An Efficient Collaborative Task Offloading Approach Based on Multi-Objective Algorithm in MEC-Assisted Vehicular Networks

• Published in: IEEE transactions on vehicular technology Vol. 74; no. 7; pp. 11249 - 11263
• Main Authors: Chen, Shuaijie, Li, Wenfeng, Sun, Jingtao, Pace, Pasquale, He, Lijun, Fortino, Giancarlo
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.07.2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Associative memory; Bayesian maximum entropy; Collaboration; Computation offloading; Computational modeling; Costs; Delay time; Delays; Edge computing; Energy consumption; Genetic algorithms; Genetics; Mobile computing; Mobile edge computing; multi-objective optimization; Multiple objective analysis; Optimization; Performance evaluation; Process control; Queueing; Reliability; Servers; task criticality; task offloading; task urgency; Vehicles
• ISSN: 0018-9545; 1939-9359
• DOI: 10.1109/TVT.2025.3543412

Mobile edge computing assisting vehicular networks to achieve collaborative computing is a significant research area, as it can effectively expand the on-board computational resources, thereby improving the responsiveness of vehicular applications and reducing energy consumption. However, the existing task offloading schemes still have various gaps and face challenges that should be addressed because task offloading problems are usually multi-objective optimization problems (MOOP) and NP-hard problems. In this respect, we formulate a new multi-objective model for vehicle-centered task offloading tacking into account the queuing and computing processes of tasks. The minimization of the average delay time, average energy consumption, and average payment cost for each vehicle is defined as a MOOP. We propose an efficient collaborative offloading approach based on task triage strategy and multi-objective optimization to address the model. Specifically, this approach can be decomposed into two stages. First, a task triage strategy is developed based on a comprehensive analysis of task urgency and task criticality to make offloading decisions for triaged tasks with extreme characteristics. Then, we propose a novel multi-objective optimization framework for the MOOP. Finally, we develop a multi-objective hybrid genetic algorithm that integrates the associative learning immediate memory strategy and genetic operations to solve the MOOP. Extensive experiments and performance evaluations based on real-world traces of taxis demonstrate the effectiveness of the proposed approach. The results indicate that the approach outperforms six other well-known multi-objective algorithms in solving the MOOP.