A Comprehensive Survey on Orbital Edge Computing: Systems, Applications, and Algorithms
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Abstract (arXiv)
The number of satellites, especially those operating in low-earth orbit (LEO), is exploding in recent years. Additionally, the use of COTS hardware into those satellites enables a new paradigm of computing: orbital edge computing (OEC). OEC entails more technically advanced steps compared to single-satellite computing. This feature allows for vast design spaces with multiple parameters, rendering several novel approaches feasible. The mobility of LEO satellites in the network and limited resources of communication, computation, and storage make it challenging to design an appropriate scheduling algorithm for specific tasks in comparison to traditional ground-based edge computing. This article comprehensively surveys the significant areas of focus in orbital edge computing, which include protocol optimization, mobility management, and resource allocation. This article provides the first comprehensive survey of OEC. Previous survey papers have only concentrated on ground-based edge computing or the integration of space and ground technologies. This article presents a review of recent research from 2000 to 2023 on orbital edge computing that covers network design, computation offloading, resource allocation, performance analysis, and optimization. Moreover, having discussed several related works, both technological challenges and future directions are highlighted in the field.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 1 Jun 2023.
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Citing papers per year: 2024: 2; 2025: 0; 2026: 2.
4 papers.
- 2024FOOL: Addressing the Downlink Bottleneck in Satellite Computing with Neural Feature Compressioncited by 6
- 2024Energy-Aware Federated Learning in Satellite Constellationscited by 3
- 2026On-Orbit Space AI: Federated, Multi-Agent, and Collaborative Algorithms for Satellite Constellationscited by 0
- 2026AstroMind: A High-Fidelity Benchmark for Spacecraft Behavior Reasoning Based on Large Language Modelscited by 0
- 2026AstroMind: A High-Fidelity Benchmark for Spacecraft Behavior Reasoning Based on Large Language Models23 May 2026
- 2026On-Orbit Space AI: Federated, Multi-Agent, and Collaborative Algorithms for Satellite Constellations15 Apr 2026
- 2024Energy-Aware Federated Learning in Satellite Constellations23 Sep 2024
- 2024FOOL: Addressing the Downlink Bottleneck in Satellite Computing with Neural Feature Compression25 Mar 2024