Towards a large scale indoor localization service with crowdsensing indoor map generation
Date of Issue2014
School of Computer Engineering
Centre for Multimedia and Network Technology
Knowing self location matters a lot in people's daily life. While Global Positioning System (GPS) provides almost perfect solution for outdoor area, it would not work in indoor areas because of no line-of-sight to satellites. However, since human tend to spend more and more time in complexly constructed buildings, helping people localize themselves in indoor space become a critical problem. Raising localization accuracy and reducing deployment cost are two main objects in indoor localization problem. High localization accuracy ensures usability of service, while low deployment cost lessens the effort people must take to use localization service. Numerous technologies have been proposed to tackle the problem. However, practical indoor localization that can provide high localization accuracy with minimum cost is still a vacancy. In the first part of this thesis, we devote ourself to explore the possibility of fingerprint based localization. Although a large number of fingerprinting based indoor localization systems have been proposed, our field experience with Google Maps Indoor (GMI), the only system available for public testing, shows that it is far from mature for indoor navigation. Motivated by the obtained insights from field studies with GMI, we propose GROPING as a self-contained indoor navigation system independent of any infrastructural support. GROPING relies on Ambient Magnetic Field fingerprints, which is formed by ``twisted'' geomagnetic field by building structures, that are far more stable than WiFi fingerprints, and it exploits crowdsensing to construct floor maps rather than expecting individual venues to supply digitized maps. Based on our experiments with 20 participants in various floors of a big shopping mall, GROPING is able to deliver a sufficient accuracy for localization and thus provides smooth navigation experience. In our experiments with ambient magnetic field fingerprint, we see that scalability of ambient magnetic field based approach is not satisfactory comparing to WiFi based approaches. By further exploration, we find that dual properties naturally existed in ambient magnetic field fingerprint and WiFi fingerprint. Therefore based on GROPING, we present MaWi - a dual-sensor enabled indoor localization system in the second part of this thesis. Central to MaWi is a novel framework combining two self-contained but complementary localization techniques: Wi-Fi and Ambient Magnetic Field. Combining the two techniques, MaWi not only achieves a high localization accuracy, but also effectively reduces human labor in building fingerprint databases: to avoid war-driving, MaWi is designed to work with low quality fingerprint databases that can be efficiently built by only one person. Our experiments demonstrate that MaWi, with a fingerprint database as scarce as one data sample at each spot, outperforms the state-of-the-art proposals working on a richer fingerprint database. Although MaWi is designed to use minimum human effort to collect fingerprints, the initial spot surveying is still an inevitable burden for all fingerprint-based localization systems. To ultimately reduce human effort in initial phase, we try to find a localization solution in a model-based methodology. In the last part of this thesis, we focus on exploiting ``multipath" phenomenon in wireless signal propagation and utilize it to fully or partially reconstruct the geometry of the indoor space, as well as locate signal source. Whereas a few physical layer techniques have been proposed to locate a signal source indoors, they all deem multipath a ``curse'' and hence take great efforts to cope with it. We, on the contrary, deem multipath a ``blessing'' and thus innovatively exploit the power of it. Essentially, with minor assumption (or knowledge) of the geometry of an indoor space, each signal path may potentially contribute a new piece of information to the location of its source. As a result, it is possible to locate the source with very few sensors (most probably just one hand-held device). At the same time, the extra information provided by multipath effect can help to fully or partially reconstruct the geometry of the indoor space, which enables a floor plan generation process missing in most of the indoor localization systems. To demonstrate these ideas, we instrument a USRP-based radio sensor prototype named iLocScan; it can simultaneously scan an indoor space (hence generate a plan for it) and position the signal source in it. Through iLocScan, we mainly aim to showcase the feasibility of harnessing multipath in assisting indoor localization, rather than to rival existing proposals in terms of localization accuracy. Nevertheless, our experiments show that iLocScan can offer satisfactory results on both source localization and space scanning.
DRNTU::Engineering::Computer science and engineering