Joaquín Torres-Sospedra

University of Valencia | UV · Department of Computer Science

A document summarizing the main features of the 119 datasets that we identified.

Improving the performance of Artificial Neural Network (ANN) regression models on small or scarce datasets, such as wireless network positioning data, can be realized by simplifying the task. One such approach includes implementing the regression model as a classifier, followed by a probabilistic mapping algorithm that transforms class probabilitie...

The importance of reproducibility and transparency in scientific research has always been a cornerstone of the scientific Ethos. Recently, after identifying the challenges in terms of reproducibility in various research fields, the necessity of wide adoption of Open Science practices has become prominent. The field of Indoor Positioning and Indoor...

In [2], we systematically reviewed the Open Science practices followed in recent publications in the field of indoor positioning, analyzing all reference papers from the 2022 and 2023 editions of the International Conference on Indoor Positioning and Indoor Navigation (IPIN). That study underscored the need for wider adoption of those open practice...

In the context of WiFi fingerprint-based indoor localization, the present work systematically investigates the generalization capabilities, within a k-Nearest Neighbor framework, of meta-distances learned through a genetic programming approach, considering sixteen well-known and widely used benchmark datasets. Our study reveals clear variations in...

Positioning in indoor scenarios using signals of opportunity is an effective solution enabling accurate and reliable performance in Global Navigation Satellite System (GNSS)-obscured scenarios. Despite the availability of numerous fingerprinting datasets utilizing various wireless signals, the challenge of device heterogeneity and sample density re...

Indoor positioning is a thriving research area which is slowly gaining market momentum. Its applications are mostly customised, ad hoc installations; ubiquitous applications analogous to GNSS for outdoors are not available because of the lack of generic platforms, widely accepted standards and interoperability protocols. In this context, the Indoor...

The ubiquity of consumer devices with sensing and computational capabilities, such as smartphones and smartwatches, has increased interest in their use in human activity recognition for healthcare monitoring applications, among others. When developing such a system, researchers rely on input data to train recognition models. In the absence of openl...

This paper describes a dataset collected in an industrial setting using a mobile unit resembling an industrial vehicle equipped with several sensors. Wi-Fi interfaces collect signals from available Access Points (APs), while motion sensors collect data regarding the mobile unit’s movement (orientation and displacement). The distinctive features of...

Dataset collected in an indoor industrial environment using a mobile unit (manually pushed trolley) that resembles an industrial vehicle equipped with several sensors, namely, Wi-Fi, wheel encoder (displacement), and Inertial Measurement Unit (IMU). Sensors were connected to a Raspberry Pi (RPi 3B +), which collected the data from the sensors. Gro...

The demand for indoor location-based services and the wide availability of mobile devices have triggered research into new positioning systems able to provide accurate indoor positions using smartphones. However, accurate solutions require a complex implementation and long-term maintenance of their infrastructure. Collaborative systems may help to...

Indoor positioning and navigation increasingly have become popular, and there are many different approaches, using different technologies. In nearly all of the approaches, the locational accuracy depends on signal propagation characteristics of the environment. What makes many of these approaches similar is the requirement of creating a signal prop...

Indoor Positioning Systems usually consider the average positioning error over a set of evaluation samples, or a quartile of that value, as the global error. However, they do not provide a metric for the uncertainty for each individual position estimation. In this paper, we apply the error propagation theory to the kNN algorithm in Wi-Fi fingerprin...

This paper focuses on the creation of a new, publicly available Wi-Fi probe request dataset. Probe requests belong to the family of management frames used by the 802.11 (Wi-Fi) protocol. As the situation changes year by year, and technology improves probe request studies are necessary to be done on up-to-date data. We provide a month-long probe req...

Main features of fingerprinting datasets in https://zenodo.org/record/7599736

The increasing popularity of wearable-based Collaborative Indoor Positioning Systems (CIPSs) has led to the development of new methods for improving positioning accuracy. However, these systems often rely on protocols, such as iBeacon, that lack sufficient privacy protection. In addition, they depend on centralized entities for the authentication a...

The Special Issue “Signal Processing and Machine Learning for Smart Sensing Applications” focused on the publication of advanced signal processing methods by means of state-of-the-art machine learning technologies for smart sensing applications [...]

