A Way to Enhance the Energy Efficient Level in the Field of WN using APSORM

In order to collect environmental data, Wireless Sensor Networks (WSNs) are made up of sensors that are placed strategically in particular locations and are powered by restricted energy sources. Because battery power is the major energy source and each sensor node has a limited energy capacity, developing energy-efficient data collection strategies for large-scale WSNs is a big task. The well-known method of clustering is intended to increase WSN energy efficiency. With the help of Adaptive Hierarchical clustering-based routing protocol and particle-Swarm Optimisation (PSO) based on Genetic Algorithms (GA), we present a fresh approach in this suggested technique. The goal of this integration is to maximise routing and extend the life of the network. By modifying dispersed data transmission using an adaptive hierarchical clustering-based routing algorithm, the suggested method minimises power usage and guarantees continuous coverage of the whole region. To efficiently balance node energy usage, a hierarchical clustering-based routing technique is first described. In addition, the use of Genetic Algorithms based on Multi-Swarm Optimisation concentrates on the selection of effective Cluster Heads (CHs), extending the network’s lifetime and enhancing routing effectiveness. The suggested PSO-Genetic Algorithm with Hill Climbing (GAHC) is quite successful, according to the study’s findings. It optimises WSN performance by improving cluster construction, lowering average energy consumption, extending lifetime computation, mitigating average packet loss, and minimising end-to-end latency.