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The Next Generation Vehicular Networks, Modeling, Algorithm and Applications

Aus der Reihe Wireless Networks
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Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

13.11.2020

Verlag

Springer

Seitenzahl

157

Maße (L/B/H)

24,1/16/1,6 cm

Gewicht

430 g

Auflage

1st ed. 2021

Sprache

Englisch

ISBN

978-3-030-56826-9

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

13.11.2020

Verlag

Springer

Seitenzahl

157

Maße (L/B/H)

24,1/16/1,6 cm

Gewicht

430 g

Auflage

1st ed. 2021

Sprache

Englisch

ISBN

978-3-030-56826-9

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: GPSR Kontakt

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  • Produktbild: The Next Generation Vehicular Networks, Modeling, Algorithm and Applications
  • Produktbild: The Next Generation Vehicular Networks, Modeling, Algorithm and Applications
  • 1 Introduction 1 1.Overview of Vehicular Networks 1 1.1 Architecture of Vehicular Networks  1 1.2 Applications in Vehicular Networks1.2 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 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 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 Vehicular Networks   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 

    References6 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 References7 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 Vehicular NetworksReferences...........................................................................