2024 |
Trilles-Oliver, Sergio; Hammad, Sahibzada Saadoon; Iskandaryan, Ditsuhi Anomaly detection based on Artificial Intelligence of Things: A Systematic Literature Mapping Journal Article Internet of Things, 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 Internet of Things, 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. |
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 IEEE Transactions on Mobile Computing, 25 (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 Data in Brief, 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- |
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 Inproceedings 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 IEEE Journal of Indoor and Seamless Positioning and Navigation, 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 Internet of Things, 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 García A; Altaba, Pablo; Huerta-Guijarro, Joaquín Examining urban polarization in five Spanish historic cities through online datasets and onsite perceptions Journal Article Habitat International, 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 Inproceedings 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 Inproceedings 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 Software Impacts, 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 IEEE Sensors Journal, 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 Inproceedings 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 Internet of Things, 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 IEEE Transactions on Mobile Computing, 22 (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 IEEE Communications Magazine, 61 (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 Internet Interventions, 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. |
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. |
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 Inproceedings 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 Journal of Biomedical Informatics, 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 Inproceedings 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 IEEE Transactions on Broadcasting, 69 (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 IEEE Internet of Things Journal, 10 (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 Data in Brief, 47 (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 IEEE Access, 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 Inproceedings 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 Internet Interventions, 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 IEEE Internet of Things Journal , 9 (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 Transactions in GIS, 26 (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 Inproceedings 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 Inproceedings 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 IEEE Transactions on Wireless Communications , 21 (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. |
Brancati, Gianluca; Chukhno, Olga; Chukhno, Nadezhda; Araniti, Giuseppe Reconfigurable Intelligent Surface Placement in 5G NR/6G: Optimization and Performance Analysis Inproceedings 2022 IEEE 33rd Annual International Symposium on Personal, Indoor and Mobile Radio Communications , pp. 1-6, IEEE, 2022, ISBN: 978-1-6654-8054-3. Abstract | Links | BibTeX | Tags: A-wear, wearables @inproceedings{Brancati2022a, title = {Reconfigurable Intelligent Surface Placement in 5G NR/6G: Optimization and Performance Analysis}, author = {Gianluca Brancati and Olga Chukhno and Nadezhda Chukhno and Giuseppe Araniti}, doi = {10.1109/PIMRC54779.2022.9978019}, isbn = {978-1-6654-8054-3}, year = {2022}, date = {2022-09-22}, booktitle = {2022 IEEE 33rd Annual International Symposium on Personal, Indoor and Mobile Radio Communications }, pages = {1-6}, publisher = {IEEE}, abstract = {The reconfigurable intelligent surface (RIS) adoption has drawn significant attention for the upcoming generation of cellular networks, i.e., 5G New Radio (NR)/6G, as a technology for forming virtual line-of-sight (LoS) links during human blockage or non-line-of-sight (NLoS) transmissions. However, the exploration of RIS placement under realistic conditions of multiple user operations has been limited by 1-2 user scenarios, but still is crucial since RIS deployment affects system performance. This paper addresses the challenge of optimal RIS deployment in 5G NR/6G cellular networks with directional antennas. Specifically, we formulate the RIS deployment problem as a facility location problem that maximizes the total data rate. We then evaluate and analyze the impact of various parameters on RIS-aided communications, such as RIS height, blockers density, number of users, and user distribution. The results confirm that the optimal RIS placement is near the BS for the case of uniform and cluster user distributions with RIS height of more than 5 m and close to the hotspots in the case of the cluster user distribution with RIS height of less than 5 m.}, keywords = {A-wear, wearables}, pubstate = {published}, tppubtype = {inproceedings} } The reconfigurable intelligent surface (RIS) adoption has drawn significant attention for the upcoming generation of cellular networks, i.e., 5G New Radio (NR)/6G, as a technology for forming virtual line-of-sight (LoS) links during human blockage or non-line-of-sight (NLoS) transmissions. However, the exploration of RIS placement under realistic conditions of multiple user operations has been limited by 1-2 user scenarios, but still is crucial since RIS deployment affects system performance. This paper addresses the challenge of optimal RIS deployment in 5G NR/6G cellular networks with directional antennas. Specifically, we formulate the RIS deployment problem as a facility location problem that maximizes the total data rate. We then evaluate and analyze the impact of various parameters on RIS-aided communications, such as RIS height, blockers density, number of users, and user distribution. The results confirm that the optimal RIS placement is near the BS for the case of uniform and cluster user distributions with RIS height of more than 5 m and close to the hotspots in the case of the cluster user distribution with RIS height of less than 5 m. |
