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Satellite Network Task Deployment Method Based on SDN and ICN

With the rapid development of 5G and the Internet of Things, satellite networks are emerging as an indispensable part of realizing wide-area coverage. The growth of the constellation of low-orbit satellites makes it possible to deploy edge computing services in satellite networks. This is, however,...

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Autores principales: Liu, Zhiguo, Dong, Xiaoqi, Wang, Lin, Feng, Jianxin, Pan, Chengsheng, Li, Yunqi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9317158/
https://www.ncbi.nlm.nih.gov/pubmed/35891119
http://dx.doi.org/10.3390/s22145439
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author Liu, Zhiguo
Dong, Xiaoqi
Wang, Lin
Feng, Jianxin
Pan, Chengsheng
Li, Yunqi
author_facet Liu, Zhiguo
Dong, Xiaoqi
Wang, Lin
Feng, Jianxin
Pan, Chengsheng
Li, Yunqi
author_sort Liu, Zhiguo
collection PubMed
description With the rapid development of 5G and the Internet of Things, satellite networks are emerging as an indispensable part of realizing wide-area coverage. The growth of the constellation of low-orbit satellites makes it possible to deploy edge computing services in satellite networks. This is, however, challenging due to the topological dynamics and limited resources of satellite networks. To improve the performance of edge computing in a satellite network, we propose a satellite network task deployment method based on SDN (software-defined network) and ICN (information-centric network). In this method, based on the full analysis of satellite network resources, a mission deployment model of a low-orbit satellite network is established. The genetic algorithm is then used to solve the proposed method. Experiments confirm that this method can effectively reduce the response delay of the tasks and the network traffic caused by task processing.
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spelling pubmed-93171582022-07-27 Satellite Network Task Deployment Method Based on SDN and ICN Liu, Zhiguo Dong, Xiaoqi Wang, Lin Feng, Jianxin Pan, Chengsheng Li, Yunqi Sensors (Basel) Article With the rapid development of 5G and the Internet of Things, satellite networks are emerging as an indispensable part of realizing wide-area coverage. The growth of the constellation of low-orbit satellites makes it possible to deploy edge computing services in satellite networks. This is, however, challenging due to the topological dynamics and limited resources of satellite networks. To improve the performance of edge computing in a satellite network, we propose a satellite network task deployment method based on SDN (software-defined network) and ICN (information-centric network). In this method, based on the full analysis of satellite network resources, a mission deployment model of a low-orbit satellite network is established. The genetic algorithm is then used to solve the proposed method. Experiments confirm that this method can effectively reduce the response delay of the tasks and the network traffic caused by task processing. MDPI 2022-07-21 /pmc/articles/PMC9317158/ /pubmed/35891119 http://dx.doi.org/10.3390/s22145439 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Liu, Zhiguo
Dong, Xiaoqi
Wang, Lin
Feng, Jianxin
Pan, Chengsheng
Li, Yunqi
Satellite Network Task Deployment Method Based on SDN and ICN
title Satellite Network Task Deployment Method Based on SDN and ICN
title_full Satellite Network Task Deployment Method Based on SDN and ICN
title_fullStr Satellite Network Task Deployment Method Based on SDN and ICN
title_full_unstemmed Satellite Network Task Deployment Method Based on SDN and ICN
title_short Satellite Network Task Deployment Method Based on SDN and ICN
title_sort satellite network task deployment method based on sdn and icn
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9317158/
https://www.ncbi.nlm.nih.gov/pubmed/35891119
http://dx.doi.org/10.3390/s22145439
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