Energy efficient heterogeneous clustering scheme using improved golden eagle optimization algorithm for WSN-based IoT

In the Internet of Things, Wireless Sensor Networks (WSNs) are networks of interconnected sensors that wirelessly collect and transmit information about the environment. Using IoT sensors, IoT applications can remotely monitor and control physical environments. Clustering in WSNs involves organizing sensor nodes into groups called clusters with one or more CHs for efficient data integration, communication and management, improving network performance and resource utilization. In WSNs, achieving energy efficiency is critical to extend network lifetime and ensure stable operation. An important aspect contributing to energy optimization is the selection of CHs. However, the lack of an efficient cluster head selection mechanism remains a significant challenge. Therefore, this study introduces an optimized multivariate cluster head selection method that leverages the Improved Golden Eagle Optimization Algorithm (IGEOA). With this approach, the selection of CHs is optimized, combining multiple objective functions designed for energy efficiency. By using this algorithm, clusters are formed based on the selected CHs. In addition, a cluster maintenance phase is integrated to supervise the post-establishment clustering of the network, which ensures optimal cluster performance and resource utilization in WSN. Evaluation through simulation illustrates that the proposed method significantly improves both performance and energy efficiency in a WSN environment.