| Issue |
ITM Web Conf.
Volume 47, 2022
2022 2nd International Conference on Computer, Communication, Control, Automation and Robotics (CCCAR2022)
|
|
|---|---|---|
| Article Number | 01003 | |
| Number of page(s) | 10 | |
| Section | Computer Science and System Design, Application | |
| DOI | https://doi.org/10.1051/itmconf/20224701003 | |
| Published online | 23 June 2022 | |
Sensor network prediction based on spatial and temporal GNN
China Ship Research and Development Academy, 100101 Beijing, China
* Corresponding author: 15248156291@qq.com
Multi-sensor prediction is a hotspot for research and development in sensor management technologies. Thanks to artificial intelligence, researchers have been able to effectively use neural networks and traditional artificial intelligence approaches to multi-sensor prediction in recent years. In this model, we try to present the sensors network as an unweighted graph, based on the GNN with spatial and temporal features, combine the characteristics of the Gated recurrent unit with temporal context, and use the Graph Neural Network to predict sensor feature. We tackle the issue of poor sensor network efficiency and sluggish speed without data fusion.
Key words: Multi-sonsor network / Graph convolutional network (GCN) / Gated recurrent unit (GRU)
© The Authors, published by EDP Sciences, 2022
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