2022
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
Mendoza-Silva, Germán Martin; Torres-Sospedra, Joaquín; Huerta-Guijarro, Joaquín
Local-level Analysis of Positioning Errors in Wi-Fi Fingerprinting Proceedings Article
In: 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.
2020
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 Proceedings Article
In: 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.
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.
2019
Torres-Sospedra, Joaquín; Moreira, A.; Mendoza-Silva, Germán Martín; Nicolau, M. J.; Matey-Sanz, Miguel; Silva, I.; Huerta-Guijarro, Joaquín; Pendão, C.
Exploiting Different Combinations of Complementary Sensor's data for Fingerprint-based Indoor Positioning in Industrial Environments Proceedings Article
In: 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.
Rojo, Jordi; Mendoza-Silva, Germán Martín; Cidral, Gabriel Ristow; Laiapea, Jorma; Parrello, Gerardo; Simó, Arnau; Stupin, Laura; Minican, Deniz; Farrés, María; Corvalán, Carmen; Unger, Florian; López, Sara Marín-; Soteras, Ignacio; Bravo, Daniel Castejón; Torres-Sospedra, Joaquín
Machine Learning applied to Wi-Fi fingerprinting: The experiences of the Ubiqum Challenge Proceedings Article
In: 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: machine learning, Wi-Fi fingerprint
@inproceedings{Rojo2019,
title = {Machine Learning applied to Wi-Fi fingerprinting: The experiences of the Ubiqum Challenge },
author = {Jordi Rojo and Germán Martín Mendoza-Silva and Gabriel Ristow Cidral and Jorma Laiapea and Gerardo Parrello and Arnau Simó and Laura Stupin and Deniz Minican and María Farrés and Carmen Corvalán and
Florian Unger and Sara Marín- López and Ignacio Soteras and Daniel Castejón Bravo 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 = {Wi-Fi Fingerprinting is widely adopted for smartphone-based indoor positioning systems due to the availability of already deployed infrastructure for communications. The UJIIndoorLoc database contains Wi-Fi data for indoor positioning in a large environment covering three multi-tier buildings collected with multiple devices. Since the evaluation set is private, the indoor positioning systems of developers and researchers can still be evaluated under the same evaluation conditions than the participants of the 2015 EvAAL-ETRI competition. This paper shows the results and the experiences of such kind of external evaluation based on a competition provided by the the students of the “Data Analytics and Machine Learning” program of the Ubiqum data academy, who applied machine learning models they learnt during the program. The results show that state-ofart Machine Learning methods provide good positioning results, but expertise on the problem is still needed},
keywords = {machine learning, Wi-Fi fingerprint},
pubstate = {published},
tppubtype = {inproceedings}
}
Wi-Fi Fingerprinting is widely adopted for smartphone-based indoor positioning systems due to the availability of already deployed infrastructure for communications. The UJIIndoorLoc database contains Wi-Fi data for indoor positioning in a large environment covering three multi-tier buildings collected with multiple devices. Since the evaluation set is private, the indoor positioning systems of developers and researchers can still be evaluated under the same evaluation conditions than the participants of the 2015 EvAAL-ETRI competition. This paper shows the results and the experiences of such kind of external evaluation based on a competition provided by the the students of the “Data Analytics and Machine Learning” program of the Ubiqum data academy, who applied machine learning models they learnt during the program. The results show that state-ofart Machine Learning methods provide good positioning results, but expertise on the problem is still needed
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
In: Data, vol. 4, no. 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.
2018
Mendoza-Silva, Germán Martín; Torres-Sospedra, Joaquín; Huerta-Guijarro, Joaquín
Locations Selection for Periodic Radio Map Update in WiFi Fingerprinting Conference
Progress in Location Based Services 2018, Springer International Publishing, Cham, 2018, ISBN: 978-3-319-71470-7.
