An Adaptive Ensemble Framework for Addressing Concept Drift in IoT Data Streams

In the modern era of digital transformation, the evolution of the fifth-generation (5G) wireless network has played a pivotal role in revolutionizing communication technology and accelerating the growth of smart technology applications. As an integral element of smart technology, the Internet of Things (IoT) is grappling with the problem of limited hardware performance.  An IoT system based on cloud and fog computing is a highly efficacious solution, but it often faces concept drift challenges in real-time data processing due to the dynamic nature of IoT environments, leading to a degradation in performance. Con- sequently, this work proposes a drift-adaptive ensemble framework called Adaptive Exponentially Weighted Average Ensemble (AEWAE) consisting of three stages: IoT data preprocessing, base model learning, and online ensembling. It is a data stream analytics framework that integrates dynamic adjustments of ensemble methods to tackle various scenarios. Experimental results on two public IoT datasets exhibit that our proposed framework surpasses current methodologies, achieving exceptional precision and effectiveness in IoT data stream analytics.