气候变化领域集成服务门户(CCPortal): Data-Driven Anomaly Detection Approach for Time-Series Streaming Data

CCPortal

DOI	10.3390/s20195646
	Data-Driven Anomaly Detection Approach for Time-Series Streaming Data
	Zhang, Minghu; Guo, Jianwen; Li, Xin; Jin, Rui
通讯作者	Guo, JW (通讯作者)
发表日期	2020
EISSN	1424-8220
卷号	20 期号:19
英文摘要	Recently, wireless sensor networks (WSNs) have been extensively deployed to monitor environments. Sensor nodes are susceptible to fault generation due to hardware and software failures in harsh environments. Anomaly detection for the time-series streaming data of sensor nodes is a challenging but critical fault diagnosis task, particularly in large-scale WSNs. The data-driven approach is becoming essential for the goal of improving the reliability and stability of WSNs. We propose a data-driven anomaly detection approach in this paper, named median filter (MF)-stacked long short-term memory-exponentially weighted moving average (LSTM-EWMA), for time-series status data, including the operating voltage and panel temperature recorded by a sensor node deployed in the field. These status data can be used to diagnose device anomalies. First, a median filter (MF) is introduced as a preprocessor to preprocess obvious anomalies in input data. Then, stacked long short-term memory (LSTM) is employed for prediction. Finally, the exponentially weighted moving average (EWMA) control chart is employed as a detector for recognizing anomalies. We evaluate the proposed approach for the panel temperature and operating voltage of time-series streaming data recorded by wireless node devices deployed in harsh field conditions for environmental monitoring. Extensive experiments were conducted on real time-series status data. The results demonstrate that compared to other approaches, the MF-stacked LSTM-EWMA approach can significantly improve the detection rate (DR) and false rate (FR). The average DR and FR values with the proposed approach are 95.46% and 4.42%, respectively. MF-stacked LSTM-EWMA anomaly detection also achieves a better F-2 score than that achieved by other methods. The proposed approach provides valuable insights for anomaly detection in WSNs by detecting anomalies in the time-series status data recorded by wireless sensor nodes.
关键词	INTEGRATED-SYSTEM INTERNET NETWORK
英文关键词	wireless sensor network; environmental monitoring; anomaly detection; fault diagnosis; data mining
语种	英语
WOS研究方向	Chemistry ; Engineering ; Instruments & Instrumentation
WOS类目	Chemistry, Analytical ; Engineering, Electrical & Electronic ; Instruments & Instrumentation
WOS记录号	WOS:000587219900001
来源期刊	SENSORS
来源机构	中国科学院青藏高原研究所
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/259807
推荐引用方式 GB/T 7714	Zhang, Minghu,Guo, Jianwen,Li, Xin,et al. Data-Driven Anomaly Detection Approach for Time-Series Streaming Data[J]. 中国科学院青藏高原研究所,2020,20(19).
APA	Zhang, Minghu,Guo, Jianwen,Li, Xin,&Jin, Rui.(2020).Data-Driven Anomaly Detection Approach for Time-Series Streaming Data.SENSORS,20(19).
MLA	Zhang, Minghu,et al."Data-Driven Anomaly Detection Approach for Time-Series Streaming Data".SENSORS 20.19(2020).