2024
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.
Klus, Lucie; Klus, Roman; Torres-Sospedra, Joaquín; Lohan, Elena Simona; Granell-Canut, Carlos; Nurmi, Jari
EWOk: Towards Efficient Multidimensional Compression of Indoor Positioning Datasets Journal Article
In: IEEE Transactions on Mobile Computing, vol. 25, no. 5, pp. 3589-3604, 2024, ISSN: 1558-0660.
Abstract | Links | BibTeX | Tags: A-wear, machine learning, prediction algorithms, Wi-Fi fingerprint
@article{Klus2024a,
title = {EWOk: Towards Efficient Multidimensional Compression of Indoor Positioning Datasets},
author = {Lucie Klus and Roman Klus and Joaquín Torres-Sospedra and Elena Simona Lohan and Carlos Granell-Canut and Jari Nurmi},
doi = {10.1109/TMC.2023.3277333},
issn = {1558-0660},
year = {2024},
date = {2024-03-01},
journal = {IEEE Transactions on Mobile Computing},
volume = {25},
number = {5},
pages = {3589-3604},
abstract = {Indoor positioning performed directly at the end-user device ensures reliability in case the network connection fails but is limited by the size of the RSS radio map necessary to match the measured array to the device’s location. Reducing the size of the RSS database enables faster processing, and saves storage space and radio resources necessary for the database transfer, thus cutting implementation and operation costs, and increasing the quality of service. In this work, we propose EWOk, an Element-Wise cOmpression using k-means, which reduces the size of the individual radio measurements within the fingerprinting radio map while sustaining or boosting the dataset’s positioning capabilities. We show that the 7-bit representation of measurements is sufficient in positioning scenarios, and reducing the data size further using EWOk results in higher compression and faster data transfer and processing. To eliminate the inherent uncertainty of k-means we propose a data-dependent, non-random initiation scheme to ensure stability and limit variance. We further combine EWOk with principal component analysis to show its applicability in combination with other methods, and to demonstrate the efficiency of the resulting multidimensional compression. We evaluate EWOk on 25 RSS fingerprinting datasets and show that it positively impacts compression efficiency, and positioning performance.},
keywords = {A-wear, machine learning, prediction algorithms, Wi-Fi fingerprint},
pubstate = {published},
tppubtype = {article}
}
Indoor positioning performed directly at the end-user device ensures reliability in case the network connection fails but is limited by the size of the RSS radio map necessary to match the measured array to the device’s location. Reducing the size of the RSS database enables faster processing, and saves storage space and radio resources necessary for the database transfer, thus cutting implementation and operation costs, and increasing the quality of service. In this work, we propose EWOk, an Element-Wise cOmpression using k-means, which reduces the size of the individual radio measurements within the fingerprinting radio map while sustaining or boosting the dataset’s positioning capabilities. We show that the 7-bit representation of measurements is sufficient in positioning scenarios, and reducing the data size further using EWOk results in higher compression and faster data transfer and processing. To eliminate the inherent uncertainty of k-means we propose a data-dependent, non-random initiation scheme to ensure stability and limit variance. We further combine EWOk with principal component analysis to show its applicability in combination with other methods, and to demonstrate the efficiency of the resulting multidimensional compression. We evaluate EWOk on 25 RSS fingerprinting datasets and show that it positively impacts compression efficiency, and positioning performance.
2023
Pascacio-de-los-Santos, Pavel
Collaborative Techniques for Indoor Positioning Systems PhD Thesis
Universitat Jaume I. INIT, 2023, ISBN: 978-952-03-2905-1.
Abstract | Links | BibTeX | Tags: A-wear, Bluetooth Low Energy, Indoor positioning, machine learning, Wi-Fi fingerprint
@phdthesis{Pascacio2023a,
title = {Collaborative Techniques for Indoor Positioning Systems},
author = {Pavel Pascacio-de-los-Santos},
url = {http://hdl.handle.net/10803/688489},
doi = {http://dx.doi.org/10.6035/14124.2023.821144},
isbn = {978-952-03-2905-1},
year = {2023},
date = {2023-06-09},
school = {Universitat Jaume I. INIT},
abstract = {This doctoral thesis focuses on developing and evaluating mobile device-based collaborative techniques to enhance the position accuracy of traditional indoor positioning systems based on RSSI (i.e., lateration and fingerprinting) in real-world conditions. During the research, first, a comprehensive systematic review of Collaborative Indoor Positioning Systems (CIPSs) was conducted to obtain a state-of-the-art; second, extensive experimental data collections considering mobile devices and collaborative scenarios were performed to create a mobile device-based BLE database and BLE and Wi-Fi radio maps for testing our collaborative and non-collaborative indoor positioning approaches; third, traditional methods to estimate distance and position were evaluated to present their limitations and challenges and two novel approaches to improve distance and positioning accuracy were proposed; finally, our proposed CIPSs using Multilayer Perceptron Artificial Neural Networks were developed to enhance the accuracy of BLE–RSSI lateration and fingerprinting-KNN methods and evaluated under real-world conditions to demonstrate its feasibility and benefits.},
keywords = {A-wear, Bluetooth Low Energy, Indoor positioning, machine learning, Wi-Fi fingerprint},
pubstate = {published},
tppubtype = {phdthesis}
}
This doctoral thesis focuses on developing and evaluating mobile device-based collaborative techniques to enhance the position accuracy of traditional indoor positioning systems based on RSSI (i.e., lateration and fingerprinting) in real-world conditions. During the research, first, a comprehensive systematic review of Collaborative Indoor Positioning Systems (CIPSs) was conducted to obtain a state-of-the-art; second, extensive experimental data collections considering mobile devices and collaborative scenarios were performed to create a mobile device-based BLE database and BLE and Wi-Fi radio maps for testing our collaborative and non-collaborative indoor positioning approaches; third, traditional methods to estimate distance and position were evaluated to present their limitations and challenges and two novel approaches to improve distance and positioning accuracy were proposed; finally, our proposed CIPSs using Multilayer Perceptron Artificial Neural Networks were developed to enhance the accuracy of BLE–RSSI lateration and fingerprinting-KNN methods and evaluated under real-world conditions to demonstrate its feasibility and benefits.
