The impact of Integrating K-Means, Mean-Shift and Centrality Clustering with Leach-C on Wireless Sensor Network Lifetime

Wireless Sensor Networks (WSNs) consist of numerous sensor nodes deployed over a specific area to monitor environmental factors such as temperature, humidity, pressure and motion. These nodes communicate wirelessly and collaborate to transmit the collected data to a central base station for further processing. WSNs have become essential in various domains, including environmental monitoring, healthcare systems, military surveillance, industrial automation, and the development of smart cities. Despite their broad range of applications, a major challenge in WSNs is the limited battery power of sensor nodes. Since replacing or recharging these batteries is often impractical, especially in remote or hazardous locations, energy efficiency becomes a critical consideration in WSN design. To address this, clustering techniques are widely adopted to enhance energy efficiency and extend network lifetime. In clustering, sensor nodes are grouped into clusters, each managed by a Cluster Head (CH). The CH is responsible for aggregating data from its cluster members and forwarding it to the base station, thereby minimizing redundant transmissions and optimizing energy usage. LEACH (Low-Energy Adaptive Clustering Hierarchy) is one of the most well-known hierarchical clustering protocols for WSNs. LEACH-C, an enhanced version, introduces centralized control by having the base station form clusters based on the nodes’ energy levels. This paper proposes integrating LEACH-C with three clustering algorithms—K-means, Mean-Shift and Closeness Centrality; to evaluate their impact on energy efficiency and network lifetime. Simulation results show that LEACH-C combined with K-means clustering achieves the best performance, significantly reducing energy consumption and prolonging the network’s lifetime.