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Covers applications of Internet of Things (IoT) in Vehicular ad-hoc
network (VANETs). Discusses use of machine learning and other
computing techniques for enhancing performance of networks. Covers
game theory-based vertical handoffs in Heterogeneous Wireless
Networks. Examines monitoring and surveillance of vehicles through
the vehicular sensor network. Discusses theoretical approaches on
software-defined vehicular Ad-hoc network.
Vehicular traffic congestion and accidents remain universal issues
in today's world. Due to the continued growth in the use of
vehicles, optimizing traffic management operations is an immense
challenge. To reduce the number of traffic accidents, improve the
performance of transportation systems, enhance road safety, and
protect the environment, vehicular ad-hoc networks have been
introduced. Current developments in wireless communication,
computing paradigms, big data, and cloud computing enable the
enhancement of these networks, equipped with wireless communication
capabilities and high-performance processing tools. Cloud-Based Big
Data Analytics in Vehicular Ad-Hoc Networks is a pivotal reference
source that provides vital research on cloud and data analytic
applications in intelligent transportation systems. While
highlighting topics such as location routing, accident detection,
and data warehousing, this publication addresses future challenges
in vehicular ad-hoc networks and presents viable solutions. This
book is ideally designed for researchers, computer scientists,
engineers, automobile industry professionals, IT practitioners,
academicians, and students seeking current research on cloud
computing models in vehicular networks.
The optimization of traffic management operations has become a
considerable challenge in today's global scope due to the
significant increase in the number of vehicles, traffic
congestions, and automobile accidents. Fortunately, there has been
substantial progress in the application of intelligent computing
devices to transportation processes. Vehicular ad-hoc networks
(VANETs) are a specific practice that merges the connectivity of
wireless technologies with smart vehicles. Despite its relevance,
empirical research is lacking on the developments being made in
VANETs and how certain intelligent technologies are being applied
within transportation systems. IoT and Cloud Computing Advancements
in Vehicular Ad-Hoc Networks provides emerging research exploring
the theoretical and practical aspects of intelligent transportation
systems and analyzing the modern techniques that are being applied
to smart vehicles through cloud technology. Featuring coverage on a
broad range of topics such as health monitoring, node localization,
and fault tolerance, this book is ideally designed for network
designers, developers, analysists, IT specialists, computing
professionals, researchers, academics, and post-graduate students
seeking current research on emerging computing concepts and
developments in vehicular ad-hoc networks.
Vehicular traffic congestion and accidents remain universal issues
in today's world. Due to the continued growth in the use of
vehicles, optimizing traffic management operations is an immense
challenge. To reduce the number of traffic accidents, improve the
performance of transportation systems, enhance road safety, and
protect the environment, vehicular ad-hoc networks have been
introduced. Current developments in wireless communication,
computing paradigms, big data, and cloud computing enable the
enhancement of these networks, equipped with wireless communication
capabilities and high-performance processing tools. Cloud-Based Big
Data Analytics in Vehicular Ad-Hoc Networks is a pivotal reference
source that provides vital research on cloud and data analytic
applications in intelligent transportation systems. While
highlighting topics such as location routing, accident detection,
and data warehousing, this publication addresses future challenges
in vehicular ad-hoc networks and presents viable solutions. This
book is ideally designed for researchers, computer scientists,
engineers, automobile industry professionals, IT practitioners,
academicians, and students seeking current research on cloud
computing models in vehicular networks.
The optimization of traffic management operations has become a
considerable challenge in today's global scope due to the
significant increase in the number of vehicles, traffic
congestions, and automobile accidents. Fortunately, there has been
substantial progress in the application of intelligent computing
devices to transportation processes. Vehicular ad-hoc networks
(VANETs) are a specific practice that merges the connectivity of
wireless technologies with smart vehicles. Despite its relevance,
empirical research is lacking on the developments being made in
VANETs and how certain intelligent technologies are being applied
within transportation systems. IoT and Cloud Computing Advancements
in Vehicular Ad-Hoc Networks provides emerging research exploring
the theoretical and practical aspects of intelligent transportation
systems and analyzing the modern techniques that are being applied
to smart vehicles through cloud technology. Featuring coverage on a
broad range of topics such as health monitoring, node localization,
and fault tolerance, this book is ideally designed for network
designers, developers, analysists, IT specialists, computing
professionals, researchers, academics, and post-graduate students
seeking current research on emerging computing concepts and
developments in vehicular ad-hoc networks.
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