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
Link To Document :
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