文章基本信息

标题：Sensors Anomaly Detection of Industrial Internet of Things Based on Isolated Forest Algorithm and Data Compression
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作者：Desheng Liu ; Hang Zhen ; Dequan Kong 等
期刊名称：Scientific Programming
印刷版ISSN：1058-9244
出版年度：2021
卷号：2021
页码：1-9
DOI：10.1155/2021/6699313
出版社：Hindawi Publishing Corporation
摘要：Aiming at solving network delay caused by large chunks of data in industrial Internet of Things, a data compression algorithm based on edge computing is creatively put forward in this paper. The data collected by sensors need to be handled in advance and are then processed by different single packet quantity K and error threshold e for multiple groups of comparative experiments, which greatly reduces the amount of data transmission under the premise of ensuring the instantaneity and effectiveness of data. On the basis of compression processing, an outlier detection algorithm based on isolated forest is proposed, which can accurately identify the anomaly caused by gradual change and sudden change and control and adjust the action of equipment, in order to meet the control requirement. As is shown by experimental simulation, the isolated forest algorithm based on partition outperforms box graph and K-means clustering algorithm based on distance in anomaly detection, which verifies the feasibility and advantages of the former in data compression and detection accuracy.