Map-assisted Indoor Positioning Utilizing Ubiquitous WiFi Signals

The demand of indoor positioning solution is on the increase dramatically, and WiFi-based indoor positioning is known as a very promising approach because of the ubiquitous WiFi signals and WiFi-compatible mobile devices. Improving the positioning accuracy is the primary target of most recent works, while the excessive deployment overhead is also a challenging problem behind. In this thesis, the author is investigating the indoor positioning problem from the aspects of indoor map information and the ubiquity of WiFi signals. This thesis proposes a set of novel WiFi positioning schemes to improve the accuracy and efficiency. Firstly, considering the access point (AP) placement is the first step to deploy indoor positioning system using WiFi, an AP placement algorithm is provided to generate the placement of APs in a given indoor environment. The AP placement algorithm utilises the floor plan information from the indoor map, in which the placement of APs is optimised to benefit the fingerprinting- based positioning. Secondly, the patterns of WiFi signals are observed and deeply analysed from sibling and spatial aspects in conjunction with pathway map from indoor map to address the problem of inconsistent WiFi signal observations. The sibling and spatial signal patterns are used to improve both positioning accuracy and efficiency. Thirdly, an AP-centred architecture is proposed by moving the positioning modules from mobile handheld to APs to facilitate the applications where mobile handheld doesn’t directly participate positioning. Meanwhile, the fingerprint technique is adopted into the AP-centred architecture to maintain comparable positioning accuracy. All the proposed works in this thesis are adequately designed, implemented and evaluated in the real-world environment and show improved performance

The Case for Approximate Intermittent Computing

We present the concept of approximate intermittent computing and concretely demonstrate its application. Intermittent computations stem from the erratic energy patterns caused by energy harvesting: computations unpredictably terminate whenever energy is insufficient and the application state is lost. Existing solutions maintain equivalence to continuous executions by creating persistent state on non-volatile memory, enabling stateful computations to cross power failures. The performance penalty is massive: system throughput reduces while energy consumption increases. In contrast, approximate intermittent computations trade the accuracy of the results for sparing the entire overhead to maintain equivalence to a continuous execution. This is possible as we use approximation to limit the extent of stateful computations to the single power cycle, enabling the system to completely shift the energy budget for managing persistent state to useful computations towards an immediate approximate result. To this end, we effectively reverse the regular formulation of approximate computing problems. First, we apply approximate intermittent computing to human activity recognition. We design an anytime variation of support vector machines able to improve the accuracy of the classification as energy is available. We build a hw/sw prototype using kinetic energy and show a 7x improvement in system throughput compared to state-of-the-art system support for intermittent computing, while retaining 83% accuracy in a setting where the best attainable accuracy is 88%. Next, we apply approximate intermittent computing in a sharply different scenario, that is, embedded image processing, using loop perforation. Using a different hw/sw prototype we build and diverse energy traces, we show a 5x improvement in system throughput compared to state-of-the-art system support for intermittent computing, while providing an equivalent output in 84% of the cases

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

Contributions to autonomous robust navigation of mobile robots in industrial applications

151 p.Un aspecto en el que las plataformas móviles actuales se quedan atrás en comparación con el punto que se ha alcanzado ya en la industria es la precisión. La cuarta revolución industrial trajo consigo la implantación de maquinaria en la mayor parte de procesos industriales, y una fortaleza de estos es su repetitividad. Los robots móviles autónomos, que son los que ofrecen una mayor flexibilidad, carecen de esta capacidad, principalmente debido al ruido inherente a las lecturas ofrecidas por los sensores y al dinamismo existente en la mayoría de entornos. Por este motivo, gran parte de este trabajo se centra en cuantificar el error cometido por los principales métodos de mapeado y localización de robots móviles,ofreciendo distintas alternativas para la mejora del posicionamiento.Asimismo, las principales fuentes de información con las que los robots móviles son capaces de realizarlas funciones descritas son los sensores exteroceptivos, los cuales miden el entorno y no tanto el estado del propio robot. Por esta misma razón, algunos métodos son muy dependientes del escenario en el que se han desarrollado, y no obtienen los mismos resultados cuando este varía. La mayoría de plataformas móviles generan un mapa que representa el entorno que les rodea, y fundamentan en este muchos de sus cálculos para realizar acciones como navegar. Dicha generación es un proceso que requiere de intervención humana en la mayoría de casos y que tiene una gran repercusión en el posterior funcionamiento del robot. En la última parte del presente trabajo, se propone un método que pretende optimizar este paso para así generar un modelo más rico del entorno sin requerir de tiempo adicional para ello

