Publications Conference paper · Delay-tolerant networks · 2023

Routing in Scalable Delay-Tolerant Space Networks with Graph Neural Networks

Matías Olmedo, Juan A. Fraire, Renato Cherini, Fabio Patrone, and Mario Marchese

International Conference on Embedded Wireless Systems and Networks (EWSN), pp. 211–218, 2023

Abstract

Space networks are challenged by long propagation delays and prolonged link disruptions, which require Delay-Tolerant Networking (DTN) mechanisms. Contact Graph Routing (CGR) exploits the a priori available topology knowledge derived from the predictable trajectories to compute optimal end-to-end routes. However, state-of-the-art CGR is limited in the scalability and stability of the computation effort, which is critical in resource-constrained spacecraft. To overcome this issue, this paper presents GAUSS: a graph neural network-based routing for scalable delaytolerant space networks. By harvesting recent advances in Graph Neural Networks (GNNs), we can improve the scalability of CGR by a factor of two while narrowing the variability down to one-third in realistic cislunar and near-Earth systems.

Cite

BibTeX

@inproceedings{DBLP:conf/ewsn/OlmedoFCPM23,
  author    = {Mat{\'{\i}}as Olmedo and Juan A. Fraire and Renato Cherini and Fabio Patrone and Mario Marchese},
  editor    = {Giancarlo Fortino and Valeria Loscr{\`{\i}} and Fabrizio Granelli and Tarek F. Abdelzaher and Antonella Guzzo and Claudio Savaglio},
  title     = {Routing in Scalable Delay-Tolerant Space Networks with Graph Neural Networks},
  booktitle = {International Conference on Embedded Wireless Systems and Networks (EWSN)},
  pages     = {211--218},
  publisher = {{ACM}},
  year      = {2023},
  url       = {https://dl.acm.org/doi/10.5555/3639940.3639967}
}

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