ORIGINAL RESEARCH article
Front. Comput. Sci.
Sec. Networks and Communications
Volume 7 - 2025 | doi: 10.3389/fcomp.2025.1666262
Machine Learning-Based Spreading Factor Optimization in LoRaWAN Networks
Provisionally accepted
- 1Qurtuba University of Sciences and Information Technology, Dera Ismail Khan, Pakistan
- 2National University of Modern Languages, Islamabad, Pakistan
- 3Prince Sultan University, Riyadh, Saudi Arabia
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The Internet of Things (IoT) has experienced rapid growth and adoption in recent years, enabling applications across diverse industries, including agriculture, logistics, smart cities, and healthcare. Long Range Wide Area Network (LoRaWAN) has emerged as a leading choice among IoT communication technologies due to its long-range, low-power, and cost-effective capabilities. However, the rapid proliferation of IoT devices has intensified the challenge of efficient resource management, particularly in spreading factor (SF) allocation for LoRaWAN networks. In this paper, we propose a Machine Learning-based Adaptive Data Rate (ML-ADR) approach for SF management to address this issue. A Long Short-Term Memory (LSTM) network was trained on a dataset generated using ns-3 for optimal SF classification. The pre-trained LSTM model was then utilized on the end-device side for efficient SF allocation with newly generated data during simulation. The results demonstrate improved packet delivery ratios and reduced energy consumption.
Keywords: LoRaWAN, Internet of Things (IoT), Machine Learning (ML), LSTM, Spreading factor (SF), Transmission Power (TP)
Received: 15 Jul 2025; Accepted: 25 Aug 2025.
Copyright: © 2025 Nisar, Ameen, Touseef Irshad, Hadi, Ahmad and Ladan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Farhan Nisar, Qurtuba University of Sciences and Information Technology, Dera Ismail Khan, Pakistan
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