2024
Din, Teodor Constatin; Huerta-Guijarro, Joaquín; Trilles-Oliver, Sergio; Torres-Sospedra, Joaquín
Feasibility Analysis of Self-oriented Antennas for Indoor Positioning based on Direction-of-Arrival and Bluetooth-Low-Energy Proceedings Article
In: 2024 IEEE 14th International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 1-6, IEEE, 2024, ISBN: 979-8-3503-6641-9.
Abstract | Links | BibTeX | Tags: Antennas, Indoor localization, Indoor positioning
@inproceedings{Din2024a,
title = {Feasibility Analysis of Self-oriented Antennas for Indoor Positioning based on Direction-of-Arrival and Bluetooth-Low-Energy},
author = {Teodor Constatin Din and Joaquín Huerta-Guijarro and Sergio Trilles-Oliver and Joaquín Torres-Sospedra},
doi = {https://doi.org/10.1109/IPIN62893.2024.10786160},
isbn = {979-8-3503-6641-9},
year = {2024},
date = {2024-10-17},
booktitle = {2024 IEEE 14th International Conference on Indoor Positioning and Indoor Navigation (IPIN)},
pages = {1-6},
publisher = {IEEE},
abstract = {Indoor positioning systems have typically relied on static anchors, such as beacons or WiFi routers, and static environmental conditions, such as magnetic fields. On the other way around, it is also common to have fixed sensing devices, such as cameras, monitoring the environment, or passive receivers collecting relevant measurements from devices being tracked (signal strength, time/direction of arrival, among others). In one form or in another, we can usually find a static device. Furthermore, the evaluation of Radio Frequency-based positioning systems has commonly relied on measurements from static evaluation locations, being a continuous evaluation less frequent in solutions relying on, for instance, fingerprinting or based on signal strength. However, there is a need for a paradigm shift in indoor positioning systems as dynamic conditions are being slowly introduced. This paper introduces an exploratory analysis of a novel approach to be integrated into existing indoor positioning solutions based on Bluetooth Low Energy-based Direction-of-Arrival solutions. The core idea is to allow the infrastructure sensing the environment to adjust the orientation of the antennas, enhance the coverage, and provide better positioning of the devices being tracked. i.e., this approach will enable the system to evolve over time, continuously adapting to current environmental conditions. We describe the low-cost infrastructure needed to enable self-orientation for a commercial Bluetooth Low Energy board providing Direction-of-Arrival measurements.},
keywords = {Antennas, Indoor localization, Indoor positioning},
pubstate = {published},
tppubtype = {inproceedings}
}
Indoor positioning systems have typically relied on static anchors, such as beacons or WiFi routers, and static environmental conditions, such as magnetic fields. On the other way around, it is also common to have fixed sensing devices, such as cameras, monitoring the environment, or passive receivers collecting relevant measurements from devices being tracked (signal strength, time/direction of arrival, among others). In one form or in another, we can usually find a static device. Furthermore, the evaluation of Radio Frequency-based positioning systems has commonly relied on measurements from static evaluation locations, being a continuous evaluation less frequent in solutions relying on, for instance, fingerprinting or based on signal strength. However, there is a need for a paradigm shift in indoor positioning systems as dynamic conditions are being slowly introduced. This paper introduces an exploratory analysis of a novel approach to be integrated into existing indoor positioning solutions based on Bluetooth Low Energy-based Direction-of-Arrival solutions. The core idea is to allow the infrastructure sensing the environment to adjust the orientation of the antennas, enhance the coverage, and provide better positioning of the devices being tracked. i.e., this approach will enable the system to evolve over time, continuously adapting to current environmental conditions. We describe the low-cost infrastructure needed to enable self-orientation for a commercial Bluetooth Low Energy board providing Direction-of-Arrival measurements.
Klus, Roman; Talvitie, Jukka; Torres-Sospedra, Joaquín; Quezada-Gaibor, Darwin; Casteleyn, Sven; Cabric, Danijela; Valkama, Mikko
C2R: A Novel Architecture for Boosting Indoor Positioning With Scarce Data Journal Article
In: IEEE Internet of Things Journal, vol. 11, iss. 20, pp. 32868-32882, 2024, ISSN: 2327-4662.
