Proximates – A Social Context Engine

  • Håkan Jonsson
  • Pierre Nugues
Part of the Communications in Computer and Information Science book series (CCIS, volume 413)


Several studies have shown the value of using proximity data to understand the social context of users. To simplify the use of social context in application development we have developed Proximates, a social context engine for mobile phones. It scans nearby Bluetooth peers to determine what devices are in proximity. We map Bluetooth MAC ids to user identities on existing social networks which then allows Proximates to infer the social context of the user. The main contribution of Proximates is its use of link attributes retrieved from Facebook for granular relationship classification. We also show that Proximates can bridge the gap between physical and digital social interactions, by showing that it can be used to measure how much time a user spends in physical proximity with his Facebook friends. In this paper we present the architecture and initial experimental results on deployment usability aspects of users of an example application. We also discuss using location for proximity detection versus direct sensing using Bluetooth.


Mobile Phone Sensing Proximity Social Context Social Sensing 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Alani, H., Szomszor, M., Cattuto, C., Van den Broeck, W., Correndo, G., Barrat, A.: Live social semantics. In: Bernstein, A., Karger, D.R., Heath, T., Feigenbaum, L., Maynard, D., Motta, E., Thirunarayan, K. (eds.) ISWC 2009. LNCS, vol. 5823, pp. 698–714. Springer, Heidelberg (2009), CrossRefGoogle Scholar
  2. 2.
    Barrat, A., Cattuto, C.: Temporal networks of face-to-face human interactions. Temporal Networks, pp. 50–55 (2013),
  3. 3.
    Beach, A., Gartrell, M., Akkala, S., Elston, J., Kelley, J., Nishimoto, K., Ray, B., Razgulin, S., Sundaresan, K., Surendar, B., Terada, M., Han, R.: WhozThat? Evolving an ecosystem for context-aware mobile social networks. Network IEEE 22(4), 50–55 (2008), CrossRefGoogle Scholar
  4. 4.
    Beach, A., Gartrell, M., Xing, X., Han, R., Lv, Q., Mishra, S., Seada, K.: Fusing mobile, sensor, and social data to fully enable context-aware computing. In: Proceedings of the Eleventh Workshop on Mobile Computing Systems Applications HotMobile 2010, p. 60 (2010),
  5. 5.
    Blom, J., Gatica-perez, D., Kiukkonen, N.: People-Centric Mobile Sensing with a Pragmatic Twist: from Behavioral Data Points to Active User Involvement. In: Mobile Devices and Services, pp. 381–384 (2011)Google Scholar
  6. 6.
    Cattuto, C., Broeck, W.V.D., Barrat, A., Colizza, V., Pinton, J.F., Vespignani, A.: Dynamics of person-to-person interactions from distributed RFID sensor networks. PloS One 5(7), 1–9 (2010), CrossRefGoogle Scholar
  7. 7.
    Cranshaw, J., Toch, E., Hong, J.: Bridging the gap between physical location and online social networks. In: Proceedings of the 12th ACM International Conference on Ubiquitous Computing (2010),
  8. 8.
    Eagle, N.: Can Serendipity Be Planned? MIT Sloan Management Review 46(1), 10–14 (2004), Google Scholar
  9. 9.
    Eagle, N., Sandy Pentland, A.: Reality mining: sensing complex social systems. Personal and Ubiquitous Computing 10(4), 255–268 (2005), CrossRefGoogle Scholar
  10. 10.
    Frank, J., Mannor, S., Precup, D.: Generating storylines from sensor data. In: Mobile Data Challenge Workshop (2012)Google Scholar
  11. 11.
    Jonsson, H.: The Data Chicken and Egg Problem. In: Proceedings of the 3rd International Workshop on Research in the Large, pp. 9–12 (2012)Google Scholar
  12. 12.
    Karam, A., Mohamed, N.: Middleware for mobile social networks: A survey. In: 45th Hawaii International Conference on System Sciences Middleware, pp. 1482–1490. IEEE (2012),,
  13. 13.
    Kiukkonen, N., Blom, J., Dousse, O., Gatica-perez, D., Laurila, J.: Towards rich mobile phone datasets: Lausanne data collection campaign. In: Proceeding of International Conference on Pervasive Services ICPS (2002)Google Scholar
  14. 14.
    Liu, S., Striegel, A.: Accurate Extraction of Face-to-Face Proximity Using Smartphones and Bluetooth. In: 2011 Proceedings of 20th International Conference on Computer Communications and Networks, ICCCN, pp. 1–5. IEEE (2011),
  15. 15.
    Mokhtar, S.B., Mcnamara, L., Capra, L.: A Middleware Service for Pervasive Social Networking. In: Proceedings of the International Workshop on Middleware for Pervasive Mobile and Embedded Computing, pp. 1–6 (2009)Google Scholar
  16. 16.
    Scholz, C., Atzmueller, M., Stumme, G.: New Insights and Methods for Predicting Face-to-Face Contacts. In: 7th Intl. AAAI Conference on Weblogs and Social Media (2013),
  17. 17.
    Schuster, D., Rosi, A., Mamei, M., Springer, T., Endler, M., Zambonelli, F.: Pervasive Social Context - Taxonomy and Survey. ACM Transactions on Intelligent Systems and Technology 9(4) (2012)Google Scholar
  18. 18.
    Tran, M.H., Han, J., Colman, A.: Social context: Supporting interaction awareness in ubiquitous environments. In: MobiQuitous, vol. 9, pp. 1–10. IEEE (2009),

Copyright information

© Springer International Publishing 2013

Authors and Affiliations

  • Håkan Jonsson
    • 1
  • Pierre Nugues
    • 1
  1. 1.Lund University, LTHLundSweden

Personalised recommendations