Generalizable Deep-Learning-Based Wireless Indoor Localization

The growing interest in indoor localization has been driven by its wide range of applications in areas such as smart homes, industrial automation, and healthcare. With the increasing reliance on wireless devices for location-based services, accurate estimation of device positions within indoor environments has become crucial. Deep learning approaches have shown promise in leveraging wireless parameters like Channel State Information (CSI) and Received Signal Strength Indicator (RSSI) to achieve precise localization. However, despite their success in achieving high accuracy, these deep learning models suffer from limited generalizability, making them unsuitable for deployment in new or dynamic environments without retraining. To address the generalizability challenge faced by conventionally trained deep learning localization models, we propose the use of meta-learning-based approaches. By leveraging meta-learning, we aim to improve the models\u27 ability to adapt to new environments without extensive retraining. Additionally, since meta-learning algorithms typically require diverse datasets from various scenarios, which can be difficult to collect specifically for localization tasks, we introduce a novel meta-learning algorithm called TB-MAML (Task Biased Model Agnostic Meta Learning). This algorithm is specifically designed to enhance generalization when dealing with limited datasets. Finally, we conduct an evaluation to compare the performance of TB-MAML-based localization with conventionally trained localization models and other meta-learning algorithms in the context of indoor localization

3D Indoor Positioning in 5G networks

Over the past two decades, the challenge of accurately positioning objects or users indoors, especially in areas where Global Navigation Satellite Systems (GNSS) are not available, has been a significant focus for the research community. With the rise of 5G IoT networks, the quest for precise 3D positioning in various industries has driven researchers to explore various machine learning-based positioning techniques. Within this context, researchers are leveraging a mix of existing and emerging wireless communication technologies such as cellular, Wi-Fi, Bluetooth, Zigbee, Visible Light Communication (VLC), etc., as well as integrating any available useful data to enhance the speed and accuracy of indoor positioning. Methods for indoor positioning involve combining various parameters such as received signal strength (RSS), time of flight (TOF), time of arrival (TOA), time difference of arrival (TDOA), direction of arrival (DOA) and more. Among these, fingerprint-based positioning stands out as a popular technique in Real Time Localisation Systems (RTLS) due to its simplicity and cost-effectiveness. Positioning systems based on fingerprint maps or other relevant methods find applications in diverse scenarios, including malls for indoor navigation and geo-marketing, hospitals for monitoring patients, doctors, and critical equipment, logistics for asset tracking and optimising storage spaces, and homes for providing Ambient Assisted Living (AAL) services. A significant challenge facing all indoor positioning systems is the objective evaluation of their performance. This challenge is compounded by the coexistence of heterogeneous technologies and the rapid advancement of computation. There is a vast potential for information fusion to be explored. These observations have led to the motivation behind our work. As a result, two novel algorithms and a framework are introduced in this thesis

Adaptive indoor positioning system based on locating globally deployed WiFi signal sources

Recent trends in data driven applications have encouraged expanding location awareness to indoors. Various attributes driven by location data indoors require large scale deployment that could expand beyond specific venue to a city, country or even global coverage. Social media, assets or personnel tracking, marketing or advertising are examples of applications that heavily utilise location attributes. Various solutions suggest triangulation between WiFi access points to obtain location attribution indoors imitating the GPS accurate estimation through satellites constellations. However, locating signal sources deep indoors introduces various challenges that cannot be addressed via the traditional war-driving or war-walking methods. This research sets out to address the problem of locating WiFi signal sources deep indoors in unsupervised deployment, without previous training or calibration. To achieve this, we developed a grid approach to mitigate for none line of site (NLoS) conditions by clustering signal readings into multi-hypothesis Gaussians distributions. We have also employed hypothesis testing classification to estimate signal attenuation through unknown layouts to remove dependencies on indoor maps availability. Furthermore, we introduced novel methods for locating signal sources deep indoors and presented the concept of WiFi access point (WAP) temporal profiles as an adaptive radio-map with global coverage. Nevertheless, the primary contribution of this research appears in utilisation of data streaming, creation and maintenance of self-organising networks of WAPs through an adaptive deployment of mass-spring relaxation algorithm. In addition, complementary database utilisation components such as error estimation, position estimation and expanding to 3D have been discussed. To justify the outcome of this research, we present results for testing the proposed system on large scale dataset covering various indoor environments in different parts of the world. Finally, we propose scalable indoor positioning system based on received signal strength (RSSI) measurements of WiFi access points to resolve the indoor positioning challenge. To enable the adoption of the proposed solution to global scale, we deployed a piece of software on multitude of smartphone devices to collect data occasionally without the context of venue, environment or custom hardware. To conclude, this thesis provides learning for novel adaptive crowd-sourcing system that automatically deals with tolerance of imprecise data when locating signal sources

