Enhance Routing Metric Based Optimized Link State Routing (OLSR) Protocol for VANET
by
Azhar Tareq Ali (1630212178)
A thesis submitted in fulfillment of the requirements for the degree of Master of Science in Computer Engineering
School of Computer and Communication Engineering UNIVERSITI MALAYSIA PERLIS
2018
UNIVERSITI MALAYSIA PERLIS
NOTES : * If the thesis is CONFIDENTIAL or RESTRICTED, please attach with the letter from the organization with the period and reasons for confidentiality or restriction.
DECLARATION OF THESIS Author’s Full Name : AZHAR TAREQ ALI
Title : ENHANCE ROUTING METRIC BASED OPTIMIZED LINK STATE ROUTING PROTOCOL (OLSR) PROTOCOL FOR VANET
Date of Birth : 11 JANUARY 1980 Academic Session : 2017/2018
I hereby declare that this thesis becomes the property of Universiti Malaysia Perlis (UniMAP) and to be placed at the library of UniMAP. This thesis is classified as:
CONFIDENTIAL (Contains confidential information under the Official Secret Act 1997)*
RESTRICTED (Contains restricted information as specified by the organization where research was done)*
OPEN ACCESS I agree that my thesis to be published as online open access (Full Text)I, the author, give permission to reproduce this thesis in whole or in part for the purpose of research or academic exchange only (except during the period of _two years, if so requested above)
Certified by:
SIGNATURE SIGNATURE OF SUPERVISOR
A4087832 IR. TS. DR. MOHD NAZRI BIN MOHD
WARIP
(NEW IC NO. /PASSPORT NO.) NAME OF SUPERVISOR Date: 04 September 2018 Date: 04 September 2018
DECLARATION OF THESIS Author’s Full Name : AZHAR TAREQ ALI
Title : ENHANCE ROUTING METRIC BASED OPTIMIZED LINK STATE ROUTING PROTOCOL (OLSR) PROTOCOL FOR VANET
Date of Birth : 11 JANUARY 1980 Academic Session : 2017/2018
©This item is protected by original copyright
ACKNOWLEDGMENT
In the Name of Allah, The Most Beneficent, The Most Merciful: “My Lord, enable me to be grateful for Your favor which You have bestowed upon me and upon my parents and to do righteousness of which You approve. And admit me by Your mercy into [the ranks of] Your righteous servants. (An-Naml:19) ”.
First and foremost, I would like to express my gratitude to my father for the support they had given to me throughout my study.
Secondly, I would like to thank and to express my gratitude to my supervisor Dr.
Mohd Nazri Bin Mohd Warip and Co-supervisor Dr. Naimah yaakob for the support and trust they had given to me throughout this journey.
Thank you, brothers and friends, for all the joy we had together. Especially to whom always beside me giving me support and coloring my days.
Last and not least, I would like to say thank you for those who no words can describe him love and support. My husband, Assoc. Dr. Waleed Khalid Abduljabbar and my children.
