Contour approximation in sensor networks

Authors:
Chiranjeeb Buragohain;Sorabh Gandhi;John Hershberger;Subhash Suri
Affiliations:
Dept. of Computer Science, University of California, Santa Barbara, CA;Dept. of Computer Science, University of California, Santa Barbara, CA;Mentor Graphics Corp., Wilsonville, OR;Dept. of Computer Science, University of California, Santa Barbara, CA
Venue:
DCOSS'06 Proceedings of the Second IEEE international conference on Distributed Computing in Sensor Systems
Year:
2006

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Abstract

We propose a distributed scheme called Adaptive-Group-Merge for sensor networks that, given a parameter k, approximates a geometric shape by a k-vertex polygon. The algorithm is well suited to the distributed computing architecture of sensor networks, and we prove that its approximation quality is within a constant factor of the optimal. We also show through simulation that our scheme outperforms several other alternatives in preserving important shape features, and achieves approximation quality almost as good as the optimal, centralized scheme. Because many applications of sensor networks involve observations and monitoring of physical phenomena, the ability to represent complex geometric shapes faithfully but using small memory is vital in many settings.