Cooperative Intelligent Transport Systems

Léo Mendiboure is a Research Fellow in Computer Science at the Université Gustave Eiffel (COSYS-ERENA team), France. His research interests include future-generation networks, automated and connected vehicles, and data processing architectures.

• Preface xiiiLéo MENDIBOUREPart 1 Introduction to Cooperative Intelligent Transport Systems 1Chapter 1 Local Interactions for Cooperative ITS: Opportunities and Constraints 3Jean-Marie BONNIN and Christophe COUTURIER1.1 Introduction 31.2 Ephemeral local interactions: concept and examples 51.2.1 Examples of services using ephemeral local interactions 51.2.2 Characteristics of ephemeral local interactions 61.2.3 Advantages of ephemeral local interactions 81.2.4 Suitability of communication technologies for this type of interaction 101.3 Local interactions serving cooperative ITS 131.3.1 Cooperative ITS services 131.3.2 Benefit of ephemeral local interactions for cooperative ITS 141.3.3 V2X communication technologies 161.3.4 Properties of C-ITS services built on local interactions 181.3.5 Limitations and constraints of implementing services built on local interactions 221.4 Role of infrastructure in cooperative ITS services 261.4.1 Infrastructures dedicated to cooperative ITS 261.4.2 Towards an active infrastructure 281.5 Conclusion and prospects 291.6 References 30Chapter 2 Evolution of Use Cases for Intelligent Transport Systems 33Sassi MAALOUL, Hasnaâ ANISS, Marion BERBINEAU and Léo MENDIBOURE2.1 Introduction 332.2 Vehicular communication technologies 342.2.1 ITS-G5/IEEE 802.11p technology 352.2.2 The 3GPP standard: C-V2X 362.2.3 Deployment of ITS technologies 372.3 Evolution of use cases 372.3.1 Classification of use cases 382.3.2 Required performance 402.3.3 Example of use cases 412.4 Challenges and future services of V2X 482.5 Conclusion 492.6 References 49Part 2 Optimization of Data Transmission for Cooperative Intelligent Transport Systems 51Chapter 3 Towards an Optimization of Data Transmission in Cooperative Intelligent Transport Systems 53Mohamed BENZAGOUTA, Ramzi BOUTAHALA, Secil ERCAN, Sassi MAALOUL, Hasnaâ ANISS, Léo MENDIBOURE, Marwane AYAIDA and Hacène FOUCHAL3.1 Introduction 533.2 Context 553.2.1 C-ITS Services 553.2.2 Communication standards 563.3 Experimental evaluation of the performance of the C-ITS message broadcasting system 583.3.1 C-Roads France project and COOPITS application 583.3.2 Experimental environment and measurements 603.3.3 Analysis of results 613.4 Discussion of the main causes 653.4.1 Absence of adaptation to actual conditions 663.4.2 Duplication of non-scalable information 663.4.3 Broadcasting of information in wide geographical areas 663.4.4 High level of security in relation to the risks involved 673.5 Recommendations and research avenues 703.5.1 Differentiation by traffic conditions 703.5.2 Smart broadcasting of constant messages 703.5.3 Smart definition of message broadcast areas 703.5.4 Security-level optimization 713.6 Conclusion 713.7 Acknowledgments 723.8 References 72Chapter 4 Efficient Hybridization of C-ITS Communication Technologies 75Badreddine Yacine YACHEUR, Toufik AHMED and Mohamed MOSBAH4.1 Introduction 754.2 Related works 774.3 Definition of a heterogeneous network architecture and design of a protocol stack 794.4 RL for selecting the mode of communication 814.4.1 Deep reinforcement learning 824.4.2 Correspondence with key elements of reinforcement learning 824.5 Performance evaluation 874.5.1 Simulation framework and scenario 874.5.2 DDQL algorithm parameters 894.5.3 Simulation results 904.6 Conclusion 934.7 References 93Chapter 5 Using SDN Technology to Control C-ITS: Towards Decentralized Approaches 97Romain DULOUT, Lylia ALOUACHE, Tidiane SYLLA, Léo MENDIBOURE, Hasnaâ ANISS, Virginie DENIAU and Yannis POUSSET5.1 Introduction 975.2 Context 995.2.1 SDN-controlled C-ITS architectures (SDVN) 995.2.2 Blockchain technology 1015.3 Application of Blockchain to SDVN architectures 1035.4 Optimization of Blockchain technology for SDVN architectures 1065.4.1 New architectures 1075.4.2 New mechanisms 1085.5 Future research avenues 1095.5.1 Optimal positioning of Blockchain nodes 1095.5.2 Energy consumption reduction 1095.5.3 Integration of AI and Blockchain 1105.5.4 A more complete integration between SDN and Blockchain 1105.6 Conclusion 1115.7 References 112Chapter 6 Application of Network Slicing in C-ITS Systems 115Abdennour RACHEDI, Toufik AHMED and Mohamed MOSBAH6.1 Introduction 1156.2 Vehicle-to-everything (V2X) communications 1166.3 Presentation of V2X technologies 1186.3.1 Its-g5 1196.3.2 Lte-v2x 1216.3.3 5g-v2x 1236.4 Network slicing for 5G-V2X 1256.4.1 Network slicing for C-V2X 1266.4.2 ITS-G5 network slicing 1286.5 Conclusion 1386.6 References 138Part 3 New Approaches to Data Processing in Cooperative Intelligent Transport Systems 141Chapter 7 A Novel Cloud Approach for Connected Vehicles 143Geoffrey WILHEM, Marwane AYAIDA and Hacène FOUCHAL7.1 Introduction 1437.2 State of the art 1447.2.1 ETSI standards for C-ITSs 