2023 |
Bravenec, Tomás Exploiting Wireless Communications for Localization: Beyond Fingerprinting PhD Thesis Universitat Jaume I. INIT, 2023. Abstract | Links | BibTeX | Tags: A-wear, data analysis methods, geoprivacy, Indoor positioning, machine learning @phdthesis{Bravenec2023d, title = {Exploiting Wireless Communications for Localization: Beyond Fingerprinting}, author = {Tomás Bravenec}, url = {http://hdl.handle.net/10803/689593}, doi = {http://dx.doi.org/10.6035/14124.2023.868082}, year = {2023}, date = {2023-12-18}, school = {Universitat Jaume I. INIT}, abstract = {The field of Location-based Services (LBS) has experienced significant growth over the past decade, driven by increasing interest in fitness tracking, robotics, and eHealth. This dissertation focuses on evaluating privacy measures in Indoor Positioning Systems (IPS), particularly in the context of ubiquitous Wi-Fi networks. It addresses non-cooperative user tracking through the exploitation of unencrypted Wi-Fi management frames, which contain enough information for device fingerprinting despite MAC address randomization. The research also explores an algorithm to estimate room occupancy based on passive Wi-Fi frame sniffing and Received Signal Strength Indicator (RSSI) measurements. Such room occupancy detection has implications for energy regulations in smart buildings. Furthermore, the thesis investigates methods to reduce computational requirements of machine learning and positioning algorithms through optimizing neural networks and employing interpolation techniques for IPS based on RSSI fingerprinting. The work contributes datasets, analysis scripts, and firmware to improve reproducibility and supports advancements in the LBS field.}, keywords = {A-wear, data analysis methods, geoprivacy, Indoor positioning, machine learning}, pubstate = {published}, tppubtype = {phdthesis} } The field of Location-based Services (LBS) has experienced significant growth over the past decade, driven by increasing interest in fitness tracking, robotics, and eHealth. This dissertation focuses on evaluating privacy measures in Indoor Positioning Systems (IPS), particularly in the context of ubiquitous Wi-Fi networks. It addresses non-cooperative user tracking through the exploitation of unencrypted Wi-Fi management frames, which contain enough information for device fingerprinting despite MAC address randomization. The research also explores an algorithm to estimate room occupancy based on passive Wi-Fi frame sniffing and Received Signal Strength Indicator (RSSI) measurements. Such room occupancy detection has implications for energy regulations in smart buildings. Furthermore, the thesis investigates methods to reduce computational requirements of machine learning and positioning algorithms through optimizing neural networks and employing interpolation techniques for IPS based on RSSI fingerprinting. The work contributes datasets, analysis scripts, and firmware to improve reproducibility and supports advancements in the LBS field. |
Bravenec, Tomás; Gould, Michael; Fryza, Tomas; Torres-Sospedra, Joaquín Influence of Measured Radio Map Interpolation on Indoor Positioning Algorithms Journal Article IEEE Sensors Journal, 17 , pp. 20044-20054, 2023, ISSN: 1530-437X. Abstract | Links | BibTeX | Tags: A-wear, Indoor positioning, radio maps @article{Bravenec2023e, title = {Influence of Measured Radio Map Interpolation on Indoor Positioning Algorithms}, author = {Tomás Bravenec and Michael Gould and Tomas Fryza and Joaquín Torres-Sospedra}, doi = {https://doi.org/10.1109/JSEN.2023.3296752}, issn = {1530-437X}, year = {2023}, date = {2023-08-01}, journal = {IEEE Sensors Journal}, volume = {17}, pages = {20044-20054}, abstract = {Indoor positioning and navigation increasingly have become popular, and there are many different approaches, using different technologies. In nearly all of the approaches, the locational accuracy depends on signal propagation characteristics of the environment. What makes many of these approaches similar is the requirement of creating a signal propagation radio map (RM) by analyzing the environment. As this is usually done on a regular grid, the collection of received signal strength indicator (RSSI) data at every reference point (RP) of an RM is a time-consuming task. With indoor positioning being in the focus of the research community, the reduction in time required for collection of RMs is very useful, as it allows researchers to spend more time with research instead of data collection. In this article, we analyze the options for reducing the time required for the acquisition of RSSI information. We approach this by collecting initial RMs of Wi-Fi signal strength using five ESP32 microcontrollers working in monitoring mode and placed around our office. We then analyze the influence the approximation of RSSI values in unreachable places has, by using linear interpolation and Gaussian process regression (GPR) to find balance among final positioning accuracy, computing complexity, and time requirements for the initial data collection. We conclude that the computational requirements can be significantly lowered, while not affecting the positioning error, by using RM with a single sample per RP generated considering many measurements.}, keywords = {A-wear, Indoor positioning, radio maps}, pubstate = {published}, tppubtype = {article} } Indoor positioning and navigation increasingly have become popular, and there are many different approaches, using different technologies. In nearly all of the approaches, the locational accuracy depends on signal propagation characteristics of the environment. What makes many of these approaches similar is the requirement of creating a signal propagation radio map (RM) by analyzing the environment. As this is usually done on a regular grid, the collection of received signal strength indicator (RSSI) data at every reference point (RP) of an RM is a time-consuming task. With indoor positioning being in the focus of the research community, the reduction in time required for collection of RMs is very useful, as it allows researchers to spend more time with research instead of data collection. In this article, we analyze the options for reducing the time required for the acquisition of RSSI information. We approach this by collecting initial RMs of Wi-Fi signal strength using five ESP32 microcontrollers working in monitoring mode and placed around our office. We then analyze the influence the approximation of RSSI values in unreachable places has, by using linear interpolation and Gaussian process regression (GPR) to find balance among final positioning accuracy, computing complexity, and time requirements for the initial data collection. We conclude that the computational requirements can be significantly lowered, while not affecting the positioning error, by using RM with a single sample per RP generated considering many measurements. |
Casanova-Marqués, Raúl; Torres-Sospedra, Joaquín; Hajny, Jan; Gould, Michael Maximizing privacy and security of collaborative indoor positioning using zero-knowledge proofs Journal Article Internet of Things, 22 , pp. 100801, 2023, ISSN: 2542-6605. Abstract | Links | BibTeX | Tags: A-wear, Bluetooth Low Energy, Indoor positioning, wearables @article{Casanova2023a, title = {Maximizing privacy and security of collaborative indoor positioning using zero-knowledge proofs}, author = {Raúl Casanova-Marqués and Joaquín Torres-Sospedra and Jan Hajny and Michael Gould}, doi = {https://doi.org/10.1016/j.iot.2023.100801}, issn = {2542-6605}, year = {2023}, date = {2023-07-01}, journal = {Internet of Things}, volume = {22}, pages = {100801}, abstract = {The increasing popularity of wearable-based Collaborative Indoor Positioning Systems (CIPSs) has led to the development of new methods for improving positioning accuracy. However, these systems often rely on protocols, such as iBeacon, that lack sufficient privacy protection. In addition, they depend on centralized entities for the authentication and verification processes. To address the limitations of existing protocols, this paper presents a groundbreaking contribution to the field of wearable-based CIPSs. We propose a decentralized Attribute-based Authentication (ABA) protocol that offers superior levels of privacy protection, untraceability, and unlinkability of user actions. Unlike existing protocols that rely on centralized entities, our approach leverages decentralized mechanisms for authentication and verification, ensuring the privacy of user location data exchange. Through extensive experimentation across multiple platforms, our results demonstrate the practicality and feasibility of the proposed protocol for real-world deployment. Overall, this work opens up new avenues for secure and privacy-preserving wearable-based CIPSs, with potential implications for the rapidly growing field of Internet of Things (IoT) applications.}, keywords = {A-wear, Bluetooth Low Energy, Indoor positioning, wearables}, pubstate = {published}, tppubtype = {article} } The increasing popularity of wearable-based Collaborative Indoor Positioning Systems (CIPSs) has led to the development of new methods for improving positioning accuracy. However, these systems often rely on protocols, such as iBeacon, that lack sufficient privacy protection. In addition, they depend on centralized entities for the authentication and verification processes. To address the limitations of existing protocols, this paper presents a groundbreaking contribution to the field of wearable-based CIPSs. We propose a decentralized Attribute-based Authentication (ABA) protocol that offers superior levels of privacy protection, untraceability, and unlinkability of user actions. Unlike existing protocols that rely on centralized entities, our approach leverages decentralized mechanisms for authentication and verification, ensuring the privacy of user location data exchange. Through extensive experimentation across multiple platforms, our results demonstrate the practicality and feasibility of the proposed protocol for real-world deployment. Overall, this work opens up new avenues for secure and privacy-preserving wearable-based CIPSs, with potential implications for the rapidly growing field of Internet of Things (IoT) applications. |
Pascacio-de-los-Santos, Pavel Collaborative Techniques for Indoor Positioning Systems PhD Thesis Universitat Jaume I. INIT, 2023, ISBN: 978-952-03-2905-1. Abstract | Links | BibTeX | Tags: A-wear, Bluetooth Low Energy, Indoor positioning, machine learning, Wi-Fi fingerprint @phdthesis{Pascacio2023a, title = {Collaborative Techniques for Indoor Positioning Systems}, author = {Pavel Pascacio-de-los-Santos}, url = {http://hdl.handle.net/10803/688489}, doi = {http://dx.doi.org/10.6035/14124.2023.821144}, isbn = {978-952-03-2905-1}, year = {2023}, date = {2023-06-09}, school = {Universitat Jaume I. INIT}, abstract = {This 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.}, keywords = {A-wear, Bluetooth Low Energy, Indoor positioning, machine learning, Wi-Fi fingerprint}, pubstate = {published}, tppubtype = {phdthesis} } This 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. |
Klus, Lucie From Compression of Wearable-based Data to Effortless Indoor Positioning PhD Thesis Tampere University. Faculty of Information Technology and Communication Sciences, 2023, ISBN: 978-952-03-2832-0. Abstract | Links | BibTeX | Tags: A-wear, Indoor positioning, machine learning, wearables @phdthesis{Klus2023a, title = {From Compression of Wearable-based Data to Effortless Indoor Positioning}, author = {Lucie Klus}, url = {http://hdl.handle.net/10803/688947}, doi = {http://dx.doi.org/10.6035/14124.2023.45900046}, isbn = {978-952-03-2832-0}, year = {2023}, date = {2023-04-27}, school = {Tampere University. Faculty of Information Technology and Communication Sciences}, abstract = {In recent years, wearable devices have become ever-present in modern society. They are typically defined as small, battery-restricted devices, worn on, in, or in very close proximity to a human body. Their performance is defined by their functionalities as much as by their comfortability and convenience. As such, they need to be compact yet powerful, thus making energy efficiency an extremely important and relevant aspect of the system. The market of wearable devices is nowadays dominated by smartwatches and fitness bands, which are capable of gathering numerous sensorbased data such as temperature, pressure, heart rate, or blood oxygen level, which have to be processed in real-time, stored, or wirelessly transferred while consuming as little energy as possible to ensure long battery life. Implementing compression schemes directly at the wearable device is one of the relevant methods to reduce the volume of data and to minimize the number of required operations while processing them, as raw measurements include plenty of redundancies that can be removed without damaging the useful information itself.}, keywords = {A-wear, Indoor positioning, machine learning, wearables}, pubstate = {published}, tppubtype = {phdthesis} } In recent years, wearable devices have become ever-present in modern society. They are typically defined as small, battery-restricted devices, worn on, in, or in very close proximity to a human body. Their performance is defined by their functionalities as much as by their comfortability and convenience. As such, they need to be compact yet powerful, thus making energy efficiency an extremely important and relevant aspect of the system. The market of wearable devices is nowadays dominated by smartwatches and fitness bands, which are capable of gathering numerous sensorbased data such as temperature, pressure, heart rate, or blood oxygen level, which have to be processed in real-time, stored, or wirelessly transferred while consuming as little energy as possible to ensure long battery life. Implementing compression schemes directly at the wearable device is one of the relevant methods to reduce the volume of data and to minimize the number of required operations while processing them, as raw measurements include plenty of redundancies that can be removed without damaging the useful information itself. |
Chukhno, Nadezhda Direct Communication radio interface for new radio multicasting and cooperative positioning PhD Thesis Università Reggio Calabria, 2023. Abstract | Links | BibTeX | Tags: 5G, A-wear, Indoor positioning @phdthesis{Chukhno2023d, title = {Direct Communication radio interface for new radio multicasting and cooperative positioning}, author = {Nadezhda Chukhno}, url = {https://hdl.handle.net/20.500.12318/136586}, year = {2023}, date = {2023-04-03}, address = {Reggio Calabria}, school = {Università Reggio Calabria}, abstract = {Recently, the popularity of Millimeter Wave (mmWave) wireless networks has increased due to their capability to cope with the escalation of mobile data demands caused by the unprecedented proliferation of smart devices in the fifth-generation (5G). Extremely high frequency or mmWave band is a fundamental pillar in the provision of the expected gigabit data rates. Hence, according to both academic and industrial communities, mmWave technology, e.g., 5G New Radio (NR) and WiGig (60 GHz), is considered as one of the main components of 5G and beyond networks. Particularly, the 3rd Generation Partnership Project (3GPP) provides for the use of licensed mmWave sub-bands for the 5G mmWave cellular networks, whereas IEEE actively explores the unlicensed band at 60 GHz for the next-generation wireless local area networks. In this regard, mmWave has been envisaged as a new technology layout for real-time heavy-traffic and wearable applications. This very work is devoted to solving the problem of mmWave band communication system while enhancing its vantages through utilizing the direct communication radio interface for NR multicasting, cooperative positioning, and mission-critical applications. The main contributions presented in this work include: (i) a set of mathematical frameworks and simulation tools to characterize multicast traffic delivery in mmWave directional systems; (ii) sidelink relaying concept exploitation to deal with the channel condition deterioration of dynamic multicast systems and to ensure mission-critical and ultra-reliable low-latency communications; (iii) cooperative positioning techniques analysis for enhancing cellular positioning accuracy for 5G+ emerging applications that require not only improved communication characteristics but also precise localization. Our study indicates the need for additional mechanisms/research that can be utilized: (i) to further improve multicasting performance in 5G/6G systems; (ii) to investigate sidelink aspects, including, but not limited to, standardization perspective and the next relay selection strategies; and (iii) to design cooperative positioning systems based on Device-to-Device (D2D) technology.}, keywords = {5G, A-wear, Indoor positioning}, pubstate = {published}, tppubtype = {phdthesis} } Recently, the popularity of Millimeter Wave (mmWave) wireless networks has increased due to their capability to cope with the escalation of mobile data demands caused by the unprecedented proliferation of smart devices in the fifth-generation (5G). Extremely high frequency or mmWave band is a fundamental pillar in the provision of the expected gigabit data rates. Hence, according to both academic and industrial communities, mmWave technology, e.g., 5G New Radio (NR) and WiGig (60 GHz), is considered as one of the main components of 5G and beyond networks. Particularly, the 3rd Generation Partnership Project (3GPP) provides for the use of licensed mmWave sub-bands for the 5G mmWave cellular networks, whereas IEEE actively explores the unlicensed band at 60 GHz for the next-generation wireless local area networks. In this regard, mmWave has been envisaged as a new technology layout for real-time heavy-traffic and wearable applications. This very work is devoted to solving the problem of mmWave band communication system while enhancing its vantages through utilizing the direct communication radio interface for NR multicasting, cooperative positioning, and mission-critical applications. The main contributions presented in this work include: (i) a set of mathematical frameworks and simulation tools to characterize multicast traffic delivery in mmWave directional systems; (ii) sidelink relaying concept exploitation to deal with the channel condition deterioration of dynamic multicast systems and to ensure mission-critical and ultra-reliable low-latency communications; (iii) cooperative positioning techniques analysis for enhancing cellular positioning accuracy for 5G+ emerging applications that require not only improved communication characteristics but also precise localization. Our study indicates the need for additional mechanisms/research that can be utilized: (i) to further improve multicasting performance in 5G/6G systems; (ii) to investigate sidelink aspects, including, but not limited to, standardization perspective and the next relay selection strategies; and (iii) to design cooperative positioning systems based on Device-to-Device (D2D) technology. |
