--Fingerprinting techniques, which are a common method for indoor localization, have been recently applied with success into outdoor settings. Particularly, the communication signals of Low Power Wide Area Networks (LPW AN) such as Sigfox, have been used for localization. In this rather recent field of study, not many publicly available datasets, which would facilitate the consistent comparison of different positioning systems, exist so far . In the current study, a published dataset of RSSI measurements on a Sigfox network deployed in Antwerp, Belgium is used to analyse the appropriate selection of preprocessing steps and to tune the hyperparameters of a kNN fingerprinting method. Initially, the tuning of hyperparameter k for a variety of distance metrics, and the selection of efficient data transformation schemes, proposed by relevant works, is presented. In addition, accuracy improvements are achieved in this study, by a detailed examination of the appropriate adjustment of the parameters of the data transformation schemes tested, and of the handling of out of range values. With the appropriate tuning of these factors, the achieved mean localization error was 298 meters, and the median error was 109 meters. T o facilitate the reproducibility of tests and comparability of results, the code and train/validation/test split used in this study are available. The recent emergence of Internet of Things (IoT) technologies has made so that a plethora of low power devices make their appearance worldwide, in people's everyday life. The concept of smart cities becomes familiar to the broad public, and numerous applications are being proposed, implemented and deployed in domains such as massive gathering of sensor measurements, automatic control, asset tracking, etc.

--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.