A Multi-Objective Joint Task Offloading Scheme for Vehicular Edge Computing

The rapid advance of Connected-Automated Vehicles (CAVs) has led to the emergence of diverse delay-sensitive and energy-constrained vehicular applications. Given the high dynamics of vehicular networks, unmanned aerial vehicles-assisted mobile edge computing (UAV-MEC) has gained attention in providing computing resources to vehicles and optimizing system costs. We model the computing offloading problem as a multi-objective optimization challenge aimed at minimizing both task processing delay and energy consumption. We propose a three-stage hybrid offloading scheme called Dynamic Vehicle Clustering Game-based Multi-objective Whale Optimization Algorithm (DVCG-MWOA) to address this problem. A novel dynamic clustering algorithm is designed based on vehicle mobility and task offloading efficiency requirements, where each UAV independently serves as the cluster head for a vehicle cluster and adjusts its position at the end of each time slot in response to vehicle movement. Within each UAV-led cluster, cooperative game theory is applied to allocate computing resources while respecting delay constraints, ensuring efficient resource utilization. To enhance offloading efficiency, we improve the multi-objective whale optimization algorithm (MOWOA), resulting in the MWOA. This enhanced algorithm determines the optimal allocation of pending tasks to different edge computing devices and the resource utilization ratio of each device, ultimately achieving a Pareto-optimal solution set for delay and energy consumption. Experimental results demonstrate that the proposed joint offloading scheme significantly reduces both delay and energy consumption compared to existing approaches, offering superior performance for vehicular networks.