Abstract | Links | BibTeX | Tags: deep learning, Indoor localization, Wi-Fi fingerprint
@article{Klus2025a,
title = {C2R: A Novel Architecture for Boosting Indoor Positioning With Scarce Data},
author = {Roman Klus and Jukka Talvitie and Joaquín Torres-Sospedra and Darwin Quezada-Gaibor and Sven Casteleyn and Danijela Cabric and Mikko Valkama},
doi = {https://doi.org/10.1109/JIOT.2024.3420122},
issn = {2327-4662},
year = {2024},
date = {2024-09-01},
urldate = {2024-09-01},
journal = {IEEE Internet of Things Journal},
volume = {11},
issue = {20},
pages = {32868-32882},
abstract = {Improving the performance of Artificial Neural Network (ANN) regression models on small or scarce datasets, such as wireless network positioning data, can be realized by simplifying the task. One such approach includes implementing the regression model as a classifier, followed by a probabilistic mapping algorithm that transforms class probabilities into the multi-dimensional regression output. In this work, we propose the so-called c2r, a novel ANN-based architecture that transforms the classification model into a robust regressor, while enabling end-to-end training. The proposed solution can remove the impact of less likely classes from the probabilistic mapping by implementing a novel, trainable differential thresholded Rectified Linear Unit layer. The proposed solution is introduced and evaluated in the indoor positioning application domain, using 23 real-world, openly available positioning datasets. The proposed C2R model is shown to achieve significant improvements over the numerous benchmark methods in terms of positioning accuracy. Specifically, when averaged across the 23 datasets, the proposed c2r improves the mean positioning error by 7.9% compared to weighted knn with k=3, from 5.43 m to 5.00 m, and by 15.4% compared to a dense neural network (DNN), from 5.91 m to 5.00 m, while adapting the learned threshold. Finally, the proposed method adds only a single training parameter to the ann, thus as shown through analytical and empirical means in the article, there is no significant increase in the computational complexity.},
keywords = {deep learning, Indoor localization, Wi-Fi fingerprint},
pubstate = {published},
tppubtype = {article}
}
Improving the performance of Artificial Neural Network (ANN) regression models on small or scarce datasets, such as wireless network positioning data, can be realized by simplifying the task. One such approach includes implementing the regression model as a classifier, followed by a probabilistic mapping algorithm that transforms class probabilities into the multi-dimensional regression output. In this work, we propose the so-called c2r, a novel ANN-based architecture that transforms the classification model into a robust regressor, while enabling end-to-end training. The proposed solution can remove the impact of less likely classes from the probabilistic mapping by implementing a novel, trainable differential thresholded Rectified Linear Unit layer. The proposed solution is introduced and evaluated in the indoor positioning application domain, using 23 real-world, openly available positioning datasets. The proposed C2R model is shown to achieve significant improvements over the numerous benchmark methods in terms of positioning accuracy. Specifically, when averaged across the 23 datasets, the proposed c2r improves the mean positioning error by 7.9% compared to weighted knn with k=3, from 5.43 m to 5.00 m, and by 15.4% compared to a dense neural network (DNN), from 5.91 m to 5.00 m, while adapting the learned threshold. Finally, the proposed method adds only a single training parameter to the ann, thus as shown through analytical and empirical means in the article, there is no significant increase in the computational complexity.
2023
Fryza, Tomas; Bravenec, Tomás; Kohl, Zdenek
Security and Reliability of Room Occupancy Detection Using Probe Requests in Smart Buildings Proceedings Article
In: 2023 33rd International Conference Radioelektronika (RADIOELEKTRONIKA, pp. 1-6, IEEE, 2023, ISBN: 979-8-3503-9835-9.
