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This reference text will benefit readers in enhancing their
understanding of the recent technologies, protocols, and challenges
in various stages of development of wireless communication and
networking. The text discusses the cellular concepts of 4G, 5G, and
6G along with their challenges. It covers topics related to
vehicular technology, wherein vehicles communicate with the traffic
and the environment around them using short-range wireless signals.
The text comprehensively covers important topics including use of
the Internet of Things (IoT) in wireless communication,
architecture, and protocols. It further covers the role of smart
antennas in emerging wireless technologies. The book Discusses
advanced techniques used in the field of wireless communication.
Covers technologies including network slicing, 5G wireless
communication, and TV white space technology. Discusses practical
applications including drone delivery systems, public safety, IoT,
virtual reality, and smart cities. Covers radio theory and
applications for wireless communication with ranges of centimeters
to hundreds of meters. Discusses important topics including
metamaterials, inductance coupling for loop antennas, bluetooth low
energy, wireless security, and wireless sensor networks. Discussing
latest technologies including 5G, 6G, IoT, vehicular technology and
TV white space technology, this text will be useful for senior
undergraduate, graduate students, and professionals in the fields
of electrical engineering, and electronics and communication
engineering.
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.
Sustainable Developments by Artificial Intelligence and Machine
Learning for Renewable Energies analyzes the changes in this energy
generation shift, including issues of grid stability with
variability in renewable energy vs. traditional baseload energy
generation. Providing solutions to current critical environmental,
economic and social issues, this book comprises various complex
nonlinear interactions among different parameters to drive the
integration of renewable energy into the grid. It considers how
artificial intelligence and machine learning techniques are being
developed to produce more reliable energy generation to optimize
system performance and provide sustainable development. As the use
of artificial intelligence to revolutionize the energy market and
harness the potential of renewable energy is essential, this
reference provides practical guidance on the application of
renewable energy with AI, along with machine learning techniques
and capabilities in design, modeling and for forecasting
performance predictions for the optimization of renewable energy
systems. It is targeted at researchers, academicians and industry
professionals working in the field of renewable energy, AI, machine
learning, grid Stability and energy generation.
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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