Mathematical modeling of resource allocation for cognitive radio sensor health monitoring system using coevolutionary quantum-behaved particle swarm optimization

Abstract

Independent interactive medical applications cooperate closely with user systems through independent information and data exchange, which realizes digital and intelligent communication for health monitoring system. Due to extensive connectivity and application services, heavy data traffic and limited energy consumption reduce the quality of application services. In this paper, a mathematical model for efficient energy harvesting resource allocation based on cognitive radio sensor network (CRSN) infrastructure is constructed to address energy limitation issues in health monitoring system applications. Because of the highly dynamic nature of available network resources, energy efficiency and communication reliability can be improved. Since the proposed optimization problem is a complex and NP hard problem, coevolutionary theory is adopted, and then a stochastic optimization algorithm based on polynomial approximation and quantum particle swarm optimization is proposed. Simulation results show that the proposed CQPSO algorithm has better performance in terms of re-transmission probability, system throughput, and energy consumption compared to other similar algorithms. Efficient resource allocation improves the performance of the health monitoring system, more effectively and real-time monitoring of human physiological and motion information, providing testers with high-precision quantitative information, and achieving early monitoring and intervention of common diseases.