Sensor network prediction based on spatial and temporal GNN
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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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EDP Sciences
2022-01-01
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| Series: | ITM Web of Conferences |
| Subjects: | |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2022/07/itmconf_cccar2022_01003.pdf |
| _version_ | 1828310857350643712 |
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| author | Liu Peng Li Zhuang Cong Yang Xu Yuheng |
| author_facet | Liu Peng Li Zhuang Cong Yang Xu Yuheng |
| author_sort | Liu Peng |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T15:51:16Z |
| format | Article |
| id | doaj.art-b1a7e3dfb1994862b36799a35b39c7cd |
| institution | Directory Open Access Journal |
| issn | 2271-2097 |
| language | English |
| last_indexed | 2024-04-13T15:51:16Z |
| publishDate | 2022-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj.art-b1a7e3dfb1994862b36799a35b39c7cd2022-12-22T02:40:51ZengEDP SciencesITM Web of Conferences2271-20972022-01-01470100310.1051/itmconf/20224701003itmconf_cccar2022_01003Sensor network prediction based on spatial and temporal GNNLiu Peng0Li Zhuang1Cong Yang2Xu Yuheng3China Ship Research and Development AcademyChina Ship Research and Development AcademyChina Ship Research and Development AcademyChina Ship Research and Development AcademyMulti-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.https://www.itm-conferences.org/articles/itmconf/pdf/2022/07/itmconf_cccar2022_01003.pdfmulti-sonsor networkgraph convolutional network (gcn)gated recurrent unit (gru) |
| spellingShingle | Liu Peng Li Zhuang Cong Yang Xu Yuheng Sensor network prediction based on spatial and temporal GNN ITM Web of Conferences multi-sonsor network graph convolutional network (gcn) gated recurrent unit (gru) |
| title | Sensor network prediction based on spatial and temporal GNN |
| title_full | Sensor network prediction based on spatial and temporal GNN |
| title_fullStr | Sensor network prediction based on spatial and temporal GNN |
| title_full_unstemmed | Sensor network prediction based on spatial and temporal GNN |
| title_short | Sensor network prediction based on spatial and temporal GNN |
| title_sort | sensor network prediction based on spatial and temporal gnn |
| topic | multi-sonsor network graph convolutional network (gcn) gated recurrent unit (gru) |
| url | https://www.itm-conferences.org/articles/itmconf/pdf/2022/07/itmconf_cccar2022_01003.pdf |
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