Leveraging Enhanced Bald Eagle Search Optimization for Energy-Aware Clustering in Wireless Sensor Networks

Wireless sensor networks (WSN) have a vital role in the Internet of Things (IoT), which aids to generate seamless data that has a great influence over the lifetime of the network. In spite of the significant applications of the WSN, various challenges like load balancing, security, storage, and energy occurs. A prominent technique that has to be implemented in WSN is clustering of sensor nodes. The sensor nodes (SNs) were accumulated to generate clusters in a cluster-based WSN scenario. The SNs also contain limited battery power. Thus, energy effectiveness in WSNs becomes crucial. The load on the SN and its distance from the base station (BS) will be the important elements of energy consumption. Hence, load balancing in accordance with the communication distance becomes essential for WSNs. In this aspect, this article develops an Enhanced Bald Eagle Search Optimization for Energy-Aware Clustering (EBESO-EAC) technique for WSN. The proposed EBESO-EAC technique mostly focuses on load balancing process via clustering the nodes in WSN. To attain this, the presented EBESO-EAC technique chooses proper set of cluster heads (CH) using a fitness function with multiple sensor parameters. Moreover, the EBESO-EAC technique is based on the integration of the traditional BESO algorithm with the Levy function during swooping phase. The simulation result of the EBESO-EAC technique was tested under a series of simulations. An extensive set of experimental analyses reported the improved performance of the EBESO-EAC technique over other existing algorithms.