Integrating the Syracuse Algorithm with K-MEAN: A Comprehensive Approach to Energy Optimization in Wireless Sensor Networks

In deploying a sensor network in a challenging environment, it is crucial to consider energy consumption to ensure an extended network lifespan. Since the inception of sensor networks, researchers have proposed various energy saving solutions outlined in the introduction. In our study, we introduce a novel approach for cluster formation and positioning of clusters and base stations to minimize energy consumption in implementing clusters using the K-MEAN algorithm. Through simulation, we demonstrate that the Syracuse WSN algorithm significantly outperforms the traditional K MEANS algorithm in conserving energy consumption.