Reasoning on Data Streams: An Approach to Adaptation in Pervasive Systems

  • Nicola Bicocchi
  • Emil Vassev
  • Franco Zambonelli
  • Mike Hinchey
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 144)

Abstract

Urban environments are increasingly invaded by devices that acquire sensor information and pave the way for innovative forms of context awareness. Collecting knowledge from loosely-structured data streams and reasoning about changes are two key elements of the process. This paper illustrates a possible way to combine these two elements in a coordinated way. We make use of a recently-developed framework for classifying data streams with service-oriented, reconfigurable components. Furthermore, we embed the KnowLang Reasoner, allowing logical and statistical reasoning on the acquired knowledge aiming to achieve self-adaptation.

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References

  1. 1.
    Smart cities Ranking of European medium-sized cities, Vienna, Austria (2007). http://tinyurl.com/bqh83np
  2. 2.
    Abeywickrama, D.B., Bicocchi, N., Zambonelli, F.: Sota: towards a general model for self-adaptive systems. In: Reddy, S., Drira, K. (eds.) WETICE, pp. 48–53. IEEE Computer Society (2012)Google Scholar
  3. 3.
    Bicocchi, N., Cecaj, A., Fontana, D., Mamei, M., Sassi, A., Zambonelli, F.: Collective awareness for human-ict collaboration in smart cities. In: WETICE, pp. 3–8 (2013)Google Scholar
  4. 4.
    Bicocchi, N., Fontana, D., Zambonelli, F.: A self-aware, reconfigurable architecture for context awareness. In: IEEE Symposium on Computers and Communications, Madeira, Portugal (2014)Google Scholar
  5. 5.
    Bicocchi, N., Lasagni, M., Zambonelli, F.: Bridging vision and commonsense for multimodal situation recognition in pervasive systems. In: International Conference on Pervasive Computing and Communications, Lugano, Switzerland (2012)Google Scholar
  6. 6.
    Kehoe, M., et al.: Understanding ibm smart cities. Redbook Series (2011)Google Scholar
  7. 7.
    Khan, W.Z., Xiang, Y., Aalsalem, M.Y., Arshad, Q.: Mobile phone sensing systems: A survey. IEEE Communication Survey and Tutorials 15, 402–427 (2013)CrossRefGoogle Scholar
  8. 8.
    Neapolitan, R.: Learning Bayesian Networks. Prentice Hall (2003)Google Scholar
  9. 9.
    Vassev, E.: KnowLang Grammar in BNF. Tech. Rep. Lero-TR-2012-04, Lero, University of Limerick, Ireland (2012)Google Scholar
  10. 10.
    Vassev, E., Hinchey, M.: Knowledge representation for cognitive robotic systems. In: Proceedings of the 15th IEEE International Symposium on Object/Component/Service-Oriented Real-Time Distributed Computing Workshops (ISCORCW 2012), pp. 156–163. IEEE Computer Society (2012)Google Scholar
  11. 11.
    Vassev, E., Hinchey, M., Gaudin, B.: Knowledge representation for self-adaptive behavior. In: Proceedings of C* Conference on Computer Science and Software Engineering (C3S2E 2012), pp. 113–117. ACM (2012)Google Scholar
  12. 12.
    Ye, J., Dobson, S., McKeever, S.: Situation identification techniques in pervasive computing: A review. Pervasive and Mobile Computing 8, 33–66 (2012)CrossRefGoogle Scholar

Copyright information

© Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2015

Authors and Affiliations

  • Nicola Bicocchi
    • 1
  • Emil Vassev
    • 2
  • Franco Zambonelli
    • 1
  • Mike Hinchey
    • 2
  1. 1.Universita di Modena e Reggio EmiliaModenaItalia
  2. 2.Lero–the Irish Software Engineering Research CentreUniversity of LimerickLimerickIreland

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