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

An indoor pedestrian localisation system with self-calibration capability

The Global Positioning System (GPS), a space-based system, employs dozens of satellites to provide location determination and navigation services around the world. However, due to the constraints to the power consuming and long-distance transmission, the strength of the GPS signal received on the mobile device is weak. Errors of the detection of the line-of-sight (LOS) propagated components of the signals are expected to be high if the users are in urban areas or in buildings, since obstacles in the surrounding environments could attenuate the LOS propagated components of the GPS signals significantly, but might upfade the multi-path components (constructive multi-path effect). Therefore, GPS should be replaced by other techniques for providing localisation services in urban areas or, especially, in indoor environments. Among all the candidates, received signal strength (RSS) location fingerprint based positioning systems attract great attentions from both the academia and industry. Usually, a time-consuming and labour-intensive site survey to collect dozens of training samples of RSS from access points (APs) in range on every reference position (RP) in the area of interest is required to build the radio map (RM), before the localisation services could be provided to users. The purpose of the thesis is to reduce the workload involved in the site survey while providing accurate localisation service from two aspects, as shown as follows. Firstly, the quantity of the training samples collected on each RP is reduced, by taking advantage of the on-line RSS measurements collected by users to calibrate the RM. The on-line RSS measurements are geo-tagged probabilistically by an implementation of particle filter to track the trajectories of the users. The employed particles in estimation of the users’ states are initialised by a supervised clustering algorithm, propagated according to the analysis of the data sourcing from inertial measurement units (IMUs), e.g., walking detection, orientation estimation, step and stepping moments detection, step length detection, etc., and corrected by the wall constraints. Furthermore, the importance weights of the particles are adjusted to reduce the negative influence of the multi-clustered distribution of the particles to the on-line localisation accuracy, by applying the on-line RSS-based localisation results when significant users' body turnings are detected. The final results confirm that the accuracy of the localisation service with the RM calibrated by the method proposed in this thesis is higher than the previously proposed approach taking advantage of expectation maximisation algorithm. Secondly, a semi-automatic site-survey method which takes advantage of a route-planning algorithm and a walking detection module to recognise automatically the index of the RP for the current site-survey task, inform the system automatically of the start/end of the process of the task on the current RP and switch automatically to the following RPs on the planned route for the following tasks. In this way, human beings' intervention to the site-survey process is greatly reduced. As a result, the errors made in the site-survey tasks, such as incorrect recognition of the index of the RP for the current task which is highly likely to occur when the technicians get absent-minded in the work, misoperations to start/end of the task for collecting RSS samples on the current RP at wrong time moments, forgetting to notify the system of the fact that the technician has moved on to the next RP, etc., are avoided. The technicians no longer feel bored or anxious in the process of fulfilment of site-survey tasks, and the working efficiency and robustness of the RM could be also improved

Information Fusion for 5G IoT: An Improved 3D Localisation Approach Using K-DNN and Multi-Layered Hybrid Radiomap

Indoor positioning is a core enabler for various 5G identity and context-aware applications requiring precise and real-time simultaneous localisation and mapping (SLAM). In this work, we propose a K-nearest neighbours and deep neural network (K-DNN) algorithm to improve 3D indoor positioning. Our implementation uses a novel data-augmentation concept for the received signal strength (RSS)-based fingerprint technique to produce a 3D fused hybrid. In the offline phase, a machine learning (ML) approach is used to train a model on a radiomap dataset that is collected during the offline phase. The proposed algorithm is implemented on the constructed hybrid multi-layered radiomap to improve the 3D localisation accuracy. In our implementation, the proposed approach is based on the fusion of the prominent 5G IoT signals of Bluetooth Low Energy (BLE) and the ubiquitous WLAN. As a result, we achieved a 91% classification accuracy in 1D and a submeter accuracy in 2D

SURIMI: supervised radio map augmentation with deep learning and a generative adversarial network for fingerprint-based indoor positioning

Indoor Positioning based on Machine Learning has drawn increasing attention both in the academy and the industry as meaningful information from the reference data can be extracted. Many researchers are using supervised, semi-supervised, and unsupervised Machine Learning models to reduce the positioning error and offer reliable solutions to the end-users. In this article, we propose a new architecture by combining Convolutional Neural Network (CNN), Long short-term memory (LSTM) and Generative Adversarial Network (GAN) in order to increase the training data and thus improve the position accuracy. The proposed combination of supervised and unsupervised models was tested in 17 public datasets, providing an extensive analysis of its performance. As a result, the positioning error has been reduced in more than 70% of them.The authors gratefully acknowledge funding from European Union’s Hori zon 2020 Research and Innovation programme under the Marie Skłodowska Curie grant agreements No. 813278 (A-WEAR: A network for dynamic wearable applications with privacy constraints, http://www.a-wear.eu/) and No. 101023072 (ORIENTATE: Low-cost Reliable Indoor Positioning in Smart Factories, http://orientate.dsi.uminho.pt)

