Outlier detection based on spatio-temporal nearest neighbors and a likelihood ratio test for sensor networks

LIU Yimin; WEN Junjie; WANG Lanjun

doi:10.16511/j.cnki.qhdxxb.2017.21.029

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Journal of Tsinghua University(Science and Technology) ›› 2017, Vol. 57 ›› Issue (11) : 1196-1201. DOI: 10.16511/j.cnki.qhdxxb.2017.21.029

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Outlier detection based on spatio-temporal nearest neighbors and a likelihood ratio test for sensor networks

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Abstract

A spatio-temporal nearest neighbors and likelihood ratio test method was developed to detect outliers caused by sensor failures in sensor networks. In the space dimension, a sensor's spatially nearest neighbors were selected using a maximum posterior probability criterion while in the time dimension, the temporal nearest neighbors were previous observations from the same sensor. Each sensor's reading was evaluated based on differences between its earlier measurements and those of its neighbors with a sensor failure model and likelihood ratio test used to detect whether the sensor had failed. Tests show that this approach gives a higher detection rate for the same false alarm rate than existing outlier detection approaches. For example, for a 10% false alarm rate, the detection rate was increased by 10%-30%.

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sensor network / outlier detection / spatial-temporal nearest neighbors / likelihood ratio test

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LIU Yimin, WEN Junjie, WANG Lanjun. Outlier detection based on spatio-temporal nearest neighbors and a likelihood ratio test for sensor networks[J]. Journal of Tsinghua University(Science and Technology). 2017, 57(11): 1196-1201 https://doi.org/10.16511/j.cnki.qhdxxb.2017.21.029

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