Abstract | Links | BibTeX | Tags: A-wear, Indoor localization, Smart Cities
@inproceedings{Bravenec2023a,
title = {Security and Reliability of Room Occupancy Detection Using Probe Requests in Smart Buildings},
author = {Tomas Fryza and Tomás Bravenec and Zdenek Kohl},
doi = {10.1109/RADIOELEKTRONIKA57919.2023.10109085},
isbn = {979-8-3503-9835-9},
year = {2023},
date = {2023-04-19},
booktitle = {2023 33rd International Conference Radioelektronika (RADIOELEKTRONIKA},
pages = {1-6},
publisher = {IEEE},
abstract = {We present new approaches for determining occupancy in smart building management systems. The solutions can be applied dually, in civil and military areas, not only for economic management but also in crisis situations when it is necessary to ensure the safety or rescue of citizens. Examining the occupancy of university workplaces can lead to future improvements in safety and energy consumption. In addition to common PIR-based motion methods, our implementation uses communication between mobile devices and infrastructure in the form of probe requests from Wi-Fi packets. The data are captured using sniffers based on ESP32 microcontrollers, then processed using Python. Thanks to this, the total number of people (respectively mobile devices) in the building can be estimated. The achieved RMSE estimation error was evaluated for minimal, small, and medium-sized room scenarios, respectively. Aspects of the use of smart building technologies are also considered in detail from the military point of view.},
keywords = {A-wear, Indoor localization, Smart Cities},
pubstate = {published},
tppubtype = {inproceedings}
}
We present new approaches for determining occupancy in smart building management systems. The solutions can be applied dually, in civil and military areas, not only for economic management but also in crisis situations when it is necessary to ensure the safety or rescue of citizens. Examining the occupancy of university workplaces can lead to future improvements in safety and energy consumption. In addition to common PIR-based motion methods, our implementation uses communication between mobile devices and infrastructure in the form of probe requests from Wi-Fi packets. The data are captured using sniffers based on ESP32 microcontrollers, then processed using Python. Thanks to this, the total number of people (respectively mobile devices) in the building can be estimated. The achieved RMSE estimation error was evaluated for minimal, small, and medium-sized room scenarios, respectively. Aspects of the use of smart building technologies are also considered in detail from the military point of view.
Torres-Sospedra, Joaquín; Quezada-Gaibor, Darwin; Nurmi, Jari; Koucheryavy, Yevgeni; Lohan, Elena Simona; Huerta-Guijarro, Joaquín
Scalable and Efficient Clustering for Fingerprint-Based Positioning Journal Article
In: IEEE Internet of Things Journal, vol. 10, no. 4, pp. 3484 - 3499, 2023, ISSN: 2327-4662.
Abstract | Links | BibTeX | Tags: Bluetooth Low Energy, Indoor localization, machine learning, Wi-Fi fingerprint
@article{Torres-Sospedra2023a,
title = {Scalable and Efficient Clustering for Fingerprint-Based Positioning},
author = {Joaquín Torres-Sospedra and Darwin Quezada-Gaibor and Jari Nurmi and Yevgeni Koucheryavy and Elena Simona Lohan and Joaquín Huerta-Guijarro},
doi = {10.1109/JIOT.2022.3230913},
issn = {2327-4662},
year = {2023},
date = {2023-02-13},
journal = {IEEE Internet of Things Journal},
volume = {10},
number = {4},
pages = {3484 - 3499},
abstract = {Indoor positioning based on IEEE 802.11 wireless LAN (Wi-Fi) fingerprinting needs a reference data set, also known as a radio map, in order to match the incoming fingerprint in the operational phase with the most similar fingerprint in the data set and then estimate the device position indoors. Scalability problems may arise when the radio map is large, e.g., providing positioning in large geographical areas or involving crowdsourced data collection. Some researchers divide the radio map into smaller independent clusters, such that the search area is reduced to less dense groups than the initial database with similar features. Thus, the computational load in the operational stage is reduced both at the user devices and on servers. Nevertheless, the clustering models are machine-learning algorithms without specific domain knowledge on indoor positioning or signal propagation. This work proposes several clustering variants to optimize the coarse and fine-grained search and evaluates them over different clustering models and data sets. Moreover, we provide guidelines to obtain efficient and accurate positioning depending on the data set features. Finally, we show that the proposed new clustering variants reduce the execution time by half and the positioning error by ≈7 % with respect to fingerprinting with the traditional clustering models.},
keywords = {Bluetooth Low Energy, Indoor localization, machine learning, Wi-Fi fingerprint},
pubstate = {published},
tppubtype = {article}
}
Indoor positioning based on IEEE 802.11 wireless LAN (Wi-Fi) fingerprinting needs a reference data set, also known as a radio map, in order to match the incoming fingerprint in the operational phase with the most similar fingerprint in the data set and then estimate the device position indoors. Scalability problems may arise when the radio map is large, e.g., providing positioning in large geographical areas or involving crowdsourced data collection. Some researchers divide the radio map into smaller independent clusters, such that the search area is reduced to less dense groups than the initial database with similar features. Thus, the computational load in the operational stage is reduced both at the user devices and on servers. Nevertheless, the clustering models are machine-learning algorithms without specific domain knowledge on indoor positioning or signal propagation. This work proposes several clustering variants to optimize the coarse and fine-grained search and evaluates them over different clustering models and data sets. Moreover, we provide guidelines to obtain efficient and accurate positioning depending on the data set features. Finally, we show that the proposed new clustering variants reduce the execution time by half and the positioning error by ≈7 % with respect to fingerprinting with the traditional clustering models.
