map (source: maps.google.com)

Figure 4.2: City movement traces on network animator

28

To study and analyze the comparative study of selected routing behavior in their

respective model, an approach of density formulation among traffic flow is used.

For this reason, city scene is further sub-classified on the basis of their

participating vehicles in a low, medium, and high density phases.

As discussed earlier, to generate simulation instance, there are certain number

of variables required to be defined within the simulation script to take action. For

different densities of city model, the common variables defined are shown in

Table 4.1 below:

Table 4.1: Common variables in city model

Variable Value

Simulation time 300 s

Topology size 4000 m x 7000 m

Routing Protocols AODV, AOMDV, DSR, DSDV

Traffic Type TCP

In this table, the variables along with their appropriate values are highlighting

the simulation‟s parameters for all the density phases of city model. According

to this, the approximated area of simulation (topology size) is defined by 4000m

x 7000m based on the selected city region with a typical simulation running time

of 300 seconds. Further, the routing protocols deployed individually for the

29

analysis of each simulation are: AODV, AOMDV, DSDV, and DSR. Lastly, the

traffic type for nodes‟ communication is commonly defined by TCP agent source

for considering reliable communication instead of UDP.

4.1.1 Low Density Model

Simulation Parameters: The low density model along with previous common

parameters comprises of 12 vehicles as nodes. These nodes are deployed and

arranged according to the provided patterns of mobility traces with a maximum

of 8 intercommunication connections, as shown in Table 4.2.

Table 4.2: City (low density) variables

Variable Value

No. of nodes 12

Max. Connections 8

Simulation Results: The analyzed results from the particular trace file of the

city‟s low density VANET scenario are assessed on the basis of two diligent

metrics of routing protocol (Section 3.2.1), i.e. „PDR’ and „Average E2E Delay.’

The outputs of their numerical calculations are placed in Table 4.3. Whereas the

logical representation for filtering and extracting the required information from

raw trace file data by mean of AWK script is given in Appendix A at the end of

the report. It is followed by detailed analyzed results in Appendix B.

30

Table 4.3: Analyzed data of city low density

CITY LOW DENSITY

AODV AOMDV DSR DSDV

Packet Delivery Ratio 99.7861% 99.7767% 52.4948% 98.9038%

Average End-to-End Delay 66.2026ms 79.5885ms 23.7363ms 68.3394ms

The observable percentage of PDR in city‟s low density model are quiet

acceptable by AODV, AOMDV, and DSDV. The results generated by DSR

routing protocol are declined sharply as compared to others. Oppositely, the

Average E2E Delay is observed very low by DSR compared to other protocol

results which are still is acceptable range of less than 150ms to 250ms.

The graphical representation of the analyzed metrics of „PDR‟ and „Average

E2E Delay‟ along with their deliverable values on top of the specified routing

protocols of this comparative study is shown in Figure 4.3 and Figure 4.4

respectively.

31

Figure 4.3: PDR at city low density

Figure 4.4: Average E2E at city low density

0.00%

20.00%

40.00%

60.00%

80.00%

100.00%

120.00%

AODV AOMDV DSR DSDV

Pc.

(%

) o

f P

acke

t R

ece

ive

d(R

ec.

Pkt

./Se

nt

Pkt

. *1

00

)

Routing Protocols

Packet Delivery Ratio

0

10

20

30

40

50

60

70

80

90

AODV AOMDV DSR DSDV

Tim

e (

mse

c.)

Routiong Protocols

Average End-to-End Delay

32

4.1.2 Medium Density Model

Simulation Parameters: The medium density model comprises of 260 vehicles

as nodes and intercommunication source type of TCP with a maximum of 150

connections. Rests of the parametric values are similar to that of previous low

density model declarations. The tabulated view of these variables and their

corresponding values are arranged in Table 4.4.

Table 4.4: City (medium density) variables

Variable Value

No. of nodes 260

Max. Connections 150

Simulation Results: The analyzed numerical results for city‟s medium density

VANET scenario from the generated trace files for individual routing protocols

are given in Table 4.5.

