Juan A. Fraire

Researcher in Space Networking and Informatics

Inria CONICET Saarland University

Publications Conference paper · Satellite IoT · 2022

Simulating LoRa-Based Direct-to-Satellite IoT Networks with FLoRaSaT

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

IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM), pp. 464–470, 2022

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Abstract

Direct-to-Satellite-IoT (DtS-IoT) is a promising approach for data transfer to/from IoT devices in remote areas where deploying terrestrial infrastructure is not appealing or feasible. In this context, Low-Earth Orbit (LEO) satellites can serve as passing-by IoT gateways to which devices can offload buffered data to. However, transmission distance and orbital dynamics, combined with highly constrained devices on the ground makes DtS-IoT a very challenging problem. In fact, existing IoT medium access control protocols, negotiations schemes, etc. need to be revised and/or extended to scale up to these challenging conditions. The intricate time-dynamic aspects of DtS-IoT networks require of adequate simulation environments to assess the expected performance of enabling technologies. To make up for the lack of such tools, we present a novel event-driven open-source end-to-end simulation tool coined FLoRaSAT. The simulator leverages Omnet++ and includes a benchmarking DtS-IoT scenario comprising 16 cross-linked LEO satellites and 1500 IoT nodes on the surface. Satellites and devices are connected using the standard LoRaWAN Low-Power Wide Area (LPWAN) protocol (Class A and B). FLoRaSAT allows the easy implementation and study of DtS-IoT radio access and core network protocols, and we take advantage of this flexibility to investigate expected network metrics and non-intuitive phenomena emerging from the resulting multi-gateway setup.

Cite

DOI 10.1109/wowmom54355.2022.00072

BibTeX
@inproceedings{DBLP:conf/wowmom/FraireMMIV22,
  author    = {Juan A. Fraire and Pablo G. Madoery and Mehdi Ait Mesbah and Oana Iova and Fabrice Valois},
  title     = {Simulating LoRa-Based Direct-to-Satellite IoT Networks with FLoRaSaT},
  booktitle = {IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM)},
  pages     = {464--470},
  publisher = {{IEEE}},
  year      = {2022},
  doi       = {10.1109/wowmom54355.2022.00072}
}

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.