Integrated Crow Lookup and Particle Optimization Algorithm-based Cluster Head Selection

A high number of sensor nodes in a wireless sensor network are dispersed randomly throughout some areas. Each node has a finite amount of energy, and when certain events that need communication with a sink node happen, it generates information. It has been demonstrated that the nodes that are physically located closer to the sink node will use up their energy reserves at a faster rate; hence, the lifetime of the network will be shortened, and a number of unfavourable outcomes will occur. As a result, the question of how to best maximise the lifetime of the network has become an important one. We came to the conclusion, based on the research fruit that was contributed by a large number of researchers, that the method for optimising the life of the network included maintaining energy efficiency, enhancing or developing certain optimised methods, and utilising the built-in functionality of nodes, among other things. Numerous other elements may also have an impact on the network's lifespan. It is determined that choosing CH by mixed heuristic algorithms is important for avoiding the decline over the past of energy over the existing system. Through this work, CH Extraction using Integrated Crow Look and Particle Optimization Technique (ICLPOT-CH) such protocol was suggested with advantages of Improved wild crows scan and augmented POT to boost WSNs' resource consistency and system longevity. To accomplish effective CH allocation, our ICLPOT- CH technique calculates important aspects like load, perpetual life, traversed length, latency in gathering data, and loss rate. Our experimental result of a suggested ICLPOT-CH validated a network throughput extension with a largest amount of active nodes and a minimum number of fatalities node, regardless of the number of edge devices, relaying cycles.