Cloud-based Indoor Positioning Platform for Context-adaptivity in GNSS-denied Scenarios

The demand for positioning, localisation and navigation services is on the rise, largely owing to the fact that such services form an integral part of applications in areas such as human activity recognition, robotics, and eHealth. Depending on the field of application, these services must accomplish high levels of accuracy, massive device connectivity, real-time response, flexibility, and integrability. Although many current solutions have succeeded in fulfilling these requirements, numerous challenges remain in terms of providing robust and reliable indoor positioning solutions. This dissertation has a core focus on improving computing efficiency, data pre-processing, and software architecture for Indoor Positioning Systems (IPSs), without throwing out position and location accuracy. Fingerprinting is the main positioning technique used in this dissertation, as it is one of the approaches used most frequently in indoor positioning solutions. The dissertation begins by presenting a systematic review of current cloud-based indoor positioning solutions for Global Navigation Satellite System (GNSS) denied scenarios. This first contribution identifies the current challenges and trends in indoor positioning applications over the last seven years (from January 2015 to May 2022). Secondly, we focus on the study of data optimisation techniques such as data cleansing and data augmentation. This second contribution is devoted to reducing the number of outliers fingerprints in radio maps and, therefore, reducing the error in position estimation. The data cleansing algorithm relies on the correlation between fingerprints, taking into account the maximum Received Signal Strength (RSS) values, whereas the Generative Adversarial Network (GAN) network is used for data augmentation in order to generate synthetic fingerprints that are barely distinguishable from real ones. Consequently, the positioning error is reduced by more than 3.5% after applying the data cleansing. Similarly, the positioning error is reduced in 8 from 11 datasets after generating new synthetic fingerprints. The third contribution suggests two algorithms which group similar fingerprints into clusters. To that end, a new post-processing algorithm for Density-based Spatial Clustering of Applications with Noise (DBSCAN) clustering is developed to redistribute noisy fingerprints to the formed clusters, enhancing the mean positioning accuracy by more than 20% in comparison with the plain DBSCAN. A new lightweight clustering algorithm is also introduced, which joins similar fingerprints based on the maximum RSS values and Access Point (AP) identifiers. This new clustering algorithm reduces the time required to form the clusters by more than 60% compared with two traditional clustering algorithms. The fourth contribution explores the use of Machine Learning (ML) models to enhance the accuracy of position estimation. These models are based on Deep Neural Network (DNN) and Extreme Learning Machine (ELM). The first combines Convolutional Neural Network (CNN) and Long short-term memory (LSTM) to learn the complex patterns in fingerprinting radio maps and improve position accuracy. The second model uses CNN and ELM to provide a fast and accurate solution for the classification of fingerprints into buildings and floors. Both models offer better performance in terms of floor hit rate than the baseline (more than 8% on average), and also outperform some machine learning models from the literature. Finally, this dissertation summarises the key findings of the previous chapters in an open-source cloud platform for indoor positioning. This software developed in this dissertation follows the guidelines provided by current standards in positioning, mapping, and software architecture to provide a reliable and scalable system

Sensing and Signal Processing in Smart Healthcare

In the last decade, we have witnessed the rapid development of electronic technologies that are transforming our daily lives. Such technologies are often integrated with various sensors that facilitate the collection of human motion and physiological data and are equipped with wireless communication modules such as Bluetooth, radio frequency identification, and near-field communication. In smart healthcare applications, designing ergonomic and intuitive human–computer interfaces is crucial because a system that is not easy to use will create a huge obstacle to adoption and may significantly reduce the efficacy of the solution. Signal and data processing is another important consideration in smart healthcare applications because it must ensure high accuracy with a high level of confidence in order for the applications to be useful for clinicians in making diagnosis and treatment decisions. This Special Issue is a collection of 10 articles selected from a total of 26 contributions. These contributions span the areas of signal processing and smart healthcare systems mostly contributed by authors from Europe, including Italy, Spain, France, Portugal, Romania, Sweden, and Netherlands. Authors from China, Korea, Taiwan, Indonesia, and Ecuador are also included