Ricci, Sara; Dzurenda, Petr; Casanova-Marqués, Raúl; Cika, Petr Threshold Signature for Privacy-Preserving Blockchain Inproceedings Business Process Management: Blockchain, Robotic Process Automation, and Central and Eastern Europe Forum. BPM 2022, Springer, Cham, 2022, ISBN: 978-3-031-16167-4. Abstract | Links | BibTeX | Tags: A-wear, blockchain @inproceedings{Ricci2022a, title = {Threshold Signature for Privacy-Preserving Blockchain}, author = {Sara Ricci and Petr Dzurenda and Raúl Casanova-Marqués and Petr Cika}, doi = {https://doi.org/10.1007/978-3-031-16168-1_7}, isbn = {978-3-031-16167-4}, year = {2022}, date = {2022-09-07}, booktitle = { Business Process Management: Blockchain, Robotic Process Automation, and Central and Eastern Europe Forum. BPM 2022}, volume = {459}, publisher = {Springer, Cham}, series = { Lecture Notes in Business Information Processing}, abstract = {Threshold signatures received renewed interest in recent years due to their practical applicability to Blockchain technology. In this article, we propose a novel (n, t)-threshold signature scheme suitable for increasing security and privacy in Blockchain technology. Our scheme allows splitting a Blockchain wallet into multiple devices so that a threshold of them is needed for signing. This increases the security of the transactions, e.g., more devices need to be compromised to recover the key and permits, and the privacy, e.g., the signing is made anonymously on behalf of the group of users sharing the Blockchain wallet. Our experimental results show that the signing algorithm requires less than 10 ms in the cases of 10 devices involved.}, keywords = {A-wear, blockchain}, pubstate = {published}, tppubtype = {inproceedings} } Threshold signatures received renewed interest in recent years due to their practical applicability to Blockchain technology. In this article, we propose a novel (n, t)-threshold signature scheme suitable for increasing security and privacy in Blockchain technology. Our scheme allows splitting a Blockchain wallet into multiple devices so that a threshold of them is needed for signing. This increases the security of the transactions, e.g., more devices need to be compromised to recover the key and permits, and the privacy, e.g., the signing is made anonymously on behalf of the group of users sharing the Blockchain wallet. Our experimental results show that the signing algorithm requires less than 10 ms in the cases of 10 devices involved. |
Quezada-Gaibor, Darwin; Torres-Sospedra, Joaquín; Nurmi, Jari; Koucheryavy, Yevgeni; Huerta-Guijarro, Joaquín 2022 IEEE 12th International Conference on Indoor Positioning and Indoor Navigation (IPIN), IEEE, 2022, ISBN: 978-1-7281-6218-8. Abstract | Links | BibTeX | Tags: deep learning, Indoor positioning, machine learning @inproceedings{Quezada2022d, title = {SURIMI: Supervised Radio Map Augmentation with Deep Learning and a Generative Adversarial Network for Fingerprint-based Indoor Positioning}, author = {Darwin Quezada-Gaibor and Joaquín Torres-Sospedra and Jari Nurmi and Yevgeni Koucheryavy and Joaquín Huerta-Guijarro}, doi = {10.1109/IPIN54987.2022.9918146}, isbn = {978-1-7281-6218-8}, year = {2022}, date = {2022-09-06}, booktitle = {2022 IEEE 12th International Conference on Indoor Positioning and Indoor Navigation (IPIN)}, number = {1-8}, publisher = {IEEE}, abstract = {Indoor Positioning based on Machine Learning has drawn increasing attention both in the academy and the industry as meaningful information from the reference data can be extracted. Many researchers are using supervised, semi-supervised, and unsupervised Machine Learning models to reduce the positioning error and offer reliable solutions to the end-users. In this article, we propose a new architecture by combining Convolutional Neural Network (CNN), Long short-term memory (LSTM) and Generative Adversarial Network (GAN) in order to increase the training data and thus improve the position accuracy. The proposed combination of supervised and unsupervised models was tested in 17 public datasets, providing an extensive analysis of its performance. As a result, the positioning error has been reduced in more than 70% of them.}, keywords = {deep learning, Indoor positioning, machine learning}, pubstate = {published}, tppubtype = {inproceedings} } Indoor Positioning based on Machine Learning has drawn increasing attention both in the academy and the industry as meaningful information from the reference data can be extracted. Many researchers are using supervised, semi-supervised, and unsupervised Machine Learning models to reduce the positioning error and offer reliable solutions to the end-users. In this article, we propose a new architecture by combining Convolutional Neural Network (CNN), Long short-term memory (LSTM) and Generative Adversarial Network (GAN) in order to increase the training data and thus improve the position accuracy. The proposed combination of supervised and unsupervised models was tested in 17 public datasets, providing an extensive analysis of its performance. As a result, the positioning error has been reduced in more than 70% of them. |
