Internet of Things data monitoring method based on recursive entropy feature extraction

Xuanxuan Li; Mingyue Yang; Qinghua Wang; Chen Zang

doi:10.1117/12.2641152

3 October 2022 Internet of Things data monitoring method based on recursive entropy feature extraction

Xuanxuan Li, Mingyue Yang, Qinghua Wang, Chen Zang

Proceedings Volume 12290, International Conference on Computer Network Security and Software Engineering (CNSSE 2022); 122900Z (2022) https://doi.org/10.1117/12.2641152
Event: International Conference on Computer Network Security and Software Engineering (CNSSE 2022), 2022, Zhuhai, China

Abstract

This paper studies the detection of node data anomalies in the Internet of Things to improve the accuracy of detection. When the content of the external data collected by the sensor nodes in the Internet of Things is random and there are great differences in the characteristics of data storage form and format, it is difficult to achieve consistency between the data of each node. The traditional detection method can not accurately propose the unified features to be detected according to the difference information, resulting in the problem of low detection accuracy. Data monitoring is the basis of the application of the Internet of Things. The current data monitoring methods have the defects of high data repetition rate and low monitoring accuracy. In order to obtain the ideal data monitoring results of the Internet of Things, a traffic prediction algorithm based on quantitative recursive feature extraction is proposed, The time series analysis model of short-term network traffic in network end-to-end routing buffer is constructed. The false nearest neighbor algorithm and average mutual information algorithm are used to reconstruct the phase space of network traffic time series, calculate the percentage of time-frequency feature points in the recursive graph plane to the total number of points in the plane, and realize the time-frequency entropy feature extraction of network traffic, The simulation results show that the algorithm can accurately predict and judge the phase trajectory of network traffic, and the prediction process has good anti-interference ability and high prediction accuracy.

Citation Download Citation

Xuanxuan Li, Mingyue Yang, Qinghua Wang, and Chen Zang "Internet of Things data monitoring method based on recursive entropy feature extraction", Proc. SPIE 12290, International Conference on Computer Network Security and Software Engineering (CNSSE 2022), 122900Z (3 October 2022); https://doi.org/10.1117/12.2641152

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KEYWORDS

Internet

Data transmission

Data communications

Data storage

Data modeling

Feature extraction

Computer networks

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