Connectivity-Based Boundary Extraction of Large-Scale 3D Sensor Networks: Algorithm and Applications
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TPDS.2013.97
Hongbo Jiang , Huazhong University of Science and Technology, Wuhan
Shengkai Zhang , Huazhong University of Science and Technology, Wuhan
Guang Tan , Chinese Academy of Sciences, Shenzhen
Chonggang Wang , InterDigital Communications, Pennsylvania
We present CABET, a novel Connectivity-bAsed Boundary Extraction scheme for large-scale Three-dimensional 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 nonuniform critical node sampling, called r&#x02B9;-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, multi-resolution extraction, and 3D localization.
G. Tan, S. Zhang, H. Jiang and C. Wang, "Connectivity-Based Boundary Extraction of Large-Scale 3D Sensor Networks: Algorithm and Applications," in IEEE Transactions on Parallel & Distributed Systems.