A Fuzzy based Methods in Wireless Body Area Network for Controlling Congestion

Abstract

With the wireless communication, the ways of communication in present era of technology has changed which helps in fastest and efficient way of communication in each and every domain. In the field of medical science, to sense the human body activities such as heartbeat, blood pressure and other activities performed by internal body parts of the human, Wireless Sensor Network is employed. Then this sensed data is transmitted to the centralized server. The information that is collected is made to transfer to the destination through a dedicated route created by routing protocols in form of data packets. Thus, the network sometimes faces the issue of congestion due to increased data traffic to the nodes. The present paper defines an enhanced congestion handling concept for Wireless Body Area Network. For this purpose, the cost function of the nodes is evaluated on the basis of major factors such as distance, residual energy and delay. Additionally, by applying the Fuzzy Inference System, the congestion control model is executed. It also improves the routing strategy by introducing the firefly algorithm based forward-looking node selection approach. For evaluation, the proposed work is simulated in MATLAB and compared with the traditional congestion technique. The simulation results show that the lifetime of the network increases by 30%. The efficiency of packet received at sink improves by 18%. Path loss in the present study is 33% less as compared to traditional approach. And, also consumes near about 8% less energy.