Deep Learning Based Solutions for Vehicular Adhoc Networks

Inbunden, Engelska, 2025

Av Jitendra Bhatia, Sudeep Tanwar, Joel J. P. C. Rodrigues, Malaram Kumhar

3 819 kr

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This book provides a holistic and comprehensive approach to deep learning for vehicular ad hoc networks (VANETs), covering various aspects such as applications, agency involvement, and potential ethical and legal issues.

Produktinformation

Utgivningsdatum2025-06-09
Mått155 x 235 x 26 mm
Vikt848 g
FormatInbunden
SpråkEngelska
SerieStudies in Computational Intelligence
Antal sidor392
FörlagSpringer Nature Switzerland AG
ISBN9789819651894

Tillhör följande kategorier

Nätverk och kommunikation inom Data och it
Artificiell intelligens inom Data och it
Systemvetenskap och AI inom Data och it

Overview of Vehicular Ad Hoc Networks.- Architecture and Protocols for data transmission in VANETs.- Applications and Challenges in VANETs.- Deep Learning Architectures for VANET.- Deep Learning for Security in VANET Secure Data Transmission in VANET.- Deep Learning for Resource Allocation in VANET.- Deep Learning for Traffic Prediction in VANET.- Traffic Prediction and modeling in Vehicular Ad Hoc Networks.- Traffic Data Collection and Processing in VANETs.- Deep Learning for Autonomous VANETs.- Implementation and Deployment of Deep Learning in Vehicular Ad Hoc Networks.- Deployment Strategies for Deep Learning in VANETs.- Energy Efficiency Deep Learning techniques in VANETs.- Case Studies and Real-World Deployment Examples.- Future Research Directions in Deep Learning for VANET.- Emerging Trends in VANETs.- Research Challenges and Open Issues in deploying deep learning models in VANETs.- Simulation/Emulation Platforms for Deep Learning in VANETs.- A framework to simulate VANETs.

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