This work analyses the impact of the first multi-building multi-floor open available dataset for Wi-Fi fingerprinting, the UJIIndoorLoc dataset, 10 years after it was presented at the Fifth International Conference on Indoor Positioning and Indoor Navigation. First, we revisit the dataset description, providing some clarifications. Second, we have methodologically analyzed all the research works that mentioned or used the dataset. This analysis has brought more insights about the real impact of the dataset on this research field, but also how this dataset has been used in other contexts. Third, we present a second-order analysis, where the most popular (in terms of citations) works using it have been analyzed. The main objective of this work is to show the impact that public databases can have in the indoor positioning research field, and highlight good practices in providing open research datasets and using them.

This study explores indoor positioning using wireless technologies, specifically 5G NR and WiFi (2.4GHz and 5GHz) frequencies, applying multilateration techniques, which are used natively in 5G NR, and the KNN supervised learning algorithm. In order to apply these positioning techniques, we will use RSSI value, due to its ease of use in real-life situations, and the Time of Arrival parameters, as this is the value used in the 5G NR integrated positioning protocol, known as LMF. This study compares 5G NR and WiFi positioning accuracy, demonstrating improvements using 5G NR. For this study, the EMSlice simulation solution was chosen, which has been configured to mimic a real high school. The generated measurements have been compared with real samples to check their quality level. This study aims to provide insights into the advantages and limitations of each technology in various indoor scenarios. The findings contribute to the development of reliable indoor positioning systems. The study revealed that the 5G NR FR1 exhibited a positioning accuracy of 2.35m RMSE, in comparison to 2.75m for 2.4 GHz Wi-Fi and 3.80m for 5 GHz Wi-Fi. Combining Time of Arrival (ToA) with RSSI achieves the highest accuracy, with an RMSE of 2.23m.

Pedestrian Dead Reckoning (PDR) plays a crucial role in indoor and outdoor localization, particularly in environments where Global Navigation Satellite Systems (GNSS) signals are limited or unavailable. In this study, we compare various PDR algorithms using real-world data collected on both indoor and outdoor tracks. Our investigation focuses on identifying optimal methods for step-detection and step-length estimation, essential components of PDR systems. We find that simpler algorithms, such as the SciPy find peaks function and simple threshold techniques, perform better than more complex approaches, showing robustness across different individuals and environmental conditions. Additionally, we propose a new method for step detection and path reconstruction and we show the critical role of Orientation data (AHRS). We highlight the importance of proper preparation and calibration for reliable trajectory reconstruction. Our findings provide valuable insights for developing and implementing effective PDR systems in both indoor and outdoor settings.

Microsoft proposed RADAR in 2000, the first indoor positioning system based on Wi-Fi fingerprinting. Since then, the indoor research community has worked not only to improve the base estimator but also on finding an optimal RSS data representation. The long-term objective is to find a positioning system that minimises the mean positioning error. Despite the relevant advances in the last 23 years, a disruptive solution has not been reached yet. The evaluation with non-open datasets and comparisons with non-optimized baselines make the analysis of the current status of fingerprinting for indoor positioning difficult. In addition, the lack of implementation details or data used for evaluation in several works make results reproducibility impossible. This paper focuses on providing a comprehensive analysis of fingerprinting with k-NN and settling the basement for replicability and reproducibility in further works, targeting to bring relevant information about k-NN when it is used as a baseline comparison of advanced fingerprint-based methods.

Currently there is no standard indoor positioning system, similar to outdoor GPS. However, WiFi signals have been used in a large number of proposals to achieve the above positioning, many of which use machine learning to do so. But what are the most commonly used techniques in machine learning? What accuracy do they achieve? Where have they been tested? This article presents a systematic review of works between 2019 and 2021 that use WiFi as the signal for positioning and machine learning models to estimate indoor position. 64 papers have been identified as relevant, which have been systematically analyzed for a better understanding of the current situation in different aspects. The results show that indoor positioning based on WiFi trends use neural network-based models, evaluated in empirical experiments. Despite this, many works still conduct an assessment in small areas, which can influence the goodness of the results presented.

Currently there is no standard indoor positioning system, similar to outdoor Global Navigation Satellite Systems. However, WiFi signals have been used in a large number of proposals to achieve indoor positioning, many of which use kNN to do so. It is nearly standard to consider the error of indoor positioning as the third quartile of the error thanks, among other reasons, to the IPIN competitions. However, this calculation obtains the global error of a system, but not the accuracy of a single measure if the true position is unknown: what is the accuracy of a single point? This work answers to this question by applying error calculation theory to the kNN algorithm of positioning using a fingerprinting method. The paper shows that an error analysis can help not only to know the accuracy of a position, but also: to understand the advantages of one distance calculation or another, in particular, Euclidean versus Manhattan; to know which elements have the main contribution to the error of position and, therefore, use this argument when deciding how to make the calculation; and also to know the quality of a single map of fingerprints as a tool to know the position. The main contribution of this work is to analyse data to know its reliability without prior information about accuracy.