©This item is protected by original copyright
iii
TABLE OF CONTENTS
PAGE
DECLARATION OF THESIS i
ACKNOWLEDGMENT ii
TABLE OF CONTENTS iii
LIST OF TABLES ix
LIST OF FIGURES x
LIST OF ABBREVIATIONS xiv
ABSTRAK xviii
ABSTRACT xx
1. CHAPTER 1: INTRODUCTION 1
1.1 Research Background 1
1.2 Problem Statements 5
1.3 Research Objectives 7
1.4 Contributions 8
1.5 Scope of Research 8
1.6 Thesis Outline 9
2. CHAPTER 2: LITERATURE REVIEW 11
2.1 Introduction 11
2.2 Evolution of VANET Architecture 11
2.3 VANET’s Routing Protocols Classification 15
2.4 Applications in VANET 18
2.5 Bio-inspired Routing in VANET 19
©This item is protected by original copyright
iv
2.6 Geographical Routing in VANET 20
2.6.1 Greedy perimeter stateless routing (GPSR) 22 2.6.2 Greedy and stateless routing protocols (GSR) 26
2.6.2.1 Contention-Based Forwarding (CBF) 26
2.6.2.2 Greedy Perimeter Coordinator Routing (GPCR) 27
2.6.2.3 Advanced Greedy Forwarding (AGF) 28
2.6.2.4 Greedy Routing with Abstract Neighbor Table (GRANT) 29
2.6.3 Street aware protocols (SAP) 29
2.6.3.1 Geographic Source Routing (GSR) 30
2.6.3.2 Spatially Aware Routing (SAR) 31
2.6.3.3 GpsrJ+ 32
2.6.3.4 Topology-assisted Geographical Routing (TO-GO) 33
2.6.4 Infrastructure assisted protocols (IAP) 34
2.6.4.1 Intelligent Routing using real-time Traffic Information in
VANET (IRTIV) 34
2.6.4.2 An Intersection UAV-Assisted VANET Routing Protocol (UVAR) 35
2.6.4.3 Intersection-Based Geographical Routing (IGRP) 36 2.6.4.4 Static-node Assisted Data-Dissemination Protocol for Vehicular
Networks (SADV) 37
2.6.5 Connectivity Aware Protocols (CAP) 39
2.6.5.1 Traffic-aware routing protocol (TARCO) 39 2.6.5.2 Anchor based Street and Traffic-Aware Routing (A-STAR) 39 2.6.5.3 Improved Greedy Traffic Aware Routing (GyTAR) 40 2.6.5.4 Vehicle Assisted Data Delivery (VADD) 41
©This item is protected by original copyright
v
2.6.5.5 Geographic Stateless VANET Routing (GeoSVR) 42
2.6.6 Real-Time Connectivity Awareness (RTCP) 43
2.6.6.1 Multi-Hop Routing for Urban VANETs (MURU) 43 2.6.6.2 Spatial and Traffic-Aware Routing (STAR) 44
2.6.6.3 Connectivity-Aware Routing (CAR) 45
2.6.6.4 Adaptive Connectivity-Aware Routing (ACAR) 46 2.6.6.5 Road Based Routing using Vehicular Traffic (RBVT) 47 2.6.6.6 Landmark Overlays for Urban Vehicular Routing Environments
(LOUVRE) 48
2.6.6.7 Back-Bone Assisted Hop Greedy (BAHG) Routing Protocol 49
2.7 Topological Routing in VANET 50
2.7.1 Proactive Routing Protocols (PRP) 50
2.7.2 Optimized Link State Routing Protocol (OLSR) 51
2.7.3 Main features of OLSR protocol 54
2.7.4 OLSR protocol operation: 55
2.7.4.1 HELLO messages: 55
2.7.4.2 TC messages 59
2.7.4.3 MID messages: 63
2.7.5 Destination-Sequenced Distance-Vector Routing Protocols (DSDV) 63
2.7.6 Reactive Routing Protocol (RRP) 64
2.7.7 Ad-Hoc On Demand Distance-Vector (AODV) 64
2.7.8 Dynamic Source Routing (DSR) 65
2.7.9 Hybrid Routing Protocols 66
2.7.10 Summary of reviewed works 67
2.7.11 Summary 73
©This item is protected by original copyright
vi
3. CHAPTER 3: METHODOLOGY 74
3.1 Introduction 74
3.2 Optimized link state routing (OLSR) 75
3.3 Routing Metrics for VANETs 77
3.4 Expected Transmission Count (ETX) 78
3.5 Expected Transmission Time for VANET (VETT) 79
3.6 Network Performances Evaluation 80
3.6.1 Throughput 80
3.6.2 Delay 80
3.6.3 Packet Delivery Ratio (PDR) 81
3.6.4 Traffic Generation 82
3.6.5 Packet Size 83
3.6.6 Number of Cars 83
3.7 Network Test-bed Design and Implementation 83
3.7.1 VANET Simulation Scenarios 84
3.7.2 Network Protocol Stack 86
3.7.3 Mobility Modules 89
3.8 Results Validation and Verification Method 92
