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AEWAE: An Efficient Ensemble Framework for Concept Drift Adaptation in IoT Data Stream. (arXiv:2305.06638v1 [cs.CR])
cs.CR updates on arXiv.org arxiv.org
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 …
analytics concept data data stream delivery framework internet internet of things iot network popular service smart smart technology stream systems technology things wireless