Evaluation and optimization of carbon emission for federal edge intelligence network

Peng ZHANG; Yong XIAO; Jiwei HU; Liang LIAO; Jianxin LYU; Zegang BAI

doi:10.11959/j.issn.2096-3750.2024.00375

您当前的位置：

首页 >

文章列表页 >

Evaluation and optimization of carbon emission for federal edge intelligence network

Theory and Technology | 更新时间：2024-06-05

- Evaluation and optimization of carbon emission for federal edge intelligence network
- Chinese Journal on Internet of Things Vol. 8, Issue 1, Pages: 98-110(2024)
- 作者机构：
  
  1. 华中科技大学，湖北武汉 430074
  2. 烽火通信科技股份有限公司，湖北武汉 430074
  3. 鹏城实验室，广东深圳 518055
  4. 琶洲实验室（黄埔），广东广州 510555
  5. 武汉烽火技术服务有限公司，湖北武汉 430074
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-3750.2024.00375
  CLC： TG391
- Revised：2023-11-10，
  
  Online First：2024-03，
  
  Published：30 March 2024
- 稿件说明：
移动端阅览
Peng ZHANG, Yong XIAO, Jiwei HU, et al. Evaluation and optimization of carbon emission for federal edge intelligence network[J]. Chinese Journal on Internet of Things, 2024, 8(1): 98-110.
DOI：

Peng ZHANG, Yong XIAO, Jiwei HU, et al. Evaluation and optimization of carbon emission for federal edge intelligence network[J]. Chinese Journal on Internet of Things, 2024, 8(1): 98-110. DOI： 10.11959/j.issn.2096-3750.2024.00375.

摘要

近年来，通信技术的持续演进导致通信网络的能耗显著增加。随着人工智能（AI

artificial intelligence）技术与算法在通信网络中的广泛应用和深度部署，未来6G智能通信网络架构和技术演进将对通信网络的节能减排带来更为严峻的挑战。基于边缘计算和分布式联邦学习的联邦边缘智能（FEI

federated edge intelligence）网络已被普遍认为是实现6G网络内生智能的关键路径之一。然而，如何评估和优化联邦边缘智能网络的综合碳排放量仍然是一大难题。为解决该问题，首先，提出了一种联邦边缘智能网络碳排放评估框架和方法。其次，基于该评估框架和方法提出3种联邦边缘智能网络碳排放优化方案，包括动态能量交易（DET

dynamic energy trading）、动态任务分配（DTA

dynamic task allocation）和动态能量交易与任务分配（DETA

dynamic energy trading and task allocation）。最后，通过自行搭建的真实硬件平台，并利用真实世界的碳强度数据集进行联邦边缘智能网络生命周期碳排放仿真。实验结果表明，3种优化方案均能在不同场景和约束条件下显著减少联邦边缘智能网络的碳排放，为下一代智能通信网络的可持续发展和实现绿色低碳6G网络提供了依据。

Abstract

In recent years

the continuous evolution of communication technology has led to a significant increase in energy consumption.With the widespread application and deep deployment of artificial intelligence (AI) technology and algorithms in telecommunication networks

the network architecture and technological evolution of network intelligent will pose even more severe challenges to the energy efficiency and emission reduction of future 6G.Federated edge intelligence (FEI)

based on edge computing and distributed federated machine learning

has been widely acknowledged as one of the key pathway for implementing network native intelligence.However

evaluating and optimizing the comprehensive carbon emissions of federated edge intelligence networks remains a significant challenge.To address this issue

a framework and a method for assessing the carbon emissions of federated edge intelligence networks were proposed.Subsequently

three carbon emission optimization schemes for FEI networks were presented

including dynamic energy trading (DET)

dynamic task allocation (DTA)

and dynamic energy trading and task allocation (DETA).Finally

by utilizing a simulation network built on real hardware and employing real-world carbon intensity datasets

FEI networks lifecycle carbon emission experiments were conducted.The experimental results demonstrate that all three optimization schemes significantly reduce the carbon emissions of FEI networks under different scenarios and constraints.This provides a basis for the sustainable development of next-generation intelligent communication networks and the realization of low-carbon 6G networks.

