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

Abstract

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