3.8.1 Validation Comparison 93
3.8.2 Simulation Model Validation 93
3.9 Summary 94
4. CHAPTER 4: RESULTS & DISCUSSION 96
4.1 Introduction 96
4.2 Evaluation of VETT in VANETs Performances 98
4.2.1 Traffic Generation (TG) 98
4.2.1.1 Throughput Comparison between OLSR, ETX, and VETT 99
©This item is protected by original copyright
vii
4.2.1.2 PDR Comparison between OLSR, ETX, and VETT 101 4.2.1.3 Delay Comparison between OLSR, ETX, and VETT 103 4.2.1.4 Packet Loss Comparison between OLSR, ETX, and VETT 105
4.2.2 Packet Size (PS) 107
4.2.2.1 Throughput Comparison between OLSR, ETX, and VETT 107 4.2.2.2 PDR Comparison between OLSR, ETX, and VETT 109 4.2.2.3 Delay comparison between OLSR, ETX, and VETT 111 4.2.2.4 Packet Loss Comparison between OLSR, ETX, and VETT 113
4.2.3 Number of Cars (NC) 115
4.2.3.1 Throughput Comparison between OLSR, ETX, and VETT 115 4.2.3.2 PDR Comparison between OLSR, ETX, and VETT 117 4.2.3.3 Delay comparison between OLSR, ETX, and VETT 119 4.2.3.4 Packet Loss Comparison between OLSR, ETX, and VETT 121 4.3 Evaluation of OLSR, ETX, and VETT Optimization Methods in VANET 123
4.3.1 Throughput Evaluation of OLSR, ETX, and VETT in VANET
Scenarios 125
4.3.2 Delay Evaluation of OLSR, ETX, and VETT in VANET Scenarios 127 4.3.3 PDR Evaluation of OLSR, ETX, and VETT in VANET Scenarios 128 4.3.4 Packet Loss Evaluation of OLSR, ETX, and VETT in VANET
Scenarios 130
4.4 Summary 132
5. CHAPTER 5: CONCLUSION 135
5.1 Conclusion 135
5.2 Future Works 138
REFERENCES 139
©This item is protected by original copyright
viii
APPENDIX A ETX-OLSR 151
APPENDIX B VETT-OLSR 157
APPENDIX C CITY SCENARIO CONFIGURATION 160 APPENDIX D HIGHWAY SCENARIO CONFIGURATION 162 APPENDIX E HYBRID SCENARIO CONFIGURATION 164
LIST OF PUBLICATIONS 166
©This item is protected by original copyright
ix
LIST OF TABLES
PAGE
Table 2.1: Layered Architecture for DSRC. 13
Table 2.2: Comparison between Three VANET Scenarios. 15 Table 2.3: Neighbour Table of Nodes in OLSR Protocol 57
Table 2.4: TC Message 61
Table 2.5: TC Message after MPR Selection 61
Table 2.6: Routing Table of Nodes after MPR Selection 62 Table 2.7: Comparison of the Reviewed VANET Routing Protocols. 69
Table 3.1: Default and modified parameter of OLSR. 77
Table 3.2: VANET Scenarios Key Simulation Parameters. 85
Table 3.3: Burst Application Parameters. 87
Table 4.1: The Simulation Parameters of VANET Scenarios. 97 Table 4.2: Routing OLSR Protocol Parameters (profile parameters) ranges. 98 Table 4.3: Traffic Generation values in Time interval and data per node in a
second. 99
Table 4.4: Profiles configuration percentage values of the evaluated routing
metrics. 124
Table 4.5: Summary of the observed performances improvements of VETT
as an averaged percentage against OLSR and ETX (for City). 133 Table 4.6: Performances improvement of OLSR, ETX, and VETT profiles of
OLSR as an averaged in city (C) and highway (H) against
different measurments. 134
©This item is protected by original copyright
x
LIST OF FIGURES
PAGE
Figure 1.1: VANET Architecture. 3
Figure 2.1: Communication Categories in VANET (Olariu & Weigle, 2009) 12 Figure 2.2: Taxonomy of VANET Routing Protocols Classification. 17
Figure 2.3: VANET Applications 18
Figure 2.4: Geographical Protocols Taxonomy and Relation Diagram. 21
Figure 2.5: Greedy forwarding algorithm. 23
Figure 2.6: Greedy forwarding failure case. 23
Figure 2.7: Perimeter forwarding. 24
Figure 2.8: The right-hand rule. 25
Figure 2.9: Packet Forwarding in GPCR. 28
Figure 2.10: The concept of SAR. 32
Figure 2.11: GpsrJ+ example; Node A will forward to Node C directly. 33
