Dataset & Code for: Dynamic Selection and Detection of Spreading Factors and Channels for End-Node Devices of LoRa Networks

This Zenodo record provides the dataset, source code, and simulation material associated with the article: “Dynamic Selection and Detection of Spreading Factors and Channels for End-Node Devices of LoRa Networks”,published in Electronics, Volume 14, Issue 17, Article 3341, August 2025 (ISSN 2079-9292, MDPI).DOI: 10.3390/electronics14173341 The record includes the Python implementation of the decentralized algorithm for dynamic selection and detection of spreading factors (SF) and channels in LoRa networks. The proposed approach enables autonomous adaptation of LoRa end-nodes, improving channel utilization and reducing collisions without requiring LoRaWAN infrastructure. The repository contains: Simulation scripts and configuration files used for performance evaluation. Experimental datasets supporting the results presented in the paper. Analysis scripts and plotting tools to reproduce the published figures. This artifact aims to ensure transparency, reproducibility, and reusability of the results, supporting further research on adaptive LoRa networking, edge intelligence, and energy-efficient IoT communications. Authors: Carles Aliagas, Roger Pueyo Centelles, Roc Meseguer, Pere Millán, and Carlos Molina.Affiliation: Universitat Rovira i Virgili (URV) and Universitat Politècnica de Catalunya (UPC).License: Creative Commons Attribution 4.0 International (CC BY 4.0).Related publication: https://doi.org/10.3390/electronics14173341Keywords: LoRa, IoT, adaptive networks, spreading factors, channel selection, decentralized algorithms, collision reduction, edge computing