Genetic Programming for Proactive Aggregation Protocols

  • Thomas Weise
  • Kurt Geihs
  • Philipp A. Baer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4431)

Abstract

We present an approach for automated generation of proactive aggregation protocols using Genetic Programming. First a short introduction into aggregation and proactive protocols is given. We then show how proactive aggregation protocols can be specified abstractly. To be able to use Genetic Programming to derive such protocol specifications, we describe a simulation based fitness assignment method. We have applied our approach successfully to the derivation of aggregation protocols. Experimental results are presented that were obtained using our own Distributed Genetic Programming Framework. The results are very encouraging and demonstrate clearly the utility of our approach.

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References

  1. 1.
    van Renesse, R.: The importance of aggregation. In: Schiper, A., Shvartsman, M.M.A.A., Weatherspoon, H., Zhao, B.Y. (eds.) Future Directions in Distributed Computing. LNCS, vol. 2584, pp. 87–92. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  2. 2.
    Chong, C.-Y., Kumar, S.P.: Sensor networks: evolution, opportunities, and challenges. Proceedings of the IEEE 91(8), 1247–1256 (2003)CrossRefGoogle Scholar
  3. 3.
    Jelasity, M., Montresor, A., Babaoglu, O.: Gossip-based aggregation in large dynamic networks. ACM Trans. Comput. Syst. 23(1), 219–252 (2005)CrossRefGoogle Scholar
  4. 4.
    Jelasity, M., Montresor, A.: Epidemic-style proactive aggregation in large overlay networks. In: Proceedings of the 24th International Conference on Distributed Computing Systems (ICDCS’04), Tokyo, Japan, Mar. 2004, pp. 102–109. IEEE Computer Society Press, Los Alamitos (2004)CrossRefGoogle Scholar
  5. 5.
    Heinzelman, W.R., Kulik, J., Balakrishnan, H.: Adaptive protocols for information dissemination in wireless sensor networks. In: MobiCom ’99: Proceedings of the 5th annual ACM/IEEE international conference on Mobile computing and networking, Seattle, Washington, United States, pp. 174–185. ACM Press, New York (1999)CrossRefGoogle Scholar
  6. 6.
    El-Fakih, K., Yamaguchi, H., Bochmann, G., Higashino, T.: A method and a genetic algorithm for deriving protocols for distributed applications with minimum communication cost. In: Proceedings of Eleventh IASTED International Conference on Parallel and Distributed Computing and Systems, Boston, USA (Nov. 1999)Google Scholar
  7. 7.
    Yamamoto, L., Tschudin, C.: Genetic evolution of protocol implementations and configurations. In: IFIP/IEEE International workshop on Self-Managed Systems and Services (SelfMan 2005), Nice, France (2005)Google Scholar
  8. 8.
    Comellas, F., Giménez, G.: Genetic programming to design communication algorithms for parallel architectures. Parallel Processing Letters 8(4), 549–560 (1998)CrossRefGoogle Scholar
  9. 9.
    de Miranda, M.N., Lima, R.N.B., Pedroza, A.C.P., de Mesquita, A.C.: HW/SW codesign of protocols based on performance optimization using genetic algorithms. Technical report (2001)Google Scholar
  10. 10.
    Weise, T., Geihs, K.: DGPF - an adaptable framework for distributed multi-objective search algorithms applied to the genetic programming of sensor networks. In: Šilc, J., Filipič, B. (eds.) Proceedings of the Second International Conference on Bioinspired Optimization Methods and their Application, BIOMA 2006, Oct. 2006, pp. 157–166. Jožef Stefan Institute, Ljubljana, Slovenia, Slovenia (2006)Google Scholar
  11. 11.
    Koza, J.R.: Genetic Programming, On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)MATHGoogle Scholar
  12. 12.
    Raidl, G.R.: A hybrid GP approach for numerically robust symbolic regression. In: Koza, J.R., Banzhaf, W., Chellapilla, K., Deb, K., Dorigo, M., Fogel, D.B., Garzon, M.H., Goldberg, D.E., Iba, H., Riolo, R. (eds.) Genetic Programming 1998: Proceedings of the Third Annual Conference, University of Wisconsin, Madison, Wisconsin, USA, pp. 323–328. Morgan Kaufmann, San Francisco (1998)Google Scholar
  13. 13.
    Distributed Genetic Programming Framework. SourceForge project, see http://sourceforge.net/projects/DGPF and http://DGPF.sourceforge.net/
  14. 14.
    Geihs, K., Weise, T.: Genetic programming techniques for sensor networks. In: Proceedings of 5. GI/ITG KuVS Fachgespräch ”Drahtlose Sensornetze”, Jul. 2006, pp. 21–25 (2006)Google Scholar
  15. 15.
    Weise, T.: Genetic programming for sensor networks. Technical report (Jan. 2006)Google Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Thomas Weise
    • 1
  • Kurt Geihs
    • 1
  • Philipp A. Baer
    • 1
  1. 1.University of Kassel, Wilhelmshöher Allee 73, D-34121 KasselGermany

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