Uncertainty handling in semantic reasoning for accurate context understanding

Context aware systems are using various sensing technologies in order to recognize end-users situations; however these technologies are vulnerable to hardware failures, energy depletion, communication problems and multiple other issues. This generates an uncertainty about the events received from the sensors, which is translated into a confidence given to these events. This confidence is used in the context-aware reasoning through a fusion of sensor data to make more accurate decisions. In this paper, we focus on handling uncertainty in sensor-based context aware applications and we propose a method for the measurement of uncertainty based on both physical and operational behaviors of the sensors. We describe how the level of uncertainty is incorporated into different layers of a semantically driven context aware system and how it is transferred to a decision engine in order to perform more accurate decisions in ambiguous observations.