2022 |
Matey-Sanz, Miguel; González-Pérez, Alberto; Casteleyn, Sven; Granell-Canut, Carlos Instrumented Timed Up and Go Test Using Inertial Sensors from Consumer Wearable Devices Inproceedings Artificial Intelligence in Medicine. AIME 2022, pp. 144-154, Springer, Cham, 2022, ISBN: 978-3031093418. Abstract | Links | BibTeX | Tags: machine learning, Mobile apps, mobile computing, symptoms, wearables @inproceedings{Matey2022a, title = {Instrumented Timed Up and Go Test Using Inertial Sensors from Consumer Wearable Devices}, author = {Miguel Matey-Sanz and Alberto González-Pérez and Sven Casteleyn and Carlos Granell-Canut}, doi = {https://doi.org/10.1007/978-3-031-09342-5_14}, isbn = {978-3031093418}, year = {2022}, date = {2022-07-09}, booktitle = {Artificial Intelligence in Medicine. AIME 2022}, volume = {13263}, pages = {144-154}, publisher = {Springer, Cham}, series = {Lectures Notes in Artificial Intelligence}, abstract = {Precision medicine pursues the ambitious goal of providing personalized interventions targeted at individual patients. Within this vision, digital health and mental health, where fine-grained monitoring of patients form the basis for so-called ecological momentary assessments and interventions, play a central role as complementary technology-based and data-driven instruments to traditional psychological treatments. Mobile devices are hereby key enablers: consumer smartphones and wearables are ubiquitously present and used in daily life, while they come with the necessary embedded physiological, inertial and movement sensors to potentially recognise user’s activities and behaviors. In this article, we explore whether real-time detection of fine-grained activities - relevant in the context of wellbeing - is feasible, applying machine learning techniques and based on sensor data collected from a consumer smartwatch device. We present the system architecture, whereby data collection is performed in the wearable device, real-time data processing and inference is delegated to the paired smartphone, and model training is performed offline. Finally, we demonstrate its use by instrumenting the well-known Timed Up and Go (TUG) test, typically used to assess the risk of fall in elderly people. Experiments show that consumer smartwatches can be used to automate the assessment of TUG tests and obtain satisfactory results, comparable with the classical manually performed version of the test.}, keywords = {machine learning, Mobile apps, mobile computing, symptoms, wearables}, pubstate = {published}, tppubtype = {inproceedings} } Precision medicine pursues the ambitious goal of providing personalized interventions targeted at individual patients. Within this vision, digital health and mental health, where fine-grained monitoring of patients form the basis for so-called ecological momentary assessments and interventions, play a central role as complementary technology-based and data-driven instruments to traditional psychological treatments. Mobile devices are hereby key enablers: consumer smartphones and wearables are ubiquitously present and used in daily life, while they come with the necessary embedded physiological, inertial and movement sensors to potentially recognise user’s activities and behaviors. In this article, we explore whether real-time detection of fine-grained activities - relevant in the context of wellbeing - is feasible, applying machine learning techniques and based on sensor data collected from a consumer smartwatch device. We present the system architecture, whereby data collection is performed in the wearable device, real-time data processing and inference is delegated to the paired smartphone, and model training is performed offline. Finally, we demonstrate its use by instrumenting the well-known Timed Up and Go (TUG) test, typically used to assess the risk of fall in elderly people. Experiments show that consumer smartwatches can be used to automate the assessment of TUG tests and obtain satisfactory results, comparable with the classical manually performed version of the test. |
