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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Veröffentlicht in:	IEEE transactions on industrial informatics Jg. 17; H. 5; S. 3620 - 3629
Hauptverfasser:	Rui, Lanlan, Zhu, Yu, Gao, Zhipeng, Qiu, Xuesong
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.05.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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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Abstract	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.
AbstractList	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.
Author	Qiu, Xuesong Zhu, Yu Gao, Zhipeng Rui, Lanlan
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SubjectTerms	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
Title	CLPM: A Cooperative Link Prediction Model for Industrial Internet of Things Using Partitioned Stacked Denoising Autoencoder
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