Indoor Positioning for BIM

Building Informational Modeling (BIM) is very popular in the construction industry in Norway today, and Omega 365 has created a suite of tools for BIM, including a 3D visualising tool for 3D models of buildings, called a BIMViewer. This tool exists in multiple forms, and one of them is an app for mobile phones, which construction workers carry with them on construction sites. When determining one's own position in the BIMViewer, it may take time to find and select the correct position. This study aims to create a feature for the BIMViewer using new technology, IEEE802.11mc and comparing it with an old method, Wi-Fi received signal strength (RSS) with the Log Distance Path Loss model. In addition, GPS was tried in order to prove it was not usable for this use case and in order to compare it with the other two methods. The main goal is to find a method that is cheap for clients to implement in regards to equipment and installation, but is precise enough to provide a good user experience. Three experiments were conducted for this study, one using only GPS and two for the other two methods. One experiment used only a single floor and the other used two floors. Both of these experiments used only 6 access points and were conducted at NyeSUS, the new hospital in Stavanger which was an active construction zone during the experiments. The experiments showed that GPS was a bad choice for the use case and that both the other methods were usable. The round trip time (RTT) method, which used the IEEE802.11mc measurements was more precise than the RSS method, however suffered from the need for more access points than the RSS method. This study concludes that both the RTT and the RSS methods may be usable, however some improvements would be needed for a truly good user experience. The study also suggests that a mix of the two methods may be beneficial

Mobility-Induced Graph Learning for WiFi Positioning

A smartphone-based user mobility tracking could be effective in finding his/her location, while the unpredictable error therein due to low specification of built-in inertial measurement units (IMUs) rejects its standalone usage but demands the integration to another positioning technique like WiFi positioning. This paper aims to propose a novel integration technique using a graph neural network called Mobility-INduced Graph LEarning (MINGLE), which is designed based on two types of graphs made by capturing different user mobility features. Specifically, considering sequential measurement points (MPs) as nodes, a user's regular mobility pattern allows us to connect neighbor MPs as edges, called time-driven mobility graph (TMG). Second, a user's relatively straight transition at a constant pace when moving from one position to another can be captured by connecting the nodes on each path, called a direction-driven mobility graph (DMG). Then, we can design graph convolution network (GCN)-based cross-graph learning, where two different GCN models for TMG and DMG are jointly trained by feeding different input features created by WiFi RTTs yet sharing their weights. Besides, the loss function includes a mobility regularization term such that the differences between adjacent location estimates should be less variant due to the user's stable moving pace. Noting that the regularization term does not require ground-truth location, MINGLE can be designed under semi- and self-supervised learning frameworks. The proposed MINGLE's effectiveness is extensively verified through field experiments, showing a better positioning accuracy than benchmarks, say root mean square errors (RMSEs) being 1.398 (m) and 1.073 (m) for self- and semi-supervised learning cases, respectively.Comment: submitted to a possible IEEE journa

Repousser les limites de l'informatique ubiquitaire par l'utilisation des smartphones

