Goal-Oriented Opportunistic Sensor Clouds

  • Marc Kurz
  • Gerold Hölzl
  • Alois Ferscha
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7566)


Activity- and context-aware systems, as they are known, established, and well evaluated in small-scale laboratory settings for years and decades, suffer from the fact, that they are limited concerning the underlying data delivering entities. The sensor systems are usually attached on the body, on objects, or in the environment, directly surrounding persons or groups whose activities or contextual information has to be detected. For sensors that are exploited in this kind of systems, it is essential that their modalities, positions and technical details are initially defined to ensure a stable and accurate system execution. In contrast to that, opportunistic sensing allows for selecting and utilizing sensors, as they happen to be accessible according to their spontaneous availability, without presumably defining the input modalities, on a goal-oriented principle. One major benefit thereby is the capability of utilizing sensors of different kinds and modalities, even immaterial sources of information like webservices, by abstracting low-level access details. This emerges the need to roll out the data federating entity as decentralized collecting point. Cloud-based technologies enable space- and time-free utilization of a vast amount of heterogeneous sensor devices reaching from simple physical devices (e.g., GPS, accelerometers, as they are conventionally included on today’s smart phones) to social media sensors, like Facebook, Twitter, or LinkedIn. This paper presents an opportunistic, cloud-based approach for large-scale activity- and context-recognition.


Wireless Sensor Network Cloud Computing Sensor Data Activity Recognition Human Activity Recognition 
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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  1. 1.
    Aarts, E., Wichert, R.: Ambient intelligence. In: Bullinger, H.J. (ed.) Technology Guide, pp. 244–249. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  2. 2.
    Armbrust, M., Fox, A., Griffith, R., Joseph, A.D., Katz, R., Konwinski, A., Lee, G., Patterson, D., Rabkin, A., Stoica, I., Zaharia, M.: A view of cloud computing. Commun. ACM 53(4), 50–58 (2010), CrossRefGoogle Scholar
  3. 3.
    Bao, L., Intille, S.: Activity Recognition from User-Annotated Acceleration Data. In: Ferscha, A., Mattern, F. (eds.) PERVASIVE 2004. LNCS, vol. 3001, pp. 1–17. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  4. 4.
    Benini, L., Farella, E., Guiducci, C.: Wireless sensor networks: Enabling technology for ambient intelligence. Microelectronics Journal 37(12), 1639–1649 (2006), CrossRefGoogle Scholar
  5. 5.
    Blunck, H., Godsk, T., Grønbaek, K., Kjaergaard, M.B., Jensen, J.L., Scharling, T., Schougaard, K.R., Toftkjaer, T.: Perpos: a platform providing cloud services for pervasive positioning. In: Proceedings of the 1st International Conference and Exhibition on Computing for Geospatial Research & Application, COM.Geo 2010, pp. pp. 11:1–11:8. ACM, New York (2010),
  6. 6.
    Calatroni, A., Roggen, D., Tröster, G.: Automatic transfer of activity recognition capabilities between body-worn motion sensors: Training newcomers to recognize locomotion. In: Eighth International Conference on Networked Sensing Systems (INSS 2011), Penghu, Taiwan (June 2011)Google Scholar
  7. 7.
    Chavarriaga, R., Sagha, H., del Millán, J.R.: Ensemble creation and reconfiguration for activity recognition: An information theoretic approach. In: IEEE Int. Conf. Systems, Man, and Cybernetics (IEEE SMC 2011) (2011)Google Scholar
  8. 8.
    Chavarriaga Lozano, R., Bayati, H., del Millán, J.R.: Unsupervised adaptation for acceleration-based activity recognition: Robustness to sensor displacement and rotation. Personal and Ubiquitous Computing (2012)Google Scholar
  9. 9.
    Corredor, I., Martínez, J.F., Familiar, M.S.: Bringing pervasive embedded networks to the service cloud: A lightweight middleware approach. Journal of Systems Architecture 57(10), 916–933 (2011), CrossRefGoogle Scholar
  10. 10.
    Dash, S.K., Sahoo, J.P., Mohapatra, S., Pati, S.P.: Sensor-Cloud: Assimilation of Wireless Sensor Network and the Cloud. In: Meghanathan, N., Chaki, N., Nagamalai, D., Akan, O., Bellavista, P., Cao, J., Dressler, F., Ferrari, D., Gerla, M., Kobayashi, H., Palazzo, S., Sahni, S., Shen, X.S., Stan, M., Xiaohua, J., Zomaya, A., Coulson, G. (eds.) CCSIT 2012, Part I. LNICST, vol. 84, pp. 455–464. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  11. 11.