2022
Klus, Lucie; Quezada-Gaibor, Darwin; Torres-Sospedra, Joaquín; Lohan, Elena Simona; Nurmi, Jari; Granell-Canut, Carlos; Huerta-Guijarro, Joaquín
Towards Accelerated Localization Performance Across Indoor Positioning Datasets Proceedings Article
In: 2022 International Conference on Localization and GNSS (ICL-GNSS), pp. 1-7, IEEE, 2022.
Abstract | Links | BibTeX | Tags: Indoor localization, machine learning
@inproceedings{Klus2022a,
title = {Towards Accelerated Localization Performance Across Indoor Positioning Datasets},
author = {Lucie Klus and Darwin Quezada-Gaibor 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/ICL-GNSS54081.2022.9797035},
year = {2022},
date = {2022-06-19},
booktitle = {2022 International Conference on Localization and GNSS (ICL-GNSS)},
pages = {1-7},
publisher = {IEEE},
abstract = {he localization speed and accuracy in the indoor scenario can greatly impact the Quality of Experience of the user. While many individual machine learning models can achieve comparable positioning performance, their prediction mechanisms offer different complexity to the system. In this work, we propose a fingerprinting positioning method for multi-building and multi-floor deployments, composed of a cascade of three models for building classification, floor classification, and 2D localization regression. We conduct an exhaustive search for the optimally performing one in each step of the cascade while validating on 14 different openly available datasets. As a result, we bring forward the best-performing combination of models in terms of overall positioning accuracy and processing speed and evaluate on independent sets of samples. We reduce the mean prediction time by 71% while achieving comparable positioning performance across all considered datasets. Moreover, in case of voluminous training dataset, the prediction time is reduced down to 1% of the benchmark's},
keywords = {Indoor localization, machine learning},
pubstate = {published},
tppubtype = {inproceedings}
}
he localization speed and accuracy in the indoor scenario can greatly impact the Quality of Experience of the user. While many individual machine learning models can achieve comparable positioning performance, their prediction mechanisms offer different complexity to the system. In this work, we propose a fingerprinting positioning method for multi-building and multi-floor deployments, composed of a cascade of three models for building classification, floor classification, and 2D localization regression. We conduct an exhaustive search for the optimally performing one in each step of the cascade while validating on 14 different openly available datasets. As a result, we bring forward the best-performing combination of models in terms of overall positioning accuracy and processing speed and evaluate on independent sets of samples. We reduce the mean prediction time by 71% while achieving comparable positioning performance across all considered datasets. Moreover, in case of voluminous training dataset, the prediction time is reduced down to 1% of the benchmark's
Renaudin, Valerie; Potorti, Francesco; Torres-Sospedra, Joaquín; Knauth, Stefan; O’keefe, Kyle; Park, Chan Gook; Sugimoto, Masanori; Wei, Dongyan; Nurmi, Jari
Guest Editorial Special Issue on Advanced Sensors and Sensing Technologies for Indoor Positioning and Navigation Journal Article
In: IEEE Sensors Journal, vol. 22, no. 6, pp. 4754-4754, 2022, ISBN: 1558-1748.
