Machine learning-based satellite routing for SAGIN IoT networks

Yuan, Xueguang, Liu, Jinlin, Du, Hang, Zhang, Yangan, Li, Feisheng et 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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Résumé

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

Type de document:	Article publié dans une revue, révisé par les pairs
Professeur:	Professeur Kadoch, Michel
Affiliation:	Génie électrique
Date de dépôt:	04 avr. 2022 18:07
Dernière modification:	23 juin 2022 13:30
URI:	https://espace2.etsmtl.ca/id/eprint/24132

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