Machine Learning-Based Satellite Routing for SAGIN IoT Networks
Due to limited coverage, radio access provided by ground communication systems is not available everywhere on the Earth. It is necessary to develop a new three-dimensional network architecture in a bid to meet various connection requirements. Space–air–ground integrated networks (SAGINs) offer large...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/11/6/862 |
| _version_ | 1797471864798314496 |
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| author | Xueguang Yuan Jinlin Liu Hang Du Yangan Zhang Feisheng Li Michel Kadoch |
| author_facet | Xueguang Yuan Jinlin Liu Hang Du Yangan Zhang Feisheng Li Michel Kadoch |
| author_sort | Xueguang Yuan |
| collection | DOAJ |
| description | Due to limited coverage, radio access provided by ground communication systems is not available everywhere on the Earth. It is necessary to develop a new three-dimensional network architecture in a bid to meet various connection requirements. Space–air–ground integrated networks (SAGINs) offer large coverage, but the communication quality of satellites is often compromised by weather conditions. To solve this problem, we propose an extended extreme learning machine (ELM) algorithm in this paper, which can predict the communication attenuation caused by rainy weather to satellite communication links, so as to avoid large path loss caused by bad weather conditions. Firstly, we use Internet of Things (IoT)-enabled sensors to collect weather-related data. Then, the system feeds the data to the extended ELM model to obtain a category prediction for blockage caused by weather. Finally, this information helps the selection of the data transmission link and thus improves the satellite routing performance. |
| first_indexed | 2024-03-09T19:55:06Z |
| format | Article |
| id | doaj.art-735d46d336ab43d79ef7585537bac19d |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-09T19:55:06Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-735d46d336ab43d79ef7585537bac19d2023-11-24T01:00:29ZengMDPI AGElectronics2079-92922022-03-0111686210.3390/electronics11060862Machine Learning-Based Satellite Routing for SAGIN IoT NetworksXueguang Yuan0Jinlin Liu1Hang Du2Yangan Zhang3Feisheng Li4Michel Kadoch5State Key Laboratory of Information Photonics and Optical Communications, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaState Key Laboratory of Information Photonics and Optical Communications, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaState Key Laboratory of Information Photonics and Optical Communications, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaState Key Laboratory of Information Photonics and Optical Communications, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaDepartment of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong 999077, ChinaDepartment of Electrical Engineering, ETS, University of Quebec, Montreal, QC H3C 3J7, CanadaDue to limited coverage, radio access provided by ground communication systems is not available everywhere on the Earth. It is necessary to develop a new three-dimensional network architecture in a bid to meet various connection requirements. Space–air–ground integrated networks (SAGINs) offer large coverage, but the communication quality of satellites is often compromised by weather conditions. To solve this problem, we propose an extended extreme learning machine (ELM) algorithm in this paper, which can predict the communication attenuation caused by rainy weather to satellite communication links, so as to avoid large path loss caused by bad weather conditions. Firstly, we use Internet of Things (IoT)-enabled sensors to collect weather-related data. Then, the system feeds the data to the extended ELM model to obtain a category prediction for blockage caused by weather. Finally, this information helps the selection of the data transmission link and thus improves the satellite routing performance.https://www.mdpi.com/2079-9292/11/6/862space–air–ground integrated networksatellite Internet of Thingslimit learning machine model |
| spellingShingle | Xueguang Yuan Jinlin Liu Hang Du Yangan Zhang Feisheng Li Michel Kadoch Machine Learning-Based Satellite Routing for SAGIN IoT Networks Electronics space–air–ground integrated network satellite Internet of Things limit learning machine model |
| title | Machine Learning-Based Satellite Routing for SAGIN IoT Networks |
| title_full | Machine Learning-Based Satellite Routing for SAGIN IoT Networks |
| title_fullStr | Machine Learning-Based Satellite Routing for SAGIN IoT Networks |
| title_full_unstemmed | Machine Learning-Based Satellite Routing for SAGIN IoT Networks |
| title_short | Machine Learning-Based Satellite Routing for SAGIN IoT Networks |
| title_sort | machine learning based satellite routing for sagin iot networks |
| topic | space–air–ground integrated network satellite Internet of Things limit learning machine model |
| url | https://www.mdpi.com/2079-9292/11/6/862 |
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