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This study introduces a new AI-focused edge computing framework for the Industrial Internet of Things (IIoT). The proposed system significantly reduces AI model training time and energy consumption in IIoT edge devices.

Keywords:
artificial intelligenceedge computingfederated learningindustrial internet of things

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Industrial Internet of Things

Background:

  • Existing Industrial Internet of Things (IIoT) edge computing frameworks face challenges including hardware-software coupling, diverse protocols, AI model deployment difficulties, limited edge device capabilities, and sensitivity to delay and energy consumption.
  • Artificial intelligence (AI) integration in edge computing is crucial for advancing industrial technology.

Purpose of the Study:

  • To propose a software-defined, AI-oriented, three-layer IIoT edge computing framework.
  • To design and implement an AI-oriented edge computing system that supports device access, cloud-based AI model deployment, and end-to-end edge processing.
  • To develop a time series-based method for device selection and computation offloading in federated learning to minimize training delay and energy usage.

Main Methods:

  • Development of a software-defined, three-layer IIoT edge computing framework.
  • Implementation of an AI-oriented edge computing system for seamless AI model integration and processing.
  • Proposal of a time series-based algorithm for intelligent device selection and computation offloading in federated learning.

Main Results:

  • The proposed AI-oriented edge computing framework and system effectively support device access and AI model deployment from the cloud.
  • The time series-based method for device selection and computation offloading significantly reduces federated learning training delay.
  • Experimental results show a 30% to 50% reduction in model training time and a 35% to 55% reduction in training energy consumption compared to random selection methods.

Conclusions:

  • The developed software-defined AI-oriented IIoT edge computing framework addresses key challenges in current systems.
  • The proposed federated learning offloading strategy enhances efficiency by reducing training time and energy consumption.
  • The implemented system demonstrates the feasibility and effectiveness of AI-driven edge computing for industrial applications.