Resource allocation method for Mobility‐Aware and Multi‐UAV‐Assisted mobile edge computing systems with energy harvesting

Due to the limited coverage of base station (BS) and battery capacity mobile users, resource allocation strategy in multiple unmanned aerial vehicles (UAVs)-assisted edge computing system with nonlinear energy harvesting is investigated this paper. The cooperation between BS multi-UAV considered, which can provide extensive for users mobility. Mobile simultaneously offload computation bits UAV, harvest from UAV. Meanwhile, mobility taken into account. Moreover, an echo state network (ESN)-based prediction algorithm utilized predicting future positions users. Therefore, UAV reach predicted users' advance ensure continuity communication. objective maximize efficiency by jointly optimizing bandwidth allocation, resources, trajectory transmitting power Then, problem formulated as a mixed-integer programming problem. quantum-behaved particle swarm optimization (QPSO) used solve Simulation results demonstrate that proposed achieve higher than other benchmark strategies. In addition, QPSO outperforms standard genetic terms efficiency.