Analysing Delay-Tolerant Networks with Correlated Mobility

  • Mikael Asplund
  • Simin Nadjm-Tehrani
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7363)

Abstract

Given a mobility pattern that entails intermittent wireless ad hoc connectivity, what is the best message delivery ratio and latency that can be achieved for a delay-tolerant routing protocol? We address this question by introducing a general scheme for deriving the routing latency distribution for a given mobility trace. Prior work on determining latency distributions has focused on models where the node mobility is characterised by independent contacts between nodes. We demonstrate through simulations with synthetic and real data traces that such models fail to predict the routing latency for cases with heterogeneous and correlated mobility. We demonstrate that our approach, which is based on characterising mobility through a colouring process, achieves a very good fit to simulated results also for such complex mobility patterns.

Keywords

Latency Delay-tolerant networks Correlated Mobility Connectivity 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Altman, E., Basar, T., Pellegrini, F.D.: Optimal monotone forwarding policies in delay tolerant mobile ad-hoc networks. Perform. Eval. 67(4) (2010), doi:10.1016/j.peva.2009.09.001Google Scholar
  2. 2.
    Aschenbruck, N., Munjal, A., Camp, T.: Trace-based mobility modeling for multi-hop wireless networks. Comput. Commun. 34(6) (2010), doi:10.1016/j.comcom.2010.11.002Google Scholar
  3. 3.
    Asplund, M.: Disconnected Discoveries: Availability Studies in Partitioned Networks. PhD thesis, Linköping University (2010), http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-60553
  4. 4.
    Asplund, M., Nadjm-Tehrani, S.: A partition-tolerant manycast algorithm for disaster area networks. In: 28th International Symposium on Reliable Distributed Systems (SRDS). IEEE (2009), doi:10.1109/SRDS.2009.16Google Scholar
  5. 5.
    Bulut, E., Geyik, S., Szymanski, B.: Efficient routing in delay tolerant networks with correlated node mobility. In: 2010 IEEE 7th International Conference on Mobile Adhoc and Sensor Systems, MASS (2010), doi:10.1109/MASS.2010.5663962Google Scholar
  6. 6.
    Cai, H., Eun, D.Y.: Toward stochastic anatomy of inter-meeting time distribution under general mobility models. In: Proceedings of the 9th ACM International Symposium on Mobile ad hoc Networking and Computing, MobiHoc 2008. ACM (2008), doi:10.1145/1374618.1374655Google Scholar
  7. 7.
    Chaintreau, A., Hui, P., Crowcroft, J., Diot, C., Gass, R., Scott, J.: Impact of human mobility on opportunistic forwarding algorithms. IEEE Trans. Mobile Comput. 6(6) (2007), doi:10.1109/TMC.2007.1060Google Scholar
  8. 8.
    Ciullo, D., Martina, V., Garetto, M., Leonardi, E.: Impact of correlated mobility on delay-throughput performance in mobile ad hoc networks. IEEE/ACM Transactions on Networking 19(6) (2011), doi:10.1109/TNET.2011.2140128Google Scholar
  9. 9.
    Garetto, M., Giaccone, P., Leonardi, E.: Capacity scaling in delay tolerant networks with heterogeneous mobile nodes. In: Proc. 8th ACM International Symposium on Mobile ad hoc Networking and Computing (MobiHoc). ACM (2007), doi:10.1145/1288107.1288114Google Scholar
  10. 10.
    Grinstead, C.M., Snell, J.L.: Introduction to Probability. American Mathematical Society (1997)Google Scholar
  11. 11.
    Hossmann, T., Spyropoulos, T., Legendre, F.: Putting contacts into context: Mobility modeling beyond inter-contact times. In: Twelfth ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc 2011). ACM (2011)Google Scholar
  12. 12.
    Karagiannis, T., Boudec, J.-Y.L., Vojnović, M.: Power law and exponential decay of intercontact times between mobile devices. IEEE Transactions on Mobile Computing 9 (2010), doi:10.1109/TMC.2010.99Google Scholar
  13. 13.
