On Selecting a Routing Protocol for Nanosatellite Swarm Networks

Routing in nanosatellites swarms presents distinct challenges, including variable node availability, constrained bandwidth, and dynamic topology. Strategies like delay-tolerant networking (DTN) can be advantageous, as they adapt to intermittent connectivity by storing and forwarding data when connections are established. Moreover, geographic routing protocols that exploit satellite positions can improve efficiency, while machine learning approaches may optimize routing decisions based on changing network conditions. What about hybrid approaches that may combine some of these methods? Basically, the crucial question is where to begin. The primary challenge for nanosatellites network designers is to determine which routing strategies to test prior to deployment. Given the vast number of existing routing protocols, testing all of them is not possible. This problem motivates the present study, which share the authors' experiences on selecting the most suitable routing algorithms for a given nanosatellites swarm. In particular, the study reports how the use of graph theory metrics helps in restricting the set of routing algorithms to be considered for network characterization and protocol selection.

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Publication Details

Published
2025-06-17
DOI
https://doi.org/10.1109/vtc2025-spring65109.2025.11174615
Primary Topic
Satellite Communication Systems
Type
article
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On Selecting a Routing Protocol for Nanosatellite Swarm Networks

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Satellite Communication Systems
article

On Selecting a Routing Protocol for Nanosatellite Swarm Networks

Bastien Tauran, Bernard Pontet, David Pradas, Riadh Dhaou, Emmanuel Lochin, Louis Basset
article en

Abstract

Routing in nanosatellites swarms presents distinct challenges, including variable node availability, constrained bandwidth, and dynamic topology. Strategies like delay-tolerant networking (DTN) can be advantageous, as they adapt to intermittent connectivity by storing and forwarding data when connections are established. Moreover, geographic routing protocols that exploit satellite positions can improve efficiency, while machine learning approaches may optimize routing decisions based on changing network conditions. What about hybrid approaches that may combine some of these methods? Basically, the crucial question is where to begin. The primary challenge for nanosatellites network designers is to determine which routing strategies to test prior to deployment. Given the vast number of existing routing protocols, testing all of them is not possible. This problem motivates the present study, which share the authors' experiences on selecting the most suitable routing algorithms for a given nanosatellites swarm. In particular, the study reports how the use of graph theory metrics helps in restricting the set of routing algorithms to be considered for network characterization and protocol selection.

École Nationale de l’Aviation Civile (FR), Centre National de la Recherche Scientifique (FR), Université Toulouse III - Paul Sabatier (FR), Institut Superieur de l'Aeronautique et de l'Espace (ISAE-SUPAERO) (FR), Centre National d'Études Spatiales (FR), Office National d'Études et de Recherches Aérospatiales (FR), Université Toulouse-I-Capitole (FR), Institut de Recherche en Informatique de Toulouse (FR), Veris Technologies (United States) (US), Université Toulouse - Jean Jaurès (FR), Institut Polytechnique de Bordeaux (FR)
Openalex Percentile: Top 65%
Satellite Communication Systems
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