Unequal clustering scheme for hotspot mitigation in IoT-enabled wireless sensor networks based on fire hawk optimization

Abstract

Wireless sensor networks (WSNs) related to the Internet of Things (IoT) have shown incredible growth with technological advancements, offering a greater number of applications all over the world. Sensor nodes are deployed closer to the base station (BS) in some scenarios and held accountable for transmitting data from neighboring and own nodes against the BS and depletes energy. In the network, this problem is referred to as the "hotspot issue" and it may be fixed using procedures that use unequal clustering. In this study, a novel approach for IoT-assisted wireless sensor networks called the Fire Hawk Optimization-based Unequal Clustering Scheme for Hotspot Mitigation (FHOUCS-HSM) is developed. In the presented FHOUCS-HSM model, a first-order radio energy model has been utilized. The FHOUCS-HSM technique follows the foraging characteristics of whistling kites, black kites, and brown falcons. The design of the FHO algorithm for unequal clustering shows the novelty of the work. In addition to this, the FHOUCS-HSM model computes a fitness function for the use of uneven cluster size and the selection of cluster heads (CH). The comparative research highlights the advantages that the FHOUCS-HSM technique has over many models that are already in use.