Sensor Network Data Fault Detection using Hierarchical Bayesian Space-Time Modeling
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.