Characteristic Analysis of Response Threshold Model and Its Application for Self-organizing Network Control

  • Takuya Iwai
  • Naoki Wakamiya
  • Masayuki Murata
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8221)

Abstract

There is an emerging research area to adopt bio-inspired algorithms to self-organize an information network system. Despite strong interests on their benefits, i.e. high robustness, adaptability, and scalability, the behavior of bio-inspired algorithms under non-negligible perturbation such as loss of information and failure of nodes observed in the realistic environment is not well investigated. Because of lack of knowledge, none can clearly identify the range of application of a bio-inspired algorithm to challenging issues of information networks. Therefore, to tackle the problem and accelerate researches in this area, we need to understand characteristics of bio-inspired algorithms from the perspective of network control. In this paper, taking a response threshold model as an example, we discuss the robustness and adaptability of bio-inspired model and its application to network control. Through simulation experiments and mathematical analysis, we show an existence condition of the equilibrium state in the lossy environment. We also clarify the influence of the environmental condition and control parameters on the transient behavior and the recovery time.

Keywords

self-organization response threshold model robustness adaptability linear stability theory 

References

  1. 1.
    Bonabeau, E., Theraulaz, G., Deneubourg, J.-L., Aron, S., Camazine, S.: Self-organization in social insects. Trends in Ecology and Evolution 12, 188–193 (1997)CrossRefGoogle Scholar
  2. 2.
    Duarte, A., Pen, I., Keller, L., Weissing, F.J.: Evolution of self-organized division of labor in a response threshold model. Behavioral Ecology and Sociobiology 66, 947–957 (2012)CrossRefGoogle Scholar
  3. 3.
    Auchmuty, J.F.G.: Bifurcation analysis of nonlinear reaction-diffusion equations I. Evolution equations and the steady state solutions. Bullebtin of Mathematical Biology 37, 323–365 (1975)MATHMathSciNetGoogle Scholar
  4. 4.
    Nishimura, J., Friedman, E.J.: Robust convergence in pulse-coupled oscillators with delays. Physical Review Letters 106, 194101 (2011)CrossRefGoogle Scholar
  5. 5.
    Gutjahr, W.J.: A graph-based ant system and its convergence. Future Generation Computer Systems 16, 873–888 (2000)CrossRefGoogle Scholar
  6. 6.
    Dressler, F., Akan, O.B.: A survey on bio-inspired networking. Computer Networks 54, 881–900 (2010)CrossRefMATHGoogle Scholar
  7. 7.
    Meisel, M., Pappasand, V., Zhang, L.: A taxonomy of biologically inspired research in computer networking. Computer Networks 54, 901–916 (2010)CrossRefMATHGoogle Scholar
  8. 8.
    Tyrrell, A., Auer, G., Bettstetter, C.: On the accuracy of firefly synchronization with delays. In: Proceedings of the International Symposium on Applied Sciences on Biomedical and Communication Technologies, pp. 1–5 (October 2008)Google Scholar
  9. 9.
    Hyodo, K., Wakamiya, N., Nakaguchi, E., Murata, M., Kubo, Y., Yanagihara, K.: Reaction-diffusion based autonomous control of wireless sensor networks. International Journal of Sensor Networks 7, 189–198 (2010)CrossRefGoogle Scholar
  10. 10.
    Bonabeau, E., Sobkowski, A., Theraulaz, G., Deneubourg, J.L.: Adaptive task allocation inspired by a model of division of labor in social insects. In: Proceedings of the International Conference on Biocomputing and Emergent Computation, pp. 36–45 (January 1997)Google Scholar
  11. 11.
    Labella, T., Dressler, F.: A bio-inspired architecture for division of labour in SANETs. Advances in Biologically Inspired Information Systems, 211–230 (December 2007)Google Scholar
  12. 12.
    Janacik, P., Heimfarth, T., Rammig, F.: Emergent topology control based on division of labour in ants. In: Proceedings of the International Conference on Advanced Information Networking and Applications, pp. 733–740 (April 2006)Google Scholar
  13. 13.
    Bonabeau, E., Henaux, F., Guérin, S., Snyers, D., Kuntz, P., Theraulaz, G.: Routing in telecommunications networks with ant-like agents. In: Albayrak, Ş., Garijo, F.J. (eds.) IATA 1998. LNCS (LNAI), vol. 1437, pp. 60–71. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  14. 14.
    Sasabe, M., Wakamiya, N., Murata, M., Miyahara, H.: Media streaming on P2P networks with bio-inspired cache replacement algorithm. In: Proceedings of the International Workshop on Biologically Inspired Approaches to Advanced Information Technology, pp. 380–395 (May 2004)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2014

Authors and Affiliations

  • Takuya Iwai
    • 1
  • Naoki Wakamiya
    • 1
  • Masayuki Murata
    • 1
  1. 1.Graduate School of Information Science and TechnologyOsaka UniversitySuitaJapan

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