2024
González-Pérez, Alberto; Díaz-Sanahuja, Laura; Matey-Sanz, Miguel; Osma, Jorge; Granell-Canut, Carlos; Bretón-López, Juana; Casteleyn, Sven
Towards a self-applied, mobile-based geolocated exposure therapy software for anxiety disorders: SyMptOMS-ET app Journal Article
In: Digital Health, vol. 10, pp. 1-17, 2024, ISBN: 2055-2076.
Abstract | Links | BibTeX | Tags: exposure therapy, mental health, mHealth, smartphone app
@article{Gonzalez-Perez2024a,
title = {Towards a self-applied, mobile-based geolocated exposure therapy software for anxiety disorders: SyMptOMS-ET app},
author = {Alberto González-Pérez and Laura Díaz-Sanahuja and Miguel Matey-Sanz and Jorge Osma and Carlos Granell-Canut and Juana Bretón-López and Sven Casteleyn},
doi = {https://doi.org/10.1177/20552076241283942},
isbn = {2055-2076},
year = {2024},
date = {2024-10-28},
urldate = {2024-10-28},
journal = {Digital Health},
volume = {10},
pages = {1-17},
abstract = {Objective
While exposure therapy (ET) has the potential to help people tolerate intense situation-specific emotions and change avoidance behaviours, no smartphone solution exists to guide the process of in-vivo ET. A geolocation-based smartphone software component was designed and developed to instrumentalize patient guidance in in-vivo ET and its psychological validity was assessed by a group of independent psychology experts.
Methods
A team of computer scientists and psychologists developed the ET Component for in-vivo ET using geolocation-based technology, following the process-centred design methodology. The ET Component was integrated into the SyMptOMS-ET Android application, which was developed following the co-design methodology. Next, nine independent psychology experts tested and evaluated the ET Component and the SyMptOMS-ET app in the field, following the think-aloud methodology. Participants also completed the Mobile Application Rating Scale (MARS) instrument to quantitatively evaluate the solutions.
Results
We present the SyMptOMS-ET app’s main features and the ET Component exposure workflow. Next, we discuss the feedback obtained and the results of the MARS instrument. Participants who tested the app were satisfied with the ET Component during exposure scenarios (score of mu4.32 out of 5 [mu 0.28] on MARS quality aspects), agreed on the soundness of the theoretical foundations of the solutions developed (score of mu4.57 [mu0.48] on MARS treatment support aspects), and provided minor think-a-loud comments to improve them.
Conclusions
The results of the expert evaluation demonstrate the psychological validity of the ET Component and the SyMptOMS-ET app. However, further studies are needed to discern the acceptability and efficacy of the mHealth tool in the target population.},
keywords = {exposure therapy, mental health, mHealth, smartphone app},
pubstate = {published},
tppubtype = {article}
}
Objective
While exposure therapy (ET) has the potential to help people tolerate intense situation-specific emotions and change avoidance behaviours, no smartphone solution exists to guide the process of in-vivo ET. A geolocation-based smartphone software component was designed and developed to instrumentalize patient guidance in in-vivo ET and its psychological validity was assessed by a group of independent psychology experts.
Methods
A team of computer scientists and psychologists developed the ET Component for in-vivo ET using geolocation-based technology, following the process-centred design methodology. The ET Component was integrated into the SyMptOMS-ET Android application, which was developed following the co-design methodology. Next, nine independent psychology experts tested and evaluated the ET Component and the SyMptOMS-ET app in the field, following the think-aloud methodology. Participants also completed the Mobile Application Rating Scale (MARS) instrument to quantitatively evaluate the solutions.
Results
We present the SyMptOMS-ET app’s main features and the ET Component exposure workflow. Next, we discuss the feedback obtained and the results of the MARS instrument. Participants who tested the app were satisfied with the ET Component during exposure scenarios (score of mu4.32 out of 5 [mu 0.28] on MARS quality aspects), agreed on the soundness of the theoretical foundations of the solutions developed (score of mu4.57 [mu0.48] on MARS treatment support aspects), and provided minor think-a-loud comments to improve them.
Conclusions
The results of the expert evaluation demonstrate the psychological validity of the ET Component and the SyMptOMS-ET app. However, further studies are needed to discern the acceptability and efficacy of the mHealth tool in the target population.
While exposure therapy (ET) has the potential to help people tolerate intense situation-specific emotions and change avoidance behaviours, no smartphone solution exists to guide the process of in-vivo ET. A geolocation-based smartphone software component was designed and developed to instrumentalize patient guidance in in-vivo ET and its psychological validity was assessed by a group of independent psychology experts.
Methods
A team of computer scientists and psychologists developed the ET Component for in-vivo ET using geolocation-based technology, following the process-centred design methodology. The ET Component was integrated into the SyMptOMS-ET Android application, which was developed following the co-design methodology. Next, nine independent psychology experts tested and evaluated the ET Component and the SyMptOMS-ET app in the field, following the think-aloud methodology. Participants also completed the Mobile Application Rating Scale (MARS) instrument to quantitatively evaluate the solutions.
Results
We present the SyMptOMS-ET app’s main features and the ET Component exposure workflow. Next, we discuss the feedback obtained and the results of the MARS instrument. Participants who tested the app were satisfied with the ET Component during exposure scenarios (score of mu4.32 out of 5 [mu 0.28] on MARS quality aspects), agreed on the soundness of the theoretical foundations of the solutions developed (score of mu4.57 [mu0.48] on MARS treatment support aspects), and provided minor think-a-loud comments to improve them.
Conclusions
The results of the expert evaluation demonstrate the psychological validity of the ET Component and the SyMptOMS-ET app. However, further studies are needed to discern the acceptability and efficacy of the mHealth tool in the target population.
Matey-Sanz, Miguel; González-Pérez, Alberto; Casteleyn, Sven; Granell-Canut, Carlos
Implementing and Evaluating the Timed Up and Go Test Automation Using Smartphones and Smartwatches Journal Article
In: IEEE Journal of Biomedical and Health Informatics, vol. 28, iss. 11, pp. 6594 - 6605, 2024, ISSN: 2168-2208.
Abstract | Links | BibTeX | Tags: activity recognition, machine learning, Mobile apps, symptoms, wearables
@article{Matey2024b,
title = {Implementing and Evaluating the Timed Up and Go Test Automation Using Smartphones and Smartwatches},
author = {Miguel Matey-Sanz and Alberto González-Pérez and Sven Casteleyn and Carlos Granell-Canut},
doi = {https://doi.org/10.1109/JBHI.2024.3456169},
issn = {2168-2208},
year = {2024},
date = {2024-09-09},
urldate = {2024-09-09},
journal = {IEEE Journal of Biomedical and Health Informatics},
volume = {28},
issue = {11},
pages = {6594 - 6605},
abstract = {Physical performance tests aim to assess the physical abilities and mobility skills of individuals for various healthcare purposes. They are often driven by experts and usually performed at their practice, and therefore they are resource-intensive and time-demanding. For tests based on objective measurements (e.g., duration, repetitions), technology can be used to automate them, allowing the patients to perform the test themselves, more frequently and anywhere, while alleviating the expert from supervising the test. The well-known Timed Up and Go (TUG) test, typically used for mobility assessment, is an ideal candidate for automation, as inertial sensors (among others) can be deployed to detect the various movements constituting the test without expert supervision. To move from expert-led testing to self-administered testing, we present a mHealth system capable of automating the TUG test using a pocket-sized smartphone or a wrist smartwatch paired with a smartphone, where data from inertial sensors are used to detect the activities carried out by the patient while performing the test and compute their results in real time. All processing (i.e., data processing, machine learning-based activity inference, results calculation) takes place on the smartphone. The use of both devices to automate the TUG test was evaluated (w.r.t. accuracy, reliability and battery consumption) and mutually compared, and set off with a reference method, obtaining excellent Bland-Altman agreement results and Intraclass Correlation Coefficient reliability. Results also suggest that the smartwatch-based system performs better than the smartphone-based system.},
keywords = {activity recognition, machine learning, Mobile apps, symptoms, wearables},
pubstate = {published},
tppubtype = {article}
}
Physical performance tests aim to assess the physical abilities and mobility skills of individuals for various healthcare purposes. They are often driven by experts and usually performed at their practice, and therefore they are resource-intensive and time-demanding. For tests based on objective measurements (e.g., duration, repetitions), technology can be used to automate them, allowing the patients to perform the test themselves, more frequently and anywhere, while alleviating the expert from supervising the test. The well-known Timed Up and Go (TUG) test, typically used for mobility assessment, is an ideal candidate for automation, as inertial sensors (among others) can be deployed to detect the various movements constituting the test without expert supervision. To move from expert-led testing to self-administered testing, we present a mHealth system capable of automating the TUG test using a pocket-sized smartphone or a wrist smartwatch paired with a smartphone, where data from inertial sensors are used to detect the activities carried out by the patient while performing the test and compute their results in real time. All processing (i.e., data processing, machine learning-based activity inference, results calculation) takes place on the smartphone. The use of both devices to automate the TUG test was evaluated (w.r.t. accuracy, reliability and battery consumption) and mutually compared, and set off with a reference method, obtaining excellent Bland-Altman agreement results and Intraclass Correlation Coefficient reliability. Results also suggest that the smartwatch-based system performs better than the smartphone-based system.
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 Forthcoming
In: IEEE Internet of Things Journal, Forthcoming, 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},
journal = {IEEE Internet of Things Journal},
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 = {forthcoming},
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.
Kotze, André; Hildemann, Moritz Jan; Santos, Vitor; Granell-Canut, Carlos
Genetic Programming to Optimize 3D Trajectories Journal Article
In: ISPRS International Journal of Geo-Information, vol. 13, no. 8, pp. 295, 2024, ISSN: 2220-9964.
Abstract | Links | BibTeX | Tags: 3D, genetic algorithms, trajectory optimisation
@article{Kotze2024a,
title = {Genetic Programming to Optimize 3D Trajectories},
author = {André Kotze and Moritz Jan Hildemann and Vitor Santos and Carlos Granell-Canut},
doi = {https://doi.org/10.3390/ijgi13080295},
issn = {2220-9964},
year = {2024},
date = {2024-08-20},
journal = {ISPRS International Journal of Geo-Information},
volume = {13},
number = {8},
pages = {295},
abstract = {Trajectory optimization is a method of finding the optimal route connecting a start and end point. The suitability of a trajectory depends on not intersecting any obstacles, as well as predefined performance metrics. In the context of unmanned aerial vehicles (UAVs), the goal is to minimize the route cost, in terms of energy or time, while avoiding restricted flight zones. Artificial intelligence techniques, including evolutionary computation, have been applied to trajectory optimization with varying degrees of success. This work explores the use of genetic programming (GP) for 3D trajectory optimization by developing a novel GP algorithm to optimize trajectories in a 3D space by encoding 3D geographic trajectories as function trees. The effects of parameterization are also explored and discussed, demonstrating the advantages and drawbacks of custom parameter settings along with additional evolutionary computational techniques. The results demonstrate the effectiveness of the proposed algorithm, which outperforms existing methods in terms of speed, automaticity, and robustness, highlighting the potential for GP-based algorithms to be applied to other complex optimization problems in science and engineering.},
keywords = {3D, genetic algorithms, trajectory optimisation},
pubstate = {published},
tppubtype = {article}
}
Trajectory optimization is a method of finding the optimal route connecting a start and end point. The suitability of a trajectory depends on not intersecting any obstacles, as well as predefined performance metrics. In the context of unmanned aerial vehicles (UAVs), the goal is to minimize the route cost, in terms of energy or time, while avoiding restricted flight zones. Artificial intelligence techniques, including evolutionary computation, have been applied to trajectory optimization with varying degrees of success. This work explores the use of genetic programming (GP) for 3D trajectory optimization by developing a novel GP algorithm to optimize trajectories in a 3D space by encoding 3D geographic trajectories as function trees. The effects of parameterization are also explored and discussed, demonstrating the advantages and drawbacks of custom parameter settings along with additional evolutionary computational techniques. The results demonstrate the effectiveness of the proposed algorithm, which outperforms existing methods in terms of speed, automaticity, and robustness, highlighting the potential for GP-based algorithms to be applied to other complex optimization problems in science and engineering.
Trilles-Oliver, Sergio; Monfort-Muriach, Aida; Cueto-Rubio, Enrique; López-Girona, Carmen; Granell-Canut, Carlos
Sucre4Stem: A K-12 Educational Tool for Integrating Computational Thinking and Programming Across Multidisciplinary Disciplines Journal Article Forthcoming
In: IEEE Transactions on Education, Forthcoming, ISSN: 0018-9359.
Abstract | Links | BibTeX | Tags: education, Internet of things, SUCRE, sucre4kids, sucre4stem
@article{Trilles2024c,
title = {Sucre4Stem: A K-12 Educational Tool for Integrating Computational Thinking and Programming Across Multidisciplinary Disciplines},
author = {Sergio Trilles-Oliver and Aida Monfort-Muriach and Enrique Cueto-Rubio and Carmen López-Girona and Carlos Granell-Canut},
doi = {https://doi.org/10.1109/TE.2024.3422666},
issn = { 0018-9359},
year = {2024},
date = {2024-07-29},
journal = {IEEE Transactions on Education},
abstract = {This article discusses the latest developments of the Sucre4Stem tool, as part of the Sucre initiative, which aims to promote interest in computational thinking and programming skills in K-12 students. The tool follows the Internet of Things approach and consists of two prominent components: 1) SucreCore and 2) SucreCode . SucreCore incorporates an advanced microcontroller packaged in a more compact design and enables wireless connectivity. SucreCode , the block-based visual programming tool, supports two different sets of blocks depending on the education grade, and facilitates wireless communication with SucreCore . At the educational level, Sucre4Stem fosters new group dynamics and encourages students to experiment real-world projects by promoting the “programming to learn” approach to concepts from other disciplines as opposed to the strategy widely applied in schools of “learning to program” in isolation.},
keywords = {education, Internet of things, SUCRE, sucre4kids, sucre4stem},
pubstate = {forthcoming},
tppubtype = {article}
}
This article discusses the latest developments of the Sucre4Stem tool, as part of the Sucre initiative, which aims to promote interest in computational thinking and programming skills in K-12 students. The tool follows the Internet of Things approach and consists of two prominent components: 1) SucreCore and 2) SucreCode . SucreCore incorporates an advanced microcontroller packaged in a more compact design and enables wireless connectivity. SucreCode , the block-based visual programming tool, supports two different sets of blocks depending on the education grade, and facilitates wireless communication with SucreCore . At the educational level, Sucre4Stem fosters new group dynamics and encourages students to experiment real-world projects by promoting the “programming to learn” approach to concepts from other disciplines as opposed to the strategy widely applied in schools of “learning to program” in isolation.
Ramos-Romero, Francisco; Trilles-Oliver, Sergio; Granell-Canut, Carlos
ARE YOU ABLE TO TELL STORIES WITH DATA? Proceedings Article
In: EDULEARN24 Proceedings, pp. 6978-6982, IATED, 2024, ISBN: 978-84-09-62938-1.
Abstract | Links | BibTeX | Tags: data visualization, storytelling
@inproceedings{Ramos2024a,
title = {ARE YOU ABLE TO TELL STORIES WITH DATA?},
author = {Francisco Ramos-Romero and Sergio Trilles-Oliver and Carlos Granell-Canut},
doi = {https://doi.org/10.21125/edulearn.2024.1653},
isbn = {978-84-09-62938-1},
year = {2024},
date = {2024-07-03},
booktitle = {EDULEARN24 Proceedings},
pages = {6978-6982},
publisher = {IATED},
abstract = {During our first education years, at the school, even at the high school, we learnt very much about many different subjects. However, in most cases, an important ability for our professional future is missed: how to create and tell stories with data and numbers. This lack of information has produced a big problem: users have access to a large amount of information, due to the current technological advances, but they are unable efficiently use data to tell stories, which is key to convert them into relevant information.
In this paper, we present a study we performed with master students aimed at improving their data visualization skills. We divided this study into several stages. Firstly, the students learn by accomplishing different tasks following a clear path divided into these sections: analysis of the audience, selecting the appropriate visuals, simplification, focus, communication with data, storytelling, and final visuals. Every task was presented in a visual and natural way, with different options, where the students should choose the correct answers, from a clear and objective point of view. All the tasks are part of a global questionnaire, which was carried out by the students so that they prove their competence in telling effective stories with numbers and data. The results shown clear lacks in different tasks such a simplification and communication with data, where most of students didn’t correctly answer the questions. As a conclusion, we can claim that more effort must be done in setting a clear objective in the visual communication field. In particular, simplifying data, focusing on the important part of the message to the audience, and also in the way and shape that data are presented.},
keywords = {data visualization, storytelling},
pubstate = {published},
tppubtype = {inproceedings}
}
During our first education years, at the school, even at the high school, we learnt very much about many different subjects. However, in most cases, an important ability for our professional future is missed: how to create and tell stories with data and numbers. This lack of information has produced a big problem: users have access to a large amount of information, due to the current technological advances, but they are unable efficiently use data to tell stories, which is key to convert them into relevant information.