Quezada-Gaibor, Darwin
Cloud-based Indoor Positioning Platform for Context-adaptivity in GNSS-denied Scenarios PhD Thesis
Universitat Jaume I. INIT, 2023.
Abstract | Links | BibTeX | Tags: A-wear, Cloud computing, Indoor positioning, machine learning, Wi-Fi fingerprint
@phdthesis{Quezada2023a,
title = {Cloud-based Indoor Positioning Platform for Context-adaptivity in GNSS-denied Scenarios},
author = {Darwin Quezada-Gaibor},
doi = {http://dx.doi.org/10.6035/14124.2023.821275},
year = {2023},
date = {2023-03-31},
school = {Universitat Jaume I. INIT},
abstract = {The demand for positioning, localisation and navigation services is on the rise, largely owing to the fact that such services form an integral part of applications in areas such as agriculture, robotics, and eHealth. Depending on the field of application, these services must accomplish high levels of accuracy, flexibility, and integrability. This dissertation focuses on improving computing efficiency, data pre-processing, and software architecture for indoor positioning solutions without leaving aside position and location accuracy. The dissertation begins by presenting a systematic review of current cloud-based indoor positioning solutions. Secondly, we focus on the study of data optimisation techniques such as data cleansing and data augmentation. The third contribution suggests two algorithms to group similar fingerprints into clusters. The fourth contribution explores the use of Machine Learning (ML) models to enhance position estimation accuracy. Finally, this dissertation summarises the key findings in an open-source cloud platform for indoor positioning.},
keywords = {A-wear, Cloud computing, Indoor positioning, machine learning, Wi-Fi fingerprint},
pubstate = {published},
tppubtype = {phdthesis}
}
The demand for positioning, localisation and navigation services is on the rise, largely owing to the fact that such services form an integral part of applications in areas such as agriculture, robotics, and eHealth. Depending on the field of application, these services must accomplish high levels of accuracy, flexibility, and integrability. This dissertation focuses on improving computing efficiency, data pre-processing, and software architecture for indoor positioning solutions without leaving aside position and location accuracy. The dissertation begins by presenting a systematic review of current cloud-based indoor positioning solutions. Secondly, we focus on the study of data optimisation techniques such as data cleansing and data augmentation. The third contribution suggests two algorithms to group similar fingerprints into clusters. The fourth contribution explores the use of Machine Learning (ML) models to enhance position estimation accuracy. Finally, this dissertation summarises the key findings in an open-source cloud platform for indoor positioning.
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
Silva, Ivo; Pendão, Cristiano; Torres-Sospedra, Joaquín; Moreira, Adriano
TrackInFactory: A Tight Coupling Particle Filter for Industrial Vehicle Tracking in Indoor Environments Journal Article
In: IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 52, no. 7, pp. 4151 - 4162, 2022, ISSN: 2168-2232.
Abstract | Links | BibTeX | Tags: Indoor positioning, Industry 4.0, sensor fusion, Wi-Fi fingerprint
@article{Silva2022ab,
title = {TrackInFactory: A Tight Coupling Particle Filter for Industrial Vehicle Tracking in Indoor Environments},
author = {Ivo Silva and Cristiano Pendão and Joaquín Torres-Sospedra and Adriano Moreira},
doi = {https://doi.org/10.1109/TSMC.2021.3091987},
issn = {2168-2232},
year = {2022},
date = {2022-07-06},
journal = {IEEE Transactions on Systems, Man, and Cybernetics: Systems},
volume = {52},
number = {7},
pages = {4151 - 4162},
abstract = {Localization and tracking of industrial vehicles have a key role in increasing productivity and improving the logistics processes of factories. Due to the demanding requirements of industrial vehicle tracking and navigation, existing systems explore technologies, such as LiDAR or ultra wide-band to achieve low positioning errors. In this article we propose TrackInFactory, a system that combines Wi-Fi with motion sensors, achieving submeter accuracy and a low maximum error. A tight coupling approach is explored in sensor fusion with a particle filter (PF). Information regarding the vehicle’s initial position and heading is not required. This approach uses the similarity of Wi-Fi samples to update the particles’ weights as they move according to motion sensor data. The PF dynamically adjusts its parameters based on a metric for estimating the confidence in position estimates, allowing to improve positioning performance. A series of simulations were performed to tune the PF. Then the approach was validated in real-world experiments with an industrial tow tractor, achieving a mean error of 0.81 m. In comparison to a loose coupling approach, this method reduced the maximum error by more than 60% and improved the overall mean error by more than 20%.},
keywords = {Indoor positioning, Industry 4.0, sensor fusion, Wi-Fi fingerprint},
pubstate = {published},
tppubtype = {article}
}
Localization and tracking of industrial vehicles have a key role in increasing productivity and improving the logistics processes of factories. Due to the demanding requirements of industrial vehicle tracking and navigation, existing systems explore technologies, such as LiDAR or ultra wide-band to achieve low positioning errors. In this article we propose TrackInFactory, a system that combines Wi-Fi with motion sensors, achieving submeter accuracy and a low maximum error. A tight coupling approach is explored in sensor fusion with a particle filter (PF). Information regarding the vehicle’s initial position and heading is not required. This approach uses the similarity of Wi-Fi samples to update the particles’ weights as they move according to motion sensor data. The PF dynamically adjusts its parameters based on a metric for estimating the confidence in position estimates, allowing to improve positioning performance. A series of simulations were performed to tune the PF. Then the approach was validated in real-world experiments with an industrial tow tractor, achieving a mean error of 0.81 m. In comparison to a loose coupling approach, this method reduced the maximum error by more than 60% and improved the overall mean error by more than 20%.
