Advertisement

Intelligent and Adaptive Pervasive Future Internet: Smart Cities for the Citizens

  • George Caridakis
  • Georgios Siolas
  • Phivos Mylonas
  • Stefanos Kollias
  • Andreas Stafylopatis
Part of the Communications in Computer and Information Science book series (CCIS, volume 384)

Abstract

Current article discusses the human centered perspective adopted in the European project SandS within the Internet of Things (IoT) framework. SandS is a complete ecosystem of users within a social network developing a collective intelligence and adapting its operation through appropriately processed feedback. In the research work discussed in this paper we will investigate SandS from the user perspective and how users can be modeled through a number of fuzzy knowledge formalism through stereotypical user profiles. Additionally, context modeling in pervasive computing systems and especially in the SandS smart home paradigm is examined through appropriate representation of context cues during overall interaction.

Keywords

Smart Cities Smart Homes Intelligent Systems User Modeling Context Aware Services Future Internet 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
  2. 2.
    Amershi, S., Conati, C.: Unsupervised and supervised machine learning in user modeling for intelligent learning environments. In: Proceedings of the 12th International Conference on Intelligent user Interfaces, pp. 72–81. ACM (2007)Google Scholar
  3. 3.
    Baader, F., Calvanese, D., McGuinness, D.L., Nardi, D., Patel-Schneider, P.F.: The Description Logic Hand-book: Theory, Implementation and Application. Cambridge University Press (2002)Google Scholar
  4. 4.
    Baldauf, M., Dustdar, S., Rosenberg, F.: A survey on context-aware systems. Int. J. Ad Hoc Ubiquitous Comput. 2(4), 263–277 (2007), http://dx.doi.org/10.1504/IJAHUC.2007.014070, doi:10.1504/IJAHUC.2007.014070CrossRefGoogle Scholar
  5. 5.
    Bettini, C., Brdiczka, O., Henricksen, K., Indulska, J., Nicklas, D., Ranganathan, A., Riboni, D.: A survey of context modelling and reasoning techniques. Pervasive and Mobile Computing 6(2), 161–180 (2010), http://www.sciencedirect.com/science/article/pii/S1574119209000510, doi:10.1016/j.pmcj.2009.06.002; Context Modelling, Reasoning and Management Google Scholar
  6. 6.
    Bolchini, C., Curino, C.A., Quintarelli, E., Schreiber, F.A., Tanca, L.: A data-oriented survey of context models. SIGMOD Rec. 36(4), 19–26 (2007), http://doi.acm.org/10.1145/1361348.1361353, doi:10.1145/1361348.1361353CrossRefGoogle Scholar
  7. 7.
  8. 8.
    Castells, P., Fernandez, M., Vallet, D.: An Adaptation of the Vector-Space Model for Ontology-Based Information Retrieval. IEEE Transactions on Knowledge and Data Engineering 19(2) (February 2007); Special issue on Knowledge and Data Engineering in the Semantic Web EraGoogle Scholar
  9. 9.
    Castells, P., Fernández, M., Vallet, D., Mylonas, P., Avrithis, Y.: Self-tuning Personalized Information Retrieval in an Ontology-Based Framework. In: Meersman, R., Tari, Z. (eds.) OTM 2005 Workshops. LNCS, vol. 3762, pp. 977–986. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  10. 10.
    Chen, H.: An Intelligent Broker Architecture for Pervasive Context-Aware Systems. PhD thesis, University of Maryland, Baltimore County (2004)Google Scholar
  11. 11.
  12. 12.
  13. 13.
    Crew cognitive radio experimentation world, http://www.crew-project.eu/
  14. 14.
    Dey, A.K.: Understanding and using context. Personal and Ubiquitous Computing 5, 4–7 (2001)CrossRefGoogle Scholar
  15. 15.
  16. 16.
    Gauch, S., Chaffee, J., Pretschner, A.: Ontology-Based Personalized Search and Browsing. Web Intelligence and Agent Systems 1(3-4), 219–234 (2004)Google Scholar
  17. 17.
    Gruber, T.R.: A Translation Approach to Portable Ontology Specification. Knowledge Acquisition 5, 199–220 (1993)CrossRefGoogle Scholar
  18. 18.
    Henricksen, K., Indulska, J., Rakotonirainy, A.: Modeling context information in pervasive computing systems. In: Mattern, F., Naghshineh, M. (eds.) PERVASIVE 2002. LNCS, vol. 2414, p. 167. Springer, Heidelberg (2002), http://dx.doi.org/10.1007/3-540-45866-2_14 CrossRefGoogle Scholar
  19. 19.
  20. 20.
    Hong, J., Suh, E., Kim, S.: Context-aware systems: A literature review and classification. Expert Systems with Applications 36(4), 8509–8522 (2009)CrossRefGoogle Scholar
  21. 21.
    Junior, P., Filgueiras, L.: User modeling with personas. In: Proceedings of the 2005 Latin American Conference on Human-Computer Interaction, pp. 277–282. ACM (2005)Google Scholar
  22. 22.
    Klir, G., Bo, Y.: Fuzzy Sets and Fuzzy Logic, Theory and Applications. Prentice Hall, New Jersey (1995)zbMATHGoogle Scholar
  23. 23.
    Kobsa, A.: Generic user modeling systems. User Modeling and User-Adapted Interaction 11(1), 49–63 (2001)CrossRefzbMATHGoogle Scholar
  24. 24.
    Kiryakov, A., Popov, B., Terziev, I., Manov, D., Ognyanoff, D.: Semantic Annotation, Indexing, and Retrieval. Journal of Web Sematics 2(1), 47–49 (2004)Google Scholar
  25. 25.
    Miyamoto, S.: Fuzzy Sets in Information Retrieval and Cluster Analysis. Kluwer Academic Publishers, Dordrecht (1990)CrossRefzbMATHGoogle Scholar
  26. 26.
  27. 27.
  28. 28.
  29. 29.
    Planetlab europe, http://www.planet-lab.eu/
  30. 30.
    Popov, B., Kiryakov, A., Ognyanoff, D., Manov, D., Kirilov, A.: KIM - A Semantic Platform for Information Extraction and Retrieval. Journal of Natural Language Engineering 10(3-4), 375–392 (2004)CrossRefGoogle Scholar
  31. 31.
  32. 32.
    W3C Recommendation, OWL Web Ontology Language Reference (February 10, 2004), http://www.w3.org/TR/owl-ref/

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • George Caridakis
    • 1
  • Georgios Siolas
    • 1
  • Phivos Mylonas
    • 1
  • Stefanos Kollias
    • 1
  • Andreas Stafylopatis
    • 1
  1. 1.Intelligent Systems, Content and Interaction LaboratoryNational Technical University of AthensGreece

Personalised recommendations