In this paper, we present a study we performed with master students aimed at improving their data visualization skills. We divided this study into several stages. Firstly, the students learn by accomplishing different tasks following a clear path divided into these sections: analysis of the audience, selecting the appropriate visuals, simplification, focus, communication with data, storytelling, and final visuals. Every task was presented in a visual and natural way, with different options, where the students should choose the correct answers, from a clear and objective point of view. All the tasks are part of a global questionnaire, which was carried out by the students so that they prove their competence in telling effective stories with numbers and data. The results shown clear lacks in different tasks such a simplification and communication with data, where most of students didn’t correctly answer the questions. As a conclusion, we can claim that more effort must be done in setting a clear objective in the visual communication field. In particular, simplifying data, focusing on the important part of the message to the audience, and also in the way and shape that data are presented.
In this paper, we present a study we performed with master students aimed at improving their data visualization skills. We divided this study into several stages. Firstly, the students learn by accomplishing different tasks following a clear path divided into these sections: analysis of the audience, selecting the appropriate visuals, simplification, focus, communication with data, storytelling, and final visuals. Every task was presented in a visual and natural way, with different options, where the students should choose the correct answers, from a clear and objective point of view. All the tasks are part of a global questionnaire, which was carried out by the students so that they prove their competence in telling effective stories with numbers and data. The results shown clear lacks in different tasks such a simplification and communication with data, where most of students didn’t correctly answer the questions. As a conclusion, we can claim that more effort must be done in setting a clear objective in the visual communication field. In particular, simplifying data, focusing on the important part of the message to the audience, and also in the way and shape that data are presented.
Trilles-Oliver, Sergio; Granell-Canut, Carlos; Ramos-Romero, Francisco
SUCRE4STEM'S DUAL APPROACH TO ENHANCING COMPUTATIONAL THINKING AND PROGRAMMING SKILLS Proceedings Article
In: EDULEARN24 Proceedings, pp. 8094-8103, IATED, 2024, ISBN: 978-84-09-62938-1.
Abstract | Links | BibTeX | Tags: Internet of things, SUCRE, sucre4stem
@inproceedings{Trilles2024b,
title = {SUCRE4STEM'S DUAL APPROACH TO ENHANCING COMPUTATIONAL THINKING AND PROGRAMMING SKILLS},
author = {Sergio Trilles-Oliver and Carlos Granell-Canut and Francisco Ramos-Romero},
doi = {https://doi.org/10.21125/edulearn.2024.1908},
isbn = {978-84-09-62938-1},
year = {2024},
date = {2024-07-03},
booktitle = {EDULEARN24 Proceedings},
pages = {8094-8103},
publisher = {IATED},
abstract = {This article highlights recent advancements in the Sucre4Stem initiative, a key component of the broader Sucre project designed to kindle interest in computational thinking and programming among K-12 students. At its core, Sucre4Stem embraces the Internet of Things (IoT) ideology and is built around two main elements: SucreCore and SucreCode. SucreCore features a sophisticated microcontroller in a sleeker design, complemented by wireless connectivity options. On the other hand, SucreCode presents a block-based visual programming interface that now offers the choice between two distinct block sets tailored to different educational levels: one for primary education students (Grades 3-6) incorporating basic programming concepts with engaging graphics, and another for high school students (Grades 7-12), which introduces more advanced concepts closer to traditional coding. This dual approach not only makes Sucre4Stem versatile across educational stages but also enhances its educational impact by fostering new group dynamics and empowering students to undertake real-world projects.},
keywords = {Internet of things, SUCRE, sucre4stem},
pubstate = {published},
tppubtype = {inproceedings}
}
This article highlights recent advancements in the Sucre4Stem initiative, a key component of the broader Sucre project designed to kindle interest in computational thinking and programming among K-12 students. At its core, Sucre4Stem embraces the Internet of Things (IoT) ideology and is built around two main elements: SucreCore and SucreCode. SucreCore features a sophisticated microcontroller in a sleeker design, complemented by wireless connectivity options. On the other hand, SucreCode presents a block-based visual programming interface that now offers the choice between two distinct block sets tailored to different educational levels: one for primary education students (Grades 3-6) incorporating basic programming concepts with engaging graphics, and another for high school students (Grades 7-12), which introduces more advanced concepts closer to traditional coding. This dual approach not only makes Sucre4Stem versatile across educational stages but also enhances its educational impact by fostering new group dynamics and empowering students to undertake real-world projects.
Matey-Sanz, Miguel; Granell-Canut, Carlos; Cárdenas, Ramón A. Mollineda
Estrategias de control y seguimiento activo de proyectos de desarrollo de software Proceedings Article
In: Actas de las Jornadas sobre Enseñanza Universitaria de la Informática (JENUI), pp. 165-172, AENUI, 2024, ISSN: 2531-0607.
Abstract | Links | BibTeX | Tags: docente
@inproceedings{Matey2024a,
title = {Estrategias de control y seguimiento activo de proyectos de desarrollo de software},
author = {Miguel Matey-Sanz and Carlos Granell-Canut and Ramón A. Mollineda Cárdenas},
url = {https://aenui.org/actas/pdf/JENUI_2024_021.pdf},
issn = {2531-0607},
year = {2024},
date = {2024-07-01},
booktitle = {Actas de las Jornadas sobre Enseñanza Universitaria de la Informática (JENUI)},
volume = {9},
pages = { 165-172},
publisher = {AENUI},
abstract = {A system of active control and monitoring actions for software development projects by teams is proposed. It has been designed within the framework of a coordination action of two subjects from the fourth year of a Degree in Computer Engineering, the cornerstone of which is an agile development project guided by acceptance tests and evolutionary design. Students face multiple complexities: realistic scenario (e.g., mobility application), development methodology (ATDD), technical requirements (e.g., executable tests, decoupled architecture), connection to public services (e.g., geocoding, route calculation) and the use of modern technologies for cross-platform development, integration and deployment. Despite having resources from two subjects and a well-defined itinerary, the risk of dropping out was significant. To reinforce the control mechanisms, a system was created with warning cards, two sessions dedicated to the defence of partial deliverable, and adaptations of the GitFlow methodology and a commit standard (Conventional commits) to actively monitor the project and the individual contribution of the students. As a result, improvements have been observed in the rate of projects completed in the
first call, the project quality, and the increase in training measured from pre and post surveys.},
keywords = {docente},
pubstate = {published},
tppubtype = {inproceedings}
}
A system of active control and monitoring actions for software development projects by teams is proposed. It has been designed within the framework of a coordination action of two subjects from the fourth year of a Degree in Computer Engineering, the cornerstone of which is an agile development project guided by acceptance tests and evolutionary design. Students face multiple complexities: realistic scenario (e.g., mobility application), development methodology (ATDD), technical requirements (e.g., executable tests, decoupled architecture), connection to public services (e.g., geocoding, route calculation) and the use of modern technologies for cross-platform development, integration and deployment. Despite having resources from two subjects and a well-defined itinerary, the risk of dropping out was significant. To reinforce the control mechanisms, a system was created with warning cards, two sessions dedicated to the defence of partial deliverable, and adaptations of the GitFlow methodology and a commit standard (Conventional commits) to actively monitor the project and the individual contribution of the students. As a result, improvements have been observed in the rate of projects completed in the
first call, the project quality, and the increase in training measured from pre and post surveys.
first call, the project quality, and the increase in training measured from pre and post surveys.
Klus, Lucie; Klus, Roman; Lohan, Elena Simona; Nurmi, Jari; Granell-Canut, Carlos; Valkama, Mikko; Talvitie, Jukka; Casteleyn, Sven; Torres-Sospedra, Joaquín
TUJI1 Dataset: Multi-device dataset for indoor localization with high measurement density Journal Article
In: Data in Brief, vol. 54, pp. 110356, 2024, ISSN: 2352-3409.
Abstract | Links | BibTeX | Tags: Indoor positioning
@article{Klus2024b,
title = {TUJI1 Dataset: Multi-device dataset for indoor localization with high measurement density},
author = {Lucie Klus and Roman Klus and Elena Simona Lohan and Jari Nurmi and Carlos Granell-Canut and Mikko Valkama and Jukka Talvitie and Sven Casteleyn and Joaquín Torres-Sospedra},
editor = {Elsevier},
doi = {https://doi.org/10.1016/j.dib.2024.110356},
issn = {2352-3409},
year = {2024},
date = {2024-06-01},
journal = {Data in Brief},
volume = {54},
pages = {110356},
abstract = {Positioning in indoor scenarios using signals of opportunity is an effective solution enabling accurate and reliable performance in Global Navigation Satellite System (GNSS)-obscured scenarios. Despite the availability of numerous fingerprinting datasets utilizing various wireless signals, the challenge of device heterogeneity and sample density remains an unanswered issue. To address this gap, this work introduces TUJI1, an anonymized IEEE 802.11 Wireless LAN (Wi-Fi) fingerprinting dataset collected using 5 different commercial devices in a fine-grained grid. The dataset contains the matched fingerprints of Received Signal Strength Indicator (RSSI) measurements with the corresponding coordinates, split into training and testing subsets for effortless and fair reproducibility.},
keywords = {Indoor positioning},
pubstate = {published},
tppubtype = {article}
}
Positioning in indoor scenarios using signals of opportunity is an effective solution enabling accurate and reliable performance in Global Navigation Satellite System (GNSS)-obscured scenarios. Despite the availability of numerous fingerprinting datasets utilizing various wireless signals, the challenge of device heterogeneity and sample density remains an unanswered issue. To address this gap, this work introduces TUJI1, an anonymized IEEE 802.11 Wireless LAN (Wi-Fi) fingerprinting dataset collected using 5 different commercial devices in a fine-grained grid. The dataset contains the matched fingerprints of Received Signal Strength Indicator (RSSI) measurements with the corresponding coordinates, split into training and testing subsets for effortless and fair reproducibility.
Casanova-Marqués, Raúl
Privacy-enhancing technologies and privacy-enhancing cryptography for wearables PhD Thesis
Brno University of Technology, 2024.
Abstract | Links | BibTeX | Tags: A-wear, cryptography, Indoor positioning, Internet of things, privacy protection, wearables
@phdthesis{Casanova2024a,
title = {Privacy-enhancing technologies and privacy-enhancing cryptography for wearables},
author = {Raúl Casanova-Marqués},
url = {http://hdl.handle.net/10803/690875},
doi = {http://dx.doi.org/10.6035/14124.2024.839804},
year = {2024},
date = {2024-04-29},
school = {Brno University of Technology},
abstract = {In response to escalating privacy concerns and the need for secure digital communication, cryptographic mechanisms have been developed to ensure impervious information exchange. However, traditional cryptographic approaches are inadequate in dynamic and resource-constrained environments, such as wearable devices. This thesis investigates attribute-based credential schemes, offering fine-grained access control based on user-specific attributes. Specifically, it assesses the effectiveness and scalability of attribute-based anonymous credential schemes within dynamic wearable device architectures. The study focuses on enhancing these schemes by incorporating user revocation while preserving privacy. Additionally, the research develops methods for attribute-based authentication protocols on smart cards with limited elliptic curve cryptography support and addresses usability challenges. Furthermore, the thesis explores the integration of anonymous authentication in collaborative indoor positioning systems to ensure privacy and security. It also delves into implementing attribute-based authentication in resource-constrained environments, including Internet of Things devices, and evaluating their feasibility in dynamic wearable device architectures.},
keywords = {A-wear, cryptography, Indoor positioning, Internet of things, privacy protection, wearables},
pubstate = {published},
tppubtype = {phdthesis}
}
In response to escalating privacy concerns and the need for secure digital communication, cryptographic mechanisms have been developed to ensure impervious information exchange. However, traditional cryptographic approaches are inadequate in dynamic and resource-constrained environments, such as wearable devices. This thesis investigates attribute-based credential schemes, offering fine-grained access control based on user-specific attributes. Specifically, it assesses the effectiveness and scalability of attribute-based anonymous credential schemes within dynamic wearable device architectures. The study focuses on enhancing these schemes by incorporating user revocation while preserving privacy. Additionally, the research develops methods for attribute-based authentication protocols on smart cards with limited elliptic curve cryptography support and addresses usability challenges. Furthermore, the thesis explores the integration of anonymous authentication in collaborative indoor positioning systems to ensure privacy and security. It also delves into implementing attribute-based authentication in resource-constrained environments, including Internet of Things devices, and evaluating their feasibility in dynamic wearable device architectures.
Trilles-Oliver, Sergio; Hammad, Sahibzada Saadoon; Iskandaryan, Ditsuhi
Anomaly detection based on Artificial Intelligence of Things: A Systematic Literature Mapping Journal Article
In: Internet of Things, vol. 25, pp. 101063, 2024, ISSN: 2542-6605.
Abstract | Links | BibTeX | Tags: Anomaly detection, Edge computing, Internet of things, TidyML
@article{Trilles2024a,
title = {Anomaly detection based on Artificial Intelligence of Things: A Systematic Literature Mapping},
author = {Sergio Trilles-Oliver and Sahibzada Saadoon Hammad and Ditsuhi Iskandaryan},
doi = {10.1016/j.iot.2024.101063},
issn = {2542-6605},
year = {2024},
date = {2024-04-01},
journal = {Internet of Things},
volume = {25},
pages = {101063},
abstract = {Advanced Machine Learning (ML) algorithms can be applied using Edge Computing (EC) to detect anomalies, which is the basis of Artificial Intelligence of Things (AIoT). EC has emerged as a solution for processing and analysing information on IoT devices. This field aims to allow the implementation of Machine/Deep Learning (DL) models on MicroController Units (MCUs). Integrating anomaly detection analysis on Internet of Things (IoT) devices produces clear benefits as it ensures the use of accurate data from the initial stage. However, this process poses a challenge due to the unique characteristics of IoT. This article presents a Systematic Literature Mapping of scientific research on the application of anomaly detection techniques in EC using MCUs. A total of 18 papers published over the period 2021–2023 were selected from a total of 162 in four databases of scientific papers. The results of this paper provide a comprehensive overview of anomaly detection using TinyML and MCUs. The main contributions of this survey are the fact that it aims to: (a) study techniques for anomaly detection in ML/DL and validation metrics used in the AIoT; (b) analyse data used in the estimation of models; (c) show how ML is applied in EC using hardware or software; (d) investigate the main microcontrollers, types of power supply, and communication technology; and (e) develop a taxonomy of ML/DL algorithms used to detect anomalies in TinyML. Finally, the benefits and challenges of this kind of TinyML analysis are described.},
keywords = {Anomaly detection, Edge computing, Internet of things, TidyML},
pubstate = {published},
tppubtype = {article}
}
Advanced Machine Learning (ML) algorithms can be applied using Edge Computing (EC) to detect anomalies, which is the basis of Artificial Intelligence of Things (AIoT). EC has emerged as a solution for processing and analysing information on IoT devices. This field aims to allow the implementation of Machine/Deep Learning (DL) models on MicroController Units (MCUs). Integrating anomaly detection analysis on Internet of Things (IoT) devices produces clear benefits as it ensures the use of accurate data from the initial stage. However, this process poses a challenge due to the unique characteristics of IoT. This article presents a Systematic Literature Mapping of scientific research on the application of anomaly detection techniques in EC using MCUs. A total of 18 papers published over the period 2021–2023 were selected from a total of 162 in four databases of scientific papers. The results of this paper provide a comprehensive overview of anomaly detection using TinyML and MCUs. The main contributions of this survey are the fact that it aims to: (a) study techniques for anomaly detection in ML/DL and validation metrics used in the AIoT; (b) analyse data used in the estimation of models; (c) show how ML is applied in EC using hardware or software; (d) investigate the main microcontrollers, types of power supply, and communication technology; and (e) develop a taxonomy of ML/DL algorithms used to detect anomalies in TinyML. Finally, the benefits and challenges of this kind of TinyML analysis are described.
Macias, Juan Emilio Zurita; Trilles-Oliver, Sergio
Machine learning-based prediction model for battery levels in IoT devices using meteorological variables Journal Article
In: Internet of Things, vol. 25, pp. 101109, 2024, ISSN: 2542-6605.
Abstract | Links | BibTeX | Tags: battery level prediction, Internet of things, machine learning
@article{Zurita2024a,
title = {Machine learning-based prediction model for battery levels in IoT devices using meteorological variables},
author = {Juan Emilio Zurita Macias and Sergio Trilles-Oliver},
doi = {10.1016/j.iot.2024.101109},
issn = {2542-6605},
year = {2024},
date = {2024-04-01},
journal = {Internet of Things},
volume = {25},
pages = {101109},
abstract = {Efficient energy management is vital for the sustainability of IoT devices employing solar harvesting systems, particularly to circumvent battery depletion during periods of diminished solar incidence. Embracing the structured methodology of CRISP-DM, this study introduces machine learning (ML) models that utilise meteorological data to predict battery charge levels in solar-powered IoT devices. These models enable proactive adjustments to the devices’ data sampling frequencies, ensuring effective energy utilisation. The proposed ML models were evaluated using authentic battery charge data and weather forecast records. The empirical results of this study corroborate the predictive prowess of the models, with an average accuracy reaching as high as 94.09% in specific test cases. This substantiates the potential of the developed methodology to significantly enhance the energy autonomy of IoT devices through predictive analytics.},
keywords = {battery level prediction, Internet of things, machine learning},
pubstate = {published},
tppubtype = {article}
}
Efficient energy management is vital for the sustainability of IoT devices employing solar harvesting systems, particularly to circumvent battery depletion during periods of diminished solar incidence. Embracing the structured methodology of CRISP-DM, this study introduces machine learning (ML) models that utilise meteorological data to predict battery charge levels in solar-powered IoT devices. These models enable proactive adjustments to the devices’ data sampling frequencies, ensuring effective energy utilisation. The proposed ML models were evaluated using authentic battery charge data and weather forecast records. The empirical results of this study corroborate the predictive prowess of the models, with an average accuracy reaching as high as 94.09% in specific test cases. This substantiates the potential of the developed methodology to significantly enhance the energy autonomy of IoT devices through predictive analytics.
