A Multi-Stage Automated Online Network Data Stream Analytics Framework for IIoT Systems
نویسندگان
چکیده
Industry 5.0 aims at maximizing the collaboration between humans and machines. Machines are capable of automating repetitive jobs, while handle creative tasks. As a critical component Industrial Internet Things (IIoT) systems for service delivery, network data stream analytics often encounter concept drift issues due to dynamic IIoT environments, causing performance degradation automation difficulties. In this paper, we propose novel Multi-Stage Automated Network Analytics (MSANA) framework adaptation in systems, consisting pre-processing, proposed Drift-based Dynamic Feature Selection (DD-FS) method, model learning & selection, Window-based Performance Weighted Probability Averaging Ensemble (W-PWPAE) model. It is complete automated that enables automatic, effective, efficient 5.0. Experimental results on two public IoT datasets demonstrate outperforms state-of-the-art methods analytics.
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ژورنال
عنوان ژورنال: IEEE Transactions on Industrial Informatics
سال: 2023
ISSN: ['1551-3203', '1941-0050']
DOI: https://doi.org/10.1109/tii.2022.3212003