Intelligent resource management in vehicular networks [electronic resource]
- 作者： Peng, Haixia.
- 其他作者：
- 其他題名：
- Wireless networks.
- 出版： Cham : Springer International Publishing :Imprint: Springer
- 叢書名： Wireless networks,
- 主題： Vehicular ad hoc networks (Computer networks)--Management. , Cyber-Physical Systems. , Communications Engineering, Networks. , Transportation Technology and Traffic Engineering.
- ISBN： 9783030965075 (electronic bk.) 、 9783030965068 (paper)
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FIND@SFXID:
CGU
- 資料類型: 電子書
- 內容註: Introduction -- Overview of Vehicular Networks -- Resource Management in Vehicular Networks -- MEC-Assisted Vehicular Networks -- Spectrum Resource Management in MEC-Assisted ADVNETs -- Multi-Dimensional Resource Management in MVNETs -- Multi-Dimensional Resource Management in UAV-Assisted MVNETs -- Conclusion.
- 摘要註: 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. Provides a comprehensive resource management design for vehicular networks; Includes multi-resource management studies in highly dynamic wireless networks; Features applications of AI technologies in multi-dimensional resource management.
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讀者標籤：
- 系統號： 005512559 | 機讀編目格式
館藏資訊
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.