CLPM: A Cooperative Link Prediction Model for Industrial Internet of Things Using Partitioned Stacked Denoising Autoencoder - Rui, Lanlan; Zhu, Yu; Gao, Zhipeng; Qiu, Xuesong (IEEE transactions on industrial informatics, 2021)
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.