ABSTRACT
In-network data storage and retrieval are fundamental functions of sensor networks. Among many proposals, geographical hash table (GHT) is perhaps most appealing as it is very simple yet powerful with low communication cost, where the key is to correctly define the bounding box. It is envisioned that the skeleton has the power to facilitate computing a precise bounding box. In existing works, the focus has been on skeleton extraction algorithms targeting for 2D sensor networks, which usually delivers a 1-manifold skeleton consisting of 1D curves. It faces a set of non-trivial challenges when 3D sensor networks are considered, in order to properly extract the surface skeleton composed of a set of 2-manifolds and possibly 1D curves.
In this paper, we study the problem of surface skeleton extraction in 3D sensor networks. We propose a scalable and distributed connectivity-based algorithm to extract the surface skeleton of 3D sensor networks. First, we propose a novel approach to identifying surface skeleton nodes by computing the \textit{extended feature nodes} such that it is robust against boundary noise, etc. We then find the maximal independent set of the identified skeleton nodes and triangulate them to form a compact representation of the 3D sensor network. Furthermore, to react to the dynamics of the sensor networks caused by node failure, insertion, etc., we design an efficient updating scheme to reconstruct the surface skeleton. Finally, we apply the extracted surface skeleton to facilitate the data storage protocol design. Extensive simulations show the robustness of the proposed algorithm to shape variation, node density, node distribution and communication radio model, and its effectiveness for data storage application with respect to load balancing.
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Index Terms
Surface skeleton extraction and its application for data storage in 3D sensor networks
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