  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.
