Crowdsourcing-Based Fingerprinting for Indoor Location in Multi-Storey Buildings

POCI-01-0247-FEDER-033479The number of available indoor location solutions has been growing, however with insufficient precision, high implementation costs or scalability limitations. As fingerprinting-based methods rely on ubiquitous information in buildings, the need for additional infrastructure is discarded. Still, the time-consuming manual process to acquire fingerprints limits their applicability in most scenarios. This paper proposes an algorithm for the automatic construction of environmental fingerprints on multi-storey buildings, leveraging the information sources available in each scenario. It relies on unlabelled crowdsourced data from users’ smartphones. With only the floor plans as input, a demand for most applications, we apply a multimodal approach that joins inertial data, local magnetic field andWi-Fi signals to construct highly accurate fingerprints. Precise movement estimation is achieved regardless of smartphone usage through Deep Neural Networks, and the transition between floors detected from barometric data. Users’ trajectories obtained with Pedestrian Dead Reckoning techniques are partitioned into clusters with Wi-Fi measurements. Straight sections from the same cluster are then compared with subsequence Dynamic Time Warping to search for similarities. From the identified overlapping sections, a particle filter fits each trajectory into the building’s floor plans. From all successfully mapped routes, fingerprints labelled with physical locations are finally obtained. Experimental results from an office and a university building show that this solution constructs comparable fingerprints to those acquired manually, thus providing a useful tool for fingerprinting-based solutions automatic setup.publishersversionpublishe

Environment-Aware Regression for Indoor Localization based on WiFi Fingerprinting

Mendoza-Silva, G., Costa, A. C., Torres-Sospedra, J., Painho, M., & Huerta, J. (2022). Environment-Aware Regression for Indoor Localization based on WiFi Fingerprinting. IEEE Sensors Journal, 22(6), 4978 - 4988. https://doi.org/10.1109/JSEN.2021.3073878Data enrichment through interpolation or regression is a common approach to deal with sample collection for Indoor Localization with WiFi fingerprinting. This paper provides guidelines on where to collect WiFi samples, and proposes a new model for received signal strength regression. The new model creates vectors that describe the presence of obstacles between an access point and the collected samples. The vectors, the distance between the access point and the positions of the samples, and the collected, are used to train a Support Vector Regression. The experiments included some relevant analyses and showed that the proposed model improves received signal strength regression in terms of regression residuals and positioning accuracy.authorsversionpublishe

Human Crowdsourcing Data for Indoor Location Applied to Ambient Assisted Living Scenarios

In the last decades, the rise of life expectancy has accelerated the demand for new technological solutions to provide a longer life with improved quality. One of the major areas of the Ambient Assisted Living aims to monitor the elderly location indoors. For this purpose, indoor positioning systems are valuable tools and can be classified depending on the need of a supporting infrastructure. Infrastructure-based systems require the investment on expensive equipment and existing infrastructure-free systems, although rely on the pervasively available characteristics of the buildings, present some limitations regarding the extensive process of acquiring and maintaining fingerprints, the maps that store the environmental characteristics to be used in the localisation phase. These problems hinder indoor positioning systems to be deployed in most scenarios. To overcome these limitations, an algorithm for the automatic construction of indoor floor plans and environmental fingerprints is proposed. With the use of crowdsourcing techniques, where the extensiveness of a task is reduced with the help of a large undefined group of users, the algorithm relies on the combination ofmultiple sources of information, collected in a non-annotated way by common smartphones. The crowdsourced data is composed by inertial sensors, responsible for estimating the users’ trajectories, Wi-Fi radio and magnetic field signals. Wi-Fi radio data is used to cluster the trajectories into smaller groups, each corresponding to specific areas of the building. Distance metrics applied to magnetic field signals are used to identify geomagnetic similarities between different users’ trajectories. The building’s floor plan is then automatically created, which results in fingerprints labelled with physical locations. Experimental results show that the proposed algorithm achieved comparable floor plan and fingerprints to those acquired manually, allowing the conclusion that is possible to automate the setup process of infrastructure-free systems. With these results, this solution can be applied in any fingerprinting-based indoor positioning system