Bravenec, Tomás; Torres-Sospedra, Joaquín; Gould, Michael; Fryza, Tomas 2022 IEEE 12th International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 1-7, IEEE, 2022, ISBN: 978-1-7281-6218-8. Abstract | Links | BibTeX | Tags: geoprivacy, wearables @inproceedings{Bravenec2022a, title = {What Your Wearable Devices Revealed About You and Possibilities of Non-Cooperative 802.11 Presence Detection During Your Last IPIN Visit}, author = {Tomás Bravenec and Joaquín Torres-Sospedra and Michael Gould and Tomas Fryza}, doi = {https://doi.org/10.1109/IPIN54987.2022.9918134}, isbn = {978-1-7281-6218-8}, year = {2022}, date = {2022-09-06}, booktitle = {2022 IEEE 12th International Conference on Indoor Positioning and Indoor Navigation (IPIN)}, pages = {1-7}, publisher = {IEEE}, abstract = {The focus on privacy-related measures regarding wireless networks grew in last couple of years. This is especially important with technologies like Wi-Fi or Bluetooth, which are all around us and our smartphones use them not just for connection to the internet or other devices, but for localization purposes as well. In this paper, we analyze and evaluate probe request frames of 802.11 wireless protocol captured during the 11 th international conference on Indoor Positioning and Indoor Navigation (IPIN) 2021. We explore the temporal occupancy of the conference space during four days of the conference as well as non-cooperatively track the presence of devices in the proximity of the session rooms using 802.11 management frames, with and without using MAC address randomization. We carried out this analysis without trying to identify/reveal the identity of the users or in any way reverse the MAC address randomization. As a result of the analysis, we detected that there are still many devices not adopting MAC randomization, because either it is not implemented, or users disabled it. In addition, many devices can be easily tracked despite employing MAC randomization.}, keywords = {geoprivacy, wearables}, pubstate = {published}, tppubtype = {inproceedings} } The focus on privacy-related measures regarding wireless networks grew in last couple of years. This is especially important with technologies like Wi-Fi or Bluetooth, which are all around us and our smartphones use them not just for connection to the internet or other devices, but for localization purposes as well. In this paper, we analyze and evaluate probe request frames of 802.11 wireless protocol captured during the 11 th international conference on Indoor Positioning and Indoor Navigation (IPIN) 2021. We explore the temporal occupancy of the conference space during four days of the conference as well as non-cooperatively track the presence of devices in the proximity of the session rooms using 802.11 management frames, with and without using MAC address randomization. We carried out this analysis without trying to identify/reveal the identity of the users or in any way reverse the MAC address randomization. As a result of the analysis, we detected that there are still many devices not adopting MAC randomization, because either it is not implemented, or users disabled it. In addition, many devices can be easily tracked despite employing MAC randomization. |
Minghini, Marco; Kotsev, Alexander; Granell-Canut, Carlos A European Approach to the Establishment of Data Spaces Journal Article Data, 7 (8), pp. 118, 2022, ISSN: 2306-5729. Abstract | Links | BibTeX | Tags: Data Infrastructures, Data spaces, Spatial Data Infrastructures (SDI) @article{Minguini2022a, title = {A European Approach to the Establishment of Data Spaces}, author = {Marco Minghini and Alexander Kotsev and Carlos Granell-Canut}, doi = {https://doi.org/10.3390/data7080118}, issn = {2306-5729}, year = {2022}, date = {2022-08-19}, journal = {Data}, volume = {7}, number = {8}, pages = {118}, abstract = {Within a context defined by the rapid increase in the availability of data, combined with the complexity of data sources, infrastructures, technologies and actors involved in data sharing flows, the European Union (EU) is devising approaches that can reap the benefits of data-driven innovation.}, keywords = {Data Infrastructures, Data spaces, Spatial Data Infrastructures (SDI)}, pubstate = {published}, tppubtype = {article} } Within a context defined by the rapid increase in the availability of data, combined with the complexity of data sources, infrastructures, technologies and actors involved in data sharing flows, the European Union (EU) is devising approaches that can reap the benefits of data-driven innovation. |
Weerapanpisit, Ponlawat; Trilles-Oliver, Sergio; Huerta-Guijarro, Joaquín; Painho, Marco A Decentralized Location-Based Reputation Management System in the IoT Using Blockchain Journal Article IEEE Internet of Things Journal, 9 (16), pp. 15100 - 15115, 2022, ISSN: 2327-4662. Abstract | Links | BibTeX | Tags: blockchain, Internet of things, location-based services, spatial indexing @article{Weerapanpisit2022a, title = {A Decentralized Location-Based Reputation Management System in the IoT Using Blockchain}, author = {Ponlawat Weerapanpisit and Sergio Trilles-Oliver and Joaquín Huerta-Guijarro and Marco Painho}, doi = {https://doi.org/10.1109/JIOT.2022.3147478}, issn = {2327-4662}, year = {2022}, date = {2022-08-15}, journal = {IEEE Internet of Things Journal}, volume = {9}, number = {16}, pages = {15100 - 15115}, abstract = {The Internet of Things (IoT) allows an object to connect to the Internet and observe or interact with a physical phenomenon. The communication technologies