Wireless Internet. 10th International Conference, WiCON 2017, Tianjin, China, December 16-17, 2017, Proceedings

Research Article

Energy-Efficient Partitioning Clustering Algorithm for Wireless Sensor Network

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  • @INPROCEEDINGS{10.1007/978-3-319-90802-1_2,
        author={Koffi Souza and Catherine Almhana and Philippe Fournier-Viger and Jalal Almhana},
        title={Energy-Efficient Partitioning Clustering Algorithm for Wireless Sensor Network},
        proceedings={Wireless Internet. 10th International Conference, WiCON 2017, Tianjin, China, December 16-17, 2017, Proceedings},
        proceedings_a={WICON},
        year={2018},
        month={5},
        keywords={Energy saving Clustering times series Smart meters Wireless Sensor Networks Data transfer},
        doi={10.1007/978-3-319-90802-1_2}
    }
    
  • Koffi Souza
    Catherine Almhana
    Philippe Fournier-Viger
    Jalal Almhana
    Year: 2018
    Energy-Efficient Partitioning Clustering Algorithm for Wireless Sensor Network
    WICON
    Springer
    DOI: 10.1007/978-3-319-90802-1_2
Koffi Souza1, Catherine Almhana1, Philippe Fournier-Viger2,*, Jalal Almhana1,*
  • 1: Université de Moncton
  • 2: Harbin Institute of Technology (Shenzhen)
*Contact email: philfv8@yahoo.com, jalal.almhana@umoncton.ca

Abstract

Wireless Sensor Networks (WSNs) have recently achieved tremendous success at both research and industry levels. WSNs are currently implemented in many areas, such as the military, environmental monitoring, and medicine. WSN nodes are battery-operated, and energy saving is critical for their survival. Several research papers have been published on how to optimize power usage. In this paper, we focus on improving power consumption by optimizing data transfer. We propose an Energy-Efficient Partitioning Algorithm to reduce data transfer and consequently improve power consumption. Using data collected from a real WSN in the City of Moncton, we implemented and compared the performance of the proposed algorithm with another data reduction algorithm. Experimental results show that our algorithm outperforms a recent data reduction technique in terms of power saving.