WBNN: a weight-based next neighbor selection algorithm for wireless body area network

Wireless body area networks (WBANs) play a pivotal role in monitoring body movements and collecting crucial health metrics such as blood pressure, heart rate, oxygen levels, and physiological conditions. These health parameters are acquired through sensor clusters strategically deployed on the human body and subsequently transmitted to fog or cloud layers for essential decision-making processes. This research study commences by enhancing energy efficiency within WBANs through the introduction of an innovative weight-based algorithm known as the weight-based next neighbor selection algorithm (WBNN). WBNN assigns specific weights to each route connecting pairs of sensors by considering the distance between them. A small weight implies a small distance, while a high value of weight implies a high distance. The total weight of each route is then computed. During the forwarding mechanism, sensor nodes opt for routes with the smallest weight, signifying reduced energy consumption. Secondly, it provides insights into various clustering schemes and routing algorithms employed for data exchange among different sensors and network layers. Lastly, the suggested algorithm is compared with the latest routing algorithms on the basis of four parameters, i.e., network utilization, number of dead nodes, throughput and energy consumption. The results show that the described protocol surpasses traditional routing protocols by achieving superior outcomes, including a 6% reduction in energy consumption, a 72% increase in throughput, and a 68% decrease in number of dead nodes. These findings underscore the potential of the proposed protocol to significantly enhance the efficiency and effectiveness of WBANs in the domain of smart healthcare applications.