Spatiotemporal Anomaly Detection in Gas Monitoring Sensor Networks

  • X. Rosalind Wang
  • Joseph T. Lizier
  • Oliver Obst
  • Mikhail Prokopenko
  • Peter Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4913)


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, thus reducing false alarms usually caused by flatline thresholds. Further, we show that the system can learn dependencies between changes of concentration in different gases and at multiple locations. We define and identify new types of events that can occur in a sensor network. In particular, we analyse joint events in a group of sensors based on learning the Bayesian model of the system, contrasting these events with merely aggregating single events. We demonstrate that anomalous events in individual gas data might be explained if considered jointly with the changes in other gases. Vice versa, a network-wide spatiotemporal anomaly may be detected even if individual sensor readings were within their thresholds. The presented Bayesian approach to spatiotemporal anomaly detection is applicable to a wide range of sensor networks.


Sensor Network Sensor Node False Alarm Bayesian Network Coal Mine 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • X. Rosalind Wang
    • 1
  • Joseph T. Lizier
    • 1
    • 2
  • Oliver Obst
    • 1
  • Mikhail Prokopenko
    • 1
  • Peter Wang
    • 1
  1. 1.CSIRO ICT CentreNorth RydeAustralia
  2. 2.School of Information TechnologiesThe University of SydneyAustralia

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