A survey of deep learning approaches for WiFi-based indoor positioning

One of the most popular approaches for indoor positioning is WiFi fingerprinting, which has been intrinsically tackled as a traditional machine learning problem since the beginning, to achieve a few metres of accuracy on average. In recent years, deep learning has emerged as an alternative approach, with a large number of publications reporting sub-metre positioning accuracy. Therefore, this survey presents a timely, comprehensive review of the most interesting deep learning methods being used for WiFi fingerprinting. In doing so, we aim to identify the most efficient neural networks, under a variety of positioning evaluation metrics for different readers. We will demonstrate that despite the new emerging WiFi signal measures (i.e. CSI and RTT), RSS produces competitive performances under deep learning. We will also show that simple neural networks outperform more complex ones in certain environments

Statistical analysis of indoor RSSI read-outs for 433 MHz, 868 MHz, 2.4 GHz and 5 GHz ISM bands

This paper presents statistical analysis of RSSI read-outs recorded in indoor environment. Many papers concerning indoor location, based on RSSI measurement, assume its normal probability density function (PDF). This is partially excused by relation to PDF of radio-receiver's noise and/or together with influence of AWGN (average white Gaussian noise) radio-channel – generally modelled by normal PDF. Unfortunately, commercial (usually unknown) methods of RSSI calculations, typically as "side-effect" function of receiver's AGC (automatic gain control), results in PDF being far different from Gaussian PDF. This paper presents results of RSSI measurements in selected ISM bands: 433/868 MHz and 2.4/5 GHz. The measurements have been recorded using low-cost integrated RF modules (at 433/868 MHz and 2.4 GHz) and 802.11 WLAN access points (at 2.4/5 GHz). Then estimated PDF of collected data is shown and compared to normal (Gaussian) PDF

Outdoor-Indoor tracking systems through geomatic techniques: data analysis for marketing and safety management

Negli ultimi decenni, l'utilizzo di sistemi di gestione delle informazioni nel trattamento dei dati edilizi ha portato a cambiamenti radicali nei metodi di produzione, documentazione e archiviazione dei dati. Dato il crescente interesse per i dati e la loro gestione, l'obiettivo di questa tesi è quello di creare un flusso di lavoro efficace e chiaro a partire dai rilievi geomatici in un'ottica di miglioramento dei dati raccolti sul territorio, sugli edifici circostanti e su quelli relativi al comportamento umano, in modo che possano essere meglio sfruttati e integrati in modelli di gestione intelligenti. Come primo passo, questa tesi mira a comprendere i limiti dell'interoperabilità e dell'integrazione dei dati nei GIS. Per promuovere l'interoperabilità dei dati GIS, è necessario analizzare i metodi di conversione nei diversi modelli di archiviazione dei dati, come CityGML e IndoorGML, definendo un dominio ontologico. Questo ha portato alla creazione di un nuovo modello arricchito, basato sulle connessioni tra i diversi elementi del modello urbano in GIS. Il secondo passo consiste nel raccogliere tutti i dati tradotti in un database a grafo sfruttando il web semantico. Il risultato offrirà vantaggi sostanziali durante l'intero ciclo di vita del progetto. Questa metodologia può essere applicata anche al patrimonio culturale, dove la gestione delle informazioni gioca un ruolo fondamentale. Un altro lavoro di ricerca è stato quello di sviluppare un sistema di gestione SMART per le attività di conservazione dei borghi storici attraverso la gestione di tipologie eterogenee di dati, dal rilievo alla documentazione tecnica. Il flusso di lavoro è stato strutturato come segue: (i) acquisizione dei dati; (ii) modellazione 3D; (iii) modellazione della conoscenza; (iv) modellazione della gestione SMART. Questa ricerca apre la strada allo sviluppo di una piattaforma web in cui importare i dati GIS per un approccio di digital twin. Tutte le ricerche svolte fino a questo punto sono state finalizzate a comprendere la capacità di creare modelli e sistemi informativi intelligenti per capire la fattibilità di ospitare dati eterogenei che potrebbero essere inclusi in futuro. Il passo successivo consiste nel comprendere il comportamento umano in uno spazio. Finora sono pochi i lavori di ricerca che si occupano di sistemi di mappatura e posizionamento che tengono conto sia degli spazi esterni che di quelli interni. Questo argomento, anche se ha pochi articoli di ricerca, rappresenta un aspetto cruciale per molte ragioni, soprattutto quando si tratta di gestire la sicurezza degli edifici danneggiati. Angelats e il suo gruppo di ricerca al CTTC hanno lavorato su questo aspetto, fornendo un sistema in grado di seguire in tempo reale le persone dall'esterno all'interno di spazi chiusi e viceversa. L'uso di sensori GNSS combinato con l'odometria inerziale visiva fornisce una traiettoria continua senza perdere il percorso seguito dall'utente monitorato. Una parte di questa tesi si è concentrata sul miglioramento della traiettoria finale ottenuta con il sistema appena descritto, effettuando test sulla traiettoria esterna del GNSS per capire il comportamento della traiettoria quando si avvicina agli edifici o quando l'utente si sposta in indoor. L'ultimo aspetto su cui si concentrerà la tesi è il tracciamento delle persone in ambienti chiusi. Il comportamento umano è al centro di numerosi studi in diversi campi, come quello scientifico, sociale ed economico. A differenza del precedente caso di studio sul tracciamento delle persone in aree esterne/interne, l'obiettivo è stato quello di raccogliere informazioni sul posizionamento dinamico delle persone in ambienti indoor, sulla base del segnale WiFi. Verrà effettuata una breve analisi dei dati per dimostrare il corretto funzionamento del sistema, per sottolineare l'importanza della conoscenza dei dati e l'uso che se ne può fare.In the last decades, the use of information management systems in the building data processing led to radical changes to the methods of data production, documentation and archiving. Given the ever-increasing interest in data and their management, the aim of this thesis is to create an effective and clear workflow starting from geomatic surveys in a perspective of improving the collected data on the territory, surrounding buildings and those related to human behaviour so they can be better exploited and integrated into smart management models As first step this thesis aims to understand the limits of data interoperability and integration in GIS filed. Before that, the data must be collected as raw data, then processed and interpret in order to obtain information. At the end of this first stage, when the information is well organized and can be well understanded and used it becomes knowledge. To promote the interoperability of GIS data, it is necessary at first to analyse methods of conversion in different data storage models such as CityGML and IndoorGML, defining an ontological domain. This has led to the creation of a new enriched model, based on connections among the different elements of the urban model in GIS environment, and to the possibility to formulate queries based on these relations. The second step consists in collecting all data translated into a specific format that fill a graph database in a semantic web environment, while maintaining those relationships. The outcome will offer substantial benefits during the entire project life cycle. This methodology can also be applied to cultural heritage where the information management plays a key role. Another research work, was to develop a SMART management system for preservation activities of historical villages through the management of heterogeneous types of data, from the survey to the technical documentation. The workflow was structured as follows: (i) Data acquisition; (ii) 3D modelling; (iii) Knowledge modelling; (iv) SMART management modelling. This research paves the way to develop a web platform where GIS data would be imported for a digital twin approach. All the research done up to this point was to understand the capability of creating smart information models and systems in order to understand the feasibility to host heterogeneous data that may be included in the future. The next step consist of understanding human behaviour in a space. So far only a few research papers are addressed towards mapping and positioning systems taking into account both outdoor and indoor spaces. This topic, even though it has few research articles, represents a crucial aspect for many reasons, especially when it comes to safety management of damaged building. Angelats and his research team at CTTC have been working on this aspect providing a system able to track in real time people from outdoor to indoor areas and vice-versa. The use of GNSS sensors combined with Visual Inertial Odometry provide a continuous trajectory without losing the path followed by the monitored user. A part of this thesis focused on enhancing the final trajectory obtained with the described system above, carrying out tests on the outdoor trajectory of GNSS in order to understand behaviour of the trajectory when it gets close to buildings or when the user moves indoor. The last aspect this thesis will focus on is the tracking of people indoor. Human behaviour is at the centre of several studies in different fields such as scientific subjects, social and economics. Differently from the previous case study of tracking people in outdoor/indoor areas, the scope was to collect information about the dynamic indoor positioning of people, based on the WiFi signal. A brief analysis of the data will be made to demonstrate the correct functioning of the system, to emphasise the importance of data knowledge and the use that can be made of it

Impact of Positioning Technology on Human Navigation

In navigation from one place to another, spatial knowledge helps us establish a destination and route while travelling. Therefore, sufficient spatial knowledge is a vital element in successful navigation. To build adequate spatial knowledge, various forms of spatial tools have been introduced to deliver spatial information without direct experience (maps, descriptions, pictures, etc.). An innovation developed in the 1970s and available on many handheld platforms from the early 2000s is the Global Position System (GPS) and related map and text-based navigation support systems. Contemporary technical achievements, such as GPS, have made navigation more effective, efficient, and comfortable in most outdoor environments. Because GPS delivers such accurate information, human navigation can be supported without specific spatial knowledge. Unfortunately, there is no universal and accurate navigation system for indoor environments. Since smartphones have become increasingly popular, we can more frequently and easily access various positioning services that appear to work both indoors and outdoors. The expansion of positioning services and related navigation technology have changed the nature of navigation. For example, routes to destination are progressively determined by a “system,” not the individual. Unfortunately we only have a partial and nascent notion of how such an intervention affects spatial behaviour. The practical purpose of this research is to develop a trustworthy positioning system that functions in indoor environments and identify those aspects those should be considered before deploying Indoor Positioning System (IPS), all towards the goal of maintaining affordable positioning accuracy, quality, and consistency. In the same way that GPS provides worry free directions and navigation support, an IPS would extend such opportunities to many of our built environments. Unfortunately, just as we know little about how GPS, or any real time navigation system, affects human navigation, there is little evidence suggesting how such a system (indoors or outdoors) changes how we find our way. For this reason, in addition to specifying an indoor position system, this research examines the difference in human’s spatial behaviour based on the availability of a navigation system and evaluates the impact of varying the levels of availability of such tools (not available, partially available, or full availability). This research relies on outdoor GPS, but when such systems are available indoors and meet the accuracy and reliability or GPS, the results will be generalizable to such situations

