Context Variation for Service Self-contextualization in Cyber-Physical Systems

  • Alexander Smirnov
  • Kurt Sandkuhl
  • Nikolay Shilov
  • Nikolay Telsya
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 208)

Abstract

Operation and configuration of Cyber-Physical Systems (CPSs) require approaches for managing the variability at design time and the dynamics at runtime caused by a multitude of component types and changing application environments. As a contribution to this area, this paper proposes to integrate concepts for variability management with approaches for self-organization in intelligent systems. Our approach exploits the idea of self-contextualization to autonomously adapt behaviors of multiple services to the current situation. More concrete, we put the “context” of CPS into the conceptual focus of our approach and propose context variants for use in self-contextualization of CPS. The main contributions of this paper are to identify challenges in variability management of CPS based on an industrial case, the integration of context variants into the reference model for self-contextualizing services and an initial validation using a case study.

Keywords

Cyber-physical systems Self-organization Self-contextualization Context variation 

Notes

Acknowledgment

The research was supported partly by projects funded by grants # 14-07-00378, # 14-07-00345, # 14-07-00363 of the Russian Foundation for Basic Research. This work was also partially financially supported by Government of Russian Federation, Grant 074-U01.

References

  1. 1.
    Serugendo, G.D.M., Gleizes, M.-P., Karageorgos, A.: Self-organisation and emergence in MAS: an overview. Informatica 30, 45–54 (2006)MATHGoogle Scholar
  2. 2.
    Preuveneers, D., Berbers, Y.: Internet of things: a context-awareness perspective. In: Yan, L., Zhang, Y., Yang, L.T., Ning, H. (eds.) The Internet of Things: From RFID to the Next-Generation Pervasive Networked Systems, pp. 287–307. Auerbach Publications, Taylor and Francis Group, New York (2008)CrossRefGoogle Scholar
  3. 3.
    Zhang, D., Huang, H., Lai, C.-F., Liang, X., Zou, Q., Guo, M.: Survey on Context-Awareness in Ubiquitous Media. Multimedia Tools Appl. 67, 1–33 (2011)MATHGoogle Scholar
  4. 4.
    Schilit, B.N., Theimer, M.M.: Disseminating active map information to mobile hosts. Network, IEEE 8(5), 22–32 (1994)CrossRefGoogle Scholar
  5. 5.
    Dey, A.K.: Understanding and using context. Pers. Ubiquitous Comput. 5(1), 4–7 (2001)CrossRefGoogle Scholar
  6. 6.
    Henricksen, K., Indulska, J., Rakotonirainy, A.: Modeling context information in pervasive computing systems. In: mattern, f, Naghshineh, M. (eds.) PERVASIVE 2002. LNCS, vol. 2414, pp. 167–180. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  7. 7.
    Wang, X., Zhang, D., Gu, T., Pung, H.: Ontology based context modeling and reasoning using OWL. In: Proceedings of the Second IEEE Annual Conference on Pervasive Computing and Communications, pp. 18–22 (2004)Google Scholar
  8. 8.
    Dey, A.K., Salber, D., Abowd, G.D.: A conceptual framework and a toolkit for supporting the rapid prototyping of context-aware applications. In: Moran, T.P., Dourish, P. (eds.) Context-Aware Computing, Special Triple Issue of Human-Computer Interaction, vol. 16, pp. 229–241 (2001)Google Scholar
  9. 9.
    Thörn, C., Sandkuhl, K.: Feature modeling: managing variability in complex systems. In: Tolk, A., Jain, L.C. (eds.) Complex Systems in Knowledge-based Environments: Theory, Models and Applications. Studies in Computational Intelligence, vol. 168, pp. 129–162. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  10. 10.
