The Autonomic Computing Paradigm in Adaptive Building / Ambient Intelligence Systems

  • Aliaksei Andrushevich
  • Stephan Tomek
  • Alexander Klapproth
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7040)

Abstract

This work is devoted to the classification and adaptation of current ambient intelligence (AmI) research activities from the viewpoint of the autonomic computing paradigm. Special attention is given to the implementation of AmI’s user-centric focus in autonomic computing.

Keywords

Ambient Intelligence Autonomic Computing self-adaptive system user-centric requirements human-centered design 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Kephart, J.O., Chess, D.M.: The Vision of Autonomic Computing. ACM Computer 36(1), 41–50 (2003)Google Scholar
  2. 2.
    Huebscher, M.C., McCann, J.A.: A Survey of Autonomic Computing\—Degrees, Models, and Applications. ACM Computing Survey 40(3), article 7 (2008)Google Scholar
  3. 3.
    Weiser, M.: The Computer for the 21st Century. ACM SIGMOBILE Mobile Computing and Communications 3(3), 3–11 (1999)CrossRefGoogle Scholar
  4. 4.
    Baldlauf, M., Schahram, D., Rosenberg, F.: A survey on context-aware systems. Journal of Ad Hoc and Ubiquitous Computing 2(4) (June 2007)Google Scholar
  5. 5.
    Kjaer, K.E.: A survey of context-aware middleware. In: Proceedings of the 25th Conference on IASTED International Multi-Conference: Software Engineering. ACTA Press (2007)Google Scholar
  6. 6.
    Bourcier, J., Diaconescu, A., Lalanda, P., McCann, J.A.: AutoHome: an Autonomic Management Framework for Pervasive Home Applications. ACM Trans. Auton. Adapt. Syst. 6(1), article 8, 1(212), 1–9 (2011)Google Scholar
  7. 7.
    Aztina, A., Izaguirre, A., Augusto, J.C.: Learning Patterns in Ambient Intelligence Environments: a Survey. Artificial Intelligence Review 34(1), 35–51 (2010)CrossRefGoogle Scholar
  8. 8.
    Andrushevich, A., Staub, M., Kistler, R., Klapproth, A.: Towards semantic buildings: Goal-driven approach for building automation service allocation and control. In: Proceedings of IEEE Conference on Emerging Technologies and Factory Automation, pp. 1–6 (September 2010)Google Scholar
  9. 9.
    Drumond, L., Girardi, R.: A Survey of Ontology Learning Procedures. In: Proceedings of the 3rd Workshop on Ontologies and Their Applications, SBIA, Brazil (2008)Google Scholar
  10. 10.
    Kim, E., Helal, S., Cook, D.: Human activity recognition and pattern discovery. IEEE Pervasive Computing 9(1), 48–53 (2010)CrossRefGoogle Scholar
  11. 11.
    Hoes, P., Hensen, J.L.M., Loomans, M.G.L.C., de Vries, B., Bourgeois, D.: User behavior in whole building simulation. Energy and Buildings 41(3), 295–302 (2009)CrossRefGoogle Scholar
  12. 12.
    Rashidi, P., Cook, D., Holder, L., Schmitter-Edgecombe, M.: Discovering activities to recognize and track in a smart environment. IEEE Transactions on Knowledge and Data Engineering 23(4), 527–539 (2011)CrossRefGoogle Scholar
  13. 13.
    Riva, G., Vatalaro, F., Davide, F., Alcañiz, M.: Interactive Context-Aware Systems Interacting with Ambient Intelligence. In: Schmidt, A. (ed.) IOS Press (2005)Google Scholar
  14. 14.
    Tazari, M.-R.: Open Distributed Framework for Adaptive User Interaction in Ambient Intelligence. In: de Ruyter, B., Wichert, R., Keyson, D.V., Markopoulos, P., Streitz, N., Divitini, M., Georgantas, N., Mana Gomez, A. (eds.) AmI 2010. LNCS, vol. 6439, pp. 227–238. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  15. 15.
    Wetter, M., Haves, P.: Building Controls Virtual Test Bed, https://gaia.lbl.gov/bcvtb

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Aliaksei Andrushevich
    • 1
  • Stephan Tomek
    • 1
  • Alexander Klapproth
    • 1
  1. 1.CEESAR-iHomeLabLucerne University of Applied Sciences and ArtsHorwSwitzerland

Personalised recommendations