A Minimum Spanning Tree-based Energy Efficient Cluster Head Election in WSN

Abstract

Over modern decades, both scientific and commercial societies have been seeing progress of Wireless Sensor Networks (WSNs). Clustering is most common form of growing WSN lifetime. The optimal no. of Cluster Heads (CHs) & structure of clusters are the main problems in clustering techniques. The paper focuses on an efficient CH preference mechanism that rotates CH between nodes with a greater energy level than others. Original energy, residual energy as well as the optimum value of CHs is assumed to be used by the algo for the choice of next category capable network cluster heads including ecosystem control, smart cities, or devices. The Fuzzy inference system is used for the clustering algorithm which displays stronger performance than the previous clustering technique. Meanwhile, a minimum spanning tree named Bellmanford algo is also constructed to establish the connection between the nodes for finding the shortest and secure path for data transmission hence resulting in faster data sending and receiving process.