Energy-Efficient Resource Allocation in Multi-UAV-Assisted Two-Stage Edge Computing for Beyond 5G Networks

Abstract

Unmanned aerial vehicle (UAV)-assisted multi-access edge computing (MEC) has become one promising solution for energy-constrained devices to run the applications with high computation demand and stringent delay requirement in beyond 5G era. In this work, we study a multi-UAV-assisted two-stage MEC system in which UAVs provide the computing and relaying services to the mobile devices. Due to the limited computing resources, each UAV executes only a portion of the offloaded tasks from its associated MDs in the first stage. Hence, in the second stage, each UAV relays the portions of the tasks to the terrestrial base station (TBS) which has rich computing resources enough to handle all the tasks relayed to it. In this regard, we formulate a joint task offloading, communication and computation resource allocation problem to minimize the energy consumption of MDs and UAVs by considering the limited resources of UAVs and the tolerable latency of the tasks. The formulated problem is a mixed-integer non-convex problem which is NP hard. To solve the formulated optimization problem, we apply the Block Successive Upper-bound Minimization (BSUM) method which guarantees to obtain the stationary points of the non-convex objective function. Finally, the extensive evaluation results are conducted to show the superior performance of our proposed framework.