Learning-based VANET Communication and Security Techniques

This timely book provides broad coverage of vehicular ad-hoc network (VANET) issues, such assecurity, and network selection. Machine learning based methods areapplied to solve these issues. This book also includes four rigorously refereedchapters from prominent international researchers working in this subjectarea. The material serves as a useful reference for researchers, graduatestudents, and practitioners seeking solutions to VANET communication andsecurity related issues. This book will also help readers understand how to usemachine learning to address the security and communication challenges in VANETs. Vehicular ad-hoc networks (VANETs) support vehicle-to-vehiclecommunications and vehicle-to-infrastructure communications to improvethe transmission security, help build unmanned-driving, and supportbooming applications of onboard units (OBUs). The high mobility of OBUsand the large-scale dynamic network with fixed roadside units (RSUs) makethe VANET vulnerable to jamming. The anti-jamming communication of VANETs can be significantly improvedby 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 ofthe OBU message, especially if the serving RSUs are blocked by jammersand/or interference, which is also demonstrated in this book. This book serves as a useful reference for researchers, graduate students, and practitioners seeking solutionsto VANET communication and security related issues.