Abstract | Links | BibTeX | Tags: Indoor localization
@article{Renaudin2022a,
title = {Guest Editorial Special Issue on Advanced Sensors and Sensing Technologies for Indoor Positioning and Navigation},
author = {Valerie Renaudin and Francesco Potorti and Joaquín Torres-Sospedra and Stefan Knauth and Kyle O’keefe and Chan Gook Park and Masanori Sugimoto and Dongyan Wei and Jari Nurmi},
doi = {https://doi.org/10.1109/JSEN.2022.3150130},
isbn = {1558-1748},
year = {2022},
date = {2022-03-22},
journal = {IEEE Sensors Journal},
volume = {22},
number = {6},
pages = {4754-4754},
abstract = {Indoor localization is a growing research field and interest is expanding in many application fields, including services, measurement, mapping, security, and standardization. The quest for appropriate tracking technologies for COVID-19 pandemic control has shown us the importance of identifying the sensors data and processing that are suitable, accurate, reliable, and respectful of privacy. A prominent area is, therefore, that of sensors, where both improved hardware solutions and more powerful data analysis are required.},
keywords = {Indoor localization},
pubstate = {published},
tppubtype = {article}
}
Indoor localization is a growing research field and interest is expanding in many application fields, including services, measurement, mapping, security, and standardization. The quest for appropriate tracking technologies for COVID-19 pandemic control has shown us the importance of identifying the sensors data and processing that are suitable, accurate, reliable, and respectful of privacy. A prominent area is, therefore, that of sensors, where both improved hardware solutions and more powerful data analysis are required.
Mendoza-Silva, Germán Martin; Costa, Ana Cristina; Torres-Sospedra, Joaquín; Painho, Marco; Huerta-Guijarro, Joaquín
Environment-Aware Regression for Indoor Localization Based on WiFi Fingerprinting Journal Article
In: IEEE Sensors Journal, vol. 22, no. 6, pp. 4978-4988, 2022, ISSN: 1558-1748.
Abstract | Links | BibTeX | Tags: Indoor localization, Wi-Fi fingerprint
@article{Mendoza-Silva2022a,
title = {Environment-Aware Regression for Indoor Localization Based on WiFi Fingerprinting},
author = {Germán Martin Mendoza-Silva and Ana Cristina Costa and Joaquín Torres-Sospedra and Marco Painho and Joaquín Huerta-Guijarro},
doi = {https://doi.org/10.1109/JSEN.2021.3073878},
issn = {1558-1748},
year = {2022},
date = {2022-03-15},
journal = {IEEE Sensors Journal},
volume = {22},
number = {6},
pages = {4978-4988},
abstract = {Data enrichment through interpolation or regression is a common approach to deal with sample collection for Indoor Localization with WiFi fingerprinting. This paper provides guidelines on where to collect WiFi samples and proposes a new model for received signal strength regression. The new model creates vectors that describe the presence of obstacles between an access point and the collected samples. The vectors, the distance between the access point and the positions of the samples, and the collected, are used to train a Support Vector Regression. The experiments included some relevant analyses and showed that the proposed model improves received signal strength regression in terms of regression residuals and positioning accuracy.},
keywords = {Indoor localization, Wi-Fi fingerprint},
pubstate = {published},
tppubtype = {article}
}
Data enrichment through interpolation or regression is a common approach to deal with sample collection for Indoor Localization with WiFi fingerprinting. This paper provides guidelines on where to collect WiFi samples and proposes a new model for received signal strength regression. The new model creates vectors that describe the presence of obstacles between an access point and the collected samples. The vectors, the distance between the access point and the positions of the samples, and the collected, are used to train a Support Vector Regression. The experiments included some relevant analyses and showed that the proposed model improves received signal strength regression in terms of regression residuals and positioning accuracy.
2021
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
In: Internet of Things, vol. 13, no. 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.
