A Multi-Stage Automated Online Network Data Stream Analytics Framework for IIoT Systems

TL;DR

This paper introduces a comprehensive multi-stage framework for automated data stream analytics in IIoT systems, effectively addressing concept drift and enhancing performance in dynamic Industry 5.0 environments.

Contribution

It presents novel methods for drift-based feature selection and ensemble learning, enabling fully automated and adaptive analytics in IIoT data streams.

Findings

Outperforms existing methods on public IoT datasets

Effectively handles concept drift in dynamic environments

Improves accuracy and efficiency of IIoT data analytics

Abstract

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.