Using Future Internet Infrastructure and Smartphones for Mobility Trace Acquisition and Social Interactions Monitoring

  • Athanasios Antoniou
  • Evangelos Theodoridis
  • Ioannis Chatzigiannakis
  • Georgios Mylonas
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7281)


Recent activity in the field of Internet-of-Things experimentation has focused on the federation of discrete testbeds, thus placing less effort in the integration of other related technologies, such as smartphones; also, while it is gradually moving to more application-oriented paths, such as urban settings, it has not dealt in large with applications having social networking features. We argue here that current IoT infrastructure, testbeds and related software technologies should be used in such a context, capturing real-world human mobility and social networking interactions, for use in evaluating and fine-tuning realistic mobility models and designing human-centric applications. We discuss a system for producing traces for a new generation of human-centric applications, utilizing technologies such as Bluetooth and focusing on human interactions. We describe the architecture for this system and the respective implementation details presenting two distinct deployments; one in an office environment and another in an exhibition/conference event with 103 active participants combined, thus covering two popular scenarios for human centric applications. Our system provides online, almost real-time, feedback and statistics and its implementation allows for rapid and robust deployment, utilizing mainstream technologies and components.


Mobile Phone Receive Signal Strength Smart City Link Prediction Human Mobility 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Coulson, G., et al.: Flexible experimentation in wireless sensor networks. Communications of the ACM (CACM) 55(1), 82–90 (2012)CrossRefGoogle Scholar
  2. 2.
    SmartSantader project,
  3. 3.
    Miluzzo, E., et al.: Darwin Phones: the Evolution of Sensing and Inference on Mobile Phones. In: MobiSys 2010, pp. 5–20 (2010)Google Scholar
  4. 4.
    Eagle, N., Pentland, A.: Reality Mining: Sensing Complex Social Systems. In: Personal and Ubiquitous Computing, pp. 255–268 (May 2006)Google Scholar
  5. 5.
    Borovoy, R., et al.: Meme tags and community mirrors: moving from conferences to collaboration. In: CSCW 1998, pp. 159–168 (1998)Google Scholar
  6. 6.
    Hui, P., et al.: Pocket switched networks and human mobility in conference environments. In: WDTN 2005, pp. 244–251 (2005)Google Scholar
  7. 7.
    Nordstrom, E., Diot, C., Gass, R., Gunningberg, P.: Experiences from measuring human mobility using Bluetooth inquiring devices. In: MobiEval 2007, pp. 15–20 (2007)Google Scholar
  8. 8.
    Nicolai, T., Yoneki, E., Behrens, N., Kenn, H.: Exploring Social Context with the Wireless Rope. In: Meersman, R., Tari, Z., Herrero, P. (eds.) OTM 2006 Workshops, part I. LNCS, vol. 4277, pp. 874–883. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  9. 9.
    Natarajan, A., Motani, M., Srinivasan, V.: Understanding Urban Interactions from Bluetooth Phone Contact Traces. In: Uhlig, S., Papagiannaki, K., Bonaventure, O. (eds.) PAM 2007. LNCS, vol. 4427, pp. 115–124. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  10. 10.
    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
  11. 11.
  12. 12.
    Brockmann, D., Hufnagel, L., Geisel, T.: The scaling laws of human travel. Nature 439(7075), 462–465 (2006)CrossRefGoogle Scholar
  13. 13.
    Gonzalez, M., Hidalgo, C., Barabasi, A.-L.: Understanding individual human mobility patterns. Nature 453(7196), 779–782 (2008)CrossRefGoogle Scholar
  14. 14.
    Song, C., Koren, T., Wang, P., Barabasi, A.-L.: Modelling the scaling properties of human mobility. Nature Physics (2010)Google Scholar
  15. 15.
    Eagle, N., Pentland, A.: Eigenbehaviors: Identifying structure in routine. Behavioral Ecology and Sociobiology 63(7), 1057–1066 (2009)CrossRefGoogle Scholar
  16. 16.
    Liben-Nowell, D., Kleinberg, J.M.: The link prediction problem for social networks. In: CIKM 2003, pp. 556–559 (2003)Google Scholar
  17. 17.
    Wang, D., Pedreschi, D., Song, C., Giannotti, F., Barabasi, A.-L.: Human Mobility. In: Social Ties, and Link Prediction KDD 2011 (2011)Google Scholar
  18. 18.
    Lambiotte, R., et al.: Geographical dispersal of mobile communication networks. Physica A: Statistical Mechanics and its Applications 387(21), 17 (2008)CrossRefGoogle Scholar
  19. 19.
    Backstrom, L., Sun, E., Marlow, C.: Find Me If You Can: Improving Geographical Prediction with Social and Spatial Proximity. North, pp. 61–70. ACM (2010)Google Scholar
  20. 20.
    Decker, G., Weske, M.: Interaction-centric modeling of process choreographies. Inf. Syst. 36, 292–312 (2011)CrossRefGoogle Scholar
  21. 21.
    Olguin, D.O., et al.: Sensible Organizations: Technology and Methodology for Automatically Measuring Organizational Behavior. IEEE Transactions on Systems, Man, and Cybernetics 39 (February 2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Athanasios Antoniou
    • 1
  • Evangelos Theodoridis
    • 1
  • Ioannis Chatzigiannakis
    • 1
  • Georgios Mylonas
    • 1
  1. 1.Computer Technology Institute and PressPatrasGreece

Personalised recommendations