Figure 2.12: Message routing in VANETs using IGRP. 37
Figure 2.13: State Transition Diagram of the Intersection Mode. 38
Figure 2.14: Selecting junctions in GyTAR. 41
Figure 2.15: The transition modes in VADD. 42
Figure 2.16: Example Scenario for Disconnected Optimal Path. 43 Figure 2.17: Functional architecture for the STAR algorithm. 45
Figure 2.18: Find path examples. 46
©This item is protected by original copyright
xi
Figure 2.19: Illustration of network for OLSR protocol. 52
Figure 2.20: Broad cast message of Node A. 52
Figure 2.21: Link state routing of node A. 52
Figure 2.22: Link state routing of node C. 53
Figure 2.23: Network topology information of each node after LSR. 53
Figure 2.24: Scenario of node A. 54
Figure 2.25: Illustration of network for determination of neighbours. 56
Figure 2.26: Evaluation of neighbour nodes. 57
Figure 2.27: Illustration of network for MPR algorithm. 58
Figure 2.28: Format of TC message. 60
Figure 2.29: Illustration of network for topology table. 60 Figure 2.30: Illustration of network for routing table. 62 Figure 3.1: Two stages optimization of the OLSR-Routing Protocol. 75
Figure 3.2: OLSR flow chart. 77
Figure 3.3: Changlun City Map (2016google, 2016). 85
Figure 3.4: Extraction for the City Scenario of Changlun map. 86 Figure 3.5: Internal Structure of running Wireless Node of OLSR protocol. 87 Figure 3.6: Snapshot of the Wireless Module Internal Structure. 89 Figure 3.7: Linear Mobility Configurations in Running Simulation. 90 Figure 3.8: Demonstration of Linear Mobility of Running Simulation. 91 Figure 3.9: Rectangular Mobility Configurations in Running Simulation. 91
©This item is protected by original copyright
xii
Figure 3.10: Changlun Map with mobility trails, blue lines represents linear mobility and red lines represents rectangular mobility. Circle
represents static mobility node placements. 92 Figure 4.1: Changlun City Map Extraction; Blue lines and red lines represents
internal city roads, Green line represents the highway road. 97 Figure 4.2: Throughput comparison between OLSR, ETX, and VETT for
different traffic generations. 100
Figure 4.3: PDR comparison between OLSR, ETX, and VETT for different
traffic generations. 102
Figure 4.4: Delay comparison between OLSR, ETX, and VETT for different
traffic generations. 104
Figure 4.5: Packet Loss comparison between OLSR, ETX, and VETT for
different traffic generations. 106
Figure 4.6: Throughput comparison between OLSR, ETX, and VETT for
different Packet Sizes. 108
Figure 4.7: PDR comparison between OLSR, ETX, and VETT for different
Packet Sizes. 110
Figure 4.8: Delay comparison between OLSR, ETX, and VETT for different
Packet Sizes. 112
Figure 4.9: PS comparison between OLSR, ETX, and VETT for different
Packet Sizes. 114
Figure 4.10: Throughput comparison between OLSR, ETX, and VETT for
different NC. 116
Figure 4.11: PDR comparison between OLSR, ETX, and VETT for different
NC. 118
Figure 4.12: Delay comparison between OLSR, ETX, and VETT for different
NC. 120
©This item is protected by original copyright
xiii
Figure 4.13: PS comparison between OLSR, ETX, and VETT for different
NC. 122
Figure 4.14: Throughput comparison between TG, PS, and Nc in City
scenario. 126
Figure 4.15: Throughput comparison between TG, PS, and Nc in Highway
scenario. 126
Figure 4.16: Delay comparison between TG, PS, and Nc in City scenario. 127 Figure 4.17: Delay comparison between TG, PS, and Nc in Highway
scenario. 128
Figure 4.18: PDR comparison between TG, PS, and Nc in City scenario. 129 Figure 4.19: PDR comparison between TG, PS, and Nc in Highway scenario.
