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Mobile Context Provider for Social Networking

  • André C. Santos
  • João M. P. Cardoso
  • Diogo R. Ferreira
  • Pedro C. Diniz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5872)

Abstract

The ability to infer user context based on a mobile device together with a set of external sensors opens up the way to new context-aware services and applications. In this paper, we describe a mobile context provider that makes use of sensors available in a smartphone as well as sensors externally connected via bluetooth. We describe the system architecture from sensor data acquisition to feature extraction, context inference and the publication of context information to well-known social networking services such as Twitter and Hi5. In the current prototype, context inference is based on decision trees, but the middleware allows the integration of other inference engines. Experimental results suggest that the proposed solution is a promising approach to provide user context to both local and network-level services.

Keywords

Sensor Node Mobile Device Sensor Reading Social Networking Service User Context 
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.

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References

  1. 1.
    Kawahara, Y., Kurasawa, H., Morikawa, H.: Recognizing user context using mobile handsets with acceleration sensors. In: IEEE Intl. Conf. on Portable Information Devices (PORTABLE 2007), pp. 1–5 (2007)Google Scholar
  2. 2.
    Welbourne, E., Lester, J., LaMarca, A., Borriello, G.: Mobile context inference using low-cost sensors. In: Strang, T., Linnhoff-Popien, C. (eds.) LoCA 2005. LNCS, vol. 3479, pp. 254–263. Springer, Heidelberg (2005)Google Scholar
  3. 3.
    Santos, A.C., Tarrataca, L., Cardoso, J.M.P., Ferreira, D.R., Diniz, P.C., Chainho, P.: Context inference for mobile applications in the UPCASE project. In: Proc. 2nd Intl. Conf. on Mobile Wireless Middleware, Operating Systems, and Applications (MOBILWARE 2009). LNICST, vol. 7, pp. 352–365. Springer, Heidelberg (2009)Google Scholar
  4. 4.
    Coutaz, J., Crowley, J.L., Dobson, S., Garlan, D.: Context is Key. Communications of the ACM 48(3), 49–53 (2005)CrossRefGoogle Scholar
  5. 5.
    Hull, R., Neaves, P., Bedford-Roberts, J.: Towards situated computing. In: Proc. of the Intl. Conf. on Wearable Computers (ISWC 1997), pp. 146–153 (1997)Google Scholar
  6. 6.
    Cheverst, K., Davies, N., Mitchell, K., Friday, A.: Experiences of Developing and Deploying a Context-Aware Tourist Guide: The GUIDE Project. In: Proc. 6th Annual Intl. Conf. on Mobile Computing and Networking, pp. 20–31. ACM, New York (2000)CrossRefGoogle Scholar
  7. 7.
    Abowd, G., Atkeson, C., Hong, J., Long, S., Kooper, R., Pinkerton, M.: Cyberguide: A Mobile Context-Aware Tour Guide. In: Proc. of the Intl. Conf. on Mobile Computing and Networking (MobiCom 1996), pp. 421–433 (1996)Google Scholar
  8. 8.
    Laerhoven, K.V.: Combining the kohonen self-organizing map and k-means for on-line classification of sensor data. In: Artificial Neural Networks (ICANN 2001), pp. 464–470 (2001)Google Scholar
  9. 9.
    Laerhoven, K.V., Cakmakci, O.: What shall we teach our pants. In: Proc. Fourth Intl Symp. Wearable Computers, ISWC 2000 (2000)Google Scholar
  10. 10.
    Randall, C., Muller, H.: Context awareness by analyzing accelerometer data. In: Proc. 4th Intl Symp. on Wearable Computers (ISWC 2000), October 2000, pp. 175–176 (2000)Google Scholar
  11. 11.
    Skaff, S., Choset, H., Rizzi, A.: Context identification for efficient multiple-model state estimation. In: Proc. of the IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS), San Diego, CA, USA, October 2007, pp. 2435–2440 (2007)Google Scholar
  12. 12.
    Krause, A., Smailagic, A., Siewiorek, D.: Context-aware mobile computing: Learning context-dependent personal preferences from a wearable sensor array. IEEE Trans. on Mobile Computing 5(2) (February 2006)Google Scholar
  13. 13.
    Healey, J., Logan, B.: Wearable wellness monitoring using ecg and accelerometer data. In: Proc. of the Ninth IEEE Intl. Symp. on Wearable Computers (ISWC 2005), pp. 220–221. IEEE Computer Society Press, Los Alamitos (2005)Google Scholar
  14. 14.
    Si, H., Kawahara, Y., Kurasawa, H., Morikawa, H., Aoyama, T.: A context-aware collaborative filtering algorithm for real world oriented content delivery service. In: Proc. of ubiPCMM (2005)Google Scholar
  15. 15.
    Himberg, J., Korpiaho, K., Mannila, H., Tikanmäki, J., Toivonen, H.: Time series segmentation for context recognition in mobile devices. In: Proc. of the 2001 IEEE Intl. Conf. on Data Mining (CDM 2001), Washington, DC, pp. 203–210. IEEE Computer Society Press, Los Alamitos (2001)CrossRefGoogle Scholar
  16. 16.
    Joly, A., Maret, P., Daigremont, J.: Context-awareness, the missing block of social networking. International Journal of Computer Science and Applications 4(2) (February 2009)Google Scholar
  17. 17.
    Quinlan, J.: Induction of Decision Trees. Machine Learning 1(1), 81–106 (1986)Google Scholar
  18. 18.
    Quinlan, J.: C4.5: Programs for Machine Learning. Morgan Kauffman, San Francisco (1993)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • André C. Santos
    • 1
  • João M. P. Cardoso
    • 2
  • Diogo R. Ferreira
    • 1
  • Pedro C. Diniz
    • 1
  1. 1.ISTTechnical University of LisbonPortugal
  2. 2.Faculty of EngineeringUniversity of PortoPortugal

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