The Degree of Global-State Awareness in Self-Organizing Systems

  • Christopher Auer
  • Patrick Wüchner
  • Hermann de Meer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5918)


Since the entities composing self-organizing systems have direct access only to information provided by their vicinity, it is a non-trivial task for them to determine properties of the global system state. However, this ability appears to be mandatory for certain self-organizing systems in order to achieve an intended functionality.

Based on Shannon’s information entropy, we introduce a formal measure that allows to determine the entities’ degree of global-state awareness. Using this measure, self-organizing systems and suitable system settings can be identified that provide the necessary information to the entities for achieving the intended system functionality.

Hence, the proposed degree supports the evaluation of functional properties during the design and management of self-organizing systems. We show this by applying the measure exemplarily to a self-organizing sensor network designed for intrusion detection. This allows us to find preferable system parameter settings.


Self-organizing systems Mathematical modeling Quantitative evaluation Information theory System design Sensor networks 


  1. 1.
    Elmenreich, W., De Meer, H.: Self-organizing networked systems for technical applications: A discussion on open issues. In: Hummel, K.A., Sterbenz, J.P.G. (eds.) IWSOS 2008. LNCS, vol. 5343, pp. 1–9. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  2. 2.
    Auer, C., Wüchner, P., De Meer, H.: A method to derive local interaction strategies for improving cooperation in self-organizing systems. In: Hummel, K.A., Sterbenz, J.P.G. (eds.) IWSOS 2008. LNCS, vol. 5343, pp. 170–181. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  3. 3.
    Holzer, R., de Meer, H., Bettstetter, C.: On autonomy and emergence in self-organizing systems. In: Hummel, K.A., Sterbenz, J.P.G. (eds.) IWSOS 2008. LNCS, vol. 5343, pp. 157–169. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  4. 4.
    Holzer, R., de Meer, H.: On modeling of self-organizing systems. In: Proc. of Autonomics 2008, Turin, Italy (September 2008)Google Scholar
  5. 5.
    Mnif, M., Müller-Schloer, C.: Quantitative emergence. In: Proc. of the 2006 IEEE Mountain Workshop on Adaptive and Learning Systems (SMCals 2006), July 2006, pp. 78–84. IEEE, Piscataway (2006)CrossRefGoogle Scholar
  6. 6.
    Holzer, R., de Meer, H.: Quantitative modeling of self-organizing properties. In: Plattner, B., Spyropoulos, T., Hummel, K.A. (eds.) Proc. of the 4th International Workshop of Self-Organizing Systems (IWSOS 2009), Zurich, Switzerland, December 2009. LNCS, Springer, Heidelberg (2009)Google Scholar
  7. 7.
    Shannon, C.E.: A mathematical theory of communication. Bell System Technical Journal 27, 379–423 (1948)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Hong, Y., Scaglione, A.: Distributed change detection in large scale sensor networks through the synchronization of pulse-coupled oscillators. In: Proc. of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2004), Montreal, Canada, May 2004, vol. 3, pp. 869–872 (2004)Google Scholar
  9. 9.
    Cover, T.M., Thomas, J.A.: Elements of information theory. Wiley-Interscience, Hoboken (1991)CrossRefzbMATHGoogle Scholar
  10. 10.
    Ernst, U., Pawelzik, K., Geisel, T.: Synchronization induced by temporal delays in pulse-coupled oscillators. Phys. Rev. Lett. 74(9), 1570–1573 (1995)CrossRefGoogle Scholar
  11. 11.
    Tyrrell, A., Auer, G., Bettstetter, C.: In: Biologically Inspired Synchronization for Wireless Networks. Vol. 69/2007 of Studies in Computational Intelligence, pp. 47–62. Springer, Heidelberg (2007)Google Scholar
  12. 12.
    Mirollo, R.E., Strogatz, S.H.: Synchronization of pulse-coupled biological oscillators. SIAM Journal on Applied Mathematics 50(6), 1645–1662 (1990)MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Lucarelli, D., Wang, I.: Decentralized synchronization protocols with nearest neighbor communication. In: Proc. of the 2nd International Conference on Embedded Networked Sensor Systems, pp. 62–68. ACM, New York (2004)CrossRefGoogle Scholar
  14. 14.
    Shalizi, C.R.: Causal Architecture, Complexity and Self-Organization in Time Series and Cellular Automata. PhD thesis, University of Wisconsin, Supervisor: Martin Olsson (2001)Google Scholar
  15. 15.
    Shalizi, C.R., Shalizi, K.L.: Blind construction of optimal nonlinear recursive predictors for discrete sequences. In: AUAI 2004: Proceedings of the 20th Conference on Uncertainty in Artificial Intelligence, pp. 504–511. AUAI Press, Arlington (2004)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2009

Authors and Affiliations

  • Christopher Auer
    • 1
  • Patrick Wüchner
    • 1
  • Hermann de Meer
    • 1
  1. 1.Faculty of Informatics and MathematicsUniversity of PassauPassauGermany

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