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Simplicial Complex Reduction Algorithm for Simplifying WSN’s Topology

  • Wenyu Ma
  • Feng YanEmail author
  • Xuzhou Zuo
  • Jin Hu
  • Weiwei Xia
  • Lianfeng Shen
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 258)

Abstract

In this paper, a reduction algorithm aiming at simplifying the topology of wireless sensor networks (WSNs) is proposed. First, we use simplicial complex as the tool to represent the topology of the WSNs. Then, we present a reduction algorithm which recurrently deletes redundant vertices and edges while keeping the homology of the network invariant. By reducing the number of simplexes, we make the simplicial complex graph nearly planar and easy for computation. Finally, the performance of the proposed scheme is investigated. Simulations under different node intensities are presented and the results indicate that the proposed algorithm performs well in reducing the number of simplexes under various situations.

Keywords

Simplicial complex Reduction algorithm Wireless sensor networks 

References

  1. 1.
    Ghrist, R., Muhammad, A.: Coverage and hole-detection in sensor networks via homology. In: Proceedings of the 4th International Conference on Information Processing in Sensor Networks, pp. 254–260. IEEE Press, Boise (2005)Google Scholar
  2. 2.
    Yan, F., Martins, P., Decreusefond, L.: Accuracy of homology based coverage hole detection for wireless sensor networks on sphere. IEEE Trans. Wireless Commun. 13(7), 3583–3595 (2014)CrossRefGoogle Scholar
  3. 3.
    Tahbaz-Salehi, A., Jadbabaie, A.: Distributed coverage verification in sensor networks without location information. IEEE Trans. Autom. Control 55(8), 1837–1849 (2008)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Kanno, J., Buchart, J.G., Selmic, R.R., Phoha, V.: Detecting coverage holes in wireless sensor networks. In: IEEE Mediterranean Conference on Control and Automation, pp. 452–457. IEEE Press, Thessaloniki (2009)Google Scholar
  5. 5.
    Yan, F., Martins, P., Decreusefond, L.: Connectivity-based distributed coverage hole detection in wireless sensor networks. In: IEEE Global Telecommunications Conference, pp. 1–6. IEEE Press, Kathmandu (2011)Google Scholar
  6. 6.
    Campos-Nanez, E., Garcia, A., Li, C.: A game-theoretic approach to efficient power management in sensor networks. Oper. Res. 56(3), 552–561 (2008)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Dlotko, P., Ghrist, R., Juda, M., Mrozek, M.: Distributed computation of coverage in sensor networks by homological methods. Appl. Algebra Eng. Commun. Comput. 23(1–2), 29–58 (2012)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Yan, F., Vergne, A., Martins, P., Decreusefond, L.: Homology-based distributed coverage hole detection in wireless sensor networks. IEEE/ACM Trans. Networking 23(6), 1705–1718 (2015)CrossRefGoogle Scholar
  9. 9.
    Vergne, A., Decreusefond, L., Martins, P.: Reduction algorithm for simplicial complexes. In: 2013 Proceedings of IEEE INFOCOM, pp. 475–479. IEEE Press, Turin (2013)Google Scholar
  10. 10.
    Cao, Z., Yan, F., Deng, S., Xia, W., Shen, L., Li, Z.: A topology control algorithm based on homology theory in software-defined sensor networks. In: IEEE/CIC International Conference on Communications, pp. 1–6. IEEE Press, Qingdao (2017)Google Scholar
  11. 11.
    An, W., Qu, N., Shao, F., Shao, F., Ci, S.: Coverage hole problem under sensing topology in flat wireless sensor networks. Wirel. Commun. Mob. Comput. 16(5), 578–589 (2016)CrossRefGoogle Scholar
  12. 12.
    Silva, V.D., Ghrist, R.: Coverage in sensor networks via persistent homology. Algebr. Geom. Topol. 7(1), 339–358 (2007)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Barmak, J.A., Minian, E.G.: Strong homotopy types, nerves and collapses. Discret. Comput. Geom. 47(2), 301–328 (2012)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Wilkerson, A.C., Moore, T.J., Swami, A., Krim, H.: Simplifying the homology of networks via strong collapses. In: IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 5258–5262. IEEE Press, Vancouver (2013)Google Scholar
  15. 15.
    Wilkerson, A.C., Chintakunta, H., Krim, H., Moore, T.J., Swami, A.: A distributed collapse of a network’s dimensionality. In: Global Conference on Signal and Information Processing, pp. 595–598. IEEE Press, Atlanta (2014)Google Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  • Wenyu Ma
    • 1
  • Feng Yan
    • 1
    • 2
    Email author
  • Xuzhou Zuo
    • 3
  • Jin Hu
    • 4
  • Weiwei Xia
    • 1
  • Lianfeng Shen
    • 1
  1. 1.National Mobile Communications Research LaboratorySoutheast UniversityNanjingChina
  2. 2.State key Laboratory of Networking and Switching TechnologyBeijing University of Posts and TelecommunicationsBeijingChina
  3. 3.School of Information and Software EngineeringUniversity of Electronic Science and Technology of ChinaChengduChina
  4. 4.724 Research Institute of CSICNanjingChina

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