Название: Cloud and IoT-Based Vehicular Ad Hoc Networks
Автор: Группа авторов
Издательство: John Wiley & Sons Limited
Жанр: Автомобили и ПДД
isbn: 9781119761822
isbn:
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Library of Congress Cataloging-in-Publication Data
ISBN 978-1-119-76183-9
Cover image: Pixabay.Com
Cover design by Russell Richardson
Set in size of 11pt and Minion Pro by Manila Typesetting Company, Makati, Philippines Printed in the USA
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Preface
As technology continues to weave itself more tightly into everyday life, socioeconomic development has become intricately tied to ever-evolving innovations. An example of this is the technology being developed to address the massive increase in the number of vehicles on the road, which has resulted in more traffic congestion and road accidents. This challenge is being addressed by developing new technologies to optimize traffic management operations. That is why it is with great pleasure that we put forth this book on the topic of Cloud- and IoT-based vehicular ad hoc networks.
Current progress in wireless communication, computing paradigms and the internet of things (IoT) have resulted in the enhancement of intelligent devices equipped with wireless communication capabilities and highefficiency processors. As a result of the development of wireless technologies there has been a rapid growth in the use of the IoT, cloud computing and the number of smart vehicles, and along with it the demand for smart devices, such as smartphones, PDAs, smart watches, smart TVs, laptops, etc., connected to the cloud. However, conventional vehicular ad hoc networks (VANETs) face several technical challenges in deployment due to less flexibility, poor connectivity, and inadequate intelligence. Cloud computing, vehicular cloud computing, IoT and VANET are the major components in the current intelligent transport system (ITS). Various research studies on VANETs, cloud concepts and the IoT show that they have significant effects on smart transportation systems.
In order to address global concerns, a collection of the innovative research in these areas is presented in “Cloud- and IoT-Based Vehicular Ad Hoc Networks”. This book covers the emerging and advanced concepts of VANETs and their integration with cloud computing and IoT, emerging wireless networking and computing models. Highlighting a wide range of topics, such as the IoT, Fog computing, and 5G, it is ideally designed for engineers, technology developers, IT specialists, policymakers, academicians, researchers, and students. The topics presented in each chapter are unique to this book and are based on the unpublished work of the contributing authors. In editing this book, we attempted to bring into the discussion all the new trends, experiments, and products that have made the vehicular ad hoc network such a dynamic area. We believe the book is a suitable reference for a larger audience, including system architects, practitioners, developers, and researchers.
Chapter 1 focuses on the need for 5G for IoT devices. The authors emphasize that faster communication can yield the full capabilities of IoT devices in various application domains such as healthcare, the industrial internet of things (IIoT), agriculture, etc. Chapter 2 deals with the fundamentals and technological details of VANET, 5G, and the need to integrate the VANET with 5G. The need for service discovery is also discussed along with the service discovery mechanism. Petty performance evaluation metrics and service discovery in the 5G-VANET milieu are also discussed. Chapter 3 primarily reviews the ARM 9 vehicle safety processor. It further focuses on its future applications, challenges, and significance in the smart transportation system. Chapter 4 focuses on the automatic emergency system in each vehicle, the use of which automatically transmits an emergency message from the location of an accident to the closest emergency center.
In Chapter 5, an interleaver-centric conflict management (ICM) solution for both vehicular and cognitive IoT (CIoT) communications is explored, which offers a coordination mechanism among the devices and/or networks to manage the conflict. Chapter 6 proposes an integrated system model to ensure safe and secure transportation, providing a very comfortable zone for humankind in terms of reliability, thereby reducing fatalities due to road accidents. Chapter 7 focuses on a wireless sensor network (WSN) in IoT and Cloud platforms. It covers the introduction of IoT Cloud and WSN architecture, and discusses the challenges and opportunities of IoT Cloud. In Chapter 8, a comparative study is done on various mechanisms for providing security and privacy to vehicles and data. The comparative analysis is very helpful to users when selecting the best model for security and privacy. In Chapter 9, the authors discuss the fundamental concepts of software defined networking (SDN), where three planes of SDN are defined and discussed in the form of SDN architecture. This chapter provides an insight into how SDN works along with a comparative review of a traditional network and SDN. The authors explain the underlying SDN security architecture and related several security threats.
The aim of Chapter 10 is to produce and design an efficient routing protocol for VANETs which can employ ad hoc on-demand distance vector routing algorithms for running the operation of both ad hoc mobile networks. Chapter 11 presents a mechanism for multilayer cluster-wise key generation for secure communication among service-oriented highly dense VANETs. The key generated from this mechanism is used as the secure key for different communications or authentications. The authors divide the whole VANET into several clusters. Chapter 12 investigates and evaluates some of the recently projected SDN-VANET methodologies and trust management systems. It also presents advanced cloud computing strategies that satisfy the requirements in VANETs and emerging technologies for future VANETs. Chapter 13 discusses the best machine learning algorithm to transmit the nodes effectively from source to destination node in order to reduce computational complexity and increase detection accuracy. It also focuses on machine learning application in ad hoc networks СКАЧАТЬ