Indoor Positioning for Monitoring Older Adults at Home: Wi-Fi and BLE Technologies in Real Scenarios

This paper presents our experience on a real case of applying an indoor localization system formonitoringolderadultsintheirownhomes. Sincethesystemisdesignedtobeusedbyrealusers, therearemanysituationsthatcannotbecontrolledbysystemdevelopersandcanbeasourceoferrors. This paper presents some of the problems that arise when real non-expert users use localization systems and discusses some strategies to deal with such situations. Two technologies were tested to provide indoor localization: Wi-Fi and Bluetooth Low Energy. The results shown in the paper suggest that the Bluetooth Low Energy based one is preferable in the proposed task

Opportunistic timing signals for pervasive mobile localization

Mención Internacional en el título de doctorThe proliferation of handheld devices and the pressing need of location-based services call for precise and accurate ubiquitous geographic mobile positioning that can serve a vast set of devices. Despite the large investments and efforts in academic and industrial communities, a pin-point solution is however still far from reality. Mobile devices mainly rely on Global Navigation Satellite System (GNSS) to position themselves. GNSS systems are known to perform poorly in dense urban areas and indoor environments, where the visibility of GNSS satellites is reduced drastically. In order to ensure interoperability between the technologies used indoor and outdoor, a pervasive positioning system should still rely on GNSS, yet complemented with technologies that can guarantee reliable radio signals in indoor scenarios. The key fact that we exploit is that GNSS signals are made of data with timing information. We then investigate solutions where opportunistic timing signals can be extracted out of terrestrial technologies. These signals can then be used as additional inputs of the multi-lateration problem. Thus, we design and investigate a hybrid system that combines range measurements from the Global Positioning System (GPS), the world’s most utilized GNSS system, and terrestrial technologies; the most suitable one to consider in our investigation is WiFi, thanks to its large deployment in indoor areas. In this context, we first start investigating standalone WiFi Time-of-flight (ToF)-based localization. Time-of-flight echo techniques have been recently suggested for ranging mobile devices overWiFi radios. However, these techniques have yielded only moderate accuracy in indoor environments because WiFi ToF measurements suffer from extensive device-related noise which makes it challenging to differentiate between direct path from non-direct path signal components when estimating the ranges. Existing multipath mitigation techniques tend to fail at identifying the direct path when the device-related Gaussian noise is in the same order of magnitude, or larger than the multipath noise. In order to address this challenge, we propose a new method for filtering ranging measurements that is better suited for the inherent large noise as found in WiFi radios. Our technique combines statistical learning and robust statistics in a single filter. The filter is lightweight in the sense that it does not require specialized hardware, the intervention of the user, or cumbersome on-site manual calibration. This makes the method we propose as the first contribution of the present work particularly suitable for indoor localization in large-scale deployments using existing legacy WiFi infrastructures. We evaluate our technique for indoor mobile tracking scenarios in multipath environments, and, through extensive evaluations across four different testbeds covering areas up to 1000m2, the filter is able to achieve a median ranging error between 1:7 and 2:4 meters. The next step we envisioned towards preparing theoretical and practical basis for the aforementioned hybrid positioning system is a deep inspection and investigation of WiFi and GPS ToF ranges, and initial foundations of single-technology self-localization. Self-localization systems based on the Time-of-Flight of radio signals are highly susceptible to noise and their performance therefore heavily rely on the design and parametrization of robust algorithms. We study the noise sources of GPS and WiFi ToF ranging techniques and compare the performance of different selfpositioning algorithms at a mobile node using those ranges. Our results show that the localization error varies greatly depending on the ranging technology, algorithm selection, and appropriate tuning of the algorithms. We characterize the localization error using real-world measurements and different parameter settings to provide guidance for the design of robust location estimators in realistic settings. These tools and foundations are necessary to tackle the problem of hybrid positioning system providing high localization capabilities across indoor and outdoor environments. In this context, the lack of a single positioning system that is able the fulfill the specific requirements of diverse indoor and outdoor applications settings has led the development of a multitude of localization technologies. Existing mobile devices such as smartphones therefore commonly rely on a multi-RAT (Radio Access Technology) architecture to provide pervasive location information in various environmental contexts as the user is moving. Yet, existing multi-RAT architectures consider the different localization technologies as monolithic entities and choose the final navigation position from the RAT that is foreseen to provide the highest accuracy in the particular context. In contrast, we propose in this work to fuse timing range (Time-of-Flight) measurements of diverse radio technologies in order to circumvent the limitations of the individual radio access technologies and improve the overall localization accuracy in different contexts. We introduce an Extended Kalman filter, modeling the unique noise sources of each ranging technology. As a rich set of multiple ranges can be available across different RATs, the intelligent selection of the subset of ranges with accurate timing information is critical to achieve the best positioning accuracy. We introduce a novel geometrical-statistical approach to best fuse the set of timing ranging measurements. We also address practical problems of the design space, such as removal of WiFi chipset and environmental calibration to make the positioning system as autonomous as possible. Experimental results show that our solution considerably outperforms the use of monolithic technologies and methods based on classical fault detection and identification typically applied in standalone GPS technology. All the contributions and research questions described previously in localization and positioning related topics suppose full knowledge of the anchors positions. In the last part of this work, we study the problem of deriving proximity metrics without any prior knowledge of the positions of the WiFi access points based on WiFi fingerprints, that is, tuples of WiFi Access Points (AP) and respective received signal strength indicator (RSSI) values. Applications that benefit from proximity metrics are movement estimation of a single node over time, WiFi fingerprint matching for localization systems and attacks on privacy. Using a large-scale, real-world WiFi fingerprint data set consisting of 200,000 fingerprints resulting from a large deployment of wearable WiFi sensors, we show that metrics from related work perform poorly on real-world data. We analyze the cause for this poor performance, and show that imperfect observations of APs with commodity WiFi clients in the neighborhood are the root cause. We then propose improved metrics to provide such proximity estimates, without requiring knowledge of location for the observed AP. We address the challenge of imperfect observations of APs in the design of these improved metrics. Our metrics allow to derive a relative distance estimate based on two observed WiFi fingerprints. We demonstrate that their performance is superior to the related work metrics.This work has been supported by IMDEA Networks InstitutePrograma Oficial de Doctorado en Ingeniería TelemáticaPresidente: Francisco Barceló Arroyo.- Secretario: Paolo Casari.- Vocal: Marco Fior

Applications across Co-located Devices

We live surrounded by many computing devices. However, their presence has yet to be fully explored to create a richer ubiquitous computing environment. There is an opportunity to take better advantage of those devices by combining them into a unified user experience. To realize this vision, we studied and explored the use of a framework, which provides the tools and abstractions needed to develop applications that distribute UI components across co-located devices. The framework comprises the following components: authentication and authorization services; a broker to sync information across multiple application instances; background services that gather the capabilities of the devices; and a library to integrate web applications with the broker, determine which components to show based on UI requirements and device capabilities, and that provides custom elements to manage the distribution of the UI components and the multiple application states. Collaboration between users is supported by sharing application states. An indoor positioning solution had to be developed in order to determine when devices are close to each other to trigger the automatic redistribution of UI components. The research questions that we set out to respond are presented along with the contributions that have been produced. Those contributions include a framework for crossdevice applications, an indoor positioning solution for pervasive indoor environments, prototypes, end-user studies and developer focused evaluation. To contextualize our research, we studied previous research work about cross-device applications, proxemic interactions and indoor positioning systems. We presented four application prototypes. The first three were used to perform studies to evaluate the user experience. The last one was used to study the developer experience provided by the framework. The results were largely positive with users showing preference towards using multiple devices under some circumstances. Developers were also able to grasp the concepts provided by the framework relatively well.Vivemos rodeados de dispositivos computacionais. No entanto, ainda não tiramos partido da sua presença para criar ambientes de computação ubíqua mais ricos. Existe uma oportunidade de combiná-los para criar uma experiência de utilizador unificada. Para realizar esta visão, estudámos e explorámos a utilização de uma framework que forneça ferramentas e abstrações que permitam o desenvolvimento de aplicações que distribuem os componentes da interface do utilizador por dispositivos co-localizados. A framework é composta por: serviços de autenticação e autorização; broker que sincroniza informação entre várias instâncias da aplicação; serviços que reúnem as capacidades dos dispositivos; e uma biblioteca para integrar aplicações web com o broker, determinar as componentes a mostrar com base nos requisitos da interface e nas capacidades dos dispositivos, e que disponibiliza elementos para gerir a distribuição dos componentes da interface e dos estados de aplicação. A colaboração entre utilizadores é suportada através da partilha dos estados de aplicação. Foi necessário desenvolver um sistema de posicionamento em interiores para determinar quando é que os dispositivos estão perto uns dos outros para despoletar a redistribuição automática dos componentes da interface. As questões de investigação inicialmente colocadas são apresentadas juntamente com as contribuições que foram produzidas. Essas contribuições incluem uma framework para aplicações multi-dispositivo, uma solução de posicionamento em interiores para computação ubíqua, protótipos, estudos com utilizadores finais e avaliação com programadores. Para contextualizar a nossa investigação, estudámos trabalhos anteriores sobre aplicações multi-dispositivo, interação proxémica e sistemas de posicionamento em interiores. Apresentámos quatro aplicações protótipo. As primeiras três foram utilizadas para avaliar a experiência de utilização. A última foi utilizada para estudar a experiência de desenvolvimento com a framework. Os resultados foram geralmente positivos, com os utilizadores a preferirem utilizar múltiplos dispositivos em certas circunstâncias. Os programadores também foram capazes de compreender a framework relativamente bem

