CLPM: A Cooperative Link Prediction Model for Industrial Internet of Things Using Partitioned Stacked Denoising Autoencoder

With the development of Industry 4.0, an increasing number of industrial Internet of Things (IIoT) mobile devices (MD), which constantly transmit data at any time, are working on the production line. However, due to node movement, signal attenuation, or physical obstacles, data must rely on the tran...

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Vydané v:IEEE transactions on industrial informatics Ročník 17; číslo 5; s. 3620 - 3629
Hlavní autori: Rui, Lanlan, Zhu, Yu, Gao, Zhipeng, Qiu, Xuesong
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 01.05.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Attenuation
Deep learning
Delays
Edge computing
Electronic devices
Industrial applications
Industrial development
Industrial Internet of Things
Industrial Internet of Things (IIoT)
Ions
link prediction
Noise reduction
Prediction models
Predictive models
Servers
stacked denoising autoencoder (SDAE)
Task analysis
ISSN:1551-3203, 1941-0050
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Popis
Shrnutí:With the development of Industry 4.0, an increasing number of industrial Internet of Things (IIoT) mobile devices (MD), which constantly transmit data at any time, are working on the production line. However, due to node movement, signal attenuation, or physical obstacles, data must rely on the transmission of relay nodes to finally reach the destination node. Based on this scenario, in this article, we propose a cooperative link prediction model (CLPM) using a stacked denoising autoencoder (SDAE) to predict links of the IIoT-based MDs at the next moment through historical link information. The layer structure of the SDAE model is partitioned so that the local MD and edge servers can cooperatively process the link prediction tasks. Experimental results show that our proposed CLPM outperforms others in terms of prediction performance and execution delay.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2020.2999318