Quezada-Gaibor, Darwin; Klus, Lucie; Torres-Sospedra, Joaquín; Lohan, Elena Simona; Nurmi, Jari; Granell-Canut, Carlos; Huerta-Guijarro, Joaquín
Data Cleansing for Indoor Positioning Wi-Fi Fingerprinting Datasets Proceedings Article
In: 2022 23rd IEEE International Conference on Mobile Data Management (MDM), pp. 349-354, IEEE, 2022, ISBN: 978-1-6654-5176-5.
Abstract | Links | BibTeX | Tags: Data science, Indoor positioning, Wi-Fi fingerprint
@inproceedings{Quezada2022c,
title = {Data Cleansing for Indoor Positioning Wi-Fi Fingerprinting Datasets},
author = {Darwin Quezada-Gaibor and Lucie Klus and Joaquín Torres-Sospedra and Elena Simona Lohan and Jari Nurmi and Carlos Granell-Canut and Joaquín Huerta-Guijarro},
doi = {https://doi.org/10.1109/MDM55031.2022.00079},
isbn = {978-1-6654-5176-5},
year = {2022},
date = {2022-06-10},
booktitle = {2022 23rd IEEE International Conference on Mobile Data Management (MDM)},
pages = {349-354},
publisher = {IEEE},
abstract = {Wearable and IoT devices requiring positioning and localisation services grow in number exponentially every year. This rapid growth also produces millions of data entries that need to be pre-processed prior to being used in any indoor positioning system to ensure the data quality and provide a high Quality of Service (QoS) to the end-user. In this paper, we offer a novel and straightforward data cleansing algorithm for WLAN fingerprinting radio maps. This algorithm is based on the correlation among fingerprints using the Received Signal Strength (RSS) values and the Access Points (APs)'s identifier. We use those to compute the correlation among all samples in the dataset and remove fingerprints with low level of correlation from the dataset. We evaluated the proposed method on 14 independent publicly-available datasets. As a result, an average of 14% of fingerprints were removed from the datasets. The 2D positioning error was reduced by 2.7% and 3D positioning error by 5.3% with a slight increase in the floor hit rate by 1.2% on average. Consequently, the average speed of position prediction was also increased by 14%.},
keywords = {Data science, Indoor positioning, Wi-Fi fingerprint},
pubstate = {published},
tppubtype = {inproceedings}
}
Wearable and IoT devices requiring positioning and localisation services grow in number exponentially every year. This rapid growth also produces millions of data entries that need to be pre-processed prior to being used in any indoor positioning system to ensure the data quality and provide a high Quality of Service (QoS) to the end-user. In this paper, we offer a novel and straightforward data cleansing algorithm for WLAN fingerprinting radio maps. This algorithm is based on the correlation among fingerprints using the Received Signal Strength (RSS) values and the Access Points (APs)'s identifier. We use those to compute the correlation among all samples in the dataset and remove fingerprints with low level of correlation from the dataset. We evaluated the proposed method on 14 independent publicly-available datasets. As a result, an average of 14% of fingerprints were removed from the datasets. The 2D positioning error was reduced by 2.7% and 3D positioning error by 5.3% with a slight increase in the floor hit rate by 1.2% on average. Consequently, the average speed of position prediction was also increased by 14%.
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
Silva, Ivo; Pendão, Cristiano; Torres-Sospedra, Joaquín; Moreira, Adriano
Quantifying the Degradation of Radio Maps in Wi-Fi Fingerprinting Proceedings Article
In: 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.
Rodriguez-Martinez, Cristina; Torres-Sospedra, Joaquín
Revisiting the Analysis of Hyperparameters in k-NN for Wi-Fi and BLE Fingerprinting: Current Status and General Results Proceedings Article
In: Proceedings of the Eleventh International Conference on Indoor Positioning and Indoor Navigation, IEEE, 2021.
