Slime mold inspired routing protocols for wireless sensor networks


Many biological systems are composed of unreliable components which self-organize effectively into systems that achieve a balance between efficiency and robustness. One such example is the true slime mold Physarum polycephalum which is an amoeba-like organism that seeks and connects 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 exploit two different mechanisms of the slime mold’s tubular network formation process via laboratory experiments and mathematical behavior modeling to design two corresponding localized routing protocols for wireless sensor networks (WSNs) that take both efficiency and robustness into account. In the first mechanism of path growth, slime mold explores its immediate surroundings to discover and connect new food sources during its growth cycle. We adapt this mechanism for a path growth routing protocol by treating data sources and sinks as singular potentials to establish routes from the sinks to all the data sources. The second mechanism of path evolution is the temporal evolution of existing tubes through nonlinear feedback in order to distribute nutrients efficiently throughout the organism. Specifically, the diameters of tubes carrying large fluxes of nutrients grow to expand their capacities, and tubes that are not used decline and disappear entirely. We adapt the tube dynamics of the slime mold for a path evolution routing protocol. In our protocol, we identify one key adaptation parameter to adjust the tradeoff between efficiency and robustness of network routes. Through extensive realistic network simulations and ideal closed form or numerical computations, we validate the effectiveness of both protocols, as well as the efficiency and robustness of the resulting network connectivity.

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  1. Adler, J., & Tso, W. W. (1974). “Decision”-making in bacteria: chemotactic response of escherichia coli to conflicting stimuli. Science, 184(4143), 1292–1294. doi:10.1126/science.184.4143.1292.

    Article  Google Scholar 

  2. Ahn, G. S., Hong, S. G., Miluzzo, E., Campbell, A. T., & Cuomo, F. (2006). Funneling-MAC: a localized, sink-oriented MAC for boosting fidelity in sensor networks. In Proceedings of the fourth ACM international conference on embedded networked sensor systems (SENSYS) (pp. 293–306). New York: ACM.

    Google Scholar 

  3. Akyildiz, I. F., & Kasimoglu, I. H. (2004). Wireless sensor and actor networks: research challenges. Ad Hoc Networks, 2(4), 351–367.

    Article  Google Scholar 

  4. Ben-Jacob, E., & Cohen, I. (1998). Cooperative organization of bacterial colonies: from genotype to morphotype. Annual Review of Microbiology, 52, 779–806.

    Article  Google Scholar 

  5. Conolly, B., Parthasarathy, P., & Dharmaraja, S. (1997). A chemical queue. The Mathematical Scientist, 22, 83–91.

    MATH  MathSciNet  Google Scholar 

  6. Culpepper, B. J., Dung, L., & Moh, M. (2004). Design and analysis of hybrid indirect transmissions (hit) for data gathering in wireless micro sensor networks. Mobile Computing and Communications Review, 8(1), 61–83. doi:

    Article  Google Scholar 

  7. Dorigo, M., & Stutzle, T. (2004). Ant colony optimization. Cambridge: MIT Press.

    Google Scholar 

  8. Ducatelle, F., Di Caro, G., & Gambardella, L. (2010). Principles and applications of swarm intelligence for adaptive routing in telecommunications networks. Swarm Intelligence, 4, 173–198. doi:10.1007/s11721-010-0040-x.

    Article  Google Scholar 

  9. Fang, Q., Gao, J., Guibas, L. J., Silva, V., & Zhang, L. (2005). Glider: gradient landmark-based distributed routing for sensor networks. In Proceedings of the 24th annual IEEE international conference on computer communications (INFOCOM) (pp. 339–350).

    Google Scholar 

  10. Gunji, Y. P., Shirakawa, T., Niizato, T., & Haruna, T. (2008). Minimal model of a cell connecting amoebic motion and adaptive transport networks. Journal of Theoretical Biology, 253(4), 659–667. doi:10.1016/j.jtbi.2008.04.017.

    Article  Google Scholar 

  11. Heinzelman, W. R., Chandrakasan, A., & Balakrishnan, H. (2000). Energy-efficient communication protocol for wireless microsensor networks. In Proceedings of the 33rd Hawaii international conference on system sciences (pp. 1–10). New York: IEEE Press.

