Multi-UAV-Assisted MEC System: Joint Association and Resource Management Framework

Abstract

In this paper, we study an energy-efficient multi- UAV-assisted multi-access edge computing (MEC) system in which unmanned aerial vehicles (UAVs) equipped with MEC servers offer computing services to the mobile devices. In particular, the mobile devices offload a portion of their computationintensive and delay-sensitive tasks to the UAVs to minimize local computing energy consumption. However, the coupling constraints of limited energy budget at UAVs and task completion deadlines make it difficult to determine device association and the amount of task to be offloaded. Moreover, the amount of computing resources assigned to the mobile devices by each UAV might vary according to the number of associated users and the amount of task offloaded from them. Therefore, in this work, we formulate a joint device association, task assignment and computing resource allocation problem to minimize the energy consumption of mobile devices and UAVs by considering the energy budget and available computing resources at the UAVs and task completion deadline constraints. To that end, we show that the proposed optimization problem is a mixed-integer nonlinear programming (MINLP) problem, which is generally a nonconvex and NP-hard problem. To solve this, we first decompose the formulated problem into three subproblems which are then solved by applying an iterative block coordinate descent (BCD) algorithm. Through the extensive simulations, we verify that our proposed algorithm outperforms the other benchmark schemes, namely, random association and offloading all.