Advertisement

Slime Mold Inspired Path Formation Protocol for Wireless Sensor Networks

  • Ke Li
  • Kyle Thomas
  • Claudio Torres
  • Louis Rossi
  • Chien-Chung Shen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6234)

Abstract

Many biological systems are composed of unreliable components which self-organize efficiently into systems that can tackle complex problems. One such example is the true slime mold Physarum polycephalum which is an amoeba-like organism that seeks food sources and efficiently distributes nutrients throughout its cell body. The distribution of nutrients is accomplished by a self-assembled resource distribution network of small tubes with varying diameter which can evolve with changing environmental conditions without any global control. In this paper, we use a phenomenological model for the tube evolution in slime mold and map it to a path formation protocol for wireless sensor networks. By selecting certain evolution parameters in the protocol, the network may evolve toward single paths connecting data sources to a data sink. In other parameter regimes, the protocol may evolve toward multiple redundant paths. We present detailed analysis of a small model network. A thorough understanding of the simple network leads to design insights into appropriate parameter selection. We also validate the design via simulation of large-scale realistic wireless sensor networks using the QualNet network simulator.

Keywords

Wireless Sensor Network Sink Node Local Computation Slime Mold Neighbor Table 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Ben-Jacob, E., Cohen, I.: Cooperative organization of bacterial colonies: From genotype to morphotype. Annual Review of Microbiology 52, 779–806 (1998)CrossRefGoogle Scholar
  2. 2.
    Li, K., Thomas, K., Rossi, L.F., Shen, C.C.: Slime-mold inspired protocol for wireless sensor networks. In: Proc. of the 2nd IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO), pp. 319–328. IEEE Press, Los Alamitos (2008)CrossRefGoogle Scholar
  3. 3.
    Nakagaki, T., Yamada, H., Toth, A.: Maze-solving by an amoeboid organism. Nature 407(6803), 470–470 (2000)CrossRefGoogle Scholar
  4. 4.
    Scalable Network Technologies, Inc.: QualNet Simulator, http://www.scalable-networks.com
  5. 5.
    Stewart, P.A.: The organization of movement in slime mold plasmodia. In: Primitive Motile Systems in Cell Biology, pp. 69–78. Academic Press, London (1964)Google Scholar
  6. 6.
    Tero, A., Kobayashi, R., Nakagaki, T.: A mathematical model for adaptive transport network in path finding by true slime mold. J. Theor. Biol. 244, 553–564 (2007)CrossRefMathSciNetGoogle Scholar
  7. 7.
    Tero, A., Takagi, S., Saigusa, T., Ito, K., Bebber, B.P., Fricker, M.D., Yumiki, K., Kobayashi, R., Nakagaki, T.: Rules for biologically inspired adaptive network design. Science 327, 439–442 (2010)CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Ke Li
    • 1
  • Kyle Thomas
    • 2
  • Claudio Torres
    • 3
  • Louis Rossi
    • 3
  • Chien-Chung Shen
    • 1
  1. 1.Computer & Info. Sciences 
  2. 2.Chemical Engineering 
  3. 3.Mathematical SciencesUniversity of DelawareNewarkUSA

Personalised recommendations