A Lightweight Concept Drift Detection and Adaptation Framework for IoT Data Streams

TL;DR

This paper introduces a lightweight, adaptive framework for IoT data stream analysis that effectively detects and adapts to concept drift, enhancing anomaly detection accuracy and efficiency without human intervention.

Contribution

It proposes a novel drift adaptation method called OASW and integrates it with an optimized LightGBM model for real-time IoT data analytics.

Findings

High accuracy in drift detection and adaptation

Efficient continuous learning on IoT streams

Outperforms state-of-the-art approaches

Abstract

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 detection use cases based on optimized LightGBM and concept drift adaptation. A novel drift adaptation method named Optimized Adaptive and Sliding Windowing (OASW) is proposed to adapt to the pattern changes of online IoT data streams. Experiments on two public datasets show the high accuracy and efficiency of our proposed adaptive LightGBM model compared against other state-of-the-art approaches. The proposed adaptive LightGBM model can perform continuous learning and drift adaptation on IoT data streams without human intervention.