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. Enabled by the high-speed, low-latency characteristics of 5G, these applications have shown significant potential in various sectors, from healthcare and transportation to energy management and beyond. 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：在数字化转型的现代时代，第五代（5G）无线网络的演进在革新通信技术、加速智能技术应用发展方面发挥了关键作用。借助5G高速率、低延迟的特性，这些应用在医疗、交通、能源管理等多个领域展现出显著潜力。作为智能技术的核心组成部分，面向服务交付的物联网系统在网络数据流分析中常因动态物联网环境遭遇概念漂移问题，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样化场景。在两个公开物联网数据集上的实验结果表明，所提框架优于现有最先进方法，在物联网数据流分析中实现了高精度与高效率。