IoT data clustering method based on parallelized subnetworks and PCA-K-means

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.