Smart Coordination of Autonomic Component Ensembles in the Context of Ad-Hoc Communication

  • Tomas Bures
  • Petr HnetynkaEmail author
  • Filip Krijt
  • Vladimir Matena
  • Frantisek Plasil
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9952)


Smart Cyber-Physical Systems (sCPS) are complex distributed decentralized systems that typically operate in an uncertain environment and thus have to be resilient to both network and individual node failures. At the same time, sCPS are commonly required to exhibit complex smart coordination while being limited in terms of resources such as network. However, optimizing network usage in a general sCPS coordination framework while maintaining the system function is complex. To better enable this, we allow incorporating key network parameters and constraints into the architecture, realized as an extension of the autonomic component ensembles paradigm. We show that when chosen well, these parameters make it possible to improve network resource usage without hampering the system utility too much. We demonstrate the parameter selection on a mobile gossip-based sCPS coordination scenario and use simulation to show the impact on overall system utility.


Smart Cyber-Physical Systems Autonomic components Ensembles Communication 



This work was partially supported by the project no. LD15051 from COST CZ (LD) programme by the Ministry of Education, Youth and Sports of the Czech Republic, partially supported by Charles University Grant Agency project No. 390615, and partially supported by Charles University institutional funding SVV-2016-260331.


  1. 1.
    Bures, T., et al.: DEECo: an ensemble-based component system. In: Proceedings of CBSE 2013, Vancouver, Canada, pp. 81–90. ACM (2013)Google Scholar
  2. 2.
    Bures, T., Gerostathopoulos, I., Hnetynka, P., Keznikl, J., Kit, M., Plasil, F.: Gossiping components for cyber-physical systems. In: Avgeriou, P., Zdun, U. (eds.) ECSA 2014. LNCS, vol. 8627, pp. 250–266. Springer, Heidelberg (2014)Google Scholar
  3. 3.
    Bures, T., et al.: Software engineering for smart cyber-physical systems – towards a research agenda: report on the first international workshop on software engineering for smart CPS. SIGSOFT Softw. Eng. Notes 40(6), 28–32 (2015)CrossRefGoogle Scholar
  4. 4.
    Bures, T., et al.: Towards intelligent ensembles. In: Proceedings of ECSAW 2015, Dubrovnik/Cavcat, Croatia, pp. 1–4. ACM (2015)Google Scholar
  5. 5.
    Cai, N., et al.: Application-oriented intelligent middleware for distributed sensing and control. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 42(6), 947–956 (2012)CrossRefGoogle Scholar
  6. 6.
    Fairbanks, G., Garlan, D.: Just Enough Software Architecture: A Risk-Driven Approach. Marshall & Brainerd, Boulder (2010)Google Scholar
  7. 7.
    Friedman, R., et al.: Gossiping on MANETs: the beauty and the beast. ACM SIGOPS Oper. Syst. Rev. 41(5), 67–74 (2007)CrossRefGoogle Scholar
  8. 8.
    Gaston, M.E., desJardins, M.: Agent-organized networks for dynamic team formation. In: Proceedings of AAMAS 2005, Utrecht, Netherlands, pp. 230–237. ACM (2005)Google Scholar
  9. 9.
    Guerrero, J., Oliver, G.: Multi-robot coalition formation in real-time scenarios. Robot. Auton. Syst. 60(10), 1295–1307 (2012)CrossRefGoogle Scholar
  10. 10.
    Hennicker, R., Klarl, A.: Foundations for ensemble modeling – the Helena approach. In: Iida, S., et al. (eds.) Specification, Algebra, and Software, pp. 359–381. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  11. 11.
    Hoch, N., et al.: The E-mobility case study. In: Wirsing, M., et al. (eds.) Software Engineering for Collective Autonomic Systems. LNCS, vol. 8998, pp. 513–533. Springer, Heidelberg (2015)Google Scholar
  12. 12.
    Kit, M., et al.: Employing domain knowledge for optimizing component communication. In: Proceedings of CBSE 2015, Montreal, Canada, pp. 59–64. ACM (2015)Google Scholar
  13. 13.
    Marin-Perianu, M., et al.: Decentralized enterprise systems: a multiplatform wireless sensor network approach. IEEE Wirel. Commun. 14(6), 57–66 (2007)CrossRefGoogle Scholar
  14. 14.
    Michalak, T., et al.: A distributed algorithm for anytime coalition structure generation. In: Proceedings of AAMAS 2010, Toronto, Canada, pp. 1007–1014, International Foundation for Autonomous Agents and Multiagent Systems (2010)Google Scholar
  15. 15.
    OMG: MDA Guide revision 2.0 (2014).
  16. 16.
    Parker, J., et al.: Exploiting spatial locality and heterogeneity of agents for search and rescue teamwork. J. Field Robot. (2015, accepted)Google Scholar
  17. 17.
    Pottie, G.J., Kaiser, W.J.: Wireless integrated network sensors. Commun. ACM 43(5), 51–58 (2000)CrossRefGoogle Scholar
  18. 18.
    Rahwan, T., et al.: Anytime coalition structure generation in multi-agent systems with positive or negative externalities. Artif. Intell. 186, 95–122 (2012)MathSciNetCrossRefzbMATHGoogle Scholar
  19. 19.
    Sandholm, T., et al.: Coalition structure generation with worst case guarantees. Artif. Intell. 111(1–2), 209–238 (1999)MathSciNetCrossRefzbMATHGoogle Scholar
  20. 20.
    Shehory, O., Kraus, S.: Methods for task allocation via agent coalition formation. Artif. Intell. 101(1–2), 165–200 (1998)MathSciNetCrossRefzbMATHGoogle Scholar
  21. 21.
    Stojmenovic, I.: Position-based routing in ad hoc networks. IEEE Commun. Mag. 40(7), 128–134 (2002)CrossRefGoogle Scholar
  22. 22.
    Vig, L., Adams, J.A.: Multi-robot coalition formation. IEEE Trans. Rob. 22(4), 637–649 (2006)CrossRefzbMATHGoogle Scholar
  23. 23.
    Wirsing, M., Hölzl, M., Tribastone, M., Zambonelli, F.: ASCENS: engineering autonomic service-component ensembles. In: Beckert, B., Damiani, F., Boer, F.S., Bonsangue, M.M. (eds.) FMCO 2011. LNCS, vol. 7542, pp. 1–24. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  24. 24.
    Wirsing, M., et al.: Software Engineering for Collective Autonomic Systems (The ASCENS Approach). Springer, Heidelberg (2015)Google Scholar
  25. 25.
    Witsch, A., Geihs, K.: An adaptive middleware core for a multi-agent coordination language. In: Proceedings of NetSys 2015, Cottbus, Germany, pp. 1–8. IEEE (2015)Google Scholar
  26. 26.
    Ye, D., et al.: Self-adaptation-based dynamic coalition formation in a distributed agent network: a mechanism and a brief survey. IEEE Trans. Parallel Distrib. Syst. 24(5), 1042–1051 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Tomas Bures
    • 1
  • Petr Hnetynka
    • 1
    Email author
  • Filip Krijt
    • 1
  • Vladimir Matena
    • 1
  • Frantisek Plasil
    • 1
  1. 1.Charles University, Faculty of Mathematics and Physics, Department of Distributed and Dependable SystemsPragueCzech Republic

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