Semantic-based discovery to support mobile context-aware service access

  • Authors:

  • Alessandra Toninelli;Antonio Corradi;Rebecca Montanari

  • Affiliations:

  • Dipartimento di Elettronica, Informatica e Sistemistica, University of Bologna, Viale Risorgimento, 2-40136 Bologna, Italy;Dipartimento di Elettronica, Informatica e Sistemistica, University of Bologna, Viale Risorgimento, 2-40136 Bologna, Italy;Dipartimento di Elettronica, Informatica e Sistemistica, University of Bologna, Viale Risorgimento, 2-40136 Bologna, Italy

  • Venue:
  • Computer Communications
  • Year:
  • 2008

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Abstract

The increasing diffusion of portable devices with wireless connectivity enables new pervasive scenarios, where users require tailored service access according to their needs, position, and execution/environment conditions (context-aware services). A crucial requirement for the context-aware service provisioning is the dynamic retrieval and interaction with local resources, i.e., resource discovery. The high degree of dynamicity and heterogeneity of mobile environments requires to rethink and/or extend traditional discovery solutions to support more intelligent service search and retrieval, personalized to user context conditions. Several research efforts have recently emerged in the field of service discovery that, based on semantic data representation and technologies, allow flexible matching between user requirements and service capabilities in open and dynamic deployment scenarios. This paper proposes a middleware-level approach to support user-centric semantic service discovery. The presented middleware, called AIDAS, exploits context-awareness based on user/device/service profile metadata to provide personalized views on services of interest, and supports semantic-based matchmaking between requested and offered service capabilities. In addition, AIDAS addresses the crucial management issue of providing resource-constrained portable devices with needed semantic support features. Semantic support services, such as ontology repositories and inference engines, typically require a large amount of computational and memory resources that might not fit the properties of all mobile devices. AIDAS addresses this issue by transparently and dynamically adapting semantic-based discovery support to the properties of different access devices.