130 Figure 4.20: Packet Loss comparison between TG, PS, and Nc in City
scenario. 131
Figure 4.21: Packet Loss comparison between TG, PS, and Nc in Highway
scenario. 132
©This item is protected by original copyright
xiv
LIST OF ABBREVIATIONS
ACAR Adaptive CAR
ACO Ant Colony Optimization
ACO-ER Efficient Routing Algorithm based-on ACO AGF Advanced Greedy Forwarding
AMR Adaptive Message Routing
AODV Ad Hoc On-demand Distance Vector
A-STAR Anchor-Based Structure and Traffic-aware Routing AWCP Adaptive Weighted Clustering Protocol
BAHG Back-bone assisted Hop Greedy BEA-OLSR Best Energy-aware OLSR
BER Bit Error Rate
BLA Bee Life Algorithm
BOA-VRP Bio-inspired Optimization Algorithm for Vehicle Routing Problem C2C-CC Car to Car Communication Consortium
CAP Connectivity-aware Routing Protocols CAR Connectivity-aware Routing
CBF Contention-based Forwarding
CF Control Factor
C-GPSR Chameleon Method GPSR
CM Chameleon Method
CM-AODV Chameleon Method AODV
CR Crossover Factor
DCF Distributed Coordination Function DE Differential Evolution
DREAM Distance Routing Effective Algorithm for Mobility DSDV Distance-Sequence Distance Vector Routing Protocol DSR Dynamic Source Routing
DSRC Dedicate Short Range Communication DTN Delay Tolerant Network
DV Distance Vector
DYMO Dynamic MANET On-demand
ETX Expected Transmission Time
FCC Federal Communication Commission
©This item is protected by original copyright
xv
FL Fuzzy Logic
GA Genetic Algorithm
GeoSVR Geographical Stateless VANET Routing
GG Gabril Graph
GLS Grid Location Service
GPCR Greedy Perimeter Forwarding Routing GPS Global Position System
GPSR Greedy Perimeter Stateless Routing
GRANT Greedy Routing with Abstract Neighbor Table GRP Geographical Routing Protocol
GSR Greedy and Stateless Routing GySTAR Improved Greedy TAR I2I Inter Infrastructure
IAP Infrastructure Assisted Protocol IETF International Engineering Task Force IGRP Intersection-based Geographical Routing IOLSR Intelligent Optimized Link State Routing
IP Internet Protocol
IRTIV Intelligent Routing Using Real-time Traffic Information in VANET IZRP Intra Zone Routing Protocol
LF Loss Function
LHS Left Hand Side
LL Lower Limit
LOUVRE Landmark Overlays for URBAN Vehicular Routing Environments
LS Link State
MAC Medium Access Control
MANET Mobile Ad-Hoc network
MAODV Multi-cast AODV
MAV-AODV Multi-cast with ACO Based-on MAODV MO PSO Multi-Objective PSO
MODE Multi-Objective Optimization Differential Evolution MPR Multi point relay
MURU Multi-hop Routing for Urban VANET NVTime Neighbor Validity Time
©This item is protected by original copyright
xvi
OA Orthogonal Array
OBSG Optimization Broadcasting scheme for VANET with GA
OBU Onboard Unit
OCM Optimization Control Message
OF Objective Function
OLSR Optimized Link State Routing
OPT Option
OT Optimization Target
PDR Packet Delivery Ratio
pPSO Parallel PSO
PRP Proactive Routing Protocols PSO Particle Swarm Optimization
QoS Quality of Service
RBVT Road-based routing Using Vehicular Traffic
REP Reply
RERP Route Reply
RERQ Route Request
RERR Route Error
RFC Request for comment
RHS Right Hand Side
RNG Relative neighborhood Graph RRP Reactive Routing Protocols
RSU Road Side Unit
RTCP Real-time connectivity Awareness
RTT Round Trip Time
SADV Static node Assisted Dissemination Protocol for VANET SAP Street-aware Routing Protocols
SAR Spatially-aware Routing
SF Smart Forwarding
SI Swarm Intelligent
SIFT Simple Forwarding Trajectory
SLAB Statistical Location-Assisted Broadcast SNR Signal-to-Noise Ratio
STAR Spatial and Traffic-aware Routing
©This item is protected by original copyright
xvii
TARCO Traffic-aware Routing Protocol
TC Topology Control
TO-GO Topology Assisted Geographical Routing TOM Taguchi Optimization Method
UL Upper Limit
USDOT United State Department of Transportation UVAR UAV-Assisted VANET Routing Protocol V2I Vehicle to Infrastructure
V2V Vehicle to Vehicle
VADD Vehicle Assisted Data Delivery VANET Vehicular Ad Hoc Network
VETT VANETs Expected Transmission Time
VT Trial Vector
WAVE Wireless Access in Vehicle Environments ZRP Zone Routing Protocol
©This item is protected by original copyright
xviii
Memantapkan Matrik Penghalaan Berdasarkan Pautan Optimum Penghalaan Bagi VANET
ABSTRAK
Rangkaian Ad Hoc Kenderaan (VANETs) menggapai minat penyelidik-penyelidik dan agensi kerajaan sebagai penyelesaian teknologi bagi sistem pengangkutan manusia.
