A machine learning approach for identifying and classifying faults in wireless sensor networks

Ehsan Ullah Warriach, Marco Aiello, Kenji Tei

研究成果: Conference contribution

14 引用 (Scopus)

抄録

Wireless Sensor Network (WSN) deployment experiences show that collected data is prone to be faulty. Faults are due to internal and external influences, such as calibration, low battery, environmental interference and sensor aging. However, only few solutions exist to deal with faulty sensory data in WSN. We develop a statistical approach to detect and identify faults in a WSN. In particular, we focus on the identification and classification of data and system fault types as it is essential to perform accurate recovery actions. Our method uses Hidden Markov Models (HMMs) to capture the fault-free dynamics of an environment and dynamics of faulty data. It then performs a structural analysis of these HMMs to determine the type of data and system faults affecting sensor measurements. The approach is validated using real data obtained from over one month of samples from motes deployed in an actual living lab.

元の言語English
ホスト出版物のタイトルProceedings - 15th IEEE International Conference on Computational Science and Engineering, CSE 2012 and 10th IEEE/IFIP International Conference on Embedded and Ubiquitous Computing, EUC 2012
ページ618-625
ページ数8
DOI
出版物ステータスPublished - 2012 12 1
外部発表Yes
イベント15th IEEE International Conference on Computational Science and Engineering, CSE 2012 and 10th IEEE/IFIP International Conference on Embedded and Ubiquitous Computing, EUC 2012 - Paphos, Cyprus
継続期間: 2012 12 52012 12 7

Other

Other15th IEEE International Conference on Computational Science and Engineering, CSE 2012 and 10th IEEE/IFIP International Conference on Embedded and Ubiquitous Computing, EUC 2012
Cyprus
Paphos
期間12/12/512/12/7

Fingerprint

Learning systems
Wireless sensor networks
Hidden Markov models
Sensors
Structural analysis
Aging of materials
Calibration
Recovery

ASJC Scopus subject areas

  • Software

これを引用

Warriach, E. U., Aiello, M., & Tei, K. (2012). A machine learning approach for identifying and classifying faults in wireless sensor networks. : Proceedings - 15th IEEE International Conference on Computational Science and Engineering, CSE 2012 and 10th IEEE/IFIP International Conference on Embedded and Ubiquitous Computing, EUC 2012 (pp. 618-625). [6417349] https://doi.org/10.1109/ICCSE.2012.90

A machine learning approach for identifying and classifying faults in wireless sensor networks. / Warriach, Ehsan Ullah; Aiello, Marco; Tei, Kenji.

Proceedings - 15th IEEE International Conference on Computational Science and Engineering, CSE 2012 and 10th IEEE/IFIP International Conference on Embedded and Ubiquitous Computing, EUC 2012. 2012. p. 618-625 6417349.

研究成果: Conference contribution

Warriach, EU, Aiello, M & Tei, K 2012, A machine learning approach for identifying and classifying faults in wireless sensor networks. : Proceedings - 15th IEEE International Conference on Computational Science and Engineering, CSE 2012 and 10th IEEE/IFIP International Conference on Embedded and Ubiquitous Computing, EUC 2012., 6417349, pp. 618-625, 15th IEEE International Conference on Computational Science and Engineering, CSE 2012 and 10th IEEE/IFIP International Conference on Embedded and Ubiquitous Computing, EUC 2012, Paphos, Cyprus, 12/12/5. https://doi.org/10.1109/ICCSE.2012.90
Warriach EU, Aiello M, Tei K. A machine learning approach for identifying and classifying faults in wireless sensor networks. : Proceedings - 15th IEEE International Conference on Computational Science and Engineering, CSE 2012 and 10th IEEE/IFIP International Conference on Embedded and Ubiquitous Computing, EUC 2012. 2012. p. 618-625. 6417349 https://doi.org/10.1109/ICCSE.2012.90
Warriach, Ehsan Ullah ; Aiello, Marco ; Tei, Kenji. / A machine learning approach for identifying and classifying faults in wireless sensor networks. Proceedings - 15th IEEE International Conference on Computational Science and Engineering, CSE 2012 and 10th IEEE/IFIP International Conference on Embedded and Ubiquitous Computing, EUC 2012. 2012. pp. 618-625
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