Integrating the Syracuse Algorithm with K-MEAN: A Comprehensive Approach to Energy Optimization in Wireless Sensor Networks
Yacouba OUATTARA
Yacouba OUATTARA, University Joseph KI-ZERBO, Ouagadougou, Kadiogo, Burkina Faso.
Manuscript received on 31 August 2024 | Revised Manuscript received on 12 September 2024 | Manuscript Accepted on 15 November 2024 | Manuscript published on 30 November 2024 | PP: 1-6 | Volume-13 Issue-4, November 2024 | Retrieval Number: 100.1/ijrte.D815313041124 | DOI: 10.35940/ijrte.D8153.13041124
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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.
Keywords: Energy, K-MEAN, Syracuse, WSN, SHEM.
Scope of the Article: Distributed Sensor Networks