VANETs bertujuan menyediakan sambungan antara kenderaan di rangkaian jalan dan infrastruktur dalam skim komunikasi ad hoc. Dalam VANETs, setiap kenderaan menggunakan mekanisme penghalaan untuk mencari laluan bagi menghantar mesejnya ke destinasi terakhir, di mana mesej-mesej tersebut dihantar dalam fesyen pelbagai hop.
Tingkah laku tersebut menekankan kesan mekanisme protokol penghalaan dalam prestasi VANETs. Dalam tahun-tahun kebelakangan ini, analisis protokol penghalaan VANETs dan impaknya terhadap prestasi rangkaian dengan senario rangkaian yang berbeza telah membangunkan pemahaman yang tepat terhadap keperluan dan matlamat untuk merancang protokol penghalaan VANETs. Tambahan pula, dalam kajian literatur, banyak mekanisme protokol penghalaan dicadangkan untuk menangani keperluan VANETs. Walau bagaimanapun, mekanisme penghalaan yang dicadangkan dalam kajian literatur tersebut hanya mempertimbangkan senario rangkaian tunggal dalam VANETs.
Walau bagaimanapun, kenderaan atau nod bergerak di dalam VANETs cenderung untuk bergerak jarak jauh, yang memberikan implikasi mengenai penglibatan mereka dalam pelbagai rangkaian senario dan topologi. Tingkah laku yang dipatuhi oleh nodus VANETs menghasilkan keperluan untuk mekanisme penghalaan yang memenuhi kriteria lebih daripada satu senario rangkaian dan topologi. Masalah ini kurang dipertimbangkan dalam kajian literatur. Oleh itu, tesis ini mencadangkan metrik penghalaan Jangkaan Penghantaran Masa VANETs (VETT) untuk menangani perubahan topologi dinamik dalam VANETs. Metrik yang dicadangkan mentakrifkan prestasi Pautan Optimum Penghalaan Stat (OLSR) dalam senario rangkaian yang berbeza sebagai masalah pengoptimuman objektif. Mekanisme yang dicadangkan ini disepadukan dengan protokol OLSR sebagai protokol penghalaan geografi. Hasil simulasi yang meluas ditunjukkan dengan membandingkan antara mekanisme penghalaan yang dioptimumkan dan yang tidak dioptimumkan. Mekanisme tersebut dinilai untuk pelbagai rangkaian metrik termasuk kepadatan lalu lintas, saiz paket dan jumlah kereta untuk dua topologi rangkaian iaitu bandar dan lebuh raya. Keputusan menunjukkan bahawa metrik penghalaan (VETT) yang dicadangkan meningkatkan prestasi OLSR untuk pelbagai senario VANETs dalam keadaan penangguhan, nisbah penghantaran paket (PDR), kehilangan paket serta penghantaran. Mekanisme pengoptimuman objektif (VETT) mengurangkan kelewatan lebih daripada 30% dan meningkatkan PDR dan keluaran melebihi 15%. Selain itu, analisis prestasi protokol penghalaan untuk senario VANETs yang berbeza menunjukkan perbezaan di dalam prestasi protokol penghalaan tunggal dalam senario yang berbeza. Ini menyokong hipotesis bahawa topologi rangkaian mempunyai impak yang besar terhadap prestasi protokol penghalaan. Tesis ini menyimpulkan bahawa pengoptimuman protokol penghalaan adalah penting bagi meningkatkan prestasi VANETs. Pengoptimuman satu objektif menghasilkan
©This item is protected by original copyright
xix
penambahbaikan prestasi penghalaan yang hebat. Walau bagaimanapun, ia tidak dapat menambahbaik lebih daripada satu prestasi secara serentak
©This item is protected by original copyright
xx
Enhance Routing Metric Based Optimized Link State Routing protocol (OLSR) Protocol for VANET
ABSTRACT
Vehicular Ad Hoc Networks (VANETs) grasp the interest of researchers and many governmental agencies as technological solution for human’s transportation systems.
