Joint Optimization between Task Offloading and Resource Allocation in Mobile Edge Computing with multiple UAVs

Energy consumption and completion time are two hot issues in UAV (Unmanned Aerial Vehicles) assisted edge computing.The current research is mainly focused on the interaction between UAVs and edge servers, while the interplay between UAVs is rarely considered.In this paper, in the considered scenario with multiple UAVs and servers, the UAVs may possess idle resources, and thus function as temporary servers to provide task offloading services for other UAVs.We begin by formulating a multi-objective joint optimization problem, which aims to balance the energy consumption and time delay in order to maximize the benefits of the system. Then we use a multi-objective genetic algorithm called Non-dominated Sorting Genetic Algorithm II (NSGA-II) to iteratively find the optimal offloading strategy.Through numerical simulation, the results obtained from the numerical simulation demonstrate the superiority of the proposed method in achieving the Pareto optimal frontier that balances energy consumption and completion time.