MESON: A Mobility-aware Dependent Task Offloading Scheme for Urban Vehicular Edge Computing

Zhao, Liang; Zhang, Enchao; Wan, Shaohua; Hawbani, Ammar; Al-Dubai, Ahmed Y.; Min, Geyong; Zomaya, Albert Y.

Abstract

Vehicular Edge Computing (VEC) is the transportation version of Mobile Edge Computing (MEC) in road scenarios. One key technology of VEC is task offloading, which allows vehicles to send their computation tasks to the surrounding Roadside Units (RSUs) or other vehicles for execution, thereby reducing computation delay and energy consumption. However, the existing task offloading schemes still have various gaps and face challenges that should be addressed because vehicles with time-varying trajectories need to process massive data with high complexity and diversity. In this paper, a VEC based computation offloading model is developed with consideration of data dependency of tasks. The minimization of the average response time and average energy consumption of the system is defined as a combinatorial optimization problem. To solve this problem, we propose a Mobility-aware dependent task offloading (MESON) Scheme for urban VEC and develop a DRL-based algorithm to train the offloading strategy. To improve the training efficiency, a vehicle mobility detection algorithm is further designed to detect the communication time between vehicles and RSUs. In this way, MESON can avoid unreasonable decisions by lowering the size of the action space. Moreover, to improve the system stability and the offloading successful rate, we design a task priority determination scheme to prioritize the tasks in the waiting queue. The experimental results show that MESON is superior compared to other task offloading schemes in terms of the average response time, average system energy consumption, and offloading successful rate.

• Journal Article Type: Article
• Acceptance Date: Jun 19, 2023
• Online Publication Date: Jun 26, 2023
• Publication Date: 2024-05
• Deposit Date: Jun 20, 2023
• Publicly Available Date: Jun 26, 2023
• Print ISSN: 1536-1233
• Electronic ISSN: 1558-0660
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 23
• Issue: 5
• Pages: 4259 - 4272
• DOI: https://doi.org/10.1109/tmc.2023.3289611
• Keywords: Mobile Edge Computing, Vehicular Edge Computing, Vehicular Networks, Deep Reinforcement Learning, Task Offloading
• Public URL: http://researchrepository.napier.ac.uk/Output/3130630