2020
Furfari, Francesco; Crivello, Antonino; Baronti, Paolo; Barsocchi, Paolo; Girolami, Michele; Palumbo, Filippo; Quezada-Gaibor, Darwin; Mendoza-Silva, Germán Martín; Torres-Sospedra, Joaquín
Discovering location based services: A unified approach for heterogeneous indoor localization systems Journal Article
In: Internet of things, vol. 13, pp. 100334, 2020.
Abstract | Links | BibTeX | Tags: A-wear, Indoor localization
@article{Furfari2020,
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 Martín Mendoza-Silva and Joaquín Torres-Sospedra },
doi = {https://doi.org/10.1016/j.iot.2020.100334 },
year = {2020},
date = {2020-02-04},
journal = {Internet of things},
volume = {13},
pages = {100334},
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 = {A-wear, Indoor localization},
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.
2018
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.
The readers will learn how to apply the necessary infrastructure to provide fingerprinting services and scalable environments to deal with fingerprint data.
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
In: 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
In: J.; Pérez-Navarro Conesa, A-; Torres-Sospedra (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}
}
Torres-Sospedra, Joaquín; Jiménez, Antonio R.; Moreira, Adriano; Lungenstrass, Tomás; Lu, Wei-Chung; Knauth, Stefan; Mendoza-Silva, Germán Martín; Seco, Fernando; Pérez-Navarro, Antoni; Nicolau, Maria João; Costa, António; Meneses, Filipe; Farina, Joaquín; Morales, Juan Pablo; Lu, Wen-Chen; Cheng, Ho-Ti; Yang, Shi-Shen; Fang, Shih-Hau; Chien, Ying-Ren; Tsao, Yu
Off-line evaluation of mobile-centric Indoor Positioning Systems: the experiences from the 2017 IPIN competition Journal Article
In: Sensors, vol. 18, no. 2, pp. 487, 2018, ISSN: 1424-8220.
Abstract | Links | BibTeX | Tags: Indoor localization, Indoor positioning
@article{Torres-Sospedra2018,
title = {Off-line evaluation of mobile-centric Indoor Positioning Systems: the experiences from the 2017 IPIN competition},
author = {Joaquín Torres-Sospedra and Antonio R. Jiménez and Adriano Moreira and Tomás Lungenstrass and Wei-Chung Lu and Stefan Knauth and Germán Martín Mendoza-Silva and Fernando Seco and Antoni Pérez-Navarro and Maria João Nicolau and António Costa and Filipe Meneses and Joaquín Farina and Juan Pablo Morales and Wen-Chen Lu and Ho-Ti Cheng and Shi-Shen Yang and Shih-Hau Fang and Ying-Ren Chien and Yu Tsao },
doi = {https://doi.org/10.3390/s18020487},
issn = {1424-8220},
year = {2018},
date = {2018-03-01},
journal = {Sensors},
volume = {18},
number = {2},
pages = {487},
abstract = {The development of indoor positioning solutions using smartphones is a growing activity with an enormous potential for everyday life and professional applications. The research activities on this topic concentrate on the development of new positioning solutions that are tested in specific environments under their own evaluation metrics. To explore the real positioning quality of smartphone-based solutions and their capabilities for seamlessly adapting to different scenarios, it is needed to find fair evaluation frameworks. The design of competitions using extensive pre-recorded datasets is a valid way to generate open data for comparing the different solutions created by research teams. In this paper, we discuss the details of the 2017 IPIN indoor localization competition, the different datasets created, the teams participating in the event, and the results they obtained. We compare these results with other competition-based approaches (Microsoft and Perf-loc) and on-line evaluation web sites. The lessons learned by organising these competitions and the benefits for the community are addressed along the paper. Our analysis paves the way for future developments on the standardization of evaluations and for creating a widely-adopted benchmark strategy for researchers and companies in the field},
keywords = {Indoor localization, Indoor positioning},
pubstate = {published},
tppubtype = {article}
}
