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Designing Smart Environments: A Paradigm Based on Learning and Prediction

  • Sajal K. Das
  • Diane J. Cook
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3776)

Abstract

We propose a learning and prediction based paradigm for designing smart home environments. The foundation of this paradigm lies in information theory as it manages uncertainties of the inhabitants’ contexts (e.g., locations or activities) in daily lives. The idea is to build compressed dictionaries of context-aware data collected from sensors and devices monitoring and/or controlling the smart environment, efficiently learn from these profiles, and finally predict inhabitant’s future contexts. Successful prediction helps automate device control operations and tasks within the environment as well as to identify anomalies. Thus, the learning and prediction based paradigm optimizes such goal functions of smart environments as minimizing maintenance cost, manual interactions and energy utilization. After identifying important features of smart environments, we present an overview of our MavHome architecture and apply the proposed paradigm to the inhabitant’s location and activity tracking and prediction, and automated decision-making capability.

Keywords

Smart Home Pervasive Computing Entropy Rate Smart Environment Mobility Process 
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.

References

  1. 1.
    Abowd, G.D.: Classroom 2000: An Experiment with the Instrumentation of a Living educational Environment. IBM Systems Journal 38(4), 508–530 (1999)CrossRefGoogle Scholar
  2. 2.
    Bhattacharya, A., Das, S.K.: LeZi-Update: An Information-Theoretic Approach for Personal Mobility Tracking in PCS Networks. Wireless Networks 8, 121–135 (2002)zbMATHCrossRefGoogle Scholar
  3. 3.
    Bobick, A., et al.: The KidsRoom: A Perceptually-Based Interactive and Immersive Story Environment. Presence 8(4), 369–393 (1999)CrossRefGoogle Scholar
  4. 4.
    Brumitt, B., et al.: Ubiquitous Computing and the Role of Geometry.  IEEE Personal Communications 7(5), 41–43 (2000)CrossRefGoogle Scholar
  5. 5.
  6. 6.
    Cleary, J.G., Witten, I.H.: Data Compression Using Adaptive Coding and Partial String Matching. IEEE Transactions on Communications 32(4), 396–402 (1984)CrossRefGoogle Scholar
  7. 7.
    Cook, D.J., Das, S.K.: Smart Environments: Technology, Protocols, and Applications. Wiley, Chichester (2005)Google Scholar
  8. 8.
    Cover, T.M., Thomas, J.A.: Elements of Information Theory. Wiley, Chichester (1991)zbMATHCrossRefGoogle Scholar
  9. 9.
    Das, S.K., Cook, D.J., et al.: The Role of Prediction Algorithms in the MavHome Smart Home Architecture. IEEE Wireless Communications 9(6), 77–84 (2002)CrossRefGoogle Scholar
  10. 10.
    Das, S.K., Cook, D.J.: Agent Based Health Monitoring in Smart Homes. In: Proc Int. Conf. on Smart Homes and Health Telematics (ICOST), Singapore (Keynote Talk) (September 2004)Google Scholar
  11. 11.
    Das, S.K., Rose, C.: Coping with Uncertainty in Wireless Mobile Networks. In: Proc. of IEEE Personal, Indoor and Mobile Radio Communications, Barcelona, Spain (September 2004) (Invited Talk)Google Scholar
  12. 12.
  13. 13.
    Fox, A., Johanson, B., Hanrahan, P., Winograd, T.: Integrating Information Appliances into an Interactive Space. IEEE Computer Graphics and Applications 20(3), 54–65 (2000)CrossRefGoogle Scholar
  14. 14.
  15. 15.
    Gopalratnam, K., Cook, D.J.: Online Sequential Prediction via Incremental Parsing: The Active LeZi Algorithm. IEEE Intelligent Systems (2005)Google Scholar
  16. 16.
    Gopalratnam, K., Cook, D.J.: Active LeZi: An Incremental Parsing Algorithm for Sequential Prediction. International Journal of Artificial Intelligence Tools 14(1-2) (2004)Google Scholar
  17. 17.
    Helal, S., et al.: Enabling Location-Aware Pervasive Computing Applications for the Elderly. In: Proc. of IEEE Int. Conf. on Pervasive Computing and Communications (PerCom 2003), March 2003, pp. 531–538 (2003)Google Scholar
  18. 18.
    Heierman, E., Youngblood, M., Cook, D.J.: Mining Temporal Sequences to Discover Interesting Patterns. In: KDD Workshop on Mining Temporal and Sequential Data (2004)Google Scholar
  19. 19.
    Heierman, E., Cook, D.J.: Improving Home Automation by Discovering Regularly Occurring Device Usage Patterns. In: Proc. of International Conf. on Data Mining (2003)Google Scholar
  20. 20.
  21. 21.
    Kidd, C., et al.: The Aware Home: A Living Laboratory for Ubiquitous Computing. In: Proceedings of the Second International Workshop on Cooperative Buildings (1999)Google Scholar
  22. 22.
    Le Gal, C., Martin, J., Lux, A., Crowley, J.L.: Smart Office: Design of an Intelligent Environment. IEEE Intelligent Systems 16(4) (July-August 2001)Google Scholar
  23. 23.
    Misra, A., Roy, A., Das, S.K.: An Information Theoretic Framework for Optimal Location Tracking in Multi-System 4G Wireless Networks. In: Proc. IEEE INFOCOM (2004)Google Scholar
  24. 24.
    Misra, A., Das, S.K.: Location Estimation (Determination and Prediction) Techniques in Smart Environments. In: Cook, D.J., Das, S.K. (eds.) Smart Environments, ch. 8, pp. 193–228. Wiley, Chichester (2005)Google Scholar
  25. 25.
    Mozer, M.: The Neural Network House: An Environment that Adapts to its Inhabitants. In: Proc. of the AAAI Spring Symposium on Intelligent Environments (1998)Google Scholar
  26. 26.
    Rissanen, J.: Stochastic Complexity in Statistical Inquiry. World Scientific, Singapore (1989)zbMATHGoogle Scholar
  27. 27.
    Roy, A., Das, S.K., Misra, A.: Exploiting Information Theory for Adaptive Mobility and Resource Management in Future Wireless Cellular Networks. IEEE Wireless Commun. 11(8), 59–64 (2004)CrossRefGoogle Scholar
  28. 28.
    Roy, A., Das, S.K., et al.: Location Aware Resource Management in Smart Homes. In: Proc. IEEE Int. Conf. on Pervasive Computing and Communications, pp. 481–488 (2003)Google Scholar
  29. 29.
  30. 30.
    Srivastava, M.B., et al.: Smart Kindergarten: Sensor-Based Wireless Networks for Smart Problem-Solving Environments. In: Proc. ACM Int. Conf. on Mobile Computing and Networking, Rome (July 2001)Google Scholar
  31. 31.
    Ziv, J., Lempel, A.: Compression of Individual Sequences via Variable Rate Coding. IEEE Transcations on Information Theory 24(5), 530–536 (1978)zbMATHCrossRefMathSciNetGoogle Scholar
  32. 32.
    Youngblood, M., Cook, D.J., Holder, L.B.: Managing Adaptive Versatile Environments. In: Proc. IEEE Int. Conf. on Pervasive Computing and Communications (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Sajal K. Das
    • 1
  • Diane J. Cook
    • 1
  1. 1.Department of Computer Science and EngineeringThe University of Texas at Arlington 

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