Sensor network data fault detection with maximum a posteriori selection and bayesian modeling
| Autor: | Kevin Ni, Greg Pottie |
|---|---|
| Rok vydání: | 2012 |
| Předmět: |
Computer Networks and Communications
Computer science business.industry Bayesian probability Fault (power engineering) computer.software_genre Bayesian inference Machine learning Fault detection and isolation Autoregressive model Data integrity Maximum a posteriori estimation Artificial intelligence Data mining business computer Wireless sensor network |
| Zdroj: | ACM Transactions on Sensor Networks. 8:1-21 |
| ISSN: | 1550-4867 1550-4859 |
| Popis: | Current sensor networks experience many faults that hamper the ability of scientists to draw significant inferences. We develop a method to systematically identify when these faults occur so that proper corrective action can be taken. We propose an adaptable modular framework that can utilize different modeling methods and approaches to identifying trustworthy sensors. We focus on using hierarchical Bayesian space-time (HBST) modeling to model the phenomenon of interest, and use maximum a posteriors selection to identify a set of trustworthy sensors. Compared to an analogous linear autoregressive system, we achieve excellent fault detection when the HBST model accurately represents the phenomenon. |
| Databáze: | OpenAIRE |
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