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Machine learning-based satellite routing for SAGIN IoT networks

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Yuan, Xueguang, Liu, Jinlin, Du, Hang, Zhang, Yangan, Li, Feisheng and Kadoch, Michel. 2022. « Machine learning-based satellite routing for SAGIN IoT networks ». Electronics, vol. 11, nº 6.
Compte des citations dans Scopus : 5.

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Abstract

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

Item Type: Peer reviewed article published in a journal
Professor:
Professor
Kadoch, Michel
Affiliation: Génie électrique
Date Deposited: 04 Apr 2022 18:07
Last Modified: 23 Jun 2022 13:30
URI: https://espace2.etsmtl.ca/id/eprint/24132

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