Reducing Power Consumption in Wireless Body Area Networks: A Novel Data Segregation and Classification Technique

Abstract

Chronic health issues such as hypertension, diabetes, high blood pressure, heart attack, and asthma are widespread in our society. Health-care wireless body area networks (WBANs) have been widely deployed by medical professionals to manage the health information of a patient remotely, and various data-management approaches have been used to handle the continuous transmission of WBAN data. The data segregation and classification approach is considered the most promising WBAN data-management technique among the extant state-of-the-art approaches. The technique plays an important role in avoiding network congestion; however, it consumes more energy from a sensor while transmitting a large packet of accumulated information over the network. This research is intended to solve this issue by introducing a novel classification scheme that separates the sensor's readings into urgent, semiurgent, and nonurgent packets. Moreover, WBAN systems are highly dependent on gateway devices. This article offers an architectural routing approach for a medical sensor that enables transmitting packets during gateway failure. The performance evaluation of this work using OMNET++ with varied performance metrics shows promising results, because verification outperforms the extant state-of-the-art methods in terms of power consumption, packet delivery ratio, and the number of transmitted packets.