{'fraction_non_alphanumeric': 0.02551521099116781, 'fraction_numerical': 0.0, 'mean_word_length': 4.964912280701754, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Sensor Network Data Fault Detection Using Bayesian Maximum a Posterior Sensor Selection and Hierarchical Bayesian Space-Time Models

Author(s): Ni, Kevin; Pottie, G J | Abstract: 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.