Next generation cloud and edge computing architectures for Real-Time Space Data Processing and Analytics

The rapid expansion of space exploration, satellite-based Earth observation, and interplanetary missions necessitates advanced computing architectures capable of handling massive, real-time data streams. Traditional centralized cloud computing models face significant challenges in terms of latency, bandwidth constraints, and reliability, especially for deep-space missions and large-scale satellite constellations. This study explores next-generation cloud and edge computing architectures designed to optimize real-time space data processing and analytics. By leveraging edge computing at satellite nodes and ground stations, data preprocessing, anomaly detection, and decision-making can occur closer to the source, reducing transmission delays and minimizing dependency on Earth-based infrastructure. Emerging technologies such as AI-driven edge inference, federated learning, and containerized microservices enhance computational efficiency and security in distributed space systems. Hybrid cloud-edge frameworks, integrating spaceborne data centers with terrestrial high-performance computing (HPC) facilities, offer scalability and adaptability for mission-critical applications. The implementation of 5G and future 6G-enabled space communication networks further accelerates real-time data exchange and collaborative processing between satellites and ground stations. Additionally, decentralized architectures using blockchain technology ensure data integrity and security, particularly for multi-tenant satellite networks and space commerce operations. Quantum computing advancements hold promise for accelerating complex data analytics tasks such as gravitational modeling and deep-space signal processing. This paper presents a comprehensive framework combining cloud and edge computing paradigms to enable autonomous decision-making, rapid situational awareness, and enhanced mission resilience. As space activities become increasingly data-intensive, deploying intelligent, adaptive computing infrastructures is crucial for ensuring the success of future space exploration and satellite applications.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-03-30
DOI
https://doi.org/10.5281/zenodo.17163519
Primary Topic
Geochemistry and Geologic Mapping
Type
article
Field-Weighted Citation Impact
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article

Next generation cloud and edge computing architectures for Real-Time Space Data Processing and Analytics

Zenodo (CERN European Organization for Nuclear Research)
Geochemistry and Geologic Mapping
article

Next generation cloud and edge computing architectures for Real-Time Space Data Processing and Analytics

article en

Abstract

The rapid expansion of space exploration, satellite-based Earth observation, and interplanetary missions necessitates advanced computing architectures capable of handling massive, real-time data streams. Traditional centralized cloud computing models face significant challenges in terms of latency, bandwidth constraints, and reliability, especially for deep-space missions and large-scale satellite constellations. This study explores next-generation cloud and edge computing architectures designed to optimize real-time space data processing and analytics. By leveraging edge computing at satellite nodes and ground stations, data preprocessing, anomaly detection, and decision-making can occur closer to the source, reducing transmission delays and minimizing dependency on Earth-based infrastructure. Emerging technologies such as AI-driven edge inference, federated learning, and containerized microservices enhance computational efficiency and security in distributed space systems. Hybrid cloud-edge frameworks, integrating spaceborne data centers with terrestrial high-performance computing (HPC) facilities, offer scalability and adaptability for mission-critical applications. The implementation of 5G and future 6G-enabled space communication networks further accelerates real-time data exchange and collaborative processing between satellites and ground stations. Additionally, decentralized architectures using blockchain technology ensure data integrity and security, particularly for multi-tenant satellite networks and space commerce operations. Quantum computing advancements hold promise for accelerating complex data analytics tasks such as gravitational modeling and deep-space signal processing. This paper presents a comprehensive framework combining cloud and edge computing paradigms to enable autonomous decision-making, rapid situational awareness, and enhanced mission resilience. As space activities become increasingly data-intensive, deploying intelligent, adaptive computing infrastructures is crucial for ensuring the success of future space exploration and satellite applications.

Zenodo (CERN European Organization for Nuclear Research)
College of Saint Rose (US)
Industry, innovation and infrastructure
Openalex Percentile: Top 33%
Geochemistry and Geologic Mapping
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Next generation cloud and edge computing architectures for Real-Time Space Data Processing and Analytics · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS