Middleware for task resolution and adaptation in pervasive environments

Driven by the heterogeneity of pervasive environments, a user task can be defined independently of devices’ resources as an assembly of abstract components, requiring services from and providing services to each other. To achieve the task’s execution, it has to be resolved in concrete components, which involves automatic matching and selection of components across various devices. Moreover, user tasks in pervasive environments are challenged by the dynamism of their execution environments. Thus, there is a need to adapt them for a continuous execution. Towards these challenges, we propose in this article a middleware that allows for each service of a user task, the best selection of the device and component used for its execution. The task resolution approach considers in addition to the functional aspects of the task, the user preferences, devices capabilities, services requirements and components preferences. The middleware also carries out adaptation of user’s tasks to cope with the dynamicity of pervasive environments. The adaptation consists of a partial reselection of devices and components that are affected by the changes. For this purpose, the middleware uses monitoring mechanisms to detect the changes during the execution of the user tasks.

[1]  Djamel Belaïd,et al.  User Preferences-Based Automatic Device Selection for Multimedia User Tasks in Pervasive Environments , 2009, 2009 Fifth International Conference on Networking and Services.

[2]  David Garlan,et al.  Aura: an Architectural Framework for User Mobility in Ubiquitous Computing Environments , 2002, WICSA.

[3]  Frank Eliassen,et al.  Using architecture models for runtime adaptability , 2006, IEEE Software.

[4]  Mohamed Bakhouya Special Issue: Adaptive Service Discovery and Composition in Ubiquitous and Pervasive Computing , 2011, TAAS.

[5]  Roy H. Campbell,et al.  A Middleware-Based Application Framework for Active Space Applications , 2003, Middleware.

[6]  Guy Bernard,et al.  A model for resource specification in mobile services , 2008, SIPE '08.

[7]  G. Klyne,et al.  Composite Capability/Preference Profiles (CC/PP) : Structure and Vocabularies , 2001 .

[8]  Djamel Belaïd,et al.  Automatic Task Resolution and Adaptation in Pervasive Environments , 2011, ICAIS.

[9]  Stéphane Frénot,et al.  MySIM: a spontaneous service integration middleware for pervasive environments , 2009, ICPS.

[10]  Gregor Schiele,et al.  PCOM - a component system for pervasive computing , 2004, Second IEEE Annual Conference on Pervasive Computing and Communications, 2004. Proceedings of the.

[11]  Seyed Masoud Sadjadi,et al.  Composing adaptive software , 2004, Computer.

[12]  Walid Gaaloul,et al.  Clustering and Managing Data Providing Services Using Machine Learning Technique , 2011, 2011 Seventh International Conference on Semantics, Knowledge and Grids.

[13]  Valérie Issarny,et al.  COCOA: COnversation-based service COmposition in pervAsive computing environments with QoS support , 2007, J. Syst. Softw..

[14]  Hamid Mukhtar,et al.  Dynamic User Task Composition Based on User Preferences , 2011, TAAS.

[15]  Schahram Dustdar,et al.  Web service clustering using multidimensional angles as proximity measures , 2009, TOIT.

[16]  Mohan Kumar,et al.  Dynamic Service Composition in Pervasive Computing , 2007, IEEE Transactions on Parallel and Distributed Systems.

[17]  Gregory D. Abowd,et al.  Towards a Better Understanding of Context and Context-Awareness , 1999, HUC.