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