An APSO-Based Resource Scheduling Algorithm for UAV-Aided MEC System
Hengkun Miao, Huasen He, Yue Ren, Yuzhuo Wang, Zihan Zhu · 2023
The widespread use of resource-intensive applications has imposed a challenge for user equipments (UEs) which may lack the necessary resources to support them. Unmanned Aerial Vehicle (UAV) aided Multi-Access Edge Computing (MEC) systems offer an innovative solution to this problem by providing additional resources. Our paper introduces a model in which a UAV outfitted with an edge server is utilized to offer service for a cluster of UEs. In each time slot, each UE has a latency-critical task that need to be processed. With the aid of UAV, UEs can choose to partially offload the computation tasks or perform local computing. A minimization problem whose goal is to reduce the overall energy consumed by jointly optimizing the computation offloading strategies and time slot scheduling is formulated. To solve this problem, an adaptive particle swarm optimization (APSO) based algorithm is proposed in this paper to search the optimal solution iteratively by analogizing the iteration procedure to the movement of particles. The parameters of the algorithm are updated through a carefully designed adaptive approach. Based on the simulation results, our proposed algorithm outperforms the benchmark algorithms by providing lower energy consumption. Besides, It is able to improve fairness among users.