Satellite Edge Computing for Real-Time and Very-High Resolution Earth Observation

In high-resolution Earth observation imagery, Low Earth Orbit (LEO) satellites capture and transmit images to ground to create an updated map of an area of interest. Such maps provide valuable information for meteorology and environmental monitoring, but can also be employed for real-time disaster d...

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Autores: Leyva-Mayorga I., Martinez-Gost M., Moretti M., Perez-Neira A., Vazquez M.A., Popovski P., Soret B.
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2023
País:España
Institución:Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
Repositorio:r-CTTC. Repositorio Institucional Producción Científica del Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
OAI Identifier:oai:cttc.fundanetsuite.com:p8305
Acceso en línea:https://cttc.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=8305
Access Level:acceso abierto
Palabra clave:Earth observation
low earth orbit (LEO) satellite communications
satellite imagery
satellite mobile edge computing (SMEC)
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spelling Satellite Edge Computing for Real-Time and Very-High Resolution Earth ObservationLeyva-Mayorga I.Martinez-Gost M.Moretti M.Perez-Neira A.Vazquez M.A.Popovski P.Soret B.Earth observationlow earth orbit (LEO) satellite communicationssatellite imagerysatellite mobile edge computing (SMEC)In high-resolution Earth observation imagery, Low Earth Orbit (LEO) satellites capture and transmit images to ground to create an updated map of an area of interest. Such maps provide valuable information for meteorology and environmental monitoring, but can also be employed for real-time disaster detection and management. However, the amount of data generated by these applications can easily exceed the communication capabilities of LEO satellites, leading to congestion and packet dropping. To avoid these problems, the Inter-Satellite Links (ISLs) can be used to distribute the data among multiple satellites and speed up processing. In this paper, we formulate a satellite mobile edge computing (SMEC) framework for real-time and very-high resolution Earth observation and optimize the image distribution and compression parameters to minimize energy consumption. Our results show that our approach increases the amount of images that the system can support by a factor of 12x and 2x when compared to directly downloading the data and to local SMEC, respectively. Furthermore, energy consumption was reduced by 11% in a real-life scenario of imaging a volcanic island, while a sensitivity analysis of the image acquisition process demonstrates that energy consumption can be reduced by up to 90%.Institute of Electrical and Electronics Engineers Inc.2023info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttps://cttc.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=8305IEEE TRANSACTIONS ON COMMUNICATIONSISSN: 00906778ISSNe: 15580857reponame:r-CTTC. Repositorio Institucional Producción Científica del Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)instname:Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)Inglésinfo:eu-repo/semantics/openAccessoai:cttc.fundanetsuite.com:p83052026-06-17T11:44:47Z
dc.title.none.fl_str_mv Satellite Edge Computing for Real-Time and Very-High Resolution Earth Observation
title Satellite Edge Computing for Real-Time and Very-High Resolution Earth Observation
spellingShingle Satellite Edge Computing for Real-Time and Very-High Resolution Earth Observation
Leyva-Mayorga I.
Earth observation
low earth orbit (LEO) satellite communications
satellite imagery
satellite mobile edge computing (SMEC)
title_short Satellite Edge Computing for Real-Time and Very-High Resolution Earth Observation
title_full Satellite Edge Computing for Real-Time and Very-High Resolution Earth Observation
title_fullStr Satellite Edge Computing for Real-Time and Very-High Resolution Earth Observation
title_full_unstemmed Satellite Edge Computing for Real-Time and Very-High Resolution Earth Observation
title_sort Satellite Edge Computing for Real-Time and Very-High Resolution Earth Observation
dc.creator.none.fl_str_mv Leyva-Mayorga I.
Martinez-Gost M.
Moretti M.
Perez-Neira A.
Vazquez M.A.
Popovski P.
Soret B.
author Leyva-Mayorga I.
author_facet Leyva-Mayorga I.
Martinez-Gost M.
Moretti M.
Perez-Neira A.
Vazquez M.A.
Popovski P.
Soret B.
author_role author
author2 Martinez-Gost M.
Moretti M.
Perez-Neira A.
Vazquez M.A.
Popovski P.
Soret B.
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv Earth observation
low earth orbit (LEO) satellite communications
satellite imagery
satellite mobile edge computing (SMEC)
topic Earth observation
low earth orbit (LEO) satellite communications
satellite imagery
satellite mobile edge computing (SMEC)
description In high-resolution Earth observation imagery, Low Earth Orbit (LEO) satellites capture and transmit images to ground to create an updated map of an area of interest. Such maps provide valuable information for meteorology and environmental monitoring, but can also be employed for real-time disaster detection and management. However, the amount of data generated by these applications can easily exceed the communication capabilities of LEO satellites, leading to congestion and packet dropping. To avoid these problems, the Inter-Satellite Links (ISLs) can be used to distribute the data among multiple satellites and speed up processing. In this paper, we formulate a satellite mobile edge computing (SMEC) framework for real-time and very-high resolution Earth observation and optimize the image distribution and compression parameters to minimize energy consumption. Our results show that our approach increases the amount of images that the system can support by a factor of 12x and 2x when compared to directly downloading the data and to local SMEC, respectively. Furthermore, energy consumption was reduced by 11% in a real-life scenario of imaging a volcanic island, while a sensitivity analysis of the image acquisition process demonstrates that energy consumption can be reduced by up to 90%.
publishDate 2023
dc.date.none.fl_str_mv 2023
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://cttc.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=8305
url https://cttc.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=8305
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers Inc.
publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers Inc.
dc.source.none.fl_str_mv IEEE TRANSACTIONS ON COMMUNICATIONS
ISSN: 00906778
ISSNe: 15580857
reponame:r-CTTC. Repositorio Institucional Producción Científica del Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
instname:Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
instname_str Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
reponame_str r-CTTC. Repositorio Institucional Producción Científica del Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
collection r-CTTC. Repositorio Institucional Producción Científica del Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,812455

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