Indoor positioning performed directly at the end-user device ensures reliability in case the network connection fails but is limited by the size of the RSS radio map necessary to match the measured array to the device’s location. Reducing the size of the RSS database enables faster processing, and saves storage space and radio resources necessary f...

Indoor positioning based on machinelearning models has attracted widespread interest in the last few years, given its high performance and usability. Supervised, semi-supervised, and unsupervised models have thus been widely used in this field not only to estimate the user position but also to compress, clean, and denoise fingerprinting datasets. S...

This paper centers on the deeper presentation of a new and publicly accessible dataset comprising of Wi-Fi probe requests. Probe requests fall within the category of management frames utilized by the 802.11 (Wi-Fi) protocol. Given the ever-evolving technological landscape and the imperative need for up-to-date data, research on probe requests remai...

Locating devices in indoor environments has become a key issue for many emerging location-based applications and intelligent spaces in different fields [...]

Indoor Positioning based on Machine Learning has drawn increasing attention both in the academy and the industry as meaningful information from the reference data can be extracted. Many researchers are using supervised, semi-supervised, and unsupervised Machine Learning models to reduce the positioning error and offer reliable solutions to the end-...

The focus on privacy-related measures regarding wireless networks grew in last couple of years. This is especially important with technologies like Wi-Fi or Bluetooth, which are all around us and our smartphones use them not just for connection to the internet or other devices, but for localization purposes as well. In this paper, we analyze and ev...

Smartphones are powerful tools with extensive sensorization that can provide useful information in research or everyday life applications. This information can be obtained from the device’s built-in sensors or through other external sensors connected physically via USB or wirelessly via Bluetooth or WiFi. This paper presents the GetSensorData appli...

Wireless networks have become an integral part of our daily lives and lately there is increased concern about privacy and protecting the identity of individual users. In this paper we address the evolution of privacy measures in Wi-Fi probe request frames. We focus on the lack of privacy measures before the implementation of MAC Address Randomizati...

Nowadays, there are a multitude of solutions for indoor positioning, as opposed to standards for outdoor positioning such as GPS. Among the different existing studies on indoor positioning, the use of Wi-Fi signals together with Machine Learning algorithms is one of the most important, as it takes advantage of the current deployment of Wi-Fi networ...

The demand to enhance distance estimation and location accuracy in a variety of Non-Line-of-Sight (NLOS) indoor environments has boosted investigation into infrastructure-less ranging and collaborative positioning approaches. Unfortunately, capturing the required measurements to support such systems is tedious and time-consuming, as it requires sim...

LoRaWAN-based positioning is emerging as an alternative positioning solution for battery-constrained IoT devices or GNSS-denied areas in urban environments. The data collected at the LoRaWAN Base Stations, such as the RSSI of received messages, can be merged to generate an RF fingerprint. Unsupervised crowdsourcing can be leveraged to build a large...

Wearable and IoT devices requiring positioning and localisation services grow in number exponentially every year. This rapid growth also produces millions of data entries that need to be pre-processed prior to being used in any indoor positioning system to ensure the data quality and provide a high Quality of Service (QoS) to the end-user. In this...

In daily life, mobile and wearable devices with high computing power, together with anchors deployed in indoor environments, form a common solution for the increasing demands for indoor location-based services. Within the technologies and methods currently in use for indoor localization, the approaches that rely on Bluetooth Low Energy (BLE) anchor...

Wearable and IoT devices requiring positioning and localisation services grow in number exponentially every year. This rapid growth also produces millions of data entries that need to be pre-processed prior to being used in any indoor positioning system to ensure the data quality and provide a high Quality of Service (QoS) to the end-user. In this...

The localization speed and accuracy in the indoor scenario can greatly impact the Quality of Experience of the user. While many individual machine learning models can achieve comparable positioning performance, their prediction mechanisms offer different complexity to the system. In this work, we propose a fingerprinting positioning method for mult...

Machine learning models have become an essential tool in current indoor positioning solutions, given their high capabilities to extract meaningful information from the environment. Convolutional neural networks (CNNs) are one of the most used neural networks (NNs) due to that they are capable of learning complex patterns from the input data. Anothe...

