Binary Quantum Elite Particle Swarm Optimization Algorithm for Spectrum Allocation in Cognitive Wireless Medical Sensor Network

Abstract

Cognitive wireless medical sensor network (CWMSN) is a new development direction of the wireless sensor network (WSN) in recent years. The requirements of CWMSN are multi-faceted such as optimized utilization of resources, energy efficiency and channel sharing. However, CWMSN also faces the problem of limited energy and spectrum. For this reason, this paper proposes a spectrum allocation model. Moreover, a new binary quantum-behaved elite particle swarm optimization algorithm (BQEPSO) based on the combination of quantum operator, elite operator and binary particle swarm optimization (BPSO) is proposed to solve the spectrum allocation problem in CWMSN. The algorithm can enable the conflict-free use of spectrum resources in CWMSN and maximize the efficiency of spectrum allocation. The BQEPSO is compared with the genetic algorithm (GA) and traditional particle swarm optimization (PSO) under the same experimental conditions. The simulation results indicate that the performance of BQEPSO is better than the other two algorithms. And BQEPSO can maintain the total throughput of the CWMSN. What’s more, BQEPSO has a high network reward and throughput.