Fast Graph - organic 3D graph for unsupervised location and mapping

It is well-known that fingerprinting-based positioning requires an exhaustive calibration phase to create a radio map, which often requires recalibration. Model-based and geometric approaches try to mitigate this effort at the expense of a lower accuracy or high computational cost. This paper introduces FastGraph, where a 3D graph is used to rapidly model the radio propagation environment. By means of unsupervised techniques, FastGraph is able to operate shortly after its deployment without previous knowledge about the environment. The proposed solution uses a novel algorithm to automatically provide location while simultaneously updating the radio map; and learn the position of the Access Points (APs) and location-specific radio propagation parameters. FastGraph has been evaluated in two real-world environments, a factory-plant and a regular university building, with results comparable to those obtained by conventional radio map-based solutions.This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT – Fundação para a Ciência eTecnologia within the Project Scope: UID/CEC/00319/2013 and the PhD fellowship PD/BD/105865/201

Recolha de dados de movimento em dispositivos móveis pessoais

Dissertação de mestrado integrado em Engenharia de ComunicaçõesEsta dissertação enquadra-se no Projeto SUM (Sensing and Understanding Human Motion Dynamics). O principal objetivo deste projeto é o estudo do movimento das pessoas num determinado meio, e o estabelecimento de modelos matemáticos para prever o seu movimento e a sua interação com os elementos presentes nos espaços físicos. Os dispositivos móveis pessoais têm um forte impacto no dia a dia dos seus utilizadores, fazendo parte das suas rotinas diárias. Além de que, a maioria destes dispositivos estão equipados com diversos sensores e interfaces que podem ser utilizados para estudar a mobilidade humana. O propósito desta dissertação centra-se no desenvolvimento de uma aplicação para dispositivos móveis, que permite a recolha de dados dos vários interfaces dos dispositivos (Wi-Fi, GPS, Bluetooth e células GSM) e o seu envio para um servidor, para posterior processamento e análise. Para motivar a utilização da aplicação e maximizar o número de utilizadores, garantindo a utilização durante um longo período de tempo, é necessário criar um serviço atrativo integrado na aplicação e estabelecer um modelo que recompense e motive os utilizadores. Por outro lado, é essencial considerar as questões de privacidade, que este tipo de estratégias de recolha de dados levanta e considerar o seu impacto na utilização da aplicação, sendo necessário a integração de mecanismos que garantam a privacidade e segurança dos utilizadores. Adicionalmente, é fundamental minimizar o consumo de recursos pela aplicação, como a utilização do processador e da memória, que nos dispositivos móveis são mais restritos do que nos restantes dispositivos, mas também reduzir os consumos energéticos de modo a maximizar a autonomia da bateria do dispositivo. Tudo isto pode ser alcançado através da criação de algoritmos eficientes para a recolha de dados, recorrendo às tecnologias do dispositivo, como por exemplo os diversos sensores, e utilizando comunicação oportunista para o envio dos dados recolhidos.This dissertation falls within the Project SUM (Sensing and Understanding Human Motion Dynamics). The main goal concerning this project is the study of people’s movement in an enclosed space, and it tries to establish mathematical models to predict the movement of people and its interaction with the elements present in physical spaces. Personal mobile devices have a heavily impact in the daily life of their users, and make part of their daily routines. In addition, most of these devices are equipped with several sensors and interfaces that may be used to study human mobility. The purpose of this dissertation is to develop an application for mobile devices that allows the collection of data from several device’s interfaces (WiFi, GPS, Bluetooth and GSM cells) and send the data to a server for later processing and analysis. To motivate the use of the application and maximize the number of users, ensuring an use for a long period of time, it becomes necessary to create an appealing service integrated in that application, and also establish a model that rewards and motivates their users. On the other hand it is necessary to take into account the privacy issues brought up by this type of strategy in collecting data, and consider its impact in the use of the application, being necessary the integration of mechanisms for ensuring the user’s privacy and security. Moreover it is necessary to minimize resource consumption by the application, such as processor and memory resources, which in mobile devices are more restricted than in other devices and also, to maximize the device’s autonomy, the energy resources need to be taken in account. All this can be achieved through the creation of efficient algorithms for data collection, using the device technology, such as the several available sensors, and using opportunistic communication to transmit the collected data

Optical fiber sensors and sensing networks: overview of the main principles and applications

