Multi-Objective Clustering Algorithm using Ordered Weighted Averaging (OWA) Operator for Heterogeneous WSN

Abstract Wireless Sensor Networks have become a vast field of study. In the past few years, it has been seen that the use of wireless sensor networks in various fields, such as health monitoring, landslide detection, agriculture field, etc., is increased. In such real-life applications, many sensor nodes are required for monitoring and collecting data from the environment, and these sensor nodes work autonomously. The network of complete sensor nodes that works on an application is termed WSN. In a WSN, clustering refers to dividing the sensor nodes into several groups called clusters. A cluster head is selected among all clusters, temporarily acting as the particular cluster's base station. This cluster head is chosen based on several factors, including the node's residual energy, distance from the base station, and distance from other sensor nodes. Mainly the cluster head is responsible for increasing the efficiency and scalability of the entire network. Utilizing the Ordered Weighted Averaging (OWA) operator, we have suggested an algorithm for cluster head selection that works better and uses less energy during data transmission. Simulation results showed that our suggested algorithm outperforms others and extends network lifetime by 5–12%.