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On the efficiency of cluster-based approaches for motion detection using body sensor networks

Lan Kun-Chan (Computer Science and Information Engineering National Cheng Kung University, Tainan City, Taiwan)
Chou Chien-Ming (Computer Science and Information Engineering National Cheng Kung University, Tainan City, Taiwan)
Wang Tzu-Nung (Computer Science and Information Engineering National Cheng Kung University, Tainan City, Taiwan)
Li Mei-Wen (Computer Science and Information Engineering National Cheng Kung University, Tainan City, Taiwan)

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