Green Optimization with Load balancing in Wireless Sensor Network using Elephant Herding Optimization

Wireless sensor networks (WSNs), which provide sensing capabilities to Internet of Things (IoT) equipment with limited energy resources, are made up of specialized transducers. Since substitution or re-energizing of batteries in sensor hubs is extremely difficult, power utilization becomes one of the pivotalmattersin WSN. Clustering calculation assumes a significant part in power management for the energy-compelled network. Optimal cluster head selection suitably adjusts the load in the sensor network, thereby reduces the energy consumption and elongates the lifetime of assisted sensors. This article centers around to an appropriate load balancing and routing technique by the utilization recently developed of Elephant Herding Optimization (EHO) algorithm that alternates the cluster location amongst nodes with the highest energy. The Scheme considered various parameter residual energy, initial energy and an optimum number of cluster head for the next cluster heads selection. The proposed model increases the lifetime of the network by keeping more nodes active even after the 2700th round. The experiment results of the trials show that the proposed EHO-based CH selection strategy outperforms the cutting-edge CH selection models.