论文标题
IIOT系统的多阶段自动化在线网络数据流分析框架
A Multi-Stage Automated Online Network Data Stream Analytics Framework for IIoT Systems
论文作者
论文摘要
行业5.0旨在最大化人类与机器之间的合作。机器能够自动化重复的工作，而人类处理创意任务。作为用于服务交付的工业互联网系统的关键组成部分，网络数据流分析经常由于动态IIOT环境而遇到概念漂移问题，从而导致性能退化和自动化困难。在本文中，我们提出了一个新型的多阶段自动化网络分析（MSANA），用于IIT系统中的概念漂移适应性框架，包括动态数据预处理，拟议的基于漂移的动态功能选择（DDD-FS）方法，动态模型学习与选择以及拟议的基于窗口的性能加权的可能性加权的可能性的可能性平均（W-PWPE）模型。这是一个完整的自动数据流分析框架，可为行业5.0中的IIOT系统提供自动，有效且有效的数据分析。两个公共物联网数据集的实验结果表明，所提出的框架的表现优于IIOT数据流分析的最先进方法。
Industry 5.0 aims at maximizing the collaboration between humans and machines. Machines are capable of automating repetitive jobs, while humans handle creative tasks. As a critical component of Industrial Internet of Things (IIoT) systems for service delivery, network data stream analytics often encounter concept drift issues due to dynamic IIoT environments, causing performance degradation and automation difficulties. In this paper, we propose a novel Multi-Stage Automated Network Analytics (MSANA) framework for concept drift adaptation in IIoT systems, consisting of dynamic data pre-processing, the proposed Drift-based Dynamic Feature Selection (DD-FS) method, dynamic model learning & selection, and the proposed Window-based Performance Weighted Probability Averaging Ensemble (W-PWPAE) model. It is a complete automated data stream analytics framework that enables automatic, effective, and efficient data analytics for IIoT systems in Industry 5.0. Experimental results on two public IoT datasets demonstrate that the proposed framework outperforms state-of-the-art methods for IIoT data stream analytics.
