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This book introduces the Internet access for vehicles as well as
novel communication and computing paradigms based on the Internet
of vehicles. To enable efficient and reliable Internet connection
for mobile vehicle users, this book first introduces analytical
modelling methods for the practical vehicle-to-roadside (V2R)
Internet access procedure, and employ the interworking of V2R and
vehicle-to-vehicle (V2V) to improve the network performance for a
variety of automotive applications. In addition, the wireless link
performance between a vehicle and an Internet access station is
investigated, and a machine learning based algorithm is proposed to
improve the link throughout by selecting an efficient modulation
and coding scheme. This book also investigates the distributed
machine learning algorithms over the Internet access of vehicles. A
novel broadcasting scheme is designed to intelligently adjust the
training users that are involved in the iteration rounds for an
asynchronous federated learning scheme, which is shown to greatly
improve the training efficiency. This book conducts the fully
asynchronous machine learning evaluations among vehicle users that
can utilize the opportunistic V2R communication to train machine
learning models. Researchers and advanced-level students who focus
on vehicular networks, industrial entities for internet of vehicles
providers, government agencies target on transportation system and
road management will find this book useful as reference. Network
device manufacturers and network operators will also want to
purchase this book.
This book introduces the Internet access for vehicles as well as
novel communication and computing paradigms based on the Internet
of vehicles. To enable efficient and reliable Internet connection
for mobile vehicle users, this book first introduces analytical
modelling methods for the practical vehicle-to-roadside (V2R)
Internet access procedure, and employ the interworking of V2R and
vehicle-to-vehicle (V2V) to improve the network performance for a
variety of automotive applications. In addition, the wireless link
performance between a vehicle and an Internet access station is
investigated, and a machine learning based algorithm is proposed to
improve the link throughout by selecting an efficient modulation
and coding scheme. This book also investigates the distributed
machine learning algorithms over the Internet access of vehicles. A
novel broadcasting scheme is designed to intelligently adjust the
training users that are involved in the iteration rounds for an
asynchronous federated learning scheme, which is shown to greatly
improve the training efficiency. This book conducts the fully
asynchronous machine learning evaluations among vehicle users that
can utilize the opportunistic V2R communication to train machine
learning models. Researchers and advanced-level students who focus
on vehicular networks, industrial entities for internet of vehicles
providers, government agencies target on transportation system and
road management will find this book useful as reference. Network
device manufacturers and network operators will also want to
purchase this book.
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