skip to main content
research-article

GPART: Partitioning Maximal Redundant Rigid and Maximal Global Rigid Components in Generic Distance Graphs

Published:09 June 2023Publication History
Skip Abstract Section

Abstract

Partitioning the Maximal Redundant Rigid Components (MRRC) and Maximal Global Rigid Components (MGRC) in generic 2D graphs are critical problem for network structure analysis, network localizability detection, and localization algorithm design. This article presents efficient algorithms to partition MRRCs and MGRCs and develops an open-sourced toolbox, GPART, for these algorithms to be conveniently used by the society. We firstly propose conditions and an efficient algorithm to merge the over-constrained regions to form the maximal redundant rigid components (MRRC). The detected MRRCs are proved to be maximal and all the MRRCs are guaranteed to be detected. The time to merge the over-constrained regions is linear to the number of nodes in the over-constrained components. To detect MGRCs, the critical problem is to decompose 3-connected components in each MRRC. We exploit SPQR-tree based method and design a local optimization algorithm, called MGRC_acce to prune the unnecessary decomposition operations so that the SPQR-tree functions can be called much less number of times. We prove the MGRCs can be detected inside MRRCs using at most O(mn) time. Then a GPART toolbox is developed and extensively tested in graphs of different densities. We show the proposed MRRC and MGRC detection algorithms are valid and MGRC_acce greatly outperforms the direct SPQR-tree based decomposition algorithm. GPART is outsourced at https://github.com/inlab-group/gpart.

