2019
Mendoza-Silva, Germán MartÃn; Matey-Sanz, Miguel; Torres-Sospedra, JoaquÃn; Huerta-Guijarro, JoaquÃn
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 MartÃn Mendoza-Silva and Miguel Matey-Sanz and JoaquÃn Torres-Sospedra and JoaquÃn Huerta-Guijarro},
doi = {https://doi.org/10.3390/data4010012},
issn = {2306-5729},
year = {2019},
date = {2019-01-04},
urldate = {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 MartÃn; Torres-Sospedra, JoaquÃn; Huerta-Guijarro, JoaquÃn
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 MartÃn Mendoza-Silva and JoaquÃn Torres-Sospedra and JoaquÃn 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 MartÃn; Richter, Philipp; Torres-Sospedra, JoaquÃn; Lohan, Elana Simona; Huerta-Guijarro, JoaquÃn
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 MartÃn Mendoza-Silva and Philipp Richter and JoaquÃn Torres-Sospedra and Elana Simona Lohan and JoaquÃn 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
Lohan, Elena Simona; Torres-Sospedra, JoaquÃn; Leppäkoski, Helena; Richter, Philipp; Peng, Zhe; Huerta-Guijarro, JoaquÃn
Wi-Fi Crowdsourced Fingerprinting Dataset for Indoor Positioning Journal Article
In: Data, vol. 2, no. 4, ARTICLE NUMBER = 32, 2017, ISSN: 2306-5729.
Abstract | Links | BibTeX | Tags: Indoor positioning, Wi-Fi fingerprint
@article{data2040032,
title = {Wi-Fi Crowdsourced Fingerprinting Dataset for Indoor Positioning},
author = {Elena Simona Lohan and JoaquÃn Torres-Sospedra and Helena Leppäkoski and Philipp Richter and Zhe Peng and JoaquÃn Huerta-Guijarro},
url = {http://www.mdpi.com/2306-5729/2/4/32},
doi = {10.3390/data2040032},
issn = {2306-5729},
year = {2017},
date = {2017-10-03},
journal = {Data},
volume = {2},
number = {4, ARTICLE NUMBER = 32},
abstract = {Benchmark open-source Wi-Fi fingerprinting datasets for indoor positioning studies are still hard to find in the current literature and existing public repositories. This is unlike other research fields, such as the image processing field, where benchmark test images such as the Lenna image or Face Recognition Technology (FERET) databases exist, or the machine learning field, where huge datasets are available for example at the University of California Irvine (UCI) Machine Learning Repository. It is the purpose of this paper to present a new openly available Wi-Fi fingerprint dataset, comprised of 4648 fingerprints collected with 21 devices in a university building in Tampere, Finland, and to present some benchmark indoor positioning results using these data. The datasets and the benchmarking software are distributed under the open-source MIT license and can be found on the EU Zenodo repository.},
keywords = {Indoor positioning, Wi-Fi fingerprint},
pubstate = {published},
tppubtype = {article}
}
Benchmark open-source Wi-Fi fingerprinting datasets for indoor positioning studies are still hard to find in the current literature and existing public repositories. This is unlike other research fields, such as the image processing field, where benchmark test images such as the Lenna image or Face Recognition Technology (FERET) databases exist, or the machine learning field, where huge datasets are available for example at the University of California Irvine (UCI) Machine Learning Repository. It is the purpose of this paper to present a new openly available Wi-Fi fingerprint dataset, comprised of 4648 fingerprints collected with 21 devices in a university building in Tampere, Finland, and to present some benchmark indoor positioning results using these data. The datasets and the benchmarking software are distributed under the open-source MIT license and can be found on the EU Zenodo repository.