Quezada-Gaibor, Darwin Cloud-based Indoor Positioning Platform for Context-adaptivity in GNSS-denied Scenarios PhD Thesis Universitat Jaume I. INIT, 2023. Abstract | Links | BibTeX | Tags: A-wear, Cloud computing, Indoor positioning, machine learning, Wi-Fi fingerprint @phdthesis{Quezada2023a, title = {Cloud-based Indoor Positioning Platform for Context-adaptivity in GNSS-denied Scenarios}, author = {Darwin Quezada-Gaibor}, doi = {http://dx.doi.org/10.6035/14124.2023.821275}, year = {2023}, date = {2023-03-31}, school = {Universitat Jaume I. INIT}, abstract = {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 agriculture, robotics, and eHealth. Depending on the field of application, these services must accomplish high levels of accuracy, flexibility, and integrability. This dissertation focuses on improving computing efficiency, data pre-processing, and software architecture for indoor positioning solutions without leaving aside position and location accuracy. The dissertation begins by presenting a systematic review of current cloud-based indoor positioning solutions. Secondly, we focus on the study of data optimisation techniques such as data cleansing and data augmentation. The third contribution suggests two algorithms to group similar fingerprints into clusters. The fourth contribution explores the use of Machine Learning (ML) models to enhance position estimation accuracy. Finally, this dissertation summarises the key findings in an open-source cloud platform for indoor positioning.}, keywords = {A-wear, Cloud computing, Indoor positioning, machine learning, Wi-Fi fingerprint}, pubstate = {published}, tppubtype = {phdthesis} } 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 agriculture, robotics, and eHealth. Depending on the field of application, these services must accomplish high levels of accuracy, flexibility, and integrability. This dissertation focuses on improving computing efficiency, data pre-processing, and software architecture for indoor positioning solutions without leaving aside position and location accuracy. The dissertation begins by presenting a systematic review of current cloud-based indoor positioning solutions. Secondly, we focus on the study of data optimisation techniques such as data cleansing and data augmentation. The third contribution suggests two algorithms to group similar fingerprints into clusters. The fourth contribution explores the use of Machine Learning (ML) models to enhance position estimation accuracy. Finally, this dissertation summarises the key findings in an open-source cloud platform for indoor positioning. |
2022 |
Pascacio-de-los-Santos, Pavel; Torres-Sospedra, Joaquín; Casteleyn, Sven; Lohan, Elena Simona A Collaborative Approach Using Neural Networks for BLE-RSS Lateration-Based Indoor Positioning Inproceedings 2022 International Joint Conference on Neural Networks (IJCNN), pp. 1-9, IEEE, 2022, ISBN: 978-1-7281-8671-9. Abstract | Links | BibTeX | Tags: Bluetooth Low Energy, Indoor positioning, machine learning @inproceedings{Pascacio2022b, title = {A Collaborative Approach Using Neural Networks for BLE-RSS Lateration-Based Indoor Positioning}, author = {Pavel Pascacio-de-los-Santos and Joaquín Torres-Sospedra and Sven Casteleyn and Elena Simona Lohan}, doi = {https://doi.org/10.1109/IJCNN55064.2022.9892484}, isbn = {978-1-7281-8671-9}, year = {2022}, date = {2022-09-30}, booktitle = {2022 International Joint Conference on Neural Networks (IJCNN)}, pages = {1-9}, publisher = {IEEE}, abstract = {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.}, keywords = {Bluetooth Low Energy, Indoor positioning, machine learning}, pubstate = {published}, tppubtype = {inproceedings} } 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. |
Quezada-Gaibor, Darwin; Torres-Sospedra, Joaquín; Nurmi, Jari; Koucheryavy, Yevgeni; Huerta-Guijarro, Joaquín 2022 IEEE 12th International Conference on Indoor Positioning and Indoor Navigation (IPIN), IEEE, 2022, ISBN: 978-1-7281-6218-8. Abstract | Links | BibTeX | Tags: deep learning, Indoor positioning, machine learning @inproceedings{Quezada2022d, title = {SURIMI: Supervised Radio Map Augmentation with Deep Learning and a Generative Adversarial Network for Fingerprint-based Indoor Positioning}, author = {Darwin Quezada-Gaibor and Joaquín Torres-Sospedra and Jari Nurmi and Yevgeni Koucheryavy and Joaquín Huerta-Guijarro}, doi = {10.1109/IPIN54987.2022.9918146}, isbn = {978-1-7281-6218-8}, year = {2022}, date = {2022-09-06}, booktitle = {2022 IEEE 12th International Conference on Indoor Positioning and Indoor Navigation (IPIN)}, number = {1-8}, publisher = {IEEE}, abstract = {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.}, keywords = {deep learning, Indoor positioning, machine learning}, pubstate = {published}, tppubtype = {inproceedings} } 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. |
Silva, Ivo; Pendão, Cristiano; Torres-Sospedra, Joaquín; Moreira, Adriano TrackInFactory: A Tight Coupling Particle Filter for Industrial Vehicle Tracking in Indoor Environments Journal Article IEEE Transactions on Systems, Man, and Cybernetics: Systems, 52 (7), pp. 4151 - 4162, 2022, ISSN: 2168-2232. Abstract | Links | BibTeX | Tags: Indoor positioning, Industry 4.0, sensor fusion, Wi-Fi fingerprint @article{Silva2022ab, title = {TrackInFactory: A Tight Coupling Particle Filter for Industrial Vehicle Tracking in Indoor Environments}, author = {Ivo Silva and Cristiano Pendão and Joaquín Torres-Sospedra and Adriano Moreira}, doi = {https://doi.org/10.1109/TSMC.2021.3091987}, issn = {2168-2232}, year = {2022}, date = {2022-07-06}, journal = {IEEE Transactions on Systems, Man, and Cybernetics: Systems}, volume = {52}, number = {7}, pages = {4151 - 4162}, abstract = {Localization and tracking of industrial vehicles have a key role in increasing productivity and improving the logistics processes of factories. Due to the demanding requirements of industrial vehicle tracking and navigation, existing systems explore technologies, such as LiDAR or ultra wide-band to achieve low positioning errors. In this article we propose TrackInFactory, a system that combines Wi-Fi with motion sensors, achieving submeter accuracy and a low maximum error. A tight coupling approach is explored in sensor fusion with a particle filter (PF). Information regarding the vehicle’s initial position and heading is not required. This approach uses the similarity of Wi-Fi samples to update the particles’ weights as they move according to motion sensor data. The PF dynamically adjusts its parameters based on a metric for estimating the confidence in position estimates, allowing to improve positioning performance. A series of simulations were performed to tune the PF. Then the approach was validated in real-world experiments with an industrial tow tractor, achieving a mean error of 0.81 m. In comparison to a loose coupling approach, this method reduced the maximum error by more than 60% and improved the overall mean error by more than 20%.}, keywords = {Indoor positioning, Industry 4.0, sensor fusion, Wi-Fi fingerprint}, pubstate = {published}, tppubtype = {article} } Localization and tracking of industrial vehicles have a key role in increasing productivity and improving the logistics processes of factories. Due to the demanding requirements of industrial vehicle tracking and navigation, existing systems explore technologies, such as LiDAR or ultra wide-band to achieve low positioning errors. In this article we propose TrackInFactory, a system that combines Wi-Fi with motion sensors, achieving submeter accuracy and a low maximum error. A tight coupling approach is explored in sensor fusion with a particle filter (PF). Information regarding the vehicle’s initial position and heading is not required. This approach uses the similarity of Wi-Fi samples to update the particles’ weights as they move according to motion sensor data. The PF dynamically adjusts its parameters based on a metric for estimating the confidence in position estimates, allowing to improve positioning performance. A series of simulations were performed to tune the PF. Then the approach was validated in real-world experiments with an industrial tow tractor, achieving a mean error of 0.81 m. In comparison to a loose coupling approach, this method reduced the maximum error by more than 60% and improved the overall mean error by more than 20%. |
Quezada-Gaibor, Darwin; Torres-Sospedra, Joaquín; Nurmi, Jari; Koucheryavy, Yevgeni; Huerta-Guijarro, Joaquín Lightweight Hybrid CNN-ELM Model for Multi-building and Multi-floor Classification Inproceedings 2022 International Conference on Localization and GNSS (ICL-GNSS), pp. 1-6, IEEE, 2022. Abstract | Links | BibTeX | Tags: Indoor positioning, machine learning @inproceedings{Quezada2022b, title = {Lightweight Hybrid CNN-ELM Model for Multi-building and Multi-floor Classification}, author = {Darwin Quezada-Gaibor and Joaquín Torres-Sospedra and Jari Nurmi and Yevgeni Koucheryavy and Joaquín Huerta-Guijarro}, doi = {https://doi.org/10.1109/ICL-GNSS54081.2022.9797021}, year = {2022}, date = {2022-06-19}, booktitle = {2022 International Conference on Localization and GNSS (ICL-GNSS)}, pages = {1-6}, publisher = {IEEE}, abstract = {Machine learning models have become an essential tool in current indoor positioning solutions, given their high capa-bilities to extract meaningful information from the environment. Convolutional neural networks (CNNs) are one of the most used neural networks (NNs) due to that they are capable of learning complex patterns from the input data. Another model used in indoor positioning solutions is the Extreme Learning Machine (ELM), which provides an acceptable generalization performance as well as a fast speed of learning. In this paper, we offer a lightweight combination of CNN and ELM, which provides a quick and accurate classification of building and floor, suitable for power and resource-constrained devices. As a result, the proposed model is 58% faster than the benchmark, with a slight improvement in the classification accuracy (by less than 1 %).}, keywords = {Indoor positioning, machine learning}, pubstate = {published}, tppubtype = {inproceedings} } Machine learning models have become an essential tool in current indoor positioning solutions, given their high capa-bilities to extract meaningful information from the environment. Convolutional neural networks (CNNs) are one of the most used neural networks (NNs) due to that they are capable of learning complex patterns from the input data. Another model used in indoor positioning solutions is the Extreme Learning Machine (ELM), which provides an acceptable generalization performance as well as a fast speed of learning. In this paper, we offer a lightweight combination of CNN and ELM, which provides a quick and accurate classification of building and floor, suitable for power and resource-constrained devices. As a result, the proposed model is 58% faster than the benchmark, with a slight improvement in the classification accuracy (by less than 1 %). |
Quezada-Gaibor, Darwin; Klus, Lucie; Torres-Sospedra, Joaquín; Lohan, Elena Simona; Nurmi, Jari; Granell-Canut, Carlos; Huerta-Guijarro, Joaquín Data Cleansing for Indoor Positioning Wi-Fi Fingerprinting Datasets Inproceedings 2022 23rd IEEE International Conference on Mobile Data Management (MDM), pp. 349-354, IEEE, 2022, ISBN: 978-1-6654-5176-5. Abstract | Links | BibTeX | Tags: Data science, Indoor positioning, Wi-Fi fingerprint @inproceedings{Quezada2022c, title = {Data Cleansing for Indoor Positioning Wi-Fi Fingerprinting Datasets}, author = {Darwin Quezada-Gaibor and Lucie Klus and Joaquín Torres-Sospedra and Elena Simona Lohan and Jari Nurmi and Carlos Granell-Canut and Joaquín Huerta-Guijarro}, doi = {https://doi.org/10.1109/MDM55031.2022.00079}, isbn = {978-1-6654-5176-5}, year = {2022}, date = {2022-06-10}, booktitle = {2022 23rd IEEE International Conference on Mobile Data Management (MDM)}, pages = {349-354}, publisher = {IEEE}, abstract = {Wearable and IoT devices requiring positioning and localisation services grow in number exponentially every year. This rapid growth also produces millions of data entries that need to be pre-processed prior to being used in any indoor positioning system to ensure the data quality and provide a high Quality of Service (QoS) to the end-user. In this paper, we offer a novel and straightforward data cleansing algorithm for WLAN fingerprinting radio maps. This algorithm is based on the correlation among fingerprints using the Received Signal Strength (RSS) values and the Access Points (APs)'s identifier. We use those to compute the correlation among all samples in the dataset and remove fingerprints with low level of correlation from the dataset. We evaluated the proposed method on 14 independent publicly-available datasets. As a result, an average of 14% of fingerprints were removed from the datasets. The 2D positioning error was reduced by 2.7% and 3D positioning error by 5.3% with a slight increase in the floor hit rate by 1.2% on average. Consequently, the average speed of position prediction was also increased by 14%.}, keywords = {Data science, Indoor positioning, Wi-Fi fingerprint}, pubstate = {published}, tppubtype = {inproceedings} } Wearable and IoT devices requiring positioning and localisation services grow in number exponentially every year. This rapid growth also produces millions of data entries that need to be pre-processed prior to being used in any indoor positioning system to ensure the data quality and provide a high Quality of Service (QoS) to the end-user. In this paper, we offer a novel and straightforward data cleansing algorithm for WLAN fingerprinting radio maps. This algorithm is based on the correlation among fingerprints using the Received Signal Strength (RSS) values and the Access Points (APs)'s identifier. We use those to compute the correlation among all samples in the dataset and remove fingerprints with low level of correlation from the dataset. We evaluated the proposed method on 14 independent publicly-available datasets. As a result, an average of 14% of fingerprints were removed from the datasets. The 2D positioning error was reduced by 2.7% and 3D positioning error by 5.3% with a slight increase in the floor hit rate by 1.2% on average. Consequently, the average speed of position prediction was also increased by 14%. |
Pascacio-de-los-Santos, Pavel; Torres-Sospedra, Joaquín; Jiménez, Antonio R; Casteleyn, Sven Mobile device-based Bluetooth Low Energy Database for range estimation in indoor environments Journal Article Scientific Data, 9 (281), 2022, ISSN: 2052-4463. Abstract | Links | BibTeX | Tags: Bluetooth Low Energy, Indoor positioning @article{Pascacio2022a, title = {Mobile device-based Bluetooth Low Energy Database for range estimation in indoor environments}, author = {Pavel Pascacio-de-los-Santos and Joaquín Torres-Sospedra and Antonio R Jiménez and Sven Casteleyn }, doi = {https://doi.org/10.1038/s41597-022-01406-2}, issn = {2052-4463}, year = {2022}, date = {2022-06-08}, journal = {Scientific Data}, volume = {9}, number = {281}, abstract = {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.}, keywords = {Bluetooth Low Energy, Indoor positioning}, pubstate = {published}, tppubtype = {article} } 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. |
