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Issue No. 04 - April (2014 vol. 25)
ISSN: 1045-9219
pp: 908-918
Hongbo Jiang , Dept. of Electron. & Inf. Eng., Huazhong Univ. of Sci. & Technol., Wuhan, China
Shengkai Zhang , Dept. of Electron. & Inf. Eng., Huazhong Univ. of Sci. & Technol., Wuhan, China
Guang Tan , Shenzhen Inst. of Adv. Technol., Shenzhen, China
Chonggang Wang , InterDigital Commun., PA, USA
Sensor networks are invariably coupled tightly with the geometric environment in which the sensor nodes are deployed. Network boundary is one of the key features that characterize such environments. While significant advances have been made for 2D cases, so far boundary extraction for 3D sensor networks has not been thoroughly studied. We present CABET, a novel Connectivity-Based Boundary Extraction scheme for large-scale 3D sensor networks. To the best of our knowledge, CABET is the first 3D-capable and pure connectivity-based solution for detecting sensor network boundaries. It is fully distributed, and is highly scalable, requiring overall message cost linear with the network size. A highlight of CABET is its non-uniform critical node sampling , called r'-sampling , that selects landmarks to form boundary surfaces with bias toward nodes embodying salient topological features. Simulations show that CABET is able to extract a well-connected boundary in the presence of holes and shape variation, with performance superior to that of some state-of-the-art alternatives. In addition, we show how CABET benefits a range of sensor network applications including 3D skeleton extraction, 3D segmentation, and 3D localization.
Three-dimensional displays, Feature extraction, Image edge detection, Shape, Knowledge engineering, Data mining, Topology,3D boundary, Sensor networks, algorithm/protocol design
Hongbo Jiang, Shengkai Zhang, Guang Tan, Chonggang Wang, "Connectivity-Based Boundary Extractionof Large-Scale 3D Sensor Networks:Algorithm and Applications", IEEE Transactions on Parallel & Distributed Systems, vol. 25, no. , pp. 908-918, April 2014, doi:10.1109/TPDS.2013.97
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