Joint Task Offloading and Resource Allocation for MEC Networks Considering UAV Trajectory

Abstract

Owing to the high flexibility and mobility, unmanned aerial vehicles (UAVs) have attracted significant attention from both academia and industry communities, especially in the UAVempowered mobile edge computing (MEC) networks. However, the repetitiveness of tasks generated by user equipments (UEs) has not been fully analyzed. In this paper, a UAV-empowered MEC network architecture is proposed. Computation tasks are divided into two categories, i.e., private tasks and public tasks, which can be executed locally or offloaded to UAVs utilized as flying MEC servers for task execution. The aim of this paper is to optimize task execution latency and network energy consumption by jointly considering UEs' offloading decisions and UAVs' route planning. To solve the challenging formulated optimization problem, an enhanced block coordinate descent algorithm is proposed, which is utilized in conjunction with the differential evolution and penalty function method. The simulation results demonstrate that the proposed scheme outperforms the random offloading strategy and fixed route strategy regarding the cumulative cost and time cost.