Deep-Green: A Dispersed Energy-Efficiency Computing Paradigm for Green Industrial IoT

TY - JOUR

T1 - Deep-Green

T2 - A Dispersed Energy-Efficiency Computing Paradigm for Green Industrial IoT

AU - Hu, Ning

AU - Tian, Zhihong

AU - Du, Xiaojiang

AU - Guizani, Nadra

AU - Zhu, Zhihan

N1 - Publisher Copyright: © 2017 IEEE.

PY - 2021/6

Y1 - 2021/6

N2 - The rapid development of the Industrial Internet of Things (IIoT) has led to the explosive growth of industrial control data. Cloud computing-based industrial control models cause vast energy consumption. Most existing solutions try to reduce the overall energy consumption by optimizing task scheduling and disregard how to reduce the load of computing and data transmission. On the other hand, due to the rigid architecture and limited capability of the edge computing platform, solutions based on edge computing urgently need to be deeply optimized in terms of data processing and energy efficiency. This paper proposes Deep-Green, which is a dispersed energy-efficient computing paradigm for the Industrial Internet of Things. The core idea of Deep-Green is to realize the joint optimization of computing and network resources by merging data transmission and data processing. Deep-Green provides a novel method of constructing an IIoT edge layer based on a dispersed computing platform. By using an energy-efficiency task scheduling algorithm, container service technology, and programmable protocol stack, the data processing service is dispatched from the cloud side to the on-site controller. Therefore, the data from manufacturing equipment can be processed while they are forwarded by the on-site industrial controller. The results of experiments show that Deep-Green can not only effectively reduce the computing load and communication overhead of the cloud-side server, but also simplify the network topology and the number of devices at the edge layer of the IIoT.

AB - The rapid development of the Industrial Internet of Things (IIoT) has led to the explosive growth of industrial control data. Cloud computing-based industrial control models cause vast energy consumption. Most existing solutions try to reduce the overall energy consumption by optimizing task scheduling and disregard how to reduce the load of computing and data transmission. On the other hand, due to the rigid architecture and limited capability of the edge computing platform, solutions based on edge computing urgently need to be deeply optimized in terms of data processing and energy efficiency. This paper proposes Deep-Green, which is a dispersed energy-efficient computing paradigm for the Industrial Internet of Things. The core idea of Deep-Green is to realize the joint optimization of computing and network resources by merging data transmission and data processing. Deep-Green provides a novel method of constructing an IIoT edge layer based on a dispersed computing platform. By using an energy-efficiency task scheduling algorithm, container service technology, and programmable protocol stack, the data processing service is dispatched from the cloud side to the on-site controller. Therefore, the data from manufacturing equipment can be processed while they are forwarded by the on-site industrial controller. The results of experiments show that Deep-Green can not only effectively reduce the computing load and communication overhead of the cloud-side server, but also simplify the network topology and the number of devices at the edge layer of the IIoT.

KW - Green Industrial Internet of Things

KW - dispersed computing

KW - industry 40

KW - smart manufacturing

UR - https://www.scopus.com/pages/publications/85102645232

UR - https://www.scopus.com/pages/publications/85102645232#tab=citedBy

U2 - 10.1109/TGCN.2021.3064683

DO - 10.1109/TGCN.2021.3064683

M3 - Article

SN - 2473-2400

VL - 5

SP - 750

EP - 764

JO - IEEE Transactions on Green Communications and Networking

JF - IEEE Transactions on Green Communications and Networking

IS - 2

M1 - 9372936

ER -