    Győrbíró, N., Fábián, Á., Hományi, G.: An activity recognition system for mobile phones. Mobile Networks and Applications 14, 82–91 (2009),, doi:10.1007/s11036-008-0112-yCrossRefGoogle Scholar
  12. 12.
    Hassan, M.M., Song, B., Huh, E.N.: A framework of sensor-cloud integration opportunities and challenges. In: Proceedings of the 3rd International Conference on Ubiquitous Information Management and Communication, ICUIMC 2009, pp. 618–626. ACM, New York (2009), Google Scholar
  13. 13.
    Hölzl, G., Kurz, M., Ferscha, A.: Goal oriented opportunistic recognition of high-level composed activities using dynamically configured hidden markov models. In: The 3rd International Conference on Ambient Systems, Networks and Technologies (ANT 2012) (August 2012)Google Scholar
  14. 14.
    Kapadia, A., Myers, S., Wang, X., Fox, G.: Secure cloud computing with brokered trusted sensor networks. In: 2010 International Symposium on Collaborative Technologies and Systems (CTS), pp. 581–592 (May 2010)Google Scholar
  15. 15.
    van Kasteren, T., Englebienne, G., Kröse, B.: An activity monitoring system for elderly care using generative and discriminative models. Personal and Ubiquitous Computing 14, 489–498 (2010),, doi:10.1007/s00779-009-0277-9CrossRefGoogle Scholar
  16. 16.
    van Kasteren, T., Noulas, A., Englebienne, G., Kröse, B.: Accurate activity recognition in a home setting. In: Proceedings of the 10th International Conference on Ubiquitous Computing, UbiComp 2008, pp. 1–9. ACM, New York (2008), CrossRefGoogle Scholar
  17. 17.
    Kurschl, W., Beer, W.: Combining cloud computing and wireless sensor networks. In: Proceedings of the 11th International Conference on Information Integration and Web-based Applications & Services, iiWAS 2009, pp. 512–518. ACM, New York (2009), Google Scholar
  18. 18.
    Kurz, M., Ferscha, A.: Sensor Abstractions for Opportunistic Activity and Context Recognition Systems. In: Lukowicz, P., Kunze, K., Kortuem, G. (eds.) EuroSSC 2010. LNCS, vol. 6446, pp. 135–148. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  19. 19.
    Kurz, M., Hölzl, G., Ferscha, A.: Dynamic adaptation of opportunistic sensor configurations for continuous and accurate activity recognition. In: Fourth International Conference on Adaptive and Self-Adaptive Systems and Applications (ADAPTIVE 2012), Nice, France, July 22-27 (2012)Google Scholar
  20. 20.
    Kurz, M., Hölzl, G., Ferscha, A., Calatroni, A., Roggen, D., Troester, G.: Real-time transfer and evaluation of activity recognition capabilities in an opportunistic system. In: Third International Conference on Adaptive and Self-Adaptive Systems and Applications (ADAPTIVE 2011), Rome, Italy, September 25-30, pp. 73–78 (2011)Google Scholar
  21. 21.
    Kurz, M., Hölzl, G., Ferscha, A., Calatroni, A., Roggen, D., Tröster, G., Sagha, H., Chavarriaga, R., del Millán, J.R., Bannach, D., Kunze, K., Lukowicz, P.: The opportunity framework and data processing ecosystem for opportunistic activity and context recognition. International Journal of Sensors, Wireless Communications and Control, Special Issue on Autonomic and Opportunistic Communications 1 (December 2011)Google Scholar
  22. 22.
    Kurz, M., Hölzl, G., Ferscha, A., Sagha, H., del Millán, J.R., Chavarriaga, R.: Dynamic quantification of activity recognition capabilities in opportunistic systems. In: Fourth Conference on Context Awareness for Proactive Systems: CAPS 2011, Budapest, Hungary, May 15-16 (2011)Google Scholar
  23. 23.
    Kwapisz, J.R., Weiss, G.M., Moore, S.A.: Activity recognition using cell phone accelerometers. SIGKDD Explor. Newsl. 12(2), 74–82 (2011), CrossRefGoogle Scholar
  24. 24.
