Towards carbon emission modeling and optimization for time-sensitive IIoT

LI Yingyu; DAI Yipeng; GE Xiaohu; SHI Guangming; XIAO Yong; LIU Yan; YU Liang; XU Han

doi:10.11959/j.issn.2096-3750.2025.00419

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Towards carbon emission modeling and optimization for time-sensitive IIoT

Theory and Technology | 更新时间：2025-04-21

- Towards carbon emission modeling and optimization for time-sensitive IIoT
- Chinese Journal on Internet of Things Vol. 9, Issue 1, Pages: 103-114(2025)
- 作者机构：
  
  1.中国地质大学（武汉）机械与电子信息学院，湖北武汉 430074
  2.琶洲实验室（黄埔），广东广州 510335
  3.华中科技大学电子信息与通信学院，湖北武汉 430074
  4.鹏城实验室，广东深圳 518055
  5.西安电子科技大学人工智能学院，陕西西安 710071
  6.电子信息对抗与仿真技术教育部重点实验室，陕西西安 710071
  7.湖北华中电力科技开发有限责任公司，湖北武汉 430074
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(62293483;62301516;62071193);The Hubei Provincial International Science and Technology Cooperation Program(2023EHA009);The Research Start-Up Fund for the “CUG Scholar” Talent Position at China University of Geosciences (Wuhan)(2021164)
- DOI：10.11959/j.issn.2096-3750.2025.00419
  CLC： TN915.5;
- Received：10 January 2023，
  
  Revised：2024-11-12，
  
  Published：30 March 2025
- 稿件说明：
移动端阅览
李莹玉,戴一鹏,葛晓虎等.面向时间敏感工业互联网的碳排放建模与优化[J].物联网学报,2025,09(01):103-114.

LI Yingyu,DAI Yipeng,GE Xiaohu,et al.Towards carbon emission modeling and optimization for time-sensitive IIoT[J].Chinese Journal on Internet of Things,2025,09(01):103-114.
李莹玉,戴一鹏,葛晓虎等.面向时间敏感工业互联网的碳排放建模与优化[J].物联网学报,2025,09(01):103-114. DOI： 10.11959/j.issn.2096-3750.2025.00419.

LI Yingyu,DAI Yipeng,GE Xiaohu,et al.Towards carbon emission modeling and optimization for time-sensitive IIoT[J].Chinese Journal on Internet of Things,2025,09(01):103-114. DOI： 10.11959/j.issn.2096-3750.2025.00419.

摘要

边缘计算和云计算中心的大规模部署，为实现绿色低碳的工业互联网（IIoT

industrial Internet of things）带来了机遇与挑战。针对时间敏感型工业互联网业务，提出了一种基于云边协同的碳排放量优化方法。首先，对云边协同架构下工业互联网中时间敏感型业务的碳排放进行了深入分析，并建立了包含云计算中心、边缘节点和骨干网数据传输的综合碳排放模型。在此基础上，考虑低时延约束，设计了一种基于交替方向乘子法（ADMM

alternative direction method of multipliers）的任务卸载优化算法，旨在最小化工业互联网的整体碳排放。为了验证所提方法的有效性，利用美国不同地区的真实碳强度数据，进行了仿真实验。仿真实验结果表明，该方法能够在保证业务低时延的前提下，显著地降低工业互联网的碳排放量，实现云边协同的优势互补。

Abstract

The large-scale deployment of edge computing and cloud computing infrastructures has brought both opportunities and challenges to the realization of the green and low-carbon industrial Internet of things (IIoT). Aiming at time-sensitive IIoT services

a carbon emission optimization method based on cloud-edge collaboration was proposed. Firstly

an in-depth analysis was conducted upon the carbon emissions of time-sensitive services in IIoT under a cloud-edge collaborative framework

and a comprehensive carbon emission model including cloud computing centers

edge nodes

and backbone network data transmission was established. Based on this

considering low-latency constraints

a task offloading optimization algorithm based on the alternative direction method of multipliers (ADMM) was designed to minimize the overall carbon emissions of the considered IIoT system. To verify the effectiveness of the proposed method

extensive numerical experiments were conducted using real carbon intensity data from different regions of the United States. The results show that the proposed method can significantly reduce the carbon emissions of the considered IIoT system while guaranteeing low latency for services

and realizing the complementary advantages of cloud-edge collaboration.

关键词

Keywords

references

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