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Learning-based VANET Communication and Security Techniques

Af: Liang Xiao, Sheng Zhou, Weihua Zhuang, Cailian Chen Engelsk Paperback

Learning-based VANET Communication and Security Techniques

Af: Liang Xiao, Sheng Zhou, Weihua Zhuang, Cailian Chen Engelsk Paperback
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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.

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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.

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Produktdetaljer
Sprog: Engelsk
Sider: 134
ISBN-13: 9783030131920
Indbinding: Paperback
Udgave:
ISBN-10: 3030131920
Kategori: Trådløs teknologi (WAP)
Udg. Dato: 10 dec 2019
Længde: 0mm
Bredde: 155mm
Højde: 235mm
Forlag: Springer Nature Switzerland AG
Oplagsdato: 10 dec 2019
Forfatter(e): Liang Xiao, Sheng Zhou, Weihua Zhuang, Cailian Chen
Forfatter(e) Liang Xiao, Sheng Zhou, Weihua Zhuang, Cailian Chen

Kategori Trådløs teknologi (WAP)

ISBN-13 9783030131920

Sprog Engelsk

Indbinding Paperback

Sider 134

Udgave

Længde 0mm

Bredde 155mm

Højde 235mm

Udg. Dato 10 dec 2019

Oplagsdato 10 dec 2019

Forlag Springer Nature Switzerland AG

Kategori sammenhænge