Modeling and Measuring Quality of Context Information in Pervasive Environments

Pervasive Environments offer new opportunities for users to dynamically access resources and services based on information characterizing their situation (context), generally assuming that this information is correct and trustworthy. In this scenario, the Quality of Context information (QoC) plays an important role for improving context-based adaptation processes and for ensuring the correct behavior of context-aware applications and services. Some research attempts have been done for modeling and measuring the quality of raw context information sensed from the environment. However, so far no attention has been paid to the quality evaluation of derived and inferred context information. This paper describes an approach for modeling and measuring quality of raw, inferred, and derived context information based on the following points of view: privacy, security, precision, completeness, and resolution. We propose a context management framework that support QoC in their various layers, protecting and providing QoC-enriched context information of users to context-aware applications and services.