Enhancing Energy in WBAN through Cognitive Radio Networks

Abstract

Wireless Body Area Networks (WBAN) provides to measure various physiological and biological data and monitor human body functionalities. In this study, a cognitive radio based coordinator node is designed for Wireless Body Area Networks. Cognitive Radio is capable of connecting various wireless access points with perception and adaptation features. A simulation model has been developed for implementing cognitive radio enabled body area network for analyzing remote health monitoring system for humans. The prominent parameters such as user speed, access point delay, and connection cost are taken into account when selecting the wireless access point. A supervised machine learning technique called Particle Swarm Optimization (PSO) is adapted in the proposed model for improving Energy efficiency and reducing transmission delay in CR enabled WBAN. Simulation is performed using MATLAB and results indicate that the use of proposed PSO for WBAN performs efficiently compared to traditional methods based on MAC protocols.