VANETs aims at providing connectivity among vehicles on the road and infrastructure network in ad hoc communications scheme. In VANETs each vehicle uses a routing mechanism to find a path for sending its messages to the last destination, where messages are sent in a multi hop fashion. The behavior emphasis the impact of the routing protocol mechanism in the performances of VANETs. In recent years, the analysis of VANETs routing protocols and their impact on the performances of network with different network scenarios has significantly developed a precise understanding of the requirements and goals for designing a VANETs routing protocol. Further, in the literature many routing protocol mechanisms are proposed to deal with VANET’s requirements. Nonetheless, proposed routing mechanisms in the literature considered a single network scenario in VANETs. However, Vehicles or moving nodes in VANETs are tending to travel in long distances, which implies their engagement in multiple network scenarios and topologies.
The adhered behavior of VANET’s nodes results in a need for a routing mechanism that addresses the requirement of more than one network scenarios and topologies. This problem is less considered in the literature. Hence, this thesis proposes VANETs Expected Transmission Time (VETT) routing metric to tackle the dynamic topology changes in VANETs. The proposed metric defines the performances of Optimized link stat routing protocol (OLSR) in different network scenarios as an objective optimization problem. The proposed mechanism is integrated with OLSR protocol as geographical routing protocol. Extensive simulation results are presented by comparing between the performances of optimized and non-optimized routing mechanisms. The mechanisms are evaluated for varying network metrics including traffic density, packet size, and number of cars for two network topologies; city and highway. The results show that the proposed routing metric (VETT) improves the performance of OLSR for multiple VANET scenarios in-terms of delay, packet delivery ratio (PDR), packet loss, and throughput. The objective optimization mechanism (VETT) reduces the delay by more than 30% and increases the PDR and throughput by more than 15%. Furthermore, the performance analysis of routing protocols for different VANET scenarios shows divergences in the performances of a single routing protocol in different scenarios. This supports the hypothesis that network topology has a major impact on the routing protocol performances. This thesis concludes that the optimization of routing protocol is necessary to improve the performances of VANET. A single objective optimization results in a great routing performances improvement. However, it is not capable of improving more than one performances simultaneously.
©This item is protected by original copyright
1
1. CHAPTER 1: INTRODUCTION
1.1 Research Background
Nowadays, the rapid evolution and cost reduction of wireless communication technologies have made them suitable for a wide spectrum of mobile and wireless applications. There are huge number of vehicles travelling along highways and streets around the world which produce millions of data being transmitted daily throughout the entire world. According to (Money, 2012), today there are approximately 6.8 billion people in the world and by 2044 that number will grow to about 9 billion (this would result in many problems, one of which is in the transportation system). As the total number of vehicles is growing from 800 million cars today to 2-4 billion by 2050, global gridlocks and traffic jams will occur in many different places. Therefore, Vehicular Ad- Hoc Networks (VANET) appears as a technology solution for the adhered issue, and grasps the attention of both government’s agencies, industries and researchers (Englund, Chen, Vinel, & Lin, 2015; Shankar & Singh, 2015).
