Abstract
We describe the application of image processing techniques for data refinement in sensor networks, by mapping network nodes to pixels in an image. Due to their localized, distributed nature, these techniques are inherently scalable and therefore desirable for use in large sensor networks. We examine two specific problems: cleaning of uncorrelated sensor noise, and the decentralized detection of edges (such as the perimeter of a chemical leak). Our simulation results show that the performance of these processing techniques depends critically upon both sensor density and radio range.
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