Trajectory-aware Dynamic Offloading for High-mobility Vehicular Edge Systems

Authors

• Haoran Xu, College of Computer and Data Science, Fuzhou University, Fuzhou 350116, China; Engineering Research Center of Big Data Intelligence, Ministry of Education, Fuzhou 350002, China; Fujian Key Laboratory of Network Computing and Intelligent Information Processing (Fuzhou University), Fuzhou 350116, China
• Zhiqin Huang, College of Computer and Data Science, Fuzhou University, Fuzhou 350116, China; Engineering Research Center of Big Data Intelligence, Ministry of Education, Fuzhou 350002, China; Fujian Key Laboratory of Network Computing and Intelligent Information Processing (Fuzhou University), Fuzhou 350116, China
• Youwu Hu, College of Computer and Data Science, Fuzhou University, Fuzhou 350116, China; Engineering Research Center of Big Data Intelligence, Ministry of Education, Fuzhou 350002, China; Fujian Key Laboratory of Network Computing and Intelligent Information Processing (Fuzhou University), Fuzhou 350116, China
• Zheyi Chen, College of Computer and Data Science, Fuzhou University, Fuzhou 350116, China; Engineering Research Center of Big Data Intelligence, Ministry of Education, Fuzhou 350002, China; Fujian Key Laboratory of Network Computing and Intelligent Information Processing (Fuzhou University), Fuzhou 350116, China

Abstract

Abstract: To address the problems of service interruptions, task failures, and resource waste caused by high mobility of intelligent vehicles (IVs) in vehicular edge computing (VEC), this paper proposes a trajectory-aware dynamic offloading (TADO) framework for high-mobility vehicular edge systems. A lightweight T-pattern trajectory-aware algorithm is designed to efficiently predict the next-hop road side unit (RSU) by mining spatio-temporal patterns from historical trajectories of vehicles, offering a forward-looking reference for offloading decisions. A joint optimization model is constructed, and a service interruption risk factor driven by trajectory prediction is introduced. An improved DRL method is developed. It takes the predicted trajectories as key state inputs and integrates parameter space noise exploration with a prioritized experience replay mechanism to learn forward-looking and robust offloading strategies in highly dynamic environments. Simulation experiments based on real-world datasets of urban vehicle trajectories demonstrate that, compared to benchmark methods, such as local computing and full edge offloading, the TADO framework significantly reduces task processing latency and system energy consumption and maintains a high task completion rate across various scenarios.

Recommended Citation

Xu, Haoran; Huang, Zhiqin; Hu, Youwu; and Chen, Zheyi (2026) "Trajectory-aware Dynamic Offloading for High-mobility Vehicular Edge Systems," Journal of System Simulation: Vol. 38: Iss. 7, Article 10.
DOI: 10.16182/j.issn1004731x.joss.25-0844
Available at: https://dc-china-simulation.researchcommons.org/journal/vol38/iss7/10

First Page

1935

Last Page

1949

CLC

TP393

Recommended Citation

Xu Haoran, Huang Zhiqin, Hu Youwu, et al. Trajectory-aware Dynamic Offloading for High-mobility Vehicular Edge Systems[J]. Journal of System Simulation, 2026, 38(7): 1935-1949.

Corresponding Author

Chen Zheyi

DOI

10.16182/j.issn1004731x.joss.25-0844