The growing commercial interest in indoor location-based services (ILBS) has spurred recent development of many indoor positioning techniques. Due to the absence of Global Positioning System (GPS) signal, many other signals have been proposed for indoor usage. Among them, Wi-Fi (802.11) emerges as a promising one due to the pervasive deployment of wireless LANs (WLANs). In particular, Wi-Fi fingerprinting has been attracting much attention recently because it does not require line-of-sight measurement of access points (APs) and achieves high applicability in complex indoor environment. This survey overviews recent advances on two major areas of Wi-Fi fingerprint localization: advanced localization techniques and efficient system deployment. Regarding advanced techniques to localize users, we present how to make use of temporal or spatial signal patterns, user collaboration, and motion sensors. Regarding efficient system deployment, we discuss recent advances on reducing offline labor-intensive survey, adapting to fingerprint changes, calibrating heterogeneous devices for signal collection, and achieving energy efficiency for smartphones. We study and compare the approaches through our deployment experiences, and discuss some future directions.

Wi-Fi fingerprinting has been extensively studied for indoor localization due to its deployability under pervasive indoor WLAN. As the signals from access points (APs) may change due to, for example, AP movement or power adjustment, the traditional approach is to conduct site survey regularly in order to maintain localization accuracy, which is costly and time-consuming. Here, we study how to accurately locate a target and automatically update fingerprints in the presence of altered AP signals (or simply, “altered APs”). We propose Localization with Altered APs and Fingerprint Updating (LAAFU) system, employing implicit crowdsourced signals for fingerprint update and survey reduction. Using novel subset sampling, LAAFU identifies any altered APs and filter them out before a location decision is made, hence maintaining localization accuracy under altered AP signals. With client locations anywhere in the region, fingerprint signals can be adaptively and transparently updated using non-parametric Gaussian process regression. We have conducted extensive experiments in our campus hall, an international airport, and a premium shopping mall. Compared with traditional weighted nearest neighbors and probabilistic algorithms, results show that LAAFU is robust against altered APs, achieving 20 percent localization error reduction with the fingerprints adaptive to environmental signal changes.

Mobile crowdsensing with increasing pervasiveness of smartphones has enabled a myriad of applications, including urban-scale signal map monitoring and revision. Despite the importance of its quality, due to the large size of a site to cover, dense crowdsourcing is neither cost-effective nor convenient for crowdsourcing participants, making it critical and challenging to balance between signal quality and crowdsourcing cost. To address this problem, we propose a novel incentive mechanism, BCCS, based on Bayesian Compressive Crowdsensing (BCS). BCCS iteratively determines the spatial grids for crowd-sourcing quality and predicts the remaining unexplored grids for deployment efficiency. BCS returns not only the predicted signal values, but also the confidence intervals for convergence and incentive control. A probabilistic user participation and measurement model is applied for incentive design, which is flexible for crowdsensing deployment. Our extensive evaluation based on two different data sets shows that BCCS achieves much higher prediction accuracy (often by more than 20%) with lower payments to the participants and fewer iterations (often by 30%) than existing solutions.

Geomagnetism has recently attracted considerable attention for indoor localization due to its pervasiveness and independence from extra infrastructure. Its location signature has been observed to be temporally stable and spatially discernible for localization purposes. This survey examines and analyzes the recent challenges and advances in geomagnetism-based indoor localization using smartphones. We first study smartphone-based geomagnetism measurements. We then review recent efforts in database construction and computation reduction, followed by state-of-the-art schemes in localizing the target. For each category, we identify practical deployment challenges and compare related studies. Finally, we summarize future directions and provide a guideline for new researchers in this field.

In fingerprint-based indoor localization, keeping fingerprints current is important for localization accuracy. Because access point (AP) signals can change over time or suddenly, the traditional approach is to survey the site regularly and frequently, which is laborious and costly. The authors propose Chameleon, a novel survey-free approach to localize users and maintain an updated fingerprint database despite fingerprint signal changes. In Chameleon, clients are both location "queriers" and unconscious "surveyors" (implicit crowdsourcing). Independent of and amenable to any fingerprint-based localization algorithm, Chameleon employs a clustering algorithm to filter out the altered AP signals to achieve high localization accuracy. Using the calculated location and the collected signals of the client, the fingerprint database can be continuously updated without manual intervention. Extensive experimental trials at The Hong Kong University of Science and Technology and at the Hong Kong International Airport confirm that Chameleon can adapt the radio map to the current signal environment and achieve low localization error despite signal changes.

In indoor localization based on Wi-Fi fingerprinting, a target sends its received signal strength indicator (RSSI) of access points (APs) to a server to estimate its position. Traditionally, the server estimates the target position by matching the RSSI with the fingerprints stored in the database. Due to signal noise in fingerprint collection and target measurement, this often results in a geographically disperse set of reference points (RPs), leading to unsatisfactory estimation accuracy. To mitigate the noise problem, we propose a novel, efficient, and highly accurate localization scheme termed Tilejunction. Based on only the first two moments of the measured signal, Tilejunction maps the target RSSI of each AP to a convex hull termed signal “tile” where the target is likely within. Using a novel comparison metric for random signals, we formulate a linear programming (LP) problem to localize the target at the junction of the tiles. To further improve its computational efficiency, Tilejunction employs an information-theoretic measure to keep only those APs whose signals show sufficient differentiation in the site. It also partitions the site into multiple clusters to substantially reduce the search space in the LP optimization. We have implemented Tilejunction. Our extensive simulation and experimental measurements show that it outperforms other recent state-of-the-art approaches (e.g. RADAR, KL-divergence, etc.) with significantly lower localization error (often by more than 30 percent).