Journal
IEEE ACCESS
Volume 7, Issue -, Pages 32754-32764Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2019.2902865
Keywords
Graph neural networks; deep learning; simulation; time series prediction; IoT
Categories
Funding
- Key Research Program of Shandong Province [2017GGX10140]
- Fundamental Research Funds for the Central Universities [2015020031]
- National Natural Science Foundation of China [61309024]
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Traditional neural networks usually concentrate on temporal data in system simulation, and lack of capabilities to reason inner logic relations between different dimensions of data collected from embedded sensors. This paper proposes a graph neural network-based modeling approach for IoT equipment (called GNNM-IoT), which considers both temporal and inner logic relations of data, in which vertices denote sensor data and edges denote relationships between vertices. The GNNM-IoT model's relationships between sensors with neural networks to produce nonlinear complex relationships. We have evaluated the GNNM-IoT using air-conditioner data from a world leading IoT company, which demonstrates that it is effective and outperforms ARIMA and LSTM methods.
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