A Survey of Crowd Sensing Opportunistic Signals for Indoor Localization <s> OIL <s> WiFi localization, the task of determining the physical location of a mobile device from wireless signal strengths, has been shown to be an accurate method of indoor and outdoor localization and a powerful building block for location-aware applications. However, most localization techniques require a training set of signal strength readings labeled against a ground truth location map, which is prohibitive to collect and maintain as maps grow large. In this paper we propose a novel technique for solving the WiFi SLAM problem using the Gaussian Process Latent Variable Model (GPLVM) to determine the latent-space locations of unlabeled signal strength data. We show how GPLVM, in combination with an appropriate motion dynamics model, can be used to reconstruct a topological connectivity graph from a signal strength sequence which, in combination with the learned Gaussian Process signal strength model, can be used to perform efficient localization. <s> BIB001 </s> A Survey of Crowd Sensing Opportunistic Signals for Indoor Localization <s> OIL <s> Smartphones can reveal crowdsourcing's full potential and let users transparently contribute to complex and novel problem solving. This emerging area is illustrated through a taxonomy that classifies the mobile crowdsourcing field and through three new applications that optimize location-based search and similarity services based on crowd-generated data. Such applications can be deployed on SmartLab, a cloud of more than 40 Android devices deployed at the University of Cyprus that provides an open testbed to facilitate research and development of smartphone applications on a massive scale. <s> BIB002 </s> A Survey of Crowd Sensing Opportunistic Signals for Indoor Localization <s> OIL <s> Radio Frequency (RF) fingerprinting, based onWiFi or cellular signals, has been a popular approach to indoor localization. However, its adoption in the real world has been stymied by the need for sitespecific calibration, i.e., the creation of a training data set comprising WiFi measurements at known locations in the space of interest. While efforts have been made to reduce this calibration effort using modeling, the need for measurements from known locations still remains a bottleneck. In this paper, we present Zee -- a system that makes the calibration zero-effort, by enabling training data to be crowdsourced without any explicit effort on the part of users. Zee leverages the inertial sensors (e.g., accelerometer, compass, gyroscope) present in the mobile devices such as smartphones carried by users, to track them as they traverse an indoor environment, while simultaneously performing WiFi scans. Zee is designed to run in the background on a device without requiring any explicit user participation. The only site-specific input that Zee depends on is a map showing the pathways (e.g., hallways) and barriers (e.g., walls). A significant challenge that Zee surmounts is to track users without any a priori, user-specific knowledge such as the user's initial location, stride-length, or phone placement. Zee employs a suite of novel techniques to infer location over time: (a) placement-independent step counting and orientation estimation, (b) augmented particle filtering to simultaneously estimate location and user-specific walk characteristics such as the stride length,(c) back propagation to go back and improve the accuracy of ocalization in the past, and (d) WiFi-based particle initialization to enable faster convergence. We present an evaluation of Zee in a large office building. <s> BIB003
In BIB002 , the authors mainly investigate the user prompting algorithms in case that improper algorithm frustrates users. They devised a user prompting algorithm based on Voronoi Diagram. By arranging the spaces of interest into Voronoi Diagram, they introduced a Spatial Uncertainty concept which relates bounded regions with unbounded regions and design user prompting algorithm on top of this. They also considered the error binds filtering problem and proposed to use clustering in RSS signal space to eliminate wrong binds. To evaluate their model, they conducted experiments in a nine-story building with about 1400 spaces and with 19 participants. Over several days, the mean error between the centroid of estimated space and the centroid of ground truth room decreases to less than 4.5 m. BIB001 . WiFi-SLAM takes the initiatives to integrate wireless signals with SLAM solutions to enable WiFi localization without much training effort. The authors propose to use Gaussian Process Latent Variable Model (GP-LVM), in combination with a motion dynamics model, to discover the latent-space locations of unlabeled Wi-Fi RSS. In their likelihood model of GP-LVM, three types of constraints are considered. The locations → signal strength constraint is captured by the GP part which means that similar locations should have similar signals. The motion dynamics part captures the location → location constraints. The last constraint signal strength → location is a back constraint that is not provided by GP-LVM and thus is implemented as a smooth internal mapping. An Isomap which could recover the overall structure of Wi-Fi traces is used to generate acceptable initialization for the optimization of whole GP-LVM model. Their experiment reports a mean localization error of 3.97 ± 0.59 meters. BIB003 . Zee is a zero-effort crowdsourcing indoor localization system which runs in the background on a mobile device. Specifically, it requires no user-specific knowledge, such as users' initial location, stride length, and phone placement. It utilizes inertial sensors to track users when they traverse a path, while simultaneously collecting Wi-Fi signals. Initially, a uniform distribution over whole floor place is assumed for the initial location of the first user; then by tracking the shape the user traverse and combining it with the floor plan, probabilities are eliminated and the predicted location converges to the ground truth one; also, backward belief propagation is leveraged to recover the whole path.