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Information 2018, 9(9), 236; https://doi.org/10.3390/info9090236

Fault-Tolerant Anomaly Detection Method in Wireless Sensor Networks

1
College of Engineering, Huaqiao University, Quanzhou 362000, China
2
Fujian Provincial Academic Engineering Research Centre in Industrial Intellectual Techniques and Systems, Quanzhou 362000, China
*
Authors to whom correspondence should be addressed.
Received: 27 August 2018 / Accepted: 12 September 2018 / Published: 18 September 2018
(This article belongs to the Section Information and Communications Technology)
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Abstract

A key issue in wireless sensor network applications is how to accurately detect anomalies in an unstable environment and determine whether an event has occurred. This instability includes the harsh environment, node energy insufficiency, hardware and software breakdown, etc. In this paper, a fault-tolerant anomaly detection method (FTAD) is proposed based on the spatial-temporal correlation of sensor networks. This method divides the sensor network into a fault neighborhood, event and fault mixed neighborhood, event boundary neighborhood and other regions for anomaly detection, respectively, to achieve fault tolerance. The results of experiment show that under the condition that 45% of sensor nodes are failing, the hit rate of event detection remains at about 97% and the false negative rate of events is above 92%. View Full-Text
Keywords: wireless sensor network; spatial-temporal correlation; fault neighborhood; event and fault mixed neighborhood; event boundary neighborhood; fault-tolerant wireless sensor network; spatial-temporal correlation; fault neighborhood; event and fault mixed neighborhood; event boundary neighborhood; fault-tolerant
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Peng, N.; Zhang, W.; Ling, H.; Zhang, Y.; Zheng, L. Fault-Tolerant Anomaly Detection Method in Wireless Sensor Networks. Information 2018, 9, 236.

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