    Logan, B., Healey, J., Philipose, M., Tapia, E.M., Intille, S.: A Long-Term Evaluation of Sensing Modalities for Activity Recognition. In: Krumm, J., Abowd, G.D., Seneviratne, A., Strang, T. (eds.) UbiComp 2007. LNCS, vol. 4717, pp. 483–500. Springer, Heidelberg (2007), CrossRefGoogle Scholar
  25. 25.
    Poolsappasit, N., Kumar, V., Madria, S., Chellappan, S.: Challenges in Secure Sensor-Cloud Computing. In: Jonker, W., Petković, M. (eds.) SDM 2011. LNCS, vol. 6933, pp. 70–84. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  26. 26.
    Ravi, N., Dandekar, N., Mysore, P., Littman, M.L.: Activity recognition from accelerometer data. In: Proceedings of the 17th Conference on Innovative Applications of Artificial Intelligence, IAAI 2005, vol. 3, pp. 1541–1546. AAAI Press (2005),
  27. 27.
    Roggen, D., Bächlin, M., Schumm, J., Holleczek, T., Lombriser, C., Tröster, G., Widmer, L., Majoe, D., Gutknecht, J.: An educational and research kit for activity and context recognition from on-body sensors. In: 2010 International Conference on Body Sensor Networks (BSN), pp. 277–282 (June 2010)Google Scholar
  28. 28.
    Roggen, D., Magnenat, S., Waibel, M., Tröster, G.: Wearable computing. IEEE Robotics Automation Magazine 18(2), 83–95 (2011)CrossRefGoogle Scholar
  29. 29.
    Roggen, D., Calatroni, A., Rossi, M., Holleczek, T., Förster, K., Tröster, G., Lukowicz, P., Bannach, D., Pirkl, G., Ferscha, A., Doppler, J., Holzmann, C., Kurz, M., Holl, G., Chavarriaga, R., Creatura, M., del Millán, J.R.: Collecting complex activity data sets in highly rich networked sensor environments. In: Proceedings of the Seventh International Conference on Networked Sensing Systems (INSS), Kassel, Germany. IEEE Computer Society Press (June 2010)Google Scholar
  30. 30.
    Roggen, D., Förster, K., Calatroni, A., Holleczek, T., Fang, Y., Troester, G., Lukowicz, P., Pirkl, G., Bannach, D., Kunze, K., Ferscha, A., Holzmann, C., Riener, A., Chavarriaga, R., del Millán, J.R.: Opportunity: Towards opportunistic activity and context recognition systems. In: Proceedings of the 3rd IEEE WoWMoM Workshop on Autonomic and Opportunistic Communications (AOC 2009), IEEE CS Press, Kos (2009)Google Scholar
  31. 31.
    Rolim, C., Koch, F., Westphall, C., Werner, J., Fracalossi, A., Salvador, G.: A cloud computing solution for patient’s data collection in health care institutions. In: Second International Conference on eHealth, Telemedicine, and Social Medicine, ETELEMED 2010, pp. 95–99 (February 2010)Google Scholar
  32. 32.
    Tapia, E., Intille, S., Larson, K., Ferscha, A., Mattern, F.: Activity Recognition in the Home Using Simple and Ubiquitous Sensors. In: Ferscha, A., Mattern, F. (eds.) PERVASIVE 2004. LNCS, vol. 3001, pp. 158–175. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  33. 33.
    Tognetti, A., Carbonaro, N., Zupone, G., De Rossi, D.: Characterization of a novel data glove based on textile integrated sensors. In: 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2006, August 30-September 3, pp. 2510–2513 (2006)Google Scholar
  34. 34.
    Ward, J., Lukowicz, P., Tröster, G., Starner, T.: Activity recognition of assembly tasks using body-worn microphones and accelerometers. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(10), 1553–1567 (2006)CrossRefGoogle Scholar
  35. 35.
    Ward, J.A., Lukowicz, P., Gellersen, H.W.: Performance metrics for activity recognition. ACM Trans. Intell. Syst. Technol. 2(1), 6:1–6:23 (2011), CrossRefGoogle Scholar
  36. 36.
    Weiss, A.: Computing in the clouds. Networker 11(4), 16–25 (2007), CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Marc Kurz
    • 1
  • Gerold Hölzl
    • 1
  • Alois Ferscha
    • 1
  1. 1.Institute for Pervasive ComputingJohannes Kepler University of LinzLinzAustria

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