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Learning-based VANET Communication and Security Techniques

  • Liang Xiao
  • Weihua Zhuang
  • Sheng Zhou
  • Cailian Chen

Part of the Wireless Networks book series (WN)

Table of contents

  1. Front Matter
    Pages i-ix
  2. Liang Xiao, Weihua Zhuang, Sheng Zhou, Cailian Chen
    Pages 1-11
  3. Liang Xiao, Weihua Zhuang, Sheng Zhou, Cailian Chen
    Pages 13-47
  4. Liang Xiao, Weihua Zhuang, Sheng Zhou, Cailian Chen
    Pages 49-77
  5. Liang Xiao, Weihua Zhuang, Sheng Zhou, Cailian Chen
    Pages 79-104
  6. Liang Xiao, Weihua Zhuang, Sheng Zhou, Cailian Chen
    Pages 105-129
  7. Liang Xiao, Weihua Zhuang, Sheng Zhou, Cailian Chen
    Pages 131-134

About this book

Introduction

This timely book provides broad coverage of vehicular ad-hoc network (VANET) issues, such as security, and network selection. Machine learning based methods are applied to solve these issues. This book also includes four rigorously refereed chapters from prominent international researchers working in this subject area. The material serves as a useful reference for researchers, graduate students, and practitioners seeking solutions to VANET communication and security related issues. This book will also help readers understand how to use machine learning to address the security and communication challenges in VANETs.

 Vehicular ad-hoc networks (VANETs) support vehicle-to-vehicle communications and vehicle-to-infrastructure communications to improve the transmission security, help build unmanned-driving, and support booming applications of onboard units (OBUs). The high mobility of OBUs and the large-scale dynamic network with fixed roadside units (RSUs) make the VANET vulnerable to jamming. 

 The anti-jamming communication of VANETs can be significantly improved by using unmanned aerial vehicles (UAVs) to relay the OBU message. UAVs help relay the OBU message to improve the signal-to-interference-plus-noise-ratio of the OBU signals, and thus reduce the bit-error-rate of the OBU message, especially if the serving RSUs are blocked by jammers and/or interference, which is also demonstrated in this book.

This book serves as a useful reference for researchers, graduate students, and practitioners seeking solutions to VANET communication and security related issues.

Keywords

Vehicular Ad-hoc Networks Security Jamming Authentication Machine Learning Reinforcement Learning Game Theory Vehicular Edge Computing Networks Heterogeneous Vehicle Network

Authors and affiliations

  • Liang Xiao
    • 1
  • Weihua Zhuang
    • 2
  • Sheng Zhou
    • 3
  • Cailian Chen
    • 4
  1. 1.Department of Communication EngineeringXiamen UniversityXiamenChina
  2. 2.Department of Electrical & Computer EngineeringUniversity of WaterlooWaterlooCanada
  3. 3.Department of Electronic EngineeringTsinghua UniversityBeijingChina
  4. 4.Department of AutomationShanghai Jiao Tong UniversityShanghaiChina

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-030-01731-6
  • Copyright Information Springer Nature Switzerland AG 2019
  • Publisher Name Springer, Cham
  • eBook Packages Engineering
  • Print ISBN 978-3-030-01730-9
  • Online ISBN 978-3-030-01731-6
  • Series Print ISSN 2366-1186
  • Series Online ISSN 2366-1445
  • Buy this book on publisher's site