Center for Embedded Network Sensing

The identification of sensors returning unreliable data is an important task when working with sensor networks. The detection of these sensors while in the field can cue human involvement in repairing problem sensors. This ensures meaningful data is collected throughout the entire length of a sensor deployment. We present a method of selecting non-faulty sensors from a given set of sensors that are expected to behave similarly. We use a Bayesian approach to select a subset of sensors which give the best probability of being correct given the data. From this we can determine whether other sensors' readings fall out of a reasonable range for the sensor set. Using data collected in a test conditions and environment data collected in the forest we verify that our method successfully selects all sensors that are expected to be correct.