A Meta-Review of Indoor Positioning Systems

An accurate and reliable Indoor Positioning System (IPS) applicable to most indoor scenarios has been sought for many years. The number of technologies, techniques, and approaches in general used in IPS proposals is remarkable. Such diversity, coupled with the lack of strict and verifiable evaluations, leads to difficulties for appreciating the true value of most proposals. This paper provides a meta-review that performed a comprehensive compilation of 62 survey papers in the area of indoor positioning. The paper provides the reader with an introduction to IPS and the different technologies, techniques, and some methods commonly employed. The introduction is supported by consensus found in the selected surveys and referenced using them. Thus, the meta-review allows the reader to inspect the IPS current state at a glance and serve as a guide for the reader to easily find further details on each technology used in IPS. The analyses of the meta-review contributed with insights on the abundance and academic significance of published IPS proposals using the criterion of the number of citations. Moreover, 75 works are identified as relevant works in the research topic from a selection of about 4000 works cited in the analyzed surveys

Fingerprint Database Enhancement by Applying Interpolation and Regression Techniques for IoT-based Indoor Localization

Most applied indoor localization is based on distance and fingerprint techniques. The distance-based technique converts specific parameters to a distance, while the fingerprint technique stores parameters as the fingerprint database. The widely used Internet of Things (IoT) technologies, e.g., Wi-Fi and ZigBee, provide the localization parameters, i.e., received signal strength indicator (RSSI). The fingerprint technique advantages over the distance-based method as it straightforwardly uses the parameter and has better accuracy. However, the burden in database reconstruction in terms of complexity and cost is the disadvantage of this technique. Some solutions, i.e., interpolation, image-based method, machine learning (ML)-based, have been proposed to enhance the fingerprint methods. The limitations are complex and evaluated only in a single environment or simulation. This paper proposes applying classical interpolation and regression to create the synthetic fingerprint database using only a relatively sparse RSSI dataset. We use bilinear and polynomial interpolation and polynomial regression techniques to create the synthetic database and apply our methods to the 2D and 3D environments. We obtain an accuracy improvement of 0.2m for 2D and 0.13m for 3D by applying the synthetic database. Adding the synthetic database can tackle the sparsity issues, and the offline fingerprint database construction will be less burden. Doi: 10.28991/esj-2021-SP1-012 Full Text: PD

Collection of a continuous long-term dataset for the evaluation of Wi-Fi-fingerprinting-based indoor positioning systems

The dataset introduced in this paper is available in two versions: lite version https://doi.org/10.5281/zenodo.6646008 (accessed on 28 July 2022) which considers Wi-Fi samples from each MD every 20 min, has a total of 382,852 Wi-Fi samples, thus making it easier to parse and analyse; full version https://doi.org/10.5281/zenodo.6928554 (accessed on 29 July 2022) which has all collected samples, with a total of 7,446,538 Wi-Fi samples.Indoor positioning and navigation have been attracting interest from the research community for quite some time. Nowadays, new fields, such as the Internet of Things, Industry 4.0, and augmented reality, are increasing the demand for indoor positioning solutions capable of delivering specific positioning performances not only in simulation but also in the real world; hence, validation in real-world environments is essential. However, collecting real-world data is a time-consuming and costly endeavor, and many research teams lack the resources to perform experiments across different environments, which are required for high-quality validation. Publicly available datasets are a solution that provides the necessary resources to perform this type of validation and to promote research work reproducibility. Unfortunately, for different reasons, and despite some initiatives promoting data sharing, the number and diversity of datasets available are still very limited. In this paper, we introduce and describe a new public dataset which has the unique characteristic of being collected over a long period (2+ years), and it can be used for different Wi-Fi-based positioning studies. In addition, we also describe the solution (Wireless Sensor Network (WSN) + mobile unit) developed to collect this dataset, allowing researchers to replicate the method and collect similar datasets in other spaces.This work has been supported by FCT—Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020, and the PhD fellowship PD/BD/137401/2018