Evaluating indoor positioning systems in a shopping mall : the lessons learned from the IPIN 2018 competition

The Indoor Positioning and Indoor Navigation (IPIN) conference holds an annual competition in which indoor localization systems from different research groups worldwide are evaluated empirically. The objective of this competition is to establish a systematic evaluation methodology with rigorous metrics both for real-time (on-site) and post-processing (off-site) situations, in a realistic environment unfamiliar to the prototype developers. For the IPIN 2018 conference, this competition was held on September 22nd, 2018, in Atlantis, a large shopping mall in Nantes (France). Four competition tracks (two on-site and two off-site) were designed. They consisted of several 1 km routes traversing several floors of the mall. Along these paths, 180 points were topographically surveyed with a 10 cm accuracy, to serve as ground truth landmarks, combining theodolite measurements, differential global navigation satellite system (GNSS) and 3D scanner systems. 34 teams effectively competed. The accuracy score corresponds to the third quartile (75th percentile) of an error metric that combines the horizontal positioning error and the floor detection. The best results for the on-site tracks showed an accuracy score of 11.70 m (Track 1) and 5.50 m (Track 2), while the best results for the off-site tracks showed an accuracy score of 0.90 m (Track 3) and 1.30 m (Track 4). These results showed that it is possible to obtain high accuracy indoor positioning solutions in large, realistic environments using wearable light-weight sensors without deploying any beacon. This paper describes the organization work of the tracks, analyzes the methodology used to quantify the results, reviews the lessons learned from the competition and discusses its future

Recent Advances in Indoor Localization Systems and Technologies

Despite the enormous technical progress seen in the past few years, the maturity of indoor localization technologies has not yet reached the level of GNSS solutions. The 23 selected papers in this book present the recent advances and new developments in indoor localization systems and technologies, propose novel or improved methods with increased performance, provide insight into various aspects of quality control, and also introduce some unorthodox positioning methods

A review of smartphones based indoor positioning: challenges and applications

The continual proliferation of mobile devices has encouraged much effort in using the smartphones for indoor positioning. This article is dedicated to review the most recent and interesting smartphones based indoor navigation systems, ranging from electromagnetic to inertia to visible light ones, with an emphasis on their unique challenges and potential real-world applications. A taxonomy of smartphones sensors will be introduced, which serves as the basis to categorise different positioning systems for reviewing. A set of criteria to be used for the evaluation purpose will be devised. For each sensor category, the most recent, interesting and practical systems will be examined, with detailed discussion on the open research questions for the academics, and the practicality for the potential clients

Sensors and Systems for Indoor Positioning

This reprint is a reprint of the articles that appeared in Sensors' (MDPI) Special Issue on “Sensors and Systems for Indoor Positioning". The published original contributions focused on systems and technologies to enable indoor applications