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

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.