Abstract | Links | BibTeX | Tags: Indoor positioning, Wi-Fi fingerprint
@conference{Mendoza-Silva2018,
title = {Locations Selection for Periodic Radio Map Update in WiFi Fingerprinting},
author = {Germán Martín Mendoza-Silva and Joaquín Torres-Sospedra and Joaquín Huerta-Guijarro},
editor = {Kiefer, Peter and Huang, Haosheng and Van de Weghe, Nico and Raubal, Martin},
url = {https://doi.org/10.1007/978-3-319-71470-7_1},
doi = {10.1007/978-3-319-71470-7_1},
isbn = {978-3-319-71470-7},
year = {2018},
date = {2018-01-01},
booktitle = {Progress in Location Based Services 2018},
pages = {3-24},
publisher = {Springer International Publishing},
address = {Cham},
abstract = {The construction and update of a radio map are usually referred as the main drawbacks of WiFi fingerprinting, a very popular method in indoor localization research. For radio map update, some studies suggest taking new measurements at some random locations, usually from the ones used in the radio map construction. In this paper, we argue that the locations should not be random, and propose how to determine them. Given the set locations where the measurements used for the initial radio map construction were taken, a subset of locations for the update measurements is chosen through optimization so that the remaining locations found in the initial measurements are best approximated through regression. The regression method is Support Vector Regression (SVR) and the optimization is achieved using a genetic algorithm approach. We tested our approach using a database of WiFi measurements collected at a relatively dense set of locations during ten months in a university library setting. The experiments results show that, if no dramatic event occurs (e.g., relevant WiFi networks are changed), our approach outperforms other strategies for determining the collection locations for periodic updates. We also present a clear guide on how to conduct the radio map updates.},
keywords = {Indoor positioning, Wi-Fi fingerprint},
pubstate = {published},
tppubtype = {conference}
}
The construction and update of a radio map are usually referred as the main drawbacks of WiFi fingerprinting, a very popular method in indoor localization research. For radio map update, some studies suggest taking new measurements at some random locations, usually from the ones used in the radio map construction. In this paper, we argue that the locations should not be random, and propose how to determine them. Given the set locations where the measurements used for the initial radio map construction were taken, a subset of locations for the update measurements is chosen through optimization so that the remaining locations found in the initial measurements are best approximated through regression. The regression method is Support Vector Regression (SVR) and the optimization is achieved using a genetic algorithm approach. We tested our approach using a database of WiFi measurements collected at a relatively dense set of locations during ten months in a university library setting. The experiments results show that, if no dramatic event occurs (e.g., relevant WiFi networks are changed), our approach outperforms other strategies for determining the collection locations for periodic updates. We also present a clear guide on how to conduct the radio map updates.
Mendoza-Silva, Germán Martín; Richter, Philipp; Torres-Sospedra, Joaquín; Lohan, Elana Simona; Huerta-Guijarro, Joaquín
Long-Term WiFi Fingerprinting Dataset for Research on Robust Indoor Positioning Journal Article
In: Data, vol. 3, no. 1, pp. 3, 2018, ISSN: 2306-5729.
Abstract | Links | BibTeX | Tags: Indoor positioning, Wi-Fi fingerprint
@article{data3010003,
title = {Long-Term WiFi Fingerprinting Dataset for Research on Robust Indoor Positioning},
author = {Germán Martín Mendoza-Silva and Philipp Richter and Joaquín Torres-Sospedra and Elana Simona Lohan and Joaquín Huerta-Guijarro},
url = {http://www.mdpi.com/2306-5729/3/1/3},
doi = {10.3390/data3010003},
issn = {2306-5729},
year = {2018},
date = {2018-01-01},
journal = {Data},
volume = {3},
number = {1},
pages = {3},
abstract = {WiFi fingerprinting, one of the most popular methods employed in indoor positioning, currently faces two major problems: lack of robustness to short and long time signal changes and difficult reproducibility of new methods presented in the relevant literature. This paper presents a WiFi RSS (Received Signal Strength) database created to foster and ease research works that address the above-mentioned two problems. A trained professional took several consecutive fingerprints while standing at specific positions and facing specific directions. The consecutive fingerprints may enable the study of short-term signals variations. The data collection spanned over 15 months, and, for each month, one type of training datasets and five types of test datasets were collected. The measurements of a dataset type (training or test) were taken at the same positions and directions every month, in order to enable the analysis of long-term signal variations. The database is provided with supporting materials and software, which give more information about the collection environment and eases the database utilization, respectively. The WiFi measurements and the supporting materials are available at the Zenodo repository under the open-source MIT license.},
keywords = {Indoor positioning, Wi-Fi fingerprint},
pubstate = {published},
tppubtype = {article}
}
WiFi fingerprinting, one of the most popular methods employed in indoor positioning, currently faces two major problems: lack of robustness to short and long time signal changes and difficult reproducibility of new methods presented in the relevant literature. This paper presents a WiFi RSS (Received Signal Strength) database created to foster and ease research works that address the above-mentioned two problems. A trained professional took several consecutive fingerprints while standing at specific positions and facing specific directions. The consecutive fingerprints may enable the study of short-term signals variations. The data collection spanned over 15 months, and, for each month, one type of training datasets and five types of test datasets were collected. The measurements of a dataset type (training or test) were taken at the same positions and directions every month, in order to enable the analysis of long-term signal variations. The database is provided with supporting materials and software, which give more information about the collection environment and eases the database utilization, respectively. The WiFi measurements and the supporting materials are available at the Zenodo repository under the open-source MIT license.