Abstract | Links | BibTeX | Tags: machine learning, Wi-Fi fingerprint
@inproceedings{Rodriguez2021a,
title = {Revisiting the Analysis of Hyperparameters in k-NN for Wi-Fi and BLE Fingerprinting: Current Status and General Results},
author = {Cristina Rodriguez-Martinez and Joaquín Torres-Sospedra},
doi = {https://doi.org/10.1109/IPIN51156.2021.9662542},
year = {2021},
date = {2021-12-15},
booktitle = {Proceedings of the Eleventh International Conference on Indoor Positioning and Indoor Navigation},
publisher = {IEEE},
abstract = {Wi-Fi Fingerprinting is a very popular technique in the field of indoor positioning, since the release of Microsoft RADAR system back in 2000. Since that milestone, the vast majority of studies and improvements in this field keep using the same base algorithm, an adaptation of the k-NN algorithm to treat geospatial data (e.g., x/y or lat/lon). One of the most relevant drawbacks of k-NN algorithm resides in its initial design, focused on resolving general classification problems. Wi-Fi fingerprinting technique is based on the measurement of the signal strength emitted by close and available Wi-Fi networks. However, the nature of signal propagation is not linear, and it is impacted by the fixed and dynamic obstacles present in the environment. This work consists in the study of k-NN algorithm parameters, k value, distance metric and data representation, to improve the efficiency of this prediction model. The evaluation will be conducted over several different heterogeneous databases and propose a model to automatically set the value of k.},
keywords = {machine learning, Wi-Fi fingerprint},
pubstate = {published},
tppubtype = {inproceedings}
}
Wi-Fi Fingerprinting is a very popular technique in the field of indoor positioning, since the release of Microsoft RADAR system back in 2000. Since that milestone, the vast majority of studies and improvements in this field keep using the same base algorithm, an adaptation of the k-NN algorithm to treat geospatial data (e.g., x/y or lat/lon). One of the most relevant drawbacks of k-NN algorithm resides in its initial design, focused on resolving general classification problems. Wi-Fi fingerprinting technique is based on the measurement of the signal strength emitted by close and available Wi-Fi networks. However, the nature of signal propagation is not linear, and it is impacted by the fixed and dynamic obstacles present in the environment. This work consists in the study of k-NN algorithm parameters, k value, distance metric and data representation, to improve the efficiency of this prediction model. The evaluation will be conducted over several different heterogeneous databases and propose a model to automatically set the value of k.
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 Proceedings Article
In: Proceedings of the 2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring), pp. 1-5, IEEE, 2021, ISBN: 978-1-7281-8965-9.
Abstract | Links | BibTeX | Tags: Indoor positioning, Wi-Fi fingerprint, Wi-Fi mapping
@inproceedings{Torres-Sospedra2021b,
title = {Ensembling Multiple Radio Maps with Dynamic Noise in Fingerprint-based Indoor Positioning},
author = {Joaquín Torres-Sospedra and Fernando J. Aranda and Fernando J. Álvarez and Darwin Quezada-Gaibor and Ivo Silva and Cristiano Pendão and Adriano Moreira},
doi = {http://dx.doi.org/10.1109/VTC2021-Spring51267.2021.9448947},
isbn = {978-1-7281-8965-9},
year = {2021},
date = {2021-06-15},
booktitle = {Proceedings of the 2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring)},
pages = {1-5},
publisher = {IEEE},
abstract = {Fingerprint-based indoor positioning is widely used in many contexts, including pedestrian and autonomous vehicles navigation. Many approaches have used traditional Machine Learning models to deal with fingerprinting, being k-NN the most common used one. However, the reference data (or radio map) is generally limited, as data collection is a very demanding task, which degrades overall accuracy. In this work, we propose a novel approach to add random noise to the radio map which will be used in combination with an ensemble model. Instead of augmenting the radio map, we create n noisy versions of the same size, i.e. our proposed Indoor Positioning model will combine n estimations obtained by independent estimators built with the n noisy radio maps. The empirical results have shown that our proposed approach improves the baseline method results in around 10% on average.},
keywords = {Indoor positioning, Wi-Fi fingerprint, Wi-Fi mapping},
pubstate = {published},
tppubtype = {inproceedings}
}
Fingerprint-based indoor positioning is widely used in many contexts, including pedestrian and autonomous vehicles navigation. Many approaches have used traditional Machine Learning models to deal with fingerprinting, being k-NN the most common used one. However, the reference data (or radio map) is generally limited, as data collection is a very demanding task, which degrades overall accuracy. In this work, we propose a novel approach to add random noise to the radio map which will be used in combination with an ensemble model. Instead of augmenting the radio map, we create n noisy versions of the same size, i.e. our proposed Indoor Positioning model will combine n estimations obtained by independent estimators built with the n noisy radio maps. The empirical results have shown that our proposed approach improves the baseline method results in around 10% on average.
Mendoza-Silva, Germán Martin; Torres-Sospedra, Joaquín; Huerta-Guijarro, Joaquín
Local-level Analysis of Positioning Errors in Wi-Fi Fingerprinting 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
Klus, Lucie; Quezada-Gaibor, Darwin; Torres-Sospedra, Joaquín; Lohan, Simona Elena; Granell-Canut, Carlos; Nurmi, Jari
RSS Fingerprinting dataset size reduction using feature-wise adaptive k-means clustering. Proceedings Article
In: Proceedings of the 12th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT), 5-8 October 2020. Online event,, pp. 195-200, 2020, ISBN: 978-1-7281-9281-9, (best paper ward).
Links | BibTeX | Tags: A-wear, Wi-Fi fingerprint
@inproceedings{Klus2020,
title = {RSS Fingerprinting dataset size reduction using feature-wise adaptive k-means clustering.},
author = {Lucie Klus and Darwin Quezada-Gaibor and Joaquín Torres-Sospedra and Simona Elena Lohan and Carlos Granell-Canut and Jari Nurmi},
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 = {195-200},
note = {best paper ward},
keywords = {A-wear, Wi-Fi fingerprint},
pubstate = {published},
tppubtype = {inproceedings}
}
Khandker, S.; Torres-Sospedra, Joaquín; Ristaniemi, T.
Analysis of Received Signal Strength Quantization in Fingerprinting Localization Journal Article
In: Sensors, vol. 20, no. 3203, 2020, ISSN: 1424-8220.
