Snow Leopard Optimization Algorithm-Based Energy Efficient Cluster Head Selection in Internet of Things

Energy stability and network lifetime are most challenging aspects in Internet of Things (IoT) in todays’ smart world. Clustering is an effective option for sustaining energy stability and network lifetime in IoT as the process of grouping nodes into clusters with efficient cluster head (CH) selection increases the probability of dramatically reducing energy consumption. However, potential selection of CHs is necessary in IoT as it is widely adopted in crucial applications which include smart agriculture and forest monitoring. In this paper, Snow Leopard Optimization Algorithm-based CH Selection Strategy (SLOACHSS) is proposed for maximizing energy stability and network lifespan by achieving optimized data routing in IoT. This proposed SLOACHSS achieves the process of clustering using steps that includes selection of CHs using SLOA and cluster formation. The process of CHs selection is achieved through the application of two different fitness functions that include energy possessed by CHs, and factor of inter-cluster and intra-cluster distance. SLOACHSS is proposed with phases of traveling of routes, hunting, reproduction, and mortality for balancing the rate of exploration and exploitation during CH selection in IoT. The simulation experiments of the proposed SLOACHSS confirm 23.21% improved energy consumption and 21.98% improved throughput in contrast to standard schemes for varying number of sensor nodes in the network.