@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}

}

@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}

}

@article{Klus2025a,

title = {C2R: A Novel Architecture for Boosting Indoor Positioning With Scarce Data},

author = {Roman Klus and Jukka Talvitie and Joaquín Torres-Sospedra and Darwin Quezada-Gaibor and Sven Casteleyn and Danijela Cabric and Mikko Valkama},

doi = {https://doi.org/10.1109/JIOT.2024.3420122},

issn = {2327-4662},

year  = {2024},

date = {2024-09-01},

urldate = {2024-09-01},

journal = {IEEE Internet of Things Journal},

volume = {11},

issue = {20},

pages = {32868-32882},

abstract = {Improving the performance of Artificial Neural Network (ANN) regression models on small or scarce datasets, such as wireless network positioning data, can be realized by simplifying the task. One such approach includes implementing the regression model as a classifier, followed by a probabilistic mapping algorithm that transforms class probabilities into the multi-dimensional regression output. In this work, we propose the so-called c2r, a novel ANN-based architecture that transforms the classification model into a robust regressor, while enabling end-to-end training. The proposed solution can remove the impact of less likely classes from the probabilistic mapping by implementing a novel, trainable differential thresholded Rectified Linear Unit layer. The proposed solution is introduced and evaluated in the indoor positioning application domain, using 23 real-world, openly available positioning datasets. The proposed C2R model is shown to achieve significant improvements over the numerous benchmark methods in terms of positioning accuracy. Specifically, when averaged across the 23 datasets, the proposed c2r improves the mean positioning error by 7.9% compared to weighted knn with k=3, from 5.43 m to 5.00 m, and by 15.4% compared to a dense neural network (DNN), from 5.91 m to 5.00 m, while adapting the learned threshold. Finally, the proposed method adds only a single training parameter to the ann, thus as shown through analytical and empirical means in the article, there is no significant increase in the computational complexity.},

keywords = {deep learning, Indoor localization, Wi-Fi fingerprint},

pubstate = {published},

tppubtype = {article}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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},

urldate = {2024-04-01},

journal = {Multimedia Tools and Applications},

volume = {83},

pages = {84047–84094},

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 = {published},

tppubtype = {article}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}