The use of fingerprinting localization techniques in outdoor IoT settings has started gaining 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 357 meters and a median error of 206 meters.

Navigating in large hospitals is a challenging task. The consequences of difficulties faced by staff, patients and visitors in finding their way in the hospital can be multiple. The HUGApp project goals are to identify the navigational needs and requirements of people within the premises of Geneva University Hospitals (HUG) before proceeding with potential solutions, such as an indoor navigation mobile app. A questionnaire was designed and distributed to staff members with the goal of understanding the current problems in wayfinding inside HUG, investigating the users' views on the creation of an indoor navigation mobile app, and specifying the user requirements for such an app. A total of 111 members of the primary care division of HUG answered the questionnaire, providing an insightful view of the healthcare professionals.

Evaluating positioning systems has become a matter of heated debate during the last years. There is no clear standard on how these technologies should be evaluated, and no predominant solution for defining the ground truth in order to compare the position estimates. In this paper, we propose a simple and inexpensive solution for tackling both of these problems in real life use cases. With the proposed methodology, it is possible to measure both static and moving targets, by creating a predefined path with checkpoints. Then, a tester, walking over them, while moving or standing still, indicates when the device was over the aforementioned checkpoints. It is also specified how to evaluate the estimates by comparing them with interpolated points of the ground truth trajectory. Two methods are proposed for performing such interpolation. Finally, in order to evaluate the performance of the positioning system as well as the perceived utility of the position estimates from the end user's point of view, a series of statistical parameters is discussed. Additionally, in the context of perceived utility by the end user, a parameter that measures the occurrence of abrupt changes in the position estimates is proposed.

In which way may an application switch instantly and reliably between an indoor and an outdoor positioning provider as a user enters and exits buildings? In this work we present a robust switching algorithm, utilizing the dynamic accuracy estimation of each position provider as a reliability indication. Our algorithm offers a fast automatic switch between the indoor and the outdoor provider, in a transparent way for the user. We also present experimental results, using GPS outdoors and a Bluetooth provider indoors. This technique was tested in our lab and was afterwards installed at the Hospital of Perugia, Italy, in the context of the Ambient Assisted Living (AAL) Virgilius project, where users can navigate with a smartphone. This study is also a result of the research done in the context of the AAL EDLAH project, for optimizing the selection of the most adequate positioning technology. Accurate position estimations are used as input for the EDLAH object detection module.