期刊名称：International Journal of Innovative Research in Science, Engineering and Technology
印刷版ISSN：2347-6710
电子版ISSN：2319-8753
出版年度：2013
卷号：2
期号：4
页码：1048
出版社：S&S Publications
摘要：Sensor networks today are widely used as they are able to capture the phenomena of the physical world that were originally difficult or impossible to obtain b y traditional techniques. Sensor network applications are tightly coupled with the geometric environment where the sensor nodes are deployed. The skeleton extraction has a great impact on the performance of location, routing, and path planning i n sensor networks. Present studies focus on using skeleton extraction for various applications in sensor networks. To achieve a better skeleton extractio n has not been studied completely. The study on skeleto n extraction fro m the computer vision community; their centralized algorithms for frequent space, however, are not immediately applicable for the discrete and distributed sensor networks. In this paper Connection oriented Skeleton Extraction algorithm to compute skeleton graph that is robust to noise, and accurate of the original topology. No centralization is needed. The skeleton graph is extracted by partitioning the boundary of the sensor network to identify the skeleton points, then generating and connecting the arcs, and finally refining the coarse skeleton graph. Our evaluation shows that is able to extract a well-connected skeleton graph in the presence of significant noise and shape variations, and state-of-the-art algorithms.