allow one IoT device to discover and communicate with another in order to exchange services, in a similar way to what humans do in their social networks. Knowing the reputation of another device is important to consider whether it is trustworthy before establishing a new connection and thus, avoid possible unexpected behaviors as a consequence. Trustworthiness, as a property of a device, can be affected by different factors including its geographical location. Hence, this research work proposes an architecture to manage reputation values of end devices in an IoT system based on the area where they are located. A cloud–fog–edge architecture is proposed, where the fog layer uses the Blockchain technology to keep the reputation management system consistent and fault tolerant across different nodes. The location-based part of the system was done by storing geographical areas in smart contracts (coined as geospatial smart contracts) and making the reputation values subject to different regions depending on the geographical location of the device. To reduce the complexity of the spatial computation, the geographical data are geocoded by either one of two different spatial indexing techniques. This work also introduced two different structures for storing geocoded areas based on either cell list or tree structure. Finally, three experiments to test the proposed architecture are presented, to deploy the architecture in IoT devices, and to compare the two geocoding techniques in smart contracts.}, keywords = {blockchain, Internet of things, location-based services, spatial indexing}, pubstate = {published}, tppubtype = {article} } The Internet of Things (IoT) allows an object to connect to the Internet and observe or interact with a physical phenomenon. The communication technologies allow one IoT device to discover and communicate with another in order to exchange services, in a similar way to what humans do in their social networks. Knowing the reputation of another device is important to consider whether it is trustworthy before establishing a new connection and thus, avoid possible unexpected behaviors as a consequence. Trustworthiness, as a property of a device, can be affected by different factors including its geographical location. Hence, this research work proposes an architecture to manage reputation values of end devices in an IoT system based on the area where they are located. A cloud–fog–edge architecture is proposed, where the fog layer uses the Blockchain technology to keep the reputation management system consistent and fault tolerant across different nodes. The location-based part of the system was done by storing geographical areas in smart contracts (coined as geospatial smart contracts) and making the reputation values subject to different regions depending on the geographical location of the device. To reduce the complexity of the spatial computation, the geographical data are geocoded by either one of two different spatial indexing techniques. This work also introduced two different structures for storing geocoded areas based on either cell list or tree structure. Finally, three experiments to test the proposed architecture are presented, to deploy the architecture in IoT devices, and to compare the two geocoding techniques in smart contracts. |
Trilles-Oliver, Sergio; Monfort-Muriach, Aida; Cueto-Rubio, Enrique; Granell-Canut, Carlos; Juan-Verdoy, Pablo Sucre4Stem: Internet of things in classrooms Inproceedings 2022 Congreso de Tecnología, Aprendizaje y Enseñanza de la Electrónica (XV Technologies Applied to Electronics Teaching Conference), pp. 1-4, IEEE, 2022, ISBN: 978-1-6654-2161-4. Abstract | Links | BibTeX | Tags: Computational thinking, Internet of things, STEM, SUCRE, sucre4stem @inproceedings{Trilles2022b, title = {Sucre4Stem: Internet of things in classrooms}, author = {Sergio Trilles-Oliver and Aida Monfort-Muriach and Enrique Cueto-Rubio and Carlos Granell-Canut and Pablo Juan-Verdoy}, doi = {https://doi.org/10.1109/TAEE54169.2022.9840703}, isbn = {978-1-6654-2161-4}, year = {2022}, date = {2022-08-09}, booktitle = {2022 Congreso de Tecnología, Aprendizaje y Enseñanza de la Electrónica (XV Technologies Applied to Electronics Teaching Conference)}, pages = {1-4}, publisher = {IEEE}, abstract = {We demonstrate Sucre4Stem, a training and informative resource for promoting computational thinking aimed at pre-university students, including visual programming through blocks, assembly of sensors and actuators in microcontrollers, network connectivity, and remote data sharing. Through the components of Sucre4Stem, students design, create and program collaborative sensorization projects that recreate real situations of the Internet of Things.}, keywords = {Computational thinking, Internet of things, STEM, SUCRE, sucre4stem}, pubstate = {published}, tppubtype = {inproceedings} } We demonstrate Sucre4Stem, a training and informative resource for promoting computational thinking aimed at pre-university students, including visual programming through blocks, assembly of sensors and actuators in microcontrollers, network connectivity, and remote data sharing. Through the components of Sucre4Stem, students design, create and program collaborative sensorization projects that recreate real situations of the Internet of Things. |
