Juan A. Fraire

Researcher in Space Networking and Informatics

Inria CONICET Saarland University

Publications Conference paper · Satellite IoT · 2022

FLoRaSat: Le simulateur pour la communication LoRa avec les satellites LEO

Juan A. Fraire, Mehdi Mesbah, Pablo Madoery, Oana Iova, and Fabrice Valois

Rencontres Francophones sur la Conception de Protocoles (CoRes), pp. 1–4, 2022

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Abstract

Le nouveau paradigme de communication, Direct-to-Satellite-IoT (DtS-IoT), est une approche très prometteuse pour le transfert de données depuis/vers des appareils IoT dans des zones reculées où le déploiement d'une infrastructure terrestre n'est pas faisable. Dans ce contexte, les satellites en orbite terrestre basse (LEO) peuvent équiper des passerelles IoT comme celles de LoRa, pour faciliter la collecte de données des capteurs situés sur Terre en ligne de vue directe. Cependant, cette communication est soumise à plusieurs défis : grandes distances de transmission (500 à 2000 km), couverture sporadique et de courte durée (environ 7 minutes), liaisons inter-satellites à faible bande passante, et appareils IoT contraints en énergie. Pour permettre à la communauté de mettre en oeuvre et d'étudier des protocoles pour la communication DtS-IoT de façon réaliste, nous proposons un nouvel outil de simulation à événements discrets : FLoRaSat. Le simulateur s'appuie sur Omnet++ et implemente un scénario de communication DtS-IoT de de bout en bout comprenant 16 satellites LEO interconnectés et 1500 noeuds IoT sur Terre qui communiquent à l'aide du protocole LoRaWAN.

Cite

BibTeX
@inproceedings{fraire2022florasat,
  author    = {Fraire, Juan A. and Mesbah, Mehdi and Madoery, Pablo and Iova, Oana and Valois, Fabrice},
  title     = {FLoRaSat: Le simulateur pour la communication LoRa avec les satellites LEO},
  booktitle = {Rencontres Francophones sur la Conception de Protocoles (CoRes)},
  pages     = {1--4},
  year      = {2022},
  url       = {https://hal.science/hal-03657890v1}
}

More on satellite IoT

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

    Large geographical regions of our planet remain uncovered by terrestrial network connections. Sparse and dense constellations of near Earth orbit satellites can bridge this gap by providing Internet of Things (IoT) connectivity on a worldwide scale in a flexible and cost-effective manner. This article presents STEREO: a novel space-terrestrial integrated IoT architecture spanning direct- and indirect-to-satellite access from IoT assets on the surface. Framed on the identified requirements, we analyze NB-IoT and LoRa/LoRaWAN features to put these technologies forward as appealing candidates for future satellite IoT deployments. Finally, we list and discuss the key open research challenges to be addressed in order to achieve successful space-terrestrial IoT integration.

  2. Sparse Satellite Constellation Design for Global and Regional Direct-to-Satellite IoT Services Gabriel Maiolini Capez, Santiago Henn, Juan A. Fraire, and Roberto Garello IEEE Transactions on Aerospace and Electronic Systems, 2022
    Abstract

    In this article, we introduce and design sparse constellations for direct-to-satellite Internet of Things (DtS-IoT). DtS-IoT does not require a ground infrastructure, because the devices are directly connected to low earth orbit satellites acting as orbiting gateways. The key idea of sparse constellations is to significantly reduce the number of in-orbit DtS-IoT satellites by a proper dimensioning of the delivery delay anyway present in resource-constrained IoT services and an optimal positioning of the orbiting gateways. First, we analyze long-range modulation (LoRa)/LoRaWAN and narrowband Internet of Things (NB-IoT) standards and derive realistic constraints on the maximum gap time between two consecutive passing-by satellites. Then, we introduce and optimize an algorithm to design quasi-optimal topologies for sparse IoT constellations. Finally, we apply our design to both global and regional coverage and we analyze the tradeoff between latency, number of orbit planes, and total number of satellites. Results show that sparse constellations can provide world-wide IoT coverage with only 12.5 and 22.5% of the satellites required by traditional dense constellations considering 3 and 2-h gaps. Also, we show that region-specific coverage of Africa and Europe can be achieved with only four and three satellites for LoRa/LoRaWAN and NB-IoT, respectively.

  3. 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.