LocateMe

Abstract

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.

Application of Deep Convolutional Neural Networks and Smartphone Sensors for Indoor Localization

“…Last but foremost, the system is tested with a single device and hence device dependence is not analyzed. Research [8,37] point out that different smartphones exhibit very different magnetic data depending on the sensitivity and accuracy of the magnetometer used in the smartphones.…”

“…Unlike the global positioning system (GPS) which serves as the chief outdoor localization technology, there is no standard technology which does the same for indoor localization. A number of technologies have been proposed so far, including optical [36], radio, acoustics, and magnetic [37,38] to perform indoor localization, yet, none of them serves without its demerits.…”

An Infrastructure-Free Indoor Localization Algorithm for Smartphones

“…As long as the internal layout remains unchanged, the magnetic anomalies are stable over time [1,26,30,34,41]. Therefore, the magnetic anomalies have the potential for accurate and pervasive indoor positioning without depending on any infrastructure [26,42].…”

“…The most problematic feature of the magnetic field is its low discernibility [1,26,34]. Construction materials and electrical appliances have little influence on the magnetic field values even at a distance of a few meters [1].…”

“…To estimate target position, MaLoc [25] fuses the inertial sensor data and step counting, heading change and hybrid magnetic measurement between two contiguous steps within a novel reliability-augmented particle filter framework. LocateMe [34] leverages sequences of magnetic readings to estimate a target’s position using smartphones. However, it only achieves a room level localization accuracy.…”