Results - A 3D Simulation Framework with Ray-Tracing Propagation for LoRaWAN Communication

Here you can find all files that we have used on our simulations. All results with their datasets will also be uploaded

Cite 📘

• Internet of Things. Elsevier. Q1 (2021-2022)

@article{RUZNIETO2023100964,
title = {A 3D simulation framework with ray-tracing propagation for LoRaWAN communication},
journal = {Internet of Things},
volume = {24},
pages = {100964},
year = {2023},
issn = {2542-6605},
doi = {https://doi.org/10.1016/j.iot.2023.100964},
url = {https://www.sciencedirect.com/science/article/pii/S2542660523002871},
author = {Andres Ruz-Nieto and Esteban Egea-Lopez and Jose-Marıa Molina-Garcıa-Pardo and Jose Santa},
keywords = {LoRaWAN, IoT, Simulation, Ray-tracing, LP-WAN},
abstract = {The adoption of Low-Power Wide-Area Networks (LP-WAN) for interconnecting remote wireless sensors has become a reality in smart scenarios, covering communications needs of large Internet of Things (IoT) deployments. The correct operation and expected performance of such network scenarios, which can range hundreds or thousands of nodes and tens of squared kilometres, should be assessed before carrying out the deployment to save installation and maintenance costs. Common network planning tools can help to roughly study potential coverage, but network simulation offers fine-grained information about network performance. Nevertheless, current simulation frameworks include limited propagation models based on statistical and empirical measurements that do not consider scenario particularities, such as terrain elevation, buildings or vegetation. This is critical in urban settings. In this line, this paper presents a simulation framework including a network simulator, a 3D engine and a ray-tracing tool, which models realistically the performance of Long-Range Wide-Area Network (LoRaWAN) communication technology. We have evaluated the performance of the solution taking as reference experimental campaigns in the city of Cartagena (Spain), comparing data obtained when simulating with the commonly employed propagation models such as Okumura–Hata or path loss. Results indicate that our framework, set-up with data from open geographical information systems, accurately fits experimental values, reporting improvements between 10% and 50% in the error committed when estimating signal strength in challenging urban streets with signal obstruction, as compared with the better performing classical model, Okumura–Hata.}
}