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ABSTRACT
We present an online method for automatically segmenting the data stream
generated at a sensor node into distinct sub-sequences. The data at each node
are assumed to be generated by a stochastic process. The data stream (sequence)
sensed at each node is split into a new sub-sequence when the Kullback-Leibler
distance between the probability distributions induced by the previously
observed data and that induced by the most recently observed data exceeds a
threshold. A distributed region growing algorithm is used to cluster spatially
proximal sensor nodes that observe similar data streams. Thus, the space being
monitored by the sensors can be divided into regions that observe similar
activity. Continuous data segmentation and region growing enables changes in
the pattern of activity to be observed as shifting spatial regions. We provide
an analysis of the time taken for convergence and the power required for
transmitting messages when the nodes are arranged in a regular grid and when
they are randomly distributed. We present simulation results that show the
performance of these algorithms on a shadow tracking application. The data used
in these simulations was obtained from a sensor network equipped with light
sensors and deployed outdoors.
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