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Review of Mobility Models for Performance Evaluation of Wireless Networks

  • Michal Gorawski
  • Krzysztof Grochla
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 242)

Abstract

Performance evaluation of protocols and mechanisms in wireless networks require a good representation of client mobility. The number of mobility models has been developed, to emulate the changes of location in time of mobile wireless devices in communication networks, such as e.g. mobile phones, tablets, netbooks, palmtops.Mobility models are used to verify the protocols and algorithms developed for wireless networks in simulation and using analytical tools. The mobility patterns of such devices converges with human movement patterns, as the mobile devices bearers. Among many propositions of human mobility modeling in the literature this paper presents and reviews techniques which are most commonly used or that give very good estimation of actual mobile device bearer behavior. The models are divided into 3 groups: random, social and hybrid.

Keywords

mobility models communication systems wireless networks cellular networks mobile devices human walk 

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References

  1. 1.
    Atkinson, R.P.D., Rhodes, C.J., Macdonald, D.W., Anderson, R.M.: Scale-free dynamics in the movement patterns of jackals. Oikos 98(1), 134–140 (2002)CrossRefGoogle Scholar
  2. 2.
    Bai, F., Helmy, A.: A survey of mobility modeling and analysis in wireless adhoc networks. In: Wireless Ad Hoc and Sensor Networks, ch. 1. Kluwer Academic Publishers (2004)Google Scholar
  3. 3.
    Bettstetter, C., Hartenstein, H., Pérez-Costa, X.: Stochastic properties of the random waypoint mobility model: Epoch length, direction distribution, and cell change rate. In: Proceedings of the 5th ACM International Workshop on Modeling, Analysis, and Simulation of Wireless and Mobile Systems (MSWiM), pp. 7–14. ACM (2002)Google Scholar
  4. 4.
    Birand, B., Zafer, M., Zussman, G., Lee, K.-W.: Dynamic graph properties of mobile networks under levy walk mobility. In: Proceedings of the 8th International Conference on Mobile Ad-Hoc and Sensor Systems (MASS 2011), pp. 292–301. IEEE Computer Society (2011)Google Scholar
  5. 5.
    Boldrini, C., Conti, M., Passarella, A.: Users mobility models for opportunistic networks: the role of physical locations. In: Proceedings of the IEEE Wireless Rural and Emergency Communications Conference, WRECOM 2007 (2007)Google Scholar
  6. 6.
    Boldrini, C., Passarella, A.: HCMM: Modelling spatial and temporal properties of human mobility driven by users social relationships. Computer Communications 33(9), 1056–1074 (2010)CrossRefGoogle Scholar
  7. 7.
    Borrel, V., Legendre, F., de Amorim, M.D., Fdida, S.: SIMPS: Using sociology for personal mobility. IEEE/ACM Transactions on Networking 17(3), 831–842 (2009)CrossRefGoogle Scholar
  8. 8.
    Brockmann, D.D., Hufnagel, L., Geisel, T.: The scaling laws of human travel. Nature 439, 462–465 (2006)CrossRefGoogle Scholar
  9. 9.
    Camp, T., Boleng, J., Davies, V.: A survey of mobility models for ad hoc network research. Wireless Communication & Mobile Computing 2(5), 483–502 (2002)CrossRefGoogle Scholar
  10. 10.
    Ekman, F., Keränen, A., Karvo, J., Ott, J.: Working day movement model. In: Proceedings of 1st ACM/SIGMOBILE Workshop on Mobility Models for Networking Research (MobilityModels 2008), pp. 33–40. ACM (2008)Google Scholar
  11. 11.
    González, M.C., Hidalgo, C.A., Barabási, A.L.: Understanding individual human mobility patterns. Nature 453, 779–782 (2008)CrossRefGoogle Scholar
  12. 12.
    Humphries, N.E., Weimerskirch, H., Queiroz, N., Southall, E.J., Sims, D.W.: Foraging success of biological levy flights recorded in situ. Proceedings of the National Academy of Sciences of the United States of America 109(19), 7169–7174 (2012)CrossRefGoogle Scholar
  13. 13.
    Karagiannis, T., Le Boudec, J.Y., Vojnović, M.: Power law and exponential decay of inter contact times between mobile devices. In: Proceedings of the 13th Annual ACM International Conference on Mobile Computing and Networking (MobiCom 1997), pp. 183–194. ACM (2007)Google Scholar
  14. 14.
    Lee, K., Hong, S., Kim, S.J., Rhee, I., Chong, S.: SLAW: Self-similar least-action human walk. IEEE/ACM Transactions on Networking 20(2), 515–529 (2010)CrossRefGoogle Scholar
  15. 15.
    Musolesi, M., Hailes, S., Mascolo, C.: An ad hoc mobility model founded on social network theory. In: Proceedings of the 7th ACM International Symposium on Modeling, Analysis and Simulation of Wireless and Mobile Systems (MSWiM 2004), pp. 20–24. ACM (2004)Google Scholar
  16. 16.
    Papageorgiou, C., Birkos, K., Dagiuklas, T., Kotsopoulos, S.: An obstacle-aware human mobility model for ad hoc networks. In: Proceedings of the 17th IEEE/ACM International Symposium on Modelling, Analysis and Simulation of Computer and Telecommunication Systems (MASCOTS 2009), pp. 1–9. IEEE (2009)Google Scholar
  17. 17.
    Ramos-Fernandez, G., Mateos, J.L., Miramontes, O., Cocho, G., Larralde, H., Ayala-Orozco, B.: Lévy walk patterns in the foraging movements of spider monkeys. Behavioral Ecology and Sociobiology 55(3), 223–230 (2004)CrossRefGoogle Scholar
  18. 18.
    Rhee, I., Lee, K., Hong, S., Kim, S.J., Chong, S.: Demystifying the levy-walk nature of human walks. Tech. rep., CS Dept., NCSU, Raleigh, NC (2008)Google Scholar
  19. 19.
    Rhee, I., Shin, M., Hong, S., Lee, K., Chong, S.: On the levy-walk nature of human mobility: Do humans walk like monkeys? IEEE/ACM Transactions on Networking 19(3), 630–643 (2011)CrossRefGoogle Scholar
  20. 20.
    Roy, R.R.: Handbook of Mobile Ad Hoc Networks for Mobility Models. Springer (2011)Google Scholar
  21. 21.
    Sims, D.W., Southall, E.J., Humphries, N.E., Hays, G.C., Bradshaw, C.J.A., et al.: Scaling laws of marine predator search behaviour. Nature 451(7182), 1098–1102 (2008)CrossRefGoogle Scholar
  22. 22.
    Song, C., Qu, Z., Blumm, N., Barabási, A.L.: Limits of predictability in human mobility. Science 327(5968), 1018–1021 (2010)MathSciNetCrossRefMATHGoogle Scholar
  23. 23.
    Yoon, J., Liu, M., Noble, B.: Random waypoint considered harmful. In: Proceedings of the 22nd Annual Joint Conference of the IEEE Computer and Communications (INFOCOM 2003), vol. 2,pp. 1312–1321. IEEE (2003)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Institute of Theoretical and Applied InformaticsPolish Academy of SciencesGliwicePoland

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