Table 4.5: Analyzed data of city medium density

CITY MEDIUM DENSITY

AODV AOMDV DSR DSDV



33

The analyzed results for this density model with 260 nodes and 150 TCP

connections, the PDR of routing protocols in AODV and AOMDV remains stable

comparing to DSDV with approximately quarter percentage of slash and DSR

with really poor performance with a very brief minor chunk of ratio. For average

E2E delay, AODV and DSR results are reasonable with an acceptable eye on

DSDV than AOMDV.

Similarly, the graphical representations of these analyzed results are plotted in

Figure 4.5 and Figure 4.6 respectively.

Figure 4.5: PDR at city medium density

0.00%

20.00%

40.00%

60.00%

80.00%

100.00%

120.00%

AODV AOMDV DSR DSDV

Pc.

(%

) o

f P

acke

t R

ece

ive

d(R

ec.

Pkt

./Se

nt

Pkt

. *1

00

)

Routing Protocols


34

Figure 4.6: Average E2E at city medium density

4.1.3 High Density Model

Simulation Parameters: The high density model comprises of 812 vehicles as

nodes with all other variable values same as that of previous medium density

model. The table for variable ~ value representation is given by Table 4.6.

Table 4.6: City (high density) variables

Variable Value

No. of nodes 812


0

50

100

150

200

250

AODV AOMDV DSR DSDV

Tim

e (

mse

c.)

Routing Protocols


35

Simulation Results: The analyzed results for city‟s high density VANET

scenario with similar to previous routing metrics of „packet delivery ratio‟ and

„average end-to-end delay‟ given in Table 4.7.

Table 4.7: Analyzed data of city high density

CITY MEDIUM DENSITY

AODV AOMDV DSR DSDV



The corresponding graphical representation of „PDR‟ and „Average E2E Delay‟

are illustrated in Figure 4.7 and Figure 4.8 respectively.

Figure 4.7: PDR at city high density

0.00%

20.00%

40.00%

60.00%

80.00%

100.00%

120.00%

AODV AOMDV DSR DSDV

Pc.

(%

) o

f P

acke

t R

ece

ive

d(R

ec.

Pkt

./Se

nt

Pkt

. *1

00

)

Routing Protocols


36

Figure 4.8: Average E2E at city high density

4.2 Highway Scene

Similar to the city outlook, highway scene is also considering the road pattern of

another bona fide trace from the Switzerland expanse. Again, the specified

regions within the movement files and their masking outlined from the Google

Maps to Network Animator are shown in Figure 4.9 and Figure 4.10

respectively.

0

20

40

60

80

100

120

AODV AOMDV DSR DSDV

Tim

e (

mse

c.)

Routing Protocols


37

Figure 4.9: Highway movement traces on Google map (source: maps.google)

Figure 4.10: Highway movement traces on network animator

38

Like city model, to make comparative study of selected routing behavior in their

respective scenes, an approach of density formulation among traffic flow is

reused. The highway model is further sub-classified on the basis of their

participating vehicles in a low, medium, and high density phases. For these

different densities, the common variables defined are shown in Table 4.8.

Table 4.8: Common variable in highway model

Variable Value

Simulation time 300 s

Topology size 14000 m x 10000 m

Routing Protocols AODV, AOMDV, DSR, DSDV

Traffic Type TCP

Most of the common density variables in Table 4.8 are similar to those of city‟s

densities with only exception of topology size. The size of simulation area is

expanded according to the highway‟s spread out. Therefore, the approximated

topology size is defined by 14000m x10000m based on the selected highway

region. The value of simulation running time is of 300 seconds, routing protocols

deployed as AODV, AOMDV, DSDV, DSR, and the traffic connection type of

TCP are kept unchanged.

39

4.2.1 Low Density Model

Simulation Parameters: The highway low density model comprises of 370

vehicles as nodes. These nodes are deployed and arranged according to the

provided patterns of mobility traces with maximum connections of 150, they are

given in the following (Table 4.9).

Table 4.9: Highway (low density) variables

Variable Value

No. of nodes 370


Simulation Results: The analyzed results of routing protocols for highway‟s low

density VANET scenario with their assessment metrics of „PDR’ and „Average

E2E Delay’ are placed in Table 4.10. The analyzing AWK script could be found

in Appendix A at the end of the report.

