The realistic modeling of mobile networks makes it necessary to find adequate models to mimic the movement of mobile nodes. In the past various such mobility models have been proposed, that either create synthetic movement patterns or are based on real-world observations. These models usually assume a constant number of mobility nodes for the simulation. Although in real-world scenarios new nodes will arrive and other nodes will leave the simulation area, only little attention has been paid to modeling these arrivals and departures of nodes.

In this paper we present an approach to easily extend mobility models to support the generation of arrivals and departures. For three standard mobility models the effect of this extension on the performance measures of a simple mobile network is shown.


Mobility models Scenario generation Arrival processes ARTA processes 


  1. 1.
    Aschenbruck, N., Ernst, R., Gerhards-Padilla, E., Schwamborn, M.: BonnMotion - a mobility scenario generation and analysis tool. In: Proceedings of the SIMUTools (2010)Google Scholar
  2. 2.
    Balachandran, A., Voelker, G., Bahl, P., Rangan, P.: Characterizing user behavior and network performance in a public wireless LAN. In: SIGMETRICS (2002)Google Scholar
  3. 3.
    Bettstetter, C.: Smooth is better than sharp: a random mobility model for simulation of wireless networks. In: Proceedings of the MSWIM (2002)Google Scholar
  4. 4.
    Bhatia, H., Lenin, R.B., Munjal, A., Ramaswamy, S., Srivastava, S.: A queuing-theoretic framework for modeling and analysis of mobility in WSNs. In: Proceedings of the PerMIS (2008)Google Scholar
  5. 5.
    Biller, B., Nelson, B.: Fitting time-series input processes for simulation. Oper. Res. 53(3), 549–559 (2005)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Biller, B., Nelson, B.L.: Modeling and generating multivariate time-series input processes using a vector autoregressive technique. ACM Trans. Model. Comput. Simul. 13(3), 211–237 (2003)CrossRefGoogle Scholar
  7. 7.
    Box, G., Jenkins, G.: Time Series Analysis - Forecasting and Control. Holden-Day, San Francisco (1970)zbMATHGoogle Scholar
  8. 8.
    Buchholz, P., Kemper, P., Kriege, J.: Multi-class markovian arrival processes and their parameter fitting. Perform. Eval. 67(11), 1092–1106 (2010)CrossRefGoogle Scholar
  9. 9.
    Buchholz, P., Kriege, J., Felko, I.: Input Modeling with Phase-Type Distributions and Markov Models - Theory and Applications. Springer, Heidelberg (2014)Google Scholar
  10. 10.
    Camp, T., Boleng, J., Davies, V.: A survey of mobility models for ad hoc network research. Wirel. Commun. Mob. Comput. 2(5), 483–502 (2002)CrossRefGoogle Scholar
  11. 11.
    Cario, M., Nelson, B.: Autoregressive to anything: time-series input processes for simulation. Oper. Res. Lett. 19(2), 51–58 (1996)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Chen, Y., Kurose, J., Towsley, D.: A mixed queueing network model of mobility in a campus wireless network. In: Proceedings of the INFOCOM (2012)Google Scholar
  13. 13.
    Hornig, R., Varga, A.: An overview of the OMNeT++ simulation environment. In: Proceedings of the SIMUTools (2008)Google Scholar
  14. 14.
    Jardosh, A., Belding-Royer, E., Almeroth, K., Suri, S.: Towards realistic mobility models for mobile ad hoc networks. In: Proceedings of the MobiCom (2003)Google Scholar
  15. 15.
    Kim, M., Kotz, D., Kim, S.: Extracting a mobility model from real user traces. In: Proceedings of the INFOCOM (2006)Google Scholar
  16. 16.
    Leland, W.E., Taqqu, M.S., Willinger, W., Wilson, D.V.: On the self-similar nature of ethernet traffic (extended version). IEEE/ACM Trans. Netw. 2(1), 1–15 (1994)CrossRefGoogle Scholar
  17. 17.
    Liang, B., Haas, Z.: Predictive distance-based mobility management for PCS networks. In: Proceedings of the INFOCOM (1999)Google Scholar
  18. 18.
    Navidi, W., Camp, T.: Stationary distributions for random waypoint models. IEEE Trans. Mobile Comput. 3(1), 99–108 (2004)CrossRefGoogle Scholar
  19. 19.
    Resta, G., Santi, P.: The QoS-RWP mobility and user behavior model for public area wireless networks. In: Proceedings of the MSWiM (2006)Google Scholar
  20. 20.
    Royer, E., Melliar-Smith, P., Moser, L.: An analysis of the optimum node density for ad hoc mobile networks. In: Proceedings of the IEEE ICC (2001)Google Scholar
  21. 21.
    Tuduce, C., Gross, T.: A mobility model based on WLAN traces and its validation. In: Proceedings of the INFOCOM (2005)Google Scholar
  22. 22.
    Yoon, J., Noble, B., Liu, M., Kim, M.: Building realistic mobility models from coarse-grained traces. In: Proceedings of the MobiSys (2006)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.TU DortmundDortmundGermany

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