🤖 AI Summary
Existing simulation tools lack integrated modeling capabilities for LoRa in non-terrestrial networks (NTNs), hindering feasibility assessment of direct LoRa-to-LEO satellite communication for large-scale IoT deployment in remote areas.
Method: We design and implement ns3-LoRa-NTN—the first full-stack ns-3 module integrating LoRa physical-layer modulation, realistic channel models, and high-fidelity LEO orbital dynamics.
Contribution/Results: Experiments confirm LoRa supports uplink transmission from end devices to LEO satellites; however, fixed spreading factors induce packet collisions due to inter-device propagation delay disparities over long distances. We identify key NTN-aware configuration optimizations—such as adaptive spreading factor assignment and timing synchronization strategies—to mitigate collision risks. This work establishes a reproducible, open-source simulation foundation for protocol design and system evaluation of integrated space–ground IoT networks.
📝 Abstract
The integration of Internet of Things (IoT) and Non-Terrestrial Networks (NTNs) has emerged as a key paradigm to provide connectivity for sensors and actuators via satellite gateways in remote areas where terrestrial infrastructure is limited or unavailable. Among other Low-Power Wide-Area Network (LPWAN) technologies for IoT, Long Range (LoRa) holds great potential given its long range, energy efficiency, and flexibility. In this paper, we explore the feasibility and performance of LoRa to support large-scale IoT connectivity through Low Earth Orbit (LEO) satellite gateways. To do so, we developed a new ns3-LoRa-NTN simulation module, which integrates and extends the ns3-LoRa and ns3-NTN modules, to enable full-stack end-to-end simulation of satellite communication in LoRa networks. Our results, given in terms of average data rate and Packet Reception Ratio (PRR), confirm that LoRa can effectively support direct communication from the ground to LEO satellites, but network optimization is required to mitigate collision probability when end nodes use the same Spreading Factors (SFs) over long distances.