An Analysis of Genetic Algorithm Based Anycast Routing in Delay and Disruption Tolerant Networks

  • Éderson R. Silva
  • Paulo R. Guardieiro
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7677)


Populations in developing countries, especially in regions that lack telecommunications infrastructure, usually do not have access to the information technology. Instead, Delay-/Disruption-Tolerant Networks (DTNs) have the capacity to interconnect areas that are underserved by traditional networks. Anycast routing can be used for many applications in DTNs, and it is useful when nodes wish to send messages to at least one, and preferably only one, of the members in a destination group. In this paper, aiming an efficient routing, it is analyzed a Genetic Algorithm (GA) based anycast routing algorithm. Simulation experiments show that the proposed algorithm can produce good results in typical scenarios including delays and disconnections in message delivery.


Anycast routing DTNs genetic algorithms subpopulation 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Khabbaz, M.J., Assi, C.M., Fawaz, W.F.: Disruption-Tolerant Networking: A Comprehensive Survey on Recent Developments and Persisting Challenges. IEEE Communications Surveys & Tutorials 14, 607–640 (2012)CrossRefGoogle Scholar
  2. 2.
    Zhang, Z.: Routing in intermittently connected mobile ad hoc networks and delay tolerant networks: overview and challenges. Communications Surveys & Tutorials 8, 24–37 (2006)CrossRefGoogle Scholar
  3. 3.
    Gong, Y., et al.: Anycast routing in delay tolerant networks. In: Global Telecommunications Conference (GLOBECOM), pp. 1–5 (2006)Google Scholar
  4. 4.
    Jain, S., Fall, D., Patra, R.: Routing in a delay tolerant network. In: Special Interest Group on Data Communications – SIGCOMM, pp. 145–158 (2004)Google Scholar
  5. 5.
    Yussof, S.: Performance analysis of genetic algorithm (GA)-based multi-constrained path routing algorithm. International Journal of the Physical Sciences 33, 7524–7539 (2011)Google Scholar
  6. 6.
    Da Silva, E.R., Guardieiro, P.R.: Anycast routing in delay tolerant networks using genetic algorithms for route decision. In: 11th International Conference on Computer and Information Technology, pp. 65–71 (2008)Google Scholar
  7. 7.
    Da Silva, E.R., Guardieiro, P.R.: An efficient genetic algorithm for anycast routing in delay/disruption tolerant networks. IEEE Communications Letters 14, 315–317 (2010)CrossRefGoogle Scholar
  8. 8.
    Goldberg, D.E.: Genetic algorithms in search, optimization and machine learning. Addison-Wesley, Massachusets (1989)zbMATHGoogle Scholar
  9. 9.
    Randaccio, L.S., Atzori, L.: Group multicast routing problem: a genetic algorithms based approach. Computer Networks 51, 3989–4004 (2007)zbMATHCrossRefGoogle Scholar
  10. 10.
    Lo, C.C., Chang, W.H.: A multiobjective hybrid genetic algorithm for the capacitated multipoint network design problem. IEEE Trans. on System, Man, and Cybernetics. 30, 461–470 (2000)CrossRefGoogle Scholar
  11. 11.
    Ferreira, A.: Building a reference combinatorial model for MANETs. IEEE Network 18, 24–29 (2004)CrossRefGoogle Scholar
  12. 12.
    Waxman, B.M.: Routing of multipoint connection. IEEE Journal on Selected Areas in Communications 6, 1617–1622 (1988)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Éderson R. Silva
    • 1
  • Paulo R. Guardieiro
    • 1
  1. 1.Faculty of Electrical EngineeringFederal University of UberlandiaUberlandiaBrazil

Personalised recommendations