Adaptive Digital Twin Migration in Vehicular Edge Computing and Networks

doi:10.1109/TVT.2024.3492349

科研成果详情

题名	Adaptive Digital Twin Migration in Vehicular Edge Computing and Networks
作者	Mou，Fangyi 1,2; Lou，Jiong 3; Tang，Zhiqing 4; Wu，Yuan 5; Jia，Weijia 4,6; Zhang，Yan 7; Zhao，Wei 8
发表日期	2025
发表期刊	IEEE Transactions on Vehicular Technology
ISSN/eISSN	0018-9545
卷号	74 期号:3 页码:4839-4854
摘要	The surge in mobile vehicles and data traffic in Vehicular Edge Computing and Networks (VECON ) requires innovative approaches for low latency, stable connectivity, and efficient resource usage in fast-moving vehicles. Existing studies have identified that utilizing digital twins (DT ) can effectively improve service quality in VECON . However, it still faces substantial challenges posed by large-scale complex DT communications in sustaining real-time collaborative endeavors. In particular, within the dynamic VECON , the decision regarding DT migration plays a pivotal role in sustaining the quality of services. In this paper, we propose an adaptive DT migration (ADM) algorithm to minimize the overall migration costs when DTs deliver services. Specifically, 1) We formulate ADM as a combinatorial optimization problem in VECON , comprehensively considering communication latency and migration latency under complex DT communications, vehicular mobilities, and dynamic states of edges; 2) An ADM algorithm based on off-policy actor-critic reinforcement learning is proposed to make migration decisions. Moreover, the ADM agent employs warm-up policies to address exploration challenges in sparse state spaces; 3) Simulations based on real-world, large-scale urban vehicular mobility datasets demonstrate that our method outperforms existing algorithms by approximately 39% on average, and it can achieve results close to the optimal.
关键词	deep reinforcement learning Digital twin migration vehicular edge computing
DOI	10.1109/TVT.2024.3492349
URL	查看来源
语种	英语English
Scopus入藏号	2-s2.0-105001090835
引用统计
文献类型	期刊论文
条目标识符	https://repository.uic.edu.cn/handle/39GCC9TT/13731
专题	北师香港浸会大学
通讯作者	Tang，Zhiqing
作者单位	1.Hong Kong Baptist University,Hong Kong,Kowloon Tong,Hong Kong 2.Beijing Normal University-Hong Kong Baptist University United International College,Faculty of Science and Technology,Zhuhai,519087,China 3.Shanghai Jiao Tong University,Department of Computer Science and Engineering,Shanghai,200240,China 4.Beijing Normal University,Institute of Artificial Intelligence and Future Networks,Zhuhai,519087,China 5.University of Macau,State Key Lab of IoT for Smart City,999078,Macao 6.BNU-HKBU United International College,Guangdong Key Lab of AI and Multi-Modal Data Processing,Zhuhai,519087,China 7.University of Oslo,Department of Informatics,Oslo,0316,Norway 8.Shenzhen University of Advanced Technology,Shenzhen,518055,China
第一作者单位	理工科技学院
推荐引用方式 GB/T 7714	Mou，Fangyi,Lou，Jiong,Tang，Zhiqinget al. Adaptive Digital Twin Migration in Vehicular Edge Computing and Networks[J]. IEEE Transactions on Vehicular Technology, 2025, 74(3): 4839-4854.
APA	Mou，Fangyi., Lou，Jiong., Tang，Zhiqing., Wu，Yuan., Jia，Weijia., .. & Zhao，Wei. (2025). Adaptive Digital Twin Migration in Vehicular Edge Computing and Networks. IEEE Transactions on Vehicular Technology, 74(3), 4839-4854.
MLA	Mou，Fangyi,et al."Adaptive Digital Twin Migration in Vehicular Edge Computing and Networks". IEEE Transactions on Vehicular Technology 74.3(2025): 4839-4854.