Energy-Efficient Multi-Factor Based Clustering Approach for Energy Harvesting Wireless Body Sensor Networks

Abstract

The medical industry, sports, entertainment, and social welfare are just a few of the many fields that can benefit from Wireless Body Area Networks (WBANs). WBANs rely heavily on Base Station Nodes (BSNs), also known as Sensor Nodes (SNs). Smaller sensor nodes are typically quite resource constrained. As a result, efficient energy use is crucial during the planning stages of WBAN designs. The radio frequencies of the sensor nodes are extremely vulnerable to noise and interference and they have limited capacity for storing energy, processing data, and archiving observations. As a result, they pose a risk to the network's efficiency, longevity, and throughput. Energy harvesting techniques used in the Internet of Things that rely on wireless sensor networks are one solution to the problem of excessive power usage. While most research on energy harvesting wireless-sensor-networks has focused on also Energy-Efficiency (EE) or Quality-Of-Service (QoS), a few recent studies have tackled the issues of clustering and routing in these systems. An effective method is required, one that makes economical use of energy while also guaranteeing high service standards. This study proposes an intelligent protocol, the Energy-Efficient Multi-Factor-Based-Clustering-Approach (2EMFCA), that takes into account both the efficiency of energy use and the dependability of communications. It uses a weighted-function specified by many factors such link statistics, neighborhood-density, residual-energy, and the pace of energy harvesting of nodes to determine a trustworthy cluster head to lead the network. When selecting a cluster's head, taking into account such criteria helps nodes conserve energy by routing data across links with a lower probability of packet loss due to signal-to-noise ratio. Improvements in network throughput, energy efficiency, and lifetime, as well as increased service availability, are all possible thanks to a reduced packet loss ratio in IoT use cases. Our suggested method beats the current low-energy efficient clustering-hierarchy and other modern protocols in terms of Network-lifetime (NL), Residual-Energy (RE), and Network-Throughput (NT), as shown in a series of investigations utilizing Network-Simulator-2(NS2).