Coyote Optimization Using Fuzzy System for Energy Efficiency in WSN

Abstract

In recent days, internet of things is widely implemented in Wireless Sensor Network (WSN). It comprises of sensor hubs associated together through the WSNs. The WSN is generally affected by the power in battery due to the linked sensor nodes. In order to extend the lifespan of WSN, clustering techniques are used for the improvement of energy consumption. Clustering methods divide the nodes in WSN and form a cluster. Moreover, it consists of unique Cluster Head (CH) in each cluster. In the existing system, Soft-K means clustering techniques are used in energy consumption in WSN. The soft-k means algorithm does not work with the large -scale wireless sensor networks, therefore it causes reliability and energy consumption problems. To overcome this, the proposed Load-Balanced Clustering conjunction with Coyote Optimization with Fuzzy Logic (LBC-COFL) algorithm is used. The main objective is to perform the lifespan by balancing the gateways with the load of less energy. The proposed algorithm is evaluated using the metrics such as energy consumption, throughput, central tendency, network lifespan, and total energy utilization.