Gómez-Cambronero, Águeda; Mann, Anna-Lisa; Mira, Adriana; Doherty, Gavin; Casteleyn, Sven
Smartphone-based serious games for mental health: a scoping review Journal Article Forthcoming
In: Multimedia Tools and Applications, Forthcoming, ISSN: 1573-7721.
Abstract | Links | BibTeX | Tags: mental health, serious games, symptoms, Systematic mapping
@article{GomezCambronero2024a,
title = {Smartphone-based serious games for mental health: a scoping review},
author = {Águeda Gómez-Cambronero and Anna-Lisa Mann and Adriana Mira and Gavin Doherty and Sven Casteleyn},
doi = {https://doi.org/10.1007/s11042-024-18971-w},
issn = {1573-7721},
year = {2024},
date = {2024-04-01},
journal = {Multimedia Tools and Applications},
abstract = {The use of smartphone-based Serious Games in mental health care is an emerging and promising research field. Combining the intrinsic characteristics of games (e.g., interactiveness, immersiveness, playfulness, user-tailoring and engaging nature) with the capabilities of smartphones (e.g., versatility, ubiquitous connectivity, built-in sensors and anywhere–anytime nature) yields great potential to deliver innovative psychological treatments, which are engaging, effective, fun and always available. This article presents a scoping review, based on the PRISMA (scoping review extension) guidelines, of the field of smartphone-based serious games for mental health care. The review combines an analysis of the technical characteristics, including game design, smartphone and game-specific features, with psychological dimensions, including type and purpose of use, underlying psychological frameworks and strategies. It also explores the integration of psychological features into Serious Games and summarizes the findings of evaluations performed. A systematic search identified 40 smartphone-based Serious Games for mental health care. The majority consist of standalone and self-administrable interventions, applying a myriad of psychological strategies to address a wide range of psychological symptoms and disorders. The findings explore the potential of Serious Games as treatments and for enhancing patient engagement; we conclude by proposing several avenues for future research in order to identify best practices and success factors.},
keywords = {mental health, serious games, symptoms, Systematic mapping},
pubstate = {forthcoming},
tppubtype = {article}
}
The use of smartphone-based Serious Games in mental health care is an emerging and promising research field. Combining the intrinsic characteristics of games (e.g., interactiveness, immersiveness, playfulness, user-tailoring and engaging nature) with the capabilities of smartphones (e.g., versatility, ubiquitous connectivity, built-in sensors and anywhere–anytime nature) yields great potential to deliver innovative psychological treatments, which are engaging, effective, fun and always available. This article presents a scoping review, based on the PRISMA (scoping review extension) guidelines, of the field of smartphone-based serious games for mental health care. The review combines an analysis of the technical characteristics, including game design, smartphone and game-specific features, with psychological dimensions, including type and purpose of use, underlying psychological frameworks and strategies. It also explores the integration of psychological features into Serious Games and summarizes the findings of evaluations performed. A systematic search identified 40 smartphone-based Serious Games for mental health care. The majority consist of standalone and self-administrable interventions, applying a myriad of psychological strategies to address a wide range of psychological symptoms and disorders. The findings explore the potential of Serious Games as treatments and for enhancing patient engagement; we conclude by proposing several avenues for future research in order to identify best practices and success factors.
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
Bravenec, Tomás
Exploiting Wireless Communications for Localization: Beyond Fingerprinting PhD Thesis
Universitat Jaume I. INIT, 2023.
Abstract | Links | BibTeX | Tags: A-wear, data analysis methods, geoprivacy, Indoor positioning, machine learning
@phdthesis{Bravenec2023d,
title = {Exploiting Wireless Communications for Localization: Beyond Fingerprinting},
author = {Tomás Bravenec},
url = {http://hdl.handle.net/10803/689593},
doi = {http://dx.doi.org/10.6035/14124.2023.868082},
year = {2023},
date = {2023-12-18},
school = {Universitat Jaume I. INIT},
abstract = {The field of Location-based Services (LBS) has experienced significant growth over the past decade, driven by increasing interest in fitness tracking, robotics, and eHealth. This dissertation focuses on evaluating privacy measures in Indoor Positioning Systems (IPS), particularly in the context of ubiquitous Wi-Fi networks. It addresses non-cooperative user tracking through the exploitation of unencrypted Wi-Fi management frames, which contain enough information for device fingerprinting despite MAC address randomization. The research also explores an algorithm to estimate room occupancy based on passive Wi-Fi frame sniffing and Received Signal Strength Indicator (RSSI) measurements. Such room occupancy detection has implications for energy regulations in smart buildings. Furthermore, the thesis investigates methods to reduce computational requirements of machine learning and positioning algorithms through optimizing neural networks and employing interpolation techniques for IPS based on RSSI fingerprinting. The work contributes datasets, analysis scripts, and firmware to improve reproducibility and supports advancements in the LBS field.},
keywords = {A-wear, data analysis methods, geoprivacy, Indoor positioning, machine learning},
pubstate = {published},
tppubtype = {phdthesis}
}
The field of Location-based Services (LBS) has experienced significant growth over the past decade, driven by increasing interest in fitness tracking, robotics, and eHealth. This dissertation focuses on evaluating privacy measures in Indoor Positioning Systems (IPS), particularly in the context of ubiquitous Wi-Fi networks. It addresses non-cooperative user tracking through the exploitation of unencrypted Wi-Fi management frames, which contain enough information for device fingerprinting despite MAC address randomization. The research also explores an algorithm to estimate room occupancy based on passive Wi-Fi frame sniffing and Received Signal Strength Indicator (RSSI) measurements. Such room occupancy detection has implications for energy regulations in smart buildings. Furthermore, the thesis investigates methods to reduce computational requirements of machine learning and positioning algorithms through optimizing neural networks and employing interpolation techniques for IPS based on RSSI fingerprinting. The work contributes datasets, analysis scripts, and firmware to improve reproducibility and supports advancements in the LBS field.
Matey-Sanz, Miguel; Casteleyn, Sven; Granell-Canut, Carlos
Dataset of inertial measurements of smartphones and smartwatches for human activity recognition Journal Article
In: Data in Brief, vol. 51, pp. 109809, 2023, ISSN: 2352-3409.
Abstract | Links | BibTeX | Tags: activity recognition, dataset, machine learning, smartphone app, smartwatch, symptoms
@article{Matey2023c,
title = {Dataset of inertial measurements of smartphones and smartwatches for human activity recognition},
author = {Miguel Matey-Sanz and Sven Casteleyn and Carlos Granell-Canut},
doi = {https://doi.org/10.1016/j.dib.2023.109809},
issn = {2352-3409},
year = {2023},
date = {2023-12-15},
journal = {Data in Brief},
volume = {51},
pages = {109809},
abstract = {This article describes a dataset for human activity recognition with inertial measurements, i.e., accelerometer and gyroscope, from a smartphone and a smartwatch placed in the left pocket and on the left wrist, respectively. Twenty-three heterogeneous subjects (μ = 44.3, σ = 14.3, 56% male) participated in the data collection, which consisted of performing five activities (seated, standing up, walking, turning, and sitting down) arranged in a specific sequence (corresponding with the TUG test). Subjects performed the sequence of activities multiple times while the devices collected inertial data at 100 Hz and were video-recorded by a researcher for data labelling purposes. The goal of this dataset is to provide smartphone- and smartwatch-based inertial data for human activity recognition collected from a heterogeneous (i.e., age-diverse, gender-balanced) set of subjects. Along with the dataset, the repository includes demographic information (age, gender), information about each sequence of activities (smartphone's orientation in the pocket, direction of turns), and a Python package with utility functions (data loading, visualization, etc). The dataset can be reused for different purposes in the field of human activity recognition, from cross-subject evaluation to comparison of recognition performance using data from smartphones and smartwatches.},
keywords = {activity recognition, dataset, machine learning, smartphone app, smartwatch, symptoms},
pubstate = {published},
tppubtype = {article}
}
This article describes a dataset for human activity recognition with inertial measurements, i.e., accelerometer and gyroscope, from a smartphone and a smartwatch placed in the left pocket and on the left wrist, respectively. Twenty-three heterogeneous subjects (μ = 44.3, σ = 14.3, 56% male) participated in the data collection, which consisted of performing five activities (seated, standing up, walking, turning, and sitting down) arranged in a specific sequence (corresponding with the TUG test). Subjects performed the sequence of activities multiple times while the devices collected inertial data at 100 Hz and were video-recorded by a researcher for data labelling purposes. The goal of this dataset is to provide smartphone- and smartwatch-based inertial data for human activity recognition collected from a heterogeneous (i.e., age-diverse, gender-balanced) set of subjects. Along with the dataset, the repository includes demographic information (age, gender), information about each sequence of activities (smartphone's orientation in the pocket, direction of turns), and a Python package with utility functions (data loading, visualization, etc). The dataset can be reused for different purposes in the field of human activity recognition, from cross-subject evaluation to comparison of recognition performance using data from smartphones and smartwatches.
Gómez-Cambronero, Águeda
"Horizon: Resilience": A Smartphone-based Serious Game Intervention for Depressive Symptoms PhD Thesis
Universitat Jaume I. INIT, 2023.
Abstract | Links | BibTeX | Tags: mental health, Mobile apps, mobile computing, serious games, symptoms
@phdthesis{GomezCambronero2023b,
title = {"Horizon: Resilience": A Smartphone-based Serious Game Intervention for Depressive Symptoms},
author = {Águeda Gómez-Cambronero},
url = {http://hdl.handle.net/10803/689528},
doi = {http://dx.doi.org/10.6035/14101.2023.544418},
year = {2023},
date = {2023-12-11},
school = {Universitat Jaume I. INIT},
abstract = {Depression is the most prevalent mental issue in our society, leading to disability and suicide deaths. The COVID-19 pandemic has intensified the need for depression treatment and prevention. While effective, evidence-based psychological treatments for depression exists, only a small percentage of those in need actually receive them. Technology, particularly smartphone-based interventions, can help maximize the reach of these treatments while ensuring their effectiveness, although it comes with challenges, such as high dropout rates. Despite the potential
of this therapy, this is a field that requires considerably more research to fully explore the benefits that smartphones have to offer. Specifically, serious games, designed with a purpose beyond entertainment, have emerged as a promising treatment tool, leveraging advance smartphone capabilities, aligning with psychological treatment principles, and enhancing user engagement.
This dissertation introduces “Horizon: Resilience”, a smartphone-based Serious Game for depressive symptoms. It is a city builder game with a decision making narrative, in which the player (patient) manages a town. The objective is to make the town progress, ensuring the steady inflow of resources and fostering the psychological resilience of its inhabitants. The game is based on the Cognitive Behavioral Therapy (CBT) framework and includes Positive Psychology (PP) techniques. These psychological techniques are woven into the game’s gameplay, feedback, economy system, quests, graphics, and story. Noteworthy is the integration of promoting Physical Activity, detected using the phone’s motion sensors, as part of gameplay. The game draws on the findings of a scoping review on smartphone-based serious games in mental health, and was informed by consultations with therapists as part of a user-centered design. Therapists and patients furthermore provided a qualitative and quantitative evaluation of a Minimum Viable Product (MVP) of the game. Their positive impressions indicate high acceptance and positive expectation regarding the use of the game as an
intervention. Lastly, a pilot randomized controlled trial protocol is outlined to assess its preliminary effectiveness-},
keywords = {mental health, Mobile apps, mobile computing, serious games, symptoms},
pubstate = {published},
tppubtype = {phdthesis}
}
Depression is the most prevalent mental issue in our society, leading to disability and suicide deaths. The COVID-19 pandemic has intensified the need for depression treatment and prevention. While effective, evidence-based psychological treatments for depression exists, only a small percentage of those in need actually receive them. Technology, particularly smartphone-based interventions, can help maximize the reach of these treatments while ensuring their effectiveness, although it comes with challenges, such as high dropout rates. Despite the potential
of this therapy, this is a field that requires considerably more research to fully explore the benefits that smartphones have to offer. Specifically, serious games, designed with a purpose beyond entertainment, have emerged as a promising treatment tool, leveraging advance smartphone capabilities, aligning with psychological treatment principles, and enhancing user engagement.
This dissertation introduces “Horizon: Resilience”, a smartphone-based Serious Game for depressive symptoms. It is a city builder game with a decision making narrative, in which the player (patient) manages a town. The objective is to make the town progress, ensuring the steady inflow of resources and fostering the psychological resilience of its inhabitants. The game is based on the Cognitive Behavioral Therapy (CBT) framework and includes Positive Psychology (PP) techniques. These psychological techniques are woven into the game’s gameplay, feedback, economy system, quests, graphics, and story. Noteworthy is the integration of promoting Physical Activity, detected using the phone’s motion sensors, as part of gameplay. The game draws on the findings of a scoping review on smartphone-based serious games in mental health, and was informed by consultations with therapists as part of a user-centered design. Therapists and patients furthermore provided a qualitative and quantitative evaluation of a Minimum Viable Product (MVP) of the game. Their positive impressions indicate high acceptance and positive expectation regarding the use of the game as an
intervention. Lastly, a pilot randomized controlled trial protocol is outlined to assess its preliminary effectiveness-
of this therapy, this is a field that requires considerably more research to fully explore the benefits that smartphones have to offer. Specifically, serious games, designed with a purpose beyond entertainment, have emerged as a promising treatment tool, leveraging advance smartphone capabilities, aligning with psychological treatment principles, and enhancing user engagement.
This dissertation introduces “Horizon: Resilience”, a smartphone-based Serious Game for depressive symptoms. It is a city builder game with a decision making narrative, in which the player (patient) manages a town. The objective is to make the town progress, ensuring the steady inflow of resources and fostering the psychological resilience of its inhabitants. The game is based on the Cognitive Behavioral Therapy (CBT) framework and includes Positive Psychology (PP) techniques. These psychological techniques are woven into the game’s gameplay, feedback, economy system, quests, graphics, and story. Noteworthy is the integration of promoting Physical Activity, detected using the phone’s motion sensors, as part of gameplay. The game draws on the findings of a scoping review on smartphone-based serious games in mental health, and was informed by consultations with therapists as part of a user-centered design. Therapists and patients furthermore provided a qualitative and quantitative evaluation of a Minimum Viable Product (MVP) of the game. Their positive impressions indicate high acceptance and positive expectation regarding the use of the game as an
intervention. Lastly, a pilot randomized controlled trial protocol is outlined to assess its preliminary effectiveness-
Matey-Sanz, Miguel; Torres-Sospedra, Joaquín; González-Pérez, Alberto; Casteleyn, Sven; Granell-Canut, Carlos
Analysis and Impact of Training Set Size in Cross-Subject Human Activity Recognition Proceedings Article
In: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, pp. 391–405, Springer, Cham, 2023, ISBN: 978-3-031-49018-7.
Abstract | Links | BibTeX | Tags: activity recognition, machine learning, smartphone app, smartwatch, symptoms
@inproceedings{Matey2023b,
title = {Analysis and Impact of Training Set Size in Cross-Subject Human Activity Recognition},
author = {Miguel Matey-Sanz and Joaquín Torres-Sospedra and Alberto González-Pérez and Sven Casteleyn and Carlos Granell-Canut},
doi = {https://doi.org/10.1007/978-3-031-49018-7_28},
isbn = {978-3-031-49018-7},
year = {2023},
date = {2023-12-01},
booktitle = {Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications},
volume = {14469},
pages = {391–405},
publisher = {Springer, Cham},
series = {Lecture Notes in Computer Science},
abstract = {The ubiquity of consumer devices with sensing and computational capabilities, such as smartphones and smartwatches, has increased interest in their use in human activity recognition for healthcare monitoring applications, among others. When developing such a system, researchers rely on input data to train recognition models. In the absence of openly available datasets that meet the model requirements, researchers face a hard and time-consuming process to decide which sensing device to use or how much data needs to be collected. In this paper, we explore the effect of the amount of training data on the performance (i.e., classification accuracy and activity-wise F1-scores) of a CNN model by performing an incremental cross-subject evaluation using data collected from a consumer smartphone and smartwatch. Systematically studying the incremental inclusion of subject data from a set of 22 training subjects, the results show that the model’s performance initially improves significantly with each addition, yet this improvement slows down the larger the number of included subjects. We compare the performance of models based on smartphone and smartwatch data. The latter option is significantly better with smaller sizes of training data, while the former outperforms with larger amounts of training data. In addition, gait-related activities show significantly better results with smartphone-collected data, while non-gait-related activities, such as standing up or sitting down, were better recognized with smartwatch-collected data.},
keywords = {activity recognition, machine learning, smartphone app, smartwatch, symptoms},
pubstate = {published},
tppubtype = {inproceedings}
}
The ubiquity of consumer devices with sensing and computational capabilities, such as smartphones and smartwatches, has increased interest in their use in human activity recognition for healthcare monitoring applications, among others. When developing such a system, researchers rely on input data to train recognition models. In the absence of openly available datasets that meet the model requirements, researchers face a hard and time-consuming process to decide which sensing device to use or how much data needs to be collected. In this paper, we explore the effect of the amount of training data on the performance (i.e., classification accuracy and activity-wise F1-scores) of a CNN model by performing an incremental cross-subject evaluation using data collected from a consumer smartphone and smartwatch. Systematically studying the incremental inclusion of subject data from a set of 22 training subjects, the results show that the model’s performance initially improves significantly with each addition, yet this improvement slows down the larger the number of included subjects. We compare the performance of models based on smartphone and smartwatch data. The latter option is significantly better with smaller sizes of training data, while the former outperforms with larger amounts of training data. In addition, gait-related activities show significantly better results with smartphone-collected data, while non-gait-related activities, such as standing up or sitting down, were better recognized with smartwatch-collected data.