    Google Scholar 

  12. Hofer, T., & Maini, P. K. (1997). Streaming instability of slime mold amoebae: an analytical model. Physical Review. E, Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics, 56, 2074–2080.

    Article  Google Scholar 

  13. Intanagonwiwat, C., Govindan, R., Estrin, D., Heidemann, J., & Silva, F. (2003). Directed diffusion for wireless sensor networking. IEEE/ACM Transactions on Networking, 11(1), 2–16. doi:10.1109/TNET.2002.808417.

    Article  Google Scholar 

  14. John, F. (1986). Partial differential equations. New York: Springer.

    Google Scholar 

  15. Kobayashi, R., Tero, A., & Nakagaki, T. (2006). Mathematical model for rhythmic protoplasmic movement in the true slime mold. Journal of Mathematical Biology, 53(2), 273–286.

    Article  MATH  MathSciNet  Google Scholar 

  16. Nakagaki, T., Yamada, H., & Toth, A. (2000). Intelligence: maze-solving by an amoeboid organism. Nature, 407(6803), 470.

    Article  Google Scholar 

  17. Nakagaki, T., Yamada, H., & Toth, A. (2001). Path finding by tube morphogenesis in an amoeboid organism. Biophysical Chemistry, 92(1–2), 47–52.

    Article  Google Scholar 

  18. Nakagaki, T., Kobayashi, R., Nishiura, Y., & Ueda, T. (2004a). Obtaining multiple separate food sources: behavioural intelligence in the physarum plasmodium. Proceedings of the Royal Society of London. Series B, Biological Sciences, 271(1554), 2305–2310.

    Article  Google Scholar 

  19. Nakagaki, T., Yamada, H., & Hara, M. (2004b). Smart network solutions in an amoeboid organism. Biophysical Chemistry, 107(1), 1–5.

    Article  Google Scholar 

  20. Rossi, L. F., Li, K., & Yackoski, J., Shen, C. C. (2007). Slime mold inspired coordinations for wireless sensor and actor networks. In First ACM workshop on sensor and actor networks (SANET) (pp. 55–56). New York: ACM.

    Google Scholar 

  21. Saleem, M., Di Caro, G. A., & Farooq, M. (2010). Swarm intelligence based routing protocol for wireless sensor networks: survey and future directions. Inf. Sci. 1–28. doi:10.1016/j.ins.2010.07.005.

  22. Scalable Network Technologies, Inc. (2011). QualNet Simulator.

  23. Stewart, P. A. (1964). The organization of movement in slime mold plasmodia. In R. D. Allen & N. Kamiya (Eds.), Primitive motile systems in cell biology (pp. 69–78). Maryland Heights: Academic Press.

    Google Scholar 

  24. Tero, A., Kobayashi, R., & Nakagaki, T. (2007). A mathematical model for adaptive transport network in path finding by true slime mold. Journal of Theoretical Biology, 244, 553–564.

    Article  MathSciNet  Google Scholar 

  25. Tero, A., Takagi, S., Saigusa, T., Ito, K., Bebber, B. P., Fricker, M. D., Yumiki, K., Kobayashi, R., & Nakagaki, T. (2010). Rules for biologically inspired adaptive network design. Science, 327, 439–442.

    Article  MathSciNet  Google Scholar 

  26. Torres, C. E., Rossi, L. F., Keffer, J., Li, K., & Shen, C. C. (2010). Modeling, analysis and simulation of ant-based network routing protocols. Swarm Intelligence, 4(3), 221–244.

    Article  Google Scholar 

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Correspondence to Chien-Chung Shen.

Additional information

Preliminary versions of this paper appeared in the following two conferences: (1) 2nd IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO), Venice, Italy, October 20–24, 2008, and (2) 7th International Conference on Swarm intelligence (ANTS), Brussels, Belgium, September 8–10, 2010.

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Li, K., Torres, C.E., Thomas, K. et al. Slime mold inspired routing protocols for wireless sensor networks. Swarm Intell 5, 183–223 (2011).

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  • Slime mold
  • Routing protocol
  • Wireless sensor network
  • Simulation stability analysis