Table 4.10: Analyzed data of highway low density

HIGHWAY LOW DENSITY

AODV AOMDV DSR DSDV



40

The PDR percentage of low density of highway is perfectly achievable by

AODV, and AOMDV. DSDV with quarter less and DSR with only single quarter

results are suspicious in their performance. In contrast, results of latter protocols

pair for average E2E delay are remarkable along with the previous one. The

graphical representations are shown in Figure 4.11 and Figure 4.12

respectively.

Figure 4.11: PDR at highway low density

0.00%

20.00%

40.00%

60.00%

80.00%

100.00%

120.00%

AODV AOMDV DSR DSDV

Pc.

(%

) o

f P

acke

t R

ece

ive

d(R

ec.

Pkt

./Se

nt

Pkt

. *1

00

)

Routing Protocols


41

Figure 4.12: Average E2E at highway low density

4.2.2 Medium Density Model

Simulation Parameters: The medium density model for highway comprises of

837 vehicles as nodes and the intercommunication source type of TCP with a

maximum of 150 connections (Table 4.11). Remaining variable values remained

unchanged.

Table 4.11: Highway (medium density) variables

Variable Value

No. of nodes 837


0

10

20

30

40

50

60

70

80

AODV AOMDV DSR DSDV

Tim

e (

mse

c.)

Routing Protocols


42

Simulation Results: The analyzed numerical results for highway‟s medium

density VANET scenario from the generated trace files for individual routing

protocols are given in Table 4.12 below:

Table 4.12: Analyzed data of highway medium density

HIGHWAY MEDIUM DENSITY

AODV AOMDV DSR DSDV



The PDR of routing protocols in AODV and AOMDV remains usual with

maximum rate. DSDV and DSR are observed to be extremely short sighted in

their performance. The average E2E delay remains under control for certain

threshold value of 150ms. The graphical representations of these analyzed

results are plotted in Figure 4.13 and Figure 4.14 respectively.

Figure 4.3: PDR at highway medium density

0.00%

20.00%

40.00%

60.00%

80.00%

100.00%

120.00%

AODV AOMDV DSR DSDV

Pc.

(%

) o

f P

acke

t R

ece

ive

d(R

ec.

Pkt

./Se

nt

Pkt

. *1

00

)

Routing Protocols


43

Figure 4.4: Average E2E at highway medium density

4.2.3 High Density Model

Simulation Parameters: The high density model comprises of 1112 vehicles as

nodes with 150 traffic connections of TCP type (Table 4.13).

Table 4.13: Highway (high density) variables

Variable Value

No. of nodes 1112


Simulation Results: The analyzed results for highway‟s high density VANET

scenario with routing metrics of „packet delivery ratio‟ and „average end-to-end

delay‟ are given in Table 4.14.

0

20

40

60

80

100

120

140

AODV AOMDV DSR DSDV

Tim

e (

mse

c.)

Routing Protocols


44

Table 4.14: Analyzed data of highway high density

HIGHWAY HIGH DENSITY

AODV AOMDV DSR DSDV



The analyzed results for PDR are nearly similar to medium density case with

minor variations observed in average E2E delay values. They are graphically

represented in Figure 4.15 and Figure 4.16 respectively.

Figure 4.15: PDR at highway high density

0.00%

20.00%

40.00%

60.00%

80.00%

100.00%

120.00%

AODV AOMDV DSR DSDV

Pc.

(%

) o

f P

acke

t R

ece

ive

d(R

ec.

Pkt

./Se

nt

Pkt

. *1

00

)

Routing Protocols


45

Figure 4.16: Average E2E at highway high density

0

20

40

60

80

100

120

140

AODV AOMDV DSR DSDV

Tim

e (

mse

c.)

Routing Protocols


46

CHAPTER 5 ANALYSIS AND DISCUSSION

In this chapter a comparative analysis of the selected routing protocols based

on their investigative metrics are thoroughly discussed. In a brief review, the

complete extent of the overall study comprises of two decisive factors: „Packet

Delivery Ratio‟ and „Average End-to-End Delay.‟ These features are directly

dependant on the formulation of their respective density models which are

classified from their parent models of City and Highway scenes. The hierarchical

designs of these momentous objectives are shown here:

Figure 5.1: Generic review

Considering the basis of the given streams along with their simulation outputs,

the enhanced plotting taken as a whole from the individually analyzed factors

(pervious chapter) with respect of routing protocols are discussed graphically

within their panoramas of city and highway results.