Westerholt, René; Acedo-Sánchez, Albert; Naranjo-Zolotov, Mijail Juanovich Exploring sense of place in relation to urban facilities – evidence from Lisbon Journal Article Cities, 127 (103750), 2022, ISSN: 0264-2751. Abstract | Links | BibTeX | Tags: Built environment, sense of place @article{Westerholt2022a, title = {Exploring sense of place in relation to urban facilities – evidence from Lisbon}, author = {René Westerholt and Albert Acedo-Sánchez and Mijail Juanovich Naranjo-Zolotov}, doi = {https://doi.org/10.1016/j.cities.2022.103750}, issn = {0264-2751}, year = {2022}, date = {2022-08-01}, journal = {Cities}, volume = {127}, number = {103750}, abstract = {Urban environments constitute the habitats in which an increasing number of people live. Place-making forms part of this living, occurring in the context of specific urban assemblages made up of facilities that serve different purposes. For example, Soho in London is characterized by entertainment facilities, while large parts of the Ruhr area in Germany are dominated by industrial features. In this article, we explore possible links between exposure to certain urban facilities and sense of place in Lisbon, Portugal. To do so, we use a web mapping-based survey that allows respondents to map and rate meaningful areas. These areas and their assessments are related to points of interest extracted from Google Places in a structural equation model using PLS-SEM. The results show that exposure to everyday urban facilities such as grocery shops is negatively correlated with place identity, while those that represent leisure locations are negatively correlated with place attachment. Both findings suggest that the temporal rhythm of exposure to certain features is an important factor. Methodologically, our study shows that scales differ between place concepts and their associated spatial footprints – an important finding for future studies. We end the article by offering conclusions and policy recommendations.}, keywords = {Built environment, sense of place}, pubstate = {published}, tppubtype = {article} } Urban environments constitute the habitats in which an increasing number of people live. Place-making forms part of this living, occurring in the context of specific urban assemblages made up of facilities that serve different purposes. For example, Soho in London is characterized by entertainment facilities, while large parts of the Ruhr area in Germany are dominated by industrial features. In this article, we explore possible links between exposure to certain urban facilities and sense of place in Lisbon, Portugal. To do so, we use a web mapping-based survey that allows respondents to map and rate meaningful areas. These areas and their assessments are related to points of interest extracted from Google Places in a structural equation model using PLS-SEM. The results show that exposure to everyday urban facilities such as grocery shops is negatively correlated with place identity, while those that represent leisure locations are negatively correlated with place attachment. Both findings suggest that the temporal rhythm of exposure to certain features is an important factor. Methodologically, our study shows that scales differ between place concepts and their associated spatial footprints – an important finding for future studies. We end the article by offering conclusions and policy recommendations. |
Dzurenda, Petr; Casanova-Marqués, Raúl; Malina, Lukas; Hajny, Jan Real-world Deployment of Privacy-Enhancing Authentication System using Attribute-based Credentials Inproceedings Proceedings of the 17th International Conference on Availability, Reliability and Security, pp. 1-9, ACM, 2022, ISBN: 9781450396707. Abstract | Links | BibTeX | Tags: A-wear, geoprivacy, wearables @inproceedings{Dzurenda2022a, title = {Real-world Deployment of Privacy-Enhancing Authentication System using Attribute-based Credentials}, author = {Petr Dzurenda and Raúl Casanova-Marqués and Lukas Malina and Jan Hajny}, doi = {https://doi.org/10.1145/3538969.3543803}, isbn = {9781450396707}, year = {2022}, date = {2022-08-01}, booktitle = {Proceedings of the 17th International Conference on Availability, Reliability and Security}, pages = {1-9}, publisher = {ACM}, abstract = {With the daily increase in digitalization and integration of the physical and digital worlds, we need to better protect users’ privacy and identity. Attribute-based Credentials (ABCs) seem to be a promising technology for this task. In this paper, we provide comprehensive analyses of the readiness, maturity, and applicability of ABCs to real-world applications. Furthermore, we introduce our Privacy-Enhancing Authentication System (PEAS), which is based on ABCs and meets all privacy requirements such as anonymity and unlinkability of the user’s activities. Besides privacy features, PEAS also provides revocation mechanisms to identify and revoke malicious users. The system is suitable for deployment in real-world scenarios and runs on a wide range of user devices (e.g., smart cards, smartphones, and wearables).}, keywords = {A-wear, geoprivacy, wearables}, pubstate = {published}, tppubtype = {inproceedings} } With the daily increase in digitalization and integration of the physical and digital worlds, we need to better protect users’ privacy and identity. Attribute-based Credentials (ABCs) seem to be a promising technology for this task. In this paper, we provide comprehensive analyses of the readiness, maturity, and applicability of ABCs to real-world applications. Furthermore, we introduce our Privacy-Enhancing Authentication System (PEAS), which is based on ABCs and meets all privacy requirements such as anonymity and unlinkability of the user’s activities. Besides privacy features, PEAS also provides revocation mechanisms to identify and revoke malicious users. The system is suitable for deployment in real-world scenarios and runs on a wide range of user devices (e.g., smart cards, smartphones, and wearables). |
