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Surface skeleton extraction and its application for data storage in 3D sensor networks

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Published:11 August 2014Publication History

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.

References

  1. D. Andrade, M. Resende, and R. Werneck. Fast local search for the maximum independent set problem. Journal of Heuristics, 18(4):525--547, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. N. Azimi, H. Gupta, X. Hou, and J. Gao. Data preservation under spatial failures in sensor networks. In Proc. of ACM MOBIHOC , 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. H. Blum. Biological shape and visual science (part i). Theoretical Biology, 38:205--287, 1973.Google ScholarGoogle ScholarCross RefCross Ref
  4. S. Bouix and K. Siddiqi. Divergence-based medial surfaces. In Proc. of European Conference on Computer Vision, 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. J. Bruck, J. Gao, and A. A. Jiang. Map: Medial axis based geometric routing in sensor betworks. In Proc. of ACM MOBICOM, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. J. DAMON. Global medial structure of regions in r3. Geometry and Topology, 10:2385--2429, 2006.Google ScholarGoogle ScholarCross RefCross Ref
  7. M. Doddavenkatappa, M. Chan, and A. Ananda. Indriya: a low-cost, 3d wireless sensor network testbed. In Proc. of ICST Conference on Testbeds and Research Infrastructures for the Development of Networks and Communities(TRIDENTCOM), 2011.Google ScholarGoogle Scholar
  8. Q. Fang, J. Gao, L. Guibas, V. de Silva, and L. Zhang. Glider: Gradient landmark-based distributed routing for sensor networks. In Proc. of IEEE INFOCOM, 2005.Google ScholarGoogle ScholarCross RefCross Ref
  9. Q. Fang, J. Gao, and L. J. Guibas. Landmark-based information storage and retrieval in sensor networks. In Proc. of IEEE INFOCOM, 2006.Google ScholarGoogle ScholarCross RefCross Ref
  10. F.F.Leymarie and B.B.Kimia. Computation of the shock scaffold for unorganized point clouds in 3d. In Proc. of IEEE Conference on Computer Vision and Pattern Recognition, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. F.F.Leymarie and B.B.Kimia. The medial scaffold of 3d unorganized point clouds. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(2):313--330, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. H. Jiang, W. Liu, D. Wang, C. Tian, X. Bai, X. Liu, and W. Liu. Case: Connectivity-based skeleton extraction in wireless sensor networks. In Proc. of IEEE INFOCOM, 2009.Google ScholarGoogle ScholarCross RefCross Ref
  13. H. Jiang, W. Liu, D. Wang, C. Tian, X. Bai, X. Liu, and W. Liu. Connectivity-based skeleton extraction in wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems, 21(5):710--721, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. H. Jiang, S. Zhang, G. Tan, and C. Wang. Cabet: Connectivity-based boundary extraction of large-scale 3d sensor networks. In Proc. of IEEE INFOCOM, 2011.Google ScholarGoogle ScholarCross RefCross Ref
  15. B. Karp and H. Kung. Gpsr: Greedy perimeter stateles routing for wireless networks. In Proc. of ACM MOBICOM, 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. R. M. Karp. Reducibility among combinatorial problems. In Complexity of Computer Computations, The IBM Research Symposia Series, pages 85--103. 1972.Google ScholarGoogle ScholarCross RefCross Ref
  17. S. Lederer, Y. Wang, and J. Gao. Connectivity-based localization of large scale sensor networks with complex shape. In Proc. of IEEE INFOCOM, 2008.Google ScholarGoogle ScholarCross RefCross Ref
  18. F. Li, J. Luo, C. Zhang, S. Xin, and Y. He. Unfold: uniform fast on-line boundary detection for dynamic 3d wireless sensor networks. In Proc. of ACM MOBIHOC, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. M. Li, Y. Liu, J. Wang, and Z. Yang. Sensor network navigation without locations. In Proc. of IEEE INFOCOM, 2009.Google ScholarGoogle ScholarCross RefCross Ref
  20. X. Li, Y. J. Kim, R. Govindan, and W. Hong. Multi-dimensional range queries in sensor networks. In Proc. of ACM SenSys, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. W. Liu, H. Jiang, X. Bai, G. Tan, C. Wang, and K. Cai. Distance transform-based skeleton extraction and its applications in sensor networks. IEEE Transactions on Parallel and Distributed Systems, 24(9):1763--1772, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. W. Liu, H. Jiang, X. Bai, G. Tan, C. Wang, W. Liu, and K. Cai. Skeleton extraction from incomplete boundaries in sensor networks based on distance transform. In Proc. of IEEE ICDCS, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. W. Liu, H. Jiang, C. Wang, C. Liu, Y. Yang, W. Liu, and B. Li. Connectivity-based and boundary-free skeleton extraction in sensor networks. In Proc. of IEEE ICDCS, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. W. Liu, H. Jiang, Y. Yang, and Z. jin. A unified framework for line-like skeleton extraction in 2d/3d sensor networks. In Proc. of IEEE ICNP, 2013.Google ScholarGoogle ScholarCross RefCross Ref