Potorti, Francesco; Torres-Sospedra, Joaquín; Quezada-Gaibor, Darwin; Jiménez, Antonio Ramón; Seco, Fernando; Pérez-Navarro, Antoni; Ortiz, Miguel; Zhu, Ni; Renaudin, Valerie; Ichikari, Ryosuke; Shimomura, Ryo; Ohta, Nozomu; Nagae, Satsuki; Kurata, Takeshi; Wei, Dongyan; Ji, Xinchun; Zhang, Wenchao; Kram, Sebastian; Stahlke, Maximilian; Mutschler, Christopher; Crivello, Antonino; Barsocchi, Paolo; Girolami, Michele; Palumbo, Filippo; Chen, Ruizhi; Wu, Yuan; Li, Wei; Yu, Yue; Xu, Shihao; Huang, Lixiong; Liu, Tao; Kuang, Jian; Niu, Xiaoji; Yoshida, Takuto; Nagata, Yoshiteru; Fukushima, Yuto; Fukatani, Nobuya; Hayashida, Nozomi; Asai, Yusuke; Urano, Kenta; Ge, Wenfei; Lee, Nien-Ting; Fang, Shih-Hau; Jie, You-Cheng; Young, Shawn-Rong; Chien, Ying-Ren; Yu, Chih-Chieh; Ma, Chengqi; Wu, Bang; Zhang, Wei; Wang, Yankun; Fan, Yonglei; Poslad, Stefan; Selviah, David R; Wang, Weixi; Yuan, Hong; Yonamoto, Yoshitomo; Yamaguchi, Masahiro; Kaichi, Tomoya; Zhou, Baoding; Liu, Xu; Gu, Zhining; Yang, Chengjing; Wu, Zhiqian; Xie, Doudou; Huang, Can; Zheng, Lingxiang; Peng, Ao; Jin, Ge; Wang, Qu; Xiong, Haiyong Luo Hao; Bao, Linfeng; Zhang, Pushuo; Zhao, Fang; Yuj, Chia-An; Hung, Chun-Hao; Antsfeld, Leonid; Chidlovskii, Boris; Jiang, Haitao; Xia, Ming; Yan, Dayu; Li, Yuhang; Dong, Yitong; Silva, Ivo; Pendão, Cristiano; Meneses, Filipe; Nicolau, Maria João; Costa, António; Moreira, Adriano; Cock, Cedric De; Plets, David; Opiela, Miroslav; Dzama, Jakub; Zhang, Liqiang; Li, Hu; Chen, Boxuan; Liu, Yu; Yean, Seanglidet; Lim, Bo Zhi; Teo, Wei Jie; Leep, Bu Sung; Oh, Hong Lye Off-Line Evaluation of Indoor Positioning Systems in Different Scenarios: The Experiences From IPIN 2020 Competition Journal Article IEEE Sensors Journal, 22 (6), pp. 5011-5054, 2022, ISSN: 1558-1748. Abstract | Links | BibTeX | Tags: Indoor positioning @article{Potorsi2022a, title = {Off-Line Evaluation of Indoor Positioning Systems in Different Scenarios: The Experiences From IPIN 2020 Competition}, author = {Francesco Potorti and Joaquín Torres-Sospedra and Darwin Quezada-Gaibor and Antonio Ramón Jiménez and Fernando Seco and Antoni Pérez-Navarro and Miguel Ortiz and Ni Zhu and Valerie Renaudin and Ryosuke Ichikari and Ryo Shimomura and Nozomu Ohta and Satsuki Nagae and Takeshi Kurata and Dongyan Wei and Xinchun Ji and Wenchao Zhang and Sebastian Kram and Maximilian Stahlke and Christopher Mutschler and Antonino Crivello and Paolo Barsocchi and Michele Girolami and Filippo Palumbo and Ruizhi Chen and Yuan Wu and Wei Li and Yue Yu and Shihao Xu and Lixiong Huang and Tao Liu and Jian Kuang and Xiaoji Niu and Takuto Yoshida and Yoshiteru Nagata and Yuto Fukushima and Nobuya Fukatani and Nozomi Hayashida and Yusuke Asai and Kenta Urano and Wenfei Ge and Nien-Ting Lee and Shih-Hau Fang and You-Cheng Jie and Shawn-Rong Young and Ying-Ren Chien and Chih-Chieh Yu and Chengqi Ma and Bang Wu and Wei Zhang and Yankun Wang and Yonglei Fan and Stefan Poslad and David R. Selviah and Weixi Wang and Hong Yuan and Yoshitomo Yonamoto and Masahiro Yamaguchi and Tomoya Kaichi and Baoding Zhou and Xu Liu and Zhining Gu and Chengjing Yang and Zhiqian Wu and Doudou Xie and Can Huang and Lingxiang Zheng and Ao Peng and Ge Jin and Qu Wang and Haiyong Luo Hao Xiong and Linfeng Bao and Pushuo Zhang and Fang Zhao and Chia-An Yuj and Chun-Hao Hung and Leonid Antsfeld and Boris Chidlovskii and Haitao Jiang and Ming Xia and Dayu Yan and Yuhang Li and Yitong Dong and Ivo Silva and Cristiano Pendão and Filipe Meneses and Maria João Nicolau and António Costa and Adriano Moreira and Cedric De Cock and David Plets and Miroslav Opiela and Jakub Dzama and Liqiang Zhang and Hu Li and Boxuan Chen and Yu Liu and Seanglidet Yean and Bo Zhi Lim and Wei Jie Teo and Bu Sung Leep and Hong Lye Oh}, doi = {https://doi.org/10.1109/JSEN.2021.3083149}, issn = {1558-1748}, year = {2022}, date = {2022-03-22}, journal = {IEEE Sensors Journal}, volume = {22}, number = {6}, pages = {5011-5054}, abstract = {Every year, for ten years now, the IPIN competition has aimed at evaluating real-world indoor localisation systems by testing them in a realistic environment, with realistic movement, using the EvAAL framework. The competition provided a unique overview of the state-of-the-art of systems, technologies, and methods for indoor positioning and navigation purposes. Through fair comparison of the performance achieved by each system, the competition was able to identify the most promising approaches and to pinpoint the most critical working conditions. In 2020, the competition included 5 diverse off-site off-site Tracks, each resembling real use cases and challenges for indoor positioning. The results in terms of participation and accuracy of the proposed systems have been encouraging. The best performing competitors obtained a third quartile of error of 1 m for the Smartphone Track and 0.5 m for the Foot-mounted IMU Track. While not running on physical systems, but only as algorithms, these results represent impressive achievements.}, keywords = {Indoor positioning}, pubstate = {published}, tppubtype = {article} } Every year, for ten years now, the IPIN competition has aimed at evaluating real-world indoor localisation systems by testing them in a realistic environment, with realistic movement, using the EvAAL framework. The competition provided a unique overview of the state-of-the-art of systems, technologies, and methods for indoor positioning and navigation purposes. Through fair comparison of the performance achieved by each system, the competition was able to identify the most promising approaches and to pinpoint the most critical working conditions. In 2020, the competition included 5 diverse off-site off-site Tracks, each resembling real use cases and challenges for indoor positioning. The results in terms of participation and accuracy of the proposed systems have been encouraging. The best performing competitors obtained a third quartile of error of 1 m for the Smartphone Track and 0.5 m for the Foot-mounted IMU Track. While not running on physical systems, but only as algorithms, these results represent impressive achievements. |
Quezada-Gaibor, Darwin; Torres-Sospedra, Joaquín; Nurmi, Jari; Koucheryavy, Yevgeni; Huerta-Guijarro, Joaquín Cloud Platforms for Context-Adaptive Positioning and Localisation in GNSS-Denied Scenarios—A Systematic Review Journal Article Sensors, 22 (1), pp. 110, 2022, ISSN: 1424-8220. Abstract | Links | BibTeX | Tags: Cloud computing, Indoor positioning @article{Quezada2022a, title = {Cloud Platforms for Context-Adaptive Positioning and Localisation in GNSS-Denied Scenarios—A Systematic Review}, author = {Darwin Quezada-Gaibor and Joaquín Torres-Sospedra and Jari Nurmi and Yevgeni Koucheryavy and Joaquín Huerta-Guijarro}, doi = {https://doi.org/10.3390/s22010110}, issn = {1424-8220}, year = {2022}, date = {2022-01-15}, journal = {Sensors}, volume = {22}, number = {1}, pages = {110}, abstract = {Cloud Computing and Cloud Platforms have become an essential resource for businesses, due to their advanced capabilities, performance, and functionalities. Data redundancy, scalability, and security, are among the key features offered by cloud platforms. Location-Based Services (LBS) often exploit cloud platforms to host positioning and localisation systems. This paper introduces a systematic review of current positioning platforms for GNSS-denied scenarios. We have undertaken a comprehensive analysis of each component of the positioning and localisation systems, including techniques, protocols, standards, and cloud services used in the state-of-the-art deployments. Furthermore, this paper identifies the limitations of existing solutions, outlining shortcomings in areas that are rarely subjected to scrutiny in existing reviews of indoor positioning, such as computing paradigms, privacy, and fault tolerance. We then examine contributions in the areas of efficient computation, interoperability, positioning, and localisation. Finally, we provide a brief discussion concerning the challenges for cloud platforms based on GNSS-denied scenarios.}, keywords = {Cloud computing, Indoor positioning}, pubstate = {published}, tppubtype = {article} } Cloud Computing and Cloud Platforms have become an essential resource for businesses, due to their advanced capabilities, performance, and functionalities. Data redundancy, scalability, and security, are among the key features offered by cloud platforms. Location-Based Services (LBS) often exploit cloud platforms to host positioning and localisation systems. This paper introduces a systematic review of current positioning platforms for GNSS-denied scenarios. We have undertaken a comprehensive analysis of each component of the positioning and localisation systems, including techniques, protocols, standards, and cloud services used in the state-of-the-art deployments. Furthermore, this paper identifies the limitations of existing solutions, outlining shortcomings in areas that are rarely subjected to scrutiny in existing reviews of indoor positioning, such as computing paradigms, privacy, and fault tolerance. We then examine contributions in the areas of efficient computation, interoperability, positioning, and localisation. Finally, we provide a brief discussion concerning the challenges for cloud platforms based on GNSS-denied scenarios. |
2021 |
Martín, Ana Jiménez; Gordo, Ismael Miranda; Domínguez, Juan Jesús García; Torres-Sospedra, Joaquín; Plaza, Sergio Lluva; Gómez, David Gualda Affinity Propagation Clustering for Older Adults Daily Routine Estimation Inproceedings Proceedings of the Eleventh International Conference on Indoor Positioning and Indoor Navigation, IEEE, 2021. Abstract | Links | BibTeX | Tags: Indoor positioning @inproceedings{Jimenez2021a, title = {Affinity Propagation Clustering for Older Adults Daily Routine Estimation}, author = {Ana Jiménez Martín and Ismael Miranda Gordo and Juan Jesús García Domínguez and Joaquín Torres-Sospedra and Sergio Lluva Plaza and David Gualda Gómez}, doi = {https://doi.org/10.1109/IPIN51156.2021.9662579}, year = {2021}, date = {2021-12-15}, booktitle = {Proceedings of the Eleventh International Conference on Indoor Positioning and Indoor Navigation}, publisher = {IEEE}, abstract = {This work proposes a system that allows estimating and monitoring daily routine changes in a sensorized home through Machine Learning and Affinity Propagation clustering techniques. Older adults often have low-activity and rather routine lives, which means that these routines can be an indicator of their physical and cognitive state in order to lead an independent life and healthy ageing. Therefore, it is important to be able to generate precise routines, as well as to monitor them, to trigger alarms in case of significant variations. This proposal defines routines based on the time spent in each of the monitored rooms. The daily time in each room is estimated trough a Bluetooth Low Energy-based indoor localization system. The localization is obtained through the Bluetooth received signal strength, which is processed with different supervised algorithms and fused with the acceleration measured by the mobile receiver, obtaining an accuracy above 96 %. From these data, the sample has been synthetically expanded to generate four different routines, on which the proposed algorithm based on Principal Component Analysis and Affinity Propagation clustering has been tested, obtaining very promising results.}, keywords = {Indoor positioning}, pubstate = {published}, tppubtype = {inproceedings} } This work proposes a system that allows estimating and monitoring daily routine changes in a sensorized home through Machine Learning and Affinity Propagation clustering techniques. Older adults often have low-activity and rather routine lives, which means that these routines can be an indicator of their physical and cognitive state in order to lead an independent life and healthy ageing. Therefore, it is important to be able to generate precise routines, as well as to monitor them, to trigger alarms in case of significant variations. This proposal defines routines based on the time spent in each of the monitored rooms. The daily time in each room is estimated trough a Bluetooth Low Energy-based indoor localization system. The localization is obtained through the Bluetooth received signal strength, which is processed with different supervised algorithms and fused with the acceleration measured by the mobile receiver, obtaining an accuracy above 96 %. From these data, the sample has been synthetically expanded to generate four different routines, on which the proposed algorithm based on Principal Component Analysis and Affinity Propagation clustering has been tested, obtaining very promising results. |
Pendão, Cristiano; Silva, Ivo; Moreira, Adriano; Torres-Sospedra, Joaquín Dioptra – A Data Generation Application for Indoor Positioning Systems Inproceedings Proceedings of the Eleventh International Conference on Indoor Positioning and Indoor Navigation, IEEE, 2021. Abstract | Links | BibTeX | Tags: Indoor positioning @inproceedings{Pendao2021a, title = {Dioptra – A Data Generation Application for Indoor Positioning Systems}, author = {Cristiano Pendão and Ivo Silva and Adriano Moreira and Joaquín Torres-Sospedra}, doi = {https://doi.org/10.1109/IPIN51156.2021.9662585}, year = {2021}, date = {2021-12-15}, booktitle = {Proceedings of the Eleventh International Conference on Indoor Positioning and Indoor Navigation}, publisher = {IEEE}, abstract = {Indoor Positioning Systems (IPSs) based on different approaches and technologies have been proposed to support localization and navigation applications in indoor environments. The fair benchmarking and comparison of these IPSs is a difficult task since each IPS is usually evaluated in very specific and controlled conditions and using private data sets, not allowing reproducibility and direct comparison between the reported results and other competing solutions. In addition, testing and evaluating an IPS in the real world is difficult and time-consuming, especially when considering evaluation in multiple environments and conditions. To enhance IPS assessment, we propose Dioptra, an open access and user-friendly application to support research, development and evaluation of IPSs through simulation. To the best of our knowledge, Dioptra is the first application specially developed to generate synthetic datasets to promote reproducibility and fair benchmarking between IPSs.}, keywords = {Indoor positioning}, pubstate = {published}, tppubtype = {inproceedings} } Indoor Positioning Systems (IPSs) based on different approaches and technologies have been proposed to support localization and navigation applications in indoor environments. The fair benchmarking and comparison of these IPSs is a difficult task since each IPS is usually evaluated in very specific and controlled conditions and using private data sets, not allowing reproducibility and direct comparison between the reported results and other competing solutions. In addition, testing and evaluating an IPS in the real world is difficult and time-consuming, especially when considering evaluation in multiple environments and conditions. To enhance IPS assessment, we propose Dioptra, an open access and user-friendly application to support research, development and evaluation of IPSs through simulation. To the best of our knowledge, Dioptra is the first application specially developed to generate synthetic datasets to promote reproducibility and fair benchmarking between IPSs. |
Aranda, Fernando J; Parralejo, Felipe; Aguilera, Teodoro; Álvarez, Fernando J; Torres-Sospedra, Joaquín Finding Optimal BLE Configuration for Indoor Positioning with Consumption Restrictions Inproceedings Proceedings of the Eleventh International Conference on Indoor Positioning and Indoor Navigation, IEEE, 2021. Abstract | Links | BibTeX | Tags: Indoor positioning @inproceedings{Aranda2021a, title = {Finding Optimal BLE Configuration for Indoor Positioning with Consumption Restrictions}, author = {Fernando J. Aranda and Felipe Parralejo and Teodoro Aguilera and Fernando J. Álvarez and Joaquín Torres-Sospedra}, doi = {https://doi.org/10.1109/IPIN51156.2021.9662563}, year = {2021}, date = {2021-12-15}, booktitle = {Proceedings of the Eleventh International Conference on Indoor Positioning and Indoor Navigation}, publisher = {IEEE}, abstract = {Bluetooth Low Energy (BLE) fingerprinting has gained a lot of research effort in recent years due to flexibility in both beacons placement and configuration. Different works have addressed the effect of the configuration parameters, mainly the transmission power (Tx) and period (Ts), over positioning accuracy but not on the system lifespan and the trade-off between these two. In this work, different configurations of one, three and six slots have been tested over the same experimental setup. Positioning accuracy was obtained using different variations of the Weighted k-Nearest Neighbours (Wk-NN) algorithm, and the system lifespan was estimated using the actual current consumption and transmission mechanism for each configuration. Experimental results have shown that Tx and the number of slots can be adjusted to optimize this trade-off; meanwhile, changes in Ts worsen Wk-NN results more than in the other parameters, showing that the minimum Ts is always the best option.}, keywords = {Indoor positioning}, pubstate = {published}, tppubtype = {inproceedings} } Bluetooth Low Energy (BLE) fingerprinting has gained a lot of research effort in recent years due to flexibility in both beacons placement and configuration. Different works have addressed the effect of the configuration parameters, mainly the transmission power (Tx) and period (Ts), over positioning accuracy but not on the system lifespan and the trade-off between these two. In this work, different configurations of one, three and six slots have been tested over the same experimental setup. Positioning accuracy was obtained using different variations of the Weighted k-Nearest Neighbours (Wk-NN) algorithm, and the system lifespan was estimated using the actual current consumption and transmission mechanism for each configuration. Experimental results have shown that Tx and the number of slots can be adjusted to optimize this trade-off; meanwhile, changes in Ts worsen Wk-NN results more than in the other parameters, showing that the minimum Ts is always the best option. |
Quezada-Gaibor, Darwin; Torres-Sospedra, Joaquín; Nurmi, Jari; Koucheryavy, Yevgeni; Huerta-Guijarro, Joaquín Lightweight Wi-Fi Fingerprinting with a Novel RSS Clustering Algorithm Inproceedings Proceedings of the Eleventh International Conference on Indoor Positioning and Indoor Navigation, IEEE, 2021. Abstract | Links | BibTeX | Tags: Indoor positioning @inproceedings{Quezada2021a, title = {Lightweight Wi-Fi Fingerprinting with a Novel RSS Clustering Algorithm}, author = {Darwin Quezada-Gaibor and Joaquín Torres-Sospedra and Jari Nurmi and Yevgeni Koucheryavy and Joaquín Huerta-Guijarro}, doi = {https://doi.org/10.1109/IPIN51156.2021.9662612}, year = {2021}, date = {2021-12-15}, booktitle = {Proceedings of the Eleventh International Conference on Indoor Positioning and Indoor Navigation}, publisher = {IEEE}, abstract = {Nowadays, several indoor positioning solutions sup-port Wi-Fi and use this technology to estimate the user position. It is characterized by its low cost, availability in indoor and outdoor environments, and a wide variety of devices support Wi-Fi technology. However, this technique suffers from scalability problems when the radio map has a large number of reference fingerprints because this might increase the time response in the operational phase. In order to minimize the time response, many solutions have been proposed along the time. The most common solution is to divide the data set into clusters. Thus, the incoming fingerprint will be compared with a specific number of samples grouped by, for instance similarity (clusters). Many of the current studies have proposed a variety of solutions based on the modification of traditional clustering algorithms in order to provide a better distribution of samples and reduce the computational load. This work proposes a new clustering method based on the maximum Received Signal Strength (RSS) values to join similar fingerprints. As a result, the proposed fingerprinting clustering method outperforms three of the most well-known clustering algorithms in terms of processing time at the operational phase of fingerprinting.}, keywords = {Indoor positioning}, pubstate = {published}, tppubtype = {inproceedings} } Nowadays, several indoor positioning solutions sup-port Wi-Fi and use this technology to estimate the user position. It is characterized by its low cost, availability in indoor and outdoor environments, and a wide variety of devices support Wi-Fi technology. However, this technique suffers from scalability problems when the radio map has a large number of reference fingerprints because this might increase the time response in the operational phase. In order to minimize the time response, many solutions have been proposed along the time. The most common solution is to divide the data set into clusters. Thus, the incoming fingerprint will be compared with a specific number of samples grouped by, for instance similarity (clusters). Many of the current studies have proposed a variety of solutions based on the modification of traditional clustering algorithms in order to provide a better distribution of samples and reduce the computational load. This work proposes a new clustering method based on the maximum Received Signal Strength (RSS) values to join similar fingerprints. As a result, the proposed fingerprinting clustering method outperforms three of the most well-known clustering algorithms in terms of processing time at the operational phase of fingerprinting. |
