Connectivity-based and Boundary-Free Skeleton Extraction in Sensor Networks

In sensor networks, skeleton (also known as medial axis) extraction is recognized as an appealing approach to support many applications such as load-balanced routing and location free segmentation. Existing solutions in the literature rely heavily on the identified boundaries, which puts limitations on the applicability of the skeleton extraction algorithm. In this paper, we conduct the first work of a connectivity-based and boundary free skeleton extraction scheme, in sensor networks. In detail, we propose a simple, distributed and scalable algorithm that correctly identifies a few skeleton nodes and connects them into a meaningful representation of the network, without reliance on any constraint on communication radio model or boundary information. The key idea of our algorithm is to exploit the necessary (but not sufficient) condition of skeleton points: the intersection area of the disk centered at a skeleton point x should be the largest one as compared to other points on the chord generated by x, where the chord is referred to as the line segment connecting x and the tangent point in the boundary. To that end, we present the concept of \epsilon-centrality of a point, quantitatively measuring how "central" a point is. Accordingly, a skeleton point should have the largest value of \epsilon-centrality as compared to other points on the chord generated by this point. Our simulation results show that the proposed algorithm works well even for networks with low node density or skewed nodal distribution, etc. In addition, we obtain two by-products, the boundaries and the segmentation result of the network.