Leveraging redundancy in sampling-interpolation applications for sensor networks: A spectral approach

An important class of sensor network applications aims at estimating the spatiotemporal behavior of a physical phenomenon, such as temperature variations over an area of interest. In such a scenario, the network essentially acts as a distributed sampling system. However, unlike in the event detection case, the notion of sensing range is largely meaningless for sampling-interpolation applications. As a result, existing techniques to exploit sensing redundancy in event detection settings, which rely on the existence of such sensing range, become unusable. Instead, this article presents a new method to exploit redundancy for the sampling class of applications by selecting a suitable set of sensors to act as sampling points. Through online estimation of process characteristics, sufficiently accurate interpolation can be achieved. We illustrate an algorithm to obtain multiple disjoint sets and demonstrate significant reductions in the number of active sensors for a wide range of synthetic sensor data.

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