    Kang, K., Cohen, S.G., Hess, J.A., Novak, W.E., Peterson, S.A.: Feature-oriented domain analysis (FODA) - feasibility study. Technical Report CMU/SEI-90-TR-21, Carnegie-Mellon University (1990)Google Scholar
  11. 11.
    Kang, K.C., Kim, S., Lee, J., Kim, K., Shin, E., Huh, M.: FORM: a feature-oriented reuse method with domain-specific reference architectures. Annals of Softw. Eng. 5, 143–168 (1998)CrossRefGoogle Scholar
  12. 12.
    Czarnecki, K., Eisenecker, U.: Generative Programming. Addison-Wesley, Reading (2000)Google Scholar
  13. 13.
    Riebisch, M.: Towards a more precise definition of feature model. In: Riebisch, M., Coplien, J.O., Streitferdt, D. (eds.) Modelling Variability for Object-Oriented Product Lines. BookOnDemand Publ. Co, Norderstedt (2003)Google Scholar
  14. 14.
    Walraven, S., Van Landuyt, D., Truyen, E., Handekyn, K., Joosen, W.: Efficient customization of multi-tenant software-as-a-service applications with service lines. J. Syst. Softw. 91, 48–62 (2014)CrossRefGoogle Scholar
  15. 15.
    Mietzner, R., Metzger, A., Leymann, F., Pohl, K.: Variability modeling to support customization and deployment of multi-tenant-aware software as a service applications. In: Proceedings of the 2009 ICSE Workshop on Principles of Engineering Service Oriented Systems, pp. 18–25. IEEE Computer Society (2009)Google Scholar
  16. 16.
    Lin, J., Sedigh, S., Miller, A.: A semantic agent framework for cyber-physical systems. In: Elçi, A., Koné, M.T., Orgun, M.A. (eds.) Semantic Agent Systems. SCI, vol. 344, pp. 189–213. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  17. 17.
    European Road Transport Research Advisory Council (2010) ERTRAC’s Strategic Research Agenda: Towards a 50 % more efficient road transport system by 2030. ERTRAC, October 2010. http://www.ertrac.org/. Accessed 04 Jan 2011
  18. 18.
    Sandkuhl, K., Borchardt, U., Lantow, B., Stamer, D., Wißotzki, M.: Towards adaptive business models for intelligent information logistics in transportation. In: 11th International Conference, BIR 2012. Higher School of Economics, Nizhny Novgorod, Russia (2012)Google Scholar
  19. 19.
    Ambient Networks Phase 2. Integrated Design for Context, Network and Policy Management, Deliverable D10.-D1 (2006). http://www.ambient-networks.org/-Files/-deliverables/-D10-D.1_PU.pdf. Accessed 09 September 2014
  20. 20.
    Hong, J., Suh, E., Kim, E.: Context-aware systems: a literature review and classification. Expert Syst. Appl. 36, 8509–8522 (2009)CrossRefGoogle Scholar
  21. 21.
    Zimmermann, A., Lorenz, A., Oppermann, R.: An operational definition of context. In: Kokinov, B., Richardson, D.C., Roth-Berghofer, T.R., Vieu, L. (eds.) CONTEXT 2007. LNCS (LNAI), vol. 4635, pp. 558–571. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  22. 22.
    Raz, D., Juhola, A.T., Serrat-Fernandez, J., Galis, A.: Fast and Efficient Context-Aware Services. John Willey, New York (2006)CrossRefGoogle Scholar
  23. 23.
    Teslya, N.: Smart space-based Lego® mindstorms EV3 robots interaction. In: Proceedings of the 16th Conference of Open Innovations Association FRUCT, pp. 195–200 (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Alexander Smirnov
    • 1
    • 3
  • Kurt Sandkuhl
    • 2
    • 3
  • Nikolay Shilov
    • 1
    • 3
  • Nikolay Telsya
    • 1
    • 3
  1. 1.SPIIRASSt. PetersburgRussia
  2. 2.University of RostockRostockGermany
  3. 3.ITMO UniversitySt. PetersburgRussia

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