Recent Progress on Security-Communication-Computing-Control Co-Optimization for Industrial Internet

The blockchain-empowered end-edge collaborative computing network is considered a promising network paradigm in the Industrial Internet, providing a secure and efficient scheduling framework. Nevertheless, the diversity of task requirements and the complexity of blockchain mechanisms have led to poor performance of this framework. Therefore, this article provides a categorized literature review of existing works on a security-communication-computing-control (S3C) co-optimization strategy based on blockchain-empowered end-edge collaborative computing intelligent scheduling frameworks and analyzes them from the perspective of the evolution of optimization algorithms from non-learning to learning-based algorithms. Furthermore, we identify key challenges for future research and summarize potential future research directions.

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Publication Details

Published
2025-05-17
DOI
https://doi.org/10.1109/yac66630.2025.11149858
Primary Topic
Freezing and Crystallization Processes
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article
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article

Recent Progress on Security-Communication-Computing-Control Co-Optimization for Industrial Internet

Freezing and Crystallization Processes
article

Recent Progress on Security-Communication-Computing-Control Co-Optimization for Industrial Internet

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Abstract

The blockchain-empowered end-edge collaborative computing network is considered a promising network paradigm in the Industrial Internet, providing a secure and efficient scheduling framework. Nevertheless, the diversity of task requirements and the complexity of blockchain mechanisms have led to poor performance of this framework. Therefore, this article provides a categorized literature review of existing works on a security-communication-computing-control (S3C) co-optimization strategy based on blockchain-empowered end-edge collaborative computing intelligent scheduling frameworks and analyzes them from the perspective of the evolution of optimization algorithms from non-learning to learning-based algorithms. Furthermore, we identify key challenges for future research and summarize potential future research directions.

Shenyang Institute of Automation (CN), Chinese Academy of Sciences (CN)
Openalex Percentile: Top 49%
Freezing and Crystallization Processes
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