Service chain caching and task offloading in two-tier UAV-assisted vehicular edge computing networks: An attention-DRL method✩

Fengyan Wu; Chao Yang; Yanqun Tang; Zhen Li; Shengli Xie

doi:10.1016/j.dcan.2024.10.005

›› 2026, Vol. 12 ›› Issue (4) :607 -617. DOI: 10.1016/j.dcan.2024.10.005

Research Article

research-article

Service chain caching and task offloading in two-tier UAV-assisted vehicular edge computing networks: An attention-DRL method^✩

Fengyan Wu ^a^,^*
, Chao Yang ^a^,^d^,^*
, Yanqun Tang ^b
, Zhen Li ^c
, Shengli Xie ^a^,^d

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^aSchool of Automation, Guangdong University of Technology and Guangdong Key Laboratory of IoT Information Technology, Guangzhou 510006, China

^b School of Electronics and Communications , Sun Yat—Sen University, Shenzhen 518107, China

^c Department of Mathematics and Information Technology, The Education University of Hong Kong , HongKong, China

^dThe Guangdong—HongKong—Macao Joint Laboratory for Smart Discrete Manufacturing and the Joint International Research Laboratory of Intelligent Information Processing and System Integration of IoT, Guangzhou, China

^* E-mail addresses: 2112204070@mail2.gdut.edu.cn (F. Wu),

yangchaoscut@aliyun.com (C. Yang),

tangyq8@mail.sysu.edu.cn (Y. Tang),

lzhen@eduhk.hk (Z. Li),

shlxie@gdut.edu.cn (S. Xie).

Fengyan Wu received her B.E. degree from Guangdong University of Technology (GDUT), Guangzhou, China, in 2022. From 2022 to 2025, She pursue the M.S. degree with the school of Automation, Guangdong University of Technology, Guangzhou, China. Now she is an engineer in CSG Digital Grid Technology (Guangdong) Co., Ltd. Her research interests include wireless communication networks, cooperative communications,and intelligent edge computing.

Chao Yang received the Ph.D. degree in Signal and Information Processing from South China University of Technology, Guangzhou, China, 2013. He currently work in the School of Automation, Guangdong University of Technology. From July, 2014 to July, 2016, he was a Research Associate in the Department of Computing, The Hong Kong Polytechnic University. His research interest focuses on VANETs, edge computing, smart grid.

Yanqun Tang received the B.Sc., M.Sc., and Ph.D. degrees from the School of Electronic Science and Engineering, National University of Defense Technology, China, in 2007, 2009 and 2013 respectively. He is currently an associate processor at the School of Electronics and Communication Engineering, Sun Yat-sen University, Shenzhen, China. His research interests are integrated sensing and communication, full duplex communications, wireless physical layer security and machine learning techniques for wireless communications.

Zhen Li received the B.Eng. degree from the School of Automation, Guangdong University of Technology, Guangzhou, China, in 2023. He is currently pursuing the M.A.Sc. degree with the Department of Electrical and Computer Engineering, Concordia University, Montreal, Canada. From 2023 to 2024, he was a Research Assistant with the Department of Mathematics and Information Technology, The Education University of Hong Kong, Hong Kong, SAR, China. His research interests include network optimization, the Internet of Things, and machine learning.

Shengli Xie received the M.S. degree in mathematics from the Central China Normal University, Wuhan, China,in 1992, and the Ph.D. degree in control theory and applications from the South China University of Technology, Guangzhou, China, in 1997. He is currently a Full Professor and the Head of the Institute of Intelligent Information Processing, Guangdong University of Technology, Guangzhou, and the Director of the Laboratory for Intelligent Information Processing, the Director of the Guangdong-Hong Kong-Macao Joint Laboratory for Smart Discrete Manufacturing. He has authored or co-authored two books and over 300 scientific papers in journals and conference proceedings. His research interests include wireless networks, automatic control, and blind signal processing.

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Abstract

Unmanned Aerial Vehicle (UAV)-assisted Vehicular Edge Computing Networks (VECNs) have emerged as a promising solution to enhance service quality for ground vehicle users. However, the growing demands from users and the limited computing and storage resources of UAVs present significant challenges in designing an efficient edge service caching scheme to minimize latency. Moreover, the integration of service caching and task offloading complicates the support of complex tasks by a single UAV. To address these challenges, this paper proposes a novel two-tier UAV-assisted VECNs framework. In this framework, multi-rotor UAVs function as hovering nodes for computational offloading, while a fixed-wing UAV serves as a mobile auxiliary cloud platform, forming a cohesive UAV group. User tasks are structured into a task chain based on the available UAVs. We integrate a joint service chain caching and task offloading scheme that considers UAV computing and storage capacities, duplicate caching, and dynamic transmission latency. To optimize task chain completion latency, we propose an Attention-based Multi-Agent Deep Q-Network (A-MADQN) algorithm. This algorithm incorporates an attention mechanism to narrow the UAV selection space, enabling the selected UAVs to collaboratively make caching and task offloading decisions. Numerical results demonstrate that the proposed algorithm significantly enhances system processing efficiency and reduces task completion latency compared to the benchmark approaches.

Keywords

Service caching / Task chain / Multi-UAV / Attention mechanism / Multi-agent DQN

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Fengyan Wu, Chao Yang, Yanqun Tang, Zhen Li, Shengli Xie. Service chain caching and task offloading in two-tier UAV-assisted vehicular edge computing networks: An attention-DRL method^✩. , 2026, 12 (4) : 607-617 DOI:10.1016/j.dcan.2024.10.005

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CRediT authorship contribution statement

Fengyan Wu: Writing – original draft, Software, Methodology. Chao Yang: Writing – review & editing, Supervision, Methodology. Yanqun Tang: Methodology. Zhen Li: Software. Shengli Xie: Writing – review & editing, Resources, Funding acquisition.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This work is supported by Guangdong Basic and Applied Basic Research Foundation (Grant No. 2024A1515012745).

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