Boundary Estimation in Sensor Networks: Theory and Methods
Sensor networks have emerged as a fundamentally new tool for monitoring spatially distributed phenomena. This paper investigates a strategy by which sensor nodes detect and estimate non-localized phenomena such as “boundaries” and “edges” (e.g., temperature gradients, variations in illumination or contamination levels). A general class of boundaries, with mild regularity assumptions, is considered, and theoretical bounds on the achievable performance of sensor network based boundary estimation are established. A hierarchical boundary estimation algorithm is proposed that achieves a near-optimal balance between mean-squared error and energy consumption.
KeywordsSensor Network Sensor Node Communication Cost Neural Information Processing System Boundary Estimation
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- 1.L. Breiman, J. Friedman, R. Olshen, and C. J. Stone. Classification and Regression Trees. Wadsworth, Belmont, CA, 1983.Google Scholar
- 2.K. Chintalapudi and R. Govindan. Localized edge detection in sensor fields. University of Southern California, Computer Science Department, Technical Report, 02-773, 2002. available at http://www.cs.usc.edu/tech-reports/technical-reports.html.
- 4.D. Ganesan, D. Estrin, and J. Heideman. DIMENSIONS: Why do we need a new data handling architecture for sensor networks? In Proceedings of IEEE/ACM HotNets-I, Princeton, NJ, October 2002.Google Scholar
- 5.E. Kolaczyk and R. Nowak. Multiscale likelihood analysis and complexity penalized estimation. Annals of Statistics (tentatively accepted for publication). Also available at http://www.ece.rice.edu/~nowak/pubs.html, 2002.
- 7.B. Laurent and P. Massart. Adaptive estimation of a quadratic functional by model selection. The Annals of Statistics, (5), October 2000.Google Scholar
- 8.Q. Li and A. Barron. Mixture density estimation. In S.A. Solla, T.K. Leen, and K.-R. Müller, editors, Advances in Neural Information Processing Systems 12. MIT Press, 2000.Google Scholar
- 9.C. Scott and R. Nowak. Dyadic classification trees via structural risk minimization. In Proc. Neural Information Processing Systems (NIPS), Vancouver, CA, Dec. 2002.Google Scholar
- 10.R. Willett and R. Nowak. Platelets: A multiscale approach to recovering edges and surfaces in photon-limited imaging. IEEE Trans. Med. Imaging, to appear in the Special Issue on Wavelets in Medical Imaging, 2003.Google Scholar