New Reconstructed Database for Cost Reduction in Indoor Fingerprinting Localization

Location fingerprinting is a technique widely suggested for challenging indoor positioning. Despite the significant benefits of this technique, it needs a considerable amount of time and energy to measure the Received Signal Strength (RSS) at Reference Points (RPs) and build a fingerprinting database to achieve an appropriate localization accuracy. Reducing the number of RPs can reduce this cost, but it noticeably degrades the accuracy of positioning. In order to alleviate this problem, this paper takes the interior architecture of the indoor area and signal propagation effects into account and proposes two novel recovery methods for creating the reconstructed database instead of the measured one. They only need a few numbers of RPs to reconstruct the database and even are able to produce a denser database. The first method is a new zone-based path-loss propagation model which employs fingerprints of different zones separately and the second one is a new interpolation method, zone-based Weighted Ring-based (WRB). The proposed methods are compared with the conventional path-loss model and six interpolation functions. Two different test environments along with a benchmarking testbed, and various RPs configurations are also utilized to verify the proposed recovery methods, based on the reconstruction errors and the localization accuracies they provide. The results indicate that by taking only 11% of the initial RPs, the new zone-based path-loss model decreases the localization error up to 26% compared to the conventional path-loss model and the proposed zone-based WRB method outperforms all the other interpolation methods and improves the accuracy by 40%

Wi-Fi Signals Database Construction using Chebyshev Wavelets for Indoor Positioning Systems

Nowadays fast and accurate positioning of assets and people is as a crucial part of many businesses, such as, warehousing, manufacturing and logistics. Applications that offer different services based on mobile user location gaining more and more attention. Some of the most common applications include location-based advertising, directory assistance, point-to-point navigation, asset tracking, emergency and fleet management. While outdoors mostly covered by the Global Positioning System, there is no one versatile solution for indoor positioning. For the past decade Wi-Fi fingerprinting based indoor positioning systems gained a lot of attention by enterprises as an affordable and flexible solution to track their assets and resources more effectively. The concept behind Wi-Fi fingerprinting is to create signal strength database of the area prior to the actual positioning. This process is known as a calibration carried out manually and the indoor positioning system accuracy highly depends on a calibration intensity. Unfortunately, this procedure requires huge amount of time, manpower and effort, which makes extensive deployment of indoor positioning system a challenging task.  approach of constructing signal strength database from a minimal number of measurements using Chebyshev wavelets approximation. The main objective of the research is to minimize the calibration workload while providing high positioning accuracy.  The field tests as well as computer simulation results showed significant improvement in signal strength prediction accuracy compared to existing approximation algorithms. Furhtermore, the proposed algorithm can recover missing signal values with much smaller number of on-site measurements compared to conventional calibration algorithm

Location-Enabled IoT (LE-IoT): A Survey of Positioning Techniques, Error Sources, and Mitigation

The Internet of Things (IoT) has started to empower the future of many industrial and mass-market applications. Localization techniques are becoming key to add location context to IoT data without human perception and intervention. Meanwhile, the newly-emerged Low-Power Wide-Area Network (LPWAN) technologies have advantages such as long-range, low power consumption, low cost, massive connections, and the capability for communication in both indoor and outdoor areas. These features make LPWAN signals strong candidates for mass-market localization applications. However, there are various error sources that have limited localization performance by using such IoT signals. This paper reviews the IoT localization system through the following sequence: IoT localization system review -- localization data sources -- localization algorithms -- localization error sources and mitigation -- localization performance evaluation. Compared to the related surveys, this paper has a more comprehensive and state-of-the-art review on IoT localization methods, an original review on IoT localization error sources and mitigation, an original review on IoT localization performance evaluation, and a more comprehensive review of IoT localization applications, opportunities, and challenges. Thus, this survey provides comprehensive guidance for peers who are interested in enabling localization ability in the existing IoT systems, using IoT systems for localization, or integrating IoT signals with the existing localization sensors