City Scene

Low Density Model

o Packet Delivery Ratio

o Average End-to-End Delay

Medium Density Model



High Density Model



Highway Scene

Low Density Model



Medium Density Model



High Density Model



47

5.1 City Results

The city observations from different approaches of varying densities with

respect to packet delivery ratio and average end-to-end delay are looking

rational for some instances while irrational for the others. These are due to the

depending consequences of routines and performances of the routing protocol.

The big pictures of these remarks are graphically plotted in Figure 5.2 and

Figure 5.3 respectively.

Figure 5.2: PDR of routing protocols in city

0.00%

20.00%

40.00%

60.00%

80.00%

100.00%

120.00%

AODV AOMDV DSR DSDV

De

live

ry P

erc

en

tage

(%

)

Routing Protocols


City Low Density

City Medium Density

City High Density

48

Figure 5.2: Average end-to-end delay of routing protocols in city

In packet delivery ratio (PDR) plotting (Figure 5.1), the more hype in the value of

percentage represents an added achievable performance of their respective

protocol. In the consideration of careful simulation results, AODV and AOMDV

made their obvious marks in all of the VANET density schemes of the given city

scenario. It is also apparent that DSDV has its acceptability for city‟s low density

variation only. To end with, DSR and DSDV (in medium and high density)

remained unsuitable for the given case of city models from showing their

approachable PDR results.

Conversely, for average end-to-end (Figure 5.2), where minimal timing values

are required, DSR majorly wins the game for all of the density levels. In addition,

for low density level, all of the routing protocols had equally acceptable values.

These rates still remained intact (in general acceptability) by AODV and DSDV

0

50

100

150

200

250

300

350

400

450

AODV AOMDV DSR DSDV

Tim

e (

mse

c)

Routing Protocols


City High Density

City Medium Density

City Low Density

49

than AOMDV for medium density. Finally, the admirable limits are crossed by

every routing protocol (except DSR) for high density phase.

5.2 Highway Results

Similar to city observations, highway results have also formed mixed variations

of balanced and imbalanced approaches inside varying densities with respect to

packet delivery ratio and average end-to-end delay. The facts of their

accumulated results by mean of graphical plotting are shown in Figure 5.3 and

Figure 5.4 respectively.

Figure 5.4: PDR of routing protocols on highway

0.00%

20.00%

40.00%

60.00%

80.00%

100.00%

120.00%

AODV AOMDV DSR DSDV

De

live

ry P

erc

en

tage

(%

)

Routing Protocols


Highway Low Density

Highway Medium Density

Highway High Density

50

Figure 5.5: Average end-to-end delay of routing protocols on highway

For packet delivery ratio (PDR) (Figure 5.3), elevate percentage means higher

performance. In view of this fact, AODV and AOMDV remained the highly

acceptable approaches for all of the density levels of highway model. In

contrast, DSR and DSDV with no chance of assumption are bearing the

comportment for this measuring metric in the specified scene of VANET.

On the other hand, the average end-to-end (Figure 5.2) remained in approach

by all of the routing protocols with a minor doubt of variation by AODV in high

density model.

5.3 Overall Evaluation

The overall evaluation is based on the findings of simulation results discussed in

previous sections of city and highway results. It is basically the formation of

0

50

100

150

200

250

300

AODV AOMDV DSR DSDV

Tim

e (

mse

c.)

Routing Protocols


Highway High Density

Highway Medium Density

Highway Low Density

51

evaluation matrix (Vehar, 1996), where rows and columns are scored according

to their corresponding weighting factors and rating values to make final score.

Carrying this procedure, the matrix is transformed accordingly for the evaluation

of individual performance of routing protocols with respect to their measuring

metrics of PDR and Average end-to-end delay. Following is table of such

generation:

