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Using body sensor networks for motion detection: a cluster‐based approach for green radio
Author(s) -
Lan Kunchan,
Chou ChienMing,
Wang Tzunung,
Li MeiWen
Publication year - 2014
Publication title -
transactions on emerging telecommunications technologies
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.366
H-Index - 47
ISSN - 2161-3915
DOI - 10.1002/ett.2559
Subject(s) - wireless sensor network , energy consumption , computer science , cluster (spacecraft) , energy (signal processing) , wireless , efficient energy use , real time computing , selection (genetic algorithm) , artificial intelligence , computer network , telecommunications , engineering , electrical engineering , mathematics , statistics
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. In addition, there is an increased interest in ‘green radio’ networks that aim to reduce the energy consumption for wireless communications to cut emissions of CO 2 . 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. Most prior works selected cluster heads either probabilistically or on the basis of 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 maximise 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. Copyright © 2012 John Wiley & Sons, Ltd.