Bellavista-Parent, Vladimir; Torres-Sospedra, Joaquín; Perez-Navarro, Antoni New trends in indoor positioning based on WiFi and machine learning: A systematic review Inproceedings Proceedings of the Eleventh International Conference on Indoor Positioning and Indoor Navigation, IEEE, 2021. Abstract | Links | BibTeX | Tags: Indoor positioning, machine learning @inproceedings{Bellavista2021a, title = {New trends in indoor positioning based on WiFi and machine learning: A systematic review}, author = {Vladimir Bellavista-Parent and Joaquín Torres-Sospedra and Antoni Perez-Navarro}, doi = {https://doi.org/10.1109/IPIN51156.2021.9662521}, year = {2021}, date = {2021-12-15}, booktitle = {Proceedings of the Eleventh International Conference on Indoor Positioning and Indoor Navigation}, publisher = {IEEE}, abstract = {Currently there is no standard indoor positioning system, similar to outdoor GPS. However, WiFi signals have been used in a large number of proposals to achieve the above positioning, many of which use machine learning to do so. But what are the most commonly used techniques in machine learning? What accuracy do they achieve? Where have they been tested? This article presents a systematic review of works between 2019 and 2021 that use WiFi as the signal for positioning and machine learning models to estimate indoor position. 64 papers have been identified as relevant, which have been systematically analyzed for a better understanding of the current situation in different aspects. The results show that indoor positioning based on WiFi trends use neural network-based models, evaluated in empirical experiments. Despite this, many works still conduct an assessment in small areas, which can influence the goodness of the results presented.}, keywords = {Indoor positioning, machine learning}, pubstate = {published}, tppubtype = {inproceedings} } Currently there is no standard indoor positioning system, similar to outdoor GPS. However, WiFi signals have been used in a large number of proposals to achieve the above positioning, many of which use machine learning to do so. But what are the most commonly used techniques in machine learning? What accuracy do they achieve? Where have they been tested? This article presents a systematic review of works between 2019 and 2021 that use WiFi as the signal for positioning and machine learning models to estimate indoor position. 64 papers have been identified as relevant, which have been systematically analyzed for a better understanding of the current situation in different aspects. The results show that indoor positioning based on WiFi trends use neural network-based models, evaluated in empirical experiments. Despite this, many works still conduct an assessment in small areas, which can influence the goodness of the results presented. |
Torres-Sospedra, Joaquín; Silva, Ivo; Klus, Lucie; Quezada-Gaibor, Darwin; Crivello, Antonino; Barsocchi, Paolo; Pendão, Cristiano; Lohan, Elena Simona; Nurmi, Jari; Moreira, Adriano Towards Ubiquitous Indoor Positioning: Comparing Systems across Heterogeneous Datasets Inproceedings Proceedings of the Eleventh International Conference on Indoor Positioning and Indoor Navigation, IEEE, 2021. Abstract | Links | BibTeX | Tags: Indoor positioning @inproceedings{Torres-Sospedra2021c, title = {Towards Ubiquitous Indoor Positioning: Comparing Systems across Heterogeneous Datasets}, author = {Joaquín Torres-Sospedra and Ivo Silva and Lucie Klus and Darwin Quezada-Gaibor and Antonino Crivello and Paolo Barsocchi and Cristiano Pendão and Elena Simona Lohan and Jari Nurmi and Adriano Moreira}, doi = {https://doi.org/10.1109/IPIN51156.2021.9662560}, year = {2021}, date = {2021-12-15}, booktitle = {Proceedings of the Eleventh International Conference on Indoor Positioning and Indoor Navigation}, publisher = {IEEE}, abstract = {The evaluation of Indoor Positioning Systems (IPSs) mostly relies on local deployments in the researchers' or partners' facilities. The complexity of preparing comprehensive experiments, collecting data, and considering multiple scenarios usually limits the evaluation area and, therefore, the assessment of the proposed systems. The requirements and features of controlled experiments cannot be generalized since the use of the same sensors or anchors density cannot be guaranteed. The dawn of datasets is pushing IPS evaluation to a similar level as machine-learning models, where new proposals are evaluated over many heterogeneous datasets. This paper proposes a way to evaluate IPSs in multiple scenarios, that is validated with three use cases. The results prove that the proposed aggregation of the evaluation metric values is a useful tool for high-level comparison of IPSs.}, keywords = {Indoor positioning}, pubstate = {published}, tppubtype = {inproceedings} } The evaluation of Indoor Positioning Systems (IPSs) mostly relies on local deployments in the researchers' or partners' facilities. The complexity of preparing comprehensive experiments, collecting data, and considering multiple scenarios usually limits the evaluation area and, therefore, the assessment of the proposed systems. The requirements and features of controlled experiments cannot be generalized since the use of the same sensors or anchors density cannot be guaranteed. The dawn of datasets is pushing IPS evaluation to a similar level as machine-learning models, where new proposals are evaluated over many heterogeneous datasets. This paper proposes a way to evaluate IPSs in multiple scenarios, that is validated with three use cases. The results prove that the proposed aggregation of the evaluation metric values is a useful tool for high-level comparison of IPSs. |
Klus, Roman; Klus, Lucie; Talvitie, Jukka; Pihlajasalo, Jaakko; Torres-Sospedra, Joaquín; Valkama, Mikko Transfer Learning for Convolutional Indoor Positioning Systems Inproceedings Proceedings of the Eleventh International Conference on Indoor Positioning and Indoor Navigation, IEEE, 2021. Abstract | Links | BibTeX | Tags: Indoor positioning @inproceedings{Klus2021a, title = {Transfer Learning for Convolutional Indoor Positioning Systems}, author = {Roman Klus and Lucie Klus and Jukka Talvitie and Jaakko Pihlajasalo and Joaquín Torres-Sospedra and Mikko Valkama}, doi = {https://doi.org/10.1109/IPIN51156.2021.9662544}, year = {2021}, date = {2021-12-15}, booktitle = {Proceedings of the Eleventh International Conference on Indoor Positioning and Indoor Navigation}, publisher = {IEEE}, abstract = {Fingerprinting is a widely used technique in indoor positioning, mainly due to its simplicity. Usually, this technique is used with the deterministic k - Nearest Neighbors (k-NN) algorithm. Utilizing a neural network model for fingerprinting positioning purposes can greatly improve the prediction speed compared to the k-NN approach, but requires a voluminous training dataset to achieve comparable performance. In many indoor positioning datasets, the number of samples is only at a level of hundreds, which results in poor performance of the neural network solution. In this work, we develop a novel algorithm based on a transfer learning approach, which combines samples from 15 different Wi-Fi RSS indoor positioning datasets, to train a single convolutional neural network model, which learns the common patterns in the combined data. The proposed model is then fine-tuned to optimally fit the individual databases. We show that the proposed solution reduces the positioning error by up to 25% compared to the benchmark model while reducing the number of outlier predictions.}, keywords = {Indoor positioning}, pubstate = {published}, tppubtype = {inproceedings} } Fingerprinting is a widely used technique in indoor positioning, mainly due to its simplicity. Usually, this technique is used with the deterministic k - Nearest Neighbors (k-NN) algorithm. Utilizing a neural network model for fingerprinting positioning purposes can greatly improve the prediction speed compared to the k-NN approach, but requires a voluminous training dataset to achieve comparable performance. In many indoor positioning datasets, the number of samples is only at a level of hundreds, which results in poor performance of the neural network solution. In this work, we develop a novel algorithm based on a transfer learning approach, which combines samples from 15 different Wi-Fi RSS indoor positioning datasets, to train a single convolutional neural network model, which learns the common patterns in the combined data. The proposed model is then fine-tuned to optimally fit the individual databases. We show that the proposed solution reduces the positioning error by up to 25% compared to the benchmark model while reducing the number of outlier predictions. |
Pascacio-de-los-Santos, Pavel; Torres-Sospedra, Joaquín; Casteleyn, Sven A Lateration Method based on Effective Combinatorial Beacon Selection for Bluetooth Low Energy Indoor Positioning Inproceedings 2021 17th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), pp. 397-402, IEEE, 2021, ISBN: 978-1-6654-2854-5. Abstract | Links | BibTeX | Tags: Indoor positioning @inproceedings{Pascacio2021c, title = {A Lateration Method based on Effective Combinatorial Beacon Selection for Bluetooth Low Energy Indoor Positioning}, author = {Pavel Pascacio-de-los-Santos and Joaquín Torres-Sospedra and Sven Casteleyn}, doi = {10.1109/WiMob52687.2021.9606419}, isbn = {978-1-6654-2854-5}, year = {2021}, date = {2021-10-15}, booktitle = {2021 17th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)}, pages = {397-402}, publisher = {IEEE}, abstract = {Nowadays, the Bluetooth Low Energy (BLE) technology joined with the Received Signal Strength Indicator technique has became a popular approach in Indoor Positioning System, thanks to the wide availability of BLE in anchors and wearable devices and the straightforward implementation of both. Consequently, methods based on geometric properties of anchors, as lateration, are capable of enhancing the positioning accuracy exploiting the growing availability of anchors and their rich geometric distribution in indoor environments. On the downside, an inappropriate selection of anchors decreases the positioning accuracy estimation. Therefore, integrating an effective beacon selection method can enhance the reliability and accuracy of these methods. In this paper, we present a novel and straightforward Lateration indoor positioning method based on effective combinatorial BLE beacon selection. The combinatorial BLE selection approach relies on a geometrical analysis (difference of triangle areas), of each beacon combination, considering the reference beacons’ position with the estimated position using lateration, and with a globally calculated virtual target position as reference. The real-world experiment demonstrated that the proposed method improves the traditional lateration with 5% to 16%, considering different evaluation metrics.}, keywords = {Indoor positioning}, pubstate = {published}, tppubtype = {inproceedings} } Nowadays, the Bluetooth Low Energy (BLE) technology joined with the Received Signal Strength Indicator technique has became a popular approach in Indoor Positioning System, thanks to the wide availability of BLE in anchors and wearable devices and the straightforward implementation of both. Consequently, methods based on geometric properties of anchors, as lateration, are capable of enhancing the positioning accuracy exploiting the growing availability of anchors and their rich geometric distribution in indoor environments. On the downside, an inappropriate selection of anchors decreases the positioning accuracy estimation. Therefore, integrating an effective beacon selection method can enhance the reliability and accuracy of these methods. In this paper, we present a novel and straightforward Lateration indoor positioning method based on effective combinatorial BLE beacon selection. The combinatorial BLE selection approach relies on a geometrical analysis (difference of triangle areas), of each beacon combination, considering the reference beacons’ position with the estimated position using lateration, and with a globally calculated virtual target position as reference. The real-world experiment demonstrated that the proposed method improves the traditional lateration with 5% to 16%, considering different evaluation metrics. |
Casanova-Marqués, Raúl; Pascacio-de-los-Santos, Pavel; Hajny, Jan; Torres-Sospedra, Joaquín Anonymous Attribute-based Credentials in Collaborative Indoor Positioning Systems Inproceedings Proceedings of the 18th International Conference on Security and Cryptography (SECRYPT 2021), pp. 791-797, SciTePress, 2021, ISBN: 978-989-758-524-1. Abstract | Links | BibTeX | Tags: geoprivacy, Indoor positioning @inproceedings{CasanovaMarques2021a, title = {Anonymous Attribute-based Credentials in Collaborative Indoor Positioning Systems}, author = {Raúl Casanova-Marqués and Pavel Pascacio-de-los-Santos and Jan Hajny and Joaquín Torres-Sospedra }, doi = {http://dx.doi.org/10.5220/0010582507910797}, isbn = {978-989-758-524-1}, year = {2021}, date = {2021-09-01}, booktitle = {Proceedings of the 18th International Conference on Security and Cryptography (SECRYPT 2021)}, pages = {791-797}, publisher = {SciTePress}, abstract = {Collaborative Indoor Positioning Systems (CIPSs) have recently received considerable attention, mainly because they address some existing limitations of traditional Indoor Positioning Systems (IPSs). In CIPSs, Bluetooth Low Energy (BLE) can be used to exchange positioning data and provide information (the Received Signal Strength Indicator (RSSI)) 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. This paper deals with privacy-enhancing technologies and their application in CIPS. In particular, we focus on cryptographic schemes which allow the verification of users without their identification, so-called Anonymous Attribute-based Credential (ABC) schemes. As the main contribution, we presen t a cryptographic scheme that allows security and privacy-friendly sharing of location information sent through BLE advertising packets. In order to demonstrate the practicality of our scheme, we also present the results from our implementation and benchmarks on different devices.}, keywords = {geoprivacy, Indoor positioning}, pubstate = {published}, tppubtype = {inproceedings} } Collaborative Indoor Positioning Systems (CIPSs) have recently received considerable attention, mainly because they address some existing limitations of traditional Indoor Positioning Systems (IPSs). In CIPSs, Bluetooth Low Energy (BLE) can be used to exchange positioning data and provide information (the Received Signal Strength Indicator (RSSI)) 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. This paper deals with privacy-enhancing technologies and their application in CIPS. In particular, we focus on cryptographic schemes which allow the verification of users without their identification, so-called Anonymous Attribute-based Credential (ABC) schemes. As the main contribution, we presen t a cryptographic scheme that allows security and privacy-friendly sharing of location information sent through BLE advertising packets. In order to demonstrate the practicality of our scheme, we also present the results from our implementation and benchmarks on different devices. |
Torres-Sospedra, Joaquín; Aranda, Fernando J; Álvarez, Fernando J; Quezada-Gaibor, Darwin; Silva, Ivo; Pendão, Cristiano; Moreira, Adriano Ensembling Multiple Radio Maps with Dynamic Noise in Fingerprint-based Indoor Positioning Inproceedings Proceedings of the 2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring), pp. 1-5, IEEE, 2021, ISBN: 978-1-7281-8965-9. Abstract | Links | BibTeX | Tags: Indoor positioning, Wi-Fi fingerprint, Wi-Fi mapping @inproceedings{Torres-Sospedra2021b, title = {Ensembling Multiple Radio Maps with Dynamic Noise in Fingerprint-based Indoor Positioning}, author = {Joaquín Torres-Sospedra and Fernando J. Aranda and Fernando J. Álvarez and Darwin Quezada-Gaibor and Ivo Silva and Cristiano Pendão and Adriano Moreira}, doi = {http://dx.doi.org/10.1109/VTC2021-Spring51267.2021.9448947}, isbn = {978-1-7281-8965-9}, year = {2021}, date = {2021-06-15}, booktitle = {Proceedings of the 2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring)}, pages = {1-5}, publisher = {IEEE}, abstract = {Fingerprint-based indoor positioning is widely used in many contexts, including pedestrian and autonomous vehicles navigation. Many approaches have used traditional Machine Learning models to deal with fingerprinting, being k-NN the most common used one. However, the reference data (or radio map) is generally limited, as data collection is a very demanding task, which degrades overall accuracy. In this work, we propose a novel approach to add random noise to the radio map which will be used in combination with an ensemble model. Instead of augmenting the radio map, we create n noisy versions of the same size, i.e. our proposed Indoor Positioning model will combine n estimations obtained by independent estimators built with the n noisy radio maps. The empirical results have shown that our proposed approach improves the baseline method results in around 10% on average.}, keywords = {Indoor positioning, Wi-Fi fingerprint, Wi-Fi mapping}, pubstate = {published}, tppubtype = {inproceedings} } Fingerprint-based indoor positioning is widely used in many contexts, including pedestrian and autonomous vehicles navigation. Many approaches have used traditional Machine Learning models to deal with fingerprinting, being k-NN the most common used one. However, the reference data (or radio map) is generally limited, as data collection is a very demanding task, which degrades overall accuracy. In this work, we propose a novel approach to add random noise to the radio map which will be used in combination with an ensemble model. Instead of augmenting the radio map, we create n noisy versions of the same size, i.e. our proposed Indoor Positioning model will combine n estimations obtained by independent estimators built with the n noisy radio maps. The empirical results have shown that our proposed approach improves the baseline method results in around 10% on average. |
