Resource Allocation Strategy for Dual UAVs-Assisted MEC System with Hybrid Solar and RF Energy Harvesting

Abstract

To address the problem that user device (UD) harvests less energy from environmental radio frequency (RF) sources, a resource allocation strategy in dual unmanned aerial vehicles (UAVs)-assisted mobile edge computing (MEC) system with hybrid energy harvesting is studied in this paper. By deploying two UAVs with hybrid solar and RF energy harvesting, they can provide edge computing services for the UDs and supply energy for the low energy UDs, respectively. When the UD has a large computing task, the computing task can be offloaded to the MEC server carried by the UAV. The computing pressure of this UD can be greatly reduced. If the energy harvested by the UD from the ambient RF sources is not enough, another UAV flies close to this UD. The UAV acts as a dedicated RF source and transfers energy to the UD. The problem of resource allocation is formulated as a mixed-integer nonlinear programming problem by jointly considering the energy consumed by UDs and UAVs. The objective is to minimize the total energy consumption under the constraints of computing capacity and energy consumption of MDs and UAVs. The suboptimal solution can be obtained by introducing a quantum-behaved particle swarm optimization (QPSO) algorithm. Simulation results verify that the proposed QPSO algorithm has lower energy consumption in contrast to the other traditional schemes.