Modeling IoT equipment with graph neural networks

Zhang, Weishan; Zhang, Yafei; Xu, Liang; Zhou, Jiehan; Liu, Yan; Gu, Mu; Liu, Xin; Yang, Su (2019-03-04)
 
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https://doi.org/10.1109/ACCESS.2019.2902865

Zhang, Weishan
Zhang, Yafei
Xu, Liang
Zhou, Jiehan
Liu, Yan
Gu, Mu
Liu, Xin
Yang, Su
Institute of Electrical and Electronics Engineers
04.03.2019

W. Zhang et al., "Modeling IoT Equipment With Graph Neural Networks," in IEEE Access, vol. 7, pp. 32754-32764, 2019. doi: 10.1109/ACCESS.2019.2902865

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doi:https://doi.org/10.1109/ACCESS.2019.2902865
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https://urn.fi/URN:NBN:fi-fe2019052917656
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Abstract

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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