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

Zero-Shot Multi-View Indoor Localization via Graph Location Networks

Indoor localization is a fundamental problem in location-based applications. Current approaches to this problem typically rely on Radio Frequency technology, which requires not only supporting infrastructures but human efforts to measure and calibrate the signal. Moreover, data collection for all locations is indispensable in existing methods, which in turn hinders their large-scale deployment. In this paper, we propose a novel neural network based architecture Graph Location Networks (GLN) to perform infrastructure-free, multi-view image based indoor localization. GLN makes location predictions based on robust location representations extracted from images through message-passing networks. Furthermore, we introduce a novel zero-shot indoor localization setting and tackle it by extending the proposed GLN to a dedicated zero-shot version, which exploits a novel mechanism Map2Vec to train location-aware embeddings and make predictions on novel unseen locations. Our extensive experiments show that the proposed approach outperforms state-of-the-art methods in the standard setting, and achieves promising accuracy even in the zero-shot setting where data for half of the locations are not available. The source code and datasets are publicly available at https://github.com/coldmanck/zero-shot-indoor-localization-release.Comment: Accepted at ACM MM 2020. 10 pages, 7 figures. Code and datasets available at https://github.com/coldmanck/zero-shot-indoor-localization-releas

Context Awareness for Navigation Applications

This thesis examines the topic of context awareness for navigation applications and asks the question, “What are the benefits and constraints of introducing context awareness in navigation?” Context awareness can be defined as a computer’s ability to understand the situation or context in which it is operating. In particular, we are interested in how context awareness can be used to understand the navigation needs of people using mobile computers, such as smartphones, but context awareness can also benefit other types of navigation users, such as maritime navigators. There are countless other potential applications of context awareness, but this thesis focuses on applications related to navigation. For example, if a smartphone-based navigation system can understand when a user is walking, driving a car, or riding a train, then it can adapt its navigation algorithms to improve positioning performance. We argue that the primary set of tools available for generating context awareness is machine learning. Machine learning is, in fact, a collection of many different algorithms and techniques for developing “computer systems that automatically improve their performance through experience” [1]. This thesis examines systematically the ability of existing algorithms from machine learning to endow computing systems with context awareness. Specifically, we apply machine learning techniques to tackle three different tasks related to context awareness and having applications in the field of navigation: (1) to recognize the activity of a smartphone user in an indoor office environment, (2) to recognize the mode of motion that a smartphone user is undergoing outdoors, and (3) to determine the optimal path of a ship traveling through ice-covered waters. The diversity of these tasks was chosen intentionally to demonstrate the breadth of problems encompassed by the topic of context awareness. During the course of studying context awareness, we adopted two conceptual “frameworks,” which we find useful for the purpose of solidifying the abstract concepts of context and context awareness. The first such framework is based strongly on the writings of a rhetorician from Hellenistic Greece, Hermagoras of Temnos, who defined seven elements of “circumstance”. We adopt these seven elements to describe contextual information. The second framework, which we dub the “context pyramid” describes the processing of raw sensor data into contextual information in terms of six different levels. At the top of the pyramid is “rich context”, where the information is expressed in prose, and the goal for the computer is to mimic the way that a human would describe a situation. We are still a long way off from computers being able to match a human’s ability to understand and describe context, but this thesis improves the state-of-the-art in context awareness for navigation applications. For some particular tasks, machine learning has succeeded in outperforming humans, and in the future there are likely to be tasks in navigation where computers outperform humans. One example might be the route optimization task described above. This is an example of a task where many different types of information must be fused in non-obvious ways, and it may be that computer algorithms can find better routes through ice-covered waters than even well-trained human navigators. This thesis provides only preliminary evidence of this possibility, and future work is needed to further develop the techniques outlined here. The same can be said of the other two navigation-related tasks examined in this thesis