Indoor Positioning and Navigation

In recent years, rapid development in robotics, mobile, and communication technologies has encouraged many studies in the field of localization and navigation in indoor environments. An accurate localization system that can operate in an indoor environment has considerable practical value, because it can be built into autonomous mobile systems or a personal navigation system on a smartphone for guiding people through airports, shopping malls, museums and other public institutions, etc. Such a system would be particularly useful for blind people. Modern smartphones are equipped with numerous sensors (such as inertial sensors, cameras, and barometers) and communication modules (such as WiFi, Bluetooth, NFC, LTE/5G, and UWB capabilities), which enable the implementation of various localization algorithms, namely, visual localization, inertial navigation system, and radio localization. For the mapping of indoor environments and localization of autonomous mobile sysems, LIDAR sensors are also frequently used in addition to smartphone sensors. Visual localization and inertial navigation systems are sensitive to external disturbances; therefore, sensor fusion approaches can be used for the implementation of robust localization algorithms. These have to be optimized in order to be computationally efficient, which is essential for real-time processing and low energy consumption on a smartphone or robot

Multi-environment Georeferencing of RGB-D Panoramic Images from Portable Mobile Mapping – a Perspective for Infrastructure Management

Hochaufgelöste, genau georeferenzierte RGB-D-Bilder sind die Grundlage für 3D-Bildräume bzw. 3D Street-View-Webdienste, welche bereits kommerziell für das Infrastrukturmanagement eingesetzt werden. MMS ermöglichen eine schnelle und effiziente Datenerfassung von Infrastrukturen. Die meisten im Aussenraum eingesetzten MMS beruhen auf direkter Georeferenzierung. Diese ermöglicht in offenen Bereichen absolute Genauigkeiten im Zentimeterbereich. Bei GNSS-Abschattung fällt die Genauigkeit der direkten Georeferenzierung jedoch schnell in den Dezimeter- oder sogar in den Meterbereich. In Innenräumen eingesetzte MMS basieren hingegen meist auf SLAM. Die meisten SLAM-Algorithmen wurden jedoch für niedrige Latenzzeiten und für Echtzeitleistung optimiert und nehmen daher Abstriche bei der Genauigkeit, der Kartenqualität und der maximalen Ausdehnung in Kauf. Das Ziel dieser Arbeit ist, hochaufgelöste RGB-D-Bilder in verschiedenen Umgebungen zu erfassen und diese genau und zuverlässig zu georeferenzieren. Für die Datenerfassung wurde ein leistungsstarkes, bildfokussiertes und rucksackgetragenes MMS entwickelt. Dieses besteht aus einer Mehrkopf-Panoramakamera, zwei Multi-Beam LiDAR-Scannern und einer GNSS- und IMU-kombinierten Navigationseinheit der taktischen Leistungsklasse. Alle Sensoren sind präzise synchronisiert und ermöglichen Zugriff auf die Rohdaten. Das Gesamtsystem wurde in Testfeldern mit bündelblockbasierten sowie merkmalsbasierten Methoden kalibriert, was eine Voraussetzung für die Integration kinematischer Sensordaten darstellt. Für eine genaue und zuverlässige Georeferenzierung in verschiedenen Umgebungen wurde ein mehrstufiger Georeferenzierungsansatz entwickelt, welcher verschiedene Sensordaten und Georeferenzierungsmethoden vereint. Direkte und LiDAR SLAM-basierte Georeferenzierung liefern Initialposen für die nachträgliche bildbasierte Georeferenzierung mittels erweiterter SfM-Pipeline. Die bildbasierte Georeferenzierung führt zu einer präzisen aber spärlichen Trajektorie, welche sich für die Georeferenzierung von Bildern eignet. Um eine dichte Trajektorie zu erhalten, die sich auch für die Georeferenzierung von LiDAR-Daten eignet, wurde die direkte Georeferenzierung mit Posen der bildbasierten Georeferenzierung gestützt. Umfassende Leistungsuntersuchungen in drei weiträumigen anspruchsvollen Testgebieten zeigen die Möglichkeiten und Grenzen unseres Georeferenzierungsansatzes. Die drei Testgebiete im Stadtzentrum, im Wald und im Gebäude repräsentieren reale Bedingungen mit eingeschränktem GNSS-Empfang, schlechter Beleuchtung, sich bewegenden Objekten und sich wiederholenden geometrischen Mustern. Die bildbasierte Georeferenzierung erzielte die besten Genauigkeiten, wobei die mittlere Präzision im Bereich von 5 mm bis 7 mm lag. Die absolute Genauigkeit betrug 85 mm bis 131 mm, was einer Verbesserung um Faktor 2 bis 7 gegenüber der direkten und LiDAR SLAM-basierten Georeferenzierung entspricht. Die direkte Georeferenzierung mit CUPT-Stützung von Bildposen der bildbasierten Georeferenzierung, führte zu einer leicht verschlechterten mittleren Präzision im Bereich von 13 mm bis 16 mm, wobei sich die mittlere absolute Genauigkeit nicht signifikant von der bildbasierten Georeferenzierung unterschied. Die in herausfordernden Umgebungen erzielten Genauigkeiten bestätigen frühere Untersuchungen unter optimalen Bedingungen und liegen in derselben Grössenordnung wie die Resultate anderer Forschungsgruppen. Sie können für die Erstellung von Street-View-Services in herausfordernden Umgebungen für das Infrastrukturmanagement verwendet werden. Genau und zuverlässig georeferenzierte RGB-D-Bilder haben ein grosses Potenzial für zukünftige visuelle Lokalisierungs- und AR-Anwendungen

