Faults in Sensory Readings: Classification and Model Learning

Sensors and Transducers

Volumn 18, Issue SPEC.ISS.1, 2013, Pages 177-187

  Baljak, Valentina ^a   Kenji, Tei ^b   Honiden, Shinichi ^a  

^a UNIVERSITY OF TOKYO   (Japan)

^b NATIONAL INSTITUTE OF INFORMATICS   (Japan)

Author keywords

Fault tolerance; Model learning; Statistical pattern recognition; Wireless sensor networks

Indexed keywords

DATA CENTRIC; DETECTION METHODS; FAULT CLASSIFICATION; MODEL LEARNING; STATISTICAL PATTERN RECOGNITION; UNDERLYING CAUSE;

DECISION TREES; FAULT DETECTION; FAULT TOLERANCE; PATTERN RECOGNITION; TIME SERIES ANALYSIS;

WIRELESS SENSOR NETWORKS;

EID: 84880270057     PISSN: None     EISSN: 17265479     Source Type: Trade Journal    
DOI: None     Document Type: Article

References (19)

1. F. Koushanfar, M. Potkonjak, and A. Sangiovannivincentelli, Fault Tolerance in Wireless Sensor Networks, in Handbook of Sensor Networks, CRC Press, 2004.
2. H. Karl and A. Willig, Protocols and Architectures for Wireless Sensor Networks, John Wiley & Sons, 2007.
3. A. Hac, Wireless Sensor Network Designs, John Wiley & Sons, 2004.
4. J. Shell, S. Coupland, and E. Goodyer, Fuzzy Data Fusion for Fault Detection in Wireless Sensor Networks, in UK Workshop on Computational Intelligence (UKCI), 2010, pp. 1-6.
5. th International Conference on Information Fusion (FUSION '09), 2009, pp. 132-139.
6. K. Ni et al., Sensor network data fault types, ACM Transactions on Sensor Networks, Vol. 5, No. 3, June 2009, pp. 25:1-25:29.
7. E. Ould-Ahmed-Vall, B. H. Ferri, and G. F. Riley, Distributed Fault-Tolerance for Event Detection Using Heterogeneous Wireless Sensor Networks, IEEE Transactions on Mobile Computing, Vol. 11, No. 12, December 2012, pp. 1994-2007.
8. A. B. Sharma, L. Golubchik, and R. Govindan, Sensor faults: Detection methods and prevalence in real-world datasets, ACM Transactions on Sensor Networks, Vol. 6, No. 6, June 2010, pp. 1-39.
9. Y. Yao, A. Sharma, L. Golubchik, and R. Govindan, Online anomaly detection for sensor systems: A simple and efficient approach, Performance Evaluation, Vol. 67, No. 11, November 2010, pp. 1059-1075.
10. R. Tiwari and M. T. Thai, On Enhancing Fault Tolerance of Virtual Backbone in a Wireless Sensor Network with Unidirectional Links, in Sensors: Theory, Algorithms, and Applications, V. L. Boginski et al., Eds., Springer, New York, 2012, pp. 3-18.
11. G. Huang, Y. Zhang, J. He, and J. Cao, Fault Tolerance in Data Gathering Wireless Sensor Networks, The Computer Journal, Vol. 54, No. 6, 2011, pp. 976-987.
12. Richard Gilstrap, Ray Baldwin, Jarren Stone, Thom Wilson, Pete Alena, Fault tolerance in ZigBee wireless sensor networks, in Proceedings of the IEEE Aerospace Conference, 2011, pp. 1-15.
13. V. Baljak, K. Tei, and S. Honiden, Classification of Faults in Sensor Readings with Statistical Pattern Recognition, in Proceedings of the 6th International Conference on Sensor Technologies and Applications (SENSORCOMM' 2012), Rome, Italy 2012, pp. 270-276.
14. S. J. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 2nd ed., Pearson Education, 2003.
15. th IEEE International Conference on Mobile Adhoc and Sensor Systems (MASS), 2011, pp. 1-6.
16. Intel Berkely Laboratory. [Online] (http://db.csail.mit.edu/labdata/labdata.htmlf)
17. O. Ghica, (2012, November) SIDnet-SWANS. [Online] (http://users.eecs.northwestern.edu/~ocg474/SIDnet.html
18. nd ed., Wiley-Interscience, 2001.
19. Java-ML. (2012, November) Java Machine Learning Library. [Online] (http://java-ml.sourceforge.net)