2017
Potortì, Francesco; Park, Sangjoon; Ruiz, Antonio Ramón Jiménez; Barsocchi, Paolo; Girolami, Michele; Crivello, Antonino; Lee, So Yeon; Lim, Jae Hyun; Torres-Sospedra, Joaquín; Seco, Fernando; Montoliu, Raul; Mendoza-Silva, Germán Martín; Rubio, Maria Del Carmen Pérez; Losada-Gutiérrez, Cristina; Espinosa, Felipe; Macias-Guarasa, Javier
Comparing the Performance of Indoor Localization Systems through the EvAAL Framework Journal Article
In: Sensors, vol. 17, no. 10, pp. 2327, 2017, ISSN: 1424-8220.
Abstract | Links | BibTeX | Tags: Indoor positioning, Wi-Fi fingerprint
@article{s17102327,
title = {Comparing the Performance of Indoor Localization Systems through the EvAAL Framework},
author = {Francesco Potortì and Sangjoon Park and Antonio Ramón Jiménez Ruiz and Paolo Barsocchi and Michele Girolami and Antonino Crivello and So Yeon Lee and Jae Hyun Lim and Joaquín Torres-Sospedra and Fernando Seco and Raul Montoliu and Germán Martín Mendoza-Silva and Maria Del Carmen Pérez Rubio and Cristina Losada-Gutiérrez and Felipe Espinosa and Javier Macias-Guarasa},
url = {http://www.mdpi.com/1424-8220/17/10/2327},
doi = {10.3390/s17102327},
issn = {1424-8220},
year = {2017},
date = {2017-10-03},
journal = {Sensors},
volume = {17},
number = {10},
pages = {2327},
abstract = {In recent years, indoor localization systems have been the object of significant research activity and of growing interest for their great expected social impact and their impressive business potential. Application areas include tracking and navigation, activity monitoring, personalized advertising, Active and Assisted Living (AAL), traceability, Internet of Things (IoT) networks, and Home-land Security. In spite of the numerous research advances and the great industrial interest, no canned solutions have yet been defined. The diversity and heterogeneity of applications, scenarios, sensor and user requirements, make it difficult to create uniform solutions. From that diverse reality, a main problem is derived that consists in the lack of a consensus both in terms of the metrics and the procedures used to measure the performance of the different indoor localization and navigation proposals. This paper introduces the general lines of the EvAAL benchmarking framework, which is aimed at a fair comparison of indoor positioning systems through a challenging competition under complex, realistic conditions. To evaluate the framework capabilities, we show how it was used in the 2016 Indoor Positioning and Indoor Navigation (IPIN) Competition. The 2016 IPIN competition considered three different scenario dimensions, with a variety of use cases: (1) pedestrian versus robotic navigation, (2) smartphones versus custom hardware usage and (3) real-time positioning versus off-line post-processing. A total of four competition tracks were evaluated under the same EvAAL benchmark framework in order to validate its potential to become a standard for evaluating indoor localization solutions. The experience gained during the competition and feedback from track organizers and competitors showed that the EvAAL framework is flexible enough to successfully fit the very different tracks and appears adequate to compare indoor positioning systems.},
keywords = {Indoor positioning, Wi-Fi fingerprint},
pubstate = {published},
tppubtype = {article}
}
In recent years, indoor localization systems have been the object of significant research activity and of growing interest for their great expected social impact and their impressive business potential. Application areas include tracking and navigation, activity monitoring, personalized advertising, Active and Assisted Living (AAL), traceability, Internet of Things (IoT) networks, and Home-land Security. In spite of the numerous research advances and the great industrial interest, no canned solutions have yet been defined. The diversity and heterogeneity of applications, scenarios, sensor and user requirements, make it difficult to create uniform solutions. From that diverse reality, a main problem is derived that consists in the lack of a consensus both in terms of the metrics and the procedures used to measure the performance of the different indoor localization and navigation proposals. This paper introduces the general lines of the EvAAL benchmarking framework, which is aimed at a fair comparison of indoor positioning systems through a challenging competition under complex, realistic conditions. To evaluate the framework capabilities, we show how it was used in the 2016 Indoor Positioning and Indoor Navigation (IPIN) Competition. The 2016 IPIN competition considered three different scenario dimensions, with a variety of use cases: (1) pedestrian versus robotic navigation, (2) smartphones versus custom hardware usage and (3) real-time positioning versus off-line post-processing. A total of four competition tracks were evaluated under the same EvAAL benchmark framework in order to validate its potential to become a standard for evaluating indoor localization solutions. The experience gained during the competition and feedback from track organizers and competitors showed that the EvAAL framework is flexible enough to successfully fit the very different tracks and appears adequate to compare indoor positioning systems.
