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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 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 SpringerBrief evaluates the cooperative effort of sensor nodes
to accomplish high-level tasks with sensing, data processing and
communication. The metrics of network-wide convergence,
unbiasedness, consistency and optimality are discussed through
network topology, distributed estimation algorithms and consensus
strategy. Systematic analysis reveals that proper deployment of
sensor nodes and a small number of low-cost relays (without sensing
function) can speed up the information fusion and thus improve the
estimation capability of wireless sensor networks (WSNs). This
brief also investigates the spatial distribution of sensor nodes
and basic scalable estimation algorithms, the consensus-based
estimation capability for a class of relay assisted sensor networks
with asymmetric communication topology, and the problem of filter
design for mobile target tracking over WSNs. From the system
perspective, the network topology is closely related to the
capability and efficiency of network-wide scalable distributed
estimation. Wireless Sensor Networks: Distributed Consensus
Estimation is a valuable resource for researchers and professionals
working in wireless communications, networks and distributed
computing. Advanced-level students studying computer science and
electrical engineering will also find the content helpful.
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