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This study introduces DOA-MSDI-CrossLinear, a novel framework for efficient industrial Internet of Things (IIoT) traffic forecasting. It resolves the trade-off between computational efficiency and prediction accuracy on resource-constrained gateways.

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Area of Science:

  • Computer Science
  • Network Engineering
  • Data Science

Background:

  • Industrial gateways are crucial for Industrial Internet of Things (IIoT) data aggregation but face a computational efficiency vs. prediction accuracy trade-off.
  • Existing deep learning models (Transformers, GNNs) are accurate but computationally intensive for gateways, while linear models lack accuracy for complex IIoT traffic.
  • IIoT traffic exhibits nonlinearity, burstiness, and time-varying characteristics, challenging traditional forecasting methods.

Purpose of the Study:

  • To propose a lightweight and theoretically grounded framework for accurate and efficient industrial IoT traffic forecasting.
  • To address the limitations of existing methods in handling complex, time-varying IIoT traffic on resource-constrained devices.
  • To redefine traffic forecasting as a hierarchical decomposition-interaction problem suitable for industrial gateways.

Main Methods:

  • Developed the DOA-MSDI-CrossLinear framework, featuring a Multi-Scale Decomposable Mixing (MDM) module for adaptive sequence decomposition and a Dual Dependency Interaction (DDI) module for capturing temporal and channel dependencies.
  • Integrated an enhanced CrossLinear model for predicting future flow values based on decomposed patterns.
  • Employed the Dream Optimization Algorithm (DOA) for bio-inspired hyperparameter tuning in non-convex optimization scenarios.

Main Results:

  • The proposed framework effectively balances computational efficiency and prediction accuracy on resource-constrained industrial gateways.
  • Demonstrated superior performance in capturing complex characteristics of IIoT traffic, including nonlinearity and burstiness.
  • Validated the effectiveness of the hierarchical decomposition-interaction approach through extensive experiments on real industrial IoT datasets.

Conclusions:

  • The DOA-MSDI-CrossLinear framework offers a viable solution for accurate and efficient industrial IoT traffic forecasting.
  • The hierarchical decomposition-interaction approach is key to modeling scale-dependent temporal correlations in industrial traffic.
  • This research provides a theoretically grounded and practically effective method for enhancing IIoT data value extraction.