Delay and Deployment Cost Optimization of Edge Computing based on C-RAN in Intelligent Plant

Ye Xiao; Fucai Luo; Chenglin Zhao; Fangmin Xu; Tao Chen

doi:10.1109/ICCWorkshops50388.2021.9473552

Delay and Deployment Cost Optimization of Edge Computing based on C-RAN in Intelligent Plant

Ye Xiao
, Fucai Luo
, Chenglin Zhao
, Fangmin Xu
, Tao Chen

Ministry of Education China
State Grid Fujian Electric Power Co. Ltd

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

Abstract

In order to minimize the deployment cost of network equipment and delay for data offloading of intelligent services in intelligent plants, this paper proposes a new edge computing architecture based on the cloud radio access network(C-RAN). In this architecture, we consider a computing offload mode which allows one sensing device (SD) to offload computing tasks to more than one access point. The problem of joint optimizing deployment cost and delay is solved based on the immune algorithm and Lagrange multiplier algorithm. The simulation result shows the significantly decrease of delay for data offloading in single-SD multi-access point mode.

Original language	English
Title of host publication	2021 IEEE International Conference on Communications Workshops, ICC Workshops 2021 - Proceedings
Publisher	IEEE Institute of Electrical and Electronic Engineers
Number of pages	6
ISBN (Electronic)	978-1-7281-9441-7
DOIs	https://doi.org/10.1109/ICCWorkshops50388.2021.9473552
Publication status	Published - Jun 2021
MoE publication type	A4 Article in a conference publication
Event	IEEE International Conference on Communications Workshops, ICC Workshops 2021 - Virtual, Online Duration: 14 Jun 2021 → 23 Jun 2021

Conference

Conference	IEEE International Conference on Communications Workshops, ICC Workshops 2021
City	Virtual, Online
Period	14/06/21 → 23/06/21

Funding

This research work is funded by 2019 Industrial Internet Innovation Development Project of Ministry of Industry and Information Technology of P.R. China “Comprehensive Security Defense Platform Project for Industrial/Enterprise Networks” and EU H2020 5G-DRIVE project (Grant Agreement (GA) No.814956).

Keywords

C-RAN
Deployment cost
edge computing
intelligent plant
low delay

Access to Document

10.1109/ICCWorkshops50388.2021.9473552

Cite this

@inproceedings{d0277e75bce24a85b03a754f33d8f7e6,

title = "Delay and Deployment Cost Optimization of Edge Computing based on C-RAN in Intelligent Plant",

abstract = "In order to minimize the deployment cost of network equipment and delay for data offloading of intelligent services in intelligent plants, this paper proposes a new edge computing architecture based on the cloud radio access network(C-RAN). In this architecture, we consider a computing offload mode which allows one sensing device (SD) to offload computing tasks to more than one access point. The problem of joint optimizing deployment cost and delay is solved based on the immune algorithm and Lagrange multiplier algorithm. The simulation result shows the significantly decrease of delay for data offloading in single-SD multi-access point mode.",

keywords = "C-RAN, Deployment cost, edge computing, intelligent plant, low delay",

author = "Ye Xiao and Fucai Luo and Chenglin Zhao and Fangmin Xu and Tao Chen",

note = "Funding Information: This research work is funded by 2019 Industrial Internet Innovation Development Project of Ministry of Industry and Information Technology of P.R. China {\textquotedblleft}Comprehensive Security Defense Platform Project for Industrial/Enterprise Networks{\textquotedblright} and EU H2020 5G-DRIVE project (Grant Agreement (GA) No.814956). Publisher Copyright: {\textcopyright} 2021 IEEE.; IEEE International Conference on Communications Workshops, ICC Workshops 2021 ; Conference date: 14-06-2021 Through 23-06-2021",

year = "2021",

month = jun,

doi = "10.1109/ICCWorkshops50388.2021.9473552",

language = "English",

booktitle = "2021 IEEE International Conference on Communications Workshops, ICC Workshops 2021 - Proceedings",