Mendoza-Silva, Germán Martín; Richter, Philipp; Torres-Sospedra, Joaquín; Lohan, Elena Simona; Huerta-Guijarro, Joaquín
Long-Term Wi-Fi fingerprinting dataset and supporting material Miscellaneous
2017.
Links | BibTeX | Tags: Wi-Fi fingerprint
@misc{zenodo2018lidbd,
title = {Long-Term Wi-Fi fingerprinting dataset and supporting material},
author = {Germán Martín Mendoza-Silva and Philipp Richter and Joaquín Torres-Sospedra and Elena Simona Lohan and Joaquín Huerta-Guijarro},
url = {https://doi.org/10.5281/zenodo.1066041},
doi = {10.5281/zenodo.1066041},
year = {2017},
date = {2017-01-01},
keywords = {Wi-Fi fingerprint},
pubstate = {published},
tppubtype = {misc}
}
2016
Torres-Sospedra, Joaquín; Mendoza-Silva, Germán Martín; Montoliu-Colás, Raúl; Belmonte-Fernández, Óscar; Benítez-Paéz, Fernando; Huerta-Guijarro, Joaquín
Ensembles of Indoor Positioning Systems Based on Fingerprinting: Simplifying Parameter Selection and Obtaining Robust Systems Proceedings Article
In: Proceedings of the Seventh International Conference on Indoor Positioning and Indoor Navigation IPIN 2016, University of Alcala, Alcalá de Henares (Madrid), Spain October 4-7, 2016, pp. in press, 2016.
BibTeX | Tags: GEO-C, Indoor positioning, Wi-Fi fingerprint
@inproceedings{TorresSospedra2016a,
title = {Ensembles of Indoor Positioning Systems Based on Fingerprinting: Simplifying Parameter Selection and Obtaining Robust Systems},
author = { Joaquín Torres-Sospedra and Germán Martín Mendoza-Silva and Raúl Montoliu-Colás and Óscar Belmonte-Fernández and Fernando Benítez-Paéz and Joaquín Huerta-Guijarro},
year = {2016},
date = {2016-01-01},
booktitle = {Proceedings of the Seventh International Conference on Indoor Positioning and Indoor Navigation IPIN 2016, University of Alcala, Alcalá de Henares (Madrid), Spain October 4-7, 2016},
pages = {in press},
keywords = {GEO-C, Indoor positioning, Wi-Fi fingerprint},
pubstate = {published},
tppubtype = {inproceedings}
}
Torres-Sospedra, Joaquín; Montoliu-Colás, Raúl; Mendoza-Silva, Germán Martín; Belmonte-Fernández, Óscar; Rambla-Risueño, David; Huerta-Guijarro, Joaquín
Providing Databases for Different Indoor Positioning Technologies: Pros and Cons of Magnetic field and Wi-Fi based Positioning Journal Article
In: Mobile information systems, pp. in press, 2016, (IF: 0.849 121/146 (Q4) Computer science – information systems 0.849 79/89 (Q4) Telecommunications ).
BibTeX | Tags: Database, Indoor positioning, Magnetic field, REPNIN, Wi-Fi fingerprint
@article{TorresSospedra2016b,
title = {Providing Databases for Different Indoor Positioning Technologies: Pros and Cons of Magnetic field and Wi-Fi based Positioning},
author = { Joaquín Torres-Sospedra and Raúl Montoliu-Colás and Germán Martín Mendoza-Silva and Óscar Belmonte-Fernández and David Rambla-Risueño and Joaquín Huerta-Guijarro},
year = {2016},
date = {2016-01-01},
journal = {Mobile information systems},
pages = {in press},
note = {IF: 0.849 121/146 (Q4) Computer science – information systems
0.849 79/89 (Q4) Telecommunications
},
keywords = {Database, Indoor positioning, Magnetic field, REPNIN, Wi-Fi fingerprint},
pubstate = {published},
tppubtype = {article}
}