Bravenec, Tomás; Torres-Sospedra, Joaquín; Gould, Michael; Fryza, Tomas
UJI Probes Revisited: Deeper Dive Into the Dataset of Wi-Fi Probe Requests Journal Article
In: IEEE Journal of Indoor and Seamless Positioning and Navigation, vol. 1, pp. 221-230, 2023, ISSN: 2832-7322.
Abstract | Links | BibTeX | Tags: A-wear, dataset, Wi-Fi
@article{Bravenec2023c,
title = {UJI Probes Revisited: Deeper Dive Into the Dataset of Wi-Fi Probe Requests},
author = {Tomás Bravenec and Joaquín Torres-Sospedra and Michael Gould and Tomas Fryza},
doi = {https://doi.org/10.1109/JISPIN.2023.3335882},
issn = {2832-7322},
year = {2023},
date = {2023-11-22},
journal = {IEEE Journal of Indoor and Seamless Positioning and Navigation},
volume = {1},
pages = {221-230},
abstract = {This article centers on the deeper presentation of a new and publicly accessible dataset comprising Wi-Fi probe requests. Probe requests fall within the category of management frames utilized by the 802.11 (Wi-Fi) protocol. Given the ever-evolving technological landscape and the imperative need for up-to-date data, research on probe requests remains essential. In this context, we present a comprehensive dataset encompassing a one-month probe request capture conducted in a university office environment. This dataset accounts for a diverse range of scenarios, including workdays, weekends, and holidays, accumulating over 1 400 000 probe requests. Our contribution encompasses a detailed exposition of the dataset, delving into its critical facets. In addition to the raw packet capture, we furnish a detailed floor plan of the office environment, commonly referred to as a radio map, to equip dataset users with comprehensive environmental information. To safeguard user privacy, all individual user information within the dataset has been anonymized. This anonymization process rigorously balances the preservation of users' privacy with the dataset's analytical utility, rendering it nearly as informative as raw data for research purposes. Furthermore, we demonstrate a range of potential applications for this dataset, including but not limited to presence detection, expanded assessment of temporal received signal strength indicator stability, and evaluation of privacy protection measures. Apart from these, we also include temporal analysis of probe request transmission frequency and period between Wi-Fi scans as well as a peak into possibilities with pattern analysis.},
keywords = {A-wear, dataset, Wi-Fi},
pubstate = {published},
tppubtype = {article}
}
This article centers on the deeper presentation of a new and publicly accessible dataset comprising Wi-Fi probe requests. Probe requests fall within the category of management frames utilized by the 802.11 (Wi-Fi) protocol. Given the ever-evolving technological landscape and the imperative need for up-to-date data, research on probe requests remains essential. In this context, we present a comprehensive dataset encompassing a one-month probe request capture conducted in a university office environment. This dataset accounts for a diverse range of scenarios, including workdays, weekends, and holidays, accumulating over 1 400 000 probe requests. Our contribution encompasses a detailed exposition of the dataset, delving into its critical facets. In addition to the raw packet capture, we furnish a detailed floor plan of the office environment, commonly referred to as a radio map, to equip dataset users with comprehensive environmental information. To safeguard user privacy, all individual user information within the dataset has been anonymized. This anonymization process rigorously balances the preservation of users' privacy with the dataset's analytical utility, rendering it nearly as informative as raw data for research purposes. Furthermore, we demonstrate a range of potential applications for this dataset, including but not limited to presence detection, expanded assessment of temporal received signal strength indicator stability, and evaluation of privacy protection measures. Apart from these, we also include temporal analysis of probe request transmission frequency and period between Wi-Fi scans as well as a peak into possibilities with pattern analysis.
Hammad, Sahibzada Saadoon; Iskandaryan, Ditsuhi; Trilles-Oliver, Sergio
An unsupervised TinyML approach applied to the detection of urban noise anomalies under the smart cities environment Journal Article
In: Internet of Things, vol. 23, pp. 100848, 2023, ISSN: 2542-6605.
Abstract | Links | BibTeX | Tags: environmental monitoring, machine learning, TinyML
@article{Saadoon2023a,
title = {An unsupervised TinyML approach applied to the detection of urban noise anomalies under the smart cities environment},
author = {Sahibzada Saadoon Hammad and Ditsuhi Iskandaryan and Sergio Trilles-Oliver},
doi = {10.1016/j.iot.2023.100848},
issn = {2542-6605},
year = {2023},
date = {2023-10-01},
journal = {Internet of Things},
volume = {23},
pages = {100848},
abstract = {Artificial Intelligence of Things (AIoT) is an emerging area of interest, and this can be used to obtain knowledge and take better decisions in the same Internet of Things (IoT) devices. IoT data are prone to anomalies due to various factors such as malfunctioning of sensors, low-cost devices, etc. Following the AIoT paradigm, this work explores anomaly detection in IoT urban noise sensor networks using a Long Short-Term Memory Autoencoder. Two autoencoder models are trained using normal data from two different sensors in the sensor network and tested for the detection of two different types of anomalies, i.e. point anomalies and collective anomalies. The results in terms of accuracy of the two models are 99.99% and 99.34%. The trained model is quantised, converted to TensorFlow Lite format and deployed on the ESP32 microcontroller (MCU). The inference time on the microcontroller is 4 ms for both models, and the power consumption of the MCU is 0.2693 W ± 0.039 and 0.3268 W ± 0.015. Heap memory consumption during the execution of the program for sensors TA120-T246187 and TA120-T246189 is 528 bytes and 744 bytes respectively.},
keywords = {environmental monitoring, machine learning, TinyML},
pubstate = {published},
tppubtype = {article}
}
Artificial Intelligence of Things (AIoT) is an emerging area of interest, and this can be used to obtain knowledge and take better decisions in the same Internet of Things (IoT) devices. IoT data are prone to anomalies due to various factors such as malfunctioning of sensors, low-cost devices, etc. Following the AIoT paradigm, this work explores anomaly detection in IoT urban noise sensor networks using a Long Short-Term Memory Autoencoder. Two autoencoder models are trained using normal data from two different sensors in the sensor network and tested for the detection of two different types of anomalies, i.e. point anomalies and collective anomalies. The results in terms of accuracy of the two models are 99.99% and 99.34%. The trained model is quantised, converted to TensorFlow Lite format and deployed on the ESP32 microcontroller (MCU). The inference time on the microcontroller is 4 ms for both models, and the power consumption of the MCU is 0.2693 W ± 0.039 and 0.3268 W ± 0.015. Heap memory consumption during the execution of the program for sensors TA120-T246187 and TA120-T246189 is 528 bytes and 744 bytes respectively.
Esparza, Juan A. García; Altaba, Pablo; Huerta-Guijarro, Joaquín
Examining urban polarization in five Spanish historic cities through online datasets and onsite perceptions Journal Article
In: Habitat International, vol. 139, pp. 102900, 2023, ISSN: 0197-3975.
Abstract | Links | BibTeX | Tags: citizen participation, local participation
@article{Garcia2023a,
title = {Examining urban polarization in five Spanish historic cities through online datasets and onsite perceptions},
author = {Juan A. García Esparza and Pablo Altaba and Joaquín Huerta-Guijarro},
doi = {https://doi.org/10.1016/j.habitatint.2023.102900},
issn = {0197-3975},
year = {2023},
date = {2023-10-01},
journal = {Habitat International},
volume = {139},
pages = {102900},
abstract = {At present, the planning and management of historic districts are faced with the challenge of striking a balance between the needs of residents and seasonal pressures from visitors. These socially bustling sites could also benefit from the data cross-referencing of cultural and social patterns in order to identify areas for improvement. This research analyses geo-referenced online datasets and data from social media applications, subsequently contrasting these with onsite data from in-person interviews. These specific variables highlight parallels and conflicts between districts designated World Heritage areas in five Spanish cities. The study provides a quantitative analysis of hotspots and coldspots within the built environment. This is followed by an examination of these two types of areas using qualitative data linked to the three most challenging issues: housing and the built environment; basic services; and cultural services. When analysing the future of historic districts three major challenges to management highlighted in the results should be considered. Firstly, even in socially active districts, imbalances and dysfunctional areas are highlighted by both online data and onsite perceptions. Secondly, the study of the dynamics of districts for observing how stakeholders adapt to this social, economic, and mobility-related polarization. Thirdly, while the study acknowledges the changes to the consumption of culture, there is still potential for improvement in hosting alternative or countercultural movements.},
keywords = {citizen participation, local participation},
pubstate = {published},
tppubtype = {article}
}
At present, the planning and management of historic districts are faced with the challenge of striking a balance between the needs of residents and seasonal pressures from visitors. These socially bustling sites could also benefit from the data cross-referencing of cultural and social patterns in order to identify areas for improvement. This research analyses geo-referenced online datasets and data from social media applications, subsequently contrasting these with onsite data from in-person interviews. These specific variables highlight parallels and conflicts between districts designated World Heritage areas in five Spanish cities. The study provides a quantitative analysis of hotspots and coldspots within the built environment. This is followed by an examination of these two types of areas using qualitative data linked to the three most challenging issues: housing and the built environment; basic services; and cultural services. When analysing the future of historic districts three major challenges to management highlighted in the results should be considered. Firstly, even in socially active districts, imbalances and dysfunctional areas are highlighted by both online data and onsite perceptions. Secondly, the study of the dynamics of districts for observing how stakeholders adapt to this social, economic, and mobility-related polarization. Thirdly, while the study acknowledges the changes to the consumption of culture, there is still potential for improvement in hosting alternative or countercultural movements.
Matey-Sanz, Miguel; Torres-Sospedra, Joaquín; Moreira, Adriano
Temporal Stability on Human Activity Recognition based on Wi-Fi CSI Proceedings Article
In: 2023 IEEE 13th International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 1-6, IEEE, 2023, ISBN: 979-8-3503-2012-1.
Abstract | Links | BibTeX | Tags: activity recognition, CSI, machine learning
@inproceedings{Matey2023a,
title = {Temporal Stability on Human Activity Recognition based on Wi-Fi CSI},
author = {Miguel Matey-Sanz and Joaquín Torres-Sospedra and Adriano Moreira},
doi = {https://doi.org/10.1109/IPIN57070.2023.10332214},
isbn = {979-8-3503-2012-1},
year = {2023},
date = {2023-09-25},
booktitle = {2023 IEEE 13th International Conference on Indoor Positioning and Indoor Navigation (IPIN)},
pages = {1-6},
publisher = {IEEE},
abstract = {Over the last years, numerous studies have emerged using Wi-Fi channel state information, enabling device-free (passive) sensing for applications such as motion detection, indoor positioning or human activity recognition. More recently, the development framework for the low-cost ESP32 microcontrollers has added support for obtaining channel state information data. In this work, we collected channel state information data for human activity recognition, where activities are relatively localized with respect to the Wi-Fi infrastructure. The data was collected in several runs, duly spaced in time, and a convolutional neural network model was used for the classification of activities. Classification performance evaluation showed a clear degradation when a model evaluated with data collected 10 minutes after the data used for training showed a 52% relative loss in the accuracy of the classification.},
keywords = {activity recognition, CSI, machine learning},
pubstate = {published},
tppubtype = {inproceedings}
}
Over the last years, numerous studies have emerged using Wi-Fi channel state information, enabling device-free (passive) sensing for applications such as motion detection, indoor positioning or human activity recognition. More recently, the development framework for the low-cost ESP32 microcontrollers has added support for obtaining channel state information data. In this work, we collected channel state information data for human activity recognition, where activities are relatively localized with respect to the Wi-Fi infrastructure. The data was collected in several runs, duly spaced in time, and a convolutional neural network model was used for the classification of activities. Classification performance evaluation showed a clear degradation when a model evaluated with data collected 10 minutes after the data used for training showed a 52% relative loss in the accuracy of the classification.
Bravenec, Tomás; Torres-Sospedra, Joaquín; Gould, Michael; Fryza, Tomas
UJI Probes: Dataset of Wi-Fi Probe Requests Proceedings Article
In: 2023 13th International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 1-6, IEEE, 2023, ISBN: 979-8-3503-2012-1.
Abstract | Links | BibTeX | Tags: A-wear, dataset, Wi-Fi
@inproceedings{Bravenec2023b,
title = {UJI Probes: Dataset of Wi-Fi Probe Requests},
author = {Tomás Bravenec and Joaquín Torres-Sospedra and Michael Gould and Tomas Fryza},
doi = {https://doi.org/10.1109/IPIN57070.2023.10332508},
isbn = {979-8-3503-2012-1},
year = {2023},
date = {2023-09-25},
booktitle = {2023 13th International Conference on Indoor Positioning and Indoor Navigation (IPIN)},
pages = {1-6},
publisher = {IEEE},
abstract = {This paper focuses on the creation of a new, publicly available Wi-Fi probe request dataset. Probe requests belong to the family of management frames used by the 802.11 (Wi-Fi) protocol. As the situation changes year by year, and technology improves probe request studies are necessary to be done on upto-date data. We provide a month-long probe request capture in an office environment, including work days, weekends, and holidays consisting of over 1 400 000 probe requests. We provide a description of all the important aspects of the dataset. Apart from the raw packet capture we also provide a Radio Map (RM) of the office to ensure the users of the dataset have all the possible information about the environment. To protect privacy, user information in the dataset is anonymized. This anonymization is done in a way that protects the privacy of users while preserving the ability to analyze the dataset to almost the same level as raw data. Furthermore, we showcase several possible use cases for the dataset, like presence detection, temporal Received Signal Strength Indicator (RSSI) stability, and privacy protection evaluation.},
keywords = {A-wear, dataset, Wi-Fi},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper focuses on the creation of a new, publicly available Wi-Fi probe request dataset. Probe requests belong to the family of management frames used by the 802.11 (Wi-Fi) protocol. As the situation changes year by year, and technology improves probe request studies are necessary to be done on upto-date data. We provide a month-long probe request capture in an office environment, including work days, weekends, and holidays consisting of over 1 400 000 probe requests. We provide a description of all the important aspects of the dataset. Apart from the raw packet capture we also provide a Radio Map (RM) of the office to ensure the users of the dataset have all the possible information about the environment. To protect privacy, user information in the dataset is anonymized. This anonymization is done in a way that protects the privacy of users while preserving the ability to analyze the dataset to almost the same level as raw data. Furthermore, we showcase several possible use cases for the dataset, like presence detection, temporal Received Signal Strength Indicator (RSSI) stability, and privacy protection evaluation.
González-Pérez, Alberto
Applying Mobile and Geospatial Technologies to Ecological Momentary Interventions PhD Thesis
Universitat Jaume I. INIT, 2023.
Abstract | Links | BibTeX | Tags: cognitive-behavioural therapy, exposure therapy, Mobile apps, mobile computing, symptoms
@phdthesis{Gonzalez-Perez2023b,
title = {Applying Mobile and Geospatial Technologies to Ecological Momentary Interventions},
author = {Alberto González-Pérez},
doi = {http://dx.doi.org/10.6035/14101.2023.533823},
year = {2023},
date = {2023-09-07},
school = {Universitat Jaume I. INIT},
abstract = {Today a large percentage of the population suffers from anxiety-related problems. This anxiety can appear in day-to-day situations. An effective therapy for these problems is exposure. In it, the person is gradually exposed to what he fears. However, these therapy sessions are long and force the patient and therapist to travel to a specific place. Here, the use of a mobile application that guides the patient during the exposure sessions can be beneficial. Until now, this application did not exist, due to the complexity of its implementation. In this doctoral thesis, the necessary tools have been implemented to facilitate the implementation of this type of solution. In addition, in collaboration with psychology professionals, a mobile application has been implemented to self-guide exposure, which has been positively assessed by an external committee of experts.},
keywords = {cognitive-behavioural therapy, exposure therapy, Mobile apps, mobile computing, symptoms},
pubstate = {published},
tppubtype = {phdthesis}
}
Today a large percentage of the population suffers from anxiety-related problems. This anxiety can appear in day-to-day situations. An effective therapy for these problems is exposure. In it, the person is gradually exposed to what he fears. However, these therapy sessions are long and force the patient and therapist to travel to a specific place. Here, the use of a mobile application that guides the patient during the exposure sessions can be beneficial. Until now, this application did not exist, due to the complexity of its implementation. In this doctoral thesis, the necessary tools have been implemented to facilitate the implementation of this type of solution. In addition, in collaboration with psychology professionals, a mobile application has been implemented to self-guide exposure, which has been positively assessed by an external committee of experts.