关键词

Keywords

references

Greenhouse gas emissions trajectories for the information and communication technology sector compatible with the UNFCCC Paris Agreement:ITU-T L . 1470 [S ] . ITU-T , 2020 .

ZHANG P , XIAO Y , LI Y Y , et al . Towards net-zero carbon emissions in network AI for 6G and beyond [J ] . IEEE Communications Magazine , 2023 , PP ( 99 ): 1 - 7 .

ZIEGLER V , VISWANATHAN H , FLINCK H , et al . 6G architecture to connect the worlds [J ] . IEEE Access , 2020 , 8 : 173508 - 173520 .

BARIAH L , MOHJAZI L , MUHAIDAT S , et al . A prospective look:key enabling technologies,applications and open research topics in 6G networks [J ] . IEEE Access , 2020 , 8 : 174792 - 174820 .

TONG W , ZHU P Y . 6G,the next horizon:from connected people and things to connected intelligence [M ] .

IMT-2030(6G)推进组 . 6G网络架构展望白皮书 [R ] . 2023 .

IMT-2030(6G) Promotion Group . 6G network architecture prospects white paper [R ] . 2023 .

IMT Vision-Framework and overall objectives of the future development of IMT for 2020 and beyond:ITU-R M . 2083 [S ] . ITU-R , 2015 .

LIU Y Q , HE Y F , LIN Y L , et al . Toward native artificial intelligence in 6G [C ] // Proceedings of 2022 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB) . Piscataway:IEEE Press , 2022 : 1 - 6 .

CHEN L , WANG H , AI M , et al . AI-native user-centric network for 6G [C ] // Proceedings of 2022 IEEE/CIC International Conference on Communications in China (ICCC Workshops) . Piscataway:IEEE Press , 2022 : 494 - 499 .

CAMELO M , GRAMAGLIA M , SOTO P , et al . DAEMON:a network intelligence plane for 6G networks [C ] // Proceedings of 2022 IEEE Globecom Workshops (GC Wkshps) . Piscataway:IEEE Press , 2022 : 1341 - 1346 .

BANCHS A , FIORE M , GARCIA-SAAVEDRA A , et al . Network intelligence in 6G:challenges and opportunities [C ] // Proceedings of the 16th ACM Workshop on Mobility in the Evolving Internet Architecture . New York:ACM , 2021 : 7 - 12 .

IMT-2030(6G)推进组 . 面向6G的智能内生体系架构研究 [R ] . 2021 .

IMT-2030(6G) Promotion Group . Research on network native intelligent architecture for 6G [R ] . 2023 .

KAUR J , KHAN M A . Sixth generation (6G) wireless technology:an overview,vision,challenges and use cases [C ] // Proceedings of 2022 IEEE Region 10 Symposium (TENSYMP) . Piscataway:IEEE Press , 2022 : 1 - 6 .

LUCCIONI A S , VIGUIER S , LIGOZAT A L , et al . Estimating the carbon footprint of BLOOM,a 176B parameter language model [EB ] . 2022 .

XIAO Y , SHI G M , LI Y Y , et al . Toward self-learning edge intelligence in 6G [J ] . IEEE Communications Magazine , 2020 , 58 ( 12 ): 34 - 40 .

XIAO Y , ZHANG X H , LI Y Y , et al . Time-sensitive learning for heterogeneous federated edge intelligence [J ] . IEEE Transactions on Mobile Computing , 2023 : 1 - 18 .

IMT-2030(6G)推进组 . 6G网络架构愿景与关键技术展望白皮书 [R ] . 2021 .

IMT-2030(6G) Promotion Group . 6G network architecture vision and key technological prospects white paper [R ] . 2021 .

LETAIEF K B , SHI Y M , LU J M , et al . Edge artificial intelligence for 6G:vision,enabling technologies,and applications [J ] . IEEE Journal on Selected Areas in Communications , 2022 , 40 ( 1 ): 5 - 36 .

WANG Q T , LIU Y , WANG Y C , et al . Deep Edge Node enables AI native toward 6G [C ] // Proceedings of 2023 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB) . Piscataway:IEEE Press , 2023 : 1 - 6 .

