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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.
This book introduces wireless traffic steering as a paradigm to
realize green communication in multi-tier heterogeneous cellular
networks. By matching network resources and dynamic mobile traffic
demand, traffic steering helps to reduce on-grid power consumption
with on-demand services provided. This book reviews existing
solutions from the perspectives of energy consumption reduction and
renewable energy harvesting. Specifically, it explains how traffic
steering can improve energy efficiency through intelligent
traffic-resource matching. Several promising traffic steering
approaches for dynamic network planning and renewable energy
demand-supply balancing are discussed. This book presents an
energy-aware traffic steering method for networks with energy
harvesting, which optimizes the traffic allocated to each cell
based on the renewable energy status. Renewable energy
demand-supply balancing is a key factor in energy dynamics, aimed
at enhancing renewable energy sustainability to reduce on-grid
energy consumption. Dynamic network planning adjusts cell density
with traffic variations to provide on-demand service, which reduces
network power consumption with quality of service provisioning
during off-peak hours. With intra- or inter-tier traffic steering,
cell density is dynamically optimized with regards to the instant
traffic load for conventional homogeneous and multi-tier
heterogeneous cellular networks, respectively. This book is
beneficial for researchers and graduate students interested in
traffic management and future wireless networking.
This book introduces wireless traffic steering as a paradigm to
realize green communication in multi-tier heterogeneous cellular
networks. By matching network resources and dynamic mobile traffic
demand, traffic steering helps to reduce on-grid power consumption
with on-demand services provided. This book reviews existing
solutions from the perspectives of energy consumption reduction and
renewable energy harvesting. Specifically, it explains how traffic
steering can improve energy efficiency through intelligent
traffic-resource matching. Several promising traffic steering
approaches for dynamic network planning and renewable energy
demand-supply balancing are discussed. This book presents an
energy-aware traffic steering method for networks with energy
harvesting, which optimizes the traffic allocated to each cell
based on the renewable energy status. Renewable energy
demand-supply balancing is a key factor in energy dynamics, aimed
at enhancing renewable energy sustainability to reduce on-grid
energy consumption. Dynamic network planning adjusts cell density
with traffic variations to provide on-demand service, which reduces
network power consumption with quality of service provisioning
during off-peak hours. With intra- or inter-tier traffic steering,
cell density is dynamically optimized with regards to the instant
traffic load for conventional homogeneous and multi-tier
heterogeneous cellular networks, respectively. This book is
beneficial for researchers and graduate students interested in
traffic management and future wireless networking.
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.
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