Iskandaryan, Ditsuhi; Ramos-Romero, Francisco; Trilles-Oliver, Sergio
A set of deep learning algorithms for air quality prediction applications Journal Article
In: Software Impacts, vol. 17, pp. 100562, 2023, ISSN: 2665-9638.
Abstract | Links | BibTeX | Tags: geospatial analysis, machine learning, spatiotemporal prediction
@article{Iskandaryan2023d,
title = {A set of deep learning algorithms for air quality prediction applications},
author = {Ditsuhi Iskandaryan and Francisco Ramos-Romero and Sergio Trilles-Oliver},
doi = {https://doi.org/10.1016/j.simpa.2023.100562},
issn = {2665-9638},
year = {2023},
date = {2023-08-10},
journal = {Software Impacts},
volume = {17},
pages = {100562},
abstract = {This paper presents a set of machine learning algorithms, including grid-based (Bidirectional Convolutional Long Short-Term Memory) and graph-based (Attention Temporal Graph Convolutional Network) algorithms to predict air quality. The methods were implemented on a spatiotemporal combination of air quality, meteorological and traffic data of the city of Madrid. The two methods are exposed to be reused for prediction in other scenarios and different air quality phenomena.},
keywords = {geospatial analysis, machine learning, spatiotemporal prediction},
pubstate = {published},
tppubtype = {article}
}
This paper presents a set of machine learning algorithms, including grid-based (Bidirectional Convolutional Long Short-Term Memory) and graph-based (Attention Temporal Graph Convolutional Network) algorithms to predict air quality. The methods were implemented on a spatiotemporal combination of air quality, meteorological and traffic data of the city of Madrid. The two methods are exposed to be reused for prediction in other scenarios and different air quality phenomena.
Bravenec, Tomás; Gould, Michael; Fryza, Tomas; Torres-Sospedra, Joaquín
Influence of Measured Radio Map Interpolation on Indoor Positioning Algorithms Journal Article
In: IEEE Sensors Journal, vol. 17, pp. 20044-20054, 2023, ISSN: 1530-437X.
Abstract | Links | BibTeX | Tags: A-wear, Indoor positioning, radio maps
@article{Bravenec2023e,
title = {Influence of Measured Radio Map Interpolation on Indoor Positioning Algorithms},
author = {Tomás Bravenec and Michael Gould and Tomas Fryza and Joaquín Torres-Sospedra},
doi = {https://doi.org/10.1109/JSEN.2023.3296752},
issn = {1530-437X},
year = {2023},
date = {2023-08-01},
journal = {IEEE Sensors Journal},
volume = {17},
pages = {20044-20054},
abstract = {Indoor positioning and navigation increasingly have become popular, and there are many different approaches, using different technologies. In nearly all of the approaches, the locational accuracy depends on signal propagation characteristics of the environment. What makes many of these approaches similar is the requirement of creating a signal propagation radio map (RM) by analyzing the environment. As this is usually done on a regular grid, the collection of received signal strength indicator (RSSI) data at every reference point (RP) of an RM is a time-consuming task. With indoor positioning being in the focus of the research community, the reduction in time required for collection of RMs is very useful, as it allows researchers to spend more time with research instead of data collection. In this article, we analyze the options for reducing the time required for the acquisition of RSSI information. We approach this by collecting initial RMs of Wi-Fi signal strength using five ESP32 microcontrollers working in monitoring mode and placed around our office. We then analyze the influence the approximation of RSSI values in unreachable places has, by using linear interpolation and Gaussian process regression (GPR) to find balance among final positioning accuracy, computing complexity, and time requirements for the initial data collection. We conclude that the computational requirements can be significantly lowered, while not affecting the positioning error, by using RM with a single sample per RP generated considering many measurements.},
keywords = {A-wear, Indoor positioning, radio maps},
pubstate = {published},
tppubtype = {article}
}
Indoor positioning and navigation increasingly have become popular, and there are many different approaches, using different technologies. In nearly all of the approaches, the locational accuracy depends on signal propagation characteristics of the environment. What makes many of these approaches similar is the requirement of creating a signal propagation radio map (RM) by analyzing the environment. As this is usually done on a regular grid, the collection of received signal strength indicator (RSSI) data at every reference point (RP) of an RM is a time-consuming task. With indoor positioning being in the focus of the research community, the reduction in time required for collection of RMs is very useful, as it allows researchers to spend more time with research instead of data collection. In this article, we analyze the options for reducing the time required for the acquisition of RSSI information. We approach this by collecting initial RMs of Wi-Fi signal strength using five ESP32 microcontrollers working in monitoring mode and placed around our office. We then analyze the influence the approximation of RSSI values in unreachable places has, by using linear interpolation and Gaussian process regression (GPR) to find balance among final positioning accuracy, computing complexity, and time requirements for the initial data collection. We conclude that the computational requirements can be significantly lowered, while not affecting the positioning error, by using RM with a single sample per RP generated considering many measurements.
Trilles-Oliver, Sergio; Monfort-Muriach, Aida; Lacomba, Diego; Granell-Canut, Carlos
Introducción a los conceptos del pensamiento computacional en educación infantil y primaria con programación tangible Proceedings Article
In: Actas de las Jornadas sobre Enseñanza Universitaria de la Informática (JENUI), pp. 257-260, AENUI, 2023, ISSN: 2531-0607.
Abstract | Links | BibTeX | Tags: Computational thinking, education, SUCRE, sucre4kids
@inproceedings{Trilles2023a,
title = {Introducción a los conceptos del pensamiento computacional en educación infantil y primaria con programación tangible},
author = {Sergio Trilles-Oliver and Aida Monfort-Muriach and Diego Lacomba and Carlos Granell-Canut},
url = {https://aenui.org/actas/pdf/JENUI_2023_032.pdf},
issn = {2531-0607},
year = {2023},
date = {2023-07-05},
booktitle = {Actas de las Jornadas sobre Enseñanza Universitaria de la Informática (JENUI)},
volume = {8},
pages = {257-260},
publisher = {AENUI},
abstract = {The Sucre programme aims to promote computational thinking and programming at each educational stage. After restructuring the programme, the Sucre4Kids initiative is reoriented to early childhood and primary education (between 5 and 10 years). Sucre4Kids introduces the basics of programming with as little friction as possible, dispensing with usual programming devices and instead using tangible elements for programming. The proposed tangible programming strategy is based on the use of cards with pictograms and each card carries a near field communication (NFC) tag that encodes the programming instructions. Students arrange the cards in sequence, representing the logical order of instructions. Depending on which sensors and actuators are wired, the reading of each card produces reactive code, that is, the execution is immediate as soon as a card is read. Sucre4Kids takes advantage of the development carried out within the Sucre programme, adapting a microcontroller with an NFC reader and a small display. The designed prototype includes several game modes in order to work with different computational thinking concepts.},
keywords = {Computational thinking, education, SUCRE, sucre4kids},
pubstate = {published},
tppubtype = {inproceedings}
}
The Sucre programme aims to promote computational thinking and programming at each educational stage. After restructuring the programme, the Sucre4Kids initiative is reoriented to early childhood and primary education (between 5 and 10 years). Sucre4Kids introduces the basics of programming with as little friction as possible, dispensing with usual programming devices and instead using tangible elements for programming. The proposed tangible programming strategy is based on the use of cards with pictograms and each card carries a near field communication (NFC) tag that encodes the programming instructions. Students arrange the cards in sequence, representing the logical order of instructions. Depending on which sensors and actuators are wired, the reading of each card produces reactive code, that is, the execution is immediate as soon as a card is read. Sucre4Kids takes advantage of the development carried out within the Sucre programme, adapting a microcontroller with an NFC reader and a small display. The designed prototype includes several game modes in order to work with different computational thinking concepts.
Casanova-Marqués, Raúl; Torres-Sospedra, Joaquín; Hajny, Jan; Gould, Michael
Maximizing privacy and security of collaborative indoor positioning using zero-knowledge proofs Journal Article
In: Internet of Things, vol. 22, pp. 100801, 2023, ISSN: 2542-6605.
Abstract | Links | BibTeX | Tags: A-wear, Bluetooth Low Energy, Indoor positioning, wearables
@article{Casanova2023a,
title = {Maximizing privacy and security of collaborative indoor positioning using zero-knowledge proofs},
author = {Raúl Casanova-Marqués and Joaquín Torres-Sospedra and Jan Hajny and Michael Gould},
doi = {https://doi.org/10.1016/j.iot.2023.100801},
issn = {2542-6605},
year = {2023},
date = {2023-07-01},
journal = {Internet of Things},
volume = {22},
pages = {100801},
abstract = {The increasing popularity of wearable-based Collaborative Indoor Positioning Systems (CIPSs) has led to the development of new methods for improving positioning accuracy. However, these systems often rely on protocols, such as iBeacon, that lack sufficient privacy protection. In addition, they depend on centralized entities for the authentication and verification processes. To address the limitations of existing protocols, this paper presents a groundbreaking contribution to the field of wearable-based CIPSs. We propose a decentralized Attribute-based Authentication (ABA) protocol that offers superior levels of privacy protection, untraceability, and unlinkability of user actions. Unlike existing protocols that rely on centralized entities, our approach leverages decentralized mechanisms for authentication and verification, ensuring the privacy of user location data exchange. Through extensive experimentation across multiple platforms, our results demonstrate the practicality and feasibility of the proposed protocol for real-world deployment. Overall, this work opens up new avenues for secure and privacy-preserving wearable-based CIPSs, with potential implications for the rapidly growing field of Internet of Things (IoT) applications.},
keywords = {A-wear, Bluetooth Low Energy, Indoor positioning, wearables},
pubstate = {published},
tppubtype = {article}
}
The increasing popularity of wearable-based Collaborative Indoor Positioning Systems (CIPSs) has led to the development of new methods for improving positioning accuracy. However, these systems often rely on protocols, such as iBeacon, that lack sufficient privacy protection. In addition, they depend on centralized entities for the authentication and verification processes. To address the limitations of existing protocols, this paper presents a groundbreaking contribution to the field of wearable-based CIPSs. We propose a decentralized Attribute-based Authentication (ABA) protocol that offers superior levels of privacy protection, untraceability, and unlinkability of user actions. Unlike existing protocols that rely on centralized entities, our approach leverages decentralized mechanisms for authentication and verification, ensuring the privacy of user location data exchange. Through extensive experimentation across multiple platforms, our results demonstrate the practicality and feasibility of the proposed protocol for real-world deployment. Overall, this work opens up new avenues for secure and privacy-preserving wearable-based CIPSs, with potential implications for the rapidly growing field of Internet of Things (IoT) applications.
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.
Chukhno, Nadezhda; Chukhno, Olga; Moltchanov, Dmitri; Molinaro, Antonella; Gaidamaka, Yuliya; Samouylov, Konstantin; Koucheryavy, Yevgeni; Araniti, Giuseppe
Optimal Multicasting in Millimeter Wave 5G NR With Multi-Beam Directional Antennas Journal Article
In: IEEE Transactions on Mobile Computing, vol. 22, no. 6, pp. 3572 - 3588, 2023, ISSN: 1558-0660.
Abstract | Links | BibTeX | Tags: A-wear, machine learning, wearables
@article{Chukhno2023a,
title = {Optimal Multicasting in Millimeter Wave 5G NR With Multi-Beam Directional Antennas},
author = {Nadezhda Chukhno and Olga Chukhno and Dmitri Moltchanov and Antonella Molinaro and Yuliya Gaidamaka and Konstantin Samouylov and Yevgeni Koucheryavy and Giuseppe Araniti},
doi = {10.1109/TMC.2021.3136298},
issn = {1558-0660},
year = {2023},
date = {2023-06-01},
journal = {IEEE Transactions on Mobile Computing},
volume = {22},
number = {6},
pages = {3572 - 3588},
abstract = {The support of multicast communications in the fifth-generation (5G) New Radio (NR) system poses unique challenges to system designers. Particularly, the highly directional antennas do not allow to serve all the user equipment devices (UEs) that belong to the same multicast session in a single transmission. The capability of modern antenna arrays to utilize multiple beams simultaneously, with potentially varying half-power beamwidth, adds a new degree of freedom to the UE scheduling. This work addresses the challenge of optimal multicasting in 5G millimeter wave (mmWave) systems by presenting a globally optimal solution for multi-beam antenna operation. The optimization problem is formulated as a special case of multi-period variable cost and size bin packing problem that allows to not impose any constraints on the number of the beams and their configurations. We also propose heuristic solutions having polynomial time complexity. Our results show that for small cell radii of up to 100 meters, a single beam is always utilized. For higher cell coverage and practical ranges of the number of users (5-50), the optimal number of beams is upper bounded by 3.},
keywords = {A-wear, machine learning, wearables},
pubstate = {published},
tppubtype = {article}
}
The support of multicast communications in the fifth-generation (5G) New Radio (NR) system poses unique challenges to system designers. Particularly, the highly directional antennas do not allow to serve all the user equipment devices (UEs) that belong to the same multicast session in a single transmission. The capability of modern antenna arrays to utilize multiple beams simultaneously, with potentially varying half-power beamwidth, adds a new degree of freedom to the UE scheduling. This work addresses the challenge of optimal multicasting in 5G millimeter wave (mmWave) systems by presenting a globally optimal solution for multi-beam antenna operation. The optimization problem is formulated as a special case of multi-period variable cost and size bin packing problem that allows to not impose any constraints on the number of the beams and their configurations. We also propose heuristic solutions having polynomial time complexity. Our results show that for small cell radii of up to 100 meters, a single beam is always utilized. For higher cell coverage and practical ranges of the number of users (5-50), the optimal number of beams is upper bounded by 3.
Chukhno, Nadezhda; Chukhno, Olga; Pizzi, Sara; Molinaro, Antonella; Iera, Antonio; Araniti, Giuseppe
Approaching 6G Use Case Requirements with Multicasting Journal Article
In: IEEE Communications Magazine, vol. 61, no. 5, pp. 144-150, 2023, ISSN: 1558-1896.
Abstract | Links | BibTeX | Tags: 6G, A-wear, Internet of things, Wi-Fi
@article{Chukhno2023c,
title = {Approaching 6G Use Case Requirements with Multicasting},
author = {Nadezhda Chukhno and Olga Chukhno and Sara Pizzi and Antonella Molinaro and Antonio Iera and Giuseppe Araniti},
doi = {10.1109/MCOM.001.2200659},
issn = {1558-1896},
year = {2023},
date = {2023-05-01},
journal = {IEEE Communications Magazine},
volume = {61},
number = {5},
pages = {144-150},
abstract = {The shift towards 6G networks is expected to be accompanied by an increased capability to support group-oriented services, such as extended reality and holographic communications, in many different contexts, from high-precision manufacturing to healthcare and remote control. This range of applications will rely heavily on multicast and mixed multicast-broadcast delivery modes. This article focuses on the technological perspectives of 6G multicasting, highlighting requirements, challenges, and enabling solutions. We then run a simulation campaign to test practical solutions and draw conclusive remarks for forthcoming 6G multicast systems.},
keywords = {6G, A-wear, Internet of things, Wi-Fi},
pubstate = {published},
tppubtype = {article}
}
The shift towards 6G networks is expected to be accompanied by an increased capability to support group-oriented services, such as extended reality and holographic communications, in many different contexts, from high-precision manufacturing to healthcare and remote control. This range of applications will rely heavily on multicast and mixed multicast-broadcast delivery modes. This article focuses on the technological perspectives of 6G multicasting, highlighting requirements, challenges, and enabling solutions. We then run a simulation campaign to test practical solutions and draw conclusive remarks for forthcoming 6G multicast systems.
Gómez-Cambronero, Águeda; Casteleyn, Sven; Bretón-López, Juana; García-Palacios, Azucena; Mira, Adriana
A smartphone-based serious game for depressive symptoms: Protocol for a pilot randomized controlled trial Journal Article
In: Internet Interventions, vol. 32, pp. 100624, 2023, ISSN: 2214-7829.
Abstract | Links | BibTeX | Tags: depression, serious games, smartphone app, symptoms
@article{GomezCambronero2023a,
title = {A smartphone-based serious game for depressive symptoms: Protocol for a pilot randomized controlled trial},
author = {Águeda Gómez-Cambronero and Sven Casteleyn and Juana Bretón-López and Azucena García-Palacios and Adriana Mira},
doi = {https://doi.org/10.1016/j.invent.2023.100624},
issn = {2214-7829},
year = {2023},
date = {2023-04-28},
journal = {Internet Interventions},
volume = {32},
pages = {100624},
abstract = {Background
Depression is the most prevalent mental disorder, with detrimental effects on the patient's well-being, high disability, and a huge associated societal and economic cost. There are evidence-based treatments, but it is difficult to reach all people in need. Internet-based interventions, and more recently smartphone-based interventions, were explored to overcome barriers to access. Evidence shows them to be effective alternatives to traditional treatments. This paper presents the protocol of a pilot study whose primary aim is to investigate the efficacy of a smartphone-based serious game intervention for patients with mild to moderate depressive symptoms.
Methods
This randomized controlled pilot trial protocol foresees two arms design: 1/ smartphone- based serious game intervention (based on Cognitive Behavior Therapy with particular emphasis on Behavioral Activation and Physical Activity), 2/ waiting list control group. The study is expected to recruit 40 participants (18+), which will be randomly assigned to one of the experimental conditions. The duration of the intervention is two months. The primary outcome measure will be depressive symptomatology. Secondary outcomes will include other variables such as physical activity, resilience, anxiety, depression impairment, and positive and negative affect. Treatment expectation, satisfaction, usability, and game playability will also be measured. The data will be analyzed based on the intention-to-treat and per protocol analyses.
