Minimizing the Carbon Footprint in LoRa-Based IoT Networks: A Machine Learning Perspective on Gateway Positioning

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Project publication · IEEE Open Journal of the Computer Society

Summary

The article applies machine learning to gateway placement in LoRa networks to reduce the carbon footprint of the deployment.

Resultados de optimización de ubicación de gateways LoRa con aprendizaje automático
Figure from the original article. Source: IEEE Open Journal of the Computer Society, reproduced without modification under CC BY 4.0.

Authors: Francisco-Jose Alvarado-Alcon, Rafael Asorey-Cacheda, Antonio-Javier Garcia-Sanchez, Joan Garcia-Haro

Journal: IEEE Open Journal of the Computer Society

DOI: 10.1109/OJCS.2025.3559331

Funding: PID2023-148214OB-C21.

UPCT Repository: http://hdl.handle.net/10317/18956

Bibliometric indicators

JCR quartileQ1
Impact factor8.2
Google Scholar citations0
JCR area
Computer Science, Theory & Methods
JCR year
2024
Scholar query
21 May 2026

Main contribution

The proposal combines prediction and optimization models to estimate the environmental impact of different network configurations. It enables infrastructure placement decisions that consider coverage, performance and emissions.

Link with BoND1

This publication reinforces BoND1 objective of designing non-cellular IoT with efficiency and sustainability criteria. The use of machine learning makes it possible to explore complex configurations without relying only on exhaustive measurement campaigns.

DOI UPCT Repository
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