  The use of fingerprinting localization techniques in outdoor IoT settings has
started to gain popularity over the recent years. Communication signals of Low
Power Wide Area Networks (LPWAN), such as LoRaWAN, are used to estimate the
location of low power mobile devices. In this study, a publicly available
dataset of LoRaWAN RSSI measurements is utilized to compare different machine
learning methods and their accuracy in producing location estimates. The tested
methods are: the k Nearest Neighbours method, the Extra Trees method and a
neural network approach using a Multilayer Perceptron. To facilitate the
reproducibility of tests and the comparability of results, the code and the
train/validation/test split of the dataset used in this study have become
available. The neural network approach was the method with the highest
accuracy, achieving a mean error of 358 meters and a median error of 204
meters.

Machine Learning;Signal Processing;Machine Learning