A Joint Optimization Method for Mobile Edge Computing System Supporting Multi-User and Multi-UAV

As the number of complex tasks and applications in Internet of Things (IoT) grows rapidly, the limited computing resources of IoT devices inevitably become insufficient to meet the required quality of service (QoS). Unmanned aerial Vehicle (UAV) -based mobile edge computing (MEC) offers a solution to ensure Qos stability and extend network coverage. In this paper, we consider a multi-UAV aided MEC system and propose an improved particle swarm optimization algorithm based on individual differential evolution and random particle generation (DE-PSO) to address the parameter coupling problem of non-convex objective function. Additionally, we propose a Trajectory Task and Power Two Floor Algorithm (TTPTFA) to minimize the energy expenditure (EE) and user latency through joint optimization of the UAV trajectory, the user and server tasks, and the communication resources. The simulation results show that the TTPTFA algorithm significantly reduces user waiting time compare to the computation uploading method (ToDeTaS) based on UAV deployment, with maintaining the same total computing and data transmission energy expenditure.