Fault Detection of WSN Based on Spatial Correlation

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Abstract:

Regarding application with smooth variation of detection, spatial correlation of sensors’ data within a small field was applied to sensor nodes’ fault diagnosis. The data were sorted into several continuous sequences by sink node. Sequence with minimum variance was regarded as normal data to determine normal nodes. For undetermined nodes, it can be determined via calculation on deviation to normal nodes’ data of vicinity area. If deviation does not exceed the threshold, the node is normal; otherwise, it is regarded as a fault node. The research on WSN in a greenhouse shows that fault node can be effectively detected in time by this method.

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1504-1510

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June 2011

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© 2011 Trans Tech Publications Ltd. All Rights Reserved

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[1] Sheth A, Hartung C, Han R. A Decentralized Fault Diagnosis System for Wireless Sensor Networks. Mobile Adhoc and Sensor Systems Conference, 2005. IEEE International Conference, 2005: 3 pp.

DOI: 10.1109/mahss.2005.1542799

Google Scholar

[2] Koushanfar F, Potkonjak M, Sangiovanni-Vincentelli A. On-Line fault detection of sensor measurements. In: Proc. of the IEEE Sensors. 2003, 2: 974−979.

DOI: 10.1109/icsens.2003.1279088

Google Scholar

[3] Kuo-Feng Ssu, Chih-Hsun Chou, Hewijin Christine Jiau, Wei-Te Hu. Detection and diagnosis of data inconsistency failures in wireless sensor networks. Computer networks (S1389-1286), 2005, 50: 1247-1260.

DOI: 10.1016/j.comnet.2005.05.034

Google Scholar

[4] Ding M., Chen D, Xing K, Cheng X. Localized fault-tolerant event boundary detection in sensor networks. Proceedings IEEE of INFOCOM 2005. 24th Annual Joint Conference of the IEEE Computer and Communications Societies. 2005, 2: 902-913.

DOI: 10.1109/infcom.2005.1498320

Google Scholar

[5] Chatzigiannakis V, Papavassiliou S. Diagnosing Anomalies and Identifying Faulty Nodes in Sensor Networks. Sensors Journal, IEEE, 2007, 7(5): 637-645.

DOI: 10.1109/jsen.2007.894147

Google Scholar

[6] Rajasegarar S, Leckie C, Palaniswami M, et al. Distributed anomaly detection in wireless sensor networks. IEEE International Conference on Communication Systems, Singapore, 2006: 1–5.

DOI: 10.1109/iccs.2006.301508

Google Scholar

[7] Krishnamachari B, Iyengar S. Distributed Bayesian algorithms for fault-tolerant event region detection in wireless sensor networks. Computers, IEEE Transactions, 2004, 53(3): 241–250.

DOI: 10.1109/tc.2004.1261832

Google Scholar

[8] Khilar P M, Mahapatra S. Intermittent Fault Diagnosis in Wireless Sensor Networks. Information Technology, 10th International Conference, 2007: 145–147.

DOI: 10.1109/icit.2007.15

Google Scholar

[9] Luo Xuanwen, Dong Ming, Huang Yinlun. On distributed fault-tolerant detection in wireless sensor networks. Computers, IEEE Transactions, 2006, 55(1): 58-70.

DOI: 10.1109/tc.2006.13

Google Scholar

[10] GAO Jian-Liang, XU Yong-Jun, LI Xiao-Wei. Weighted-Median Based Distributed Fault Detection for Wireless Sensor Networks Journal of Software, 2007, 18(5): 1208-1217.

DOI: 10.1360/jos181208

Google Scholar

[11] M C Vuran, O B Akan, I F Akyildiz. Spatio-temporal correlation: Theory and applications for wireless sensor networks. Computer Networks Journal (Elsevier), 2004, 45(3): 245–25.

DOI: 10.1016/j.comnet.2004.03.007

Google Scholar