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Issue No.05 - May (2010 vol.21)
pp: 710-721
Hongbo Jiang , Huazhong University of Science and Technology, Wuhan
Wenping Liu , Huazhong University of Science and Technology, Wuhan
Dan Wang , The Hong Kong Polytechnic University, Hong Kong
Chen Tian , Huazhong University of Science and Technology, Wuhan
Xiang Bai , Huazhong University of Science and Technology, Wuhan
Xue Liu , McGill University, Quebec
Ying Wu , Northwestern University, Evanston
Wenyu Liu , Huazhong University of Science and Technology, Wuhan
ABSTRACT
Many sensor network applications are tightly coupled with the geometric environment where the sensor nodes are deployed. The topological skeleton extraction for the topology has shown great impact on the performance of such services as location, routing, and path planning in wireless sensor networks. Nonetheless, current studies focus on using skeleton extraction for various applications in wireless sensor networks. How to achieve a better skeleton extraction has not been thoroughly investigated. There are studies on skeleton extraction from the computer vision community; their centralized algorithms for continuous space, however, are not immediately applicable for the discrete and distributed wireless sensor networks. In this paper, we present a novel Connectivity-bAsed Skeleton Extraction (CASE) algorithm to compute skeleton graph that is robust to noise, and accurate in preservation of the original topology. In addition, CASE is distributed as no centralized operation is required, and is scalable as both its time complexity and its message complexity are linearly proportional to the network size. The skeleton graph is extracted by partitioning the boundary of the sensor network to identify the skeleton points, then generating the skeleton arcs, connecting these arcs, and finally refining the coarse skeleton graph. We believe that CASE has broad applications and present a skeleton-assisted segmentation algorithm as an example. Our evaluation shows that CASE is able to extract a well-connected skeleton graph in the presence of significant noise and shape variations, and outperforms the state-of-the-art algorithms.
INDEX TERMS
Sensor networks, algorithm/protocol design, skeleton extraction.
CITATION
Hongbo Jiang, Wenping Liu, Dan Wang, Chen Tian, Xiang Bai, Xue Liu, Ying Wu, Wenyu Liu, "Connectivity-Based Skeleton Extraction in Wireless Sensor Networks", IEEE Transactions on Parallel & Distributed Systems, vol.21, no. 5, pp. 710-721, May 2010, doi:10.1109/TPDS.2009.109
REFERENCES
[1] X. Bai, L.J. Latecki, and W. Liu, "Skeleton Pruning by Contour Partitioning with Discrete Curve Evolution," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 29, no. 3, pp. 449-462, Mar. 2007.
[2] H. Blum, "Biological Shape and Visual Science (Part i)," J. Theoretical Biology, vol. 38, pp. 205-287, 1973.
[3] J. Bruck, J. Gao, and A.A. Jiang, "MAP: Medial Axis Based Geometric Routing in Sensor Networks," Proc. ACM MobiCom, 2005.
[4] J. Bruck, J. Gao, and A.A. Jiang, "MAP: Medial Axis Based Geometric Routing in Sensor Networks," Wireless Networks, vol. 13, no. 6, pp. 609-616, 2007.
[5] C. Buragohain, D. Agrawal, and S. Suri, "Distributed Navigation Algorithms for Sensor Networks," Proc. IEEE INFOCOM, 2006.
[6] E. Cayirci and T. Coplu, "Sendrom: Sensor Networks for Disaster Relief Operations Management," Wireless Networks, vol. 13, no. 3, pp. 409-423, 2007.
[7] H.I. Choi, S.W. Choi, and H.P. Moon, "Mathematcal Theory of Medial Axis Transform," Pacific J. Math, vol. 181, no. 1, pp. 57-88, 1997.
[8] W.-P. Choi, K.-M. Lam, and W.-C. Siu, "Extraction of the Euclidean Skeleton Based on a Connectivity Criterion," Pattern Recognition, vol. 36, pp. 721-729, 2003.
[9] D. Dong, Y. Liu, and X. Liao, "Fine-Grained Boundary Recognition in Wireless Ad Hoc and Sensor Networks by Topological Methods," Proc. ACM MobiHoc, 2009.
[10] Q. Fang, J. Gao, and L. Guibas, "Locating and Bypassing Routing Holes in Sensor Networks," Proc. Mobile Networks and Applications, 2006.
[11] S.P. Fekete, A. Kroller, D. Pfisterer, S. Fischer, and C. Buschmann, "Locating and Bypassing Routing Holes in Sensor Networks," Proc. Int'l Workshop Algorithmic Aspects of Wireless Sensor Networks, 2004.
[12] M. Fennell and R. Wishner, "Battlefield Awareness via Synergistic SAR and MTI Exploitation," IEEE Aerospace and Electronic Systems Magazine, vol. 13, no. 2, pp. 39-43, Feb. 1998.
[13] R. Ghrist and A. Muhammad, "Coverage and Hole-Detection in Sensor Networks via Homology," Proc. IEEE Information Processing in Sensor Networks (IPSN), 2004.
[14] P. Golland and E. Grimson, "Fixed Topology Skeletons," Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), vol. 1, pp. 10-17, 2000.
[15] H. Jiang, W. Liu, D. Wang, T. Chen, X. Bai, X. Liu, Y. Wu, and W. Liu, "CASE: Connectivity-Based Skeleton Extraction in Wireless Sensor Networks," Proc. IEEE INFOCOM, pp. 2916-2920, Apr. 2009.
[16] L.J. Latecki and R. Lakamper, "Shape Similarity Measure Based on Correspondence of Visual Parts," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 10, pp. 1185-1190, Oct. 2000.
[17] E.L. Lawer, Combinatorial Optimization: Networks and Matroids. Holt, Rinehart and Winston, 1976.
[18] S. Lederer, Y. Wang, and J. Gao, "Connectivity-Based Localization of Large Scale Sensor Networks with Complex Shape," Proc. IEEE INFOCOM, 2008.
[19] L. Lin and H. Lee, "A Dynamic Medial Axis Model for Sensor Networks," Proc. IEEE Int'l Conf. Embedded and Real-Time Computing Systems and Applications, pp. 146-156, Aug. 2007.
[20] H. Rom and G. Medioni, "Hierarchical Decomposition and Axial Shape Description," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 15, no. 10, pp. 973-981, Oct. 1993.
[21] O. Saukh, R. Sauter, M. Gauger, P.J. Marron, and K. Rothernel, "On Boundary Recognition without Location Information in Wireless Sensor Networks," Proc. Information Processing in Sensor Networks (IPSN), 2008.
[22] S. Schaefer and C. Yuksel, "Example-Based Skeleton Extraction," Proc. Eurographics Symp. Geometry Processing, 2007.
[23] R. Szewczyk, E. Osterweil, J. Polastre, M. Hamilton, A. Mainwaring, and D. Estrin, "Habitat Monitoring with Sensor Networks," Comm. ACM, vol. 47, no. 6, pp. 34-40, 2004.
[24] Y. Wang, J. Gao, and J.S. Mitchell, "Boundary Recognition in Sensor Networks by Topological Methods," Proc. ACM MobiCom, Sept. 2006.
[25] X. Zhu, R. Sarkar, and J. Gao, "Shape Segmentation and Applications in Sensor Networks," Proc. IEEE INFOCOM, pp. 1838-1846, May 2007.
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