Although a large number of WiFi 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. In this paper, we first report our field studies with GMI, as well as experiment results aiming to explain our unsatisfactory GMI experience. Then motivated by the obtained insights, we propose GROPING as a self-contained indoor navigation system independent of any infrastructural support. GROPING relies on geomagnetic fingerprints 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.

Fine-grained localization is extremely important to accurately locate a user indoors. Although innovative solutions have already been proposed, there is no solution that is universally accepted, easily implemented, user centric, and, most importantly, works in the absence of GSM coverage or WiFi availability. The advent of sensor rich smartphones has paved a way to develop a solution that can cater to these requirements. By employing a smartphone's built-in magnetic field sensor, magnetic signatures were collected inside buildings. These signatures displayed a uniqueness in their patterns due to the presence of different kinds of pillars, doors, elevators, etc., that consist of ferromagnetic materials like steel or iron. We theoretically analyze the cause of this uniqueness and then present an indoor localization solution by classifying signatures based on their patterns. However, to account for user walking speed variations so as to provide an application usable to a variety of users, we follow a dynamic time-warping-based approach that is known to work on similar signals irrespective of their variations in the time axis. Our approach resulted in localization distances of approximately 2m--6m with accuracies between 80--100% implying that it is sufficient to walk short distances across hallways to be located by the smartphone. The implementation of the application on different smartphones yielded response times of less than five secs, thereby validating the feasibility of our approach and making it a viable solution.

Many mobile phone applications such as pedometers and navigation systems rely on orientation sensors that most smartphones are now equipped with. Unfortunately, these sensors rely on measured accelerometer and magnetic field data to determine the orientation. Thus, accelerations upon the phone which arise from everyday use alter orientation information. Similarly, external magnetic interferences from indoor/urban settings affect the heading calculation, resulting in inaccurate directional information. The inability to determine the orientation during everyday use inhibits many potential mobile applications development. In this work, we exploit the newly built-in gyroscope in the Nexus S smartphone to address the interference problems associated with the orientation sensor. We first perform drift error analysis and apply this to gyroscope calculations. We test simple as well as complex rotations seen in walking applications. We lastly test the gyroscope's resistance to described interferences. Experiments show angular calculations with percent error no larger than 6% from actual rotated values. Further, we are able to determine the phone's orientation at any time, in magnetically-interfered areas, with the phone accelerating. With this accurate information we can virtually orient the phone to better use mobile-acquired data. This shows that the presence of a gyroscope in smartphones will certainly aid in numerous applications.

Identifying and locating oneself in different hallways of high rise buildings forms the classic indoor localization problem. GPS does not work indoors and WiFi may not be omnipresent. This paper presents a novel approach to ambient magnetic fields based indoor localization. We present a system that classifies magnetic signatures using dynamic time warping. Specifically, by aligning similar magnetic signatures that differ in magnitude or time, we classify the signatures and infer the location irrepective of the person and his/her mode of commuting. A Nexus One smartphone was employed, utilizing its builtin magnetic field sensor to create a user friendly localization application solely on the phone. By using a variety of subjects including sighted, blindfolded and people using wheelchairs to handle the human speed variation problem, we evaluated the system across 26 and 15 hallways of two different buildings and obtained accuracies of 92.6%, and 91.1% respectively. With these encouraging results, we believe our proposed solution is user independent and caters to a wide range of hallways.