BeeHive: An Efficient Fault-Tolerant Routing Algorithm Inspired by Honey Bee Behavior

  • Horst F. Wedde
  • Muddassar Farooq
  • Yue Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3172)


Bees organize their foraging activities as a social and communicative effort, indicating both the direction, distance and quality of food sources to their fellow foragers through a ”dance” inside the bee hive (on the ”dance floor”). In this paper we present a novel routing algorithm, BeeHive, which has been inspired by the communicative and evaluative methods and procedures of honey bees. In this algorithm, bee agents travel through network regions called foraging zones. On their way their information on the network state is delivered for updating the local routing tables. BeeHive is fault tolerant, scalable, and relies completely on local, or regional, information, respectively. We demonstrate through extensive simulations that BeeHive achieves a similar or better performance compared to state-of-the-art algorithms.


Source Node Data Packet Packet Delay Average Throughput Average Packet Delay 
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  1. 1.
    Barán, B., Sosa, R.: A new approach for antnet routing. In: Proceedings of the Ninth International Conference on Computer, Communications and Networks (2000)Google Scholar
  2. 2.
    Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press, Oxford (1999)zbMATHGoogle Scholar
  3. 3.
    Di Caro, G., Dorigo, M.: AntNet: Distributed stigmergetic control for communication networks. Journal of Artificial Intelligence 9, 317–365 (1998)zbMATHGoogle Scholar
  4. 4.
    Di Caro, G., Dorigo, M.: Two ant colony algorithms for best-effort routing in datagram networks. In: Proceedings of the Tenth IASTED International Conference on Parallel and Distributed Computing and Systems (PDCS 1998), pp. 541–546. IASTED/ACTA Press (1998)Google Scholar
  5. 5.
    Dijkstra, E.: A note on two problems in connection with graphs. Numerical Mathematics 1, 269–271 (1959)zbMATHCrossRefMathSciNetGoogle Scholar
  6. 6.
    Goldberg, D.: Genetic algorithms in search, optimization and machine learning. Addison Wesley, Reading (1989)zbMATHGoogle Scholar
  7. 7.
    Grassé, P.: La reconstruction du nid et les coordinations interindividuelles chez bellicositermes natalensis et cubitermes sp. la théorie de la stigmergie: essai d’interprétation du comportement des termites constructeurs. Insectes Sociaux 6, 41–81 (1959)Google Scholar
  8. 8.
    Liang, S., Zincir-Heywood, A., Heywood, M.: The effect of routing under local information using a social insect metaphor. In: Proceedings of IEEE Congress on Evolutionary Computing (May 2002)Google Scholar
  9. 9.
    Liang, S., Zincir-Heywood, A., Heywood, M.: Intelligent packets for dynamic network routing using distributed genetic algorithm. In: Proceedings of Genetic and Evolutionary Computation Conference. GECCO (July 2002)Google Scholar
  10. 10.
    Nii, P.: The blackboard model of problem solving. AI Mag 7(2), 38–53 (1986)Google Scholar
  11. 11.
    Seeley, T.: The Wisdom of the Hive. Harvard University Press, London (1995)Google Scholar
  12. 12.
    Varga, A.: OMNeT++: Discrete event simulation system: User manual,
  13. 13.
    von Frisch, K.: The Dance Language and Orientation of Bees. Harvard University Press, Cambridge (1967)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Horst F. Wedde
    • 1
  • Muddassar Farooq
    • 1
  • Yue Zhang
    • 1
  1. 1.Informatik IIIUniversity of DortmundDortmundGermany

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