Implementation of Enhanced Energy Aware Clustering Based Routing (EEACBR)Algorithm to Improve Network Lifetime in WSN’s

This Because of significant advancements in processor, communication, and low-power utilization of embedded computer devices, WSN (Wireless Sensor Network) is quickly becoming the most common technology used in commercial and industrial applications. WSNs are becoming an advanced technology applied in various fields. In Wireless sensor networks, the usage of energy is seen as the most significant challenge. In this work, a new method based on a genetic algorithm was developed to reduce energy use. Initially, the algorithm separates the network into clusters or individual cells. The evolutionary algorithm was then applied to the problem of determining the optimal size of a network in terms of its number of nodes. Once the nodes are distributed around the environment, the chromosomal length is adjusted to be equal to the total number of nodes to allow for gradual convergence. This shortens the chromosomes and helps us locate the optimal option more quickly. Meanwhile, the K-Means algorithm is given the cluster heads from each chromosome as input, facilitating a rapid iteration of the clustering process. Results from an implementation of the proposed method in the NS2 simulator demonstrate that it improves upon prior algorithms in terms of both network longevity and throughput.