WLAN-paikannuksen elinkaaren tukeminen

The advent of GPS positioning at the turn of the millennium provided consumers with worldwide access to outdoor location information. For the purposes of indoor positioning, however, the GPS signal rarely penetrates buildings well enough to maintain the same level of positioning granularity as outdoors. Arriving around the same time, wireless local area networks (WLAN) have gained widespread support both in terms of infrastructure deployments and client proliferation. A promising approach to bridge the location context then has been positioning based on WLAN signals. In addition to being readily available in most environments needing support for location information, the adoption of a WLAN positioning system is financially low-cost compared to dedicated infrastructure approaches, partly due to operating on an unlicensed frequency band. Furthermore, the accuracy provided by this approach is enough for a wide range of location-based services, such as navigation and location-aware advertisements. In spite of this attractive proposition and extensive research in both academia and industry, WLAN positioning has yet to become the de facto choice for indoor positioning. This is despite over 20 000 publications and the foundation of several companies. The main reasons for this include: (i) the cost of deployment, and re-deployment, which is often significant, if not prohibitive, in terms of work hours; (ii) the complex propagation of the wireless signal, which -- through interaction with the environment -- renders it inherently stochastic; (iii) the use of an unlicensed frequency band, which means the wireless medium faces fierce competition by other technologies, and even unintentional radiators, that can impair traffic in unforeseen ways and impact positioning accuracy. This thesis addresses these issues by developing novel solutions for reducing the effort of deployment, including optimizing the indoor location topology for the use of WLAN positioning, as well as automatically detecting sources of cross-technology interference. These contributions pave the way for WLAN positioning to become as ubiquitous as the underlying technology.GPS-paikannus avattiin julkiseen käyttöön vuosituhannen vaihteessa, jonka jälkeen sitä on voinut käyttää sijainnin paikantamiseen ulkotiloissa kaikkialla maailmassa. Sisätiloissa GPS-signaali kuitenkin harvoin läpäisee rakennuksia kyllin hyvin voidakseen tarjota vastaavaa paikannustarkkuutta. Langattomat lähiverkot (WLAN), mukaan lukien tukiasemat ja käyttölaitteet, yleistyivät nopeasti samoihin aikoihin. Näiden verkkojen signaalien käyttö on siksi alusta asti tarjonnut lupaavia mahdollisuuksia sisätilapaikannukseen. Useimmissa ympäristöissä on jo valmiit WLAN-verkot, joten paikannuksen käyttöönotto on edullista verrattuna järjestelmiin, jotka vaativat erillisen laitteiston. Tämä johtuu osittain lisenssivapaasta taajuusalueesta, joka mahdollistaa kohtuuhintaiset päätelaitteet. WLAN-paikannuksen tarjoama tarkkuus on lisäksi riittävä monille sijaintipohjaisille palveluille, kuten suunnistamiselle ja paikkatietoisille mainoksille. Näistä lupaavista alkuasetelmista ja laajasta tutkimuksesta huolimatta WLAN-paikannus ei ole kuitenkaan pystynyt lunastamaan paikkaansa pääasiallisena sisätilapaikannusmenetelmänä. Vaivannäöstä ei ole puutetta; vuosien saatossa on julkaistu yli 20 000 tieteellistä artikkelia sekä perustettu useita yrityksiä. Syitä tähän kehitykseen on useita. Ensinnäkin, paikannuksen pystyttäminen ja ylläpito vaativat aikaa ja vaivaa. Toiseksi, langattoman signaalin eteneminen ja vuorovaikutus ympäristön kanssa on hyvin monimutkaista, mikä tekee mallintamisesta vaikeaa. Kolmanneksi, eri teknologiat ja laitteet kilpailevat lisenssivapaan taajuusalueen käytöstä, mikä johtaa satunnaisiin paikannustarkkuuteen vaikuttaviin tietoliikennehäiriöihin. Väitöskirja esittelee uusia menetelmiä joilla voidaan merkittävästi pienentää paikannusjärjestelmän asennuskustannuksia, jakaa ympäristö automaattisesti osiin WLAN-paikannusta varten, sekä tunnistaa mahdolliset langattomat häiriölähteet. Nämä kehitysaskeleet edesauttavat WLAN-paikannuksen yleistymistä jokapäiväiseen käyttöön

Analysis and evaluation of Wi-Fi indoor positioning systems using smartphones

This paper attempts to analyze the main algorithms used in Machine Learning applied to the indoor location. New technologies are facing new challenges. Satellite positioning has become a typical application of mobile phones, but stops working satisfactorily in enclosed spaces. Currently there is a problem in positioning which is unresolved. This circumstance motivates the research of new methods. After the introduction, the first chapter presents current methods of positioning and the problem of positioning indoors. This part of the work shows globally the current state of the art. It mentions a taxonomy that helps classify the different types of indoor positioning and a selection of current commercial solutions. The second chapter is more focused on the algorithms that will be analyzed. It explains how the most widely used of Machine Learning algorithms work. The aim of this section is to present mathematical algorithms theoretically. These algorithms were not designed for indoor location but can be used for countless solutions. In the third chapter, we learn gives tools work: Weka and Python. the results obtained after thousands of executions with different algorithms and parameters showing main problems of Machine Learning shown. In the fourth chapter the results are collected and the conclusions drawn are shown