TY - GEN
T1 - Edge Computing and Communication for Energy-Efficient Earth Surveillance with LEO Satellites
AU - Gost, Marc M.
AU - Leyva-Mayorga, Israel
AU - Perez-Neira, Ana
AU - Vazquez, Miguel Angel
AU - Soret, Beatriz
AU - Moretti, Marco
N1 - Funding Information:
Part of the research has been supported by the project SatNEx-V, co-funded by the European Space Agency (ESA). This work has also received funding by the Spanish ministry of science and innovation under project IRENE (PID2020-115323RB-C31 / AEI / 10.13039/501100011033) and grant from the Spanish ministry of economic affairs and digital transformation and of the European union – NextGenerationEU [UNICO-5G I+D/AROMA3D-Space (TSI-063000-2021-70).
Funding Information:
This work has been performed under a programme of and funded by the European Space Agency. The view expressed herein can in no way be taken to reflect the official opinion of the European Space Agency.
Publisher Copyright:
© 2022 IEEE.
PY - 2022/7/11
Y1 - 2022/7/11
N2 - Modern satellites deployed in low Earth orbit (LEO) accommodate processing payloads that can be exploited for edge computing. Furthermore, by implementing inter-satellite links, the LEO satellites in a constellation can route the data end-to-end (E2E). These capabilities can be exploited to greatly improve the current store-and-forward approaches in Earth surveillance systems. However, they give rise to an NP-hard problem of joint communication and edge computing resource management (RM). In this paper, we propose an algorithm that allows the satellites to select between computing the tasks at the edge or at a cloud server and to allocate an adequate power for communication. The overall objective is to minimize the energy consumption at the satellites while fulfilling specific service E2E latency constraints for the computing tasks. Experimental results show that our algorithm achieves energy savings of up to 18% when compared to the selected benchmarks with either 1) fixed edge computing decisions or 2) maximum power allocation.
AB - Modern satellites deployed in low Earth orbit (LEO) accommodate processing payloads that can be exploited for edge computing. Furthermore, by implementing inter-satellite links, the LEO satellites in a constellation can route the data end-to-end (E2E). These capabilities can be exploited to greatly improve the current store-and-forward approaches in Earth surveillance systems. However, they give rise to an NP-hard problem of joint communication and edge computing resource management (RM). In this paper, we propose an algorithm that allows the satellites to select between computing the tasks at the edge or at a cloud server and to allocate an adequate power for communication. The overall objective is to minimize the energy consumption at the satellites while fulfilling specific service E2E latency constraints for the computing tasks. Experimental results show that our algorithm achieves energy savings of up to 18% when compared to the selected benchmarks with either 1) fixed edge computing decisions or 2) maximum power allocation.
UR - http://www.scopus.com/inward/record.url?scp=85134749810&partnerID=8YFLogxK
U2 - 10.1109/ICCWorkshops53468.2022.9814483
DO - 10.1109/ICCWorkshops53468.2022.9814483
M3 - Article in proceeding
AN - SCOPUS:85134749810
SN - 978-1-6654-2672-5
T3 - 2022 IEEE International Conference on Communications Workshops, ICC Workshops 2022
SP - 556
EP - 561
BT - 2022 IEEE International Conference on Communications Workshops, ICC Workshops 2022
PB - IEEE Communications Society
T2 - 2022 IEEE International Conference on Communications Workshops, ICC Workshops 2022
Y2 - 16 May 2022 through 20 May 2022
ER -