    Keränen, A., Ott, J.: Increasing reality for dtn protocol simulations. Technical report, Helsinki University of Technology, Networking Laboratory (2007)Google Scholar
  14. 14.
    Keränen, A., Ott, J., Kärkkäinen, T.: The ONE simulator for dtn protocol evaluation. In: Proceedings of the 2nd International Conference on Simulation Tools and Techniques, Simutools, ICST (2009), doi:10.4108/ICST.SIMUTOOLS2009.5674Google Scholar
  15. 15.
    Khelil, A., Becker, C., Tian, J., Rothermel, K.: An epidemic model for information diffusion in manets. In: Proc. 5th ACM International Workshop on Modeling Analysis and Simulation of Wireless and Mobile Systems (MSWiM). ACM (2002), doi:10.1145/570758.570768Google Scholar
  16. 16.
    Kuiper, E., Nadjm-Tehrani, S., Yuan, D.: A framework for performance analysis of geographic delay-tolerant routing. EURASIP Journal on Wireless Communications and Networking (to appear, 2012)Google Scholar
  17. 17.
    Lahde, S., Doering, M., Pöttner, W.-B., Lammert, G., Wolf, L.: A practical analysis of communication characteristics for mobile and distributed pollution measurements on the road. Wireless Communications and Mobile Computing 7(10) (2007), doi:10.1002/wcm.522Google Scholar
  18. 18.
    Lee, C.-H., Eun, D.Y.: Exploiting heterogeneity in mobile opportunistic networks: An analytic approach. In: 2010 7th Annual IEEE Communications Society Conference on Sensor Mesh and Ad Hoc Communications and Networks, SECON (2010), doi:10.1109/SECON.2010.5508265Google Scholar
  19. 19.
    Lu, R., Lin, X., Shen, X.: Spring: A social-based privacy-preserving packet forwarding protocol for vehicular delay tolerant networks. In: 2010 Proceedings of IEEE INFOCOM (2010), doi:10.1109/INFCOM.2010.5462161Google Scholar
  20. 20.
    Passarella, A., Conti, M.: Characterising Aggregate Inter-contact Times in Heterogeneous Opportunistic Networks. In: Domingo-Pascual, J., Manzoni, P., Palazzo, S., Pont, A., Scoglio, C. (eds.) NETWORKING 2011, Part II. LNCS, vol. 6641, pp. 301–313. Springer, Heidelberg (2011), doi:10.1007/978-3-642-20798-3_23CrossRefGoogle Scholar
  21. 21.
    Piorkowski, M., Sarafijanovic-Djukic, N., Grossglauser, M.: A parsimonious model of mobile partitioned networks with clustering. In: First International Conference on Communication Systems and Networks (COMSNETS). IEEE (2009), doi:10.1109/COMSNETS.2009.4808865Google Scholar
  22. 22.
    Resta, G., Santi, P.: A framework for routing performance analysis in delay tolerant networks with application to non cooperative networks. IEEE Transactions on Parallel and Distributed Systems 23(1) (2011), doi:10.1109/TPDS.2011.99Google Scholar
  23. 23.
    Spyropoulos, T., Psounis, K., Raghavendra, C.: Efficient routing in intermittently connected mobile networks: The single-copy case. IEEE/ACM Trans. Netw. 16(1) (2008), doi:10.1109/TNET.2007.897962Google Scholar
  24. 24.
    Spyropoulos, T., Turletti, T., Obraczka, K.: Routing in delay-tolerant networks comprising heterogeneous node populations. IEEE Trans. Mobile Comput. 8(8) (2009), doi:10.1109/TMC.2008.172Google Scholar
  25. 25.
    Yoon, J., Liu, M., Noble, B.: Random waypoint considered harmful. In: Proc. INFOCOM 2003. Twenty-Second Annual Joint Conference of the IEEE Computer and Communications Societies. IEEE (2003), doi:10.1109/INFCOM.2003.1208967Google Scholar
  26. 26.
    Zhang, X., Neglia, G., Kurose, J., Towsley, D.: Performance modeling of epidemic routing. Comput. Netw. 51(10) (2007), doi:10.1016/j.comnet.2006.11.028Google Scholar
  27. 27.
    Zhu, H., Fu, L., Xue, G., Zhu, Y., Li, M., Ni, L.: Recognizing exponential inter-contact time in vanets. In: Proceedings of IEEE INFOCOM (2010), doi:10.1109/INFCOM.2010.5462263Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Mikael Asplund
    • 1
  • Simin Nadjm-Tehrani
    • 1
  1. 1.Department of Computer and Information ScienceLinköping UniversitySweden

Personalised recommendations