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GRM: Group Regularity Mobility Model

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Published:21 November 2017Publication History

ABSTRACT

In this work we propose, implement, and evaluate Group Regularity Model (GRM), a novel mobility model that accounts for the role of group meetings regularity in human mobility. We show that existing mobility models for humans do not capture the regularity of human group meetings present in real mobility traces. We characterize the statistical properties of such group meetings in real mobility traces and design GRM accordingly. We show that GRM maintains the typical pairwise contact properties of real traces, such as contact duration and inter-contact time distributions. In addition, GRM accounts for the role of group mobility, presenting group meetings regularity and social communities' structure. Finally, we evaluate state-of-art social-aware protocols for opportunistic routing and show that their performance in synthetic traces generated by GRM is similar to their performance in real-world traces.

References

  1. Albert-László Barabási and Réka Albert. 1999. Emergence of scaling in random networks. science 286, 5439 (1999), 509--512.Google ScholarGoogle Scholar
  2. Sven Berg. 1988. Snowball sampling. Encyclopedia of statistical sciences (1988).Google ScholarGoogle Scholar
  3. U. Brandes et al. 2003. Experiments on graph clustering algorithms. In European Symposium on Algorithms. Springer, 568--579. Google ScholarGoogle ScholarCross RefCross Ref
  4. A. Chaintreau et al. 2007. Impact of Human Mobility on Opportunistic Forwarding Algorithms. IEEE TMC 6, 6 (June 2007), 606--620. https://doi.org/10.1109/TMC. 2007.1060Google ScholarGoogle Scholar
  5. N. Cruz and H. Miranda. 2015. Recurring contact opportunities within groups of devices. In 12th EAI Mobiquitous. 160--169.Google ScholarGoogle Scholar
  6. Nathan Eagle and Alex Pentland. 2006. Reality mining: sensing complex social systems. Personal and ubiquitous computing 10, 4 (2006), 255--268. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Frans Ekman et al. 2008. Working day movement model. In Proceedings of the 1st ACM SIGMOBILE workshop on Mobility models. ACM, 33--40. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Santo Fortunato. 2010. Community detection in graphs. Physics reports 486, 3 (2010), 75--174. Google ScholarGoogle ScholarCross RefCross Ref
  9. Marta Gonzalez et al. 2008. Understanding individual human mobility patterns. Nature 453, 7196 (2008), 779--782. Google ScholarGoogle ScholarCross RefCross Ref
  10. Tristan Henderson et al. 2008. The changing usage of a mature campus-wide wireless network. Computer Networks 52, 14 (2008), 2690--2712. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Xiaoyan Hong et al. 1999. A Group Mobility Model for Ad Hoc Wireless Networks. In 2nd ACM MSWIM. 53--60.Google ScholarGoogle Scholar
  12. Pan Hui et al. 2011. Bubble rap: Social-based forwarding in delay-tolerant networks. IEEE TMC 10, 11 (2011), 1576--1589. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Ari Keränen et al. 2009. The ONE simulator for DTN protocol evaluation. In Proceedings of the 2nd international conference on simulation tools and techniques. 55. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Sokol Kosta et al. 2014. Large-scale synthetic social mobile networks with SWIM. IEEE TMC 13, 1 (2014), 116--129. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Kyunghan Lee et al. 2009. Slaw: A new mobility model for human walks. In INFOCOM 2009, IEEE. 855--863. Google ScholarGoogle ScholarCross RefCross Ref
  16. Yong Li et al. 2014. Social-aware D2D communications: qualitative insights and quantitative analysis. Communications Magazine, IEEE 52, 6 (2014), 150--158. Google ScholarGoogle ScholarCross RefCross Ref
  17. Ivan O. Nunes, Clayson Celes, Pedro O.S. Vaz de Melo, and Antonio A.F. Loureiro. 2017. GROUPS-NET: Group meetings aware routing in multi-hop D2D networks. Computer Networks (2017), 94 -- 108. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. I. O. Nunes et al. 2016. Group Mobility: Detection, Tracking and Characterization. In IEEE ICC.Google ScholarGoogle Scholar
  19. "I. O. Nunes et al. 2016. Leveraging D2D Multi-Hop Communication Through Social Group Meetings Awareness. Wireless Communications Magazine, IEEE (2016).Google ScholarGoogle Scholar
  20. Gergely Palla et al. 2005. Uncovering the overlapping community structure of complex networks in nature and society. Nature 435, 7043 (2005), 814--818. Google ScholarGoogle ScholarCross RefCross Ref
  21. Joanne Treurniet. 2014. A Taxonomy and Survey of Microscopic Mobility Models from the Mobile Networking Domain. ACM Computing Surveys (CSUR) 47, 1 (2014), 14.Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. K. H. Wang and Baochun Li. 2002. Group mobility and partition prediction in wireless ad-hoc networks. In IEEE ICC 2002. Google ScholarGoogle ScholarCross RefCross Ref

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          cover image ACM Conferences
          MSWiM '17: Proceedings of the 20th ACM International Conference on Modelling, Analysis and Simulation of Wireless and Mobile Systems
          November 2017
          340 pages
          ISBN:9781450351621
          DOI:10.1145/3127540

          Copyright © 2017 ACM

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          Publication History

          • Published: 21 November 2017

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          MSWiM '17 Paper Acceptance Rate29of142submissions,20%Overall Acceptance Rate398of1,577submissions,25%

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