Juan A. Fraire

Researcher in Space Networking and Informatics

Inria CONICET Saarland University

Publications Journal article · Satellite IoT · 2024

Satellite Visibility Prediction for Constrained Devices in Direct-to-Satellite IoT Systems

Raydel Ortigueira, Samuel Montejo-Sánchez, Santiago Henn, Juan A. Fraire, and Sandra Céspedes

IEEE Sensors Journal, vol. 24, no. 16, pp. 26630–26644, 2024

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Abstract

The Internet of Things (IoT) enables the transport of small amounts of data from/to constrained devices such as sensors and actuators. When these devices are located in remote areas without terrestrial network coverage, the direct-to-satellite IoT (DtS-IoT) systems become cost-effective solutions. DtS-IoT allows data exchange with remote nodes through low Earth orbit (LEO) nanosatellites, which are reachable only during short intervals, even during passes with high elevation angles. Therefore, if the constrained nodes become active late concerning a visibility interval, they may miss a transmission opportunity. In the same way, if the nodes become active prematurely, they may incur unnecessary energy consumption. Knowing the duration and start of each visibility interval beforehand allows the constrained devices to increase the probability of successful transmissions and save energy simultaneously. The precise prediction of satellite visibility intervals requires sizable energy and computational costs, a significant challenge for constrained devices, particularly for devices devoid of Internet access on the ground. In this article, we propose a fast and efficient algorithm for satellite visibility prediction that considers the two-body model as a reference and includes a mechanism for the constrained ground nodes to update the orbital information. Numerical and simulation results show that our solution decreases the computation cost by up to 66.53% compared to state-of-the-art visibility prediction algorithms. Furthermore, we show that for each pass of the satellite, the constrained devices successfully update the orbital information 87.12% of the time, this process being completely independent of any supplementary Internet connectivity.

Cite

DOI 10.1109/jsen.2024.3418728

BibTeX
@article{ortigueira2024satellite,
  author    = {Ortigueira, Raydel and Montejo-S{\'a}nchez, Samuel and Henn, Santiago and Fraire, Juan A. and C{\'e}spedes, Sandra},
  title     = {Satellite Visibility Prediction for Constrained Devices in Direct-to-Satellite IoT Systems},
  journal   = {IEEE Sensors Journal},
  volume    = {24},
  number    = {16},
  pages     = {26630--26644},
  publisher = {IEEE},
  year      = {2024},
  doi       = {10.1109/jsen.2024.3418728}
}

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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  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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