Mendoza-Silva, Germán Martin; Torres-Sospedra, Joaquín; Huerta-Guijarro, Joaquín Local-level Analysis of Positioning Errors in Wi-Fi Fingerprinting Inproceedings Proceedings of the 2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring), pp. 1-5, IEEE, 2021, ISBN: 978-1-7281-8964-2. Abstract | Links | BibTeX | Tags: Indoor positioning, Wi-Fi fingerprint @inproceedings{Mendoza-Silva2021a, title = {Local-level Analysis of Positioning Errors in Wi-Fi Fingerprinting}, author = {Germán Martin Mendoza-Silva and Joaquín Torres-Sospedra and Joaquín Huerta-Guijarro}, doi = {https://doi.org/10.1109/VTC2021-Spring51267.2021.9448936}, isbn = {978-1-7281-8964-2}, year = {2021}, date = {2021-06-15}, booktitle = {Proceedings of the 2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring)}, pages = {1-5}, publisher = {IEEE}, abstract = {Nowadays, Location Based Services run over a net of heterogeneous devices (mainly smartphones) with different location capabilities thanks to, for instance, signals of opportunity as Wi-Fi. In contrast to professional deployments in controlled scenarios, the positioning error highly depends not only on the environment but also on the location. Traditional metrics for evaluating indoor positioning system may fail in obtaining lower-level details on the reported results. This paper introduces a way to perform a local-level analysis of the positioning errors. Our approach is based on analyses of the position-wise variance of positioning errors.}, keywords = {Indoor positioning, Wi-Fi fingerprint}, pubstate = {published}, tppubtype = {inproceedings} } Nowadays, Location Based Services run over a net of heterogeneous devices (mainly smartphones) with different location capabilities thanks to, for instance, signals of opportunity as Wi-Fi. In contrast to professional deployments in controlled scenarios, the positioning error highly depends not only on the environment but also on the location. Traditional metrics for evaluating indoor positioning system may fail in obtaining lower-level details on the reported results. This paper introduces a way to perform a local-level analysis of the positioning errors. Our approach is based on analyses of the position-wise variance of positioning errors. |
Pascacio-de-los-Santos, Pavel; Casteleyn, Sven; Torres-Sospedra, Joaquín Smartphone Distance Estimation Based on RSSI-Fuzzy Classification Approach Inproceedings Proceedings of the 2021 International Conference on Localization and GNSS (ICL-GNSS), pp. 1-6, IEEE, 2021, ISBN: 978-1-7281-9645-9. Abstract | Links | BibTeX | Tags: Indoor positioning, machine learning @inproceedings{Pascacio2021b, title = {Smartphone Distance Estimation Based on RSSI-Fuzzy Classification Approach}, author = {Pavel Pascacio-de-los-Santos and Sven Casteleyn and Joaquín Torres-Sospedra}, doi = {https://doi.org/10.1109/ICL-GNSS51451.2021.9452226}, isbn = {978-1-7281-9645-9}, year = {2021}, date = {2021-06-01}, booktitle = {Proceedings of the 2021 International Conference on Localization and GNSS (ICL-GNSS)}, pages = {1-6}, publisher = {IEEE}, abstract = {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.}, keywords = {Indoor positioning, machine learning}, pubstate = {published}, tppubtype = {inproceedings} } 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. |
Furfari, Francesco; Crivello, Antonino; Baronti, Paolo; Barsocchi, Paolo; Girolami, Michele; Palumbo, Filippo; Quezada-Gaibor, Darwin; Mendoza-Silva, Germán Martin; Torres-Sospedra, Joaquín Discovering location based services: A unified approach for heterogeneous indoor localization systems Journal Article Internet of Things, 13 (1001511), 2021, ISSN: 2542-6605. Abstract | Links | BibTeX | Tags: Indoor localization, Indoor positioning, location-based services @article{Furfari2021, title = {Discovering location based services: A unified approach for heterogeneous indoor localization systems}, author = {Francesco Furfari and Antonino Crivello and Paolo Baronti and Paolo Barsocchi and Michele Girolami and Filippo Palumbo and Darwin Quezada-Gaibor and Germán Martin Mendoza-Silva and Joaquín Torres-Sospedra}, doi = {https://doi.org/10.1016/j.iot.2020.100334}, issn = {2542-6605}, year = {2021}, date = {2021-03-01}, journal = {Internet of Things}, volume = {13}, number = {1001511}, abstract = {The technological solutions and communication capabilities offered by the Internet of Things paradigm, in terms of raising availability of wearable devices, the ubiquitous internet connection, and the presence on the market of service-oriented solutions, have allowed a wide proposal of Location Based Services (LBS). In a close future, we foresee that companies and service providers will have developed reliable solutions to address indoor positioning, as basis for useful location based services. These solutions will be different from each other and they will adopt different hardware and processing techniques. This paper describes the proposal of a unified approach for Indoor Localization Systems that enables the cooperation between heterogeneous solutions and their functional modules. To this end, we designed an integrated architecture that, abstracting its main components, allows a seamless interaction among them. Finally, we present a working prototype of such architecture, which is based on the popular Telegram application for Android, as an integration demonstrator. The integration of the three main phases –namely the discovery phase, the User Agent self-configuration, and the indoor map retrieval/rendering– demonstrates the feasibility of the proposed integrated architecture.}, keywords = {Indoor localization, Indoor positioning, location-based services}, pubstate = {published}, tppubtype = {article} } The technological solutions and communication capabilities offered by the Internet of Things paradigm, in terms of raising availability of wearable devices, the ubiquitous internet connection, and the presence on the market of service-oriented solutions, have allowed a wide proposal of Location Based Services (LBS). In a close future, we foresee that companies and service providers will have developed reliable solutions to address indoor positioning, as basis for useful location based services. These solutions will be different from each other and they will adopt different hardware and processing techniques. This paper describes the proposal of a unified approach for Indoor Localization Systems that enables the cooperation between heterogeneous solutions and their functional modules. To this end, we designed an integrated architecture that, abstracting its main components, allows a seamless interaction among them. Finally, we present a working prototype of such architecture, which is based on the popular Telegram application for Android, as an integration demonstrator. The integration of the three main phases –namely the discovery phase, the User Agent self-configuration, and the indoor map retrieval/rendering– demonstrates the feasibility of the proposed integrated architecture. |
Pascacio-de-los-Santos, Pavel; Casteleyn, Sven; Torres-Sospedra, Joaquín; Lohan, Elena Simona; Nurmi, Jari Collaborative Indoor Positioning Systems: A Systematic Review Journal Article Sensors, 21 (3), pp. 1002, 2021. Abstract | Links | BibTeX | Tags: A-wear, Indoor positioning @article{Pascacio-de-los-Santos2021, title = {Collaborative Indoor Positioning Systems: A Systematic Review}, author = {Pavel Pascacio-de-los-Santos and Sven Casteleyn and Joaquín Torres-Sospedra and Elena Simona Lohan and Jari Nurmi}, doi = {https://doi.org/10.3390/s21031002 }, year = {2021}, date = {2021-02-03}, journal = {Sensors}, volume = {21}, number = {3}, pages = {1002}, abstract = {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}, keywords = {A-wear, Indoor positioning}, pubstate = {published}, tppubtype = {article} } 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 |
Silva, Germán Mendoza M; Torres-Sospedra, Joaquín; Potorti, Francesco; Moreira, Adriano; Knauth, Stefan; Berkvens, Rafael; Huerta-Guijarro, Joaquín Beyond Euclidean Distance for Error Measurement in Pedestrian Indoor Location Journal Article IEEE Transactions on Instrumentation and Measurement, 70 (1001511), 2021. Abstract | Links | BibTeX | Tags: Indoor positioning @article{Mendoza-Silva2021, title = {Beyond Euclidean Distance for Error Measurement in Pedestrian Indoor Location}, author = {Germán M. Mendoza Silva and Joaquín Torres-Sospedra and Francesco Potorti and Adriano Moreira and Stefan Knauth and Rafael Berkvens and Joaquín Huerta-Guijarro}, doi = {https://doi.org/10.1109/TIM.2020.3021514}, year = {2021}, date = {2021-02-02}, journal = {IEEE Transactions on Instrumentation and Measurement}, volume = {70}, number = {1001511}, abstract = {Indoor positioning systems (IPSs) suffer from a lack of standard evaluation procedures enabling credible comparisons: this is one of the main challenges hindering their widespread market adoption. Traditionally, accuracy evaluation is based on positioning errors defined as the Euclidean distance between the true positions and the estimated positions. While Euclidean is simple, it ignores obstacles and floor transitions. In this article, we describe procedures that measure a positioning error defined as the length of the pedestrian path that connects the estimated position to the true position. The procedures apply pathfinding on floor maps using visibility graphs (VGs) or navigational meshes (NMs) for vector maps and fast marching (FM) for raster maps. Multifloor and multibuilding paths use the information on vertical in-building communication ways and outdoor paths. The proposed measurement procedures are applied to position estimates provided by the IPSs that participated in the EvAAL-ETRI 2015 competition. Procedures are compared in terms of pedestrian path realism, indoor model complexity, path computation time, and error magnitudes. The VGs algorithm computes shortest distance paths; NMs produce very similar paths with significantly shorter computation time; and FM computes longer, more natural-looking paths at the expense of longer computation time and memory size. The 75th percentile of the measured error differs among the methods from 2.2 to 3.7 m across the evaluation sets. }, keywords = {Indoor positioning}, pubstate = {published}, tppubtype = {article} } Indoor positioning systems (IPSs) suffer from a lack of standard evaluation procedures enabling credible comparisons: this is one of the main challenges hindering their widespread market adoption. Traditionally, accuracy evaluation is based on positioning errors defined as the Euclidean distance between the true positions and the estimated positions. While Euclidean is simple, it ignores obstacles and floor transitions. In this article, we describe procedures that measure a positioning error defined as the length of the pedestrian path that connects the estimated position to the true position. The procedures apply pathfinding on floor maps using visibility graphs (VGs) or navigational meshes (NMs) for vector maps and fast marching (FM) for raster maps. Multifloor and multibuilding paths use the information on vertical in-building communication ways and outdoor paths. The proposed measurement procedures are applied to position estimates provided by the IPSs that participated in the EvAAL-ETRI 2015 competition. Procedures are compared in terms of pedestrian path realism, indoor model complexity, path computation time, and error magnitudes. The VGs algorithm computes shortest distance paths; NMs produce very similar paths with significantly shorter computation time; and FM computes longer, more natural-looking paths at the expense of longer computation time and memory size. The 75th percentile of the measured error differs among the methods from 2.2 to 3.7 m across the evaluation sets. |
2020 |
Quezada-Gaibor, Darwin; Klus, Lucie; Torres-Sospedra, Joaquín; Lohan, Simona Elena; Nurmi, Jari; Huerta-Guijarro, Joaquín Improving DBSCAN for Indoor Positioning Using Wi-Fi Radio Maps in Wearable and IoT Devices Inproceedings Proceedings of the 12th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT), 5-8 October 2020. Online event, pp. 208-213, 2020, ISBN: 978-1-7281-9281-9. Links | BibTeX | Tags: A-wear, Indoor positioning, Internet of things, wearables, Wi-Fi mapping @inproceedings{Quezada-Gaibor2020, title = {Improving DBSCAN for Indoor Positioning Using Wi-Fi Radio Maps in Wearable and IoT Devices}, author = {Darwin Quezada-Gaibor and Lucie Klus and Joaquín Torres-Sospedra and Simona Elena Lohan and Jari Nurmi and Joaquín Huerta-Guijarro}, doi = {http://www.doi.org/10.1109/ICUMT51630.2020.9222411}, isbn = {978-1-7281-9281-9}, year = {2020}, date = {2020-09-17}, booktitle = {Proceedings of the 12th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT), 5-8 October 2020. Online event}, pages = {208-213}, keywords = {A-wear, Indoor positioning, Internet of things, wearables, Wi-Fi mapping}, pubstate = {published}, tppubtype = {inproceedings} } |
Potortì, Francesco; Park, Sangjoon; Crivello, Antonino; Palumbo, Filippo; Girolami, Michele; Barsocchi, Paolo; Lee, Soyeon; Torres-Sospedra, Joaquín; Jimenez, Antonio Ramón; Pérez-Navarro, Antoni; Mendoza-Silva, Germán Martin; Seco, Fernando; Ortiz, Miguel; Perul, Johan; Renaudin, Valerie; Kang, Hyunwoong; Park, Soyoung; Lee, Jae Hong; Park, Chan Gook; Ha, Jisu; Han, Jaeseung; Park, Changjun; Kim, Keunhye; Lee, Yonghyun; Gye, Seunghun; Lee, Keumryeol; Kim, Eunjee; Choi, Jeongsik; Choi, Yang-Seok; Talwar, Shilpa; Cho, Seong Yun; Ben-Moshe, Boaz; Scherbakov, Alex; Antsfeld, Leonid; Sansano-Sansano, Emilio; Chidlovskii, Boris; Kronenwett, Nikolai; Prophet, Silvia; Landau, Yael; Marbel, Revital; Zheng, Lingxiang; Peng, Ao; Lin, Zhichao; Wu, Bang; Ma, Chengqi; Poslad, Stefan; Selviah, David R; Wu, Wei; Ma, Zixiang; Zhang, Wenchao; Wei, Dongyan; Yuan, Hong; Jiang, Jun-Bang; Huang, Shao-Yung; Liu, Jing-Wen; Su, Kuan-Wu; Leu, Jenq-Shiou; Nishiguchi, Kazuki; Bousselham, Walid; Uchiyama, Hideaki; Thomas, Diego; Shimada, Atsushi; Taniguchi, Rin-Ichiro; Cortés, Vicente; Lungenstrass, Tomás; Ashraf, Imran; Lee, Chanseok; Ali, Muhammad Usman; Im, Yeongjun; Kim, Gunzung; Eom, Jeongsook; Hur, Soojung; Park, Yongwan; Opiela, Miroslav; Moreira, Adriano; Nicolau, Maria João; Pendão, Cristiano; Silva, Ivo; Meneses, Filipe; Costa, António; Trogh, Jens; Plets, David; Chien, Ying-Ren; Chang, Tzu-Yu; Fang, Shih-Hau; Tsao, Yu The IPIN 2019 Indoor Localisation Competition: Description and Results Journal Article IEEE Access, 8 , pp. 206674-20671, 2020, ISSN: 2169-3536. Abstract | Links | BibTeX | Tags: Indoor positioning @article{Potortì2020, title = {The IPIN 2019 Indoor Localisation Competition: Description and Results}, author = {Francesco Potortì and Sangjoon Park and Antonino Crivello and Filippo Palumbo and Michele Girolami and Paolo Barsocchi and Soyeon Lee and Joaquín Torres-Sospedra and Antonio Ramón Jimenez and Antoni Pérez-Navarro and Germán Martin Mendoza-Silva and Fernando Seco and Miguel Ortiz and Johan Perul and Valerie Renaudin and Hyunwoong Kang and Soyoung Park and Jae Hong Lee and Chan Gook Park and Jisu Ha and Jaeseung Han and Changjun Park and Keunhye Kim and Yonghyun Lee and Seunghun Gye and Keumryeol Lee and Eunjee Kim and Jeongsik Choi and Yang-Seok Choi and Shilpa Talwar and Seong Yun Cho and Boaz Ben-Moshe and Alex Scherbakov and Leonid Antsfeld and Emilio Sansano-Sansano and Boris Chidlovskii and Nikolai Kronenwett and Silvia Prophet and Yael Landau and Revital Marbel and Lingxiang Zheng and Ao Peng and Zhichao Lin and Bang Wu and Chengqi Ma and Stefan Poslad and David R. Selviah and Wei Wu and Zixiang Ma and Wenchao Zhang and Dongyan Wei and Hong Yuan and Jun-Bang Jiang and Shao-Yung Huang and Jing-Wen Liu and Kuan-Wu Su and Jenq-Shiou Leu and Kazuki Nishiguchi and Walid Bousselham and Hideaki Uchiyama and Diego Thomas and Atsushi Shimada and Rin-Ichiro Taniguchi and Vicente Cortés and Tomás Lungenstrass and Imran Ashraf and Chanseok Lee and Muhammad Usman Ali and Yeongjun Im and Gunzung Kim and Jeongsook Eom and Soojung Hur and Yongwan Park and Miroslav Opiela and Adriano Moreira and Maria João Nicolau and Cristiano Pendão and Ivo Silva and Filipe Meneses and António Costa and Jens Trogh and David Plets and Ying-Ren Chien and Tzu-Yu Chang and Shih-Hau Fang and Yu Tsao}, doi = {10.1109/ACCESS.2020.3037221}, issn = {2169-3536}, year = {2020}, date = {2020-09-01}, journal = {IEEE Access}, volume = {8}, pages = {206674-20671}, abstract = {IPIN 2019 Competition, sixth in a series of IPIN competitions, was held at the CNR Research Area of Pisa (IT), integrated into the program of the IPIN 2019 Conference. It included two on-site real-time Tracks and three off-site Tracks. The four Tracks presented in this paper were set in the same environment, made of two buildings close together for a total usable area of 1000 m 2 outdoors and and 6000 m 2 indoors over three floors, with a total path length exceeding 500 m. IPIN competitions, based on the EvAAL framework, have aimed at comparing the accuracy performance of personal positioning systems in fair and realistic conditions: past editions of the competition were carried in big conference settings, university campuses and a shopping mall. Positioning accuracy is computed while the person carrying the system under test walks at normal walking speed, uses lifts and goes up and down stairs or briefly stops at given points. Results presented here are a showcase of state-of-the-art systems tested side by side in real-world settings as part of the on-site real-time competition Tracks. Results for off-site Tracks allow a detailed and reproducible comparison of the most recent positioning and tracking algorithms in the same environment as the on-site Tracks. }, keywords = {Indoor positioning}, pubstate = {published}, tppubtype = {article} } IPIN 2019 Competition, sixth in a series of IPIN competitions, was held at the CNR Research Area of Pisa (IT), integrated into the program of the IPIN 2019 Conference. It included two on-site real-time Tracks and three off-site Tracks. The four Tracks presented in this paper were set in the same environment, made of two buildings close together for a total usable area of 1000 m 2 outdoors and and 6000 m 2 indoors over three floors, with a total path length exceeding 500 m. IPIN competitions, based on the EvAAL framework, have aimed at comparing the accuracy performance of personal positioning systems in fair and realistic conditions: past editions of the competition were carried in big conference settings, university campuses and a shopping mall. Positioning accuracy is computed while the person carrying the system under test walks at normal walking speed, uses lifts and goes up and down stairs or briefly stops at given points. Results presented here are a showcase of state-of-the-art systems tested side by side in real-world settings as part of the on-site real-time competition Tracks. Results for off-site Tracks allow a detailed and reproducible comparison of the most recent positioning and tracking algorithms in the same environment as the on-site Tracks. |
