Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Machine Learning - Special issue on learning with probabilistic representations
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Tutorial on maximum likelihood estimation
Journal of Mathematical Psychology
Distributed collaboration for event detection in wireless sensor networks
MPAC '05 Proceedings of the 3rd international workshop on Middleware for pervasive and ad-hoc computing
Contour map matching for event detection in sensor networks
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
An online support vector machine for abnormal events detection
Signal Processing - Special section: Advances in signal processing-assisted cross-layer designs
Macro Programming through Bayesian Networks: Distributed Inference and Anomaly Detection
PERCOM '07 Proceedings of the Fifth IEEE International Conference on Pervasive Computing and Communications
Detecting non-trivial computation in complex dynamics
ECAL'07 Proceedings of the 9th European conference on Advances in artificial life
Efficient Signal Processing and Anomaly Detection in Wireless Sensor Networks
EvoWorkshops '09 Proceedings of the EvoWorkshops 2009 on Applications of Evolutionary Computing: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG
Mote-Based Online Anomaly Detection Using Echo State Networks
DCOSS '09 Proceedings of the 5th IEEE International Conference on Distributed Computing in Sensor Systems
Spatio-temporal event detection using dynamic conditional random fields
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
CRITIS'09 Proceedings of the 4th international conference on Critical information infrastructures security
Ensemble based sensing anomaly detection in wireless sensor networks
Expert Systems with Applications: An International Journal
Proceedings of the 15th ACM international conference on Modeling, analysis and simulation of wireless and mobile systems
Hi-index | 0.00 |
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.