The Next Generation Vehicular Networks, Modeling, Algorithm and Applications

• Main Authors: Su, Zhou, Hui, Yilong, Luan, Tom H, Liu, Qiaorong, Xing, Rui
• Format: eBook
• Language: English
• Published: Cham Springer International Publishing AG 2020; Springer International Publishing
• Edition: 1
• Series: Wireless Networks
• Subjects: Artificial Intelligence; Computer Science; Computer Systems Organization and Communication Networks; Vehicular ad hoc networks (Computer networks); Wireless and Mobile Communication
• ISBN: 3030568261; 9783030568269
• ISSN: 2366-1186; 2366-1445
• DOI: 10.1007/978-3-030-56827-6

This book proposes the novel network envisions and framework design principles, in order to systematically expound the next generation vehicular networks, including the modelling, algorithms and practical applications. It focuses on the key enabling technologies to design the next generation vehicul...

Table of Contents:

• Intro -- Preface -- Contents -- Acronyms -- 1 Introduction -- 1.1 Overview of Vehicular Networks -- 1.1.1 Architecture of Vehicular Networks -- 1.1.2 Applications in Vehicular Networks -- 1.2 Overview of Enabling Technologies -- 1.2.1 Advanced Communication-5G -- 1.2.2 Mobile Edge Computing -- 1.2.3 Network Function Virtualization -- 1.2.4 Software Defined Network -- 1.2.5 Computation Offloading -- 1.2.6 Blockchain -- 1.2.7 Information Centric Networks -- 1.2.8 Edge Caching -- 1.2.9 Autonomous Driving -- 1.2.10 Artificial Intelligence -- 1.3 Aim of the Book -- References -- 2 Reputation Based Content Delivery in Information Centric Vehicular Networks -- 2.1 Introduction -- 2.2 Overview of Information Centric Vehicular Networks -- 2.2.1 Content Delivery in Vehicular Networks -- 2.2.2 ICN Based Content Delivery -- 2.2.3 Challenges of Content Delivery in Information Centric Vehicular Networks -- 2.3 Reputation Based Vehicular Networks -- 2.4 Framework of Reputation Based Content Delivery in Information Centric Vehicular Networks -- 2.4.1 Network Architecture -- 2.4.2 Framework of Reputation Based Content Delivery in Information Centric Vehicular Networks -- 2.5 Simulation -- 2.5.1 Setting -- 2.5.2 Results Analysis -- 2.6 Summary -- References -- 3 Contract Based Edge Caching in Vehicular Networks -- 3.1 Introduction -- 3.2 Edge Caching in Vehicular Social Networks -- 3.2.1 Vehicular Social Networks -- 3.2.2 Edge Caching in Vehicular Networks -- 3.2.3 Challenges of Edge Caching in Vehicular Networks -- 3.3 Contract Based Edge Caching in Vehicular Networks -- 3.4 Framework of Contract Based Edge Caching in VehicularNetworks -- 3.4.1 Network Architecture -- 3.4.2 Framework of Contract Based Edge Caching in Vehicular Networks -- 3.5 Simulation -- 3.5.1 Setting -- 3.5.2 Results Analysis -- 3.6 Summary -- References
• 4 Stackelberg Game Based Computation Offloading in Vehicular Networks -- 4.1 Introduction -- 4.2 System Model -- 4.2.1 Network Model -- 4.2.2 Communication Model -- 4.2.3 Task Execution Model -- 4.3 Stackelberg Game Analysis -- 4.3.1 Benefits of Vehicles -- 4.3.2 Benefits of MEC Server -- 4.3.3 Stackelberg Game -- 4.4 The Equilibrium Solution of Stackelberg Game -- 4.4.1 Stage 2: Offloading Strategy of Vehicles -- 4.4.2 Stage 1: Pricing Strategy of MEC Servers -- 4.4.3 Stackelberg Game Equilibrium -- 4.5 Simulation -- 4.5.1 Setting -- 4.5.2 Results Analysis -- 4.6 Summary -- References -- 5 Auction Based Secure Computation Offloading in VehicularNetworks -- 5.1 Introduction -- 5.2 System Model -- 5.2.1 Network Model -- 5.2.2 Task Model -- 5.3 Analysis of Secure Offloading Strategy in Edge-Cloud Networks -- 5.3.1 Task Offloading Scheme Based on First Price Sealed Auction -- 5.3.2 TSVM-Based Detection Scheme -- 5.4 Simulation -- 5.4.1 Setting -- 5.4.2 Results Analysis -- 5.5 Summary -- References -- 6 Bargain Game Based Secure Content Delivery in VehicularNetworks -- 6.1 Introduction -- 6.2 Problem Formulation -- 6.2.1 Deployment of RSUs -- 6.2.2 Attack and Defence in Vehicular Networks -- 6.2.3 Trust Evaluation of Vehicles -- 6.2.4 Trust Value of RSUs -- 6.2.5 Deployment of AU -- 6.2.6 Bargain Game Between RSUs and Vehicles -- 6.3 Simulation -- 6.3.1 Setting -- 6.3.2 Results Analysis -- 6.4 Summary -- References -- 7 Deep Learning Based Autonomous Driving in Vehicular Networks -- 7.1 Introduction -- 7.2 Overview of Deep Learning Based Autonomous Driving in Vehicular Networks -- 7.2.1 Autonomous Driving -- 7.2.2 Autonomous Driving with Vehicular Networks -- 7.2.3 Deep Learning Based Autonomous Driving -- 7.3 Architecture of Deep Learning Based Autonomous Driving in Vehicular Networks -- 7.3.1 Network Architecture -- 7.3.2 Composition of Learning Group
• 7.4 Learning with Groups: Deep Learning Based Autonomous Driving in Vehicular Networks -- 7.4.1 Topology of Learning Groups -- 7.4.2 Cooperative Learning Within a Group -- 7.4.3 Allocation of Profits/Costs Within a Group -- 7.5 Simulation -- 7.5.1 Setting -- 7.5.2 Results Analysis -- 7.6 Summary -- References -- 8 Conclusions and Future Directions -- 8.1 Conclusions -- 8.2 Future Research Directions -- 8.2.1 Trading Mechanism in Vehicular Networks -- 8.2.2 Security and Privacy in Vehicular Networks -- 8.2.3 Big Data in Vehicular Networks -- 8.2.4 QoE Aware Services in Vehicular Networks -- 8.2.5 Smart Transportation Systems with Vehicular Networks -- 8.2.6 Resource Integration and Allocation in VehicularNetworks -- References