Resource Allocation and Task Offloading for Mobile Edge Computing System Based on UAV-EC Collaboration

The swift expansion of Mobile IoT Devices (MIDs) is challenging the capabilities of conventional Mobile Edge Computing (MEC) systems. Although the integration of Unmanned Aerial Vehicle (UAV) introduces a novel approach by bolstering computational capacity for MIDs, the intrinsic computational constraints of UAV hinder current UAV-assisted MEC systems from efficiently addressing the enhancing demand for compute-intensive services as MIDs proliferate. This paper introduces a system that integrates UAV and ground Edge Cloud (EC) to provide efficient services for a large number of MIDs. We propose an optimization problem focused on minimizing energy efficiency (EE), with the aim of achieving minimizing EE through the joint optimization of task computation and offloading strategies. To solve this problem, we utilize an Adaptive Particle Swarm Optimization (APSO) algorithm, which incorporates a dynamic adaptation mechanism that adjusts the inertia weight. According to the optimization objectives and constraints, we design the fitness function to measure EE of the system and iteratively obtains the optimal solution. Simulation results indicate that the proposed method outperforms benchmark methods.