Statistical Analysis of Contact Patterns between Human-Carried Mobile Devices

Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 58)


In this paper, we focus on analyzing the impact of human-to-human contact patterns on opportunistic communication in Pocket Switched Networks (PSNs). We take advantage of statistical methods to consider the distributions of two different types of inter-contact time as well as the number of contacts between human-carried mobile devices. Different from the results from recent studies, we present empirical evidence that power law with exponential cutoff characterizes all three distributions of interest better than other possible long-tail distributions. We further show that each of the investigated distributions has a finite mean value. Having a finite mean value is of importance for each distribution, as it facilitates the design of distributed community detection algorithms as well as social-based forwarding algorithms. Finally, we make the recommendation to exploit the average number of contacts as a threshold for each device to determine their friend-set, which is a precondition for some distributed community detection algorithms.


Statistical Analysis Contact Pattern Pocket Switched Networks 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Hui, P., Chaintreau, A., Gass, R., Scott, J., Crowcroft, J., Diot, C.: Pocket switched networks and human mobility in conference environments. In: 2005 ACM SIGCOMM Workshop on Delay-Tolerant Networking, pp. 244–251 (2005)Google Scholar
  2. 2.
    Chaintreau, A., Hui, P., Crowcroft, J., Diot, C., Gass, R., Scott, J.: Impact of Human Mobility on Opportunistic Forwarding Algorithms. IEEE Transactions on Mobile Computing 6(6), 606–620 (2007)CrossRefGoogle Scholar
  3. 3.
  4. 4.
    Eagle, N., Pentland, A.: Reality mining: sensing complex social systems. Personal and Ubiquitous Computing 10(4), 255–268 (2006)CrossRefGoogle Scholar
  5. 5.
    Cabero, J.M., Molina, V., Urteaga, I., Liberal, F., Martín, J.L.: Acquisition of human traces with Bluetooth technology: Challenges and proposals. Ad Hoc Networks (published online June 6, 2012), doi:10.1016/j.adhoc.2012.05.007Google Scholar
  6. 6.
    Clauset, A.: Finding local community structure in networks. Physical Review E 72, 026132 (2005)CrossRefGoogle Scholar
  7. 7.
    Hui, P., Yoneki, E., Chan, S.Y., Crowcroft, J.: Distributed community detection in delay tolerant networks. In: 2nd ACM/IEEE International Workshop on Mobility in the Evolving Internet Architecture, pp. 1–8 (2007)Google Scholar
  8. 8.
    Daly, E.M., Haahr, M.: Social network analysis for routing in disconnected delay-tolerant MANETs. In: 8th ACM International Symposium on Mobile Ad Hoc Networking and Computing, pp. 32–40 (2007)Google Scholar
  9. 9.
    Hui, P., Crowcroft, J., Yoneki, E.: Bubble rap: social-based forwarding in delay tolerant networks. In: 9th ACM International Symposium on Mobile Ad Hoc Networking and Computing, pp. 241–250 (2008)Google Scholar
  10. 10.
    Bulut, E., Szymanski, B.K.: Friendship Based Routing in Delay Tolerant Mobile Social Networks. In: IEEE Global Telecommunications Conference (GLOBECOM 2010), pp. 1–5 (2010)Google Scholar
  11. 11.
    Mei, A., Morabito, G., Santi, P., Stefa, J.: Social-aware stateless forwarding in pocket switched networks. In: 30th IEEE International Conference on Computer Communications (INFOCOM 2011), pp. 251–255 (2011)Google Scholar
  12. 12.
    Karagiannis, T., Boudec, J.-Y.L., Vojnovi, M.: Power law and exponential decay of inter contact times between mobile devices. In: 13th Annual ACM International Conference on Mobile Computing and Networking, pp. 183–194 (2007)Google Scholar
  13. 13.
    Gaito, S., Pagani, E., Rossi, G.P.: Fine-Grained Tracking of Human Mobility in Dense Scenarios. In: 6th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON), pp. 40–42 (2009)Google Scholar
  14. 14.
    Albert, R., Barabási, A.-L.: Statistical mechanics of complex networks. Reviews of Modern Physics 74(1), 47–97 (2002)MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Clauset, A., Shalizi, C.R., Newman, M.E.J.: Power-Law Distributions in Empirical Data. SIAM Review 51(4), 661–703 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  16. 16.
    Vuong, Q.H.: Likelihood Ratio Tests for Model Selection and Non-Nested Hypotheses. Econometrica 57(2), 307–333 (1989)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering 2013

Authors and Affiliations

  1. 1.Communications Networks, TZIUniversity of BremenGermany
  2. 2.Department of Computer Science & EngineeringOcean University of ChinaChina

Personalised recommendations