Novel Method for Outlier Nodes Detection and Localization in Wireless Sensor Networks

Junzheng JIANG; Jie YANG; Shan OUYANG

doi:10.11999/JEIT171207

Volume 40 Issue 10

Sep. 2018

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Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(10): 2358-2364

Junzheng JIANG, Jie YANG, Shan OUYANG. Novel Method for Outlier Nodes Detection and Localization in Wireless Sensor Networks[J]. Journal of Electronics & Information Technology, 2018, 40(10): 2358-2364. doi: 10.11999/JEIT171207

Citation:

Junzheng JIANG, Jie YANG, Shan OUYANG. Novel Method for Outlier Nodes Detection and Localization in Wireless Sensor Networks[J]. Journal of Electronics & Information Technology, 2018, 40(10): 2358-2364. doi: 10.11999/JEIT171207

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Novel Method for Outlier Nodes Detection and Localization in Wireless Sensor Networks

doi: 10.11999/JEIT171207

1.
School of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, China
2.
Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing, Guilin 541004, China

Funds: The National Natural Science Foundation of China (61761011, 61371186), The Natural Science Foundation of Guangxi (2017GXNSFAA198173), The Innovation Project of GUET Graduate Education (2018YJCX34)

Received Date: 2017-12-21
Rev Recd Date: 2018-05-18

Available Online: 2018-07-30

Publish Date: 2018-10-01

Abstract

Abstract

The outlier nodes detection and localization in Wireless Sensor Networks (WSNs) is a crucial step in ensuring the accuracy and reliability of network data acquisition. Based on the theory of graph signal processing, a novel algorithm is presented for outlier detection and localization in WSNs. The new algorithm first builds the graph signal model of the network, then detect the location of the outlier based on the method of vertex-domain and graph frequency-domain joint analysis. Specifically speaking, the first step of algorithm is extracting the high-frequency component of the signal using a high-pass graph filter. In the second step, the network is decomposed into a set of sub-graphs, and then the specific frequency components of the output signal in sub-graphs are filtered out. The third step is to locate the suspected outlier center-nodes of sub-graphs based on the threshold of the filtered sub-graphs signal. Finally, the outlier nodes in the network are detected and located by comparing the set of nodes of each sub-graph with the set of suspected outlier nodes. Experimental results show that compared with the existing outlier detection methods in networks, the proposed method not only has higher probability of outlier detection, but also has a higher positioning rate of outlier nodes.
- Wireless Sensor Networks (WSNs),
- Outlier detection,
- Graph signal processing,
- Sub-graphs,
- Vertex-domain and graph frequency-domain joint analysis

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References

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