Energy Efficient Clustering and Sink Mobility in Heterogeneous Wireless Sensor Network using Balanced Spider Monkey Optimization

Utilizing the mobile sinks which enhances the network lifetime as well as energy consumption when there is the eruption traffic situation in Wireless Sensor Networks (WSN). Therefore, it is significant to manage the traffic by utilizing the mobile sinks in Energy-Efficient (EE) mobility approach. The energy resources limitation in Sensor Nodes (SN) presents a challenging issue that minimizes network lifetime of individual nodes as well as entire system as a result of energy depletion. In this research, the Balanced Spider Monkey Optimization (BSMO) is proposed for obtaining an EE Cluster Head (CH) selection and sink mobility in heterogenous WSN. The sensor nodes with a predetermined less energy level qualify for the procedure of selection of CH. The optimization-based clustering and sink mobility approach is developed for designing an optimal transmission path by CH selection. The proposed method achieves better results and it is evaluated by various performance metrices like energy consumption, throughput and No. of alive nodes of values about 0.0024, 4.87 and 99.35 respectively when compared to the existing methods like Particle Swarm Optimization (PSO) and Energy-aware Routing algorithm using Clustering and Mobile Sinks (ERCMS).