Paper: Spatiotemporal Anomaly Detection in Gas Monitoring Sensor Networks January 31
Our paper “Spatiotemporal Anomaly Detection in Gas Monitoring Sensor Networks” is currently being presented at the European conference on Wireless Sensor Networks (EWSN’08) in Bologna, Italy. In this paper, we use Bayesian Networks as a means for unsupervised learning and anomaly (event) detection in gas monitoring sensor networks for underground coal mines. We show that the Bayesian Network model can learn cyclical baselines for gas concentrations, and by this reduce false alarms usually caused by flatline thresholds. You can check out the details here.
Technorati Tags: paper, sensor networks, bayesian networks, anomaly detection, coal mines
