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LEARNING-BASED VANET COMMUNICATION AND SECURITY TECHNIQUES IBD

LIANG XIAO / SHENG ZHOU / WEIHUA ZHUANG
SPRINGER
12 / 2019
9783030131920
Inglés

Sinopsis

This timely book provides broad coverage of vehicular ad-hoc network (VANET) issues, such asásecurity, and network selection. Machine learning based methods areáapplied to solve these issues. This book also includes four rigorously refereedáchapters from prominent international researchers working in this subjectáarea. The material serves as a useful reference for researchers, graduateástudents, and practitioners seeking solutions to VANET communication andásecurity related issues. This book will also help readers understand how to useámachine learning to address the security and communication challenges in VANETs.áVehicular ad-hoc networks (VANETs) support vehicle-to-vehicleácommunications and vehicle-to-infrastructure communications to improveáthe transmission security, help build unmanned-driving, and supportábooming applications of onboard units (OBUs). The high mobility of OBUsáand the large-scale dynamic network with fixed roadside units (RSUs) makeáthe VANET vulnerable to jamming.ááThe anti-jamming communication of VANETs can be significantly improvedáby 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 ofáthe OBU message, especially if the serving RSUs are blocked by jammersáand/or interference, which is also demonstrated in this book.This book serves as a useful reference for researchers, graduate students, and practitioners seeking solutionsáto VANET communication and security related issues.