Les smartphones occupent une place de plus en plus importante dans notre quotidien. Ils permettent d’accéder, en tout temps et en tout lieu, à de nombreux services facilitant la vie de l’utilisateur et jouent ainsi un rôle important dans nos modes de vie à l’ère de l’informatique ubiquitaire. De nombreux travaux ont montré par exemple qu’il était possible de proposer avec ces dispositifs mobiles bien plus que ce que l’on peut faire avec un simple ordinateur. L’espace des services qu’ils offrent s’est ainsi grandement étendu dans des domaines extrêmement variés. Cependant, leur mise en œuvre présente souvent des limites en raison des importantes contraintes importantes d’utilisation qu’elles font peser sur l’utilisateur, du déploiement infrastructurel qu’elles requièrent ou de la précision limitée qu’elles permettent d’atteindre. Dans cette thèse, nous nous proposons de repousser ces limites. Nous avons retenu la condition d’utilisation la condition d’utilisation la moins contraignante : celle d’un téléphone simplement tenu dans la main. Nous proposons alors HandRate, le premier système capable de suivre la fréquence cardiaque d’un utilisateur dans ce contexte d’utilisation. Nous exploitons ensuite ce même signal vibratoire de la main pour construire HoldPass, un système capable d’authentifier un utilisateur dans ces mêmes conditions d’utilisation. HandRate et HoldPass peuvent ainsi jouer un rôle important dans la lutte contre les maladies cardio-vasculaires et une authentification biométrique plus sûre et plus difficile à outrepasser. Pour ajouter du contexte à ces informations médicales, nous mettons à profit les avancées récentes du standard WiFi et proposons un algorithme, FUSIC, permettant de fournir une localisation plus précise à l’intérieur des bâtiments. Des prototypes de chacune de ces solutions ont été implantés sur du matériel réel et ont donné lieu à des expérimentations en vraie grandeur, incluant parfois la participation de centaines d’utilisateurs. Elles montrent des améliorations sensibles des performances tout en minimisant les contraintes d’utilisation

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

Perception Intelligence Integrated Vehicle-to-Vehicle Optical Camera Communication.

Ubiquitous usage of cameras and LEDs in modern road and aerial vehicles open up endless opportunities for novel applications in intelligent machine navigation, communication, and networking. To this end, in this thesis work, we hypothesize the benefit of dual-mode usage of vehicular built-in cameras through novel machine perception capabilities combined with optical camera communication (OCC). Current key conception of understanding a line-of-sight (LOS) scenery is from the aspect of object, event, and road situation detection. However, the idea of blending the non-line-of-sight (NLOS) information with the LOS information to achieve a see-through vision virtually is new. This improves the assistive driving performance by enabling a machine to see beyond occlusion. Another aspect of OCC in the vehicular setup is to understand the nature of mobility and its impact on the optical communication channel quality. The research questions gathered from both the car-car mobility modelling, and evaluating a working setup of OCC communication channel can also be inherited to aerial vehicular situations like drone-drone OCC. The aim of this thesis is to answer the research questions along these new application domains, particularly, (i) how to enable a virtual see-through perception in the car assisting system that alerts the human driver about the visible and invisible critical driving events to help drive more safely, (ii) how transmitter-receiver cars behaves while in the mobility and the overall channel performance of OCC in motion modality, (iii) how to help rescue lost Unmanned Aerial Vehicles (UAVs) through coordinated localization with fusion of OCC and WiFi, (iv) how to model and simulate an in-field drone swarm operation experience to design and validate UAV coordinated localization for group of positioning distressed drones. In this regard, in this thesis, we present the end-to-end system design, proposed novel algorithms to solve the challenges in applying such a system, and evaluation results through experimentation and/or simulation

MEMS Accelerometers

Micro-electro-mechanical system (MEMS) devices are widely used for inertia, pressure, and ultrasound sensing applications. Research on integrated MEMS technology has undergone extensive development driven by the requirements of a compact footprint, low cost, and increased functionality. Accelerometers are among the most widely used sensors implemented in MEMS technology. MEMS accelerometers are showing a growing presence in almost all industries ranging from automotive to medical. A traditional MEMS accelerometer employs a proof mass suspended to springs, which displaces in response to an external acceleration. A single proof mass can be used for one- or multi-axis sensing. A variety of transduction mechanisms have been used to detect the displacement. They include capacitive, piezoelectric, thermal, tunneling, and optical mechanisms. Capacitive accelerometers are widely used due to their DC measurement interface, thermal stability, reliability, and low cost. However, they are sensitive to electromagnetic field interferences and have poor performance for high-end applications (e.g., precise attitude control for the satellite). Over the past three decades, steady progress has been made in the area of optical accelerometers for high-performance and high-sensitivity applications but several challenges are still to be tackled by researchers and engineers to fully realize opto-mechanical accelerometers, such as chip-scale integration, scaling, low bandwidth, etc