Abstract | Links | BibTeX | Tags: Indoor positioning, Wi-Fi fingerprint
@article{Khandker2020,
title = {Analysis of Received Signal Strength Quantization in Fingerprinting Localization},
author = {S. Khandker and Joaquín Torres-Sospedra and T. Ristaniemi},
doi = {https://doi.org/10.3390/s20113203},
issn = {1424-8220},
year = {2020},
date = {2020-07-09},
journal = {Sensors},
volume = {20},
number = {3203},
abstract = {In recent times, Received Signal Strength (RSS)-based Wi-Fi fingerprinting localization has become one of the most promising techniques for indoor localization. The primary aim of RSS is to check the quality of the signal to determine the coverage and the quality of service. Therefore, fine-resolution RSS is needed, which is generally expressed by 1-dBm granularity. However, we found that, for fingerprinting localization, fine-granular RSS is unnecessary. A coarse-granular RSS can yield the same positioning accuracy. In this paper, we propose quantization for only the effective portion of the signal strength for fingerprinting localization. We found that, if a quantized RSS fingerprint can carry the major characteristics of a radio environment, it is sufficient for localization. Five publicly open fingerprinting databases with four different quantization strategies were used to evaluate the study. The proposed method can help to simplify the hardware configuration, enhance security, and save approximately 40–60% storage space and data traffic},
keywords = {Indoor positioning, Wi-Fi fingerprint},
pubstate = {published},
tppubtype = {article}
}
In recent times, Received Signal Strength (RSS)-based Wi-Fi fingerprinting localization has become one of the most promising techniques for indoor localization. The primary aim of RSS is to check the quality of the signal to determine the coverage and the quality of service. Therefore, fine-resolution RSS is needed, which is generally expressed by 1-dBm granularity. However, we found that, for fingerprinting localization, fine-granular RSS is unnecessary. A coarse-granular RSS can yield the same positioning accuracy. In this paper, we propose quantization for only the effective portion of the signal strength for fingerprinting localization. We found that, if a quantized RSS fingerprint can carry the major characteristics of a radio environment, it is sufficient for localization. Five publicly open fingerprinting databases with four different quantization strategies were used to evaluate the study. The proposed method can help to simplify the hardware configuration, enhance security, and save approximately 40–60% storage space and data traffic
Torres-Sospedra, Joaquín; Quezada-Gaibor, Darwin; Mendoza-Silva, Germán Martín; Nurmi, Jari; Koucheryavy, Yevgeni; Huerta-Guijarro, Joaquín
New Cluster Selection and Fine-grained Search for k-Means Clustering and Wi-Fi Fingerprinting 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
Khandker, S.; Torres-Sospedra, Joaquín; Ristaniemi, T.
Improving RF fingerprinting methods by means of D2D communication protocol Journal Article
In: Electronics, vol. 8, pp. 97, 2019, (IF:).
Links | BibTeX | Tags: Wi-Fi fingerprint
@article{Khandker2019,
title = {Improving RF fingerprinting methods by means of D2D communication protocol},
author = {S. Khandker and Joaquín Torres-Sospedra and T. Ristaniemi},
doi = {10.3390/electronics8010097},
year = {2019},
date = {2019-02-01},
journal = {Electronics},
volume = {8},
pages = {97},
note = {IF:},
keywords = {Wi-Fi fingerprint},
pubstate = {published},
tppubtype = {article}
}
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.
Sansano, Emilio; Montoliu, Raul; Belmonte-Fernández, Óscar; Torres-Sospedra, Joaquín
Indoor Positioning and Fingerprinting: The R package ipft Journal Article
In: The R-Journal., vol. 11, no. 1, 2019.
Links | BibTeX | Tags: Indoor positioning, r, Wi-Fi fingerprint
@article{Sansano2019,
title = {Indoor Positioning and Fingerprinting: The R package ipft},
author = {Emilio Sansano and Raul Montoliu and Óscar Belmonte-Fernández and Joaquín Torres-Sospedra},
editor = {
},
doi = {https://doi.org/10.32614/RJ-2019-010},
year = {2019},
date = {2019-01-01},
journal = {The R-Journal.},
volume = {11},
number = {1},
keywords = {Indoor positioning, r, Wi-Fi fingerprint},
pubstate = {published},
tppubtype = {article}
}
2018
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.
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}
}
Montoliu, Raul; Belmonte, Oscar; Sansano, Emilio; Torres-Sospedra, Joaquín
A new methodology for long-term maintenance of WiFi fingerprinting radio maps Proceedings Article
In: Proceedings of the Ninth International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 1-7, IEEE, 2018, ISBN: 78-1-5386-5635-8/18.
Abstract | BibTeX | Tags: Indoor positioning, Wi-Fi fingerprint
@inproceedings{ipin2018longterm,
title = {A new methodology for long-term maintenance of WiFi fingerprinting radio maps},
author = {Raul Montoliu and Oscar Belmonte and Emilio Sansano and Joaquín Torres-Sospedra },
isbn = {78-1-5386-5635-8/18},
year = {2018},
date = {2018-01-01},
booktitle = {Proceedings of the Ninth International Conference on Indoor Positioning and Indoor Navigation (IPIN)},
pages = {1-7},
publisher = {IEEE},
abstract = {One of the main problems of Indoor Positioning Systems (IPSs) based on WiFi fingerprinting is the radio map maintenance. It is well known that the creation of the radio map is a tedious and long-time task. In addition, if sometime after its creation, some access points are removed from the environment the accuracy of the IPS can be dramatically affected. This paper presents a new methodology to deal with this problem using imputation based techniques. An extensive set of experiments, comparing different imputation techniques, has been performed to demonstrate the benefits of using the proposed approach, showing that the proposed method is able to reduce the localization error in almost one meter with respect to a wellknown solution.},
keywords = {Indoor positioning, Wi-Fi fingerprint},
pubstate = {published},
tppubtype = {inproceedings}
}
One of the main problems of Indoor Positioning Systems (IPSs) based on WiFi fingerprinting is the radio map maintenance. It is well known that the creation of the radio map is a tedious and long-time task. In addition, if sometime after its creation, some access points are removed from the environment the accuracy of the IPS can be dramatically affected. This paper presents a new methodology to deal with this problem using imputation based techniques. An extensive set of experiments, comparing different imputation techniques, has been performed to demonstrate the benefits of using the proposed approach, showing that the proposed method is able to reduce the localization error in almost one meter with respect to a wellknown solution.
