Spatiotemporal anomaly detection in gas monitoring sensor networks

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4913 LNCS, Issue , 2008, Pages 90-105

  Wang, X Rosalind ^a   Lizier, Joseph T ^a,b   Obst, Oliver ^a   Prokopenko, Mikhail ^a   Wang, Peter ^a  

^a CSIRO   (Australia)

^b UNIVERSITY OF SYDNEY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

ALARM SYSTEMS; BAYESIAN NETWORKS; COAL GAS; COAL MINES; CONCENTRATION (PROCESS); DISTRIBUTED PARAMETER NETWORKS; GAS DETECTORS; GASES; HYBRID SENSORS; INFERENCE ENGINES; INTELLIGENT NETWORKS; MINING; SENSORS; SPEECH ANALYSIS; SPEECH RECOGNITION; STRUCTURES (BUILT OBJECTS); WIRELESS SENSOR NETWORKS;

ANOMALOUS EVENTS; ANOMALY DETECTIONS; BAYESIAN APPROACHES; BAYESIAN MODELLING; BAYESIAN-NETWORK MODELS; EUROPEAN; FALSE ALARMS; GAS CONCENTRATIONS; GAS MONITORING; OF SENSORS; SENSOR READINGS; UNDERGROUND COAL MINES;

SENSOR NETWORKS;

EID: 49949106302     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-77690-1_6     Document Type: Conference Paper

References (25)

1. Wang, P., Wang, X.R., Guo, Y., Gerasimov, V., Prokopenko, M., Fillon, O., Haustein, K., Rowan, G.: Anomaly detection in coal-mining sensor data, report 2: Feasibility study and demonstration. Technical Report 07/084, CSIRO, ICT Centre (2007)
2. Xue, W., Luo, Q., Chen, L., Liu, Y.: Contour map matching for event detection in sensor networks. In: Proceedings of the 2006 ACM SIGMOD international conference on Management of data, Chicago, IL, USA, pp. 145-156 (2006)
3. Ypma, A., Duin, R.P.W.: Novelty detection using self-organizing maps. In: Progress in Connectionist-Based Information Systems, vol. 2, pp. 1322-1325. Springer, London (1997)
4. Davy, M., Desobry, F., Gretton, A., Doncarli, C.: An online support vector machine for abnormal events detection. Signal Processing 86(8), 2009-2025 (2006)
5. Mamei, M., Nagpal, R.: Macro programming through Bayesian Networks: Distributed inference and anomaly detection. In: PerCom. 2007. Fifth Annual IEEE International Conference on Pervasive Computing and Communications, Los Alamitos, CA, USA, pp. 87-96 (2007)
6. Kumar, A.V.U.P., Reddy, A.M.V., Janakiram, D.: Distributed collaboration for event detection in wireless sensor networks. In: Proceedings of the 3rd international workshop on Middleware for pervasive and ad-hoc computing, pp. 1-8 (2005)
7. Jiao, B., Son, S.H., Stankovic, J.A.: GEM: Generic event service middleware for wireless sensor networks. In: Second International Workshop on Networked Sensing Systems (2005)
8. Friedman, N., Koller, D.: Being Bayesian about network structure: A Bayesian approach to structure discovery in Bayesian Networks. Machine Learning 50, 95-126 (2003)
9. Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian network classifiers. Machine Learning 29, 131-163 (1997)
10. Jensen, F.V.: Bayesian Networks and Decision Graphs. Springer, New York (2001)
11. Pearl, J.: Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann, San Francisco (1988)
12. Packard, N.H., Crutchfield, J.P., Farmer, J.D., Shaw, R.S.: Geometry from a time series. Phys. Rev. Lett. 45(9), 712-716 (1980)
13. MacKay, D.J.: Information Theory, Learning and Inference. Cambridge University Press, Cambridge (2003)
14. Myung, I.J.: Tutorial on maximum likelihood estimation. Journal of Mathematical Psychology 47, 90-100 (2003)
15. Fraser, A.M., Swinney, H.L.: Independent coordinates for strange attractors from mutual information. Phys. Rev. A 33(2), 1134-1140 (1986)
16. Massey, F.J.: The Kolmogorov-Smirnov test for goodness of fit. Journal of the American Statistical Association 46(253), 68-78 (1951)
17. Menzies, T., Allen, D., Orrego, A.: Bayesian anomaly detection. In: ICML 2006. Workshop on Machine learning Algorithms for Surveillance and Event Detection, PA, USA (June 2006)
18. Schreiber, T.: Measuring information transfer. Phys. Rev. Lett. 85(2), 461-464 (2000)
19. Lizier, J.T., Prokopenko, M., Zomaya, A.Y.: Detecting non-trivial computation in complex dynamics. In: Almeida e Costa, F., et al. (eds.) ECAL 2007. LNCS (LNAI), vol. 4648, pp. 895-904. Springer, Heidelberg (2007)
20. Grassberger, P., Procaccia, I.: Estimation of the Kolmogorov entropy from a chaotic signal. Phys. Rev. A 28(4), 2591-2593 (1983)
21. Takens, F.: Detecting strange attractors in turbulence. Lecture Notes in Mathematics, vol. 898, pp. 366-381. Springer, Heidelberg (1981)
22. Takens, F.: Invariants related to dimension and entropy. In: Atas do 13 Colóquio Brasiliero do Matemática, Rio de Janeiro (1983)
23. Theiler, J.: Spurious dimension from correlation algorithms applied to limited time-series data. Phys. Rev. A 34(3), 2427-2432 (1986)
24. Dhamala, M., Lai, Y., Kostelich, E.: Analyses of transient chaotic time series. Phys. Rev. E 64(5), 56207-56216 (2001)
25. Kugiumtzis, D., Lillekjendlie, B., Christophersen, N.: Chaotic time series part I: Estimation of some invariant properties in state space. Modeling, Identification and Control 15, 205-224 (1994)