Optimized Clustering Approach for WSN Using WCLUST Algorithm

Abstract

The proliferation of Internet of Things applications has resulted in easy device integration in Wireless Sensor Networks, enabling real-time data collection and coordination. However, seamless integration is still plagued by issues such as energy constraints and high energy consumption due to inefficient data communication. Many studies explored clustering methods to address such issues. Here, we introduce the Whale-Based Algorithm for cluster establishment and a Selection Factor for Cluster Head selection—key steps that minimize paths and save energy. With the use of the proposed method, our approach improves clustering quality with the establishment of dense and energy-saving clusters. The Selection Factor provides dynamic selection of the most suitable Cluster Heads according to residual energy, neighboring nodes, and distance to the sink. The proposed method attains the clustering quality accuracy of 97% and drastically decreases the run time with respect to the Ant-Based Clustering algorithm.