  25. W. Liu, D. Wang, H. Jiang, W. Liu, and C. Wang. Approximate convex decomposition based localization in wireless sensor networks. In Proc. of IEEE INFOCOM, 2012.Google ScholarGoogle ScholarCross RefCross Ref
  26. S. Ratnasamy, B. Karp, S. Shenker, D. Estrin, R. Govindan, L. Yin, and F. Yu. Data-centric storage in sensornets with ght, a geographic hash table. Mobile Networks and Applications, 8(4):427--442, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. D. Reniers and A. Telea. Segmenting simplified surface skeletons. In Proc. of the 14th IAPR International Conference on Discrete Geometry for Computer Imagery, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. D. Reniers, J. J. Wijk, and A.Telea. Computing multiscale curve and surface skeletons of genus 0 shapes using a global importance measure. IEEE Transactions on Visualization and Computer Graphics, 14(2):355--368, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. R. Sarkar, W. Zeng, J. Gao, and X. D. Gu. Covering space for in-network sensor data storage. In Proc. of ACM/IEEE IPSN, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. R. Sarkar, X. Zhu, and J. Gao. Double rulings for information brokerage in sensor networks. IEEE/ACM Transactions on Networking, 17(6):1902--1915, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. R. Tam and W. Heidrich. Shape simplification based on the medial axis transform. In Proc. of IEEE Visualization Conference, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. G. Tan, H. Jiang, S. Zhang, and A. Kermarrec. Connectivity-based and anchor-free localization in large-scale 2d/3d sensor networks. In Proc. of ACM MOBIHOC, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. G. Werner-Allen, P. Swieskowski, and M. Welsh. Motelab: a wireless sensor network testbed. In Proc. of ACM/IEEE IPSN, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. S. Xia, N. Ding, M. Jin, H. Wu, and Y. Yang. Medial axis construction and applications in 3D wireless sensor networks. In Proc. of IEEE INFOCOM, mini-conference, 2013.Google ScholarGoogle ScholarCross RefCross Ref
  35. S. Xia, X. Yin, H. Wu, M. Jin, and X. Gu. Deterministic greedy routing with guaranteed delivery in 3d wireless sensor networks. In Proc. of ACM MOBIHOC, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. X. Yu, X. Yin, W. Han, J. Gao, and X. D. Gu. Scalable routing in 3D high genus sensor networks using graph embedding. In Proc. of IEEE INFOCOM, mini-conference, 2012.Google ScholarGoogle ScholarCross RefCross Ref
  37. H. Zhou, N. Ding, M. Jin, S. Xia, and H. Wu. Distributed algorithms for bottleneck identification and segmentation in 3D wireless sensor networks. In Proc. of IEEE SECON, 2011.Google ScholarGoogle ScholarCross RefCross Ref
  38. H. Zhou, M. Jin, and H. Wu. A distributed delaunay triangulation algorithm based on centroidal voronoi tessellation for wireless sensor networks. In Proc. of ACM MOBIHOC, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. D. Zhu, Q. Tao, J. Xing, Y. Wang, W. Liu, and H. Jiang. The extraction and evaluation of skeleton in sensor networks. In Proc. of IEEE Ninth International Conference on Mobile Ad-hoc and Sensor Networks (MSN), 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. X. Zhu, R. Sarkar, and J. Gao. Shape segmentation and applications in sensor networks. In Proc. of IEEE INFOCOM, 2007.Google ScholarGoogle ScholarDigital LibraryDigital Library

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      • Published in

        cover image ACM Conferences
        MobiHoc '14: Proceedings of the 15th ACM international symposium on Mobile ad hoc networking and computing
        August 2014
        460 pages
        ISBN:9781450326209
        DOI:10.1145/2632951

        Copyright © 2014 ACM

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        Publication History

        • Published: 11 August 2014

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        MobiHoc '14 Paper Acceptance Rate40of211submissions,19%Overall Acceptance Rate296of1,843submissions,16%

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