Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/28555
Title: CASE : connectivity-Based skeleton extraction in wireless sensor networks
Authors: Jiang, H
Liu, W
Wang, D 
Tian, C
Bai, X
Liu, X
Wu, Y
Liu, W
Keywords: Graph theory
Telecommunication network topology
Wireless sensor networks
Issue Date: 2009
Publisher: IEEE
Source: IEEE INFOCOM 2009, 19-25 April 2009, Rio de Janeiro, p. 2916-2920 How to cite?
Abstract: Many sensor network applications are tightly coupled with the geometric environment where the sensor nodes are deployed. The topological skeleton extraction has shown great impact on the performance of such services as location, routing, and path planning in sensor networks. Nonetheless, current studies focus on using skeleton extraction for various applications in sensor networks. How to achieve a better skeleton extraction has not been thoroughly investigated. There are studies on skeleton extraction from the computer vision community; their centralized algorithms for continuous space, however, is not immediately applicable for the discrete and distributed sensor networks. In this paper we present CASE: a novel connectivity-based skeleton extraction algorithm to compute skeleton graph that is robust to noise, and accurate in preservation of the original topology. In addition, no centralized operation is required. The skeleton graph is extracted by partitioning the boundary of the sensor network to identify the skeleton points, then generating the skeleton arcs, connecting these arcs, and finally refining the coarse skeleton graph. Our evaluation shows that CASE is able to extract a well-connected skeleton graph in the presence of significant noise and shape variations, and outperforms state-of-the-art algorithms.
URI: http://hdl.handle.net/10397/28555
ISBN: 978-1-4244-3512-8
978-1-4244-3513-5 (E-ISBN)
ISSN: 0743-166X
DOI: 10.1109/INFCOM.2009.5062258
Appears in Collections:Conference Paper

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