Abstract
Many pervasive applications depend on data from sensors that are placed in the applications physical environment. In these applications, the quality of the sensor data—e.g., its accuracy or a failed object detection—is of crucial importance for the application knowledge base and processing results. However, through the increasing complexity and the proprietary of sensors, applications cannot directly request information about the quality of the sensor measurements. However, an indirect quality assessment is possible by using additional simple sensors. Our approach uses information from these additional sensors to construct upper and lower bounds of the probability of failed measurements, which in turn can be used by the applications to adapt their decisions. Within this framework it is possible to fuse multiple heterogeneous indirect sensors through the aggregation of multiple quality evidences. This approach is evaluated using sensor data to detect the quality of template matching sensors.
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Kuka, C., Nicklas, D. (2012). Approximating Complex Sensor Quality Using Failure Probability Intervals. In: Hüllermeier, E., Link, S., Fober, T., Seeger, B. (eds) Scalable Uncertainty Management. SUM 2012. Lecture Notes in Computer Science(), vol 7520. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33362-0_22
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DOI: https://doi.org/10.1007/978-3-642-33362-0_22
Publisher Name: Springer, Berlin, Heidelberg
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