Mobile device-based Bluetooth Low Energy Database for range estimation in indoor environments

The demand to enhance distance estimation and location accuracy in a variety of Non-Line-of-Sight (NLOS) indoor environments has boosted investigation into infrastructure-less ranging and collaborative positioning approaches. Unfortunately, capturing the required measurements to support such systems is tedious and time-consuming, as it requires simultaneous measurements using multiple mobile devices, and no such database are available in literature. This article presents a Bluetooth Low Energy (BLE) database, including Received-Signal-Strength (RSS) and Ground-Truth (GT) positions, for indoor positioning and ranging applications, using mobile devices as transmitters and receivers. The database is composed of three subsets: one devoted to the calibration in an indoor scenario; one for ranging and collaborative positioning under Non-Line-of-Sight conditions; and one for ranging and collaborative positioning in real office conditions. As a validation of the dataset, a baseline analysis for data visualization, data filtering and collaborative distance estimation applying a path-loss based on the Levenberg-Marquardt Least Squares Trilateration method are included

Collaborative Indoor Positioning Systems: A Systematic Review

Research and development in Collaborative Indoor Positioning Systems (CIPSs) is growing steadily due to their potential to improve on the performance of their non-collaborative counterparts. In contrast to the outdoors scenario, where Global Navigation Satellite System is widely adopted, in (collaborative) indoor positioning systems a large variety of technologies, techniques, and methods is being used. Moreover, the diversity of evaluation procedures and scenarios hinders a direct comparison. This paper presents a systematic review that gives a general view of the current CIPSs. A total of 84 works, published between 2006 and 2020, have been identified. These articles were analyzed and classified according to the described system’s architecture, infrastructure, technologies, techniques, methods, and evaluation. The results indicate a growing interest in collaborative positioning, and the trend tend to be towards the use of distributed architectures and infrastructure-less systems. Moreover, the most used technologies to determine the collaborative positioning between users are wireless communication technologies (Wi-Fi, Ultra-WideBand, and Bluetooth). The predominant collaborative positioning techniques are Received Signal Strength Indication, Fingerprinting, and Time of Arrival/Flight, and the collaborative methods are particle filters, Belief Propagation, Extended Kalman Filter, and Least Squares. Simulations are used as the main evaluation procedure. On the basis of the analysis and results, several promising future research avenues and gaps in research were identified

L/F-CIPS: Collaborative indoor positioning for smartphones with lateration and fingerprinting

The demand for indoor location-based services and the wide availability of mobile devices have triggered research into new positioning systems able to provide accurate indoor positions using smartphones. However, accurate solutions require a complex implementation and long-term maintenance of their infrastructure. Collaborative systems may help to alleviate these drawbacks. In this paper, we propose a smartphone-based collaborative architecture using neural networks and received signal strength, which exploits the built-in wireless communication technologies in smartphones and the collaboration between devices to improve traditional positioning systems without additional deployment. Experiments are carried out in two real-world scenarios, demonstrating that our proposed architecture enhances the position accuracy of traditional indoor positioning systems.The authors gratefully acknowledge funding from European Union’s Horizon 2020 RIA programme under the Marie Skłodowska Curie grant agreement No. 813278 (A-WEAR: A network for dynamic wearable applications with privacy constraints) and No. 101023072 (ORIENTATE: Low-cost Reliable Indoor Positioning in Smart Factories). The associate editor coordinating the review of this article and approving it for publication was Prof. Name Surname (Corresponding authors: J. Torres-Sospedra and S. Casteleyn)

L/F-CIPS: collaborative indoor positioning for smartphones with lateration and fingerprinting

The demand for indoor location-based services (LBS) and the wide availability of mobile devices have triggered research into new positioning systems able to provide accurate indoor positions using smartphones. However, accurate solutions require a complex implementation and long-term maintenance of their infrastructure. Collaborative systems may help alleviate these drawbacks. In this article, we propose a smartphone-based collaborative architecture using neural networks and received signal strength (RSS), which exploits the built-in wireless communication technologies in smartphones and the collaboration between devices to improve the traditional positioning systems without additional deployment. Experiments are carried out in two real-world scenarios, demonstrating that our proposed architecture enhances the position accuracy of the traditional indoor positioning systems (IPSs)

A Collaborative Approach Using Neural Networks for BLE-RSS Lateration-Based Indoor Positioning

In daily life, mobile and wearable devices with high computing power, together with anchors deployed in indoor en-vironments, form a common solution for the increasing demands for indoor location-based services. Within the technologies and methods currently in use for indoor localization, the approaches that rely on Bluetooth Low Energy (BLE) anchors, Received Signal Strength (RSS), and lateration are among the most popular, mainly because of their cheap and easy deployment and accessible infrastructure by a variety of devices. Never-theless, such BLE- and RSS-based indoor positioning systems are prone to inaccuracies, mostly due to signal fluctuations, poor quantity of anchors deployed in the environment, and/or inappropriate anchor distributions, as well as mobile device hardware variability. In this paper, we address these issues by using a collaborative indoor positioning approach, which exploits neighboring devices as additional anchors in an extended positioning network. The collaborating devices' information (i.e., estimated positions and BLE- RSS) is processed using a multilayer perceptron (MLP) neural network by taking into account the device specificity in order to estimate the relative distances. After this, the lateration is applied to collaboratively estimate the device position. Finally, the stand-alone and collaborative position estimates are combined, providing the final position estimate for each device. The experimental results demonstrate that the proposed collaborative approach outperforms the stand-alone lateration method in terms of positioning accuracy.acceptedVersionPeer reviewe