Table 5.1: Overall Evaluation Matrix

Routing

Protocols

Weighting

Factor

Evaluative Routing Metrics

Total

Score

Packet Delivery Ratio Average End-to-End Delay

Rating Score Rating Score

AODV 4 4 16 2 8 24

AOMDV 4 4 16

2 8 24

DSDV 3 2 6 3 9 15

DSR 2 1 2 4 8 10

The major scope of this matrix is associated with the cross relation of routing

protocols and their evaluative metrics. They are distributed in the table by first

column and part of first row respectively. The weighting factor column is

assigned to each criterion based on its effect on the success of its consideration

depending on their generated results from previous chapter. They are classified

as: AODV - 4, AOMDV – 4, DSDV – 3, and DSR – 2. There is a quantitative

52

rating to each qualitative value such as: poor, fair, good, and very-good. These

values are further associated with their respective numerical values of 1, 2, 3,

and 4 correspondingly. The assignments of these values are done by

characterizing the overall performance of all the simulation results according to

their output levels of the generated results. After the weights are assigned to

each category, the calculation phase is established. It is where the assigned

weights are multiplied by their relative rating to determine the specified

operation score (as in case of PDR and Average E2E delay). At the end of each

score the summation is being performed on to get the total score of every

individual participant from their operative scores. The member with high score in

total will be assigned the ranking accordingly and fairly on the basis of its overall

performance results mentioned earlier.

Total score column of the given table clearly shows up to standard

performances of AODV and AOMDV on equality basis, despite of extra

enhancement by AOMDV discussed in literature review. Furthermore, DSDV

and DSR lagged behind their performance (especially in PDR than E2E) and

remained unsuitable for the given case of VANET environment of city and

highway with varying densities. The graphical demonstration of this overall

evaluation matrix approach is shown in Figure 5.6.

53

Figure 5.6: Graphical representation of overall evaluation matrix

0

5

10

15

20

25

30

AODV AOMDV DSDV DSR

Total Score

Total Score

54

CHAPTER 6 CONCLUSIONS AND FUTURE WORK

In this proportional study of specified routing protocols in VANET environment,

certain considerable and realistic approaches of ad hoc networks are thoroughly

examined. The involvement of vehicular traces of city and highway models

really played their vital role. Moreover, the representations of these models

along with their variable density levels have generated faction wise numerous

practical results. These results now could be used for the assortment and

selection of routing protocols for specified domains.

The generic choice of routing protocols: AODV, AOMDV, DSDV, and DSR are

made on the basis of vigilant thoughts of related work in the same domain of ad

hoc networks. Similarly, for analyses purpose among the variety of available

routing metrics, the options are prepared for packet delivery ratio (PDR) and

average end-to-end delay. It is due to their classical and comprehensible

differentiation in the context of routing measures.

The simulation test beds for two scenarios of city and highway are deployed by

ns-2 along with their injecting level of densities. The density scale was defined

by the number of participating vehicles within the projected scene of city and

highway. They are classified as: low, medium, and high, depending on the

vehicular strength and devises results.

The generated and analyzed results for city and highway models apparently

remained competitive in between AODV and AOMDV routing protocols. The

55

PDR values of both of them were tussling in between 97% to 99%

approximately, whereas, the Average E2E Delay remained within the threshold

limit of 250ms by all of the participating protocols.

In a nutshell, the end results by mean of extensive and rigorous simulations

within the particular deployable test beds are quite rational and pragmatic in

comparison to the real roads and traffic scenarios. As an outcome, AOMDV and

AODV - two favorite ad hoc routing protocols - are found the most appropriate

selection for reasonably adjustable protocols at the network layer of given

cases, i.e. city and highway models in VANET with varying traffic concentration.

The test bed(s) created as an endurance of this work can be utilize in a variety

of manners for the future work, both in an immediate and steady ways.

The two main instantaneous diversions can be drive through from:

1. Investigative study of numerous protocols of OSI layers by mean of their

corresponding parametric value, similar or revolutionize as mentioned in

this work.

2. Formation of practical traces with the help of coordination modeling. The

city, highway, and relevant coordinates can be obtained from subsequent

research schools of regional roads and transport studies.

The other move of gradual and continuing approach of future work can be made

with the help exponential mapping; utilizing Google Maps can be a good start

from first to last. This could be comprises of plans from regional to national, and

56

from national to international road maps and traffic study. These mapping

schemes will further lead towards the geographical constraints in-between

digital and physical scopes, for example, Quality of Service (QoS) issues,

reliability check, security concerns, and likewise reflections to ponder upon in

VANETs.

57

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Chen, A., B. Khorashadi, et al. 2007, Impact of transmission power on TCP performance in vehicular ad hoc networks, Fourth Annual Conference on Wireless on Demand Network Systems and Services, WONS '07.

