Enhancing LoRa-based Outdoor Localization Accuracy Using Machine Learning

The Internet of Things is gaining significant relevance, driving increasing interest in location-
based services using wireless signals, particularly Low Power Wide Area Network (LPWAN) technology.
LoRa (Long Range), together with LoRaWAN, is a prominent LPWAN standard that provides long-range
connectivity and low energy consumption, making it viable for IoT-based positioning systems in smart
cities. For localization systems leveraging LoRa signals, Machine Learning (ML) approaches are being
increasingly explored, as ML-based solutions offer a powerful way to enhance the accuracy of positioning.
In this study, we propose various ML approaches for LoRa-based positioning in outdoor environments. We
evaluate six different ML models: k-NN, CNN, SVR, ANN, XG-Boost, and LightGBM-using an open-
source urban LoRaWAN dataset. We further propose a Hybrid Model that combines convolutional feature
extraction with gradient-boosted regression. This architecture integrates the strengths of deep learning and
tree-based models, aiming to capture both temporal signal patterns and structured input correlations for
improved localization accuracy. The models are trained offline and tested for performance in terms of
localization accuracy, mean square error, and computational efficiency. Additionally, we investigate the
impact of different Feature Vector (FV) subsets on localization performance by analyzing the significance of
LoRaWAN signal attributes. Our results highlight the effectiveness of ML models in enhancing localization
accuracy for LoRa-based outdoor positioning systems, demonstrating performance improvements ranging
from 10% to 73% compared to previous ML studies in outdoor localization.