• DocumentCode
    2798958
  • Title

    A QoS Adaptive Multi-path Reinforcement Learning Routing Algorithm for MANET

  • Author

    Ziane, Saïda ; Mellouk, Abdelhamid

  • Author_Institution
    Univ. of Paris Xll-Val de Marne, Paris
  • fYear
    2007
  • fDate
    13-16 May 2007
  • Firstpage
    659
  • Lastpage
    664
  • Abstract
    The goals of QoS routing are in general twofold: selecting routes with satisfied QoS requirement, and achieving global efficiency in resource utilization. The prediction of these goals in real time is quite difficult, making the effectiveness of "traditional" protocols based on analytical models questionable. In this paper we first discuss some key design considerations in providing QoS routing support, and present a review of previous work addressing the problem of route selection in interaction with QoS constraints. We then devise a solution based on swarm intelligence paradigm based on reinforcement learning approach that we find more adapted for this kind of problems. Finally, we discuss some possible future directions for providing efficient QoS routing mechanisms in wireless ad hoc networks.
  • Keywords
    ad hoc networks; adaptive systems; learning (artificial intelligence); mobile radio; multimedia communication; multipath channels; quality of service; resource allocation; routing protocols; telecommunication computing; telecommunication traffic; MANET; QoS adaptive multipath reinforcement learning routing algorithm; QoS constraints; mobile ad hoc network; multimedia traffic; quality of service; resource utilization; routing protocol; swarm intelligence paradigm; wireless ad hoc networks; Ad hoc networks; Bandwidth; Learning; Mobile ad hoc networks; Network topology; Particle swarm optimization; Quality of service; Routing protocols; Streaming media; Telecommunication traffic;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Systems and Applications, 2007. AICCSA '07. IEEE/ACS International Conference on
  • Conference_Location
    Amman
  • Print_ISBN
    1-4244-1030-4
  • Electronic_ISBN
    1-4244-1031-2
  • Type

    conf

  • DOI
    10.1109/AICCSA.2007.370701
  • Filename
    4231029