Ramos-Romero, Francisco; Iskandaryan, Ditsuhi; Koribska, Iva Data visualisation for teachers: how to read, interpret and show data correctly Inproceedings EDULEARN22 Proceedings, pp. 8022-8022, IATED, 2022, ISBN: 978-84-09-42484-9. Abstract | Links | BibTeX | Tags: data visualization @inproceedings{Ramos2022a, title = {Data visualisation for teachers: how to read, interpret and show data correctly}, author = {Francisco Ramos-Romero and Ditsuhi Iskandaryan and Iva Koribska}, doi = {10.21125/edulearn.2022.1885}, isbn = {978-84-09-42484-9}, year = {2022}, date = {2022-07-25}, booktitle = {EDULEARN22 Proceedings}, volume = {1}, pages = {8022-8022}, publisher = {IATED}, abstract = {Nowadays, visual information such as charts, diagrams, infographics and so forth are omnipresent in social media, presentations, online scientific papers, that is, in the digital world. We have all heard the well-known sentence about pictures: a picture is worth a thousand words. In the context of charts, it is essential to read the information with attention and care, otherwise we may not understand the core underlying message. Thus, charts, line graphs, bar graphs, plots, etc. can provide us value information, such as trends or patterns hidden by numbers, if we properly read them. However, charts can also be, intentionally or not, confusing. Thus, we can claim that charts could lie in different ways such as a having a poor design, showing inaccurate or insufficient data, or presenting misleading patterns. In this work, we aim at improving the teacher’s skills in detecting the poor-practice in chart creation, although much of this poor practice is not deliberate, often it is due to the lack of knowledge from the user, but sometimes it could also be by reasons ethically questionable. So, we divided this work in three stages. First, teachers receive information and material for the preparation, design, and delivery of effective and efficient charts. In the second stage, they brought to the classroom examples of charts not correctly created, and the content and message from them are carefully analysed. Finally, they modified those examples and showed in the classroom the changes and the reasons to support them. Finally, teachers understood that the creation of charts is relatively easy, but the ways how data can represented or misrepresented is key in the communication process.}, keywords = {data visualization}, pubstate = {published}, tppubtype = {inproceedings} } Nowadays, visual information such as charts, diagrams, infographics and so forth are omnipresent in social media, presentations, online scientific papers, that is, in the digital world. We have all heard the well-known sentence about pictures: a picture is worth a thousand words. In the context of charts, it is essential to read the information with attention and care, otherwise we may not understand the core underlying message. Thus, charts, line graphs, bar graphs, plots, etc. can provide us value information, such as trends or patterns hidden by numbers, if we properly read them. However, charts can also be, intentionally or not, confusing. Thus, we can claim that charts could lie in different ways such as a having a poor design, showing inaccurate or insufficient data, or presenting misleading patterns. In this work, we aim at improving the teacher’s skills in detecting the poor-practice in chart creation, although much of this poor practice is not deliberate, often it is due to the lack of knowledge from the user, but sometimes it could also be by reasons ethically questionable. So, we divided this work in three stages. First, teachers receive information and material for the preparation, design, and delivery of effective and efficient charts. In the second stage, they brought to the classroom examples of charts not correctly created, and the content and message from them are carefully analysed. Finally, they modified those examples and showed in the classroom the changes and the reasons to support them. Finally, teachers understood that the creation of charts is relatively easy, but the ways how data can represented or misrepresented is key in the communication process. |
González-Pérez, Alberto; Granell-Canut, Carlos; Cárdenas, Ramón Mollineda A Coordinación de asignaturas dirigida por un proyecto de desarrollo ágil con evaluación unificada Inproceedings Actas de las Jornadas sobre Enseñanza Universitaria de la Informática (JENUI), pp. 127-134, AENUI, 2022, ISBN: 2531-0607. Abstract | BibTeX | Tags: docente @inproceedings{Gonzalez-Perez2022c, title = {Coordinación de asignaturas dirigida por un proyecto de desarrollo ágil con evaluación unificada}, author = {Alberto González-Pérez and Carlos Granell-Canut and Ramón A. Mollineda Cárdenas}, isbn = {2531-0607}, year = {2022}, date = {2022-07-15}, booktitle = {Actas de las Jornadas sobre Enseñanza Universitaria de la Informática (JENUI)}, volume = {7}, pages = {127-134}, publisher = {AENUI}, abstract = {Este trabajo presenta una acción de coordinación entre dos asignaturas de un Grado en Ingeniería Informática, concebida para recrear un escenario realista de desarrollo ágil de un proyecto de software dirigido por pruebas de aceptación y diseño evolutivo. A diferencia de una experiencia anterior, que se limitaba a un proyecto conjunto, esta propuesta promueve una integración profunda de todas las actividades docentes y de evaluación de las asignaturas Diseño de Software y Paradigmas de Software, las cuales se imparten en un mismo período lectivo. La primera está orientada al estudio de patrones de diseño, mientras que la segunda introduce el paradigma Desarrollo Dirigido por Pruebas de Aceptación (ATDD), el cual promueve