BOLLA R , BRUSCHI R , LOMBARDO C , et al . 6G enablers for zero-carbon network slices and vertical edge services [J ] . IEEE Networking Letters , 2023 , 5 ( 3 ): 173 - 176 .

ZHAO B R , CUI Q M , LIANG S Y , et al . Green concerns in federated learning over 6G [J ] . China Communications , 2022 , 19 ( 3 ): 50 - 69 .

WANG Q , XIAO Y , ZHU H X , et al . Towards energy-efficient federated edge intelligence for IoT networks [C ] // Proceedings of 2021 IEEE 41st International Conference on Distributed Computing Systems Workshops (ICDCSW) . Piscataway:IEEE Press , 2021 : 55 - 62 .

XIAO Y , LI Y Y , SHI G M , et al . Optimizing resource-efficiency for federated edge intelligence in IoT networks [C ] // Proceedings of 2020 International Conference on Wireless Communications and Signal Processing (WCSP) . Piscataway:IEEE Press , 2020 : 86 - 92 .

IVANOVA D , BARRETT J , WIEDENHOFER D , et al . Quantifying the potential for climate change mitigation of consumption options [J ] . Environmental Research Letters , 2020 , 15 ( 9 ): 093001 .

XIAO Y , NIYATO D , HAN Z , et al . Dynamic energy trading for energy harvesting communication networks:a stochastic energy trading game [J ] . IEEE Journal on Selected Areas in Communications , 2015 , 33 ( 12 ): 2718 - 2734 .

XIAO Y , NIYATO D , WANG P , et al . Dynamic energy trading for wireless powered communication networks [J ] . IEEE Communications Magazine , 2016 , 54 ( 11 ): 158 - 164 .

XIAO Y , KRUNZ M . Dynamic network slicing for scalable fog computing systems with energy harvesting [J ] . IEEE Journal on Selected Areas in Communications , 2018 , 36 ( 12 ): 2640 - 2654 .

GE X H , JIA H M , ZHONG Y , et al . Energy efficient optimization of wireless-powered 5G full duplex cellular networks:a mean field game approach [J ] . IEEE Transactions on Green Communications and Networking , 2019 , 3 ( 2 ): 455 - 467 .

XIAO Y , HIRZALLAH M , KRUNZ M . Distributed resource allocation for network slicing over licensed and unlicensed bands [J ] . IEEE Journal on Selected Areas in Communications , 2018 , 36 ( 10 ): 2260 - 2274 .

XIAO Y , KRUNZ M . Distributed optimization for energy-efficient fog computing in the tactile Internet [J ] . IEEE Journal on Selected Areas in Communications , 2018 , 36 ( 11 ): 2390 - 2400 .

LECUN Y , BOTTOU L , BENGIO Y , et al . Gradient-based learning applied to document recognition [J ] . Proceedings of the IEEE , 1998 , 86 ( 11 ): 2278 - 2324 .

MCMAHAN H B , MOORE E , RAMAGE D , et al . Communicationefficient learning of deep networks from decentralized data [C ] // Proceedings of 19th International Conference on Artificial Intelligence and Statistics . Cambridge:PMLR Press , 2016 : 37 - 29 .

DE CHALENDAR J A , TAGGART J , BENSON S M . Tracking emissions in the US electricity system [J ] . Proceedings of the National Academy of Sciences of the United States of America , 2019 , 116 ( 51 ): 25497 - 25502 .

Views

300

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Towards carbon emission modeling and optimization for time-sensitive IIoT

Data services and data plane for 6G mobile communication network

Carbon emission monitoring based on internet of things with cloud-tube-edge-end structure

Cloud native network for intelligent Internet of everything

Intelligent reflecting surface design for 6G-assited Internet of things

Related Author

XU Han

YU Liang

LIU Yan

XIAO Yong

SHI Guangming

GE Xiaohu

DAI Yipeng

LI Yingyu

Related Institution

Hubei Huazhong Electric Power Technology Development Co., Ltd.

Key Laboratory of Electronic Information Countermeasure and Simulation Technology, Ministry of Education, Xi′an

School of Artificial Intelligence, Xidian University, Xi′an

School of Electronic Information and Communications, Huazhong University of Science and Technology

Pazhou Laboratory (Huangpu)

⁰