  Remote sensors are becoming the standard for observing and recording
ecological data in the field. Such sensors can record data at fine temporal
resolutions, and they can operate under extreme conditions prohibitive to human
access. Unfortunately, sensor data streams exhibit many kinds of errors ranging
from corrupt communications to partial or total sensor failures. This means
that the raw data stream must be cleaned before it can be used by domain
scientists. In our application environment|the H.J. Andrews Experimental
Forest|this data cleaning is performed manually. This paper introduces a
Dynamic Bayesian Network model for analyzing sensor observations and
distinguishing sensor failures from valid data for the case of air temperature
measured at 15 minute time resolution. The model combines an accurate
distribution of long-term and short-term temperature variations with a single
generalized fault model. Experiments with historical data show that the
precision and recall of the method is comparable to that of the domain expert.
The system is currently being deployed to perform real-time automated data
cleaning.
