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Intelligent resources management for vehicular social networks = societal perspectives and current issues in the digital era /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Intelligent resources management for vehicular social networks/ by Haixia Zhang, Dongyang Li, Tong Xue.
Reminder of title:	societal perspectives and current issues in the digital era /
Author:	Zhang, Haixia.
other author:	Li, Dongyang.
Published:	Cham :Springer Nature Switzerland : : 2025.,
Description:	xii, 134 p. :ill. (some col.), digital ; : 24 cm.;
Contained By:	Springer Nature eBook
Subject:	Intelligent transportation systems. -
Online resource:	https://doi.org/10.1007/978-3-031-80169-3
ISBN:	9783031801693

Intelligent resources management for vehicular social networks = societal perspectives and current issues in the digital era /
Zhang, Haixia.

Intelligent resources management for vehicular social networkssocietal perspectives and current issues in the digital era /[electronic resource] :by Haixia Zhang, Dongyang Li, Tong Xue. - Cham :Springer Nature Switzerland :2025. - xii, 134 p. :ill. (some col.), digital ;24 cm. - Wireless networks,2366-1445. - Wireless networks..

Introduction -- Vehicular Social Networks -- Learning-based Vehicle Behaviors Prediction in VSNs -- Social-mobility-aware Communication Resource Management -- Social-aware Caching Resource Management in VSNs -- Joint Communication and Computation Resource Management in VSNs -- Social-enabled Physical Layer Secure Transmission -- Future Research Directions -- Conclusion.

This book explores integrating behavior prediction with artificial intelligence-driven resource management to provide a transformative framework for optimizing vehicular social networks (VSNs). The book starts by providing an overview of the key issues encountered in VSNs, including the dynamic and unpredictable nature of vehicular mobility, varying communication demands, and the need for efficient resource allocation. A significant portion of the book is dedicated to exploring behavior prediction models for vehicles in VSNs. By analyzing the past movements, interactions, and social behaviors of vehicles, this book presents various prediction algorithms to anticipate future positions, communication patterns, and resource requirements. With behavior prediction as a foundation, the book delves into the design and implementation of intelligent resource management systems for VSNs. It demonstrates how predictive capabilities empower these systems to allocate communication, computing and caching resources dynamically. The book extensively evaluates the proposed intelligent resource management approach through extensive simulations and practical experiments. The results showcase the effectiveness of the system, highlighting significant improvements in network performance compared to traditional resource allocation methods. These findings validate the potential of behavior prediction and intelligent resource management in revolutionizing VSNs. Finally, this book provides conclusions and promising directions, hoping to stimulate future research outcomes in the field of vehicular networks from different perspectives. The book serves as an invaluable resource for researchers, engineers, and industry professionals interested in advancing the field of vehicular networks and harnessing behavior prediction to create efficient, safe, and intelligent VSNs. Integrates behavior prediction with AI-driven resource management as a transformative framework for optimizing VSNs; Provides detail on harnessing behavior prediction to create efficient, safe, and intelligent vehicular social networks; Delves into the design and implementation of intelligent resource management systems for VSNs.

ISBN: 9783031801693

Standard No.: 10.1007/978-3-031-80169-3doiSubjects--Topical Terms:

866387
Intelligent transportation systems.

LC Class. No.: TE228.3

Dewey Class. No.: 388.312

Intelligent resources management for vehicular social networks = societal perspectives and current issues in the digital era /
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505 0 $a Introduction -- Vehicular Social Networks -- Learning-based Vehicle Behaviors Prediction in VSNs -- Social-mobility-aware Communication Resource Management -- Social-aware Caching Resource Management in VSNs -- Joint Communication and Computation Resource Management in VSNs -- Social-enabled Physical Layer Secure Transmission -- Future Research Directions -- Conclusion.
520 $a This book explores integrating behavior prediction with artificial intelligence-driven resource management to provide a transformative framework for optimizing vehicular social networks (VSNs). The book starts by providing an overview of the key issues encountered in VSNs, including the dynamic and unpredictable nature of vehicular mobility, varying communication demands, and the need for efficient resource allocation. A significant portion of the book is dedicated to exploring behavior prediction models for vehicles in VSNs. By analyzing the past movements, interactions, and social behaviors of vehicles, this book presents various prediction algorithms to anticipate future positions, communication patterns, and resource requirements. With behavior prediction as a foundation, the book delves into the design and implementation of intelligent resource management systems for VSNs. It demonstrates how predictive capabilities empower these systems to allocate communication, computing and caching resources dynamically. The book extensively evaluates the proposed intelligent resource management approach through extensive simulations and practical experiments. The results showcase the effectiveness of the system, highlighting significant improvements in network performance compared to traditional resource allocation methods. These findings validate the potential of behavior prediction and intelligent resource management in revolutionizing VSNs. Finally, this book provides conclusions and promising directions, hoping to stimulate future research outcomes in the field of vehicular networks from different perspectives. The book serves as an invaluable resource for researchers, engineers, and industry professionals interested in advancing the field of vehicular networks and harnessing behavior prediction to create efficient, safe, and intelligent VSNs. Integrates behavior prediction with AI-driven resource management as a transformative framework for optimizing VSNs; Provides detail on harnessing behavior prediction to create efficient, safe, and intelligent vehicular social networks; Delves into the design and implementation of intelligent resource management systems for VSNs.
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