Improving IoT based WSN Lifetime using Cuckoo Search Optimization

A Wireless Sensor Network (WSN) is a collection of energy- and processing-constrained devices that collect information on a variety of occurrences. Clustering organization, which groups sensors hierarchically and designates one of them as a CH, is an efficient way to extend the lifetime of a wireless sensor nodes. A such CH is in charge of individual behaviors including such collecting data from other cluster sensors and relaying it to the base station over the internet. The process of collecting data is enhanced and, as a result, the lifetime of the network is extended when using a cluster-head organization. The existing I-Sep approach, considered for comparison in this research, distinguish cluster heads based on energy and has the disadvantage of consuming more energy if the higher energy node is far away from the BS. Cuckoo search algorithm was deployed to solve the energy-efficient CH selection, which presented several criteria such as fitness function, computational cost, and so on, and quickly chose a better CH that used less energy. The simulation findings reveal that, when compared to the existing method I-SEP, the dynamic cluster head selection method using CSA provides more energy efficiency in terms of the number of alive nodes, dead nodes, remaining energy and throughput of the network.