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.
