2021 |
Silva, Ivo; Pendão, Cristiano; Torres-Sospedra, Joaquín; Moreira, Adriano Quantifying the Degradation of Radio Maps in Wi-Fi Fingerprinting Inproceedings Proceedings of the Eleventh International Conference on Indoor Positioning and Indoor Navigation, IEEE, 2021. Abstract | Links | BibTeX | Tags: Wi-Fi fingerprint, Wi-Fi mapping @inproceedings{Silva2021ab, title = {Quantifying the Degradation of Radio Maps in Wi-Fi Fingerprinting}, author = {Ivo Silva and Cristiano Pendão and Joaquín Torres-Sospedra and Adriano Moreira}, doi = {https://doi.org/10.1109/IPIN51156.2021.9662558}, year = {2021}, date = {2021-12-15}, booktitle = {Proceedings of the Eleventh International Conference on Indoor Positioning and Indoor Navigation}, publisher = {IEEE}, abstract = {One of the most common assumptions regarding indoor positioning systems based on Wi-Fi fingerprinting is that the Radio Map (RM) becomes outdated and has to be updated to maintain the positioning performance. It is known that propagation effects, the addition/removal of Access Points (APs), changes in the indoor layout, among others, cause RMs to become outdated. However, there is a lack of studies that show how the RM degrades over time. In this paper, we describe an empirical study, based on real-world experiments, to evaluate how and why RMs degrade over time. We conducted site surveys and deployed monitoring devices to analyse the radio environment of one building over 2+ years, which allowed us to identify significant changes/events that caused the degradation of RMs. To quantify the RM degradation, we use the positioning error and propose the RM degradation ratio, a metric to directly compare two RMs and measure how different they are. Obtained results show that the positioning performance is much better when RMs are collected on the same day as the test data, and although RM degradation tends to increase over time, it only leads to large positioning errors when significant changes occur in the Wi-Fi infrastructure, making previous RMs outdated.}, keywords = {Wi-Fi fingerprint, Wi-Fi mapping}, pubstate = {published}, tppubtype = {inproceedings} } One of the most common assumptions regarding indoor positioning systems based on Wi-Fi fingerprinting is that the Radio Map (RM) becomes outdated and has to be updated to maintain the positioning performance. It is known that propagation effects, the addition/removal of Access Points (APs), changes in the indoor layout, among others, cause RMs to become outdated. However, there is a lack of studies that show how the RM degrades over time. In this paper, we describe an empirical study, based on real-world experiments, to evaluate how and why RMs degrade over time. We conducted site surveys and deployed monitoring devices to analyse the radio environment of one building over 2+ years, which allowed us to identify significant changes/events that caused the degradation of RMs. To quantify the RM degradation, we use the positioning error and propose the RM degradation ratio, a metric to directly compare two RMs and measure how different they are. Obtained results show that the positioning performance is much better when RMs are collected on the same day as the test data, and although RM degradation tends to increase over time, it only leads to large positioning errors when significant changes occur in the Wi-Fi infrastructure, making previous RMs outdated. |
Torres-Sospedra, Joaquín; Aranda, Fernando J; Álvarez, Fernando J; Quezada-Gaibor, Darwin; Silva, Ivo; Pendão, Cristiano; Moreira, Adriano Ensembling Multiple Radio Maps with Dynamic Noise in Fingerprint-based Indoor Positioning Inproceedings Proceedings of the 2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring), pp. 1-5, IEEE, 2021, ISBN: 978-1-7281-8965-9. Abstract | Links | BibTeX | Tags: Indoor positioning, Wi-Fi fingerprint, Wi-Fi mapping @inproceedings{Torres-Sospedra2021b, title = {Ensembling Multiple Radio Maps with Dynamic Noise in Fingerprint-based Indoor Positioning}, author = {Joaquín Torres-Sospedra and Fernando J. Aranda and Fernando J. Álvarez and Darwin Quezada-Gaibor and Ivo Silva and Cristiano Pendão and Adriano Moreira}, doi = {http://dx.doi.org/10.1109/VTC2021-Spring51267.2021.9448947}, isbn = {978-1-7281-8965-9}, year = {2021}, date = {2021-06-15}, booktitle = {Proceedings of the 2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring)}, pages = {1-5}, publisher = {IEEE}, abstract = {Fingerprint-based indoor positioning is widely used in many contexts, including pedestrian and autonomous vehicles navigation. Many approaches have used traditional Machine Learning models to deal with fingerprinting, being k-NN the most common used one. However, the reference data (or radio map) is generally limited, as data collection is a very demanding task, which degrades overall accuracy. In this work, we propose a novel approach to add random noise to the radio map which will be used in combination with an ensemble model. Instead of augmenting the radio map, we create n noisy versions of the same size, i.e. our proposed Indoor Positioning model will combine n estimations obtained by independent estimators built with the n noisy radio maps. The empirical results have shown that our proposed approach improves the baseline method results in around 10% on average.}, keywords = {Indoor positioning, Wi-Fi fingerprint, Wi-Fi mapping}, pubstate = {published}, tppubtype = {inproceedings} } Fingerprint-based indoor positioning is widely used in many contexts, including pedestrian and autonomous vehicles navigation. Many approaches have used traditional Machine Learning models to deal with fingerprinting, being k-NN the most common used one. However, the reference data (or radio map) is generally limited, as data collection is a very demanding task, which degrades overall accuracy. In this work, we propose a novel approach to add random noise to the radio map which will be used in combination with an ensemble model. Instead of augmenting the radio map, we create n noisy versions of the same size, i.e. our proposed Indoor Positioning model will combine n estimations obtained by independent estimators built with the n noisy radio maps. The empirical results have shown that our proposed approach improves the baseline method results in around 10% on average. |
