On the Efficiency of Cluster-based Approaches for Motion Detection using Body Sensor Networks

Kun-chan Lan1, Chien-Ming Chou1, Tzu-nung Wang1 and Mei-Wen Li1

  1. Computer Science and Information Engineering, National Cheng Kung University
    No.1, University Road, Tainan City 701, Taiwan
    klan@csie.ncku.edu.tw

Abstract

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.

Key words

body sensor network, motion detection, energy conservation, KNN

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS110310053L

Publication information

Volume 8, Issue 4 (October 2011)
Cyber-Physical Networks and Software
Year of Publication: 2011
ISSN: 1820-0214 (Print) 2406-1018 (Online)
Publisher: ComSIS Consortium

Full text

DownloadAvailable in PDF
Portable Document Format

How to cite

Lan, K., Chou, C., Wang, T., Li, M.: On the Efficiency of Cluster-based Approaches for Motion Detection using Body Sensor Networks. Computer Science and Information Systems, Vol. 8, No. 4, 1051-1071. (2011)