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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.
