Joint Association, Deployment and Flight Trajectory Optimization for Multi-UAV-enabled Large-Scale Mobile Edge Computing

This work investigates how multiple unmanned aerial vehicles (UAVs) assist the large-scale IoT devices (its count
$\geq$
100) in the edge computing system in accomplishing their tasks. The UAVs serve the latter as edge servers, and fly to footholds to collect task data from the latter, execute tasks locally and return results to the latter. The goal of this work is to minimize overall energy consumption by jointly optimizing the association between each UAV and ground-based IoT devices, deployments of UAVs, and their flight trajectories. To achieve this, this work proposes a joint optimization approach (JOA). It has three parts: 1) an improved k-means method is designed to handle the association between each UAV and ground-based IoT devices, where the number of clusters is equal to that of UAVs, which means that each UAV is responsible for the IoT devices within a cluster; 2) for the deployments of UAVs, an improved fireworks algorithm (IFWA) with variable-length encoding strategy and population size update strategy is proposed to optimize the number and locations of footholds of each UAV, where each member of the population symbolizes a UAV foothold, and each firework and its offspring are considered as the deployment of UAV. Also, the population size update strategy is employed to dynamically change the number of footholds; and 3) regarding UAV flight trajectory, a pre-computed greedy algorithm based on the footholds of UAVs obtained by IFWA is proposed to minimize the total UAV distance. The proposed approach is verified on ten large-scale instances, and the results demonstrate its effectiveness in achieving minimal energy consumption when compared to other state-of-the-art methods.