Optical fiber sensors present several advantages in relation to other types of sensors. These advantages are essentially related to the optical fiber properties, i.e., small, lightweight, resistant to high temperatures and pressure, electromagnetically passive, among others. Sensing is achieved by exploring the properties of light to obtain measurements of parameters, such as temperature, strain, or angular velocity. In addition, optical fiber sensors can be used to form an Optical Fiber Sensing Network (OFSN) allowing manufacturers to create versatile monitoring solutions with several applications, e.g., periodic monitoring along extensive distances (kilometers), in extreme or hazardous environments, inside structures and engines, in clothes, and for health monitoring and assistance. Most of the literature available on this subject focuses on a specific field of optical sensing applications and details their principles of operation. This paper presents a more broad overview, providing the reader with a literature review that describes the main principles of optical sensing and highlights the versatility, advantages, and different real-world applications of optical sensing. Moreover, it includes an overview and discussion of a less common architecture, where optical sensing and Wireless Sensor Networks (WSNs) are integrated to harness the benefits of both worlds.This work was supported by FCT—Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020

Energy consumption in personal mobile devices sensing applications

Personal mobile devices have a strong impact in the daily life of their users, making part of their daily routines. Most of these devices are equipped with several sensors and interfaces that may be used to study human mobility and its interaction with the elements present in physical spaces. Our goal was to develop an application for Android smartphones that could be used to collect data from several of the devices' interfaces (Wi-Fi, GPS, Bluetooth and GSM), and to send that data to a server for later processing and analysis. In order to maximise the autonomy of the devices, energy resources must be used efficiently. This paper focus on a power-consumption saving solution for mobile phone-based sensing systems in the context of human motion analysis. Experiments were conducted with the objective of comparing power-consumption in different situations using our solution. Results have shown that, considering current power consumption patterns, carefully designed solutions for mobile phone-based sensing for observing human motion may enhance energy efficiency satisfactorily. In this particular domain, we have explored periodic sampling of the sensors and the suspension of the sampling process in the Android operating system whenever the device is not moving and we report such results in this paper.Research group supported by FEDER Funds through the COMPETE and National Funds through FCT Fundação para a Ciência e a Tecnologia under the projects n. 13843 and PEst- OE/EEI/UI0319/2014

Collection of a continuous long-term dataset for the evaluation of Wi-Fi-fingerprinting-based indoor positioning systems

The dataset introduced in this paper is available in two versions: lite version https://doi.org/10.5281/zenodo.6646008 (accessed on 28 July 2022) which considers Wi-Fi samples from each MD every 20 min, has a total of 382,852 Wi-Fi samples, thus making it easier to parse and analyse; full version https://doi.org/10.5281/zenodo.6928554 (accessed on 29 July 2022) which has all collected samples, with a total of 7,446,538 Wi-Fi samples.Indoor positioning and navigation have been attracting interest from the research community for quite some time. Nowadays, new fields, such as the Internet of Things, Industry 4.0, and augmented reality, are increasing the demand for indoor positioning solutions capable of delivering specific positioning performances not only in simulation but also in the real world; hence, validation in real-world environments is essential. However, collecting real-world data is a time-consuming and costly endeavor, and many research teams lack the resources to perform experiments across different environments, which are required for high-quality validation. Publicly available datasets are a solution that provides the necessary resources to perform this type of validation and to promote research work reproducibility. Unfortunately, for different reasons, and despite some initiatives promoting data sharing, the number and diversity of datasets available are still very limited. In this paper, we introduce and describe a new public dataset which has the unique characteristic of being collected over a long period (2+ years), and it can be used for different Wi-Fi-based positioning studies. In addition, we also describe the solution (Wireless Sensor Network (WSN) + mobile unit) developed to collect this dataset, allowing researchers to replicate the method and collect similar datasets in other spaces.This work has been supported by FCT—Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020, and the PhD fellowship PD/BD/137401/2018

Real-world deployment of low-cost indoor positioning systems for industrial applications

The deployment of an Indoor Position System (IPS) in the real-world raised many challenges, such as installation of infrastructure, the calibration process or modelling of the building's floor plan. For Wi-Fi-based IPSs, deployments often require a laborious and time-consuming site survey to build a Radio Map (RM), which tends to become outdated over time due to several factors. In this paper, we evaluate different deployment methods of a Wi-Fi-based IPS in an industrial environment. The proposed solution works in scenarios with different space restrictions and automatically builds a RM using industrial vehicles in operation. Localization and tracking of industrial vehicles, equipped with low-cost sensors, is achieved with a particle filter, which combines Wi-Fi measurements with heading and displacement data. This allows to automatically annotate and add new samples to a RM, named vehicle Radio Map (vRM), without human intervention. In industrial environments, vRMs can be used with Wi-Fi fingerprinting to locate human operators, industrial vehicles, or other assets, allowing to improve logistics, monitoring of operations, and safety of operators. Experiments in an industrial building show that the proposed solution is capable of automatically building a high-quality vRM in different scenarios, i.e., considering a complete floor plan, a partial floor plan or without a floor plan. Obtained results revealed that vRMs can be used in Wi-Fi fingerprinting with better accuracy than a traditional RM. Sub-meter accuracies were obtained for an industrial vehicle prototype after deployment in a real building.This work was supported in part by the Fundacao para a Ciencia e Tecnologia-FCT through the Research and Development Units Project Scope under Grant UIDB/00319/2020 and in part by the Ph.D. Fellowship under Grant PD/BD/137401/2018. The associate editor coordinating the review of this article and approving it for publication was Prof. Masanori Sugimoto