Discussion
The study aims to establish initial evidence for the efficacy of a smartphone-based serious game intervention, to serve as input for a larger-scale randomized control trial. The intervention exploits advanced smartphone capabilities, such as the use of a serious game as delivery mode, with the potential benefit of engagement and treatment adherence, and motion sensors to monitor and stimulate physical activity. As a secondary objective, the study aims to gather initial evidence on the user's expectations, satisfaction, usability and playability of the serious game as a treatment.},
keywords = {depression, serious games, smartphone app, symptoms},
pubstate = {published},
tppubtype = {article}
}
Background
Depression is the most prevalent mental disorder, with detrimental effects on the patient's well-being, high disability, and a huge associated societal and economic cost. There are evidence-based treatments, but it is difficult to reach all people in need. Internet-based interventions, and more recently smartphone-based interventions, were explored to overcome barriers to access. Evidence shows them to be effective alternatives to traditional treatments. This paper presents the protocol of a pilot study whose primary aim is to investigate the efficacy of a smartphone-based serious game intervention for patients with mild to moderate depressive symptoms.
Methods
This randomized controlled pilot trial protocol foresees two arms design: 1/ smartphone- based serious game intervention (based on Cognitive Behavior Therapy with particular emphasis on Behavioral Activation and Physical Activity), 2/ waiting list control group. The study is expected to recruit 40 participants (18+), which will be randomly assigned to one of the experimental conditions. The duration of the intervention is two months. The primary outcome measure will be depressive symptomatology. Secondary outcomes will include other variables such as physical activity, resilience, anxiety, depression impairment, and positive and negative affect. Treatment expectation, satisfaction, usability, and game playability will also be measured. The data will be analyzed based on the intention-to-treat and per protocol analyses.
Discussion
The study aims to establish initial evidence for the efficacy of a smartphone-based serious game intervention, to serve as input for a larger-scale randomized control trial. The intervention exploits advanced smartphone capabilities, such as the use of a serious game as delivery mode, with the potential benefit of engagement and treatment adherence, and motion sensors to monitor and stimulate physical activity. As a secondary objective, the study aims to gather initial evidence on the user's expectations, satisfaction, usability and playability of the serious game as a treatment.
Depression is the most prevalent mental disorder, with detrimental effects on the patient's well-being, high disability, and a huge associated societal and economic cost. There are evidence-based treatments, but it is difficult to reach all people in need. Internet-based interventions, and more recently smartphone-based interventions, were explored to overcome barriers to access. Evidence shows them to be effective alternatives to traditional treatments. This paper presents the protocol of a pilot study whose primary aim is to investigate the efficacy of a smartphone-based serious game intervention for patients with mild to moderate depressive symptoms.
Methods
This randomized controlled pilot trial protocol foresees two arms design: 1/ smartphone- based serious game intervention (based on Cognitive Behavior Therapy with particular emphasis on Behavioral Activation and Physical Activity), 2/ waiting list control group. The study is expected to recruit 40 participants (18+), which will be randomly assigned to one of the experimental conditions. The duration of the intervention is two months. The primary outcome measure will be depressive symptomatology. Secondary outcomes will include other variables such as physical activity, resilience, anxiety, depression impairment, and positive and negative affect. Treatment expectation, satisfaction, usability, and game playability will also be measured. The data will be analyzed based on the intention-to-treat and per protocol analyses.
Discussion
The study aims to establish initial evidence for the efficacy of a smartphone-based serious game intervention, to serve as input for a larger-scale randomized control trial. The intervention exploits advanced smartphone capabilities, such as the use of a serious game as delivery mode, with the potential benefit of engagement and treatment adherence, and motion sensors to monitor and stimulate physical activity. As a secondary objective, the study aims to gather initial evidence on the user's expectations, satisfaction, usability and playability of the serious game as a treatment.
Klus, Lucie
From Compression of Wearable-based Data to Effortless Indoor Positioning PhD Thesis
Tampere University. Faculty of Information Technology and Communication Sciences, 2023, ISBN: 978-952-03-2832-0.
Abstract | Links | BibTeX | Tags: A-wear, Indoor positioning, machine learning, wearables
@phdthesis{Klus2023a,
title = {From Compression of Wearable-based Data to Effortless Indoor Positioning},
author = {Lucie Klus},
url = {http://hdl.handle.net/10803/688947},
doi = {http://dx.doi.org/10.6035/14124.2023.45900046},
isbn = {978-952-03-2832-0},
year = {2023},
date = {2023-04-27},
school = {Tampere University. Faculty of Information Technology and Communication Sciences},
abstract = {In recent years, wearable devices have become ever-present in modern society. They
are typically defined as small, battery-restricted devices, worn on, in, or in very close
proximity to a human body. Their performance is defined by their functionalities as
much as by their comfortability and convenience. As such, they need to be compact
yet powerful, thus making energy efficiency an extremely important and relevant
aspect of the system. The market of wearable devices is nowadays dominated by
smartwatches and fitness bands, which are capable of gathering numerous sensorbased
data such as temperature, pressure, heart rate, or blood oxygen level, which
have to be processed in real-time, stored, or wirelessly transferred while consuming
as little energy as possible to ensure long battery life. Implementing compression
schemes directly at the wearable device is one of the relevant methods to reduce the
volume of data and to minimize the number of required operations while processing
them, as raw measurements include plenty of redundancies that can be removed
without damaging the useful information itself.},
keywords = {A-wear, Indoor positioning, machine learning, wearables},
pubstate = {published},
tppubtype = {phdthesis}
}
In recent years, wearable devices have become ever-present in modern society. They
are typically defined as small, battery-restricted devices, worn on, in, or in very close
proximity to a human body. Their performance is defined by their functionalities as
much as by their comfortability and convenience. As such, they need to be compact
yet powerful, thus making energy efficiency an extremely important and relevant
aspect of the system. The market of wearable devices is nowadays dominated by
smartwatches and fitness bands, which are capable of gathering numerous sensorbased
data such as temperature, pressure, heart rate, or blood oxygen level, which
have to be processed in real-time, stored, or wirelessly transferred while consuming
as little energy as possible to ensure long battery life. Implementing compression
schemes directly at the wearable device is one of the relevant methods to reduce the
volume of data and to minimize the number of required operations while processing
them, as raw measurements include plenty of redundancies that can be removed
without damaging the useful information itself.
are typically defined as small, battery-restricted devices, worn on, in, or in very close
proximity to a human body. Their performance is defined by their functionalities as
much as by their comfortability and convenience. As such, they need to be compact
yet powerful, thus making energy efficiency an extremely important and relevant
aspect of the system. The market of wearable devices is nowadays dominated by
smartwatches and fitness bands, which are capable of gathering numerous sensorbased
data such as temperature, pressure, heart rate, or blood oxygen level, which
have to be processed in real-time, stored, or wirelessly transferred while consuming
as little energy as possible to ensure long battery life. Implementing compression
schemes directly at the wearable device is one of the relevant methods to reduce the
volume of data and to minimize the number of required operations while processing
them, as raw measurements include plenty of redundancies that can be removed
without damaging the useful information itself.
Nüst, Daniel; Ostermann, Frank O.; Granell-Canut, Carlos
A peer review process for higher reproducibility of publications in GIScience can also work for Earth System Sciences Proceedings Article
In: European Geosciences Union (EGU) General Assembly 2023, pp. EGU23-15384, Copernicus Publications, 2023.
Abstract | Links | BibTeX | Tags: AGILE, GIScience, Reproducibility, Reproducible research
@inproceedings{nust2023a,
title = {A peer review process for higher reproducibility of publications in GIScience can also work for Earth System Sciences},
author = {Daniel Nüst and Frank O. Ostermann and Carlos Granell-Canut},
doi = {https://doi.org/10.5194/egusphere-egu23-15384},
year = {2023},
date = {2023-04-24},
booktitle = {European Geosciences Union (EGU) General Assembly 2023},
pages = {EGU23-15384},
publisher = {Copernicus Publications},
abstract = {The Reproducible AGILE initiative (https://reproducible-agile.github.io/) successfully established a code execution procedure following the CODECHECK principles (https://doi.org/10.12688/f1000research.51738.2) at the AGILE conference series (https://agile-online.org/conference). The AGILE conference is a medium-sized community-led conference in the domains of Geographic Information Science (GIScience), geoinformatics, and related fields. The conference is organised under the umbrella of the Association of Geographic Information Laboratories in Europe (AGILE).},
keywords = {AGILE, GIScience, Reproducibility, Reproducible research},
pubstate = {published},
tppubtype = {inproceedings}
}
The Reproducible AGILE initiative (https://reproducible-agile.github.io/) successfully established a code execution procedure following the CODECHECK principles (https://doi.org/10.12688/f1000research.51738.2) at the AGILE conference series (https://agile-online.org/conference). The AGILE conference is a medium-sized community-led conference in the domains of Geographic Information Science (GIScience), geoinformatics, and related fields. The conference is organised under the umbrella of the Association of Geographic Information Laboratories in Europe (AGILE).
González-Pérez, Alberto; Matey-Sanz, Miguel; Granell-Canut, Carlos; Díaz-Sanahuja, Laura; Bretón-López, Juana; Casteleyn, Sven
AwarNS: A framework for developing context-aware reactive mobile applications for health and mental health Journal Article
In: Journal of Biomedical Informatics, vol. 141, pp. 104359, 2023, ISSN: 1532-0464.
Abstract | Links | BibTeX | Tags: context-aware computing, digital phenotyping, location-based services, mHealth, smartphone app, symptoms
@article{Gonzalez-Perez2023a,
title = {AwarNS: A framework for developing context-aware reactive mobile applications for health and mental health},
author = {Alberto González-Pérez and Miguel Matey-Sanz and Carlos Granell-Canut and Laura Díaz-Sanahuja and Juana Bretón-López and Sven Casteleyn},
doi = {10.1016/j.jbi.2023.104359},
issn = {1532-0464},
year = {2023},
date = {2023-04-20},
journal = {Journal of Biomedical Informatics},
volume = {141},
pages = {104359},
abstract = {In recent years, interest and investment in health and mental health smartphone apps have grown significantly. However, this growth has not been followed by an increase in quality and the incorporation of more advanced features in such applications. This can be explained by an expanding fragmentation of existing mobile platforms along with more restrictive privacy and battery consumption policies, with a consequent higher complexity of developing such smartphone applications. To help overcome these barriers, there is a need for robust, well-designed software development frameworks which are designed to be reliable, power-efficient and ethical with respect to data collection practices, and which support the sense-analyse-act paradigm typically employed in reactive mHealth applications. In this article, we present the AwarNS Framework, a context-aware modular software development framework for Android smartphones, which facilitates transparent, reliable, passive and active data sampling running in the background (sense), on-device and server-side data analysis (analyse), and context-aware just-in-time offline and online intervention capabilities (act). It is based on the principles of versatility, reliability, privacy, reusability, and testability. It offers built-in modules for capturing smartphone and associated wearable sensor data (e.g. IMU sensors, geolocation, Wi-Fi and Bluetooth scans, physical activity, battery level, heart rate), analysis modules for data transformation, selection and filtering, performing geofencing analysis and machine learning regression and classification, and act modules for persistence and various notification deliveries. We describe the framework’s design principles and architecture design, explain its capabilities and implementation, and demonstrate its use at the hand of real-life case studies implementing various mobile interventions for different mental disorders used in clinical practice.},
keywords = {context-aware computing, digital phenotyping, location-based services, mHealth, smartphone app, symptoms},
pubstate = {published},
tppubtype = {article}
}
In recent years, interest and investment in health and mental health smartphone apps have grown significantly. However, this growth has not been followed by an increase in quality and the incorporation of more advanced features in such applications. This can be explained by an expanding fragmentation of existing mobile platforms along with more restrictive privacy and battery consumption policies, with a consequent higher complexity of developing such smartphone applications. To help overcome these barriers, there is a need for robust, well-designed software development frameworks which are designed to be reliable, power-efficient and ethical with respect to data collection practices, and which support the sense-analyse-act paradigm typically employed in reactive mHealth applications. In this article, we present the AwarNS Framework, a context-aware modular software development framework for Android smartphones, which facilitates transparent, reliable, passive and active data sampling running in the background (sense), on-device and server-side data analysis (analyse), and context-aware just-in-time offline and online intervention capabilities (act). It is based on the principles of versatility, reliability, privacy, reusability, and testability. It offers built-in modules for capturing smartphone and associated wearable sensor data (e.g. IMU sensors, geolocation, Wi-Fi and Bluetooth scans, physical activity, battery level, heart rate), analysis modules for data transformation, selection and filtering, performing geofencing analysis and machine learning regression and classification, and act modules for persistence and various notification deliveries. We describe the framework’s design principles and architecture design, explain its capabilities and implementation, and demonstrate its use at the hand of real-life case studies implementing various mobile interventions for different mental disorders used in clinical practice.
Fryza, Tomas; Bravenec, Tomás; Kohl, Zdenek
Security and Reliability of Room Occupancy Detection Using Probe Requests in Smart Buildings Proceedings Article
In: 2023 33rd International Conference Radioelektronika (RADIOELEKTRONIKA, pp. 1-6, IEEE, 2023, ISBN: 979-8-3503-9835-9.
Abstract | Links | BibTeX | Tags: A-wear, Indoor localization, Smart Cities
@inproceedings{Bravenec2023a,
title = {Security and Reliability of Room Occupancy Detection Using Probe Requests in Smart Buildings},
author = {Tomas Fryza and Tomás Bravenec and Zdenek Kohl},
doi = {10.1109/RADIOELEKTRONIKA57919.2023.10109085},
isbn = {979-8-3503-9835-9},
year = {2023},
date = {2023-04-19},
booktitle = {2023 33rd International Conference Radioelektronika (RADIOELEKTRONIKA},
pages = {1-6},
publisher = {IEEE},
abstract = {We present new approaches for determining occupancy in smart building management systems. The solutions can be applied dually, in civil and military areas, not only for economic management but also in crisis situations when it is necessary to ensure the safety or rescue of citizens. Examining the occupancy of university workplaces can lead to future improvements in safety and energy consumption. In addition to common PIR-based motion methods, our implementation uses communication between mobile devices and infrastructure in the form of probe requests from Wi-Fi packets. The data are captured using sniffers based on ESP32 microcontrollers, then processed using Python. Thanks to this, the total number of people (respectively mobile devices) in the building can be estimated. The achieved RMSE estimation error was evaluated for minimal, small, and medium-sized room scenarios, respectively. Aspects of the use of smart building technologies are also considered in detail from the military point of view.},
keywords = {A-wear, Indoor localization, Smart Cities},
pubstate = {published},
tppubtype = {inproceedings}
}
We present new approaches for determining occupancy in smart building management systems. The solutions can be applied dually, in civil and military areas, not only for economic management but also in crisis situations when it is necessary to ensure the safety or rescue of citizens. Examining the occupancy of university workplaces can lead to future improvements in safety and energy consumption. In addition to common PIR-based motion methods, our implementation uses communication between mobile devices and infrastructure in the form of probe requests from Wi-Fi packets. The data are captured using sniffers based on ESP32 microcontrollers, then processed using Python. Thanks to this, the total number of people (respectively mobile devices) in the building can be estimated. The achieved RMSE estimation error was evaluated for minimal, small, and medium-sized room scenarios, respectively. Aspects of the use of smart building technologies are also considered in detail from the military point of view.
Chukhno, Nadezhda
Direct Communication radio interface for new radio multicasting and cooperative positioning PhD Thesis
Università Reggio Calabria, 2023.
