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Evolving proactive aggregation protocols

Published: 26 March 2008 Publication History

Abstract

We present an approach for the automated synthesis of proactive aggregation protocols using Genetic Programming and discuss major decisions in modeling and simulating distributed aggregation protocols. We develop a genotype, which is an abstract specification form for aggregation protocols. Finally we show the evolution of a distributed average protocol under various conditions to demonstrate the utility of our approach.

References

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Weise, T., Geihs, K.: Genetic programming techniques for sensor networks. In: 5. GI/ITG KuVS Fachgespräch "Drahtlose Sensornetze", Stuttgart, Germany, pp. 21-25 (2006).
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Weise, T., Geihs, K.: Dgpf - an adaptable framework for distributed multi-objective search algorithms applied to the genetic programming of sensor networks. In: 2nd International Conference on Bioinspired Optimization Methods and their Application, BIOMA 2006, pp. 157-166. Ljubljana, Slovenia (2006).
[3]
Weise, T., Geihs, K., Baer, P.A.: Genetic Programming for Proactive Aggregation Protocols. In: Beliczynski, B., Dzielinski, A., Iwanowski, M., Ribeiro, B. (eds.) ICANNGA 2007. LNCS, vol. 4431, pp. 167-173. Springer, Heidelberg (2007).
[4]
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).
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Chong, C.-Y., Kumar, S.P.: Sensor networks: evolution, opportunities, and challenges. Proceedings of the IEEE 91(8), 1247-1256 (2003).
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Jelasity, M., Montresor, A., Babaoglu, O.: Gossip-based aggregation in large dynamic networks. ACM Trans. Comput. Syst. 23(3), 219-252 (2005).
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Kempe, D., Dobra, A., Gehrke, J.: Gossip-based computation of aggregate information. In: Proceedings of 44th Symposium on Foundations of Computer Science (FOCS 2003), Cambridge, USA, pp. 482-491. IEEE Computer Society Press, Los Alamitos (2003).
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Koza, J.R.: Genetic Programming, On the Programming of Computers by Means of Natural Selection. The MIT Press, Cambridge (1992), ISBN: 0262111705.
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Nguyen, X.H., et al.: Solving the symbolic regression problem with tree-adjunct grammar guided genetic programming: the comparative results. In: IEEE Congress on Evolutionary Computation, CEC 2002, Honolulu, USA, pp. 1326-1331 (2002).
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Lopes, H.S., Weinert, W.R.: EGIPSYS: an enhanced gene expression programming approach for symbolic regression problems. Int. J. of Ap. Math. and Com. Sci. 14 (2004).
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Weise, T.: Global Optimization Algorithms - Theory and Application (2007), http://www.it-weise.de/

Cited By

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  • (2021)Genetic Improvement of Routing Protocols for Delay Tolerant NetworksACM Transactions on Evolutionary Learning and Optimization10.1145/34536831:1(1-37)Online publication date: 20-May-2021

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Published In

cover image Guide Proceedings
EuroGP'08: Proceedings of the 11th European conference on Genetic programming
March 2008
374 pages
ISBN:3540786708

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 26 March 2008

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  • (2021)Genetic Improvement of Routing Protocols for Delay Tolerant NetworksACM Transactions on Evolutionary Learning and Optimization10.1145/34536831:1(1-37)Online publication date: 20-May-2021

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