Location-Enabled IoT (LE-IoT): A Survey of Positioning Techniques, Error Sources, and Mitigation

The Internet of Things (IoT) has started to empower the future of many industrial and mass-market applications. Localization techniques are becoming key to add location context to IoT data without human perception and intervention. Meanwhile, the newly-emerged Low-Power Wide-Area Network (LPWAN) technologies have advantages such as long-range, low power consumption, low cost, massive connections, and the capability for communication in both indoor and outdoor areas. These features make LPWAN signals strong candidates for mass-market localization applications. However, there are various error sources that have limited localization performance by using such IoT signals. This paper reviews the IoT localization system through the following sequence: IoT localization system review -- localization data sources -- localization algorithms -- localization error sources and mitigation -- localization performance evaluation. Compared to the related surveys, this paper has a more comprehensive and state-of-the-art review on IoT localization methods, an original review on IoT localization error sources and mitigation, an original review on IoT localization performance evaluation, and a more comprehensive review of IoT localization applications, opportunities, and challenges. Thus, this survey provides comprehensive guidance for peers who are interested in enabling localization ability in the existing IoT systems, using IoT systems for localization, or integrating IoT signals with the existing localization sensors

An IoT based Virtual Coaching System (VSC) for Assisting Activities of Daily Life

Nowadays aging of the population is becoming one of the main concerns of theworld. It is estimated that the number of people aged over 65 will increase from 461million to 2 billion in 2050. This substantial increment in the elderly population willhave significant consequences in the social and health care system. Therefore, in thecontext of Ambient Intelligence (AmI), the Ambient Assisted Living (AAL) has beenemerging as a new research area to address problems related to the aging of the population. AAL technologies based on embedded devices have demonstrated to be effectivein alleviating the social- and health-care issues related to the continuous growing of theaverage age of the population. Many smart applications, devices and systems have beendeveloped to monitor the health status of elderly, substitute them in the accomplishment of activities of the daily life (especially in presence of some impairment or disability),alert their caregivers in case of necessity and help them in recognizing risky situations.Such assistive technologies basically rely on the communication and interaction be-tween body sensors, smart environments and smart devices. However, in such contextless effort has been spent in designing smart solutions for empowering and supportingthe self-efficacy of people with neurodegenerative diseases and elderly in general. Thisthesis fills in the gap by presenting a low-cost, non intrusive, and ubiquitous VirtualCoaching System (VCS) to support people in the acquisition of new behaviors (e.g.,taking pills, drinking water, finding the right key, avoiding motor blocks) necessary tocope with needs derived from a change in their health status and a degradation of theircognitive capabilities as they age. VCS is based on the concept of extended mind intro-duced by Clark and Chalmers in 1998. They proposed the idea that objects within theenvironment function as a part of the mind. In my revisiting of the concept of extendedmind, the VCS is composed of a set of smart objects that exploit the Internet of Things(IoT) technology and machine learning-based algorithms, in order to identify the needsof the users and react accordingly. In particular, the system exploits smart tags to trans-form objects commonly used by people (e.g., pillbox, bottle of water, keys) into smartobjects, it monitors their usage according to their needs, and it incrementally guidesthem in the acquisition of new behaviors related to their needs. To implement VCS, thisthesis explores different research directions and challenges. First of all, it addresses thedefinition of a ubiquitous, non-invasive and low-cost indoor monitoring architecture byexploiting the IoT paradigm. Secondly, it deals with the necessity of developing solu-tions for implementing coaching actions and consequently monitoring human activitiesby analyzing the interaction between people and smart objects. Finally, it focuses on the design of low-cost localization systems for indoor environment, since knowing theposition of a person provides VCS with essential information to acquire information onperformed activities and to prevent risky situations. In the end, the outcomes of theseresearch directions have been integrated into a healthcare application scenario to imple-ment a wearable system that prevents freezing of gait in people affected by Parkinson\u2019sDisease