Abstract | Links | BibTeX | Tags: 5G, A-wear, Indoor positioning
@phdthesis{Chukhno2023d,
title = {Direct Communication radio interface for new radio multicasting and cooperative positioning},
author = {Nadezhda Chukhno},
url = {https://hdl.handle.net/20.500.12318/136586},
year = {2023},
date = {2023-04-03},
address = {Reggio Calabria},
school = {Università Reggio Calabria},
abstract = {Recently, the popularity of Millimeter Wave (mmWave) wireless networks has increased due to their capability to cope with the escalation of mobile data demands caused by the unprecedented proliferation of smart devices in the fifth-generation (5G). Extremely high frequency or mmWave band is a fundamental pillar in the provision of the expected gigabit data rates. Hence, according to both academic and industrial communities, mmWave technology, e.g., 5G New Radio (NR) and WiGig (60 GHz), is considered as one of the main components of 5G and beyond networks. Particularly, the 3rd Generation Partnership Project (3GPP) provides for the use of licensed mmWave sub-bands for the 5G mmWave cellular networks, whereas IEEE actively explores the unlicensed band at 60 GHz for the next-generation wireless local area networks. In this regard, mmWave has been envisaged as a new technology layout for real-time heavy-traffic and wearable applications. This very work is devoted to solving the problem of mmWave band communication system while enhancing its vantages through utilizing the direct communication radio interface for NR multicasting, cooperative positioning, and mission-critical applications. The main contributions presented in this work include: (i) a set of mathematical frameworks and simulation tools to characterize multicast traffic delivery in mmWave directional systems; (ii) sidelink relaying concept exploitation to deal with the channel condition deterioration of dynamic multicast systems and to ensure mission-critical and ultra-reliable low-latency communications; (iii) cooperative positioning techniques analysis for enhancing cellular positioning accuracy for 5G+ emerging applications that require not only improved communication characteristics but also precise localization. Our study indicates the need for additional mechanisms/research that can be utilized: (i) to further improve multicasting performance in 5G/6G systems; (ii) to investigate sidelink aspects, including, but not limited to, standardization perspective and the next relay selection strategies; and (iii) to design cooperative positioning systems based on Device-to-Device (D2D) technology.},
keywords = {5G, A-wear, Indoor positioning},
pubstate = {published},
tppubtype = {phdthesis}
}
Recently, the popularity of Millimeter Wave (mmWave) wireless networks has increased due to their capability to cope with the escalation of mobile data demands caused by the unprecedented proliferation of smart devices in the fifth-generation (5G). Extremely high frequency or mmWave band is a fundamental pillar in the provision of the expected gigabit data rates. Hence, according to both academic and industrial communities, mmWave technology, e.g., 5G New Radio (NR) and WiGig (60 GHz), is considered as one of the main components of 5G and beyond networks. Particularly, the 3rd Generation Partnership Project (3GPP) provides for the use of licensed mmWave sub-bands for the 5G mmWave cellular networks, whereas IEEE actively explores the unlicensed band at 60 GHz for the next-generation wireless local area networks. In this regard, mmWave has been envisaged as a new technology layout for real-time heavy-traffic and wearable applications. This very work is devoted to solving the problem of mmWave band communication system while enhancing its vantages through utilizing the direct communication radio interface for NR multicasting, cooperative positioning, and mission-critical applications. The main contributions presented in this work include: (i) a set of mathematical frameworks and simulation tools to characterize multicast traffic delivery in mmWave directional systems; (ii) sidelink relaying concept exploitation to deal with the channel condition deterioration of dynamic multicast systems and to ensure mission-critical and ultra-reliable low-latency communications; (iii) cooperative positioning techniques analysis for enhancing cellular positioning accuracy for 5G+ emerging applications that require not only improved communication characteristics but also precise localization. Our study indicates the need for additional mechanisms/research that can be utilized: (i) to further improve multicasting performance in 5G/6G systems; (ii) to investigate sidelink aspects, including, but not limited to, standardization perspective and the next relay selection strategies; and (iii) to design cooperative positioning systems based on Device-to-Device (D2D) technology.
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.
Iskandaryan, Ditsuhi
Universitat Jaume I. INIT, 2023.
Abstract | Links | BibTeX | Tags: air quality prediction, machine learning, spatiotemporal prediction
@phdthesis{Iskandaryan2023c,
title = {Study and Prediction of Air Quality in Smart Cities through Machine Learning Techniques Considering Spatiotemporal Components},
author = {Ditsuhi Iskandaryan},
doi = {http://dx.doi.org/10.6035/14101.2023.726676},
year = {2023},
date = {2023-03-07},
school = {Universitat Jaume I. INIT},
abstract = {Air quality is considered one of the top concerns. Information and knowledge about air quality can assist in effectively monitoring and controlling concentrations, reducing or preventing its harmful impacts and consequences. The complexity of air quality dependence on various components in spatiotemporal dimensions creates additional challenges to acquire this information. The current dissertation proposes machine learning and deep learning technologies that are capable of capturing and processing multidimensional information and complex dependencies controlling air quality. The following components come together to formulate the novelty of the current work: spatiotemporal forecast of the defined prediction target (nitrogen dioxide); incorporation and integration of air quality, meteorological and traffic data with their features/variables in spatiotemporal dimensions within a certain spatial extent and temporal interval; the consideration of coronavirus disease 2019 as an external key factor impacting air quality level; and provision of the code and data implemented to incentivise and guarantee reproducibility.},
keywords = {air quality prediction, machine learning, spatiotemporal prediction},
pubstate = {published},
tppubtype = {phdthesis}
}
Air quality is considered one of the top concerns. Information and knowledge about air quality can assist in effectively monitoring and controlling concentrations, reducing or preventing its harmful impacts and consequences. The complexity of air quality dependence on various components in spatiotemporal dimensions creates additional challenges to acquire this information. The current dissertation proposes machine learning and deep learning technologies that are capable of capturing and processing multidimensional information and complex dependencies controlling air quality. The following components come together to formulate the novelty of the current work: spatiotemporal forecast of the defined prediction target (nitrogen dioxide); incorporation and integration of air quality, meteorological and traffic data with their features/variables in spatiotemporal dimensions within a certain spatial extent and temporal interval; the consideration of coronavirus disease 2019 as an external key factor impacting air quality level; and provision of the code and data implemented to incentivise and guarantee reproducibility.
Chukhno, Nadezhda; Chukhno, Olga; Moltchanov, Dmitri; Gaydamaka, Anna; Samuylov, Andrey; Molinaro, Antonella; Koucheryavy, Yevgeni; Iera, Antonio
The Use of Machine Learning Techniques for Optimal Multicasting in 5G NR Systems Journal Article
In: IEEE Transactions on Broadcasting, vol. 69, no. 1, pp. 201-214, 2023, ISSN: 1557-9611.
Abstract | Links | BibTeX | Tags: A-wear, machine learning, wearables
@article{Chukhno2023b,
title = {The Use of Machine Learning Techniques for Optimal Multicasting in 5G NR Systems},
author = {Nadezhda Chukhno and Olga Chukhno and Dmitri Moltchanov and Anna Gaydamaka and Andrey Samuylov and Antonella Molinaro and Yevgeni Koucheryavy and Antonio Iera},
doi = {10.1109/TBC.2022.3206595},
issn = {1557-9611},
year = {2023},
date = {2023-03-01},
journal = {IEEE Transactions on Broadcasting},
volume = {69},
number = {1},
pages = {201-214},
abstract = {Multicasting is a key feature of cellular systems, which provides an efficient way to simultaneously disseminate a large amount of traffic to multiple subscribers. However, the efficient use of multicast services in fifth-generation (5G) New Radio (NR) is complicated by several factors, including inherent base station (BS) antenna directivity as well as the exploitation of antenna arrays capable of creating multiple beams concurrently. In this work, we first demonstrate that the problem of efficient multicasting in 5G NR systems can be formalized as a special case of multi-period variable cost and size bin packing problem (BPP). However, the problem is known to be NP-hard, and the solution time is practically unacceptable for large multicast group sizes. To this aim, we further develop and test several machine learning alternatives to address this issue. The numerical analysis shows that there is a trade-off between accuracy and computational complexity for multicast grouping when using decision tree-based algorithms. A higher number of splits offers better performance at the cost of an increased computational time. We also show that the nature of the cell coverage brings three possible solutions to the multicast grouping problem: (i) small-range radii are characterized by a single multicast subgroup with wide beamwidth, (ii) middle-range deployments have to be solved by employing the proposed algorithms, and (iii) BS at long-range radii sweeps narrow unicast beams to serve multicast users.},
keywords = {A-wear, machine learning, wearables},
pubstate = {published},
tppubtype = {article}
}
Multicasting is a key feature of cellular systems, which provides an efficient way to simultaneously disseminate a large amount of traffic to multiple subscribers. However, the efficient use of multicast services in fifth-generation (5G) New Radio (NR) is complicated by several factors, including inherent base station (BS) antenna directivity as well as the exploitation of antenna arrays capable of creating multiple beams concurrently. In this work, we first demonstrate that the problem of efficient multicasting in 5G NR systems can be formalized as a special case of multi-period variable cost and size bin packing problem (BPP). However, the problem is known to be NP-hard, and the solution time is practically unacceptable for large multicast group sizes. To this aim, we further develop and test several machine learning alternatives to address this issue. The numerical analysis shows that there is a trade-off between accuracy and computational complexity for multicast grouping when using decision tree-based algorithms. A higher number of splits offers better performance at the cost of an increased computational time. We also show that the nature of the cell coverage brings three possible solutions to the multicast grouping problem: (i) small-range radii are characterized by a single multicast subgroup with wide beamwidth, (ii) middle-range deployments have to be solved by employing the proposed algorithms, and (iii) BS at long-range radii sweeps narrow unicast beams to serve multicast users.
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.
Iskandaryan, Ditsuhi; Ramos-Romero, Francisco; Trilles-Oliver, Sergio
Reconstructing secondary data based on air quality, meteorological and traffic data considering spatiotemporal components Journal Article
In: Data in Brief, vol. 47, no. 108957, 2023, ISSN: 352-3409.
Abstract | Links | BibTeX | Tags: geospatial analysis, geospatial data, nitrogen dioxide prediction, spatiotemporal prediction
@article{Iskandaryan2023b,
title = {Reconstructing secondary data based on air quality, meteorological and traffic data considering spatiotemporal components},
author = {Ditsuhi Iskandaryan and Francisco Ramos-Romero and Sergio Trilles-Oliver},
doi = {https://doi.org/10.1016/j.dib.2023.108957},
issn = {352-3409},
year = {2023},
date = {2023-02-06},
journal = {Data in Brief},
volume = {47},
number = {108957},
abstract = {This paper introduces the reconstructed dataset along with procedures to implement air quality prediction, which consists of air quality, meteorological and traffic data over time, and their monitoring stations and measurement points. Given the fact that those monitoring stations and measurement points are located in different places, it is important to incorporate their time series data into a spatiotemporal dimension. The output can be used as input for various predictive analyses, in particular, we used the reconstructed dataset as input for grid-based (Convolutional Long Short-Term Memory and Bidirectional Convolutional Long Short-Term Memory) and graph-based (Attention Temporal Graph Convolutional Network) machine learning algorithms. The raw dataset is obtained from the Open Data portal of the Madrid City Council.},
keywords = {geospatial analysis, geospatial data, nitrogen dioxide prediction, spatiotemporal prediction},
pubstate = {published},
tppubtype = {article}
}
This paper introduces the reconstructed dataset along with procedures to implement air quality prediction, which consists of air quality, meteorological and traffic data over time, and their monitoring stations and measurement points. Given the fact that those monitoring stations and measurement points are located in different places, it is important to incorporate their time series data into a spatiotemporal dimension. The output can be used as input for various predictive analyses, in particular, we used the reconstructed dataset as input for grid-based (Convolutional Long Short-Term Memory and Bidirectional Convolutional Long Short-Term Memory) and graph-based (Attention Temporal Graph Convolutional Network) machine learning algorithms. The raw dataset is obtained from the Open Data portal of the Madrid City Council.
Iskandaryan, Ditsuhi; Ramos-Romero, Francisco; Trilles-Oliver, Sergio
Graph Neural Network for Air Quality Prediction: A Case Study in Madrid Journal Article
In: IEEE Access, vol. 11, pp. 2729-2742, 2023, ISSN: 2169-3536.
Abstract | Links | BibTeX | Tags: air quality prediction, machine learning, spatiotemporal prediction
@article{Iskandaryan2023a,
title = {Graph Neural Network for Air Quality Prediction: A Case Study in Madrid},
author = {Ditsuhi Iskandaryan and Francisco Ramos-Romero and Sergio Trilles-Oliver},
doi = {10.1109/ACCESS.2023.3234214},
issn = {2169-3536},
year = {2023},
date = {2023-01-04},
journal = {IEEE Access},
volume = {11},
pages = {2729-2742},
abstract = {Air quality monitoring, modelling and forecasting are considered pressing and challenging topics for citizens and decision-makers, including the government. The tools used to achieve the above goals vary depending on the opportunities provided by technological development. Much attention is currently being paid to machine learning and deep learning methods, which, compared to domain knowledge methods, often perform better in terms of capturing, computing and processing multidimensional information and complex dependencies. The technique introduced in this work is an Attention Temporal Graph Convolutional Network based on a combination of Attention, a Gated Recurrent Unit and a Graph Convolutional Network. In the framework of the current study, it is initially suggested to use the presented approach in the domain of air quality prediction. The proposed method was tested using air quality, meteorological and traffic data obtained from the city of Madrid for the periods January-June 2019 and January-June 2022. The evaluation metrics, including Root Mean Square Error, Mean Absolute Error and Pearson Correlation Coefficient, confirmed the proposed model’s advantages compared with the reference models (Temporal Graph Convolutional Network, Long Short-Term Memory and Gated Recurrent Unit).},
keywords = {air quality prediction, machine learning, spatiotemporal prediction},
pubstate = {published},
tppubtype = {article}
}
Air quality monitoring, modelling and forecasting are considered pressing and challenging topics for citizens and decision-makers, including the government. The tools used to achieve the above goals vary depending on the opportunities provided by technological development. Much attention is currently being paid to machine learning and deep learning methods, which, compared to domain knowledge methods, often perform better in terms of capturing, computing and processing multidimensional information and complex dependencies. The technique introduced in this work is an Attention Temporal Graph Convolutional Network based on a combination of Attention, a Gated Recurrent Unit and a Graph Convolutional Network. In the framework of the current study, it is initially suggested to use the presented approach in the domain of air quality prediction. The proposed method was tested using air quality, meteorological and traffic data obtained from the city of Madrid for the periods January-June 2019 and January-June 2022. The evaluation metrics, including Root Mean Square Error, Mean Absolute Error and Pearson Correlation Coefficient, confirmed the proposed model’s advantages compared with the reference models (Temporal Graph Convolutional Network, Long Short-Term Memory and Gated Recurrent Unit).
2022
Chukhno, Olga; Chukhno, Nadezhda; Pizzi, Sara; Molinaro, Antonella; Iera, Antonio; Araniti, Giuseppe
Modeling Reconfigurable Intelligent Surfaces-aided Directional Communications for Multicast Services Proceedings Article
In: GLOBECOM 2022 - 2022 IEEE Global Communications Conference, pp. 5850-5855, IEEE, 2022, ISBN: 978-1-6654-3541-3.
Abstract | Links | BibTeX | Tags: A-wear, wearables
@inproceedings{Chukhno2022d,
title = {Modeling Reconfigurable Intelligent Surfaces-aided Directional Communications for Multicast Services},
author = {Olga Chukhno and Nadezhda Chukhno and Sara Pizzi and Antonella Molinaro and Antonio Iera and Giuseppe Araniti},
doi = {10.1109/GLOBECOM48099.2022.10000930},
isbn = {978-1-6654-3541-3},
year = {2022},
date = {2022-12-08},
booktitle = {GLOBECOM 2022 - 2022 IEEE Global Communications Conference},
pages = {5850-5855},
publisher = {IEEE},
abstract = {According to the 6G vision, the evolution of wireless communication systems will soon lead to the possibility of supporting Tbps communications, as well as satisfying, individually or jointly, a plethora of other very stringent quality requirements related to latency, bitrate, and reliability. The achievement of these goals will naturally raise many research issues within radio communications. In this context, a promising 6G wireless communications enabler is the reconfigurable intelligent surface (RIS) hardware architecture, which has already been recognized as a game-changing way to turn any naturally passive wireless communication setting into an active one. This paper investigates RIS-aided multicast 6G communications by first modeling the system delay as a first-come-first-served (FCFS) M/D/1 queue and analyzing the behavior under different blockage conditions. Then the study of multi-beam operation scenarios, covering multicast and RIS-aided multicast communications, is conducted by leveraging an M/D/c queue model. Achieved results show that large-size RISs outperform even slightly obstructed direct BS-to-user paths. In contrast, RISs of smaller sizes require the design of sophisticated power control and sharing mechanisms to achieve better performance.},
keywords = {A-wear, wearables},
pubstate = {published},
tppubtype = {inproceedings}
}
According to the 6G vision, the evolution of wireless communication systems will soon lead to the possibility of supporting Tbps communications, as well as satisfying, individually or jointly, a plethora of other very stringent quality requirements related to latency, bitrate, and reliability. The achievement of these goals will naturally raise many research issues within radio communications. In this context, a promising 6G wireless communications enabler is the reconfigurable intelligent surface (RIS) hardware architecture, which has already been recognized as a game-changing way to turn any naturally passive wireless communication setting into an active one. This paper investigates RIS-aided multicast 6G communications by first modeling the system delay as a first-come-first-served (FCFS) M/D/1 queue and analyzing the behavior under different blockage conditions. Then the study of multi-beam operation scenarios, covering multicast and RIS-aided multicast communications, is conducted by leveraging an M/D/c queue model. Achieved results show that large-size RISs outperform even slightly obstructed direct BS-to-user paths. In contrast, RISs of smaller sizes require the design of sophisticated power control and sharing mechanisms to achieve better performance.
Osma, Jorge; Martínez-García, Laura; Prado-Abril, Javier; Perís-Baquero, Óscar; González-Pérez, Alberto
In: Internet Interventions, vol. 30, pp. 100577, 2022, ISSN: 2214-7829.