The development of indoor positioning solutions using smartphones is a growing activity with an enormous potential for everyday life and professional applications. The research activities on this topic concentrate on the development of new positioning solutions that are tested in specific environments under their own evaluation metrics. To explore the real positioning quality of smartphone-based solutions and their capabilities for seamlessly adapting to different scenarios, it is needed to find fair evaluation frameworks. The design of competitions using extensive pre-recorded datasets is a valid way to generate open data for comparing the different solutions created by research teams. In this paper, we discuss the details of the 2017 IPIN indoor localization competition, the different datasets created, the teams participating in the event, and the results they obtained. We compare these results with other competition-based approaches (Microsoft and Perf-loc) and on-line evaluation web sites. The lessons learned by organising these competitions and the benefits for the community are addressed along the paper. Our analysis paves the way for future developments on the standardization of evaluations and for creating a widely-adopted benchmark strategy for researchers and companies in the field
2017
Torres-Sospedra, Joaquín; Jiménez, Antonio R; Knauth, Stefan; Moreira, Adriano; Beer, Yair; Fetzer, Toni; Ta, Viet-Cuong; Montoliu, Raul; Seco, Fernando; Mendoza-Silva, Germán Martín; Belmonte, Oscar; Koukofikis, Athanasios; Nicolau, Maria João; Costa, António; Meneses, Filipe; Ebner, Frank; Deinzer, Frank; Vaufreydaz, Dominique; Dao, Trung-Kien; Castelli, Eric
The Smartphone-Based Offline Indoor Location Competition at IPIN 2016: Analysis and Future Work Journal Article
In: Sensors, vol. 17, no. 3, ARTICLE NUMBER = 557, 2017, ISSN: 1424-8220.
Abstract | Links | BibTeX | Tags: Indoor localization
@article{s17030557,
title = {The Smartphone-Based Offline Indoor Location Competition at IPIN 2016: Analysis and Future Work},
author = {Joaquín Torres-Sospedra and Antonio R Jiménez and Stefan Knauth and Adriano Moreira and Yair Beer and Toni Fetzer and Viet-Cuong Ta and Raul Montoliu and Fernando Seco and Germán Martín Mendoza-Silva and Oscar Belmonte and Athanasios Koukofikis and Maria João Nicolau and António Costa and Filipe Meneses and Frank Ebner and Frank Deinzer and Dominique Vaufreydaz and Trung-Kien Dao and Eric Castelli},
url = {http://www.mdpi.com/1424-8220/17/3/557},
doi = {10.3390/s17030557},
issn = {1424-8220},
year = {2017},
date = {2017-03-10},
journal = {Sensors},
volume = {17},
number = {3, ARTICLE NUMBER = 557},
abstract = {This paper presents the analysis and discussion of the off-site localization competition track, which took place during the Seventh International Conference on Indoor Positioning and Indoor Navigation (IPIN 2016). Five international teams proposed different strategies for smartphone-based indoor positioning using the same reference data. The competitors were provided with several smartphone-collected signal datasets, some of which were used for training (known trajectories), and others for evaluating (unknown trajectories). The competition permits a coherent evaluation method of the competitors’ estimations, where inside information to fine-tune their systems is not offered, and thus provides, in our opinion, a good starting point to introduce a fair comparison between the smartphone-based systems found in the literature. The methodology, experience, feedback from competitors and future working lines are described.},
keywords = {Indoor localization},
pubstate = {published},
tppubtype = {article}
}
This paper presents the analysis and discussion of the off-site localization competition track, which took place during the Seventh International Conference on Indoor Positioning and Indoor Navigation (IPIN 2016). Five international teams proposed different strategies for smartphone-based indoor positioning using the same reference data. The competitors were provided with several smartphone-collected signal datasets, some of which were used for training (known trajectories), and others for evaluating (unknown trajectories). The competition permits a coherent evaluation method of the competitors’ estimations, where inside information to fine-tune their systems is not offered, and thus provides, in our opinion, a good starting point to introduce a fair comparison between the smartphone-based systems found in the literature. The methodology, experience, feedback from competitors and future working lines are described.
