Data faults in sensor networks must be marked to ensure accurate inferences. We introduce a two phase semi-realtime end-to-end Bayesian fault detection system for sensor networks. The first phase selects a subset of agreeing sensors from which a model of expected behavior is derived. The second phase uses this subset to derive and tag questionable sensor data. To accurately model the data, we use a hierarchical Bayesian space-time (HBST) model, as compared to the linear autoregressive modeling used in previous works. Applying this system to simulated and real world data, results are excellent when the phenomenon is well modeled by the HBST model. It achieves high detection rates and almost zero false detection rates. Results also indicate that in cases of critically low spatial sampling density a more accurate model is required.

We present a new application of hierarchical Bayesian space-time (HBST) modeling: data fault detection in sensor networks primarily used in environmental monitoring situations. To show the effectiveness of HBST modeling, we develop a rudimentary tagging system to mark data that does not fit with given models. Using this, we compare HBST modeling against first order linear autoregressive (AR) modeling, which is a commonly used alternative due to its simplicity. We show that while HBST is more complex, it is much more accurate than linear AR modeling as evidenced in greatly reduced false detection rates while maintaining similar, if not better detection rates. HBST modeling reduces false detection rates 41.5% to 96.5% when paired with our simple fault detection method. We also see that HBST modeling is more robust to model mismatches and unmodeled dynamics than linear AR modeling.

Little of the work in sensor network studies related to data quality has presented a detailed study of sensor faults and fault models. We provide a comprehensive look at sensor network data fault types and a unified basis for describing sensor faults backed up by real world deployment examples. We also identify several considerations one must take into account when developing a fault detection or diagnosis system. We suggest a broad framework of important considerations when developing a data fault detection system and discuss some assumptions that can be made in this context. Based upon experience and previous work we define a series of features to consider when modeling sensor data for either fault detection or fault correction. We define three main headings of types of features and explore the effect that each type has on a sensor. Then we list all common faults that we have observed in actual sensor network deployments. We provide an example of how one may use such a taxonomy of faults.