Khandker, S; Torres-Sospedra, Joaquín; Ristaniemi, T Analysis of Received Signal Strength Quantization in Fingerprinting Localization Journal Article Sensors, 20 (3203), 2020, ISSN: 1424-8220. Abstract | Links | BibTeX | Tags: Indoor positioning, Wi-Fi fingerprint @article{Khandker2020, title = {Analysis of Received Signal Strength Quantization in Fingerprinting Localization}, author = {S. Khandker and Joaquín Torres-Sospedra and T. Ristaniemi}, doi = {https://doi.org/10.3390/s20113203}, issn = {1424-8220}, year = {2020}, date = {2020-07-09}, journal = {Sensors}, volume = {20}, number = {3203}, abstract = {In recent times, Received Signal Strength (RSS)-based Wi-Fi fingerprinting localization has become one of the most promising techniques for indoor localization. The primary aim of RSS is to check the quality of the signal to determine the coverage and the quality of service. Therefore, fine-resolution RSS is needed, which is generally expressed by 1-dBm granularity. However, we found that, for fingerprinting localization, fine-granular RSS is unnecessary. A coarse-granular RSS can yield the same positioning accuracy. In this paper, we propose quantization for only the effective portion of the signal strength for fingerprinting localization. We found that, if a quantized RSS fingerprint can carry the major characteristics of a radio environment, it is sufficient for localization. Five publicly open fingerprinting databases with four different quantization strategies were used to evaluate the study. The proposed method can help to simplify the hardware configuration, enhance security, and save approximately 40–60% storage space and data traffic}, keywords = {Indoor positioning, Wi-Fi fingerprint}, pubstate = {published}, tppubtype = {article} } In recent times, Received Signal Strength (RSS)-based Wi-Fi fingerprinting localization has become one of the most promising techniques for indoor localization. The primary aim of RSS is to check the quality of the signal to determine the coverage and the quality of service. Therefore, fine-resolution RSS is needed, which is generally expressed by 1-dBm granularity. However, we found that, for fingerprinting localization, fine-granular RSS is unnecessary. A coarse-granular RSS can yield the same positioning accuracy. In this paper, we propose quantization for only the effective portion of the signal strength for fingerprinting localization. We found that, if a quantized RSS fingerprint can carry the major characteristics of a radio environment, it is sufficient for localization. Five publicly open fingerprinting databases with four different quantization strategies were used to evaluate the study. The proposed method can help to simplify the hardware configuration, enhance security, and save approximately 40–60% storage space and data traffic |
Torres-Sospedra, Joaquín; Quezada-Gaibor, Darwin; Mendoza-Silva, Germán Martín; Nurmi, Jari; Koucheryavy, Yevgeni; Huerta-Guijarro, Joaquín New Cluster Selection and Fine-grained Search for k-Means Clustering and Wi-Fi Fingerprinting Inproceedings 2020 International Conference on Localization and GNSS (ICL-GNSS), Tampere, Finland, 2020, pp. 1-6, IEEE, 2020, ISBN: 978-1-7281-6455-7. Abstract | Links | BibTeX | Tags: A-wear, Indoor positioning, Wi-Fi fingerprint @inproceedings{Torres-Sospedra2020, title = {New Cluster Selection and Fine-grained Search for k-Means Clustering and Wi-Fi Fingerprinting}, author = {Joaquín Torres-Sospedra and Darwin Quezada-Gaibor and Germán Martín Mendoza-Silva and Jari Nurmi and Yevgeni Koucheryavy and Joaquín Huerta-Guijarro}, doi = {http://www.doi.org/10.1109/ICL-GNSS49876.2020.9115419 }, isbn = {978-1-7281-6455-7}, year = {2020}, date = {2020-07-02}, booktitle = {2020 International Conference on Localization and GNSS (ICL-GNSS), Tampere, Finland, 2020}, pages = {1-6}, publisher = {IEEE}, abstract = {Wi-Fi fingerprinting is a popular technique for Indoor Positioning Systems (IPSs) thanks to its low complexity and the ubiquity of WLAN infrastructures. However, this technique may present scalability issues when the reference dataset (radio map) is very large. To reduce the computational costs, k-Means Clustering has been successfully applied in the past. However, it is a general-purpose algorithm for unsupervised classification. This paper introduces three variants that apply heuristics based on radio propagation knowledge in the coarse and fine-grained searches. Due to the heterogeneity either in the IPS side (including radio map generation) and in the network infrastructure, we used an evaluation framework composed of 16 datasets. In terms of general positioning accuracy and computational costs, the best proposed k-means variant provided better general positioning accuracy and a significantly better computational cost –around 40% lower– than the original k-means.}, keywords = {A-wear, Indoor positioning, Wi-Fi fingerprint}, pubstate = {published}, tppubtype = {inproceedings} } Wi-Fi fingerprinting is a popular technique for Indoor Positioning Systems (IPSs) thanks to its low complexity and the ubiquity of WLAN infrastructures. However, this technique may present scalability issues when the reference dataset (radio map) is very large. To reduce the computational costs, k-Means Clustering has been successfully applied in the past. However, it is a general-purpose algorithm for unsupervised classification. This paper introduces three variants that apply heuristics based on radio propagation knowledge in the coarse and fine-grained searches. Due to the heterogeneity either in the IPS side (including radio map generation) and in the network infrastructure, we used an evaluation framework composed of 16 datasets. In terms of general positioning accuracy and computational costs, the best proposed k-means variant provided better general positioning accuracy and a significantly better computational cost –around 40% lower– than the original k-means. |
Karmacharya, Amrit; Mendoza-Silva, German Martín; Torres-Sospedra, Joaquín Sensor Fusion and Well Condition Triangle Approach for BLE-based Indoor Positioning Inproceedings Ometov, A; Nurmi, Jaarmi; Lohan, Elena Simona; Torres-Sospedra, Joaquín; (Eds), Kuusniemi H (Ed.): Proceedings of the International Conference on Localization and GNSS (ICL GNSS 2020) CEUR Workshop Proceedings. Tampere, Finland, June 2-4 2020, CEUR, 2020, ISSN: 1613-0073 . Abstract | Links | BibTeX | Tags: Indoor positioning, Mastergeotech @inproceedings{Karmacharya2020b, title = {Sensor Fusion and Well Condition Triangle Approach for BLE-based Indoor Positioning}, author = {Amrit Karmacharya and German Martín Mendoza-Silva and Joaquín Torres-Sospedra }, editor = {A. Ometov and Jaarmi Nurmi and Elena Simona Lohan and Joaquín Torres-Sospedra and H. Kuusniemi (Eds)}, url = {http://ceur-ws.org/Vol-2626/paper6.pdf }, issn = {1613-0073 }, year = {2020}, date = {2020-06-15}, booktitle = {Proceedings of the International Conference on Localization and GNSS (ICL GNSS 2020) CEUR Workshop Proceedings. Tampere, Finland, June 2-4 2020}, volume = {2626}, publisher = {CEUR}, abstract = {GPS has been a de-facto standard for outdoor positioning. For indoor positioning different systems exist. But there is no general solution to fit all situations. A popular choice among service provider is Bluetooth Low Energy (BLE) based Indoor Positioning System (IPS) . BLE has low cost, low power consumption, and it is compatible with newer smartphones. This paper introduces two ways for accuracy improvement i) a new algorithm for BLE-based IPS based on well-condition triangle and ii) fusion of BLE position estimates with IMU position estimates was implemented. Fusion generally gives better results but a noteworthy result from fusion was that the position estimates during turns were accurate. When used separately, both BLE and IMU estimates showed errors in turns. Fusion with IMU improved the accuracy of BLE based positioning.}, keywords = {Indoor positioning, Mastergeotech}, pubstate = {published}, tppubtype = {inproceedings} } GPS has been a de-facto standard for outdoor positioning. For indoor positioning different systems exist. But there is no general solution to fit all situations. A popular choice among service provider is Bluetooth Low Energy (BLE) based Indoor Positioning System (IPS) . BLE has low cost, low power consumption, and it is compatible with newer smartphones. This paper introduces two ways for accuracy improvement i) a new algorithm for BLE-based IPS based on well-condition triangle and ii) fusion of BLE position estimates with IMU position estimates was implemented. Fusion generally gives better results but a noteworthy result from fusion was that the position estimates during turns were accurate. When used separately, both BLE and IMU estimates showed errors in turns. Fusion with IMU improved the accuracy of BLE based positioning. |
Aranda, F J; Parralejo, F; Álvarez, F J; Torres-Sospedra, Joaquín Multi-Slot BLE Raw Database for Accurate Positioning in Mixed Indoor/Outdoor Environments Journal Article Data, 5 , pp. 67, 2020. Links | BibTeX | Tags: Indoor positioning @article{Aranda2020, title = {Multi-Slot BLE Raw Database for Accurate Positioning in Mixed Indoor/Outdoor Environments}, author = {F.J. Aranda and F. Parralejo and F.J. Álvarez and Joaquín Torres-Sospedra}, doi = {http://www.doi.org/10.3390/data5030067}, year = {2020}, date = {2020-03-11}, journal = {Data}, volume = {5}, pages = {67}, keywords = {Indoor positioning}, pubstate = {published}, tppubtype = {article} } |
Karmacharya, Amrit Sensor fusion of IMU and BLE using a well-condition triangle approach for BLE positioning Masters Thesis INIT, UJI, 2020. Abstract | Links | BibTeX | Tags: Indoor positioning, Mastergeotech @mastersthesis{Karmacharya2020, title = {Sensor fusion of IMU and BLE using a well-condition triangle approach for BLE positioning }, author = {Amrit Karmacharya}, editor = {Joaquín Torres-Sospedra and Cristian Kray and Mauro Castelli (supervisors)}, url = {http://hdl.handle.net/10362/95137}, year = {2020}, date = {2020-03-05}, address = {Castellón}, school = {INIT, UJI}, abstract = {GPS has been a de-facto standard for outdoor positioning. For indoor positioning different systems exist. But there is no general solution to fit all situations. A popular choice among service provider is BLE-based IPS. BLE-has low cost, low power consumption, and tit is are compatible with newer smartphones. These factors make it suitable for mass market applications with an estimated market of 10 billion USD by 2020. Although, BLEbased IPS have advantages over its counterparts, it has not solved the position accuracy problem yet. More research is needed to meet the position accuracy required for indoor LBS. In this thesis, two ways for accuracy improvement were tested i) a new algorithm for BLE-based IPS was proposed and ii) fusion of BLE position estimates with IMU position estimates was implemented. The first way exploits a concept from control survey called well-conditioned triangle. Theoretically, a well-conditioned triangle is an equilateral triangle but for in practice, triangles whose angles are greater than 30° and less than 120° are considered well-conditioned. Triangles which do not satisfy well-condition are illconditioned. An estimated position has the least error if the geometry from which it is estimated satisfy well-condition. Ill-conditioned triangle should not be used for position estimation. The proposed algorithm checked for well-condition among the closest detected beacons and output estimates only when the beacons geometry satisfied well-condition. The proposed algorithm was compared with weighted centroid (WC) algorithm. Proposed algorithm did not improve on the accuracy but the variance in error was highly reduced. The second way tested was fusion of BLE and IMU using Kálmán filter. Fusion generally gives better results but a noteworthy result from fusion was that the position estimates during turns were accurate. When used separately, both BLE and IMU estimates showed errors in turns. Fusion with IMU improved the accuracy. More research is required to improve accuracy of BLE-based IPS. Reproducibility self-assessment (https://osf.io/j97zp/): 2, 2, 2, 1, 2 (input data, prepossessing, methods, computational environment, results).}, keywords = {Indoor positioning, Mastergeotech}, pubstate = {published}, tppubtype = {mastersthesis} } GPS has been a de-facto standard for outdoor positioning. For indoor positioning different systems exist. But there is no general solution to fit all situations. A popular choice among service provider is BLE-based IPS. BLE-has low cost, low power consumption, and tit is are compatible with newer smartphones. These factors make it suitable for mass market applications with an estimated market of 10 billion USD by 2020. Although, BLEbased IPS have advantages over its counterparts, it has not solved the position accuracy problem yet. More research is needed to meet the position accuracy required for indoor LBS. In this thesis, two ways for accuracy improvement were tested i) a new algorithm for BLE-based IPS was proposed and ii) fusion of BLE position estimates with IMU position estimates was implemented. The first way exploits a concept from control survey called well-conditioned triangle. Theoretically, a well-conditioned triangle is an equilateral triangle but for in practice, triangles whose angles are greater than 30° and less than 120° are considered well-conditioned. Triangles which do not satisfy well-condition are illconditioned. An estimated position has the least error if the geometry from which it is estimated satisfy well-condition. Ill-conditioned triangle should not be used for position estimation. The proposed algorithm checked for well-condition among the closest detected beacons and output estimates only when the beacons geometry satisfied well-condition. The proposed algorithm was compared with weighted centroid (WC) algorithm. Proposed algorithm did not improve on the accuracy but the variance in error was highly reduced. The second way tested was fusion of BLE and IMU using Kálmán filter. Fusion generally gives better results but a noteworthy result from fusion was that the position estimates during turns were accurate. When used separately, both BLE and IMU estimates showed errors in turns. Fusion with IMU improved the accuracy. More research is required to improve accuracy of BLE-based IPS. Reproducibility self-assessment (https://osf.io/j97zp/): 2, 2, 2, 1, 2 (input data, prepossessing, methods, computational environment, results). |
2019 |
Renaudin, Valerie; Ortiz, M; Perul, J; Torres-Sospedra, Joaquín; Jimenez, Ramón A; Pérez-Navarro, Antoni; Mendoza-Silva, Germán Martín; Seco, F; Landau, Y; Marbel, R; Ben-Moshe, B; Zheng, X; Ye, F; Kuang, J; Li, Y; Niu, X; Landa, V; Hacohen, S; Shvalb, N; Lu, C; Uchiyama, H; Thomas, D; Shimada, A; Taniguchi, R; Ding, Z; Xu, F; Kronenwett, N; Vladimirov, B; Lee, S; Cho, E; Jun, S; Lee, C; Park, S; Lee, Y; Rew, J; Park, C; Jeong, H; Han, J; Lee, K; Zhang, W; Li, X; Wei, D; Zhang, Y; Park, S Y; Park, C G; Knauth, S; Pipelidis, G; Tsiamitros, N; Lungenstrass, T; Pablo Morales, J; Trogh, J; Plets, D; Opiela, M; Shih-Hau Fang Tsao, Y; Chien, Y -R; Yang, S -S; Ye, S -J; Ali, M U; Hur, S; Park, Y I Evaluating Indoor Positioning Systems in a Shopping Mall: The Lessons Learned from the IPIN 2018 Competition Journal Article IEEE Access, 7 , pp. 148594 – 148628, 2019. Abstract | Links | BibTeX | Tags: Indoor positioning @article{Renaudin2019, title = {Evaluating Indoor Positioning Systems in a Shopping Mall: The Lessons Learned from the IPIN 2018 Competition}, author = {Valerie Renaudin and M. Ortiz and J. Perul and Joaquín Torres-Sospedra and A. Ramón Jimenez and Antoni Pérez-Navarro and Germán Martín Mendoza-Silva and F. Seco and Y. Landau and Marbel, R. and Ben-Moshe, B. and Zheng, X. and Ye, F. and Kuang, J. and Li, Y. and Niu, X. and Landa, V. and Hacohen, S. and Shvalb, N. and Lu, C. and Uchiyama, H. and Thomas, D. and Shimada, A. and Taniguchi, R. and Ding, Z. and Xu, F. and Kronenwett, N. and Vladimirov, B. and Lee, S. and Cho, E. and Jun, S. and Lee, C. and Park, S. and Lee, Y. and Rew, J. and Park, C. and Jeong, H. and Han, J. and Lee, K. and Zhang, W. and Li, X. and Wei, D. and Zhang, Y. and Park, S. Y. and Park, C. G. and Knauth, S. and Pipelidis, G. and Tsiamitros, N. and Lungenstrass, T. and Pablo Morales, J. and Trogh, J. and Plets, D. and Opiela, M. and Shih-Hau Fang Tsao, Y. and Chien, Y.-R. and Yang, S.-S. and Ye, S.-J. and Ali, M. U. and S. Hur and Y.I. Park}, url = {https://ieeexplore.ieee.org/document/8852722 }, year = {2019}, date = {2019-12-01}, journal = {IEEE Access}, volume = {7}, pages = {148594 – 148628}, abstract = {The Indoor Positioning and Indoor Navigation (IPIN) conference holds an annual competition in which indoor localization systems from different research groupsworldwide are evaluated empirically. The objective of this competition is to establish a systematic evaluation methodology with rigorous metrics both for real-time (on-site) and post-processing (off-site) situations, in a realistic environment unfamiliar to the prototype developers. For the IPIN 2018 conference, this competition was held on September 22nd, 2018, in Atlantis, a large shopping mall in Nantes (France). Four competition tracks (two on-site and two off-site) were designed. They consisted of several 1 km routes traversing several oors of the mall. Along these paths, 180 points were topographically surveyed with a 10 cm accuracy, to serve as ground truth landmarks, combining theodolite measurements, differential global navigation satellite system (GNSS) and 3D scanner systems. 34 teams effectively competed. The accuracy score corresponds to the third quartile (75th percentile) of an error metric that combines the horizontal positioning error and the oor detection. The best results for the on-site tracks showed an accuracy score of 11.70 m (Track 1) and 5.50 m (Track 2), while the best results for the off-site tracks showed an accuracy score of 0.90 m (Track 3) and 1.30m(Track 4). These results showed that it is possible to obtain high accuracy indoor positioning solutions in large, realistic environments using wearable light-weight sensors without deploying any beacon. This paper describes the organization work of the tracks, analyzes the methodology used to quantify the results, reviews the lessons learned from the competition and discusses its future}, keywords = {Indoor positioning}, pubstate = {published}, tppubtype = {article} } The Indoor Positioning and Indoor Navigation (IPIN) conference holds an annual competition in which indoor localization systems from different research groupsworldwide are evaluated empirically. The objective of this competition is to establish a systematic evaluation methodology with rigorous metrics both for real-time (on-site) and post-processing (off-site) situations, in a realistic environment unfamiliar to the prototype developers. For the IPIN 2018 conference, this competition was held on September 22nd, 2018, in Atlantis, a large shopping mall in Nantes (France). Four competition tracks (two on-site and two off-site) were designed. They consisted of several 1 km routes traversing several oors of the mall. Along these paths, 180 points were topographically surveyed with a 10 cm accuracy, to serve as ground truth landmarks, combining theodolite measurements, differential global navigation satellite system (GNSS) and 3D scanner systems. 34 teams effectively competed. The accuracy score corresponds to the third quartile (75th percentile) of an error metric that combines the horizontal positioning error and the oor detection. The best results for the on-site tracks showed an accuracy score of 11.70 m (Track 1) and 5.50 m (Track 2), while the best results for the off-site tracks showed an accuracy score of 0.90 m (Track 3) and 1.30m(Track 4). These results showed that it is possible to obtain high accuracy indoor positioning solutions in large, realistic environments using wearable light-weight sensors without deploying any beacon. This paper describes the organization work of the tracks, analyzes the methodology used to quantify the results, reviews the lessons learned from the competition and discusses its future |