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
Moreira, A.; Silva, I.; Meneses, F.; Nicolau, M. J.; Pendão, C.; Torres-Sospedra, Joaquín
Multiple simultaneous Wi-Fi measurements in fingerprinting indoor positioning Proceedings Article
In: Proceedings of the Eigth International Conference on Indoor Positioning and Indoor Navigation. Sapporo, Japan, 18-21 Sep 2017., IEEE, 2017, ISBN: 978-1-5090-6299-17 .
Abstract | Links | BibTeX | Tags: Indoor positioning, Wi-Fi fingerprint
@inproceedings{Moreira2017,
title = {Multiple simultaneous Wi-Fi measurements in fingerprinting indoor positioning},
author = {A. Moreira and I. Silva and F. Meneses and M.J. Nicolau and C. Pendão and Joaquín Torres-Sospedra},
doi = {0.1109/IPIN.2017.8115914},
isbn = {978-1-5090-6299-17 },
year = {2017},
date = {2017-12-01},
booktitle = {Proceedings of the Eigth International Conference on Indoor Positioning and Indoor Navigation. Sapporo, Japan, 18-21 Sep 2017.},
publisher = {IEEE},
abstract = {The accuracy of fingerprinting-based positioning methods accuracy is limited by the fluctuations in the radio signal intensity mainly due to reflections, refractions, and multipath interference, among other factors. We consider that the fluctuations (often modelled as a Gaussian process for simplification purposes) can be minimized by exploiting the richness of multiple signals collected simultaneously through independent network interfaces. This paper introduces an analysis of Wi-Fi signals' statistics using simultaneous measurements which shows that RSSI values obtained from independent devices are not highly correlated. The low correlation between Wi-Fi interfaces might be exploited to improve the positioning accuracy. The validation of the proposed fingerprinting approach in a real scenario shows that the mean and maximum error in positioning can be reduced by more than 40% when five Wi-Fi interfaces are simultaneously used for fingerprinting},
keywords = {Indoor positioning, Wi-Fi fingerprint},
pubstate = {published},
tppubtype = {inproceedings}
}
The accuracy of fingerprinting-based positioning methods accuracy is limited by the fluctuations in the radio signal intensity mainly due to reflections, refractions, and multipath interference, among other factors. We consider that the fluctuations (often modelled as a Gaussian process for simplification purposes) can be minimized by exploiting the richness of multiple signals collected simultaneously through independent network interfaces. This paper introduces an analysis of Wi-Fi signals' statistics using simultaneous measurements which shows that RSSI values obtained from independent devices are not highly correlated. The low correlation between Wi-Fi interfaces might be exploited to improve the positioning accuracy. The validation of the proposed fingerprinting approach in a real scenario shows that the mean and maximum error in positioning can be reduced by more than 40% when five Wi-Fi interfaces are simultaneously used for fingerprinting
Torres-Sospedra, Joaquín; Moreira, Adriano
Analysis of Sources of Large Positioning Errors in Deterministic Fingerprinting Journal Article
In: Sensors, vol. 17, no. 12, pp. ARTICLE NUMBER = 2736, 2017, ISSN: 1424-8220.
Abstract | Links | BibTeX | Tags: Indoor positioning, Wi-Fi fingerprint
@article{s17122736,
title = {Analysis of Sources of Large Positioning Errors in Deterministic Fingerprinting},
author = { Joaquín Torres-Sospedra and Adriano Moreira},
url = {http://www.mdpi.com/1424-8220/17/12/2736},
doi = {10.3390/s17122736},
issn = {1424-8220},
year = {2017},
date = {2017-10-27},
journal = {Sensors},
volume = {17},
number = {12},
pages = {ARTICLE NUMBER = 2736},
abstract = {Wi-Fi fingerprinting is widely used for indoor positioning and indoor navigation due to the ubiquity of wireless networks, high proliferation of Wi-Fi-enabled mobile devices, and its reasonable positioning accuracy. The assumption is that the position can be estimated based on the received signal strength intensity from multiple wireless access points at a given point. The positioning accuracy, within a few meters, enables the use of Wi-Fi fingerprinting in many different applications. However, it has been detected that the positioning error might be very large in a few cases, which might prevent its use in applications with high accuracy positioning requirements. Hybrid methods are the new trend in indoor positioning since they benefit from multiple diverse technologies (Wi-Fi, Bluetooth, and Inertial Sensors, among many others) and, therefore, they can provide a more robust positioning accuracy. In order to have an optimal combination of technologies, it is crucial to identify when large errors occur and prevent the use of extremely bad positioning estimations in hybrid algorithms. This paper investigates why large positioning errors occur in Wi-Fi fingerprinting and how to detect them by using the received signal strength intensities.},
keywords = {Indoor positioning, Wi-Fi fingerprint},
pubstate = {published},
tppubtype = {article}
}
Wi-Fi fingerprinting is widely used for indoor positioning and indoor navigation due to the ubiquity of wireless networks, high proliferation of Wi-Fi-enabled mobile devices, and its reasonable positioning accuracy. The assumption is that the position can be estimated based on the received signal strength intensity from multiple wireless access points at a given point. The positioning accuracy, within a few meters, enables the use of Wi-Fi fingerprinting in many different applications. However, it has been detected that the positioning error might be very large in a few cases, which might prevent its use in applications with high accuracy positioning requirements. Hybrid methods are the new trend in indoor positioning since they benefit from multiple diverse technologies (Wi-Fi, Bluetooth, and Inertial Sensors, among many others) and, therefore, they can provide a more robust positioning accuracy. In order to have an optimal combination of technologies, it is crucial to identify when large errors occur and prevent the use of extremely bad positioning estimations in hybrid algorithms. This paper investigates why large positioning errors occur in Wi-Fi fingerprinting and how to detect them by using the received signal strength intensities.
