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The Next Generation Vehicular Networks, Modeling, Algorithm and Applications

The Next Generation Vehicular Networks, Modeling, Algorithm and Applications (Paperback)

Tom H. Luan, Zhou Su, Yilong Hui, Qiaorong Liu, Rui Xing (지은이)
Springer
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The Next Generation Vehicular Networks, Modeling, Algorithm and Applications
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· 제목 : The Next Generation Vehicular Networks, Modeling, Algorithm and Applications (Paperback) 
· 분류 : 외국도서 > 컴퓨터 > 네트워킹 > 일반
· ISBN : 9783030568290
· 쪽수 : 157쪽
· 출판일 : 2021-11-13

목차

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 De?ned Network 
 1.2.5 Computation Of?oading 
 1.2.6 Blockchain 
 1.2.7 Information Centric Networks 
 1.2.8 Edge Caching 
 1.2.9 Autonomous Driving 
 1.2.10 Arti?cial Intelligence 
1.3 Aim of the Book 
2 ReputationBasedContentDeliveryinInformationCentricVehicularNetworks
  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 
       2.4.1 Network Architecture
       2.4.2 FrameworkofReputationBasedContentDeliveryinInformationCentricVehicular Networks 
  2.5 Simulation 
      2.5.1 Setting 
      2.5.2 Results Analysis 
   2.6 Summary 
References
3 Contract Based Edge Cachingi n 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 Vehicular Networks 
         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 Of?oading 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 Bene?ts of Vehicles
        4.3.2 Bene?ts of MEC Server
        4.3.3 Stackelberg Game
   4.4 The Equilibrium Solution of Stackelberg Game 
        4.4.1 Stage 2: Of?oading 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 Of?oading in Vehicular Networks 
  5.1 Introduction 
  5.2 System Model 
      5.2.1 Network Model
      5.2.2 Task Model 
  5.3 Analysis of Secure Of?oading Strategy in Edge-Cloud Networks
      5.3.1 Task Of?oading 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 Vehicular Networks
  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 Pro?ts/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 Vehicular Networks
References........................................................................... 


저자소개

Zhou Su (지은이)    정보 더보기
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Yilong Hui (지은이)    정보 더보기
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Qiaorong Liu (지은이)    정보 더보기
펼치기
Rui Xing (지은이)    정보 더보기
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