Vehicular Cyber Physical Systems - Adaptive Connectivity and Security (Paperback, Softcover reprint of the original 1st ed. 2017)

This book provides probabilistic, deterministic and geolocation-aware approaches for adaptive connectivity, robust security and privacy-aware communications for vehicular cyber physical systems (CPS). It presents mathematical models and numerical results obtained from experiments and simulations, and a trade-off between connectivity, security and privacy for vehicular communications. Connectivity between vehicles is crucial for vehicular CPS. Intelligent vehicular CPS provides not only road safety and traffic efficiency by exchanging information among vehicles, but also offers infotainment services to passengers using a variety of wireless technologies to forward the traffic/trajectory information with Vehicle-to-Vehicle (V2V), vehicular ad hoc network (VANET), and Vehicle-to-Roadside-to-Vehicle (V2R2V) communications. The book covers how to ensure that the message received from other vehicles is secure and trustworthy, rather than malicious. Further, it reveals how to make sure that the privacy of participants is not revealed while validating the received message. Researchers and professionals working with vehicular networks, smart systems, cyber physical systems, and mobile privacy will find this book valuable.

General

Imprint:	Springer International Publishing AG
Country of origin:	Switzerland
Release date:	June 2018
First published:	2017
Authors:	Danda B. Rawat • Chandra Bajracharya
Dimensions:	235 x 155 x 5mm (L x W x T)
Format:	Paperback
Pages:	75
Edition:	Softcover reprint of the original 1st ed. 2017
ISBN-13:	978-3-319-83065-0
Categories:	Books > Computing & IT > Computer communications & networking > Network security Books > Professional & Technical > Energy technology & engineering > Electrical engineering > Power generation & distribution Books > Professional & Technical > Electronics & communications engineering > Communications engineering / telecommunications > General
LSN:	3-319-83065-1
Barcode:	9783319830650

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