A model personal energy meter

Every day each of us consumes a significant amount of energy, both directly through transport, heating and use of appliances, and indirectly from our needs for the production of food, manufacture of goods and provision of services. This dissertation investigates a personal energy meter which can record and apportion an individual's energy usage in order to supply baseline information and incentives for reducing our environmental impact. If the energy costs of large shared resources are split evenly without regard for individual consumption each person minimises his own losses by taking advantage of others. Context awareness offers the potential to change this balance and apportion energy costs to those who cause them to be incurred. This dissertation explores how sensor systems installed in many buildings today can be used to apportion energy consumption between users, including an evaluation of a range of strategies in a case study and elaboration of the overriding principles that are generally applicable. It also shows how second-order estimators combined with location data can provide a proxy for fine-grained sensing. A key ingredient for apportionment mechanisms is data on energy usage. This may come from metering devices or buildings directly, or from profiling devices and using secondary indicators to infer their power state. A mechanism for profiling devices to determine the energy costs of specific activities, particularly applicable to shared programmable devices is presented which can make this process simpler and more accurate. By combining crowdsourced building-inventory information and a simple building energy model it is possible to estimate an individual's energy use disaggregated by device class with very little direct sensing. Contextual information provides crucial cues for apportioning the use and energy costs of resources, and one of the most valuable sources from which to infer context is location. A key ingredient for a personal energy meter is a low cost, low infrastructure location system that can be deployed on a truly global scale. This dissertation presents a description and evaluation of the new concept of inquiry-free Bluetooth tracking that has the potential to offer indoor location information with significantly less infrastructure and calibration than other systems. Finally, a suitable architecture for a personal energy meter on a global scale is demonstrated using a mobile phone application to aggregate energy feeds based on the case studies and technologies developed

A New Front-End System For UAV-Based Antenna Measurements For Polarimetric Weather Radars

Radar system calibration is vital for ensuring optimal performance, especially in weather radars that have stringent requirements for co-polarization mismatch. In-field calibration is essential, particularly for mobile weather radars, as environmental conditions can vary between deployments. Traditionally, conventional far-field ranges or airborne systems such as helicopters and aircraft have been used to measure and calibrate radar systems. However, in recent years, Unmanned Aerial Systems (UAS) have emerged as a cost-effective and flexible alternative for antenna measurement and radar calibration. Previous studies have demonstrated the feasibility of using UAS for far-field antenna measurements across various operating frequencies. These works have achieved high accuracy in characterizing and calibrating polarimetric weather radar systems, meeting critical requirements such as co-polarization mismatch below 0.1 dB and cross-polarization isolation below -45 dB. However, existing UAS-based systems are complex to operate, requiring multiple equipment both on the UAS and the ground station. They are primarily limited to one-way transmission from the UAS to the AUT and lack the capability to switch between RX and TX measurements or H- and V-polarization without physical modifications. The objective of this thesis is to develop a lightweight and self-contained front-end system for UAS-based in-situ antenna characterization. This system will eliminate the need for additional RF instruments on the ground, providing remote real-time control to switch between RX and TX modes in both V- and H-polarization. It will also facilitate the transmission and reception of measurement data over long distances, enabling far-field measurements beyond 120 m. The proposed system aims to address the limitations of existing UAS-based calibration systems, offering a sophisticated and accurate solution for measuring the strictest radar systems. By developing a versatile and lightweight front-end system, this research seeks to advance the field of UAS-based antenna characterization and contribute to the improvement of radar calibration techniques

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

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity