Space Computing: Architectures, Challenges, and Future Directions

In recent years, the rapid advancement of space technologies has given rise to sophisticated space computing systems, which have become crucial for various applications such as Earth observation, communication, and scientific research. This survey paper provides a comprehensive overview of the history, current state, and future trends in space computing systems. We discuss the evolution of these systems, highlighting important milestones from early satellite communication networks to modern space–air–ground integrated networks (SAGIN). Despite their advancements, space computing systems face numerous challenges, including resource constraints and unstable network links. To address these challenges, we explore key enabling technologies that are critical for the future of space computing. These include virtualization and resource pooling, which enhance resource utilization; distributed storage, which provides resilience and efficiency; advanced scheduling and orchestration techniques, which optimize task allocation across heterogeneous resources; collaborative AI training and inference, which leverage distributed satellite networks for real-time data processing; and robust security and privacy measures, which are vital for safeguarding sensitive information in space operations. By examining existing systems and identifying open research challenges, this paper aims to provide a road map for future research and development in the field of space computing systems. Our findings highlight the potential of emerging technologies to transform space operations and underscore the importance of continued innovation in overcoming current limitations.

Authors

Institutions

Publication Details

Journal
Intelligent Computing
Published
2025-01-01
DOI
https://doi.org/10.34133/icomputing.0134
Primary Topic
Modular Robots and Swarm Intelligence
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Space Computing: Architectures, Challenges, and Future Directions

Zhiwen He, Zhuoran Zhang, Zhiguo Wan, Ivan Edmar Carvajal Roca et al.
Intelligent Computing
Modular Robots and Swarm Intelligence
article

Space Computing: Architectures, Challenges, and Future Directions

Zhiwen He, Zhuoran Zhang, Zhiguo Wan, Ivan Edmar Carvajal Roca, Hui Zhang, Hua Wang, Haitao Wang, Junxiao Xue, Erzhong Xue, Chao Li
article en

Abstract

In recent years, the rapid advancement of space technologies has given rise to sophisticated space computing systems, which have become crucial for various applications such as Earth observation, communication, and scientific research. This survey paper provides a comprehensive overview of the history, current state, and future trends in space computing systems. We discuss the evolution of these systems, highlighting important milestones from early satellite communication networks to modern space–air–ground integrated networks (SAGIN). Despite their advancements, space computing systems face numerous challenges, including resource constraints and unstable network links. To address these challenges, we explore key enabling technologies that are critical for the future of space computing. These include virtualization and resource pooling, which enhance resource utilization; distributed storage, which provides resilience and efficiency; advanced scheduling and orchestration techniques, which optimize task allocation across heterogeneous resources; collaborative AI training and inference, which leverage distributed satellite networks for real-time data processing; and robust security and privacy measures, which are vital for safeguarding sensitive information in space operations. By examining existing systems and identifying open research challenges, this paper aims to provide a road map for future research and development in the field of space computing systems. Our findings highlight the potential of emerging technologies to transform space operations and underscore the importance of continued innovation in overcoming current limitations.

Intelligent ComputingVol. 4
Tianjin University (CN), South China Normal University (CN), Zhejiang Lab (CN)
Sustainable cities and communities
Openalex Percentile: Top 77%
Modular Robots and Swarm Intelligence
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.