The Degree of Global-State Awareness in Self-Organizing Systems
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.
KeywordsSelf-organizing systems Mathematical modeling Quantitative evaluation Information theory System design Sensor networks
- 4.Holzer, R., de Meer, H.: On modeling of self-organizing systems. In: Proc. of Autonomics 2008, Turin, Italy (September 2008)Google Scholar
- 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
- 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
- 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
- 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.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