2016
Mendoza-Silva, Germán Martín; Torres-Sospedra, Joaquín; Huerta-Guijarro, Joaquín; Montoliu, Raul; Benítez, Fernando; Belmonte, Oscar
Situation Goodness Method for Weighted Centroid-Based Wi-Fi APs Localization Conference
Progress in Location-Based Services 2016, Springer International Publishing, Cham, 2016, ISBN: 978-3-319-47289-8.
Abstract | Links | BibTeX | Tags: Indoor localization, Interpolation, LBS, Weighted centroid, Wi-Fi mapping
@conference{Mendoza-Silva2017,
title = {Situation Goodness Method for Weighted Centroid-Based Wi-Fi APs Localization},
author = {Germán Martín Mendoza-Silva and Joaquín Torres-Sospedra and Joaquín Huerta-Guijarro and Raul Montoliu and Fernando Benítez and Oscar Belmonte},
editor = {Gartner, Georg and Huang, Haosheng},
url = {http://dx.doi.org/10.1007/978-3-319-47289-8_2},
doi = {10.1007/978-3-319-47289-8_2},
isbn = {978-3-319-47289-8},
year = {2016},
date = {2016-10-13},
booktitle = {Progress in Location-Based Services 2016},
pages = {27--47},
publisher = {Springer International Publishing},
address = {Cham},
abstract = {Knowing the location of Wi-Fi antennas may be critical for indoor localization. However, in a real environment, their positions may be unknown since they can be managed by external entities. This paper introduces a new method for evaluating the suitability of using the weighted centroid method for the 2D localization of a Wi-Fi AP. The method is based on the idea that the weighted centroid method provides its best results when there are fingerprints taken around the AP. In order to find the probability of being in the presence of such situations, a natural neighbor interpolation method is used to find the regions with the highest signal strengths. A geometrical method is then used to characterize that probability based on the distribution of those regions in relation to the AP position estimation given by the weighted centroid method. The paper describes the testing location and the used Wi-Fi fingerprints database. That database is used to create new databases that recreate different sampling possibilities through a samples deletion strategy. The original database and the newly created ones are then used to evaluate the localization results of several AP localization methods and the new method proposed in this paper. The evaluation results have shown that the proposed method is able to provide a proper probability for the suitability of using the weighted centroid method for localizing a Wi-Fi AP.},
keywords = {Indoor localization, Interpolation, LBS, Weighted centroid, Wi-Fi mapping},
pubstate = {published},
tppubtype = {conference}
}
Knowing the location of Wi-Fi antennas may be critical for indoor localization. However, in a real environment, their positions may be unknown since they can be managed by external entities. This paper introduces a new method for evaluating the suitability of using the weighted centroid method for the 2D localization of a Wi-Fi AP. The method is based on the idea that the weighted centroid method provides its best results when there are fingerprints taken around the AP. In order to find the probability of being in the presence of such situations, a natural neighbor interpolation method is used to find the regions with the highest signal strengths. A geometrical method is then used to characterize that probability based on the distribution of those regions in relation to the AP position estimation given by the weighted centroid method. The paper describes the testing location and the used Wi-Fi fingerprints database. That database is used to create new databases that recreate different sampling possibilities through a samples deletion strategy. The original database and the newly created ones are then used to evaluate the localization results of several AP localization methods and the new method proposed in this paper. The evaluation results have shown that the proposed method is able to provide a proper probability for the suitability of using the weighted centroid method for localizing a Wi-Fi AP.
Jordán, Emilio Troncho
A prospective geoinformatic approach to indoor navigation for Unmanned Air System (UAS) by use of quick response (QR) codes Masters Thesis
2016.
BibTeX | Tags: Indoor localization, Indoor positioning, Mastergeotech, unmanned air systems
@mastersthesis{Jordán2016,
title = {A prospective geoinformatic approach to indoor navigation for Unmanned Air System (UAS) by use of quick response (QR) codes },
author = {Emilio Troncho Jordán},
editor = {Ignacio Guerrero (supervisor) and Torsten Prinz (co-supervisor) and Roberto Henriques (co-supervisor)},
year = {2016},
date = {2016-02-26},
keywords = {Indoor localization, Indoor positioning, Mastergeotech, unmanned air systems},
pubstate = {published},
tppubtype = {mastersthesis}
}