A Model for Using Machine Learning in Smart Environments

  • Sakari Stenudd
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7096)


This work presents a model for using machine learning in the adaptive control of smart environments. The model is based on an investigation of the existing works regarding smart environments and an analysis of the machine learning uses within them. Four different categories of machine learning in smart environments were identified: prediction, recognition, detection and optimisation. These categories can be deployed to different phases of a self-adaptive application utilising the adaptation loop structure. The use of machine learning in one phase of the adaptation loop was demonstrated by carrying out an experiment utilising neural networks in the prediction of latencies.


control loop adaptive systems self-adaptive software prediction 


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  1. 1.
    Aztiria, A., Izaguirre, A., Augusto, J.C.: Learning patterns in ambient intelligence environments: a survey. Artificial Intelligence Review 34(1), 35–51 (2010)CrossRefGoogle Scholar
  2. 2.
    Berkhin, P.: A survey of clustering data mining techniques. In: Grouping Multidimensional Data, pp. 25–71. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  3. 3.
    Cook, D.J., Das, S.K.: Smart Environments: Technologies, Protocols, and Applications. John Wiley, Hoboken (2005)Google Scholar
  4. 4.
    Cook, D.J., Das, S.K.: How smart are our environments? an updated look at the state of the art. Pervasive and Mobile Computing 3(2), 53–73 (2007)CrossRefGoogle Scholar
  5. 5.
    Das, S.K., Cook, D.J.: Designing Smart Environments: A Paradigm Based on Learning and Prediction. In: Pal, S.K., Bandyopadhyay, S., Biswas, S. (eds.) PReMI 2005. LNCS, vol. 3776, pp. 80–90. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  6. 6.
    Das, S.K., Cook, D.J., Battacharya, A., Heierman III, E.O., Lin, T.Y.: The role of prediction algorithms in the mavhome smart home architecture. IEEE Wireless Communications 9(6), 77–84 (2002)CrossRefGoogle Scholar
  7. 7.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. John Wiley, New York (2001)zbMATHGoogle Scholar
  8. 8.
    Fernandez-Montes, A., Ortega, J.A., Alvarez, J.A., Gonzalez-Abril, L.: Smart environment software reference architecture. In: Fifth International Joint Conference on INC, IMS and IDC, NCM 2009, August 25-27, pp. 397–403 (2009)Google Scholar
  9. 9.
    Hagras, H., Callaghan, V., Colley, M., Clarke, G., Pounds-Cornish, A., Duman, H.: Creating an ambient-intelligence environment using embedded agents. IEEE Intelligent Systems 19(6), 12–20 (2004)CrossRefGoogle Scholar
  10. 10.
    Igel, C., Glasmachers, T., Heidrich-Meisner, V.: Shark. Journal of Machine Learning Research 9, 993–996 (2008)zbMATHGoogle Scholar
  11. 11.
    Igel, C., Hüsken, M.: Empirical evaluation of the improved Rprop learning algorithms. Neurocomputing 50(1), 105–124 (2003)CrossRefzbMATHGoogle Scholar
  12. 12.
    Intille, S.S., Larson, K., Tapia, E.M., Beaudin, J.S., Kaushik, P., Nawyn, J., Rockinson, R.: Using a Live-in Laboratory for Ubiquitous Computing Research. In: Fishkin, K.P., Schiele, B., Nixon, P., Quigley, A. (eds.) PERVASIVE 2006. LNCS, vol. 3968, pp. 349–365. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  13. 13.
    Jakkula, V.R., Crandall, A.S., Cook, D.J.: Enhancing anomaly detection using temporal pattern discovery. In: Advanced Intelligent Environments, pp. 175–194. Springer, US (2009)CrossRefGoogle Scholar
  14. 14.
    Kephart, J.O., Chess, D.M.: The vision of autonomic computing. IEEE Computer 36(1), 41–50 (2003)CrossRefGoogle Scholar
  15. 15.
    Logan, B., Healey, J., Philipose, M., Tapia, E.M., Intille, S.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
  16. 16.
    Mitchell, T.M.: Machine learning. McGraw-Hill, New York (1997)zbMATHGoogle Scholar
  17. 17.
    Mozer, M.C.: The neural network house: An environment that adapts to its inhabitants. In: Proc. AAAI Spring Symposium on Intelligent Environments (1998)Google Scholar
  18. 18.
    Patcha, A., Park, J.M.: An overview of anomaly detection techniques: Existing solutions and latest technological trends. Computer Networks 51(12), 3448–3470 (2007)CrossRefGoogle Scholar
  19. 19.
    Salehie, M., Tahvildari, L.: Self-adaptive software: Landscape and research challenges. ACM Trans. Auton. Adapt. Syst. 4, 14:1–14:42 (2009)Google Scholar
  20. 20.
    Toninelli, A., Pantsar-Syväniemi, S., Bellavista, P., Ovaska, E.: Supporting context awareness in smart environments: a scalable approach to information interoperability. In: Proceedings of the International Workshop on Middleware for Pervasive Mobile and Embedded Computing, pp. 1–4. ACM (2009)Google Scholar
  21. 21.
    Youngblood, G.M., Cook, D.J., Holder, L.B.: Managing adaptive versatile environments. Pervasive and Mobile Computing 1(4), 373–403 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Sakari Stenudd
    • 1
  1. 1.VTT Technical Research Centre of FinlandOuluFinland

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