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15 August 2023 IoT data clustering method based on parallelized subnetworks and PCA-K-means
Xingyi Du, Jing Hu, Ran Chen, Jingce Yang, Tiecheng Song
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Proceedings Volume 12719, Second International Conference on Electronic Information Technology (EIT 2023); 127191G (2023) https://doi.org/10.1117/12.2685799
Event: Second International Conference on Electronic Information Technology (EIT 2023), 2023, Wuhan, China
Abstract
Internet of Things (IoT) data is characterized by large amount of data and high real-time performance. Processing IoT data through complex event processing techniques requires setting complex rules, but the rules tend to change as the business changes. Rule engine can set rules through separate configuration files to filter to data or things matching the rules without modifying device data or management platform code. In this paper, in order to solve the performance of the rule engine at large data volume, we design a rule network-based reduced-dimensional K-Means raw data clustering method to cluster devices by the difference of device operation data while implementing parallelized rule networks according to the rule types. After the experimental analysis of the method, the results show that the reduced-dimensional K-Means raw data clustering method based on rule networks has smaller event loss and effectively reduces redundancy.
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Xingyi Du, Jing Hu, Ran Chen, Jingce Yang, and Tiecheng Song "IoT data clustering method based on parallelized subnetworks and PCA-K-means", Proc. SPIE 12719, Second International Conference on Electronic Information Technology (EIT 2023), 127191G (15 August 2023); https://doi.org/10.1117/12.2685799
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KEYWORDS
Internet of things
Data acquisition
Data processing
Design rules
Principal component analysis
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