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Chia-Chen, H., H. Chan, et al. 2008, Mobility Pattern Aware Routing for Heterogeneous Vehicular Networks. IEEE Wireless Communications and Networking Conference, WCNC 2008.

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Djenouri, D., W. Soualhi, et al. 2008, VANET's Mobility Models and Overtaking: An Overview, 3rd International Conference on Information and Communication Technologies: From Theory to Applications, ICTTA 2008.

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KUROSE, J.F., and ROSS, K.W. 2004, Computer Networking: A Top Down Approach Featuring The Internet, 3rd ed., Addison Wesley.

LANG, D. 2003, A Comprehensive Overview About Selected Ad Hoc Networking Routing Protocols, DCS, Technische Uni. Munich, Germany, pp. 47.

Maeda, K., K. Sato, et al. 2005, Getting urban pedestrian flow from simple observation: realistic mobility generation in wireless network simulation, Proceedings of the 8th ACM international symposium on Modeling, analysis and simulation of wireless and mobile systems, Montral, Quebec, Canada, ACM.

ttp://www.isi.edu/nsnam/ns/ns-tutorial/tutorial-0

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Mahesh, K. and R. Samir, Ad hoc on-demand multipath distance vector routing, Computer Science Department, University of California, Los Angeles, CA 90095-1596, U.S.A.

Padmini, M, Routing protocols for Ad Hoc Mobile Wireless Networks, available at: < http://www.cse.wustl.edu/~jain/cis788-99/ftp/adhoc_routing/>

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61

APPENDIX A

TCL & AWK SCRIPTS WITH TRAFFIC PATTERN FILE

Page

Sample A – 1 MAIN TCL script file 62

Sample A – 2 Traffic pattern file 66

Sample A – 4 AWK script 68

62

# ===========================================================

# MAIN TCL script file

# ===========================================================

#=========================================================== # #Simple VANET Scenario # #Junaid M. Shaikh # #Courtesy: Marc Gries Tutorial # #=========================================================== #=========================================================== # Define Options #=========================================================== set opt(chan) Channel/WirelessChannel ;# channel type set opt(prop) Propagation/TwoRayGround ;# radio-propagation model set opt(netif) Phy/WirelessPhy ;# network interface type set opt(mac) Mac/802_11 ;# MAC type set opt(ifq) Queue/DropTail/PriQueue ;# Interface queue type ##Following option should be enabled for DRS as (rp) #set opt(ifq) CMUPriQueue ;# Interface queue type set opt(ll) LL ;# Link layer type set opt(ant) Antenna/OmniAntenna ;# Antenna type set opt(ifqlen) 100 ;# max packet in ifq set opt(nn) 812 ;# number of mobilenodes #set opt(rp) AODV ;# ad-hoc routing protocol #set opt(rp) AOMDV ;# ad-hoc routing protocol ##set opt(rp) DSR ;# ad-hoc routing protocol set opt(rp) DSDV ;# ad-hoc routing protocol set opt(sc) chd.tcl" ;# node movement file set opt(cp) "tcp-812-150-ch" ;# traffic file

63

# ============================ # Define simulation area range # ============================ #set opt(x) 3000 #set opt(y) 3000 set opt(x) 4011 set opt(y) 7011 #=========================================================== # Main Program #========================================================== # =========================== # Creating simulator instance # =========================== set ns_ [new Simulator] $ns_ color 0 Brown #$ns_ color 0 Blue # ==================================================== # Creating network animator (NAM) and Trace (tr) files # ==================================================== set nf [open vanet.nam w] set tf [open vanet.tr w] $ns_ namtrace-all-wireless $nf $opt(x) $opt(y) $ns_ trace-all $tf # ============================= # Creating topological boundary # ============================= set topo [new Topography] $topo load_flatgrid $opt(x) $opt(y) # ======================================== # Create general operations director (god) # ======================================== set god_ [create-god $opt(nn)]

