Advertisement

Evolutionary Intelligence

, Volume 5, Issue 4, pp 245–259 | Cite as

Evolutionary linkage creation between information sources in P2P networks

  • Kei Ohnishi
  • Mario Köppen
  • Kaori Yoshida
Research Paper
  • 270 Downloads

Abstract

The present paper proposes a peer-to-peer (P2P) information retrieval and sharing system that evolutionarily creates linkages of information sources that are useful for both information publishers and information users, where information is managed in a decentralized manner. The proposed system relies on interactions among information publishers who actually generate information and have the greatest knowledge of the information, information users who use the information, and a network that creates useful linkages of information sources (information publishers). In order to enhance the value of their own information sources, information publishers propose new linkages of information sources that indicate information sources with which they would like to have their own information sources co-occur. The information users evaluate the linkages proposed by the information publishers. The network evolutionarily reconstructs the topological structures of the P2P network based on the fitness obtained from the users. Simulation results suggest that it is possible to find more information sources that users desire using the topological structures reconstructed by the proposed system, as compared to the use of non-reconstructed topological structures.

Keywords

Information sources P2P networks Evolutionary algorithms Information retrieval 

References

  1. 1.
  2. 2.
  3. 3.
    Mathes A (2004) Folksonomies—cooperative classification and communication through shared metadata. http://www.adammathes.com/academic/computer-mediated-communication/folksonomies.html
  4. 4.
    Lua EK, Crowcroft J, Pias M, Sharma R, Lim S (2005) A survey and comparison of peer-to-peer overlay network schemes. IEEE Commun Surv Tutor 7(2):72–93CrossRefGoogle Scholar
  5. 5.
    Ripeanu M, Iamnitchi A, Foster I (2002) Mapping the Gnutella network. IEEE Internet Comput 6(1):50–57CrossRefGoogle Scholar
  6. 6.
    Stutzbach D, Rejaie R, Sen S (2008) Characterizing unstructured overlay topologies in modern P2P file-sharing systems. IEEE/ACM Trans Netw (TON) 16(2):267–280CrossRefGoogle Scholar
  7. 7.
    Ohnishi K, Oie Y (2010) Evolutionary P2P networking that fuses evolutionary computation and P2P networking together. IEICE Trans Commun E93-B(2):317–328CrossRefGoogle Scholar
  8. 8.
    Bäck T (1996) Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms. Oxford University Press, OxfordzbMATHGoogle Scholar
  9. 9.
    Koo SGM, Lee CSG, Kannan K (2004) A genetic-algorithm-based neighbor-selection strategy for hybrid peer-to-peer networks. In: Proceedings of the international conference on computer communications and networks (ICCCN 2004), pp 469–474Google Scholar
  10. 10.
    Srivatsa M, Gedik B, Liu L (2006) Large scaling unstructured peer-to-peer networks with heterogeneity-aware topology and routing. IEEE Trans Parallel Distrib Syst 17(11):1277–1293CrossRefGoogle Scholar
  11. 11.
    Pournaras E, Exarchakos G, Antonopoulos N (2008) Load-driven neighbourhood reconfiguration of Gnutella overlay. Comput Commun 31(13):3030–3039CrossRefGoogle Scholar
  12. 12.
    Merz P, Wolf S (2006) Evolutionary local search for designing peer-to-peer overlay topologies based on minimum routing cost spanning trees. In: Proceedings of the 9th international conference on parallel problem solving from nature (PPSN IX), pp 272–281Google Scholar
  13. 13.
    Munetomo M, Takai Y, Sato Y (1997) An adaptive network routing algorithm employing path genetic operators. In: Proceedings of the seventh international conference on genetic algorithms, pp 643–649Google Scholar
  14. 14.
    Imai P, Tschudin C (2010) Practical online network stack evolution. SASO 2010 workshop on self-adaptive networkingGoogle Scholar
  15. 15.
    Goldberg D, Nichols D, Oki BM, Terry D (1992) Using collaborative filtering to weave an information tapestry. Commun ACM 35(12):61–70CrossRefGoogle Scholar
  16. 16.
    Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, New YorkzbMATHGoogle Scholar
  17. 17.
    Adamic LA, Huberman BA (2000) The nature of markets in the world wide web. Q J Electron Commer 1(1):5–12Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Kyushu Institute of TechnologyFukuokaJapan

Personalised recommendations