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.
Lohan, Elena Simona; Torres-Sospedra, Joaquín; Leppäkoski, Helena; Richter, Philipp; Peng, Zhe; Huerta-Guijarro, Joaquín
Wi-Fi Crowdsourced Fingerprinting Dataset for Indoor Positioning Journal Article
In: Data, vol. 2, no. 4, pp. 32, 2017, ISSN: 2306-5729.
Abstract | Links | BibTeX | Tags: crowdsourcing, Indoor positioning, Wi-Fi fingerprint
@article{Lohan2017,
title = {Wi-Fi Crowdsourced Fingerprinting Dataset for Indoor Positioning},
author = {Elena Simona Lohan and Joaquín Torres-Sospedra and Helena Leppäkoski and Philipp Richter and Zhe Peng and Joaquín 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},
pages = {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 = {crowdsourcing, 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.
Lohan, Elena Simona; Torres-Sospedra, Joaquín; Leppäkoski, Helena; Richter, Philipp; Peng, Zhe; Huerta-Guijarro, Joaquín
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 Joaquín Torres-Sospedra and Helena Leppäkoski and Philipp Richter and Zhe Peng and Joaquín 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.
Torres-Sospedra, Joaquín; Belmonte, Oscar; Montoliu, Raul; Trilles-Oliver, Sergio; Calia, Andrea
In-home monitoring system based on WiFi fingerprints for Ambient Assisted Living Journal Article
In: Journal of Ambient Intelligence and Smart Environments (Accepted), 2017.
BibTeX | Tags: Monitoring, Wi-Fi fingerprint
@article{torres2017aal,
title = {In-home monitoring system based on WiFi fingerprints for Ambient Assisted Living},
author = {Joaquín Torres-Sospedra and Oscar Belmonte and Raul Montoliu and Sergio Trilles-Oliver and Andrea Calia},
year = {2017},
date = {2017-05-22},
journal = {Journal of Ambient Intelligence and Smart Environments (Accepted)},
keywords = {Monitoring, Wi-Fi fingerprint},
pubstate = {published},
tppubtype = {article}
}
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}
}
Torres-Sospedra, Joaquín; Moreira, Adriano; Knauth, Stefan; Berkvens, Rafael; Montoliu-Colás, Raúl; Belmonte-Fernández, Óscar; Trilles-Oliver, Sergio; Nicolau, Maria Joao; Meneses, Filipe; Costa, Antonio; Koukofikis, Athanasious; Weyn, Marteen; Peremans, Herbert
Realistic Evaluation of Indoor Positioning Systems Based on Wi-Fi Fingerprinting: The 2015 EvAAL-ETRI Competition Journal Article
In: Journal of ambient intelligence and smart environments, vol. 9, pp. 263–279, 2017, ISSN: 1876-1364.
Links | BibTeX | Tags: Indoor positioning, REPNIN, Wi-Fi fingerprint
@article{Torres-Sospedra2017,
title = {Realistic Evaluation of Indoor Positioning Systems Based on Wi-Fi Fingerprinting: The 2015 EvAAL-ETRI Competition},
author = {Joaquín Torres-Sospedra and Adriano Moreira and Stefan Knauth and Rafael Berkvens and Raúl Montoliu-Colás and Óscar Belmonte-Fernández and Sergio Trilles-Oliver and Maria Joao Nicolau and Filipe Meneses and Antonio Costa and Athanasious Koukofikis and Marteen Weyn and Herbert Peremans},
doi = {10.3233/AIS-170421},
issn = {1876-1364},
year = {2017},
date = {2017-01-01},
journal = {Journal of ambient intelligence and smart environments},
volume = {9},
pages = {263–279},
keywords = {Indoor positioning, REPNIN, Wi-Fi fingerprint},
pubstate = {published},
tppubtype = {article}
}
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; Belmonte-Fernández, Óscar; Montoliu-Colás, Raúl; Trilles-Oliver, Sergio; Calia, Andrea
How feasible is WiFi fingerprint-based indoor positioning for in-home monitoring? Proceedings Article
In: Proceedings of the 12th International Conference on Intelligent Environment, London, 14-16 September 2016, pp. in press, 2016.
BibTeX | Tags: Indoor positioning, REPNIN, Wi-Fi fingerprint
@inproceedings{TorresSospedra2016,
title = {How feasible is WiFi fingerprint-based indoor positioning for in-home monitoring?},
author = {Joaquín Torres-Sospedra and Óscar Belmonte-Fernández and Raúl Montoliu-Colás and Sergio Trilles-Oliver and Andrea Calia},
year = {2016},
date = {2016-01-01},
booktitle = {Proceedings of the 12th International Conference on Intelligent Environment, London, 14-16 September 2016},
pages = {in press},
keywords = {Indoor positioning, REPNIN, 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}
}
2015
Torres-Sospedra, Joaquín; Montoliu-Colás, Raúl; Trilles-Oliver, Sergio; Belmonte-Fernández, Óscar; Huerta-Guijarro, Joaquín
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 = { Joaquín Torres-Sospedra and Raúl Montoliu-Colás and Sergio Trilles-Oliver and Óscar Belmonte-Fernández and Joaquín 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.
