Modeling IoT equipment with graph neural networks
|Author:||Zhang, Weishan1; Zhang, Yafei1; Xu, Liang2,3;|
1School of Computer and Communication Engineering, China University of Petroleum, Qingdao 266580, China
2School of Computer and Communication Engineering, Beijing University of Science and Technology, Beijing 100083, China
3Qingdao Deep Intelligence Information Technology Co., Ltd., Qingdao 266200, China
4Faculty of Information Technology and Electrical Engineering, University of Oulu, 90014 Oulu, Finland
5Faculty of Engineering and Computer Science, Concordia University, Montreal, QC H3G 1M8, Canada
6Beijing Aerospace Smart Manufacturing Technology Development Co., Ltd., Beijing 100854, China
7School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150006, China
8College of Computer Science and Technology, Fudan University, Shanghai 200433, China
|Online Access:||PDF Full Text (PDF, 1.6 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2019052917656
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2019-05-29
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.
|Pages:||32754 - 32764|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
This work was supported in part by the Key Research Program of Shandong Province under Grant 2017GGX10140, in part by the Fundamental Research Funds for the Central Universities under Grant 2015020031, and in part by the National Natural Science Foundation of China under Grant 61309024.
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