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A Lightweight Concept Drift Detection and Adaptation Framework for IoT Data Streams
[article]
2021
arXiv
pre-print
In recent years, with the increasing popularity of "Smart Technology", the number of Internet of Things (IoT) devices and systems have surged significantly. Various IoT services and functionalities are based on the analytics of IoT streaming data. However, IoT data analytics faces concept drift challenges due to the dynamic nature of IoT systems and the ever-changing patterns of IoT data streams. In this article, we propose an adaptive IoT streaming data analytics framework for anomaly
arXiv:2104.10529v1
fatcat:tx3or6snhnh6xhbxyab4y2cz6u