Advertisement

An Empirical Study of the Potential for Context-Aware Power Management

  • Colin Harris
  • Vinny Cahill
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4717)

Abstract

Context-aware power management (CAPM) uses context (e.g., user location) likely to be available in future ubiquitous computing environments, to effectively power manage a building’s energy consuming devices. The objective of CAPM is to minimise overall energy consumption while maintaining user-perceived device performance.

The principal context required by CAPM is when the user is not using and when the user is about to use a device. Accurately inferring this user context is challenging and there is a balance between how much energy additional context can save and how much it will cost energy wise. This paper presents results from a detailed user study that investigated the potential of such CAPM.

The results show that CAPM is a hard problem. It is possible to get within 6% of the optimal policy, but policy performance is very dependent on user behaviour. Furthermore, adding more sensors to improve context inference can actually increase overall energy consumption.

Keywords

Idle Time Face Detection Idle Period Threshold Policy Building Energy Consumption 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    E.U.: Towards a european strategy for the security of energy supply (2000), http://europa.eu.int/comm/energy_transport/en/lpi_lv_en1.html
  2. 2.
    Benini, L., Bogliolo, A., Micheli, G.D.: A survey of design techniques for system-level dynamic power management. IEEE Transactions on Very Large Scale Integration (VLSI) Systems 8(3), 299–316 (2000)CrossRefGoogle Scholar
  3. 3.
    Douglis, F., Krishnan, P., Marsh, B.: Thwarting the power-hungry disk. In: USENIX Winter, pp. 292–306 (1994)Google Scholar
  4. 4.
    Tetri, E.: Profitability of switching off fluorescent lamps: Take-a-break. In: RIGHT LIGHT 4, vol. 1, pp. 113–116 (1997)Google Scholar
  5. 5.
    Harris, C., Cahill, V.: Exploiting user behaviour for context-aware power management. In: International Conference On Wireless and Mobile Computing, Networking and Communications, pp. 122–130. IEEE Computer Society Press, Los Alamitos (2005)Google Scholar
  6. 6.
    Mozer, M.: Adaptive house project, http://www.cs.colorado.edu/~mozer/nnh/
  7. 7.
    Mozer, M.: 12. In: Smart environments: Technologies, protocols, and applications, pp. 273–294. J. Wiley and Sons, Chichester (2004)Google Scholar
  8. 8.
    Oliver, N., Horvitz, E., Garg, A.: Layered representations for human activity recognition. In: Fourth IEEE Int. Conf. on Multimodal Interfaces, pp. 3–8. IEEE Computer Society Press, Los Alamitos (2002)CrossRefGoogle Scholar
  9. 9.
    Oliver, N., Horvitz, E.: S-seer: Selective perception in a multimodal office activity recognition system. In: Multimodal Interaction and Related Machine Learning Algorithms, pp. 122–135 (2004)Google Scholar
  10. 10.
    Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Computer Vision and Pattern Recognition (2001)Google Scholar
  11. 11.
    Rabiner, L., Juang, B.H.: Fundamentals of speech recognition. Prentice-Hall, Inc., Upper Saddle River, NJ, USA (1993)Google Scholar
  12. 12.
    Dietterich, T.: Statistical tests for comparing supervised classification learning algorithms. Technical report, Department of Computer Science, Oregon State University (1996)Google Scholar
  13. 13.
    Spiegelhalter, D.J., Lauritzen, S.L.: Sequential updating of conditional probabilities on directed graphical structures. Networks 20, 579–605 (1990)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Colin Harris
    • 1
  • Vinny Cahill
    • 1
  1. 1.Distributed Systems Group, Department of Computer Science, Trinity College, Dublin 2Ireland

Personalised recommendations