Torres-Sospedra, Joaquín; Moreira, A; Mendoza-Silva, Germán Martín; Nicolau, M J; Matey-Sanz, Miguel; Silva, I; Huerta-Guijarro, Joaquín; Pendão, C Proceedings of the Tenth International Conference on Indoor Positioning and Indoor Navigation, 30 Sept. – 3 Oct. 2019, Pisa, Italy. , IEEE, 2019, ISBN: 978-1-7281-1788-1 . Abstract | Links | BibTeX | Tags: Indoor positioning, Sensors, Wi-Fi fingerprint @inproceedings{Torres-Sospedra2019b, title = {Exploiting Different Combinations of Complementary Sensor's data for Fingerprint-based Indoor Positioning in Industrial Environments}, author = {Joaquín Torres-Sospedra and A. Moreira and Germán Martín Mendoza-Silva and M. J. Nicolau and Miguel Matey-Sanz and I. Silva and Joaquín Huerta-Guijarro and C. Pendão }, doi = {10.1109/IPIN.2019.8911758 }, isbn = {978-1-7281-1788-1 }, year = {2019}, date = {2019-12-01}, booktitle = {Proceedings of the Tenth International Conference on Indoor Positioning and Indoor Navigation, 30 Sept. – 3 Oct. 2019, Pisa, Italy. }, publisher = {IEEE}, abstract = {Wi-Fi fingerprinting is a popular technique for smartphone-based indoor positioning. However, well-known RF propagation issues create signal fluctuations that translate into large positioning errors. Large errors limit the usage of Wi-Fi fingerprinting in industrial environments, where the reliability of position estimates is a key requirement. One successful approach to deal with signal fluctuations is to average the signals collected simultaneously through independent Wi-Fi interfaces. Another successful approach is to average the estimates provided by models built on independent radio maps. This paper explores multiple combinations of both approaches and determines the procedure to select the best model based on them through a simulated environment. The evaluation of the proposed model in a real-world industrial scenario shows that the positioning error (according to different metrics including the 95th and 99th percentiles) is highly improved with respect to the traditional fingerprint.}, keywords = {Indoor positioning, Sensors, Wi-Fi fingerprint}, pubstate = {published}, tppubtype = {inproceedings} } Wi-Fi fingerprinting is a popular technique for smartphone-based indoor positioning. However, well-known RF propagation issues create signal fluctuations that translate into large positioning errors. Large errors limit the usage of Wi-Fi fingerprinting in industrial environments, where the reliability of position estimates is a key requirement. One successful approach to deal with signal fluctuations is to average the signals collected simultaneously through independent Wi-Fi interfaces. Another successful approach is to average the estimates provided by models built on independent radio maps. This paper explores multiple combinations of both approaches and determines the procedure to select the best model based on them through a simulated environment. The evaluation of the proposed model in a real-world industrial scenario shows that the positioning error (according to different metrics including the 95th and 99th percentiles) is highly improved with respect to the traditional fingerprint. |
K. L. Li, ; Zlatanova, S; Torres-Sospedra, Joaquín; Pérez-Navarro, Antoni; Laoudias, C; Moreira, A Survey on Indoor Map Standards and Formats Inproceedings Proceedings of the Tenth International Conference on Indoor Positioning and Indoor Navigation, 30 Sept. – 3 Oct. 2019, Pisa, Italy, IEEE, 2019, ISBN: 978-1-7281-1788-1 . Abstract | BibTeX | Tags: Indoor positioning, OGC, Standards @inproceedings{Li2019, title = {Survey on Indoor Map Standards and Formats}, author = {K. L. Li, and S. Zlatanova and Joaquín Torres-Sospedra and Antoni Pérez-Navarro and C. Laoudias and A. Moreira}, isbn = {978-1-7281-1788-1 }, year = {2019}, date = {2019-12-01}, booktitle = {Proceedings of the Tenth International Conference on Indoor Positioning and Indoor Navigation, 30 Sept. – 3 Oct. 2019, Pisa, Italy}, publisher = {IEEE}, abstract = {With the adoption of indoor positioning solutions, which enable for a variety of location-based spatial services, a number of indoor map standards and formats have been proposed in the last decade. As each of these indoor map standard has its own purpose, the strengths and weaknesses are necessary to be understood and analyzed before selecting one of them for a given application. The Indoor Map Subcommittee has been established under IPIN/ISC in 2017. Among others, the goal of this working group is to compare available indoor map standards, provide a guideline for their application and advise on changes to their standardization development organizations if necessary. In this paper we present a survey of indoor map standards as an achievement of the subcommittee. The scope of the survey covers official standards such as IFC of BuildingSmart, IndoorGML and CityGML of OGC, and Indoor OpenStreetMap. We present several use-cases to show and discuss how to build indoor maps.}, keywords = {Indoor positioning, OGC, Standards}, pubstate = {published}, tppubtype = {inproceedings} } With the adoption of indoor positioning solutions, which enable for a variety of location-based spatial services, a number of indoor map standards and formats have been proposed in the last decade. As each of these indoor map standard has its own purpose, the strengths and weaknesses are necessary to be understood and analyzed before selecting one of them for a given application. The Indoor Map Subcommittee has been established under IPIN/ISC in 2017. Among others, the goal of this working group is to compare available indoor map standards, provide a guideline for their application and advise on changes to their standardization development organizations if necessary. In this paper we present a survey of indoor map standards as an achievement of the subcommittee. The scope of the survey covers official standards such as IFC of BuildingSmart, IndoorGML and CityGML of OGC, and Indoor OpenStreetMap. We present several use-cases to show and discuss how to build indoor maps. |
Jiménez, Antonio R; Seco, Fernando; Torres-Sospedra, Joaquín Tools for smartphone multi-sensor data registration and GT mapping for positioning applications Inproceedings Proceedings of the Tenth International Conference on Indoor Positioning and Indoor Navigation, 30 Sept. – 3 Oct. 2019, Pisa, Italy, IEEE, 2019, ISBN: 978-1-7281-1788-1 . Abstract | BibTeX | Tags: Indoor positioning, mobile GIS, Sensors @inproceedings{Jiménez2019, title = {Tools for smartphone multi-sensor data registration and GT mapping for positioning applications}, author = {Antonio R. Jiménez and Fernando Seco and Joaquín Torres-Sospedra }, isbn = {978-1-7281-1788-1 }, year = {2019}, date = {2019-12-01}, booktitle = {Proceedings of the Tenth International Conference on Indoor Positioning and Indoor Navigation, 30 Sept. – 3 Oct. 2019, Pisa, Italy}, publisher = {IEEE}, abstract = {Nowadays smartphones have impressive sensing and computation capabilities, allowing the registration and processing of multiple sources of information. This power enables the creation of useful applications, such as seamless location both outdoors and indoors. Research teams pay less interest in standardizing the acquisition and processing of sensor data than to research and innovation tasks, so each group develops its own private software tools to collect data. We want to contribute by creating a framework that allows a more coherent datastream registration and algorithm performance comparison. In this paper we present an open-source framework to make possible multi-sensor registration, which includes GetSensorData, our logging Android application. In order to ease the creation and sharing of experiments among different researchers around the world, the framework also includes the data format definition, the data parsers and the procedures to calibrate maps and to define the ground-truth trajectory for subsequent position algorithm performance comparison. Finally, we review applications of these tools in the IPIN competition as well as in teaching activities}, keywords = {Indoor positioning, mobile GIS, Sensors}, pubstate = {published}, tppubtype = {inproceedings} } Nowadays smartphones have impressive sensing and computation capabilities, allowing the registration and processing of multiple sources of information. This power enables the creation of useful applications, such as seamless location both outdoors and indoors. Research teams pay less interest in standardizing the acquisition and processing of sensor data than to research and innovation tasks, so each group develops its own private software tools to collect data. We want to contribute by creating a framework that allows a more coherent datastream registration and algorithm performance comparison. In this paper we present an open-source framework to make possible multi-sensor registration, which includes GetSensorData, our logging Android application. In order to ease the creation and sharing of experiments among different researchers around the world, the framework also includes the data format definition, the data parsers and the procedures to calibrate maps and to define the ground-truth trajectory for subsequent position algorithm performance comparison. Finally, we review applications of these tools in the IPIN competition as well as in teaching activities |
Gómez-Cambronero, Águeda; González-Pérez, Alberto; Miralles-Tena, Ignacio; Casteleyn, Sven Mixed reality escape room to promote learning in indoor environments Inproceedings EDULEARN19 Proceedings of 10th International Conference on Education and New Learning Technologies, pp. 5857-5864, IATED, Palma de Mallorca, 2019, ISBN: 978-84-09-12031-4 . Links | BibTeX | Tags: geogames, Indoor positioning, RyC-Casteleyn, videogame @inproceedings{Gómez-Cambronero2019, title = {Mixed reality escape room to promote learning in indoor environments}, author = {Águeda Gómez-Cambronero and Alberto González-Pérez and Ignacio Miralles-Tena and Sven Casteleyn}, doi = {10.21125/edulearn.2019.1412 }, isbn = {978-84-09-12031-4 }, year = {2019}, date = {2019-09-01}, booktitle = {EDULEARN19 Proceedings of 10th International Conference on Education and New Learning Technologies}, pages = {5857-5864}, publisher = {IATED}, address = {Palma de Mallorca}, keywords = {geogames, Indoor positioning, RyC-Casteleyn, videogame}, pubstate = {published}, tppubtype = {inproceedings} } |
Mendoza-Silva, Germán Martín; Matey-Sanz, Miguel; Torres-Sospedra, Joaquín; Huerta-Guijarro, Joaquín BLE RSS Measurements Dataset for Research on Accurate Indoor Positioning Journal Article Data, 4 (1), pp. 12, 2019, ISSN: 2306-5729. Abstract | Links | BibTeX | Tags: academic libraries, Indoor positioning, Wi-Fi fingerprint @article{germanb, title = {BLE RSS Measurements Dataset for Research on Accurate Indoor Positioning}, author = {Germán Martín Mendoza-Silva and Miguel Matey-Sanz and Joaquín Torres-Sospedra and Joaquín Huerta-Guijarro}, doi = {https://doi.org/10.3390/data4010012}, issn = {2306-5729}, year = {2019}, date = {2019-01-04}, journal = {Data}, volume = {4}, number = {1}, pages = {12}, abstract = {RSS-based indoor positioning is a consolidated research field for which several techniques have been proposed. Among them, Bluetooth Low Energy (BLE) beacons are a popular option for practical applications. This paper presents a new BLE RSS database that was created to aid in the development of new BLE RSS-based positioning methods and to encourage their reproducibility and comparability. The measurements were collected in two university zones: an area among bookshelves in a library and an area of an office space. Each zone had its own batch of deployed iBKS 105 beacons, configured to broadcast advertisements every 200 ms. The collection in the library zone was performed using three Android smartphones of different brands and models, with beacons broadcasting at −12 dBm transmission power, while in the other zone the collection was performed using of one those smartphones with beacons configured to advertise at the −4 dBm, −12 dBm and −20 dBm transmission powers. Supporting materials and scripts are provided along with the database, which annotate the BLE readings, provide details on the collection, the environment, and the BLE beacon deployments, ease the database usage, and introduce the reader to BLE RSS-based positioning and its challenges. The BLE RSS database and its supporting materials are available at the Zenodo repository under the open-source MIT license.}, keywords = {academic libraries, Indoor positioning, Wi-Fi fingerprint}, pubstate = {published}, tppubtype = {article} } RSS-based indoor positioning is a consolidated research field for which several techniques have been proposed. Among them, Bluetooth Low Energy (BLE) beacons are a popular option for practical applications. This paper presents a new BLE RSS database that was created to aid in the development of new BLE RSS-based positioning methods and to encourage their reproducibility and comparability. The measurements were collected in two university zones: an area among bookshelves in a library and an area of an office space. Each zone had its own batch of deployed iBKS 105 beacons, configured to broadcast advertisements every 200 ms. The collection in the library zone was performed using three Android smartphones of different brands and models, with beacons broadcasting at −12 dBm transmission power, while in the other zone the collection was performed using of one those smartphones with beacons configured to advertise at the −4 dBm, −12 dBm and −20 dBm transmission powers. Supporting materials and scripts are provided along with the database, which annotate the BLE readings, provide details on the collection, the environment, and the BLE beacon deployments, ease the database usage, and introduce the reader to BLE RSS-based positioning and its challenges. The BLE RSS database and its supporting materials are available at the Zenodo repository under the open-source MIT license. |
Mendoza-Silva, Germán Martín; Torres-Sospedra, Joaquín; Huerta-Guijarro, Joaquín A Meta-Review of Indoor Positioning Systems Journal Article Sensors, 19 (20), pp. 4507, 2019. Links | BibTeX | Tags: Indoor positioning @article{Mendoza-Silva2019b, title = {A Meta-Review of Indoor Positioning Systems }, author = {Germán Martín Mendoza-Silva and Joaquín Torres-Sospedra and Joaquín Huerta-Guijarro}, doi = {https://doi.org/10.3390/s19204507 }, year = {2019}, date = {2019-01-01}, journal = {Sensors}, volume = {19}, number = {20}, pages = {4507}, keywords = {Indoor positioning}, pubstate = {published}, tppubtype = {article} } |
Sansano, Emilio; Montoliu, Raul; Belmonte-Fernández, Óscar; Torres-Sospedra, Joaquín Indoor Positioning and Fingerprinting: The R package ipft Journal Article The R-Journal., 11 (1), 2019. Links | BibTeX | Tags: Indoor positioning, r, Wi-Fi fingerprint @article{Sansano2019, title = {Indoor Positioning and Fingerprinting: The R package ipft}, author = {Emilio Sansano and Raul Montoliu and Óscar Belmonte-Fernández and Joaquín Torres-Sospedra}, editor = { }, doi = {https://doi.org/10.32614/RJ-2019-010}, year = {2019}, date = {2019-01-01}, journal = {The R-Journal.}, volume = {11}, number = {1}, keywords = {Indoor positioning, r, Wi-Fi fingerprint}, pubstate = {published}, tppubtype = {article} } |
2018 |
Belmonte-Fernández, Óscar; Montoliu, Raúl; Torres-Sospedra, Joaquín; Sansano-Sansano, Emilio; Chia-Aguilar, Daniel A radiosity-based method to avoid calibration for indoor positioning systems Journal Article Expert Systems with Applications, 105 , pp. 89 - 101, 2018, ISSN: 0957-4174. Links | BibTeX | Tags: Classification algorithm, Indoor positioning, machine learning, Radiosity @article{BELMONTEFERNANDEZ201889, title = {A radiosity-based method to avoid calibration for indoor positioning systems}, author = {Óscar Belmonte-Fernández and Raúl Montoliu and Joaquín Torres-Sospedra and Emilio Sansano-Sansano and Daniel Chia-Aguilar}, url = {http://www.sciencedirect.com/science/article/pii/S0957417418302112}, doi = {https://doi.org/10.1016/j.eswa.2018.03.054}, issn = {0957-4174}, year = {2018}, date = {2018-09-01}, journal = {Expert Systems with Applications}, volume = {105}, pages = {89 - 101}, keywords = {Classification algorithm, Indoor positioning, machine learning, Radiosity}, pubstate = {published}, tppubtype = {article} } |