diseños que progresan en paralelo a la especificación de requisitos funcionales mediante pruebas de aceptación ejecutables. Las pruebas sirven tanto de guía en el uso de buenas prácticas de diseño, como de medida objetiva de progreso. Además, se propone un método para cuantificar el progreso en la formación del alumnado a partir de la medición de las diferencias entre versiones pre y post de un cuestionario. Los resultados reflejaron una valoración positiva del realismo del proyecto, de su capacidad motivadora y de la libertad para elegir tecnologías. Se obtuvieron incrementos notables en el uso de API de terceros, en métodos de desarrollo guiados por prueba, en la creación de pruebas de aceptación y en el uso de patrones de diseño. Finalmente, entre el alumnado que presentó el proyecto en primera convocatoria, creció el porcentaje de notas superiores a 9 puntos.}, keywords = {docente}, pubstate = {published}, tppubtype = {inproceedings} } Este trabajo presenta una acción de coordinación entre dos asignaturas de un Grado en Ingeniería Informática, concebida para recrear un escenario realista de desarrollo ágil de un proyecto de software dirigido por pruebas de aceptación y diseño evolutivo. A diferencia de una experiencia anterior, que se limitaba a un proyecto conjunto, esta propuesta promueve una integración profunda de todas las actividades docentes y de evaluación de las asignaturas Diseño de Software y Paradigmas de Software, las cuales se imparten en un mismo período lectivo. La primera está orientada al estudio de patrones de diseño, mientras que la segunda introduce el paradigma Desarrollo Dirigido por Pruebas de Aceptación (ATDD), el cual promueve diseños que progresan en paralelo a la especificación de requisitos funcionales mediante pruebas de aceptación ejecutables. Las pruebas sirven tanto de guía en el uso de buenas prácticas de diseño, como de medida objetiva de progreso. Además, se propone un método para cuantificar el progreso en la formación del alumnado a partir de la medición de las diferencias entre versiones pre y post de un cuestionario. Los resultados reflejaron una valoración positiva del realismo del proyecto, de su capacidad motivadora y de la libertad para elegir tecnologías. Se obtuvieron incrementos notables en el uso de API de terceros, en métodos de desarrollo guiados por prueba, en la creación de pruebas de aceptación y en el uso de patrones de diseño. Finalmente, entre el alumnado que presentó el proyecto en primera convocatoria, creció el porcentaje de notas superiores a 9 puntos. |
IF Journal
Journal
Book
Book chapter
Congress
Thesis & M. Thesis
2024 |
Anomaly detection based on Artificial Intelligence of Things: A Systematic Literature Mapping Journal Article Internet of Things, 25 , pp. 101063, 2024, ISSN: 2542-6605. |
Machine learning-based prediction model for battery levels in IoT devices using meteorological variables Journal Article Internet of Things, 25 , pp. 101109, 2024, ISSN: 2542-6605. |
EWOk: Towards Efficient Multidimensional Compression of Indoor Positioning Datasets Journal Article IEEE Transactions on Mobile Computing, 25 (5), pp. 3589-3604, 2024, ISSN: 1558-0660. |
2023 |
Exploiting Wireless Communications for Localization: Beyond Fingerprinting PhD Thesis Universitat Jaume I. INIT, 2023. |
Dataset of inertial measurements of smartphones and smartwatches for human activity recognition Journal Article Data in Brief, 51 , pp. 109809, 2023, ISSN: 2352-3409. |
"Horizon: Resilience": A Smartphone-based Serious Game Intervention for Depressive Symptoms PhD Thesis Universitat Jaume I. INIT, 2023. |
Analysis and Impact of Training Set Size in Cross-Subject Human Activity Recognition Inproceedings Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, pp. 391–405, Springer, Cham, 2023, ISBN: 978-3-031-49018-7. |
UJI Probes Revisited: Deeper Dive Into the Dataset of Wi-Fi Probe Requests Journal Article IEEE Journal of Indoor and Seamless Positioning and Navigation, 1 , pp. 221-230, 2023, ISSN: 2832-7322. |
An unsupervised TinyML approach applied to the detection of urban noise anomalies under the smart cities environment Journal Article Internet of Things, 23 , pp. 100848, 2023, ISSN: 2542-6605. |
Examining urban polarization in five Spanish historic cities through online datasets and onsite perceptions Journal Article Habitat International, 139 , pp. 102900, 2023, ISSN: 0197-3975. |
Temporal Stability on Human Activity Recognition based on Wi-Fi CSI Inproceedings 2023 IEEE 13th International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 1-6, IEEE, 2023, ISBN: 979-8-3503-2012-1. |
UJI Probes: Dataset of Wi-Fi Probe Requests Inproceedings 2023 13th International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 1-6, IEEE, 2023, ISBN: 979-8-3503-2012-1. |
Applying Mobile and Geospatial Technologies to Ecological Momentary Interventions PhD Thesis Universitat Jaume I. INIT, 2023. |
A set of deep learning algorithms for air quality prediction applications Journal Article Software Impacts, 17 , pp. 100562, 2023, ISSN: 2665-9638. |
Influence of Measured Radio Map Interpolation on Indoor Positioning Algorithms Journal Article IEEE Sensors Journal, 17 , pp. 20044-20054, 2023, ISSN: 1530-437X. |
Introducción a los conceptos del pensamiento computacional en educación infantil y primaria con programación tangible Inproceedings Actas de las Jornadas sobre Enseñanza Universitaria de la Informática (JENUI), pp. 257-260, AENUI, 2023, ISSN: 2531-0607. |
Maximizing privacy and security of collaborative indoor positioning using zero-knowledge proofs Journal Article Internet of Things, 22 , pp. 100801, 2023, ISSN: 2542-6605. |