2014
Nadal-García, Vicent-Francisco
Combining WLAN fingerprint-based localization with sensor data for indoor navigation using mobile devices Masters Thesis
Universitat Jaume I, 2014.
BibTeX | Tags: Indoor positioning, mobile GIS, Sensors, Wi-Fi fingerprint
@mastersthesis{NadalGarcia2013,
title = {Combining WLAN fingerprint-based localization with sensor data for indoor navigation using mobile devices},
author = { Vicent-Francisco Nadal-García},
editor = {Raúl Montoliu-Colás (supervisor) and Roberto Henriques (co-supervisor) and Óscar Belmonte-Fernández (co-supervisor)},
year = {2014},
date = {2014-06-02},
school = {Universitat Jaume I},
keywords = {Indoor positioning, mobile GIS, Sensors, Wi-Fi fingerprint},
pubstate = {published},
tppubtype = {mastersthesis}
}
2013
Avariento-Vicent, Joan Pere
WIFI indoor positioning for mobile devices, an application for the UJI Smart Campus Masters Thesis
Universitat Jaume I, 2013.
BibTeX | Tags: mobile GIS, Smart Campus, SMARTUJI, Wi-Fi fingerprint
@mastersthesis{AvarientoVicent2013,
title = {WIFI indoor positioning for mobile devices, an application for the UJI Smart Campus},
author = { Joan Pere Avariento-Vicent},
editor = {Francisco Ramos-Romero (supervisor) and Andrés Muñoz-Zuluaga (co-supervisor) and André Barriguinha (co-supervisor)},
year = {2013},
date = {2013-03-01},
school = {Universitat Jaume I},
keywords = {mobile GIS, Smart Campus, SMARTUJI, Wi-Fi fingerprint},
pubstate = {published},
tppubtype = {mastersthesis}
}
0000
Quezada-Gaibor, Darwin; Klus, Lucie; Klus, Roman; Lohan, Elena Simona; Nurmi, Jari; Valkama, Mikko; Huerta-Guijarro, Joaquín; Torres-Sospedra, Joaquín
Autoencoder Extreme Learning Machine for Fingerprint-Based Positioning: A Good Weight Initialization is Decisive Journal Article
In: IEEE Journal of Indoor and Seamless Positioning and Navigation, vol. 1, pp. 53-68, 0000, ISSN: 2832-7322.
Abstract | Links | BibTeX | Tags: A-wear, Autoencoder, Indoor positioning, Wi-Fi fingerprint
@article{Quezada2023b,
title = {Autoencoder Extreme Learning Machine for Fingerprint-Based Positioning: A Good Weight Initialization is Decisive},
author = {Darwin Quezada-Gaibor and Lucie Klus and Roman Klus and Elena Simona Lohan and Jari Nurmi and Mikko Valkama and Joaquín Huerta-Guijarro and Joaquín Torres-Sospedra},
doi = {https://doi.org/10.1109/JISPIN.2023.3299433},
issn = {2832-7322},
journal = {IEEE Journal of Indoor and Seamless Positioning and Navigation},
volume = {1},
pages = {53-68},
abstract = {Indoor positioning based on machine-learning (ML) models has attracted widespread interest in the last few years, given its high performance and usability. Supervised, semisupervised, and unsupervised models have thus been widely used in this field, not only to estimate the user position, but also to compress, clean, and denoise fingerprinting datasets. Some scholars have focused on developing, improving, and optimizing ML models to provide accurate solutions to the end user. This article introduces a novel method to initialize the input weights in autoencoder extreme learning machine (AE-ELM), namely factorized input data (FID), which is based on the normalized form of the orthogonal component of the input data. AE-ELM with FID weight initialization is used to efficiently reduce the radio map. Once the dimensionality of the dataset is reduced, we use k -nearest neighbors to perform the position estimation. This research work includes a comparative analysis with several traditional ways to initialize the input weights in AE-ELM, showing that FID provide a significantly better reconstruction error. Finally, we perform an assessment with 13 indoor positioning datasets collected from different buildings and in different countries. We show that the dimensionality of the datasets can be reduced more than 11 times on average, while the positioning error suffers only a small increment of 15% (on average) in comparison to the baseline.},
keywords = {A-wear, Autoencoder, Indoor positioning, Wi-Fi fingerprint},
pubstate = {published},
tppubtype = {article}
}
Indoor positioning based on machine-learning (ML) models has attracted widespread interest in the last few years, given its high performance and usability. Supervised, semisupervised, and unsupervised models have thus been widely used in this field, not only to estimate the user position, but also to compress, clean, and denoise fingerprinting datasets. Some scholars have focused on developing, improving, and optimizing ML models to provide accurate solutions to the end user. This article introduces a novel method to initialize the input weights in autoencoder extreme learning machine (AE-ELM), namely factorized input data (FID), which is based on the normalized form of the orthogonal component of the input data. AE-ELM with FID weight initialization is used to efficiently reduce the radio map. Once the dimensionality of the dataset is reduced, we use k -nearest neighbors to perform the position estimation. This research work includes a comparative analysis with several traditional ways to initialize the input weights in AE-ELM, showing that FID provide a significantly better reconstruction error. Finally, we perform an assessment with 13 indoor positioning datasets collected from different buildings and in different countries. We show that the dimensionality of the datasets can be reduced more than 11 times on average, while the positioning error suffers only a small increment of 15% (on average) in comparison to the baseline.