FCM clustering and FLS based CH selection to enhance sustainability of wireless sensor networks for environmental monitoring applications

Abstract

Wireless Sensor Networks (WSNs) are the physical monitoring infrastructure of the Internet of Things (IoT) technology. For IoT based monitoring systems, the WSNs need to be sustainable with a maximum number of alive nodes so that the monitoring is effective. The sensor nodes are battery-driven and hence energy efficiency is one of the major challenges. Based on the clustering methods and the selection of Cluster Head (CH), the energy consumption of the sensor nodes can be minimized. In this research work, a clustering protocol based on fuzzy techniques is proposed to improve the stability and sustainability of WSN. Fuzzy techniques are used to tackle uncertainties occurring in wireless sensor networks. Clusters are formed based on Fuzzy-c-means (FCM) algorithm. The aim is to group the nodes properly so as to reduce intra-cluster communication distances. The CHs are then selected based on the Fuzzy Logic System (FLS). The performance of the proposed protocol is observed for an increase in the coverage area and node density. The proposed protocol is also analyzed for different sink locations. Due to better network stability and sustainability, the proposed protocol can be used for large scale IoT based monitoring systems.