Mobile Wireless Middleware, Operating Systems, and Applications. Third International Conference, Mobilware 2010, Chicago, IL, USA, June 30 - July 2, 2010. Revised Selected Papers

Research Article

A Self-organizing Approach for Building and Maintaining Knowledge Networks

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  • @INPROCEEDINGS{10.1007/978-3-642-17758-3_13,
        author={Gabriella Castelli and Marco Mamei and Franco Zambonelli},
        title={A Self-organizing Approach for Building and Maintaining Knowledge Networks},
        proceedings={Mobile Wireless Middleware, Operating Systems, and Applications. Third International Conference, Mobilware 2010, Chicago, IL, USA, June 30 - July 2, 2010. Revised Selected Papers},
        proceedings_a={MOBILWARE},
        year={2012},
        month={10},
        keywords={Distributed Middleware Knowledge Networks Context Awareness},
        doi={10.1007/978-3-642-17758-3_13}
    }
    
  • Gabriella Castelli
    Marco Mamei
    Franco Zambonelli
    Year: 2012
    A Self-organizing Approach for Building and Maintaining Knowledge Networks
    MOBILWARE
    Springer
    DOI: 10.1007/978-3-642-17758-3_13
Gabriella Castelli1,*, Marco Mamei1,*, Franco Zambonelli1,*
  • 1: University of Modena and Reggio Emilia
*Contact email: gabriella.castelli@unimore.it, marco.mamei@unimore.it, franco.zambonelli@unimore.it

Abstract

Pervasive and mobile devices can generate huge amounts of contextual data, from which knowledge about situations occurring in the world can be inferred for the use of pervasive services. Due to the overwhelming amount of data and the distributed and dynamic nature of pervasive systems, this may be not a trivial task. Indeed the management of contextual data should be run by a dedicate middleware layer, i.e., knowledge networks in charge of organizing and aggregating such data to facilitate its exploitation by pervasive services. In this paper we introduce a unsupervised, distributed and self-organizing approach to build and maintain such a layer based on simple agents that organize and extract useful information from the data space. We also present a Java-based implementation of the approach and discuss experimental results.