Context-aware QoI computation in multi-sensor systems

Multi-sensor systems are increasingly being deployed in many application scenarios due to the enormous potential they can offer. However, as the processing of sensory data often results in imprecise outcome, measuring the quality of information (QoI) in these systems has become an important issue. The measurement of QoI is usually performed by processing the elementary data provided by the heterogeneous sensors, which is also influenced by the techniques involved in sensor management. However, the effect of context, such as environmental geometry, sensor placement, orientation, time, and other parameters in computing QoI has not yet been explored extensively in the literature. This paper proposes a context evolution model and studies its impact in the QoI computation. In particular, we show that the dynamic context information can be utilized to manage a multi-sensor system to improve its QoI.