Abstract: The combination of unmanned aerial vehicles(UAVs) and mobile edge computing(MEC) technology breaks the limitations of traditional terrestrial communications.The effective line-of-sight channel provided by UAVs can greatly improve the communication quality between edge servers and mobile devices(MDs).To further enhance the quality-of-service(QoS) of MEC systems,a multi-UAV-enabled MEC system model is designed.In the proposed model,UAVs are regarded as edge servers to offer computing services for MDs,aiming to minimize the average task response time by jointly optimizing UAV deployment and computation offloading.Based on the problem definition,a two-layer joint optimization method(PSO-GA-G) is proposed.On one hand,the outer layer of the proposed method utilizes a discrete particle swarm optimization algorithm combined with genetic algorithm operators(PSO-GA) to optimize the UAV deployment.On the other hand,the inner layer of the proposed method adopts a greedy algorithm to optimize the computation offloading.Extensive simulation experiments verify the feasibility and effectiveness of the proposed method.The results show that the proposed method can achieve shorter average task response time,compared to other baseline methods.

Key words: Computation offloading, Discrete particle swarm optimization algorithm, Greedy algorithm, Mobile edge computing, Unmanned aerial vehicle deployment