With the evolution of the fifth-generation (5G) wireless network, smart technology based on the Internet of Things (IoT) has become increasingly popular. As a crucial component of smart technology, IoT systems for service delivery often face concept drift issues in network data stream analytics due to dynamic IoT environments, resulting in performance degradation. In this article, we propose a drift-adaptive 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 demonstrate that our proposed framework outperforms state-of-the-art methods, achieving high accuracy and efficiency in IoT data stream analytics.

翻译：随着第五代（5G）无线网络的发展，基于物联网（IoT）的智能技术日益普及。作为智能技术的核心组成部分，面向服务提供的物联网系统在网络数据流分析中常因动态物联网环境面临概念漂移问题，导致性能下降。本文提出一种名为自适应指数加权平均集成（AEWAE）的漂移自适应框架，包含三个阶段：物联网数据预处理、基模型学习与在线集成。该数据流分析框架通过集成方法的动态调整应对多种场景。在两个公开物联网数据集上的实验结果表明，所提框架在物联网数据流分析中实现了高精度与高效率，性能优于现有最先进方法。