A classification and modeling of the quality of contextual information in smart spaces

Authors:
Hyun Lee;Jae Sung Choi;Ramez Elmasri
Affiliations:
Computer Science and Engineering, University of Texas at Arlington, 76019, USA;Computer Science and Engineering, University of Texas at Arlington, 76019, USA;Computer Science and Engineering, University of Texas at Arlington, 76019, USA
Venue:
PERCOM '09 Proceedings of the 2009 IEEE International Conference on Pervasive Computing and Communications
Year:
2009

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Abstract

Reliable contextual information should be generated to provide pervasive services to the occupant in smart spaces. This is difficult for several reasons. First, the number of ways to describe an event or an object is unlimited and there is no standard regarding granularity of context information in context classification schemes. Second, the quality of a given piece of contextual information is not guaranteed by uncertainty. In this paper, we propose a pragmatic context classification and a generalized context modeling scheme based on sensor fusion techniques. To make a pragmatic context classification, we introduce two approaches, “occupant-centered pragmatic approach” and “relation-dependency” approach. To improve the quality of given contextual information by reducing uncertainty, we introduce “state-space based sensor fusion modeling” as a generalized context modeling. Finally, we show an example within the applied scenario as an evidential network.