VANET is a special case of Mobile Ad hoc Network (MANET) application, having an impact on the wireless communications and Intelligent Transport System (ITS) (Chang, Xiang, Shi, & Lin, 2009). The main goals of VANET is to exchange information between vehicle’s driver to avoid unpleasant traffic situations, enhance traffic management and offer infotainment services. These services can help reduce road collision by 82% as stated in the United State Department of Transportation (USDOT) report (Kenney, 2011). VANET can provide road safety in means of intersection collision warning, emergency warning between vehicles, road condition information exchange
©This item is protected by original copyright
2
between vehicles, and post-crash estimation systems (Al-Sultan, Al-Doori, Al-Bayatti, &
Zedan, 2014).
VANET is composed of Roadside Units (RSU) and Vehicle “On-Board Units”
(OBU). Both RSU and OBU are equipped with embedded systems to provide communication, position information and capable of intelligent computations.
Intercommunication between RSUs and OBUs divides VANET architecture into three communication ways: Vehicle to Vehicle (V2V), Vehicle to Infrastructure (V2I), and Inter-infrastructure communication (I2I). Figure 1.1 depicts the three communication infrastructures of VANET. Further, communication between VANET units is defined by two standards: Dedicated Short Range Communication (DSRC) and Wireless Access in Vehicle Environments (WAVE) (Li, 2010). The United States Federal Communications Commission (FCC) assigned the 75 MHz spectrum in the 5.9 GH band for DSRC communication. The physical and Medium Access Control layer are operated with IEEE802.11p protocol standards (Kenney, 2011). IEEE proposed a full protocol stack for VANET in 1906.1 draft which known as WAVE (Li, 2010). WAVE contains six sub layers, and each layer is dedicated for specific network functionalities (Blum, 2015).
©This item is protected by original copyright
3
Figure 1.1: VANET Architecture.
WAVE is faced with many research challenges. Challenges faced by WAVE standard that addresses the limitations of VANET are reviewed and investigated by many researchers (Al-Sultan et al., 2014; Felipe Cunha et al., 2016; Eze, Zhang, & Liu, 2014;
Liang, Li, Zhang, Wang, & Bie, 2015; ur Rehman, Khan, Zia, & Zheng, 2013). Research issues in VANET are a direct result of the unique characteristics in VANET such as the high dynamic nature of VANET topology and the predictable mobility of road vehicles (Liang et al., 2015). Moreover, the similarity between VANET and MANET makes VANET inherits most of MANET research problems such as the instability in wireless connectivity and the multi-hop communication fashion. This work focuses on the problem of the dynamic nature of VANET’s topology, and the problem of designing a robust routing protocol for VANET.
The fact that, vehicles are tending to travel in long distances is a major factor for the high dynamic nature in VANET. It’s also causes the same node to be attached to
©This item is protected by original copyright
4
different network scenarios. This problem necessitated the need for a robust routing protocol design, which cops with different network requirements. For example, a routing protocol might have a good performance in one network scenario such as a city, however, this performance may have degraded or contrasted for another scenario such as highway network. The contrasts in the protocol performances is due to change in the network topology parameters including number of nodes and mobility trajectory. In the literatures many protocols are designed for a certain topology and scenario requirements (Bitam, Mellouk, & Zeadally, 2015; H. Cheng & Cao, 2008; Eiza, Ni, & Member, 2013; Fazio, Rango, & Sottile, 2015; Hajlaoui, Guyennet, & Moulahi, 2016; In, 2008; Jerbi & Senouci, 2009; Kopp, Member, Tyson, & Pose, 2016). However less literature addresses the multi- scenario issue in VANET. This thesis developed a new routing metric named Expected Transmission Time in VNET (VETT) to improve the performances of Optimized link stat routing protocol (OLSR).
In this thesis VETT will be developed to improve the performance of OLSR. The protocol is firstly optimized on three different VANET scenarios (City, Highway and Hybrid) in-terms of three routing parameters (Traffic generation (TG), Packet Size (PS) and Number of cars (NC)) to define and justify a certain performance metric. Further the selected protocol is modified to automatically change their routing parameters values according to the current network scenario.
The work in this thesis is based-on simulation experiments. The discrete event simulator OMNET++5.1 and the INET3.5 network framework is used for the simulation purpose. OLSR is a standard protocol implementation and based-on the INET 3.5 modules that are an implementation of this protocol draft. Further the VANET topologies