Collaborative Techniques for Indoor Positioning Systems PhD Thesis Universitat Jaume I. INIT, 2023, ISBN: 978-952-03-2905-1. |
Optimal Multicasting in Millimeter Wave 5G NR With Multi-Beam Directional Antennas Journal Article IEEE Transactions on Mobile Computing, 22 (6), pp. 3572 - 3588, 2023, ISSN: 1558-0660. |
Approaching 6G Use Case Requirements with Multicasting Journal Article IEEE Communications Magazine, 61 (5), pp. 144-150, 2023, ISSN: 1558-1896. |
A smartphone-based serious game for depressive symptoms: Protocol for a pilot randomized controlled trial Journal Article Internet Interventions, 32 , pp. 100624, 2023, ISSN: 2214-7829. |
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. |
A peer review process for higher reproducibility of publications in GIScience can also work for Earth System Sciences Inproceedings European Geosciences Union (EGU) General Assembly 2023, pp. EGU23-15384, Copernicus Publications, 2023. |
AwarNS: A framework for developing context-aware reactive mobile applications for health and mental health Journal Article Journal of Biomedical Informatics, 141 , pp. 104359, 2023, ISSN: 1532-0464. |
Security and Reliability of Room Occupancy Detection Using Probe Requests in Smart Buildings Inproceedings 2023 33rd International Conference Radioelektronika (RADIOELEKTRONIKA, pp. 1-6, IEEE, 2023, ISBN: 979-8-3503-9835-9. |
Direct Communication radio interface for new radio multicasting and cooperative positioning PhD Thesis Università Reggio Calabria, 2023. |
Cloud-based Indoor Positioning Platform for Context-adaptivity in GNSS-denied Scenarios PhD Thesis Universitat Jaume I. INIT, 2023. |
Universitat Jaume I. INIT, 2023. |
The Use of Machine Learning Techniques for Optimal Multicasting in 5G NR Systems Journal Article IEEE Transactions on Broadcasting, 69 (1), pp. 201-214, 2023, ISSN: 1557-9611. |
Scalable and Efficient Clustering for Fingerprint-Based Positioning Journal Article IEEE Internet of Things Journal, 10 (4), pp. 3484 - 3499, 2023, ISSN: 2327-4662. |
Reconstructing secondary data based on air quality, meteorological and traffic data considering spatiotemporal components Journal Article Data in Brief, 47 (108957), 2023, ISSN: 352-3409. |
Graph Neural Network for Air Quality Prediction: A Case Study in Madrid Journal Article IEEE Access, 11 , pp. 2729-2742, 2023, ISSN: 2169-3536. |
2022 |
Modeling Reconfigurable Intelligent Surfaces-aided Directional Communications for Multicast Services Inproceedings GLOBECOM 2022 - 2022 IEEE Global Communications Conference, pp. 5850-5855, IEEE, 2022, ISBN: 978-1-6654-3541-3. |
Internet Interventions, 30 , pp. 100577, 2022, ISSN: 2214-7829. |
Placement of Social Digital Twins at the Edge for Beyond 5G IoT Networks Journal Article IEEE Internet of Things Journal , 9 (23), pp. 23927 - 23940, 2022, ISSN: 2327-4662. |
Emotive facets of place meet urban analytics Journal Article Transactions in GIS, 26 (7), pp. 2954–2974, 2022, ISSN: 1361-1682. |
Spatiotemporal Prediction of Nitrogen Dioxide Based on Graph Neural Networks Inproceedings Advances and New Trends in Environmental Informatics. ENVIROINFO 2022. , pp. 111–128, Springer, Cham, 2022, ISBN: 978-3-031-18311-9. |
A Collaborative Approach Using Neural Networks for BLE-RSS Lateration-Based Indoor Positioning Inproceedings 2022 International Joint Conference on Neural Networks (IJCNN), pp. 1-9, IEEE, 2022, ISBN: 978-1-7281-8671-9. |
A Holistic Assessment of Directional Deafness in mmWave-Based Distributed 3D Networks Journal Article IEEE Transactions on Wireless Communications , 21 (9), pp. 7491 - 7505, 2022, ISSN: 1558-2248. |
Reconfigurable Intelligent Surface Placement in 5G NR/6G: Optimization and Performance Analysis Inproceedings 2022 IEEE 33rd Annual International Symposium on Personal, Indoor and Mobile Radio Communications , pp. 1-6, IEEE, 2022, ISBN: 978-1-6654-8054-3. |
Threshold Signature for Privacy-Preserving Blockchain Inproceedings Business Process Management: Blockchain, Robotic Process Automation, and Central and Eastern Europe Forum. BPM 2022, Springer, Cham, 2022, ISBN: 978-3-031-16167-4. |
2022 IEEE 12th International Conference on Indoor Positioning and Indoor Navigation (IPIN), IEEE, 2022, ISBN: 978-1-7281-6218-8. |
2022 IEEE 12th International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 1-7, IEEE, 2022, ISBN: 978-1-7281-6218-8. |
A European Approach to the Establishment of Data Spaces Journal Article Data, 7 (8), pp. 118, 2022, ISSN: 2306-5729. |
A Decentralized Location-Based Reputation Management System in the IoT Using Blockchain Journal Article IEEE Internet of Things Journal, 9 (16), pp. 15100 - 15115, 2022, ISSN: 2327-4662. |
Sucre4Stem: Internet of things in classrooms Inproceedings 2022 Congreso de Tecnología, Aprendizaje y Enseñanza de la Electrónica (XV Technologies Applied to Electronics Teaching Conference), pp. 1-4, IEEE, 2022, ISBN: 978-1-6654-2161-4. |
Exploring sense of place in relation to urban facilities – evidence from Lisbon Journal Article Cities, 127 (103750), 2022, ISSN: 0264-2751. |
Real-world Deployment of Privacy-Enhancing Authentication System using Attribute-based Credentials Inproceedings Proceedings of the 17th International Conference on Availability, Reliability and Security, pp. 1-9, ACM, 2022, ISBN: 9781450396707. |
Data visualisation for teachers: how to read, interpret and show data correctly Inproceedings EDULEARN22 Proceedings, pp. 8022-8022, IATED, 2022, ISBN: 978-84-09-42484-9. |
Coordinación de asignaturas dirigida por un proyecto de desarrollo ágil con evaluación unificada Inproceedings Actas de las Jornadas sobre Enseñanza Universitaria de la Informática (JENUI), pp. 127-134, AENUI, 2022, ISBN: 2531-0607. |