Silva, Ivo; Pendão, Cristiano; Torres-Sospedra, Joaquín; Moreira, Adriano TrackInFactory: A Tight Coupling Particle Filter for Industrial Vehicle Tracking in Indoor Environments Journal Article IEEE Transactions on Systems, Man, and Cybernetics: Systems, 52 (7), pp. 4151 - 4162, 2022, ISSN: 2168-2232. Abstract | Links | BibTeX | Tags: Indoor positioning, Industry 4.0, sensor fusion, Wi-Fi fingerprint @article{Silva2022ab, title = {TrackInFactory: A Tight Coupling Particle Filter for Industrial Vehicle Tracking in Indoor Environments}, author = {Ivo Silva and Cristiano Pendão and Joaquín Torres-Sospedra and Adriano Moreira}, doi = {https://doi.org/10.1109/TSMC.2021.3091987}, issn = {2168-2232}, year = {2022}, date = {2022-07-06}, journal = {IEEE Transactions on Systems, Man, and Cybernetics: Systems}, volume = {52}, number = {7}, pages = {4151 - 4162}, abstract = {Localization and tracking of industrial vehicles have a key role in increasing productivity and improving the logistics processes of factories. Due to the demanding requirements of industrial vehicle tracking and navigation, existing systems explore technologies, such as LiDAR or ultra wide-band to achieve low positioning errors. In this article we propose TrackInFactory, a system that combines Wi-Fi with motion sensors, achieving submeter accuracy and a low maximum error. A tight coupling approach is explored in sensor fusion with a particle filter (PF). Information regarding the vehicle’s initial position and heading is not required. This approach uses the similarity of Wi-Fi samples to update the particles’ weights as they move according to motion sensor data. The PF dynamically adjusts its parameters based on a metric for estimating the confidence in position estimates, allowing to improve positioning performance. A series of simulations were performed to tune the PF. Then the approach was validated in real-world experiments with an industrial tow tractor, achieving a mean error of 0.81 m. In comparison to a loose coupling approach, this method reduced the maximum error by more than 60% and improved the overall mean error by more than 20%.}, keywords = {Indoor positioning, Industry 4.0, sensor fusion, Wi-Fi fingerprint}, pubstate = {published}, tppubtype = {article} } Localization and tracking of industrial vehicles have a key role in increasing productivity and improving the logistics processes of factories. Due to the demanding requirements of industrial vehicle tracking and navigation, existing systems explore technologies, such as LiDAR or ultra wide-band to achieve low positioning errors. In this article we propose TrackInFactory, a system that combines Wi-Fi with motion sensors, achieving submeter accuracy and a low maximum error. A tight coupling approach is explored in sensor fusion with a particle filter (PF). Information regarding the vehicle’s initial position and heading is not required. This approach uses the similarity of Wi-Fi samples to update the particles’ weights as they move according to motion sensor data. The PF dynamically adjusts its parameters based on a metric for estimating the confidence in position estimates, allowing to improve positioning performance. A series of simulations were performed to tune the PF. Then the approach was validated in real-world experiments with an industrial tow tractor, achieving a mean error of 0.81 m. In comparison to a loose coupling approach, this method reduced the maximum error by more than 60% and improved the overall mean error by more than 20%. |
Iskandaryan, Ditsuhi; Ramos-Romero, Francisco; Trilles-Oliver, Sergio Comparison of Nitrogen Dioxide Predictions During a Pandemic and Non-pandemic Scenario in the City of Madrid using a Convolutional LSTM Network Journal Article International Journal of Computational Intelligence and Applications, 21 (2), pp. 2250014, 2022, ISSN: 1757-5885. Abstract | Links | BibTeX | Tags: air quality prediction, machine learning @article{Iskandaryan2022d, title = {Comparison of Nitrogen Dioxide Predictions During a Pandemic and Non-pandemic Scenario in the City of Madrid using a Convolutional LSTM Network}, author = {Ditsuhi Iskandaryan and Francisco Ramos-Romero and Sergio Trilles-Oliver}, doi = {https://doi.org/10.1142/S1469026822500146}, issn = {1757-5885}, year = {2022}, date = {2022-06-21}, journal = {International Journal of Computational Intelligence and Applications}, volume = {21}, number = {2}, pages = {2250014}, abstract = {Traditionally, machine learning technologies with the methods and capabilities available, combined with a