2020 |
Quezada-Gaibor, Darwin; Klus, Lucie; Torres-Sospedra, Joaquín; Lohan, Simona Elena; Nurmi, Jari; Huerta-Guijarro, Joaquín Improving DBSCAN for Indoor Positioning Using Wi-Fi Radio Maps in Wearable and IoT Devices Inproceedings Proceedings of the 12th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT), 5-8 October 2020. Online event, pp. 208-213, 2020, ISBN: 978-1-7281-9281-9. Links | BibTeX | Tags: A-wear, Indoor positioning, Internet of things, wearables, Wi-Fi mapping @inproceedings{Quezada-Gaibor2020, title = {Improving DBSCAN for Indoor Positioning Using Wi-Fi Radio Maps in Wearable and IoT Devices}, author = {Darwin Quezada-Gaibor and Lucie Klus and Joaquín Torres-Sospedra and Simona Elena Lohan and Jari Nurmi and Joaquín Huerta-Guijarro}, doi = {http://www.doi.org/10.1109/ICUMT51630.2020.9222411}, isbn = {978-1-7281-9281-9}, year = {2020}, date = {2020-09-17}, booktitle = {Proceedings of the 12th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT), 5-8 October 2020. Online event}, pages = {208-213}, keywords = {A-wear, Indoor positioning, Internet of things, wearables, Wi-Fi mapping}, pubstate = {published}, tppubtype = {inproceedings} } |
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. |
Mendoza-Silva, Germán Martín; Rodríguez-Pupo, Luis Enrique; Torres-Sospedra, Joaquín; Huerta-Guijarro, Joaquín Solutions for signal mapping campaigns of Wi-Fi networks Proceeding JIIDE 2016 Barcelona (27-30/09/2016), 2016. Abstract | Links | BibTeX | Tags: Citizen Science, Data Infrastructures, Mobile apps, Web, Wi-Fi, Wi-Fi mapping @proceedings{mendozasolutions, title = {Solutions for signal mapping campaigns of Wi-Fi networks}, author = {Germán Martín Mendoza-Silva and Luis Enrique Rodríguez-Pupo and Joaquín Torres-Sospedra and Joaquín Huerta-Guijarro}, url = {http://www.idee.es/resources/presentaciones/JIIDE16/2016/34_art_2_UJI_SolucionesMapeadoWiFi.pdf}, year = {2016}, date = {2016-09-27}, publisher = {JIIDE 2016 Barcelona (27-30/09/2016)}, abstract = {The boom of smart mobile devices with several types of sensors has enabled applications that engage people in collecting information about their surroundings so that they can contribute to citizen science projects. In this paper, we address a set of software solutions that aim to enable the general public to participate in WiFi signal samples collection campaigns. We expect these solutions to be appealing for researchers working in WiFi-based indoor positioning due to the widespread presence of WiFi antennas, the popularity of smartphone able to connect to those antennas, and because it is usually required to create a WiFi fingerprint database, which is a very time-consuming activity. The solutions set addresses three step in the WiFi signal samples database creation process: The campaign planning, the WiFi signal collection and the database construction and sharing. By the end of the process, the research community is provided with sets of geo-located points whose attributes include the signal intensities of the detected WiFi access points. The solutions set that we described in this paper can be extended to include campaigns focused on measuring other physical phenomena by using other sensors found in mobile devices. }, keywords = {Citizen Science, Data Infrastructures, Mobile apps, Web, Wi-Fi, Wi-Fi mapping}, pubstate = {published}, tppubtype = {proceedings} } The boom of smart mobile devices with several types of sensors has enabled applications that engage people in collecting information about their surroundings so that they can contribute to citizen science projects. In this paper, we address a set of software solutions that aim to enable the general public to participate in WiFi signal samples collection campaigns. We expect these solutions to be appealing for researchers working in WiFi-based indoor positioning due to the widespread presence of WiFi antennas, the popularity of smartphone able to connect to those antennas, and because it is usually required to create a WiFi fingerprint database, which is a very time-consuming activity. The solutions set addresses three step in the WiFi signal samples database creation process: The campaign planning, the WiFi signal collection and the database construction and sharing. By the end of the process, the research community is provided with sets of geo-located points whose attributes include the signal intensities of the detected WiFi access points. The solutions set that we described in this paper can be extended to include campaigns focused on measuring other physical phenomena by using other sensors found in mobile devices. |
IF Journal
Journal
Book
Book chapter
Congress
Thesis & M. Thesis
2021 |
Quantifying the Degradation of Radio Maps in Wi-Fi Fingerprinting Inproceedings Proceedings of the Eleventh International Conference on Indoor Positioning and Indoor Navigation, IEEE, 2021. |
Ensembling Multiple Radio Maps with Dynamic Noise in Fingerprint-based Indoor Positioning Inproceedings Proceedings of the 2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring), pp. 1-5, IEEE, 2021, ISBN: 978-1-7281-8965-9. |
2020 |
Improving DBSCAN for Indoor Positioning Using Wi-Fi Radio Maps in Wearable and IoT Devices Inproceedings Proceedings of the 12th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT), 5-8 October 2020. Online event, pp. 208-213, 2020, ISBN: 978-1-7281-9281-9. |
2016 |
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. |
Solutions for signal mapping campaigns of Wi-Fi networks Proceeding JIIDE 2016 Barcelona (27-30/09/2016), 2016. |