Learning Temporal and Spatial Features Jointly: A Unified Framework for Space-Time Data Prediction in Industrial IoT Networks
Yinghui Zhang, Hu An, Yaxuan Xing, Yang Liu, Tiankui Zhang
Abstract
Industrial Internet-of-Things (IIoT) networks, as the application of Internet-of-Things (IoT) networks to modern industry, are growing rapidly with the digital transformation accelerates. Mobile sensors are widely adopted in monitoring key information of massive IIoT networks in an ad hoc fashion or retrofitting on to an existing infrastructure. Hence, owing to the characteristics of scalability, unstable locations, and highly unstructured, achieving perfect prediction for mobile multisensor is a challenging problem. In this article, a dynamic space-time prediction algorithm combining temporal convolutional network and graph convolutional network (TCN-GCN) is proposed to provide reliable multiple nodes prediction for detection and maintenance in IIoT networks. In particularly, an adaptive learning graph process is exclusively designed according to the dynamic characteristics of mobile sensors. Moreover, the improved dilated time convolution network and dynamic graph convolution network (DGCN) are combined to effectively capture the time dependence and topology information. Furthermore, an advanced loss function is applied to avoid overfitting. Numerical simulations show that the proposed TCN-GCN prediction algorithm exhibits higher effectiveness in accuracy and complexity than convolutional long short-term memory (ConvLSTM), temporal graph convolutional network (T-GCN), and residual graph ConvLSTM (Graph-ResLSTM).