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A Model for Using Machine Learning in Smart Environments

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Grid and Pervasive Computing Workshops (GPC 2011)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 7096))

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Abstract

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.

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Author information

Authors and Affiliations

  1. VTT Technical Research Centre of Finland, P.O. Box 1100, FIN-90571, Oulu, Finland

    Sakari Stenudd

Authors
  1. Sakari Stenudd
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Oulu, 90014, Oulu, Finland

    Mika Rautiainen

  2. Aalto University, Helsinki, Finland

    Timo Korhonen  & Edward Mutafungwa  & 

  3. VTT Technical Research Centre of Finland, Oulu, Finland

    Eila Ovaska , Artem Katasonov , Antti Evesti  & Heikki Ailisto , ,  & 

  4. University of St Andrews, St. Andrews, UK

    Aaron Quigley

  5. Nokia Research Center, Oulu, Finland

    Jonna Häkkilä

  6. Microsoft Research, Cambridge, UK

    Natasa Milic-Frayling

  7. Department of Computer Science and Engineering, University of Oulu, Oulu, Finland

    Jukka Riekki

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© 2012 Springer-Verlag Berlin Heidelberg

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Stenudd, S. (2012). A Model for Using Machine Learning in Smart Environments. In: Rautiainen, M., et al. Grid and Pervasive Computing Workshops. GPC 2011. Lecture Notes in Computer Science, vol 7096. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27916-4_4

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  • DOI: https://doi.org/10.1007/978-3-642-27916-4_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-27915-7

  • Online ISBN: 978-3-642-27916-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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