Contextual Reasoning under Uncertainty in Sensor Data Stream Monitoring

Contextual data streams monitoring plays an important role in the identification of abnormalities in many application domains. Abnormalities are related to negative effects that, consequently, affect people's quality of living. A number of sensors could be placed in various locations undertaking the responsibility of monitoring specific phenomena. Sensors report their measurements to a central system that is capable of situational reasoning. The system, through decision making, responds to any event related to the observed phenomena. In this paper, the authors propose a mechanism that builds on top of the sensors measurements and derives the appropriate decisions for the immediate identification of events. The proposed system adopts data fusion and prediction time series regression methods for efficiently aggregating sensors observations. They also adopt Fuzzy Logic for handling the uncertainty on the decision making. The authors perform a set of simulations over real data and report on the advantages and disadvantages of the proposed system.