An Energy-Efficient and Dynamic Clustering Approach for Wireless Sensor Networks

Abstract

Abstract Wireless sensor networks (WSNs) consist of multiple sensor nodes that are often randomly distributed in the environment and operate independently without human intervention. However, the limited power supply of these nodes presents a significant challenge for WSNs, making the development of energy-saving algorithms critical to increase network lifespan. Node clustering is a commonly used solution to reduce data transfer nodes and achieve data aggregation. Furthermore, the mobility of nodes in the network is often overlooked but can impact energy consumption. This study proposes a Dynamic Dual Head Clustering algorithm for WSNs (DDHCWSN) to reduce energy consumption. Instead of re-clustering in each round, the DDHCWSN updates existing clusters based on energy and position, allowing normal nodes to send data to the closest cluster head node. The algorithm considers two cluster heads, one for data aggregation and the other for sending data to the base station, which can have a better effect on network power consumption. The proposed algorithm was compared with six different algorithms in six different scenarios, and it performed well.