Based on ETEO Pattern Abnormal Event Detection in Wireless Sensor Networks

Bo Tian; Dian Hong Wang; Fen Xiong Chen; Zheng Pu Zhang

doi:10.4028/www.scientific.net/AMR.926-930.1886

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 926-930Based on ETEO Pattern Abnormal Event Detection in...

Based on ETEO Pattern Abnormal Event Detection in Wireless Sensor Networks

Abstract:

This paper presents a new algorithm for the detection of abnormal events in Wireless Sensor Networks (WSN). Abnormal events are sets of data points that correspond to interesting patterns in the underlying phenomenon that the network monitors. This algorithm is inspired from time-series data mining techniques and transforms a stream of sensor readings into an Extension Temporal Edge Operator (ETEO) of time series pattern representation, and then extracts the three eigenvalue of each sub-pattern, that is, patterns length, patterns slope and patterns mean to map time series to feature space, and finally uses local outlier factor to detect abnormal pattern in this feature space. Experiments on synthetic and real data show that the definition of pattern outlier is reasonable and this algorithm is efficient to detect outliers in WSN.

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Periodical:

Advanced Materials Research (Volumes 926-930)

Pages:

1886-1889

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.926-930.1886

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Online since:

May 2014

Authors:

Bo Tian, Dian Hong Wang*, Fen Xiong Chen, Zheng Pu Zhang

Keywords:

Abnormal Event Detection, ETEO Compressed Pattern, Local Outlier Factor, Wireless Sensor Network (WSN)

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* - Corresponding Author

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