Mendoza-Silva, Germán MartÃn; Richter, Philipp; Torres-Sospedra, JoaquÃn; Lohan, Elena Simona; Huerta-Guijarro, JoaquÃn
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 MartÃn Mendoza-Silva and Philipp Richter and JoaquÃn Torres-Sospedra and Elena Simona Lohan and JoaquÃn 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, JoaquÃn; Mendoza-Silva, Germán MartÃn; Montoliu-Colás, Raúl; Belmonte-Fernández, Óscar; BenÃtez-Paéz, Fernando; Huerta-Guijarro, JoaquÃn
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 = { JoaquÃn Torres-Sospedra and Germán MartÃn Mendoza-Silva and Raúl Montoliu-Colás and Óscar Belmonte-Fernández and Fernando BenÃtez-Paéz and JoaquÃn 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, JoaquÃn; Montoliu-Colás, Raúl; Mendoza-Silva, Germán MartÃn; Belmonte-Fernández, Óscar; Rambla-Risueño, David; Huerta-Guijarro, JoaquÃn
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 = { JoaquÃn Torres-Sospedra and Raúl Montoliu-Colás and Germán MartÃn Mendoza-Silva and Óscar Belmonte-Fernández and David Rambla-Risueño and JoaquÃn 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}
}
2015
Torres-Sospedra, JoaquÃn; Montoliu-Colás, Raúl; Trilles-Oliver, Sergio; Belmonte-Fernández, Óscar; Huerta-Guijarro, JoaquÃn
Comprehensive analysis of distance and similarity measures for Wi-Fi fingerprinting indoor positioning systems Journal Article
In: Expert Systems with Applications, vol. 42, no. 23, pp. 9263-9278, 2015, ISSN: 09574174, (IF: 2.981, Q1).
Abstract | Links | BibTeX | Tags: Distance measures, Indoor positioning, SMARTWAYS, Wi-Fi fingerprint
@article{TorresSospedra2015a,
title = {Comprehensive analysis of distance and similarity measures for Wi-Fi fingerprinting indoor positioning systems},
author = { JoaquÃn Torres-Sospedra and Raúl Montoliu-Colás and Sergio Trilles-Oliver and Óscar Belmonte-Fernández and JoaquÃn Huerta-Guijarro},
url = {http://hdl.handle.net/10234/158880},
doi = {10.1016/j.eswa.2015.08.013},
issn = {09574174},
year = {2015},
date = {2015-01-01},
journal = {Expert Systems with Applications},
volume = {42},
number = {23},
pages = {9263-9278},
abstract = {Recent advances in indoor positioning systems led to a business interest in those applications and services where a precise localization is crucial. Wi-Fi fingerprinting based on machine learning and expert systems are commonly used in the literature. They compare a current fingerprint to a database of fingerprints, and then return the most similar one/ones according to: 1) a distance function, 2) a data representation method for received signal strength values, and 3) a thresholding strategy. However, most of the previous works simply use the Euclidean distance with the raw unprocessed data. There is not any previous work that studies which is the best distance function, which is the best way of representing the data and which is the effect of applying thresholding. In this paper, we present a comprehensive study using 51 distance metrics, 4 alternatives to represent the raw data (2 of them proposed by us), a thresholding based on the RSS values and the public UJIIndoorLoc database. The results shown in this paper demonstrate that researchers and developers should take into account the conclusions arisen in this work in order to improve the accuracy of their systems. The IPSs based on k-NN are improved by just selecting the appropriate configuration (mainly distance function and data representation). In the best case, 13-NN with Sørensen distance and the powed data representation, the error in determining the place (building and floor) has been reduced in more than a 50% and the positioning accuracy has been increased in 1.7 m with respect to the 1-NN with Euclidean distance and raw data commonly used in the literature. Moreover, our experiments also demonstrate that thresholding should not be applied in multi-building and multi-floor environments.},
note = {IF: 2.981, Q1},
keywords = {Distance measures, Indoor positioning, SMARTWAYS, Wi-Fi fingerprint},
pubstate = {published},
tppubtype = {article}
}
Recent advances in indoor positioning systems led to a business interest in those applications and services where a precise localization is crucial. Wi-Fi fingerprinting based on machine learning and expert systems are commonly used in the literature. They compare a current fingerprint to a database of fingerprints, and then return the most similar one/ones according to: 1) a distance function, 2) a data representation method for received signal strength values, and 3) a thresholding strategy. However, most of the previous works simply use the Euclidean distance with the raw unprocessed data. There is not any previous work that studies which is the best distance function, which is the best way of representing the data and which is the effect of applying thresholding. In this paper, we present a comprehensive study using 51 distance metrics, 4 alternatives to represent the raw data (2 of them proposed by us), a thresholding based on the RSS values and the public UJIIndoorLoc database. The results shown in this paper demonstrate that researchers and developers should take into account the conclusions arisen in this work in order to improve the accuracy of their systems. The IPSs based on k-NN are improved by just selecting the appropriate configuration (mainly distance function and data representation). In the best case, 13-NN with Sørensen distance and the powed data representation, the error in determining the place (building and floor) has been reduced in more than a 50% and the positioning accuracy has been increased in 1.7 m with respect to the 1-NN with Euclidean distance and raw data commonly used in the literature. Moreover, our experiments also demonstrate that thresholding should not be applied in multi-building and multi-floor environments.