The evolution of modern cyber-physical systems and the tremendous growth in the number of interconnected Internet of Things (IoT) devices are already paving new ways for the development of improved data collection and processing methods [...]

Multiple sensors are embedded in wearable devices [...]

Indoor localization is a growing research field and interest is expanding in many application fields, including services, measurement, mapping, security, and standardization. The quest for appropriate tracking technologies for COVID-19 pandemic control has shown us the importance of identifying the sensors data and processing that are suitable, acc...

Wi-Fi fingerprinting is a well-known technique used for indoor positioning. It relies on a pattern recognition method that compares the captured operational fingerprint with a set of previously collected reference samples (radio map) using a similarity function. The matching algorithms suffer from a scalability problem in large deployments with a h...

Indoor Positioning based on wifi fingerprinting needs a reference dataset, also known as a radio map, in order to match the incoming fingerprint in the operational phase with the most similar fingerprint in the dataset and then estimate the device position indoors. Scalability problems may arise when the radio map is large, e.g., providing position...

Cloud Computing and Cloud Platforms have become an essential resource for businesses, due to their advanced capabilities, performance, and functionalities. Data redundancy, scalability, and security, are among the key features offered by cloud platforms. Location-Based Services (LBS) often exploit cloud platforms to host positioning and localisatio...

The pandemic situation has driven to several measures to prevent the spread of COVID-19. One of these measures is social distance and, as a consequence, limitation of capacity of indoor closed spaces. This makes necessary the deployment of systems that help to control occupancy of spaces. This work proposes a low-cost system to control access to an...

Nowadays, several indoor positioning solutions support Wi-Fi and use this technology to estimate the user position. It is characterized by its low cost, availability in indoor and outdoor environments, and a wide variety of devices support Wi-Fi technology. However, this technique suffers from scalability problems when the radio map has a large num...

Indoor Positioning Systems are attracting many researchers, being Bluetooth Low Energy gaining some attention recently. The recent need to track people, ensuring low density of people in some areas, made the research community to provide unintrusive tracking. In contrast to traditional direct ngerprinting, where the user utilizes their mobile to es...

Wi-Fi Fingerprinting is a very popular technique in the field of indoor positioning, since the release of Microsoft RADAR system back in 2000. Since that milestone, the vast majority of studies and improvements in this field keep using the same base algorithm, an adaptation of the k-NN algorithm to treat geospatial data (e.g., x/y or lat/lon). One...

One of the most common assumptions regarding indoor positioning systems based on Wi-Fi fingerprinting is that the Radio Map (RM) becomes outdated and has to be updated to maintain the positioning performance. It is known that propagation effects, the addition/removal of Access Points (APs), changes in the indoor layout, among others, cause RMs to b...

Bluetooth Low Energy (BLE) fingerprinting has gained a lot of research effort in recent years due to flexibility in both beacons placement and configuration. Different works have addressed the effect of the configuration parameters, mainly the transmission power (Tx) and period (Ts), over positioning accuracy but not on the system lifespan and the...

The evaluation of Indoor Positioning Systems (IPS) mostly relies on local deployments in the researchers' or partners' facilities. The complexity of preparing comprehensive experiments, collecting data, and considering multiple scenarios usually limits the evaluation area and, therefore, the assessment of the proposed systems. The requirements and...

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 t...

Emerging communication network applications require a location accuracy of less than 1m in more than 95% of the service area. For this purpose, 5G New Radio (NR) technology is designed to facilitate high-accuracy continuous localization. In 5G systems, the existence of high-density small cells and the possibility of the device-to-device (D2D) commu...

Localization and tracking of industrial vehicles have a key role in increasing productivity and improving the logistics processes of factories. Due to the demanding require- ments of industrial vehicle tracking and navigation, existing systems explore technologies, such as LiDAR or ultra wide-band to achieve low positioning errors. In this article...

Every year, for ten years now, the IPIN competition has aimed at evaluating real-world indoor localisation systems by testing them in a realistic environment, with realistic movement, using the EvAAL framework. The competition provided a unique overview of the state-of-the-art of systems, technologies, and methods