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Large-scale sensor networks impose energy and communication constraints, thus it is difficult to collect data from each individual sensor node and process it at the sink. In this paper, the authors propose an efficient data-collection scheme that can be used for event monitoring or network-wide diagnosis. The scheme relies on the well-known representation of data contour maps, which trade off accuracy with the amount of samples. The scheme consists of three novel algorithms to build contour maps: Distributed spatial and temporal data suppression, contour reconstruction at the sink via interpolation and smoothing, and an efficient mechanism to convey routing information over multiple hops.
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