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.
翻译:近些年来,随着“智能技术”的日益普及,物联网(IoT)装置和系统的数目大为增加,各种IoT服务和功能以IoT流数据的分析方法为基础,然而,IoT数据分析方法由于IoT系统的动态性质和IoT数据流的不断变化的模式而面临概念漂移的挑战。在本篇文章中,我们提议了一个适应性IoT流数据分析框架,用于根据优化的光基BM和概念漂移适应来检测异常现象使用案例。建议采用一种名为优化适应和滑动窗口(OASW)的新式漂移适应方法,以适应在线IoT数据流的模式变化。关于两个公共数据集的实验表明,与其他最先进的方法相比,我们拟议的适应性光基BM模型具有很高的准确性和效率。拟议的适应性LightGBM模型可以在没有人类干预的情况下对IoT数据流进行持续学习和漂移适应。