Resource Allocation Strategy for Mobile Edge Computing System with Hybrid Energy Harvesting

Abstract

Aiming at the problem that mobile terminal (MT) harvests less energy from ambient radio frequency (RF) sources, the resource allocation strategy in mobile edge computing (MEC) system with hybrid energy harvesting is investigated in this paper. By deploying multiple magnetic induction energy quick charging stations (MI-CSs) within the coverage area of the base station, the MT can supplement extra energy at a nearby MI-CS when the energy harvested from ambient RF sources is about to be exhausted. The MT offloads computing task to edge server by leveraging MEC technology. The resource allocation problem is formulated as an optimization problem. The objective is to minimize the total energy consumption of MTs under the constraints of computing capability range of MT, maximal computing resource of edge server, computing delay of task, and battery energy of MT. The suboptimal solution is obtained by adopting the quantum-behaved particle swarm optimization (QPSO) algorithm. Simulation results show that the QPSO algorithm has less energy consumption compared with the standard particle swarm optimization algorithm and the fixed computing resource allocation method.