Intelligent Resource Management In Vehicular Networks
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Intelligent Resource Management in Vehicular Networks
This book provides a comprehensive investigation on new technologies for future vehicular networks. The authors propose different schemes to efficiently manage the multi-dimensional resources for supporting diversified applications. The authors answer the questions of why connected and automated vehicle technology should be considered; how the multi-access edge computing (MEC) and unmanned aerial vehicle (UAV) technologies can be helpful to vehicular networks; how to efficiently manage the multi-dimensional resources to support different vehicular applications with guaranteed quality-of-service (QoS) requirements; and how to adopt optimization and AI technologies to achieve resource management in vehicular networks. The book is pertinent to researchers, professionals, academics and students in vehicular technologies.
Intelligent Resources Management for Vehicular Social Networks
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.
Intelligent Computing and Communication for the Internet of Vehicles
This book investigates intelligent network resource management for IoV, with the objective of maximizing the communication and computing performance of vehicle users. Focusing on two representative use cases in IoV, i.e., safety message broadcast and autonomous driving, the authors propose link-layer protocol design and application-layer computing task scheduling to achieve the objective given the unique characteristics and requirements of IoV. In particular, this book illustrates the challenges of resource management for IoV due to network dynamics, such as time-varying traffic intensity and vehicle mobility, and presents intelligent resource management solutions to adapt to the network dynamics. The Internet of Vehicles (IoV) enables vehicle-to-everything connectivity and supports a variety of applications for vehicles on the road. Intelligent resource management is critical for satisfying demanding communication and computing requirements on IoV, while the highly dynamic network environments pose challenges to the design of resource management schemes. This book provides insights into the significance of adaptive resource management in improving the performance of IoV. The customized communication protocol and computing scheduling scheme are designed accordingly by taking the network dynamics information as an integral design factor. Moreover, the decentralized designs of the proposed solutions guarantee low signaling overhead and high scalability. A comprehensive literature review summarizing recent resource management schemes in IoV, followed by the customized design of communication and computing solutions for the two IoV use cases is included which can serve as a useful reference for professionals from both academia and industry in the area of IoV and resource management. Researchers working within this field and computer science and electrical engineering students will find this book useful as well.