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The Spatial-Temporal Anomaly Detection Algorithm in Wireless Sensor Networks
The Spatial-Temporal Anomaly Detection Algorithm in Wireless Sensor Networks
Journal from gdlhub / 2016-11-16 07:17:17By : Liu Xin, Zhang Shaoliang, Telkomnika
Created : 2015-09-01, with 1 files
Keyword : wireless sensor networks, spatial–temporal anomaly, data stream
Url : http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/2010
As the traditional anomaly detection algorithms cannot effectively identify the spatial-temporal anomaly of the wireless sensor networks (WSNs), taking the CO2 concentration collected by WSNs for example, we propose the spatial-temporal anomaly detection algorithm in wireless sensor network. First we use the 3 rules to realize the anomaly detection of the adaptive threshold. Then extract the eigenvalue (average) of sliding window to be detected, construct the spatial-temporal matrix for the relationship between neighbor nodes in the specified interval, use the fuzzy clustering method to analyze the eigenvalue of adjacent nodes in spatial-temporal correlation and classify them, and identify abnormal leakage probability according to the results of the classification. Finally, use real data to verify this algorithm and analyze the parameters selected , the results show that the algorithm is high detection rate and low false alarm rate.
Description Alternative :As the traditional anomaly detection algorithms cannot effectively identify the spatial-temporal anomaly of the wireless sensor networks (WSNs), taking the CO2 concentration collected by WSNs for example, we propose the spatial-temporal anomaly detection algorithm in wireless sensor network. First we use the 3 rules to realize the anomaly detection of the adaptive threshold. Then extract the eigenvalue (average) of sliding window to be detected, construct the spatial-temporal matrix for the relationship between neighbor nodes in the specified interval, use the fuzzy clustering method to analyze the eigenvalue of adjacent nodes in spatial-temporal correlation and classify them, and identify abnormal leakage probability according to the results of the classification. Finally, use real data to verify this algorithm and analyze the parameters selected , the results show that the algorithm is high detection rate and low false alarm rate.
Property | Value |
---|---|
Publisher ID | gdlhub |
Organization | Telkomnika |
Contact Name | Herti Yani, S.Kom |
Address | Jln. Jenderal Sudirman |
City | Jambi |
Region | Jambi |
Country | Indonesia |
Phone | 0741-35095 |
Fax | 0741-35093 |
Administrator E-mail | elibrarystikom@gmail.com |
CKO E-mail | elibrarystikom@gmail.com |
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