Abstract
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.
Highlights
With the rapid development and integration of the Internet of ings (IoT) and cloud computing technology, we have gradually entered the era of “Internet of ings, comprehensive perception” [1]
In order to provide a better computing platform for the Internet of ings, a cloud computing center with strong computing power and mass storage, this paper proposes an edge collaborative cloud architecture with the help of edge devices processing massive data and private data in edge computing
An algorithm of data compression and anomaly detection based on edge computing comes into being. e data collected by sensors are preprocessed to reduce the amount of data transmission, so as to greatly reduce the cloud computing load
Summary
With the rapid development and integration of the Internet of ings (IoT) and cloud computing technology, we have gradually entered the era of “Internet of ings, comprehensive perception” [1]. In the rule-based method, researchers have proposed sequence dependence and speed constraint, which can effectively use the characteristics in time series to repair highly abnormal data. This method can hardly meet the needs of sequence anomaly detection with variable patterns [10]. In order to provide a better computing platform for the Internet of ings, a cloud computing center with strong computing power and mass storage, this paper proposes an edge collaborative cloud architecture with the help of edge devices processing massive data and private data in edge computing On this basis, an algorithm of data compression and anomaly detection based on edge computing comes into being.
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