Original scientific paper

https://doi.org/10.17559/TV-20160108183528

Study on sensor fault instability prediction for the Internet of agricultural things based on largest Lyapunov exponent

Yong Li ; Open Laboratory of Geo-spatial Information Technology and Application of Guangdong Province, Guangzhou Institute of Geography, 100 Xianliezhong Road, Guangzhou 510070, China
Jingfeng Yang ; Open Laboratory of Geo-spatial Information Technology and Application of Guangdong Province, Guangzhou Institute of Geography, 100 Xianliezhong Road, Guangzhou 510070, China
Nanfeng Zhang ; Chief Engineer of Guangzhou Entry Exit Inspection and Quarantine Bureau laboratory
Ji Yang ; Open Laboratory of Geo-spatial Information Technology and Application of Guangdong Province, Guangzhou Institute of Geography, 100 Xianliezhong Road, Guangzhou 510070, China
Handong Zhou ; Guangzhou Yuntu Information Technology Co., South China Agricultural University College of Engineering, 19 Tangdongdong Road, Guangzhou 510665, China
Jiarong He ; Post-graduate student in South China University of Technology

Abstract

This study uses the largest Lyapunov exponent algorithm to predict the fault types in the wireless sensor network of the Internet of Agricultural Things. System fault data are collected from the Internet of Agricultural Things, which is composed of a calibrated TDR soil moisture sensor network, to develop a sensor fault instability prediction model based on the largest Lyapunov exponent algorithm. To verify the applicability of this model in forecasting training samples under various conditions, this study tests and compares such algorithm with the C4.5 algorithm model as a fault data account for different percentages of training samples. The largest Lyapunov exponent instability prediction method is also applied on the training set that mostly comprises normal data. The algorithm achieves a prediction accuracy of 90,43 %, which is 5,55 % higher than that of the C4.5 algorithm (84,88 %). Different algorithms demonstrate a certain degree of adaptability in various application conditions. The largest Lyapunov exponent instability prediction method achieves better results when many accurate samples are used. The results from the application adaptability test show that the sensor fault instability prediction model based on the largest Lyapunov exponent algorithm provides a reliable approach for collecting sensor fault information collection and predicting faults in the Internet of Agricultural Things.

Keywords

fault instability prediction; internet of agricultural things; largest Lyapunov exponent algorithm; sensor

Hrčak ID:

153154

URI

https://hrcak.srce.hr/153154

Publication date:

19.2.2016.

Article data in other languages: croatian

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