1457.2.2 Vehicular cloud computing 1467.2.3 Information-centric networking 1477.3 The GeoVCDN approach 1507.3.1 A centralized context-cloud architecture 1507.3.2 Geographic routing ICN protocol 1537.3.3 Discussion 1607.4 Analytical model 1607.4.1 Description of the model 1617.4.2 Network modeling 1617.4.3 Communication environment modeling 1647.4.4 Message dissemination modeling 1657.4.5 Approaches 1737.4.6 Discussion 1797.5 Evaluation 1807.5.1 Simulator description 1807.5.2 Simulation results for network load 1827.6 Simulation results for data utility 1867.6.1 Simulation results for data validity 1867.6.2 Simulation results for data freshness 1877.6.3 Discussion of the simulation 1917.7 Use case study 1917.7.1 Scenario 1927.7.2 Discussion 1947.8 Conclusion 1957.9 Acknowledgment 1967.10 References 196Chapter 8 Optimal Placement of Edge Servers in C-ITS Systems 199Sabri KHAMARI, Toufik AHMED and Mohamed MOSBAH8.1 Introduction 1998.2 Context 2018.2.1 Vehicular applications 2018.2.2 Multi-access edge computing (MEC) 2018.2.3 Deployment of MEC systems 2018.3 State of the art 2028.4 OptPlacement: efficient edge server placement 2038.4.1 System modeling 2048.4.2 Methodology and simulation 2088.4.3 Performance evaluation 2138.5 Conclusion 2188.6 References 219Chapter 9 Risk Estimation: A Necessity for the Connected Autonomous Vehicle 223Dominique GRUYER, Sio-Song IENG, Sébastien GLASER,Sébastien DEMMEL, Charles TATKEU and Sabrine BELMEKKI9.1 Context and objectives 2239.2 Estimation of risk local to the ego-vehicle: some existing metrics 2269.3. Development of communication strategy to extend risk: CBL and CBL-G 2329.4 Computation of cooperative risks: extended local risk and global risk 2349.5 Impact of global risk and anticipation of risky situations 2369.6 Discussion 2429.7 Conclusion and prospects 2469.8 References 247Chapter 10 Resilience of Collective Perception in C-ITS – Deep Multi-Agent Reinforcement Learning 251Imed GHNAYA, Hasnaâ ANISS, Marion BERBINEAU,Mohamed MOSBAH and Toufik AHMED10.1 Introduction 25210.1.1 Background and issue 25210.1.2 Motivation and contribution 25310.2 State of the art 25510.2.1 Standardization of collective perception by ETSI 25610.2.2 Perception data selection and exchange techniques 25710.3 Mathematical modeling of the cooperative driving environment 25810.3.1 Awareness and perception data exchange 25910.3.2 Utility of perception data in the driving environment 26010.4 Multi-agent learning with DRL for selection and exchange of perception data 26110.4.1 System design 26210.4.2 Learning algorithm 26310.5 Simulations, results and evaluations 26510.5.1 Simulation tools, scenarios and parameters 26510.5.2 Results and evaluations 26610.6 Conclusion 26910.7 References 270Part 4 Securing Cooperative Intelligent Transport Systems 273Chapter 11 Distance-Bounding Protocols 275David GÉRAULT, Pascal LAFOURCADE and Léo ROBERT11.1 Introduction 27611.2 Relations between threats for DB protocols 27811.2.1 Threat models 27811.2.2 Relation between different threat models 28111.3 Overview of existing protocols 28311.3.1 Improvement of attacks 28411.3.2 Comparison of DB protocols 28711.4 References 288Chapter 12 Context-Aware Security and Privacy as a Service for the Connected and Autonomous Vehicle 295Tidiane SYLLA, Mohamed Aymen CHALOUF,Léo MENDIBOURE and Francine KRIEF12.1 Introduction 29512.2 Security, privacy and trust of connected and autonomous vehicle applications 29712.2.1 Main applications of the connected and autonomous vehicle 29712.2.2 Security, privacy and trust services for the connected and autonomous vehicle 30012.3 Security and privacy architecture 30312.3.1 Context-aware security and privacy 30312.3.2 Gaps in existing solutions 30512.3.3 Proposed solution 30612.4 Self-adaptive selection of network access technologies 31212.4.1 Infrastructure edge computing 31312.4.2 Orchestration and placement of services 31512.5 Main research works to be conducted 31712.6 Conclusion 31812.7 References 319Chapter 13 Vehicular Wireless Communications: Risks and Detection of Attacks 321Jonathan VILLAIN, Virginie DENIAU and Christophe GRANSART13.1 Introduction 32113.2 General characteristics of wireless communications for connected vehicles 32213.2.1 Challenges related to the connected vehicle 32213.2.2 V2V communications 32313.2.3 V2I communications 32413.3 Characteristics of wireless communications 32513.3.1 Principle of wireless communications 32513.3.2 Long-range communications 32513.3.3 Short-range communications 32613.3.4 Advent of 5G 32613.4 Susceptibility of communications and risks incurred 32713.4.1 Principle of attacks targeting layers 1 and 2 of communication systems 32713.4.2 Sybil attack 32813.4.3 Deauthentication frame attack 32813.4.4 Black-hole attack 32913.4.5 Jamming attack 33013.4.6 Flooding attack 33113.4.7 Risks and performance indicators 33113.5 Attack detection 33213.5.1 Need for a detection system 33213.5.2 Detection method 33313.5.3 AI for detection 33513.6 Conclusion 33813.7 References 338List of Authors 341Index 345