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.
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