Special Issue on Orbital Edge: Data Centres in Space

Important Dates

Submissions Due: 1 May 2026

Publication: November/December 2027

Low Earth Orbit (LEO) satellites enable applications such as global internet connectivity, remote sensing, Earth observation, and scientific research, with significant growth driven by advancements in satellite miniaturization, lower launch costs, and increasing demand for broadband access. The integration of edge computing capabilities into LEO satellite systems gives rise to the new field of Orbital Edge Computing (OEC). OEC represents a transformative shift in space-based data processing, enabling real-time analytics, reduced latency, and enhanced autonomy in remote and bandwidth-constrained environments. A space-based data centre refers to compute infrastructure—specialized computational processors, storage, and inter-satellite networking—hosted directly onboard satellites or orbital platforms, enabling AI/ML workloads to run in orbit rather than on Earth. This reduces latency of communication from satellites-to-Earth, enabling some data to be directly processed on-board the satellite before transferring this to a terrestrial data centres.

In parallel with the development of OEC, Federated Learning (FL) emerged as a promising new approach in distributed machine learning by enabling model training across decentralized data sources without transferring raw data.  Federated learning is especially well-suited to space environments because satellites generate large amounts of sensor data but face constraints such as intermittent connectivity, limited bandwidth, radiation-hardened compute limits, and energy availability. Recent research demonstrates that FL can be adapted explicitly for orbital dynamics and for distributed constellations of satellites acting as space-based data centres.

This Special Issue solicits articles which explores how Machine Learning and Data-Driven applications can be deployed on LEO satellites. The objective of this special issue is to enable researchers and practitioners to share their research results in this area. We encourage novel and high quality contributions related to the following areas:

• Coordination in LEO satellites to support AI/ML applications
• Energy harvesting in LEO satellites
• Satellite-Terrestrial systems integration
• Demonstrator applications deployed on orbital-edge platforms
• Simulation environments for orbital edge
• Serverless & data stream processing in orbital edge
• Infrastructure optimization and resource management in orbital edge
• Performance modelling, placement and evaluation in orbital edge
• Data, storage and network management in orbital edge
• Accelerators support for orbital edge systems
• Fault tolerance, reliability and resilience in orbital edge systems
• Federated learning on orbital edge systems

Submission Guidelines

For author information and guidelines on submission criteria, visit the Author’s Information Page. Please submit papers through the IEEE Author Portal., system, and be sure to select the special issue or special section name. Manuscripts should not be published or currently submitted for publication elsewhere. Please submit only full papers intended for review, not abstracts, to the IEEE Author Portal. If requested, abstracts should be sent by email to the guest editors directly.

In addition to submitting your paper to IEEE Software, you are also encouraged to upload the data related to your paper to IEEE DataPort. IEEE DataPort is IEEE's data platform that supports the storage and publishing of datasets while also providing access to thousands of research datasets. Uploading your dataset to IEEE DataPort will strengthen your paper and will support research reproducibility. Your paper and the dataset can be linked, providing a good opportunity for you to increase the number of citations you receive. Data can be uploaded to IEEE DataPort prior to submitting your paper or concurrent with the paper submission.

Guest Editors

• Bahman Javadi, Western Sydney University, Australia
• Massimo Villari, University of Messina, Italy
• Omer Rana, Cardiff University, UK