Intelligent Transportation Systems (eBook)

Shyam Chakraborty is the Managing Director and Founder of Oy Trinnect Ltd which deals in a revolutionary telemedicine program to help healthcare providers provide high quality healthcare across the Finland. Dr. Chakraborty has 28 International (16 IEEE) journal papers, 43 conference papers, 3 books, 7 World patents, industrial research reports, and 3 graduated PhD scholars. He was awarded the Senior Academy Fellowship of Academy of Finland from 2000-2004. Prior to founding his company Trinnect he taught wireless networking at LNM Institute of Information Technology in Jaipur India in 2012-2013.Nazmul. H. Siddique is a lecturer specializing in Computational Intelligence, Mobile Robotics, Object Oriented Programming and Cybernetics at the School of Computing and Intelligent Systems of the University of Ulster. In 2012 he was awarded the DEL/Connect NI Grant—Innovation Visit to Japan; and in 2011 he was awarded Daiwa Anglo-Japanese Foundation Grant – Stochastic Regression Modelling.Syed Faraz Hasan is a Senior Lecturer at the School of Engineering and Advanced Technology, Massey University, New Zealand, where he leads the Telecommunication and Network Engineering (TNE) research group. He has extensive experience of leading and delivering on externally funded research projects. He is a member of the Science Policy Exchange panel which is led by New Zealand Prime Minister’s Chief Science Advisor. He is a Senior Member of IEEE. Dr. Hasan obtained his PhD degree in 2011 from University of Ulster, UK, and Bachelor’s degree (Electrical Engineering) in 2008 from NED University of Engineering and Technology, Pakistan.  His main area of expertise is wireless communication. 

Foreword 6
Preface 7
Contents 9
Abbreviations 13
List of Figures 16
List of Tables 20
1 Wireless Technology for Vehicles 22
1.1 Wireless Local Area Networks 23
1.2 Expanding the Mobility Domain of WLANs 25
1.2.1 Vehicular Communications 26
1.2.2 V2V and R2V Communications 26
1.3 Autonomous and Connected Vehicles Vision 28
1.4 Wireless Technologies for Vehicles 30
1.4.1 Cellular Networks and D2D Communication 31
1.4.2 802.1x Technologies 33
1.4.3 Cognitive Radio 34
1.5 802.11-Based VC: Challenges 34
1.5.1 Disruption Tolerance 34
1.5.2 Handover Latency 36
1.5.3 Security Issues 37
1.6 Summary 37
2 Basics of Vehicular Communication 39
2.1 Disruption Tolerant Networking 40
2.1.1 Systems and Architectures 40
2.1.2 New and Modified Protocols 43
2.1.3 Prediction-Based Techniques 45
2.2 Handover Latency in Wireless Networks 47
2.2.1 Detection, Search, and Probing Delay 48
2.2.2 Authentication and Address Allocation Delay 50
2.3 Handovers in Vehicular Communication 51
2.3.1 Mobility Management and Heterogeneity 54
2.4 IEEE Standards for Vehicular Communication 55
2.4.1 Wireless Access in Vehicular Environments: 802.11p 56
2.4.2 Fast Transition: 802.11r 57
2.4.3 High Throughput: 802.11n 58
2.4.4 Very High Throughput: 802.11ac 58
2.4.5 IEEE 802.11ax: Work in Progress 60
2.5 Summary 61
3 Performance Indicators of Vehicular Communication 62
3.1 The Vehicular Context 63
3.2 Key Parameters: RSS and Data Rate 63
3.3 Measurement and Analysis 64
3.3.1 Signal Strength 65
3.3.2 Data Rate 68
3.3.2.1 Experimental Setup 68
3.3.2.2 Observations and Analysis 70
3.3.3 Correlation Between Data Rates and RSS 72
3.4 Application: Traffic Congestion Monitoring 74
3.4.1 Extended MULE Concept 75
3.4.1.1 Comparison with Other Works 76
3.4.1.2 X-MULE Issues 76
3.4.2 Roadside Infrastructure 77
3.4.2.1 Encounter Duration 79
3.4.3 Communication Mechanism 80
3.5 Case Study: In-Vehicle Infotainment 84
3.6 Summary 85
4 Markov Representation of Vehicular Communications 87
4.1 Markov Models 88
4.1.1 Fundamentals of Markov Chains 88
4.1.2 Markov Process in R2V Communications 89
4.2 Estimating the Transition Probability 91
4.2.1 Data Collection 91
4.2.2 Probability Distribution of Dataset 92
4.2.3 Calculating Transition Probability 97
4.2.4 Long Term Error Rate 99
4.3 Three-State Markov Model 101
4.4 Towards Hidden Markov Model 103
4.5 Summary 104
5 Disruption in Vehicular Communications 105
5.1 Hidden Markov Models 105
5.2 HMM Representation of R2V Communication 106
5.2.1 Model Structure 107
5.2.2 Estimating Model Parameters 109
5.2.3 Model Generality, Limitations, and Need 112
5.2.3.1 Online and Offline Calculations 112
5.3 Observation Sequence of HMM 114
5.4 Probabilistic Measures of Disruption 117
5.4.1 Forward Algorithm 118
5.4.2 State Probability 120
5.4.3 Encounter Probability 121
5.5 Traffic Pattern Analysis 123
5.5.1 Drive Tests 123
5.5.2 Variation in Disruption with Traffic Patterns 125
5.6 Summary 128
6 Inter ISP Roaming for Vehicular Communications 129
6.1 Intra- and Inter ISP Roaming 130
6.2 Wireless Internet Service Provider Roaming 132
6.2.1 WISPr Architecture 133
6.3 Wireless Roaming for Data Offloading 135
6.4 Modifications in HMM 137
6.4.1 Effectiveness of WISPr 138
6.5 Summary 140
7 Handover Latency in Vehicular Communication 142
7.1 Handovers in WLANs 142
7.2 Experiments and Observations 143
7.2.1 Measurement Setup 144
7.2.2 Observations in Vehicular Environments 144
7.3 Latency Analysis 147
7.3.1 DHCP Delay 147
7.3.1.1 Legacy DHCP 148
7.3.1.2 Assessing Individual DHCP Processes 148
7.3.2 EAP Delay 151
7.3.3 Scanning Delay 152
7.3.4 Delays Due to Background Applications 152
7.4 Reducing Scanning Phase Delay 153
7.4.1 Scanning Orthogonal Channels 154
7.4.2 AP Performance on Orthogonal Channels 157
7.5 Summary 160
8 Cellular Technology-Based Vehicular Communication 161
8.1 Vehicular D2D Communication 162
8.1.1 Quality of Service Issues 162
8.1.2 Contextual Awareness 163
8.2 D2D Support in LTE-A Networks 163
8.3 Resource Allocation 164
8.3.1 Vehicular Perspective 165
8.3.2 Moving Personal Cells 166
8.3.3 State of the Art: Resource Allocation 167
8.4 Device Discovery 168
8.4.1 Dedicated Discovery Resources 169
8.4.2 Existing Discovery Mechanisms 170
8.5 Summary 171
9 Epilogue 172
9.1 Future of ITS 172
9.2 Disruption Tolerance 173
9.3 Handover Latency 173
9.4 D2D-based Vehicular Communication 174
9.5 Data Handling in Vehicular Sensor Networks 174
9.6 Location Invariant Models 175
A Backward Algorithm 177
B EAP Authentication Mechanism 179
C Software Tools 181
C.1 IPerf 181
C.2 Vistumbler 181
C.3 Windows Network Monitor 182
C.4 Others 182
References 183
Index 194