Juan A. Fraire

Researcher in Space Networking and Informatics

Inria CONICET Saarland University

Publications Journal article · Satellite IoT · 2025

FLoRaSat 2: Simulating Cross-Linked Direct-to-Satellite IoT LEO Constellations with Joint Access and Routing Evaluation

Alexander Y. Choquenaira-Florez, Benoit Coeugnet, Robin Ohs, Juan A. Fraire, and Hervé Rivano

Wiley International Journal of Satellite Communications and Networking, 2025

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Abstract

Direct-to-Satellite IoT (DtS-IoT) has emerged as a promising solution for extending IoT connectivity to remote regions where terrestrial infrastructure is impractical or economically unfeasible. In such scenarios, Low-Earth Orbit (LEO) satellites act as mobile in-orbit gateways, but their dynamic nature and reliance on constrained Inter-Satellite Links (ISLs) introduce significant challenges for medium access, routing, and end-to-end performance evaluation. To address these issues, we present FLORASAT 2, an open-source, event-driven simulator based on OMNET++. The first version of FLORASAT focused on device-to-satellite communication, providing a unique tool to study LoRa/LoRaWAN-based DtS-IoT systems. The present work advances FLORASAT 2 into a full end-to-end simulation framework by integrating Medium Access Control (MAC) protocols with constellation-grade routing models, including constellation creation, dynamic ISL topology control, and a sandbox for benchmarking routing algorithms such as Directed, DDRA, and DiscoRoute. Notably, it introduces K-MAC, a repetition-based MAC scheme designed to improve resilience under Doppler effects, congestion, and intermittent visibility, while expanding the satellite and device modules to support multiple MAC protocols beyond LoRaWAN. To the best of our knowledge, FLORASAT 2 is the only open-source simulator capable of jointly evaluating access and routing layers for constellation-grade DtS-IoT networks. Through extended benchmark scenarios on Starlink-and Iridiumlike constellations, we demonstrate how the integration of MAC and routing enables a more realistic and holistic assessment of network performance, positioning FLORASAT 2 as a versatile platform for the design and optimization of next-generation satellite IoT systems.

Cite

BibTeX
@article{choquenaira2025florasat2,
  author  = {Choquenaira-Florez, Alexander Y. and Coeugnet, Benoit and Ohs, Robin and Fraire, Juan A. and Rivano, Herv{\'e}},
  title   = {{FLoRaSat} 2: Simulating Cross-Linked Direct-to-Satellite {IoT} {LEO} Constellations with Joint Access and Routing Evaluation},
  journal = {Wiley International Journal of Satellite Communications and Networking},
  year    = {2025},
  url     = {https://hal.science/hal-05424077}
}

More on satellite IoT

  1. On the role of machine learning in satellite internet of things: A survey of techniques, challenges, and future directions Alexander Ylnner Choquenaira Florez, Juan A. Fraire, Hannah B. Pasandi, and Hervé Rivano Elsevier Computer Networks, 2025
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    The drive towards an interconnected world via satellites is reshaping the landscape of communication technologies. This survey comprehensively reviews studies in the Satellite Internet of Things (SIoT) domain, focusing on the role of Machine Learning (ML) techniques. Indeed, the global data collection scale in SIoT is ideally suited for data-intensive and sophisticated ML approaches. We highlight the innovative use of ML to address specific SIoT challenges, aiming to identify current trends, methodologies, and outcomes. We considered theoretical, practical, and experimental research, organizing existing publications into a new taxonomy that intersects ML and SIoT categories. Our taxonomy reveals that Deep Learning (DL), Reinforcement Learning (RL), and Federated Learning (FL) are widely applied to address radio access schemes, resource and network management, and application-specific issues. This survey identifies critical gaps in current research on ML applications in SIoT, such as the lack of differentiation between space-based and ground-based processing, insufficient integration of SIoT-specific metrics, and the oversight of limited computational resources on orbiting satellites. These issues raise concerns about the feasibility and efficiency of proposed solutions. We propose promising research directions based on the derived insights to effectively bridge the gap between ML researchers and industrial SIoT entities.

  2. DORSAL: Downlink Optimization for Robust Direct-to-Satellite LoRaWAN Juan A. Fraire, Oana Iova, Carlos Fernandez Hernandez, Fabrice Valois, Hervé Rivano, Cesar Azurdia-Meza, Marcos Diaz, Miguel Gutiérrez Gaitán, Diego Dujovne, Samuel Montejo Sánchez, and Richard Demo Souza IEEE Internet of Things Journal, 2026
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    Delivering downlink (DL) ACKs to Class A LoRaWAN devices via low Earth orbit (LEO) satellite constellations requires predicting end-to-end propagation delays of 10–500 ms to hit a 1-s receive window, a timing challenge absent from terrestrial schedulers. We present downlink optimization for robust direct-to-satellite LoRaWAN (DORSAL), the first mixed-integer linear programming (MILP) scheduler for DL delivery in direct-to-satellite (DtS) LoRaWAN networks. It introduces the downlink arrival receive-window timing (DART) as the central constraint to jointly optimize gateway (GW) selection and packet injection timing across ISL-capable and bent-pipe architectures. Evaluation on a Walker 5× 5 polar constellation (25 satellites, 650 km) and a commercial ground station (GS) network yields five results: 1) DART infeasibility blocks 73% of bent-pipe deliveries regardless of scheduler quality: DART-unaware approaches collapse from 93% nominal to only 20% real delivery; 2) ISL routing raises %ACK by + 67.4 pp over bent-pipe (91.6% versus 24.2% at n_mathrm GS=3 ground stations, ISM band) by eliminating the three-way simultaneous-visibility constraint, with 73% of ACKs delivered via ≥ 2 -hop paths; 3) ISL %ACK is nearly flat across n_mathrm GSin 3,6,12 GSs (91.6%–92.7%), showing that a minimal three-station polar network captures the full scheduling benefit of an ISL-equipped constellation; 4) ISL hardware speed has negligible impact up to 300 ms/hop: %ACK is flat from 0 to 300 ms, then degrades in two discrete steps (−5.3 pp at 500 ms, −2 pp at 700 ms) as successive hop-count tiers lose causal injection feasibility; and 5) the MILP solves to proven optimality within seconds for up to 10^3 devices, confirming that the sparse conflict-graph structure keeps the problem tractable at realistic Internet of Things (IoT) subscriber counts.

  3. Space-Terrestrial Integrated Internet of Things: Challenges and Opportunities Juan A. Fraire, Oana Iova, and Fabrice Valois IEEE Communications Magazine, 2022
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