64

# ================== # Node configuration # ================== $ns_ node-config -adhocRouting $opt(rp) \ -llType $opt(ll) \ -macType $opt(mac) \ -ifqType $opt(ifq) \ -ifqLen $opt(ifqlen) \ -antType $opt(ant) \ -propType $opt(prop) \ -phyType $opt(netif) \ -topoInstance $topo \ -agentTrace ON \ -routerTrace ON \ -macTrace OFF \ -movementTrace ON \ -channel [new $opt(chan)] # ================= # Creation of nodes # ================= #set node_(0) [$ns_ node] #set node_(1) [$ns_ node] for {set j 0} {$j < 812} {incr j} { set node_($j) [$ns_ node] } #$ns_ color 1 white #$ns_ color 2 red #$ns_ color 3 green # ================= # Creating mobility # ================= source $opt(sc) for {set i 0} {$i < 812} {incr i} { $ns_ initial_node_pos $node_($i) 812 } #$ns_ at 0.1 "$node_(1) setdest 1.0 70.0 50.0" #$ns_ at 0.1 "$node_(0) setdest 299.0 50.0 50.0"

65

# ================ # Creating traffic # ================ source $opt(cp) #set tcp [new Agent/TCP] #$tcp set class_ 2 #set sink [new Agent/TCPSink] #$ns_ attach-agent $node_(0) $tcp #$ns_ attach-agent $node_(1) $sink #$ns_ connect $tcp $sink #set ftp [new Application/FTP] #$ftp attach-agent $tcp #$ns_ at 0.1 "$ftp start" # ===================== # Simulation end timing # ===================== $ns_ at 300 "finish" #=========================================================== # End of Simulation #=========================================================== proc finish {} { global ns_ nf tf $ns_ flush-trace close $nf close $tf exec nam vanet.nam & #exec xgraph simple-vanet.tr -geometry 800x400 & exit 0 } #=========================================================== # Running Simulation #=========================================================== puts "Starting Simulation..." $ns_ run ################# #------EOF------# #################

66

#===========================================================

# Traffic Pattern File

#===========================================================

# # nodes: 812, max conn: 150, send rate: 0.0, seed: 0.0 # # # 0 connecting to 1 at time 39.923569513449245 # set tcp_(0) [$ns_ create-connection TCP $node_(0) TCPSink $node_(1) 0] $tcp_(0) set window_ 32 $tcp_(0) set packetSize_ 512 set ftp_(0) [$tcp_(0) attach-source FTP] $ns_ at 39.923569513449245 "$ftp_(0) start" # # 1 connecting to 2 at time 88.06065001900339 # set tcp_(1) [$ns_ create-connection TCP $node_(1) TCPSink $node_(2) 0] $tcp_(1) set window_ 32 $tcp_(1) set packetSize_ 512 set ftp_(1) [$tcp_(1) attach-source FTP] $ns_ at 88.06065001900339 "$ftp_(1) start" # # 1 connecting to 3 at time 114.97263667870902 # set tcp_(2) [$ns_ create-connection TCP $node_(1) TCPSink $node_(3) 0] $tcp_(2) set window_ 32 $tcp_(2) set packetSize_ 512 set ftp_(2) [$tcp_(2) attach-source FTP] $ns_ at 114.97263667870902 "$ftp_(2) start" # # 4 connecting to 5 at time 100.22347272384141 # set tcp_(3) [$ns_ create-connection TCP $node_(4) TCPSink $node_(5) 0] $tcp_(3) set window_ 32 $tcp_(3) set packetSize_ 512 set ftp_(3) [$tcp_(3) attach-source FTP] $ns_ at 100.22347272384141 "$ftp_(3) start" # # 5 connecting to 6 at time 165.62555372045634 # set tcp_(4) [$ns_ create-connection TCP $node_(5) TCPSink $node_(6) 0] $tcp_(4) set window_ 32