publisher = "IEEE Institute of Electrical and Electronic Engineers",

address = "United States",

}

Xiao, Y, Luo, F, Zhao, C, Xu, F & Chen, T 2021, Delay and Deployment Cost Optimization of Edge Computing based on C-RAN in Intelligent Plant. in 2021 IEEE International Conference on Communications Workshops, ICC Workshops 2021 - Proceedings. IEEE Institute of Electrical and Electronic Engineers, IEEE International Conference on Communications Workshops, ICC Workshops 2021, Virtual, Online, 14/06/21. https://doi.org/10.1109/ICCWorkshops50388.2021.9473552

Delay and Deployment Cost Optimization of Edge Computing based on C-RAN in Intelligent Plant. / Xiao, Ye; Luo, Fucai; Zhao, Chenglin et al.
2021 IEEE International Conference on Communications Workshops, ICC Workshops 2021 - Proceedings. IEEE Institute of Electrical and Electronic Engineers, 2021.

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

TY - GEN

T1 - Delay and Deployment Cost Optimization of Edge Computing based on C-RAN in Intelligent Plant

AU - Xiao, Ye

AU - Luo, Fucai

AU - Zhao, Chenglin

AU - Xu, Fangmin

AU - Chen, Tao

N1 - Funding Information: This research work is funded by 2019 Industrial Internet Innovation Development Project of Ministry of Industry and Information Technology of P.R. China “Comprehensive Security Defense Platform Project for Industrial/Enterprise Networks” and EU H2020 5G-DRIVE project (Grant Agreement (GA) No.814956). Publisher Copyright: © 2021 IEEE.

PY - 2021/6

Y1 - 2021/6

N2 - In order to minimize the deployment cost of network equipment and delay for data offloading of intelligent services in intelligent plants, this paper proposes a new edge computing architecture based on the cloud radio access network(C-RAN). In this architecture, we consider a computing offload mode which allows one sensing device (SD) to offload computing tasks to more than one access point. The problem of joint optimizing deployment cost and delay is solved based on the immune algorithm and Lagrange multiplier algorithm. The simulation result shows the significantly decrease of delay for data offloading in single-SD multi-access point mode.

AB - In order to minimize the deployment cost of network equipment and delay for data offloading of intelligent services in intelligent plants, this paper proposes a new edge computing architecture based on the cloud radio access network(C-RAN). In this architecture, we consider a computing offload mode which allows one sensing device (SD) to offload computing tasks to more than one access point. The problem of joint optimizing deployment cost and delay is solved based on the immune algorithm and Lagrange multiplier algorithm. The simulation result shows the significantly decrease of delay for data offloading in single-SD multi-access point mode.

KW - C-RAN

KW - Deployment cost

KW - edge computing

KW - intelligent plant

KW - low delay

UR - https://www.scopus.com/pages/publications/85112816956

U2 - 10.1109/ICCWorkshops50388.2021.9473552

DO - 10.1109/ICCWorkshops50388.2021.9473552

M3 - Conference article in proceedings

AN - SCOPUS:85112816956

BT - 2021 IEEE International Conference on Communications Workshops, ICC Workshops 2021 - Proceedings

PB - IEEE Institute of Electrical and Electronic Engineers

T2 - IEEE International Conference on Communications Workshops, ICC Workshops 2021

Y2 - 14 June 2021 through 23 June 2021

ER -

Delay and Deployment Cost Optimization of Edge Computing based on C-RAN in Intelligent Plant

Abstract

Conference

Funding

Keywords

Access to Document

Other files and links

Fingerprint

5G-DRIVE: 5G HarmoniseD Research and TrIals for serVice Evolution between EU and China

Cite this

Delay and Deployment Cost Optimization of Edge Computing based on C-RAN in Intelligent Plant

Abstract

Conference

Funding

Keywords

Access to Document

Other files and links

Fingerprint

Projects

5G-DRIVE: 5G HarmoniseD Research and TrIals for serVice Evolution between EU and China

Cite this