geospatial dimension, can perform predictive analyzes of air quality with greater accuracy. However, air pollution is influenced by many external factors, one of which has recently been caused by the restrictions applied to curb the relentless advance of COVID-19. These sudden changes in air quality levels can negatively influence current forecasting models. This work compares air pollution forecasts during a pandemic and non-pandemic period under the same conditions. The ConvLSTM algorithm was applied to predict the concentration of nitrogen dioxide using data from the air quality and meteorological stations in Madrid. The proposed model was applied for two scenarios: pandemic (January–June 2020) and non-pandemic (January–June 2019), each with sub-scenarios based on time granularity (1-h, 12-h, 24-h and 48-h) and combination of features. The Root Mean Square Error was taken as the estimation metric, and the results showed that the proposed method outperformed a reference model, and the feature selection technique significantly improved the overall accuracy.}, keywords = {air quality prediction, machine learning}, pubstate = {published}, tppubtype = {article} } Traditionally, machine learning technologies with the methods and capabilities available, combined with a geospatial dimension, can perform predictive analyzes of air quality with greater accuracy. However, air pollution is influenced by many external factors, one of which has recently been caused by the restrictions applied to curb the relentless advance of COVID-19. These sudden changes in air quality levels can negatively influence current forecasting models. This work compares air pollution forecasts during a pandemic and non-pandemic period under the same conditions. The ConvLSTM algorithm was applied to predict the concentration of nitrogen dioxide using data from the air quality and meteorological stations in Madrid. The proposed model was applied for two scenarios: pandemic (January–June 2020) and non-pandemic (January–June 2019), each with sub-scenarios based on time granularity (1-h, 12-h, 24-h and 48-h) and combination of features. The Root Mean Square Error was taken as the estimation metric, and the results showed that the proposed method outperformed a reference model, and the feature selection technique significantly improved the overall accuracy. |
Quezada-Gaibor, Darwin; Torres-Sospedra, Joaquín; Nurmi, Jari; Koucheryavy, Yevgeni; Huerta-Guijarro, Joaquín Lightweight Hybrid CNN-ELM Model for Multi-building and Multi-floor Classification Inproceedings 2022 International Conference on Localization and GNSS (ICL-GNSS), pp. 1-6, IEEE, 2022. Abstract | Links | BibTeX | Tags: Indoor positioning, machine learning @inproceedings{Quezada2022b, title = {Lightweight Hybrid CNN-ELM Model for Multi-building and Multi-floor Classification}, author = {Darwin Quezada-Gaibor and Joaquín Torres-Sospedra and Jari Nurmi and Yevgeni Koucheryavy and Joaquín Huerta-Guijarro}, doi = {https://doi.org/10.1109/ICL-GNSS54081.2022.9797021}, year = {2022}, date = {2022-06-19}, booktitle = {2022 International Conference on Localization and GNSS (ICL-GNSS)}, pages = {1-6}, publisher = {IEEE}, abstract = {Machine learning models have become an essential tool in current indoor positioning solutions, given their high capa-bilities to extract meaningful information from the environment. Convolutional neural networks (CNNs) are one of the most used neural networks (NNs) due to that they are capable of learning complex patterns from the input data. Another model used in indoor positioning solutions is the Extreme Learning Machine (ELM), which provides an acceptable generalization performance as well as a fast speed of learning. In this paper, we offer a lightweight combination of CNN and ELM, which provides a quick and accurate classification of building and floor, suitable for power and resource-constrained devices. As a result, the proposed model is 58% faster than the benchmark, with a slight improvement in the classification accuracy (by less than 1 %).