Conesa, Jordi; Pérez-Navarro, Antoni; Torres-Sospedra, Joaquín; Montoliu, Raul Geographical and Fingerprinting Data for Positioning and Navigation Systems: Challenges, Experiences and Technology Roadmap Book Academic Press, 2018, ISBN: 9780128131893. Abstract | BibTeX | Tags: Indoor localization, Indoor positioning, Wi-Fi fingerprint @book{Conesa2018, title = {Geographical and Fingerprinting Data for Positioning and Navigation Systems: Challenges, Experiences and Technology Roadmap}, author = {Jordi Conesa and Antoni Pérez-Navarro and Joaquín Torres-Sospedra and Raul Montoliu }, editor = {Jordi Conesa and Antoni Pérez-Navarro and Joaquín Torres-Sospedra and Raul Montoliu }, isbn = {9780128131893}, year = {2018}, date = {2018-08-01}, publisher = {Academic Press}, abstract = {Geographical and Fingerprinting Data for Positioning and Navigation Systems: Challenges, Experiences and Technology Roadmap explores the state-of-the -art software tools and innovative strategies to provide better understanding of positioning and navigation in indoor environments using fingerprinting techniques. The book provides the different problems and challenges of indoor positioning and navigation services and shows how fingerprinting can be used to address such necessities. This advanced publication provides the useful references educational institutions, industry, academic researchers, professionals, developers and practitioners need to apply, evaluate and reproduce this book’s contributions. The readers will learn how to apply the necessary infrastructure to provide fingerprinting services and scalable environments to deal with fingerprint data.}, keywords = {Indoor localization, Indoor positioning, Wi-Fi fingerprint}, pubstate = {published}, tppubtype = {book} } Geographical and Fingerprinting Data for Positioning and Navigation Systems: Challenges, Experiences and Technology Roadmap explores the state-of-the -art software tools and innovative strategies to provide better understanding of positioning and navigation in indoor environments using fingerprinting techniques. The book provides the different problems and challenges of indoor positioning and navigation services and shows how fingerprinting can be used to address such necessities. This advanced publication provides the useful references educational institutions, industry, academic researchers, professionals, developers and practitioners need to apply, evaluate and reproduce this book’s contributions. The readers will learn how to apply the necessary infrastructure to provide fingerprinting services and scalable environments to deal with fingerprint data. |
Torres-Sospedra, Joaquín; Richter, Philipp; Mendoza-Silva, Germán Martín; Lohan, Elena Simona; Huerta-Guijarro, Joaquín Supporting Materials for Characterising the Alteration in the AP Distribution with the RSS Distance and the Position Estimates Inproceedings Proceedings of the Ninth International Conference on Indoor Positioning and Indoor Navigation (IPIN), 2018, pp. 1-7, IEEE, 2018, ISBN: 978-1-5386-5635-8/18. Abstract | Links | BibTeX | Tags: Indoor positioning @inproceedings{zenodo2018ipin, title = {Supporting Materials for Characterising the Alteration in the AP Distribution with the RSS Distance and the Position Estimates}, author = {Joaquín Torres-Sospedra and Philipp Richter and Germán Martín Mendoza-Silva and Elena Simona Lohan and Joaquín Huerta-Guijarro}, url = {https://doi.org/10.5281/zenodo.1305414}, doi = {10.5281/zenodo.1305414}, isbn = {978-1-5386-5635-8/18}, year = {2018}, date = {2018-07-01}, booktitle = {Proceedings of the Ninth International Conference on Indoor Positioning and Indoor Navigation (IPIN), 2018}, number = {art. no. 8533791 }, pages = {1-7}, publisher = {IEEE}, abstract = {Fingerprinting is widely used for indoor positioning, where pattern matching techniques are usually applied to signals from APs or Beacons. However, the real-time monitoring of the emitters is not an easy task in most cases. When an alteration in the emitters is not detected or properly fixed, it might have a severe impact in the accuracy of the indoor positioning algorithm. Simple but common alterations are energy failure, emitter replacement, wrong emitter placement after maintenance and AP displacement. This paper explores how the AP alteration might be automatically detected by computing the average of the RSS distance to the best match over multiple operational points. The experimental setup consider one simulated and two real scenarios to validate the proposed metric for detecting AP alternation. The results show that it is possible to detect AP alteration when it has a considerable impact in the IPS accuracy}, keywords = {Indoor positioning}, pubstate = {published}, tppubtype = {inproceedings} } Fingerprinting is widely used for indoor positioning, where pattern matching techniques are usually applied to signals from APs or Beacons. However, the real-time monitoring of the emitters is not an easy task in most cases. When an alteration in the emitters is not detected or properly fixed, it might have a severe impact in the accuracy of the indoor positioning algorithm. Simple but common alterations are energy failure, emitter replacement, wrong emitter placement after maintenance and AP displacement. This paper explores how the AP alteration might be automatically detected by computing the average of the RSS distance to the best match over multiple operational points. The experimental setup consider one simulated and two real scenarios to validate the proposed metric for detecting AP alternation. The results show that it is possible to detect AP alteration when it has a considerable impact in the IPS accuracy |
Montoliu-Colás, Raúl; Sansano, E; Torres-Sospedra, Joaquín; Belmonte-Fernández, Óscar IndoorLoc Platform: A Web Tool to Support the Comparison of Indoor Positioning Systems Book Chapter Conesa, Jordi; Pérez-Navarro, Antonio; Torres-Sospedra, Joaquín; Montoliu-Colás, Raúl (Ed.): Geographical and Fingerprinting Data for Positioning and Navigation Systems: Challenges, Experiences and Technology Roadmap, Chapter 12, pp. 225-247, Academic Press, 2018, ISBN: 9780128131893. BibTeX | Tags: Indoor localization, Indoor positioning @inbook{Montoliu-Colás25.0, title = {IndoorLoc Platform: A Web Tool to Support the Comparison of Indoor Positioning Systems}, author = {Raúl Montoliu-Colás and E. Sansano and Joaquín Torres-Sospedra and Óscar Belmonte-Fernández}, editor = {Jordi Conesa and Antonio Pérez-Navarro and Joaquín Torres-Sospedra and Raúl Montoliu-Colás }, isbn = {9780128131893}, year = {2018}, date = {2018-03-29}, booktitle = {Geographical and Fingerprinting Data for Positioning and Navigation Systems: Challenges, Experiences and Technology Roadmap}, pages = {225-247}, publisher = {Academic Press}, chapter = {12}, keywords = {Indoor localization, Indoor positioning}, pubstate = {published}, tppubtype = {inbook} } |
Pérez-Navarro, Antoni; Torres-Sospedra, Joaquín; Montoliu-Colás, Raul; Conesa, Jordi; Berkvens, Rafael; Caso, Giuseppe; Costa, Constantinos; Dorigatti, Nicola; Hernández, Noelia; Knauth, Stefan; Lohan, Elena Simona; Machaj, Juraj; Moreira, Adriano; Wilk, Pawel Challenges of Fingerprinting in Indoor Positioning and Navigation Book Chapter Conesa J.; Pérez-Navarro, Torres-Sospedra Montoliu A-; J ; R (Ed.): Geographical and Fingerprinting Data for Positioning and Navigation Systems: Challenges, Experiences and Technology Roadmap, Chapter 1, pp. 1-20, Academic Press, 2018, ISBN: 9780128131893. BibTeX | Tags: Indoor localization, Indoor positioning, Wi-Fi fingerprint @inbook{Pérez-Navarro25.0, title = {Challenges of Fingerprinting in Indoor Positioning and Navigation}, author = {Antoni Pérez-Navarro and Joaquín Torres-Sospedra and Raul Montoliu-Colás and Jordi Conesa and Rafael Berkvens and Giuseppe Caso and Constantinos Costa and Nicola Dorigatti and Noelia Hernández and Stefan Knauth and Elena Simona Lohan and Juraj Machaj and Adriano Moreira and Pawel Wilk}, editor = {Conesa, J.; Pérez-Navarro, A-; Torres-Sospedra, J.; Montoliu, R.}, isbn = {9780128131893}, year = {2018}, date = {2018-03-15}, booktitle = {Geographical and Fingerprinting Data for Positioning and Navigation Systems: Challenges, Experiences and Technology Roadmap}, pages = {1-20}, publisher = {Academic Press}, chapter = {1}, keywords = {Indoor localization, Indoor positioning, Wi-Fi fingerprint}, pubstate = {published}, tppubtype = {inbook} } |
IF Journal
Journal
Book
Book chapter
Congress
Thesis & M. Thesis
2023 |
Exploiting Wireless Communications for Localization: Beyond Fingerprinting PhD Thesis Universitat Jaume I. INIT, 2023. |
Influence of Measured Radio Map Interpolation on Indoor Positioning Algorithms Journal Article IEEE Sensors Journal, 17 , pp. 20044-20054, 2023, ISSN: 1530-437X. |
Maximizing privacy and security of collaborative indoor positioning using zero-knowledge proofs Journal Article Internet of Things, 22 , pp. 100801, 2023, ISSN: 2542-6605. |
Collaborative Techniques for Indoor Positioning Systems PhD Thesis Universitat Jaume I. INIT, 2023, ISBN: 978-952-03-2905-1. |
From Compression of Wearable-based Data to Effortless Indoor Positioning PhD Thesis Tampere University. Faculty of Information Technology and Communication Sciences, 2023, ISBN: 978-952-03-2832-0. |
Direct Communication radio interface for new radio multicasting and cooperative positioning PhD Thesis Università Reggio Calabria, 2023. |
Cloud-based Indoor Positioning Platform for Context-adaptivity in GNSS-denied Scenarios PhD Thesis Universitat Jaume I. INIT, 2023. |
2022 |
A Collaborative Approach Using Neural Networks for BLE-RSS Lateration-Based Indoor Positioning Inproceedings 2022 International Joint Conference on Neural Networks (IJCNN), pp. 1-9, IEEE, 2022, ISBN: 978-1-7281-8671-9. |
2022 IEEE 12th International Conference on Indoor Positioning and Indoor Navigation (IPIN), IEEE, 2022, ISBN: 978-1-7281-6218-8. |
TrackInFactory: A Tight Coupling Particle Filter for Industrial Vehicle Tracking in Indoor Environments Journal Article IEEE Transactions on Systems, Man, and Cybernetics: Systems, 52 (7), pp. 4151 - 4162, 2022, ISSN: 2168-2232. |
Lightweight Hybrid CNN-ELM Model for Multi-building and Multi-floor Classification Inproceedings 2022 International Conference on Localization and GNSS (ICL-GNSS), pp. 1-6, IEEE, 2022. |
Data Cleansing for Indoor Positioning Wi-Fi Fingerprinting Datasets Inproceedings 2022 23rd IEEE International Conference on Mobile Data Management (MDM), pp. 349-354, IEEE, 2022, ISBN: 978-1-6654-5176-5. |
Mobile device-based Bluetooth Low Energy Database for range estimation in indoor environments Journal Article Scientific Data, 9 (281), 2022, ISSN: 2052-4463. |
Off-Line Evaluation of Indoor Positioning Systems in Different Scenarios: The Experiences From IPIN 2020 Competition Journal Article IEEE Sensors Journal, 22 (6), pp. 5011-5054, 2022, ISSN: 1558-1748. |
Cloud Platforms for Context-Adaptive Positioning and Localisation in GNSS-Denied Scenarios—A Systematic Review Journal Article Sensors, 22 (1), pp. 110, 2022, ISSN: 1424-8220. |
2021 |
Affinity Propagation Clustering for Older Adults Daily Routine Estimation Inproceedings Proceedings of the Eleventh International Conference on Indoor Positioning and Indoor Navigation, IEEE, 2021. |
Dioptra – A Data Generation Application for Indoor Positioning Systems Inproceedings Proceedings of the Eleventh International Conference on Indoor Positioning and Indoor Navigation, IEEE, 2021. |
Finding Optimal BLE Configuration for Indoor Positioning with Consumption Restrictions Inproceedings Proceedings of the Eleventh International Conference on Indoor Positioning and Indoor Navigation, IEEE, 2021. |
Lightweight Wi-Fi Fingerprinting with a Novel RSS Clustering Algorithm Inproceedings Proceedings of the Eleventh International Conference on Indoor Positioning and Indoor Navigation, IEEE, 2021. |
New trends in indoor positioning based on WiFi and machine learning: A systematic review Inproceedings Proceedings of the Eleventh International Conference on Indoor Positioning and Indoor Navigation, IEEE, 2021. |
Towards Ubiquitous Indoor Positioning: Comparing Systems across Heterogeneous Datasets Inproceedings Proceedings of the Eleventh International Conference on Indoor Positioning and Indoor Navigation, IEEE, 2021. |
Transfer Learning for Convolutional Indoor Positioning Systems Inproceedings Proceedings of the Eleventh International Conference on Indoor Positioning and Indoor Navigation, IEEE, 2021. |
A Lateration Method based on Effective Combinatorial Beacon Selection for Bluetooth Low Energy Indoor Positioning Inproceedings 2021 17th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), pp. 397-402, IEEE, 2021, ISBN: 978-1-6654-2854-5. |
Anonymous Attribute-based Credentials in Collaborative Indoor Positioning Systems Inproceedings Proceedings of the 18th International Conference on Security and Cryptography (SECRYPT 2021), pp. 791-797, SciTePress, 2021, ISBN: 978-989-758-524-1. |
Ensembling Multiple Radio Maps with Dynamic Noise in Fingerprint-based Indoor Positioning Inproceedings Proceedings of the 2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring), pp. 1-5, IEEE, 2021, ISBN: 978-1-7281-8965-9. |
Local-level Analysis of Positioning Errors in Wi-Fi Fingerprinting Inproceedings Proceedings of the 2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring), pp. 1-5, IEEE, 2021, ISBN: 978-1-7281-8964-2. |
Smartphone Distance Estimation Based on RSSI-Fuzzy Classification Approach Inproceedings Proceedings of the 2021 International Conference on Localization and GNSS (ICL-GNSS), pp. 1-6, IEEE, 2021, ISBN: 978-1-7281-9645-9. |
Discovering location based services: A unified approach for heterogeneous indoor localization systems Journal Article Internet of Things, 13 (1001511), 2021, ISSN: 2542-6605. |
Collaborative Indoor Positioning Systems: A Systematic Review Journal Article Sensors, 21 (3), pp. 1002, 2021. |
Beyond Euclidean Distance for Error Measurement in Pedestrian Indoor Location Journal Article IEEE Transactions on Instrumentation and Measurement, 70 (1001511), 2021. |
2020 |
Improving DBSCAN for Indoor Positioning Using Wi-Fi Radio Maps in Wearable and IoT Devices Inproceedings Proceedings of the 12th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT), 5-8 October 2020. Online event, pp. 208-213, 2020, ISBN: 978-1-7281-9281-9. |
The IPIN 2019 Indoor Localisation Competition: Description and Results Journal Article IEEE Access, 8 , pp. 206674-20671, 2020, ISSN: 2169-3536. |
Analysis of Received Signal Strength Quantization in Fingerprinting Localization Journal Article Sensors, 20 (3203), 2020, ISSN: 1424-8220. |
New Cluster Selection and Fine-grained Search for k-Means Clustering and Wi-Fi Fingerprinting Inproceedings 2020 International Conference on Localization and GNSS (ICL-GNSS), Tampere, Finland, 2020, pp. 1-6, IEEE, 2020, ISBN: 978-1-7281-6455-7. |
Sensor Fusion and Well Condition Triangle Approach for BLE-based Indoor Positioning Inproceedings Ometov, A; Nurmi, Jaarmi; Lohan, Elena Simona; Torres-Sospedra, Joaquín; (Eds), Kuusniemi H (Ed.): Proceedings of the International Conference on Localization and GNSS (ICL GNSS 2020) CEUR Workshop Proceedings. Tampere, Finland, June 2-4 2020, CEUR, 2020, ISSN: 1613-0073 . |
Multi-Slot BLE Raw Database for Accurate Positioning in Mixed Indoor/Outdoor Environments Journal Article Data, 5 , pp. 67, 2020. |
Sensor fusion of IMU and BLE using a well-condition triangle approach for BLE positioning Masters Thesis INIT, UJI, 2020. |
2019 |
Evaluating Indoor Positioning Systems in a Shopping Mall: The Lessons Learned from the IPIN 2018 Competition Journal Article IEEE Access, 7 , pp. 148594 – 148628, 2019. |
Proceedings of the Tenth International Conference on Indoor Positioning and Indoor Navigation, 30 Sept. – 3 Oct. 2019, Pisa, Italy. , IEEE, 2019, ISBN: 978-1-7281-1788-1 . |
Survey on Indoor Map Standards and Formats Inproceedings Proceedings of the Tenth International Conference on Indoor Positioning and Indoor Navigation, 30 Sept. – 3 Oct. 2019, Pisa, Italy, IEEE, 2019, ISBN: 978-1-7281-1788-1 . |
Tools for smartphone multi-sensor data registration and GT mapping for positioning applications Inproceedings Proceedings of the Tenth International Conference on Indoor Positioning and Indoor Navigation, 30 Sept. – 3 Oct. 2019, Pisa, Italy, IEEE, 2019, ISBN: 978-1-7281-1788-1 . |
Mixed reality escape room to promote learning in indoor environments Inproceedings EDULEARN19 Proceedings of 10th International Conference on Education and New Learning Technologies, pp. 5857-5864, IATED, Palma de Mallorca, 2019, ISBN: 978-84-09-12031-4 . |
BLE RSS Measurements Dataset for Research on Accurate Indoor Positioning Journal Article Data, 4 (1), pp. 12, 2019, ISSN: 2306-5729. |
A Meta-Review of Indoor Positioning Systems Journal Article Sensors, 19 (20), pp. 4507, 2019. |
Indoor Positioning and Fingerprinting: The R package ipft Journal Article The R-Journal., 11 (1), 2019. |
2018 |
A radiosity-based method to avoid calibration for indoor positioning systems Journal Article Expert Systems with Applications, 105 , pp. 89 - 101, 2018, ISSN: 0957-4174. |
Geographical and Fingerprinting Data for Positioning and Navigation Systems: Challenges, Experiences and Technology Roadmap Book Academic Press, 2018, ISBN: 9780128131893. |
Supporting Materials for Characterising the Alteration in the AP Distribution with the RSS Distance and the Position Estimates Inproceedings Proceedings of the Ninth International Conference on Indoor Positioning and Indoor Navigation (IPIN), 2018, pp. 1-7, IEEE, 2018, ISBN: 978-1-5386-5635-8/18. |
IndoorLoc Platform: A Web Tool to Support the Comparison of Indoor Positioning Systems Book Chapter Conesa, Jordi; Pérez-Navarro, Antonio; Torres-Sospedra, Joaquín; Montoliu-Colás, Raúl (Ed.): Geographical and Fingerprinting Data for Positioning and Navigation Systems: Challenges, Experiences and Technology Roadmap, Chapter 12, pp. 225-247, Academic Press, 2018, ISBN: 9780128131893. |
Challenges of Fingerprinting in Indoor Positioning and Navigation Book Chapter Conesa J.; Pérez-Navarro, Torres-Sospedra Montoliu A-; J ; R (Ed.): Geographical and Fingerprinting Data for Positioning and Navigation Systems: Challenges, Experiences and Technology Roadmap, Chapter 1, pp. 1-20, Academic Press, 2018, ISBN: 9780128131893. |