Managing Sensor Data Uncertainty: A Data Quality Approach

Managing Sensor Data Uncertainty: A Data Quality Approach

Claudia C. Gutiérrez Rodríguez (I3S Laboratory, Nice Sophia-Antipolis University, Sophia-Antipolis, France) and Sylvie Servigne (LIRIS – UMR 5205, INSA de Lyon, Université de Lyon, Villeurbanne, France)
DOI: 10.4018/jaeis.2013010103
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With an increasingly technological improvement, sensors infrastructure actually supports many current and promising environmental applications. Environmental Monitoring Systems built on such sensors removes geographical, temporal and other restraints while increasing both the coverage and the quality of real world understanding. However, a main issue for such applications is the uncertainty of data coming from sensors, which may impact experts’ decisions. In this paper, the authors address this problem with an approach dedicated to provide environmental monitoring applications and users with data quality information.
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The Problem Of Sensor Data Quality

For years, data quality characterizes a key problem for all kind of organizations (Wang & Strong, 1996). Actually, emerging applications in geospatial domain and manipulating sensor data also reveal this important issue. Considering monitoring as a primary key on environmental crisis management systems, an early and reliable detection of critical events is crucial for systems achievement. Environmental monitoring thus requires an efficient acquisition of information coming from sensors (spread over large areas), an interpretation of complex observation patterns at different temporal and spatial scales, as well as reliable and understandable results. These facts led us to wonder: how to evaluate and provide users with quality information?

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