A Reproducible Comparison of RSSI Fingerprinting Localization Methods Using LoRaWAN

--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 (LPW AN), such as LoRaW AN, are used to estimate the location of low power mobile devices. In this study, a publicly available dataset of LoRaW AN 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. T o 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. The proliferation of the usage of Internet-of-Things (IoT) technologies and Low Power Wide Area Networks (LPW AN), such as LoRaW AN or Sigfox, over the last decade has created a new landscape in the field of outdoor localization. Low power devices of LPW ANs cannot afford the battery consumption of a chip-set of a Global Navigation Satellite System (GNSS), such as the GPS.