In sensor networks community, the skeleton (or medial axis), as an important infrastructure which can correctly capture the topological and geometrical features of the underlying network, has been widely used for facilitating routing, navigation, segmentation, etc. Even though there are a handful of skeleton extraction solutions, the measurement of the goodness of the derived skeleton is often application-oriented, and there is no quantitative metric for this task. In this paper, we study the problem of skeleton extraction and conduct the first work on quantitative evaluation of skeleton in sensor networks. Different from traditional schemes which assume complete or incomplete boundaries, the proposed skeleton extraction algorithm is based on mere connectivity information, without reliance on any boundary information. More specifically, for each node we compute its variability factor based on the neighborhood sizes of the node and its neighbors, which can reflect how central a sensor node is to the network, and a sensor node identifies itself as a skeleton node if its variability factor is locally maximal. Next, we present a light-weight scheme to connect these skeleton nodes. Finally, we proposed a metric, named visibility coefficient, to quantitatively evaluate the derived skeleton.
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