With the adoption of indoor positioning solutions, which enable for a variety of location-based spatial services, a number of indoor map standards and formats have been proposed in the last decade. As each of these indoor map standard has its own purpose, the strengths and weaknesses are necessary to be understood and analyzed before selecting one of them for a given application. The Indoor Map Subcommittee has been established under IPIN/ISC in 2017. Among others, the goal of this working group is to compare available indoor map standards, provide a guideline for their application and advise on changes to their standardization development organizations if necessary. In this paper we present a survey of indoor map standards as an achievement of the subcommittee. The scope of the survey covers official standards such as IFC of BuildingSmart, IndoorGML and CityGML of OGC, and Indoor OpenStreetMap. We present several use-cases to show and discuss how to build indoor maps.

Chronic pain is a very common problem worldwide and helping people coping with it is fundamental for improving their quality of life. Since smartphones are available anywhere and anytime for all users, the present work proposes the development of an App that helps users to change their mood when facing low back and cervical pain. The App will drive the user thorough several screens that will help him or her to challenge their negative thoughts for more positive ones. This process will be driven thorough some messages and questions proposed by reserachers with expertise on health and pain management, but also thorough messages and questions proposed by users themselves. The main contributions of this work are: 1) using an App to face pain thorugh a process of cognitive restructuring; and 2) sending messages and questions based on the context of the user, by taking into account his or her previous answers, the environment and time of the day.

This paper presents a study of indoor positioning in public zones of the Parc Taulí Hospital in Sabadell. It is a challenging scenario because: (1) it combines wide spaces with middle sized and narrow spaces; (2) it is a shielded zone where no signals are available, and therefore, no WiFi signal can be used for positioning; and (3) it is not possible to deploy beacons for positioning. The goal of this work is to test whether it is possible to get indoor positioning in a real and challenging scenario by using only the magnetic field. The positioning precision requires to locate the part of the hospital where the user is. The proposed solution defines “virtual corridors” to improve positioning in wide areas. To validate the work, magnetic field data have been recorded from the scenario, using different smartphones and by different persons. The obtained magnetic data curves have been compared by using dynamic time warping distance. Results show that it is possible to characterize every path with the magnetic field. The main contributions of the present paper are: (1) defining “virtual corridors” as a way to position using magnetic field in 2D spaces; and (2) showing that even in wide spaces, like the hall of a hospital, it is possible to find magnetic anomalies linked to positions.

The research presented in this paper is focused on In-home positioning systems. These indoor environments are characterized by a reduced number of AP, narrow spaces and very important radio signal attenuation. Existing indoor techniques seems to be not totally suitable for these scenarios; this paper proposes an add-on for helping these techniques by the use of wearable ultrasound sensors (distance measurements). One important step for indoor techniques using RF signals is the off-line phase and the generation of Radio Maps. In order to considerably reduce the time needed for the off-line phase, this paper uses an existing tool for generating maps automatically. Therefore, a considerable gain of time is obtained. On the other hand, the combination of WiFi and range techniques allows us to do coarse and fine location. An IT artifact named UTOPIA (UlTrasOund Positioning Indoor App) has been developed to track all data needed for computing indoor position like RSSI, distances to walls, number of steps, heading and more. The project uses two maps for positioning: a WiFi map automatically generated; and an ultrasound map, that takes into account what should be the ultrasound value at every single point of the building and for several directions of the sensor. To get position, the values obtained by smartphone are compared with WiFi map. A particle filter technique is used to propagate position and ultrasound values are used to get the weight in the particle filter. Results obtained show that the methodology is coherent with real scenario and can be used for helping existing indoor position techniques in these specific scenarios (In-home environments). The main contributions of this paper are: 1) offering an alternative system suitable for in-home features that allows to calculate positioning by using WiFi and ultrasound; 2) avoiding the off-line mapping phase of WiFi fingerprinting by comparing with an automatically calculated WiFi map; and 3) using the map for improving positioning by generating an ultrasound reference map with the value of ultrasound signal at every single point of the building, and for several directions.

For outdoors, Global Network Navigation Systems have become the standard mechanism of positioning; but for indoor environments, there is still no standard system. Several mechanisms have been proposed and, among them, some of the more extended are those that use Wi-Fi signal for positioning, and mainly, those known as Wi-Fi fingerprinting. However, actual Wi-Fi mechanisms depends on environmental conditions: instability of Wi-Fi, differences in sensors of Smartphones, etc. One of these uncontrolled effects is the interaction between RF and human bodies. Human bodies interact with Wi-Fi radiation and, therefore, affects the signal received.

At the end of their technical studies, students have to present a final project in order to finish their degree. With this project, students prove that they have got all of the competences they should have acquired. Some competencies, such as the ability to communicate orally or the ability to argue may be evaluated during a face-to-face dissertation defense, but how to translate this evaluation scenario to a virtual environment is a challenge. This paper faces the problem of presenting dissertations in a virtual environment, to evaluate the aforementioned competences. The solution proposed, called Present@, is focused in teaching and technical dimensions, and is complemented with some materials and tutorials that help students to acquire those competences. Therefore, the paper shows Present@ as a solution to the problems that virtual universities have to face when dealing with the evaluation of final degree projects and particularly in their defense. Present@ has been tested over 131 students during six semesters and the results show that it actually helps students to acquire the desired competences in virtual environments. In addition, the analysis about the use of the tool has denoted that videos are an efficient mechanism to evaluate the ability to communicate of students and to work on transversal competencies. Some students also commented that the use of videos make the dissertation defense more natural and closer to face-to-face environments.