67

$tcp_(4) set packetSize_ 512 set ftp_(4) [$tcp_(4) attach-source FTP] $ns_ at 165.62555372045634 "$ftp_(4) start" # # 8 connecting to 9 at time 89.648865521721945 # set tcp_(5) [$ns_ create-connection TCP $node_(8) TCPSink $node_(9) 0] $tcp_(5) set window_ 32 $tcp_(5) set packetSize_ 512 set ftp_(5) [$tcp_(5) attach-source FTP] $ns_ at 89.648865521721945 "$ftp_(5) start" # # 8 connecting to 10 at time 1.337946458411378 # set tcp_(6) [$ns_ create-connection TCP $node_(8) TCPSink $node_(10) 0] $tcp_(6) set window_ 32 $tcp_(6) set packetSize_ 512 set ftp_(6) [$tcp_(6) attach-source FTP] $ns_ at 1.337946458411378 "$ftp_(6) start" # # 9 connecting to 10 at time 124.93961080207471 # set tcp_(7) [$ns_ create-connection TCP $node_(9) TCPSink $node_(10) 0] $tcp_(7) set window_ 32 $tcp_(7) set packetSize_ 512 set ftp_(7) [$tcp_(7) attach-source FTP] $ns_ at 124.93961080207471 "$ftp_(7) start" # # 9 connecting to 11 at time 157.06980878350782 # set tcp_(8) [$ns_ create-connection TCP $node_(9) TCPSink $node_(11) 0] $tcp_(8) set window_ 32 $tcp_(8) set packetSize_ 512 set ftp_(8) [$tcp_(8) attach-source FTP] $ns_ at 157.06980878350782 "$ftp_(8) start" # # 10 connecting to 11 at time 26.31274839225819 # set tcp_(9) [$ns_ create-connection TCP $node_(10) TCPSink $node_(11) 0] $tcp_(9) set window_ 32 $tcp_(9) set packetSize_ 512 set ftp_(9) [$tcp_(9) attach-source FTP] $ns_ at 26.31274839225819 "$ftp_(9) start" . . . .

68

#===========================================================

# AWK Script for calculating: Packet Delivery Ratio and Average End-to-End Delay

#===========================================================

#! /bin/awk -f #***************************************************************************** #To run this AWK script type: #./wireless_qos.tcl <trace file> #***************************************************************************** BEGIN { seqno = -1; count = 0; spkt = 0; rpkt = 0; dpkt = 0; fpkt = 0; ack = 0; } { #packet delivery ratio # Sent tcp packets if($4 == "AGT" && $1 == "s" && seqno < $6) { seqno = $6; } # Received tcp packets else if (($4 == "AGT") && ($1 == "r")){ rpkt++; } # Dropped tcp packets else if ($1 == "D" && $7 == "tcp" && $8 > 512){ dpkt++; } # Forwarded tcp packets if($1 == "f" && $7 == "tcp"){ fpkt++; }

69

# end-to-end delay if($4 == "AGT" && $1 == "s") { start_time[$6] = $2; } else if(($7 == "tcp") && ($1 == "r")) { end_time[$6] = $2; } else if($1 == "D" && $7 == "tcp") { end_time[$6] = -1; } } END { for(i=0; i<=seqno; i++) { if(end_time[i] > 0) { delay[i] = end_time[i] - start_time[i]; count++; } else { delay[i] = -1; } } for(i=0; i<count; i++) { if(delay[i] > 0) { n_to_n_delay = n_to_n_delay + delay[i]; } } n_to_n_delay = n_to_n_delay/count; # Output print " Generated/Sent TCP Packets = " seqno+1; print " Received TCP Packets = " rpkt; print " Dropped TCP Packets = " dpkt; print " Forwarded TCP Packets = " fpkt; print " Packet delivery ratio = " rpkt/(seqno+1)*100 "%"; print " Average end-to-end delay = " n_to_n_delay * 1000 " ms"; }

70

APPENDIX B

ANALYZED SIMULATION RESULTS

Page

Results B – 1 City Results 71

Results B – 2 Highway Results 72

71

CITY RESULTS

CITY LOW DENSITY

AODV AOMDV DSR DSDV

Generated TCP Packets 39272 37619 71830 40593

Received TCP Packets 39188 37535 37707 40148


Average End-to-End Delay 66.2026 79.5885 23.7363 68.3394

CITY MEDIUM DENSITY

AODV AOMDV DSR DSDV





CITY HIGH DENSITY

AODV AOMDV DSR DSDV





72

HIGHWAY RESULTS

HIGHWAY LOW DENSITY

AODV AOMDV DSR DSDV





HIGHWAY MEDIUM DENSITY

AODV AOMDV DSR DSDV





HIGHWAY HIGH DENSITY

AODV AOMDV DSR DSDV





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A COMPARATIVE ANALYSIS OF ROUTING PROTOCOLS IN VANET ... · PDF filea comparative analysis of routing protocols in vanet enviromnent using realistic vehicular traces ... chapter 1