A Reproducible Comparison of RSSI Fingerprinting Localization Methods Using LoRaWAN
This work addresses localization for low-power IoT devices, but it is incremental as it compares existing methods on a new dataset.
The study compared machine learning methods for fingerprinting localization using LoRaWAN RSSI measurements, finding that a neural network approach achieved the highest accuracy with a mean error of 358 meters and a median error of 204 meters.
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.