WARMHEX-BCCLR: BIO-INSPIRED HEXAGONAL CLUSTERING FOR LONGEVITY OF IOT-WSN SURVIVAL TIME

Wireless Sensor Networks (WSN)vis the base network for the advance IoT based network derivation. In WSNs, many sensor nodes work together to gather data from the environment. This data is first sent to a cluster head, which then passes it on to the base station for processing. In this paper, a nature-inspired clustering architecture known as SwarmHex, is proposed by integrating honeycomb and hexagonal structures to optimize Wireless Sensor Networks (WSNs). The proposed work enhances data transmission efficiency by structuring the communication pathway from individual nodes to cluster heads, and from cluster heads to the base station, thereby improving overall network longevity and energy utilization. The placement of nodes in the edges of the architecture provides the compact packing of sensors within a cluster with equal distance between the sensors tackle the coverage problem and transmission of data through the architecture using linear direction pattern with effective cluster head selection strategy based on priority helps the sensor nodes in efficient CH selection and data transfer. The proposed bio-inspired clustering SwarmHex-BCCLR shows best in class performance compared to the existing standard algorithms named as Ant Colony Optimization (ACO), Grey Wolf Optimization (GWO), Sandpiper Hybrid Optimization (SHO) and Whale Optimization Algorithm (WOA) that leads to longevity contribution to increased survival time of the WSN.