REFERENCES

  1. [1] Connelly Robert, Jordán Tibor, and Whiteley Walter. 2013. Generic global rigidity of body–bar frameworks. Journal of Combinatorial Theory, Series B 103, 6 (2013), 689705.Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. [2] Gortler Steven J., Healy Alexander D., and Thurston Dylan P.. 2010. Characterizing generic global rigidity. American Journal of Mathematics 132, 4 (2010), 897939.Google ScholarGoogle ScholarCross RefCross Ref
  3. [3] Gutwenger Carsten and Mutzel Petra. 2001. A linear time implementation of SPQR-Trees. In Graph Drawing, Goos Gerhard, Hartmanis Juris, Leeuwen Jan van, and Marks Joe (Eds.). Vol. 1984. Springer Heidelberg, Heidelberg, 7790. DOI:. Series Title: Lecture Notes in Computer Science.Google ScholarGoogle ScholarCross RefCross Ref
  4. [4] Hendrickson Bruce. 1992. Conditions for unique graph realizations. SIAM Journal on Computing 21, 1 (1992), 6584.Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. [5] Hopcroft J. E. and Tarjan R. E.. 1972. Finding the triconnected components of a graph. Technical Report TR 140, Dept. of Computer Science, Cornell Univ.Google ScholarGoogle Scholar
  6. [6] Hopcroft J. E. and Tarjan R. E.. 1973. Dividing a graph into triconnected components. SIAM J. Comput. 2, 3 (1973), 135158. DOI:. arXiv:https://doi.org/10.1137/0202012Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. [7] Jackson Bill. 2007. Notes on the rigidity of graphs. In Levico Conference Notes, Vol. 4.Google ScholarGoogle Scholar
  8. [8] Jackson Bill and Jordán Tibor. 2005. Connected rigidity matroids and unique realizations of graphs. Journal of Combinatorial Theory, Series B 94, 1 (May2005), 129. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. [9] Jacobs Donald J. and Hendrickson Bruce. 1997. An algorithm for two-dimensional rigidity percolation: The Pebble game. J. Comput. Phys. 137, 2 (1997), 346365.Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. [10] Laman G.. 1970. On graphs and rigidity of plane skeletal structures. Journal of Engineering Mathematics 4, 4 (Oct.1970), 331340. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  11. [11] Liu Yunhao, Yang Zheng, Wang Xiaoping, and Jian Lirong. 2010. Location, localization, and localizability. Journal of Computer Science and Technology 25, 2 (2010), 274297.Google ScholarGoogle ScholarCross RefCross Ref
  12. [12] Moore David C., Leonard J., Rus D., and Teller S.. 2004. Robust distributed network localization with noisy range measurements. In SenSys’04. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. [13] Ping Haodi, Wang Yongcai, and Li Deying. 2020. HGO: Hierarchical graph optimization for accurate, efficient, and robust network localization. In Proceedings of the 29th International Conference on Computer Communications and Networks, ICCCN. 19. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  14. [14] Haodi Ping, Yongcai Wang, Xingfa Shen, Deying Li, and Wenping Chen. 2022. On node localizability identification in barycentric linear localization. ACM Trans. Sen. Netw. 19, 1, Article 19 (Dec 2022), 26 pages. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. [15] H. Ping, Y. Wang, D. Li, and T. Sun. 2022. Flipping free conditions and their application in sparse network localization. IEEE Transactions on Mobile Computing 21, 3 (2022), 986–1003.Google ScholarGoogle Scholar
  16. [16] Sau Buddhadeb and Mukhopadhyaya Krishnendu. 2013. Localizability of wireless sensor networks: Beyond wheel extension. In Stabilization, Safety, and Security of Distributed Systems, Higashino Teruo, Katayama Yoshiaki, Masuzawa Toshimitsu, Potop-Butucaru Maria, and Yamashita Masafumi (Eds.). Lecture Notes in Computer Science, Springer International Publishing, Cham, 326340. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  17. [17] Servatius Brigitte and Servatius Herman. 2010. Rigidity, global rigidity, and graph decomposition. European Journal of Combinatorics 31, 4 (2010), 11211135.Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. [18] Ileana Streinu and Audrey Lee. 2008. Pebble game algorithms and (k, l)-sparse graphs. Discrete Mathematics & Theoretical Computer Science 308, 8 (2008), 1425–1437. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. [19] Sun T., Wang Y., Li D., Gu Z., and Xu J.. 2018. WCS: Weighted component stitching for sparse network localization. IEEE/ACM Transactions on Networking 26, 5 (2018), 22422253.Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. [20] Tarjan Robert. 1971. Depth-first search and linear graph algorithms. In Proceedings of the 12th Annual Symposium on Switching and Automata Theory (swat 1971). 114121. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. [21] Wang Y., Sun T., Rao G., and Li D.. 2018. Formation tracking in sparse airborne networks. IEEE Journal on Selected Areas in Communications 36, 9 (2018), 20002014.Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. [22] Wu Hejun, Ding Ao, Liu Weiwei, Li Lvzhou, and Yang Zheng. 2017. Triangle extension: Efficient localizability detection in wireless sensor networks. IEEE Transactions on Wireless Communications 16, 11 (Nov.2017), 74197431. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  23. [23] Yang Zheng and Liu Yunhao. 2010. Understanding node localizability of wireless Ad-hoc networks. In Proceedings of the 2010 IEEE INFOCOM. 19. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  24. [24] Yang Z., Liu Y., and Li X.-Y.. 2009. Beyond trilateration: On the localizability of wireless ad-hoc networks. In Proceedings of the IEEE INFOCOM 2009. 23922400. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  25. [25] Zhao H., Wei J., Huang S., Zhou L., and Tang Q.. 2019. Regular topology formation based on artificial forces for distributed mobile robotic networks. IEEE Transactions on Mobile Computing 18, 10 (2019), 24152429.Google ScholarGoogle ScholarCross RefCross Ref

Index Terms

  1. GPART: Partitioning Maximal Redundant Rigid and Maximal Global Rigid Components in Generic Distance Graphs

        Recommendations

        Comments

        Login options

        Check if you have access through your login credentials or your institution to get full access on this article.

        Sign in

        Full Access

        • Published in

          cover image ACM Transactions on Sensor Networks
          ACM Transactions on Sensor Networks  Volume 19, Issue 4
          November 2023
          622 pages
          ISSN:1550-4859
          EISSN:1550-4867
          DOI:10.1145/3593034
          Issue’s Table of Contents

          Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 9 June 2023
          • Online AM: 29 April 2023
          • Accepted: 13 April 2023
          • Revised: 30 March 2023
          • Received: 12 September 2022
          Published in tosn Volume 19, Issue 4

          Permissions

          Request permissions about this article.

          Request Permissions

          Check for updates

          Qualifiers

          • research-article
        • Article Metrics

          • Downloads (Last 12 months)121
          • Downloads (Last 6 weeks)9

          Other Metrics

        PDF Format

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader

        Full Text

        View this article in Full Text.

        View Full Text

        HTML Format

        View this article in HTML Format .

        View HTML Format