Machine learning in vehicular networking: an overview

Tan, K. , Bremner, D. , Le Kernec, J. , Zhang, L. and Imran, M. (2022) Machine learning in vehicular networking: an overview. Digital Communications and Networks, 8(1), pp. 18-24. (doi: 10.1016/j.dcan.2021.10.007)

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As vehicle complexity and road congestion increase, combined with the emergence of electric vehicles, the need for intelligent transportation systems to improve on-road safety and transportation efficiency using vehicular networks has become essential. The evolution of high mobility wireless networks will provide improved support for connected vehicles through highly dynamic heterogeneous networks. Particularly, 5G deployment introduces new features and technologies that enable operators to capitalize on emerging infrastructure capabilities. Machine Learning (ML), a powerful methodology for adaptive and predictive system development, has emerged in both vehicular and conventional wireless networks. Adopting data-centric methods enables ML to address highly dynamic vehicular network issues faced by conventional solutions, such as traditional control loop design and optimization techniques. This article provides a short survey of ML applications in vehicular networks from the networking aspect. Research topics covered in this article include network control containing handover management and routing decision making, resource management, and energy efficiency in vehicular networks. The findings of this paper suggest more attention should be paid to network forming/deforming decision making. ML applications in vehicular networks should focus on researching multi-agent cooperated oriented methods and overall complexity reduction while utilizing enabling technologies, such as mobile edge computing for real-world deployment. Research datasets, simulation environment standardization, and method interpretability also require more research attention.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Zhang, Professor Lei and Tan, Kang and Imran, Professor Muhammad and Le Kernec, Dr Julien and Bremner, Dr Duncan
Authors: Tan, K., Bremner, D., Le Kernec, J., Zhang, L., and Imran, M.
College/School:College of Science and Engineering > School of Engineering > Systems Power and Energy
Journal Name:Digital Communications and Networks
ISSN (Online):2352-8648
Published Online:28 October 2021
Copyright Holders:Copyright © 2021 Chongqing University of Posts and Telecommunications
First Published:First published in Digital Communications and Networks 8(1): 18-24
Publisher Policy:Reproduced under a Creative Commons License

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
304481Resource Orchestration for Diverse Radio SystemsLei ZhangEngineering and Physical Sciences Research Council (EPSRC)EP/S02476X/1ENG - Systems Power & Energy