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An Adaptive Ensemble Framework for Addressing Concept Drift in IoT Data Streams
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  • Yafeng Wu ,
  • Lan Liu ,
  • Yongjie Yu ,
  • Guiming Chen ,
  • Junhan Hu
Yafeng Wu
Guangdong Polytechnic Normal University
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Lan Liu
Guangdong Polytechnic Normal University

Corresponding Author:[email protected]

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Yongjie Yu
Guangdong Polytechnic Normal University
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Guiming Chen
Guangdong Polytechnic Normal University
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Junhan Hu
Guangdong Polytechnic Normal University
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Abstract

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.
24 Dec 2023Submitted to TechRxiv
02 Jan 2024Published in TechRxiv