Anonymous Attribute-Based Credentials in Collaborative Indoor Positioning Systems

Collaborative Indoor Positioning Systems have recently received considerable attention, mainly because they address some of the existing limitations of traditional Indoor Positioning System. In Collaborative Indoor Positioning Systems, Bluetooth Low Energy can be used to exchange positioning data and provide information (the Received Signal Strength Indicator) to establish the relative distance between the actors. The collaborative models exploit the position of actors and the relative position among them to allow positioning to external actors or improve the accuracy of the existing actors. However, the traditional protocols (e.g. iBeacon) are not yet ready for providing sufficient privacy protection. Therefore, this paper deals with privacy-enhancing technologies and their application in Collaborative Indoor Positioning System. In particular, we focus on cryptographic schemes which allow the verification of users without their identification, so-called Anonymous Attribute-Based Credentials schemes. As the main contribution, we present a cryptographic scheme that allows security and privacy-friendly sharing of location information sent through Bluetooth Low Energy advertising packets. In order to demonstrate the practicality of our scheme, we also present the results from our implementation and benchmarks on different devices

Collaborative Techniques for Indoor Positioning Systems

Doctorat internacionalThis doctoral thesis focuses on developing and evaluating mobile device-based collaborative techniques to enhance the position accuracy of traditional indoor positioning systems based on RSSI (i.e., lateration and fingerprinting) in real-world conditions. During the research, first, a comprehensive systematic review of Collaborative Indoor Positioning Systems (CIPSs) was conducted to obtain a state-of-the-art; second, extensive experimental data collections considering mobile devices and collaborative scenarios were performed to create a mobile device-based BLE database and BLE and Wi-Fi radio maps for testing our collaborative and non-collaborative indoor positioning approaches; third, traditional methods to estimate distance and position were evaluated to present their limitations and challenges and two novel approaches to improve distance and positioning accuracy were proposed; finally, our proposed CIPSs using Multilayer Perceptron Artificial Neural Networks were developed to enhance the accuracy of BLE–RSSI lateration and fingerprinting-KNN methods and evaluated under real-world conditions to demonstrate its feasibility and benefits

Smartphone Distance Estimation Based on RSSI-Fuzzy Classification Approach

Positioning people indoors has known an exponential growth in the last few years, especially thanks to Bluetooth Low Energy (BLE) technology and the Received Signal Strength Indicator (RSSI) technique. This approach is available in wearable devices, is easy to implement and has energy consumption advantages. However, the relative distance calculation is inaccurate, as the strength of BLE signals significantly fluctuates in indoor environments. Typical coping mechanisms, such as path-loss propagation models, require mathematical modeling and time-consuming calibration, that depend on the environment. In this paper, we propose a novel distance estimator based on RSSI-fuzzy classification of the BLE signals. Fuzzy-logic improves the robustness and accuracy of RSSI-based estimators, does not require an explicit propagation model and is easy and intuitive to (graphically) tune (using basic statistical analysis). The estimator's suitability and the feasibility to provide an easy implementation were experimentally demonstrated in two scenarios with real-world data.acceptedVersionPeer reviewe

A Collaborative Approach Using Neural Networks for BLE-RSS Lateration-Based Indoor Positioning

Ponencia presentada en: 2022 International Joint Conference on Neural Networks (IJCNN), 18-23 July 2022.In daily life, mobile and wearable devices with high computing power, together with anchors deployed in indoor environments, form a common solution for the increasing demands for indoor location-based services. Within the technologies and methods currently in use for indoor localization, the approaches that rely on Bluetooth Low Energy (BLE) anchors, Received Signal Strength (RSS), and lateration are among the most popular, mainly because of their cheap and easy deployment and accessible infrastructure by a variety of devices. Nevertheless, such BLE- and RSS-based indoor positioning systems are prone to inaccuracies, mostly due to signal fluctuations, poor quantity of anchors deployed in the environment, and/or inappropriate anchor distributions, as well as mobile device hardware variability. In this paper, we address these issues by using a collaborative indoor positioning approach, which exploits neighboring devices as additional anchors in an extended positioning network. The collaborating devices’ information (i.e., estimated positions and BLE-RSS) is processed using a multilayer perceptron (MLP) neural network by taking into account the device specificity in order to estimate the relative distances. After this, the lateration is applied to collaboratively estimate the device position. Finally, the stand-alone and collaborative position estimates are combined, providing the final position estimate for each device. The experimental results demonstrate that the proposed collaborative approach outperforms the standalone lateration method in terms of positioning accuracy

A Survey on Wearable Technology: History, State-of-the-Art and Current Challenges

Technology is continually undergoing a constituent development caused by the appearance of billions new interconnected “things” and their entrenchment in our daily lives. One of the underlying versatile technologies, namely wearables, is able to capture rich contextual information produced by such devices and use it to deliver a legitimately personalized experience. The main aim of this paper is to shed light on the history of wearable devices and provide a state-of-the-art review on the wearable market. Moreover, the paper provides an extensive and diverse classification of wearables, based on various factors, a discussion on wireless communication technologies, architectures, data processing aspects, and market status, as well as a variety of other actual information on wearable technology. Finally, the survey highlights the critical challenges and existing/future solutions.publishedVersionPeer reviewe