Evaluation of medium access and a positioning system in wireless underground sensor networks

Wireless Underground Networks (WUN) have many applications, such as border surveillance, agriculture monitoring, and infrastructure monitoring. Recent studies have shown that they are feasible and have deployment advantages over wired networks, but only a few WUN evaluations in multiple access scenarios have been done. This paper presents a simulation study on medium access for a WUN with 4 nodes buried, and one node aboveground. The simulations were carried out using the ns-3 simulator and they evaluate both Wi-Fi, and Lr-Wpan networks for dry and wet soils. We verified that for the same number of concurrent nodes, the use of the RTS/CTS mechanism has a much higher influence than the soil water content. Furthermore, a study about the feasibility of using Wi-Fi fingerprinting for positioning above the ground based on the buried infrastructure revealed promising results.This work has been partially supported by COM- PETE: POCI-01-0145-FEDER-007043 and FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013. This work is financed by the ERDF – European Regional Development Fund through the Opera- tional Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme within project «POCI-01- 0145-FEDER-006961», and by National Funds through the FCT – Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) as part of project UID/EEA/50014/2013. The first and second authors would like to thank the support from the Portuguese Foundation for Science and Technology (FCT) under the fellowships PD/BD/105861/2014, and PD/BD/105865/2014

TrackInFactory: A Tight Coupling Particle Filter for Industrial Vehicle Tracking in Indoor Environments

Localization and tracking of industrial vehicles have a key role in increasing productivity and improving the logistics processes of factories. Due to the demanding requirements 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 we propose TrackInFactory, a system that combines Wi-Fi with motion sensors, achieving submeter accuracy and a low maximum error. A tight coupling approach is explored in sensor fusion with a particle filter (PF). Information regarding the vehicle's initial position and heading is not required. This approach uses the similarity of Wi-Fi samples to update the particles' weights as they move according to motion sensor data. The PF dynamically adjusts its parameters based on a metric for estimating the confidence in position estimates, allowing to improve positioning performance. A series of simulations were performed to tune the PF. Then the approach was validated in real-world experiments with an industrial tow tractor, achieving a mean error of 0.81 m. In comparison to a loose coupling approach, this method reduced the maximum error by more than 60% and improved the overall mean error by more than 20%

Floor plan-free particle filter for indoor positioning of industrial vehicles

Industry 4.0 is triggering the rapid development of solutions for indoor localization of industrial ve- hicles in the factories of the future. Either to support indoor navigation or to improve the operations of the factory, the localization of industrial vehicles imposes demanding requirements such as high accuracy, coverage of the entire operating area, low convergence time and high reliability. Industrial vehicles can be located using Wi-Fi fingerprinting, although with large positioning errors. In addition, these vehicles may be tracked with motion sensors, however an initial position is necessary and these sensors often suffer from cumulative errors (e.g. drift in the heading). To overcome these problems, we propose an indoor positioning system (IPS) based on a particle filter that combines Wi-Fi fingerprinting with data from motion sensors (displacement and heading). Wi-Fi position estimates are obtained using a novel approach, which explores signal strength measurements from multiple Wi-Fi interfaces. This IPS is capable of locating a vehicle prototype without prior knowledge of the starting position and heading, without depending on the building’s floor plan. An average positioning error of 0.74 m was achieved in performed tests in a factory-like building.FCT – Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020, the PhD fellowship PD/BD/137401/2018 and the Technological Development in the scope of the projects in co-promotion no 002814/2015 (iFACTORY 2015-2018

Quantifying the degradation of radio maps in Wi-Fi fingerprinting

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 become outdated. However, there is a lack of studies that show how the RM degrades over time. In this paper, we describe an empirical study, based on real-world experiments, to evaluate how and why RMs degrade over time. We conducted site surveys and deployed monitoring devices to analyse the radio environment of one building over 2+ years, which allowed us to identify significant changes/events that caused the degradation of RMs. To quantify the RM degradation, we use the positioning error and propose the RM degradation ratio, a metric to directly compare two RMs and measure how different they are. Obtained results show that the positioning performance is much better when RMs are collected on the same day as the test data, and although RM degradation tends to increase over time, it only leads to large positioning errors when significant changes occur in the Wi-Fi infrastructure, making previous RMs outdated.This work has been supported by FCT - Fundacao para a Ciencia e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020 and the PhD fellowship PD/BD/137401/2018. J. Torres-Sospedra acknowledges funding from Torres Quevedo programme (PTQ2018-009981)