Resource Allocation and Trajectory Optimization in Unmanned Aerial Vehicle-assisted Mobile Edge Computing

Edge computing offers a groundbreaking architecture for supplying computing, storage, and networking resources to propel the Internet of Things forward. By situating them at the network's edge, this model makes computational power more accessible to users. If tasks are executed entirely at the edge, energy and resource constraints of edge nodes may lead to poor performance. Therefore, it is widely recognized that offloading certain tasks to cloud data centers (CDCs), which possess abundant execution resources, is advantageous. However, implementing CDCs is not widespread and lacks flexibility in isolated regions. This presents challenges and high costs for reliably completing tasks quickly. Consequently, employing more adaptable unmanned aerial vehicles (UAVs) as CDCs in specific scenarios is crucial. The work presents the idea of mobile edge computing supported by the UAV. By considering the needs of user services, we enhance the energy efficiency of the UAV by optimizing their trajectories, transmission power, and computational load distribution. Furthermore, the work introduces an improved algorithm called GeneticSimulated-annealing-based Particle Swarm Optimizer (GSPSO) to optimize the energy efficiency of the UAV. Experimental simulations show that regarding the energy efficiency of the UAV, GSPSO exhibits superior search efficiency, surpassing genetic algorithm, simulated annealing, and particle swarm optimization by 7.39%, 15.03%, and 27.93%, respectively.