Abstract | Links | BibTeX | Tags: emotional disorders, mental health, smartphone app, thematic content analysis
@article{Osma2022a,
title = {Developing a smartphone App based on the Unified Protocol for the transdiagnostic treatment of emotional disorders: A qualitative analysis of users and professionals' perspectives},
author = {Jorge Osma and Laura Martínez-García and Javier Prado-Abril and Óscar Perís-Baquero and Alberto González-Pérez},
doi = {https://doi.org/10.1016/j.invent.2022.100577},
issn = {2214-7829},
year = {2022},
date = {2022-12-01},
journal = {Internet Interventions},
volume = {30},
pages = {100577},
abstract = {Emotional Disorders have become the most prevalent mental disorders in the world. In relation to their high prevalence, mental health care from public health services faces major challenges. Consequently, finding solutions to deliver cost-effective evidence-based treatments has become a main goal of today's clinical psychology. Smartphone apps for mental health have emerged as a potential tool to deal with it. However, despite their effectiveness and advantages, several studies suggest the need to involve patients and professionals in the design of these apps from the first stage of the development process. Thus, this study aimed to identify, from both a group of users and professionals, the needs, opinions, expectations and design aspects of a future smartphone app based in the Unified Protocol (UP), that will allow to develop the subsequent technical work of the app engineers. Two focus groups were conducted, one with 7 professionals and the other with 9 users, both groups familiar with the UP. A thematic content analysis based in grounded theory was performed in order to define emergent categories of analysis derived from the interview data. The results revealed 8 common topics in both focus groups and 5 specific key topics were identified in the professionals' focus group. Of the total proposals, 93 % of the professionals' and 78 % of the users' are implemented in the preliminary version of the app.},
keywords = {emotional disorders, mental health, smartphone app, thematic content analysis},
pubstate = {published},
tppubtype = {article}
}
Emotional Disorders have become the most prevalent mental disorders in the world. In relation to their high prevalence, mental health care from public health services faces major challenges. Consequently, finding solutions to deliver cost-effective evidence-based treatments has become a main goal of today's clinical psychology. Smartphone apps for mental health have emerged as a potential tool to deal with it. However, despite their effectiveness and advantages, several studies suggest the need to involve patients and professionals in the design of these apps from the first stage of the development process. Thus, this study aimed to identify, from both a group of users and professionals, the needs, opinions, expectations and design aspects of a future smartphone app based in the Unified Protocol (UP), that will allow to develop the subsequent technical work of the app engineers. Two focus groups were conducted, one with 7 professionals and the other with 9 users, both groups familiar with the UP. A thematic content analysis based in grounded theory was performed in order to define emergent categories of analysis derived from the interview data. The results revealed 8 common topics in both focus groups and 5 specific key topics were identified in the professionals' focus group. Of the total proposals, 93 % of the professionals' and 78 % of the users' are implemented in the preliminary version of the app.
Chukhno, Olga; Chukhno, Nadezhda; Araniti, Giuseppe; Campolo, Claudia; Iera, Antonio; Molinaro, Antonella
Placement of Social Digital Twins at the Edge for Beyond 5G IoT Networks Journal Article
In: IEEE Internet of Things Journal , vol. 9, no. 23, pp. 23927 - 23940, 2022, ISSN: 2327-4662.
Abstract | Links | BibTeX | Tags: A-wear, digital twin, Internet of things
@article{Chukhno2022c,
title = {Placement of Social Digital Twins at the Edge for Beyond 5G IoT Networks},
author = {Olga Chukhno and Nadezhda Chukhno and Giuseppe Araniti and Claudia Campolo and Antonio Iera and Antonella Molinaro},
doi = {0.1109/JIOT.2022.3190737},
issn = {2327-4662},
year = {2022},
date = {2022-12-01},
journal = {IEEE Internet of Things Journal },
volume = {9},
number = {23},
pages = {23927 - 23940},
abstract = {As the fifth-generation (5G) and beyond (5G+/6G) networks move forward, and a wide variety of new advanced Internet of Things (IoT) applications are offered, effective methodologies for discovering time-relevant information, services, and resources are being demanded. To this end, computing-, storage-, and battery-constrained IoT devices are progressively augmented via digital twins (DTs) hosted on edge servers. According to recent research results, a further feature these devices may acquire is social behavior; this latter offers enormous possibilities for fast and trustworthy service discovery, although it requires new orchestration policies of DTs at the network edge. This work addresses the dynamic placement of DTs with social capabilities [social digital twins (SDTs)] at the edge, by providing an optimal solution under IoT device mobility and by accounting for edge network deployment specifics, types of devices, and their social peculiarities. The optimization problem is formulated as a particular case of the quadratic assignment problem (QAP); also, an approximation algorithm is proposed and two relaxation techniques are applied to reduce computation complexity. Results show that the proposed placement policy ensures a latency among SDTs up to 1.4 times lower than the one obtainable with a traditional proximity-based only placement while still guaranteeing appropriate proximity between physical devices and their virtual counterparts. Moreover, the proposed heuristic closely approximates the optimal solution while guaranteeing the lowest computational time.},
keywords = {A-wear, digital twin, Internet of things},
pubstate = {published},
tppubtype = {article}
}
As the fifth-generation (5G) and beyond (5G+/6G) networks move forward, and a wide variety of new advanced Internet of Things (IoT) applications are offered, effective methodologies for discovering time-relevant information, services, and resources are being demanded. To this end, computing-, storage-, and battery-constrained IoT devices are progressively augmented via digital twins (DTs) hosted on edge servers. According to recent research results, a further feature these devices may acquire is social behavior; this latter offers enormous possibilities for fast and trustworthy service discovery, although it requires new orchestration policies of DTs at the network edge. This work addresses the dynamic placement of DTs with social capabilities [social digital twins (SDTs)] at the edge, by providing an optimal solution under IoT device mobility and by accounting for edge network deployment specifics, types of devices, and their social peculiarities. The optimization problem is formulated as a particular case of the quadratic assignment problem (QAP); also, an approximation algorithm is proposed and two relaxation techniques are applied to reduce computation complexity. Results show that the proposed placement policy ensures a latency among SDTs up to 1.4 times lower than the one obtainable with a traditional proximity-based only placement while still guaranteeing appropriate proximity between physical devices and their virtual counterparts. Moreover, the proposed heuristic closely approximates the optimal solution while guaranteeing the lowest computational time.
Acedo-Sánchez, Albert; González-Pérez, Alberto; Granell-Canut, Carlos; Casteleyn, Sven
Emotive facets of place meet urban analytics Journal Article
In: Transactions in GIS, vol. 26, no. 7, pp. 2954–2974, 2022, ISSN: 1361-1682.
Abstract | Links | BibTeX | Tags: data analysis methods, sense of place, symptoms
@article{Acedo2022a,
title = {Emotive facets of place meet urban analytics},
author = {Albert Acedo-Sánchez and Alberto González-Pérez and Carlos Granell-Canut and Sven Casteleyn},
doi = {https://doi.org/10.1111/tgis.12990},
issn = {1361-1682},
year = {2022},
date = {2022-11-30},
journal = {Transactions in GIS},
volume = {26},
number = {7},
pages = {2954–2974},
abstract = {The lack of a well-established and unified place theory across disciplines is decelerating its formalization, evolution, and especially its pragmatic implications and applicability. In this article, we identify research gaps in the emotive facets of place scholarship. We found that it: (1) rarely joins physical, social, and individual variables in the same model; (2) omits the immediately perceived and sensory dimensions; (3) disregards the analysis of how individual–place emotive relationships vary across time; and (4) overlooks the difficulties of reducing multifaceted emotive facets of place into geographic features. Next, we examine these research gaps through the lens of technology-based advancements in urban analytics. Finally, we discuss the need to combine social-oriented research and (spatial) data-driven disciplines to enrich and expand the research area of emotive facets of place and connected disciplines.},
keywords = {data analysis methods, sense of place, symptoms},
pubstate = {published},
tppubtype = {article}
}
The lack of a well-established and unified place theory across disciplines is decelerating its formalization, evolution, and especially its pragmatic implications and applicability. In this article, we identify research gaps in the emotive facets of place scholarship. We found that it: (1) rarely joins physical, social, and individual variables in the same model; (2) omits the immediately perceived and sensory dimensions; (3) disregards the analysis of how individual–place emotive relationships vary across time; and (4) overlooks the difficulties of reducing multifaceted emotive facets of place into geographic features. Next, we examine these research gaps through the lens of technology-based advancements in urban analytics. Finally, we discuss the need to combine social-oriented research and (spatial) data-driven disciplines to enrich and expand the research area of emotive facets of place and connected disciplines.
Iskandaryan, Ditsuhi; Ramos-Romero, Francisco; Trilles-Oliver, Sergio
Spatiotemporal Prediction of Nitrogen Dioxide Based on Graph Neural Networks Proceedings Article
In: Advances and New Trends in Environmental Informatics. ENVIROINFO 2022. , pp. 111–128, Springer, Cham, 2022, ISBN: 978-3-031-18311-9.
Abstract | Links | BibTeX | Tags: air quality prediction, machine learning
@inproceedings{Iskandaryan2022e,
title = {Spatiotemporal Prediction of Nitrogen Dioxide Based on Graph Neural Networks},
author = {Ditsuhi Iskandaryan and Francisco Ramos-Romero and Sergio Trilles-Oliver},
doi = {https://doi.org/10.1007/978-3-031-18311-9_7},
isbn = {978-3-031-18311-9},
year = {2022},
date = {2022-11-10},
booktitle = {Advances and New Trends in Environmental Informatics. ENVIROINFO 2022. },
pages = {111–128},
publisher = {Springer, Cham},
series = {Progress in IS},
abstract = {Air quality prediction, especially spatiotemporal prediction, is still a challenging issue. Considering the impact of numerous factors on air quality causes difficulties in integrating these factors in a spatiotemporal dimension and developing a model to make efficient predictions. At the same time, machine learning and deep learning development bring advanced approaches to addressing these challenges and propose novel solutions. The current work introduces one of the most advanced methods, an attention temporal graph convolutional network, which was implemented on datasets constructed by combining air quality, meteorological and traffic data on a spatiotemporal axis. The datasets were obtained from the city of Madrid for the periods January-June 2019 and January–June 2020. The evaluation metrics, the Root Mean Square Error and the Mean Absolute Error confirmed the proposed model’s advantages compared with long short-term memory (reference model). Particularly, it outperformed the latter method by 14.18% and 3.78%, respectively.},
keywords = {air quality prediction, machine learning},
pubstate = {published},
tppubtype = {inproceedings}
}
Air quality prediction, especially spatiotemporal prediction, is still a challenging issue. Considering the impact of numerous factors on air quality causes difficulties in integrating these factors in a spatiotemporal dimension and developing a model to make efficient predictions. At the same time, machine learning and deep learning development bring advanced approaches to addressing these challenges and propose novel solutions. The current work introduces one of the most advanced methods, an attention temporal graph convolutional network, which was implemented on datasets constructed by combining air quality, meteorological and traffic data on a spatiotemporal axis. The datasets were obtained from the city of Madrid for the periods January-June 2019 and January–June 2020. The evaluation metrics, the Root Mean Square Error and the Mean Absolute Error confirmed the proposed model’s advantages compared with long short-term memory (reference model). Particularly, it outperformed the latter method by 14.18% and 3.78%, respectively.
Pascacio-de-los-Santos, Pavel; Torres-Sospedra, Joaquín; Casteleyn, Sven; Lohan, Elena Simona
A Collaborative Approach Using Neural Networks for BLE-RSS Lateration-Based Indoor Positioning Proceedings Article
In: 2022 International Joint Conference on Neural Networks (IJCNN), pp. 1-9, IEEE, 2022, ISBN: 978-1-7281-8671-9.
Abstract | Links | BibTeX | Tags: Bluetooth Low Energy, Indoor positioning, machine learning
@inproceedings{Pascacio2022b,
title = {A Collaborative Approach Using Neural Networks for BLE-RSS Lateration-Based Indoor Positioning},
author = {Pavel Pascacio-de-los-Santos and Joaquín Torres-Sospedra and Sven Casteleyn and Elena Simona Lohan},
doi = {https://doi.org/10.1109/IJCNN55064.2022.9892484},
isbn = {978-1-7281-8671-9},
year = {2022},
date = {2022-09-30},
booktitle = {2022 International Joint Conference on Neural Networks (IJCNN)},
pages = {1-9},
publisher = {IEEE},
abstract = {In daily life, mobile and wearable devices with high computing power, together with anchors deployed in indoor en-vironments, form a common solution for the increasing demands for indoor location-based services. Within the technologies and methods currently in use for indoor localization, the approaches that rely on Bluetooth Low Energy (BLE) anchors, Received Signal Strength (RSS), and lateration are among the most popular, mainly because of their cheap and easy deployment and accessible infrastructure by a variety of devices. Never-theless, such BLE- and RSS-based indoor positioning systems are prone to inaccuracies, mostly due to signal fluctuations, poor quantity of anchors deployed in the environment, and/or inappropriate anchor distributions, as well as mobile device hardware variability. In this paper, we address these issues by using a collaborative indoor positioning approach, which exploits neighboring devices as additional anchors in an extended positioning network. The collaborating devices' information (i.e., estimated positions and BLE- RSS) is processed using a multilayer perceptron (MLP) neural network by taking into account the device specificity in order to estimate the relative distances. After this, the lateration is applied to collaboratively estimate the device position. Finally, the stand-alone and collaborative position estimates are combined, providing the final position estimate for each device. The experimental results demonstrate that the proposed collaborative approach outperforms the stand-alone lateration method in terms of positioning accuracy.},
keywords = {Bluetooth Low Energy, Indoor positioning, machine learning},
pubstate = {published},
tppubtype = {inproceedings}
}
In daily life, mobile and wearable devices with high computing power, together with anchors deployed in indoor en-vironments, form a common solution for the increasing demands for indoor location-based services. Within the technologies and methods currently in use for indoor localization, the approaches that rely on Bluetooth Low Energy (BLE) anchors, Received Signal Strength (RSS), and lateration are among the most popular, mainly because of their cheap and easy deployment and accessible infrastructure by a variety of devices. Never-theless, such BLE- and RSS-based indoor positioning systems are prone to inaccuracies, mostly due to signal fluctuations, poor quantity of anchors deployed in the environment, and/or inappropriate anchor distributions, as well as mobile device hardware variability. In this paper, we address these issues by using a collaborative indoor positioning approach, which exploits neighboring devices as additional anchors in an extended positioning network. The collaborating devices' information (i.e., estimated positions and BLE- RSS) is processed using a multilayer perceptron (MLP) neural network by taking into account the device specificity in order to estimate the relative distances. After this, the lateration is applied to collaboratively estimate the device position. Finally, the stand-alone and collaborative position estimates are combined, providing the final position estimate for each device. The experimental results demonstrate that the proposed collaborative approach outperforms the stand-alone lateration method in terms of positioning accuracy.
Chukhno, Olga; Chukhno, Nadezhda; Galinina, Olga; Andreev, Sergey; Gaidamaka, Yuliya; Samouylov, Konstantin; Araniti, Giuseppe
A Holistic Assessment of Directional Deafness in mmWave-Based Distributed 3D Networks Journal Article
In: IEEE Transactions on Wireless Communications , vol. 21, no. 9, pp. 7491 - 7505, 2022, ISSN: 1558-2248.
Abstract | Links | BibTeX | Tags: A-wear, wearables
@article{Chukhno2022b,
title = {A Holistic Assessment of Directional Deafness in mmWave-Based Distributed 3D Networks},
author = {Olga Chukhno and Nadezhda Chukhno and Olga Galinina and Sergey Andreev and Yuliya Gaidamaka and Konstantin Samouylov and Giuseppe Araniti},
doi = {10.1109/TWC.2022.3159086},
issn = {1558-2248},
year = {2022},
date = {2022-09-22},
journal = {IEEE Transactions on Wireless Communications },
volume = {21},
number = {9},
pages = {7491 - 7505},
abstract = {The adoption of abundant millimeter-wave (mmWave) spectrum offers higher capacity for short-range connectivity in various Unmanned Aerial Vehicle (UAV)-centric communications scenarios. In contrast to the conventional cellular paradigm, where the coordination of connected nodes is highly centralized, the distributed deployments, such as those operating over unlicensed frequency bands, maintain robust interactions in the absence of central control. These agile decentralized systems are being naturally created by dynamic UAV swarms that form a temporary 3D structure without reliance on remote management or pre-established network infrastructures. While much effort has been invested in the performance assessment of distributed, directional, and 3D systems individually, a combination of these three angles allows capturing more realistic UAV swarm scenarios and produces a novel research perspective. This work addresses one of the fundamental challenges in mmWave-based 3D networks– directional deafness– which is known to adversely affect the overall system performance. Particularly, we develop a mathematical framework by taking into account the peculiarities of 3D directional and distributed deployments. We provide a holistic analytical assessment of directional deafness and propose several powerful approximations that capture realistic antenna patterns.},
keywords = {A-wear, wearables},
pubstate = {published},
tppubtype = {article}
}
The adoption of abundant millimeter-wave (mmWave) spectrum offers higher capacity for short-range connectivity in various Unmanned Aerial Vehicle (UAV)-centric communications scenarios. In contrast to the conventional cellular paradigm, where the coordination of connected nodes is highly centralized, the distributed deployments, such as those operating over unlicensed frequency bands, maintain robust interactions in the absence of central control. These agile decentralized systems are being naturally created by dynamic UAV swarms that form a temporary 3D structure without reliance on remote management or pre-established network infrastructures. While much effort has been invested in the performance assessment of distributed, directional, and 3D systems individually, a combination of these three angles allows capturing more realistic UAV swarm scenarios and produces a novel research perspective. This work addresses one of the fundamental challenges in mmWave-based 3D networks– directional deafness– which is known to adversely affect the overall system performance. Particularly, we develop a mathematical framework by taking into account the peculiarities of 3D directional and distributed deployments. We provide a holistic analytical assessment of directional deafness and propose several powerful approximations that capture realistic antenna patterns.