}, keywords = {Indoor positioning, machine learning}, pubstate = {published}, tppubtype = {inproceedings} } Machine learning models have become an essential tool in current indoor positioning solutions, given their high capa-bilities to extract meaningful information from the environment. Convolutional neural networks (CNNs) are one of the most used neural networks (NNs) due to that they are capable of learning complex patterns from the input data. Another model used in indoor positioning solutions is the Extreme Learning Machine (ELM), which provides an acceptable generalization performance as well as a fast speed of learning. In this paper, we offer a lightweight combination of CNN and ELM, which provides a quick and accurate classification of building and floor, suitable for power and resource-constrained devices. As a result, the proposed model is 58% faster than the benchmark, with a slight improvement in the classification accuracy (by less than 1 %). |
Klus, Lucie; Quezada-Gaibor, Darwin; Torres-Sospedra, Joaquín; Lohan, Elena Simona; Nurmi, Jari; Granell-Canut, Carlos; Huerta-Guijarro, Joaquín Towards Accelerated Localization Performance Across Indoor Positioning Datasets Inproceedings 2022 International Conference on Localization and GNSS (ICL-GNSS), pp. 1-7, IEEE, 2022. Abstract | Links | BibTeX | Tags: Indoor localization, machine learning @inproceedings{Klus2022a, title = {Towards Accelerated Localization Performance Across Indoor Positioning Datasets}, author = {Lucie Klus and Darwin Quezada-Gaibor and Joaquín Torres-Sospedra and Elena Simona Lohan and Jari Nurmi and Carlos Granell-Canut and Joaquín Huerta-Guijarro}, doi = {https://doi.org/10.1109/ICL-GNSS54081.2022.9797035}, year = {2022}, date = {2022-06-19}, booktitle = {2022 International Conference on Localization and GNSS (ICL-GNSS)}, pages = {1-7}, publisher = {IEEE}, abstract = {he localization speed and accuracy in the indoor scenario can greatly impact the Quality of Experience of the user. While many individual machine learning models can achieve comparable positioning performance, their prediction mechanisms offer different complexity to the system. In this work, we propose a fingerprinting positioning method for multi-building and multi-floor deployments, composed of a cascade of three models for building classification, floor classification, and 2D localization regression. We conduct an exhaustive search for the optimally performing one in each step of the cascade while validating on 14 different openly available datasets. As a result, we bring forward the best-performing combination of models in terms of overall positioning accuracy and processing speed and evaluate on independent sets of samples. We reduce the mean prediction time by 71% while achieving comparable positioning performance across all considered datasets. Moreover, in case of voluminous training dataset, the prediction time is reduced down to 1% of the benchmark's}, keywords = {Indoor localization, machine learning}, pubstate = {published}, tppubtype = {inproceedings} } he localization speed and accuracy in the indoor scenario can greatly impact the Quality of Experience of the user. While many individual machine learning models can achieve comparable positioning performance, their prediction mechanisms offer different complexity to the system. In this work, we propose a fingerprinting positioning method for multi-building and multi-floor deployments, composed of a cascade of three models for building classification, floor classification, and 2D localization regression. We conduct an exhaustive search for the optimally performing one in each step of the cascade while validating on 14 different openly available datasets. As a result, we bring forward the best-performing combination of models in terms of overall positioning accuracy and processing speed and evaluate on independent sets of samples. We reduce the mean prediction time by 71% while achieving comparable positioning performance across all considered datasets. Moreover, in case of voluminous training dataset, the prediction time is reduced down to 1% of the benchmark's |
