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Wireless Networks

Learning-based VANET Communication and Security Techniques

Authors: Xiao, L., Zhuang, W., Zhou, S., Chen, C.

  • Investigates the fundamental principles for “communication and security issues in VANETs”, including the system fundamentals and algorithm fundamentals. These fundamental principles help to fully and deeply understand the communication and security issues for vehicular ad-hoc networks
  • Shows how machine learning techniques can solve problems in vehicular ad-hoc networks
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  • Investigates in various aspects, including “authentication in VANETs”, “VANET communication against smart jamming”, “task offloading in vehicular edge computing networks”, and “network selection on heterogeneous vehicle network.”
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Buy this book

eBook $109.00
price for USA in USD (gross)
  • ISBN 978-3-030-01731-6
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $149.99
price for USA in USD
  • ISBN 978-3-030-01730-9
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
About this book

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.

Table of contents (6 chapters)

  • Introduction

    Xiao, Liang (et al.)

    Pages 1-11

  • Learning-Based Rogue Edge Detection in VANETs with Ambient Radio Signals

    Xiao, Liang (et al.)

    Pages 13-47

  • Learning While Offloading: Task Offloading in Vehicular Edge Computing Network

    Xiao, Liang (et al.)

    Pages 49-77

  • Intelligent Network Access System for Vehicular Real-Time Service Provisioning

    Xiao, Liang (et al.)

    Pages 79-104

  • UAV Relay in VANETs Against Smart Jamming with Reinforcement Learning

    Xiao, Liang (et al.)

    Pages 105-129

Buy this book

eBook $109.00
price for USA in USD (gross)
  • ISBN 978-3-030-01731-6
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $149.99
price for USA in USD
  • ISBN 978-3-030-01730-9
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
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Bibliographic Information

Bibliographic Information
Book Title
Learning-based VANET Communication and Security Techniques
Authors
Series Title
Wireless Networks
Copyright
2019
Publisher
Springer International Publishing
Copyright Holder
Springer Nature Switzerland AG
eBook ISBN
978-3-030-01731-6
DOI
10.1007/978-3-030-01731-6
Hardcover ISBN
978-3-030-01730-9
Series ISSN
2366-1186
Edition Number
1
Number of Pages
IX, 134
Number of Illustrations
6 b/w illustrations, 48 illustrations in colour
Topics