IoT-O, a Core-Domain IoT Ontology to Represent Connected Devices Networks

  • Nicolas Seydoux
  • Khalil Drira
  • Nathalie Hernandez
  • Thierry Monteil
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10024)

Abstract

Smart objects are now present in our everyday lives, and the Internet of Things is expanding both in number of devices and in volume of produced data. These devices are deployed in dynamic ecosystems, with spatial mobility constraints, intermittent network availability depending on many parameters (e.g. battery level or duty cycle), etc. To capture knowledge describing such evolving systems, open, shared and dynamic knowledge representations are required. These representations should also have the ability to adapt over time to the changing state of the world. That is why we propose IoT-O, a core-domain modular IoT ontology proposing a vocabulary to describe connected devices and their relation with their environment. First, existing IoT ontologies are described and compared to requirements an IoT ontology should be compliant with. Then, after a detailed description of its modules, IoT-O is instantiated in a home automation use case to illustrate how it supports the description of evolving systems.

References

  1. 1.
    Ganz, F., Puschmann, D., Barnaghi, P., Carrez, F.: A practical evaluation of information processing and abstraction techniques for the internet of things. IEEE Internet Things J. 2(4), 340–354 (2015)CrossRefGoogle Scholar
  2. 2.
    Gyrard, A., Serrano, M., Atemezing, G.A.: Semantic web methodologies, best practices and ontology engineering applied to Internet of Things. In: IEEE 2nd World Forum on Internet of Things (WF-IoT), pp. 412–417. IEEE (2015)Google Scholar
  3. 3.
    Murdock, P.: White paper: semantic interoperability for the web of things (2016)Google Scholar
  4. 4.
    Alaya, M.B., Medjiah, S., Monteil, T., Drira, K.: Toward semantic interoperability in oneM2M architecture. IEEE Commun. Mag. 53(12), 35–41 (2015)CrossRefGoogle Scholar
  5. 5.
    Lemaignan, S.: Grounding the interaction: knowledge management for interactive robots. Ph.D. thesis (2012)Google Scholar
  6. 6.
    del Carmen Suarez de Figueroa Baonza, M.: NeOn methodology for building ontology networks: specification, sheduling and reuse. Ph.D. thesis (2010)Google Scholar
  7. 7.
    Aquin, M.: Modularizing ontologies. In: Suárez-Figueroa, M.C., Gómez-Pérez, A., Motta, E., Gangemi, A. (eds.) Ontology Engineering in a Networked World, pp. 213–233. Springer, Heidelberg (2012)Google Scholar
  8. 8.
    Gangemi, A.: Ontology design patterns for semantic web content. In: Gil, Y., Motta, E., Benjamins, V.R., Musen, M.A. (eds.) ISWC 2005. LNCS, vol. 3729, pp. 262–276. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  9. 9.
    Scharffe, F., Euzenat, J., Fensel, D.: Towards design patterns for ontology alignment. In: Proceedings of the 2008 ACM Symposium on Applied Computing - SAC 2008, p. 2321. ACM Press, New York, March 2008Google Scholar
  10. 10.
    Henson, C., Sheth, A., Thirunarayan, K.: Semantic perception: converting sensory observations to abstractions. IEEE Internet Comput. 16(2), 26–34 (2012)CrossRefGoogle Scholar
  11. 11.
    Chatzigiannakis, I., Hasemann, H., Karnstedt, M., Kleine, O., Kröller, A., Leggieri, M., Pfisterer, D., Römer, K., Truong, C.: True self-configuration for the loT. In: 3rd International Conference on the Internet of Things (IOT) (2012)Google Scholar
  12. 12.
    Han, S.N., Lee, G.M., Crespi, N.: Towards automated service composition using policy ontology in building automation system. In: 2012 IEEE Ninth International Conference on Services Computing, pp. 685–686 (2012)Google Scholar
  13. 13.
    Janowicz, K., Compton, M.: The stimulus-sensor-observation ontology design pattern and its integration into the semantic sensor network ontology. In: Proceedings of the 9th International Semantic Web Conference, 3rd International Workshop on Semantic Sensor Networks, pp. 7–11 (2010)Google Scholar
  14. 14.
    Kephart, J., Chess, D.: The vision of autonomic computing. Computer 36(1), 41–50 (2003)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Le-Phuoc, D., Quoc, H., Parreira, J.X., Hauswirth, M.: The linked sensor middleware-connecting the real world and the semantic web. In: Semantic Web Challenge 2011. Number April 2005, pp. 1–8 (2011)Google Scholar
  16. 16.
    Kopecký, J., Vitvar, T., Bournez, C., Farrell, J.: SAWSDL: semantic annotations for WSDL and XML schema. IEEE Internet Comput. 11(6), 60–67 (2007)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Nicolas Seydoux
    • 1
    • 2
    • 3
  • Khalil Drira
    • 2
    • 3
  • Nathalie Hernandez
    • 1
  • Thierry Monteil
    • 2
    • 3
  1. 1.IRIT Maison de la RechercheUniversity of Toulouse Jean JaurèsToulouseFrance
  2. 2.CNRS, LAASToulouseFrance
  3. 3.Univ de Toulouse, INSA, LAASToulouseFrance

Personalised recommendations