• DocumentCode
    2238461
  • Title

    an affinity-driven clustering approach for service discovery and composition for pervasive computing

  • Author

    Gaber, J. ; Bakhouya, Mohamed

  • Author_Institution
    Lab. Syst. et Transp., Univ. de Technologie de Belfort-Montbeliard, Belfort
  • fYear
    2006
  • fDate
    26-29 June 2006
  • Firstpage
    277
  • Lastpage
    280
  • Abstract
    Pervasive computing is a new paradigm with a goal to provide computing and communication services all the time and everywhere. In this paper, a service emergence model for the implementation of pervasive computing applications is presented. In this model, ad hoc or composite services are represented by an organization or group of autonomous agents. Agents establish relationships based on affinities. Affinity corresponds to the adequacy with which two services could bind to create a composed service or to point out a similar service. These affinities are adjusted or reinforced by user satisfaction regarding the provided service and dynamic network condition changes. Simulations of this proposed service emergence model with NS2 are also presented
  • Keywords
    mobile agents; ubiquitous computing; affinity-driven clustering approach; autonomous agents; composite services; mobile ad hoc network; pervasive computing; reinforcement learning; service composition; service discovery; service emergence model; user satisfaction; Pervasive computing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Pervasive Services, 2006 ACS/IEEE International Conference on
  • Conference_Location
    Lyon
  • Print_ISBN
    1-4244-0237-9
  • Type

    conf

  • DOI
    10.1109/PERSER.2006.1652241
  • Filename
    1652241