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On the Efficiency of Cluster-based Approaches for Motion Detection using Body Sensor Networks
Computer Science and Information Systems, Tome 8 (2011) no. 4 Cet article a éte moissonné depuis la source Computer Science and Information Systems website

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Body Sensor Networks (BSN) are an emerging application that places sensors on the human body. Given that a BSN is typically powered by a battery, one of the most critical challenges is how to prolong the lifetime of all sensor nodes. Recently, using clusters to reduce the energy consumption of BSN has shown promising results. One of the important parameters in these cluster-based algorithms is the selection of cluster heads (CHs). Most prior works selected CHs either probabilistically or based on nodes’ residual energy. In this work, we first discuss the efficiency of cluster-based approaches for saving energy. We then propose a novel cluster head selection algorithm to maximize the lifetime of a BSN for motion detection. Our results show that we can achieve above 90% accuracy for the motion detection, while keeping energy consumption as low as possible.
Keywords: body sensor network, motion detection, energy conservation, KNN 
@article{CSIS_2011_8_4_a6,
     author = {Kun-chan Lan and Chien-Ming Chou and Tzu-nung Wang and Mei-Wen Li},
     title = {On the {Efficiency} of {Cluster-based} {Approaches} for {Motion} {Detection} using {Body} {Sensor} {Networks}},
     journal = {Computer Science and Information Systems},
     year = {2011},
     volume = {8},
     number = {4},
     url = {http://geodesic.mathdoc.fr/item/CSIS_2011_8_4_a6/}
}
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Kun-chan Lan; Chien-Ming Chou; Tzu-nung Wang; Mei-Wen Li. On the Efficiency of Cluster-based Approaches for Motion Detection using Body Sensor Networks. Computer Science and Information Systems, Tome 8 (2011) no. 4. http://geodesic.mathdoc.fr/item/CSIS_2011_8_4_a6/