Iskandaryan, Ditsuhi; Sabatino, Silvana Di; Ramos-Romero, Francisco; Trilles-Oliver, Sergio Exploratory Analysis and Feature Selection for the Prediction of Nitrogen Dioxide Inproceedings AGILE GIScience Series (Proceedings of the 25th AGILE Conference on Geographic Information Science), Copernicus Publications, 2022. Abstract | Links | BibTeX | Tags: air quality prediction, machine learning @inproceedings{Iskandaryan2022c, title = {Exploratory Analysis and Feature Selection for the Prediction of Nitrogen Dioxide}, author = {Ditsuhi Iskandaryan and Silvana Di Sabatino and Francisco Ramos-Romero and Sergio Trilles-Oliver}, doi = { https://doi.org/10.5194/agile-giss-3-6-2022}, year = {2022}, date = {2022-06-15}, booktitle = {AGILE GIScience Series (Proceedings of the 25th AGILE Conference on Geographic Information Science)}, volume = {3}, number = {6}, publisher = {Copernicus Publications}, abstract = {Nitrogen dioxide is one of the most hazardous pollutants identified by the World Health Organisation. Predicting and reducing pollutants is becoming a very urgent task and many methods have been used to predict their concentration, such as physical or machine learning models. In addition to choosing the right model, it is also critical to choose the appropriate features. This work focuses on the spatiotemporal prediction of nitrogen dioxide concentration using Bidirectional Convolutional LSTM integrated with the exploration of nitrogen dioxide and associated features, as well as the implementation of feature selection methods. The Root Mean Square Error and the Mean Absolute Error were used to evaluate the proposed approach.}, keywords = {air quality prediction, machine learning}, pubstate = {published}, tppubtype = {inproceedings} } Nitrogen dioxide is one of the most hazardous pollutants identified by the World Health Organisation. Predicting and reducing pollutants is becoming a very urgent task and many methods have been used to predict their concentration, such as physical or machine learning models. In addition to choosing the right model, it is also critical to choose the appropriate features. This work focuses on the spatiotemporal prediction of nitrogen dioxide concentration using Bidirectional Convolutional LSTM integrated with the exploration of nitrogen dioxide and associated features, as well as the implementation of feature selection methods. The Root Mean Square Error and the Mean Absolute Error were used to evaluate the proposed approach. |
Lemmens, Rob; Albrecht, Florian; Lang, Stefan; Casteleyn, Sven; Stelmaszczuk-Górska, Martyna; Olijslagers, Marc; Begiu, Mariana; Granell-Canut, Carlos; Augustijn, Ellen-Wine; Carsten Pathe, Eva-Maria Missoni-Steinbacher ; Monfort-Muriach, Aida Updating and using the EO4GEO Body of Knowledge for (AI) concept annotation Inproceedings AGILE GIScience Series (Proceedings of the 25th AGILE Conference on Geographic Information Science), Copernicus Publications, 2022. Abstract | Links | BibTeX | Tags: Body of Knowledge, education, EO4GEO, GIScience @inproceedings{Lemmens2022a, title = {Updating and using the EO4GEO Body of Knowledge for (AI) concept annotation}, author = {Rob Lemmens and Florian Albrecht and Stefan Lang and Sven Casteleyn and Martyna Stelmaszczuk-Górska and Marc Olijslagers and Mariana Begiu and Carlos Granell-Canut and Ellen-Wine Augustijn and Carsten Pathe, Eva-Maria Missoni-Steinbacher and Aida Monfort-Muriach}, doi = {https://doi.org/10.5194/agile-giss-3-44-2022}, year = {2022}, date = {2022-06-15}, booktitle = {AGILE GIScience Series (Proceedings of the 25th AGILE Conference on Geographic Information Science)}, volume = {3}, number = {44}, publisher = {Copernicus Publications}, abstract = {The EO4GEO Body of Knowledge (BoK) serves as a vocabulary for the domain of geoinformation and earth observation, supporting the annotation of online resources. This paper presents how the BoK is designed, maintained and improved. We discuss how the BoK content can be extended, using the example of integrating artificial intelligence (AI) concepts and show how annotation is done by adding persistent concept identifiers in the metadata of training materials. This platform allows us to share online information with clarified semantics. A prolonged use necessitates the incentivisation of an active expert community and a further adoption of infrastructure standards.}, keywords = {Body of Knowledge, education, EO4GEO, GIScience}, pubstate = {published}, tppubtype = {inproceedings} } The EO4GEO Body of Knowledge (BoK) serves as a vocabulary for the domain of geoinformation and earth observation, supporting the annotation of online resources. This paper presents how the BoK is designed, maintained and improved. We discuss how the BoK content can be extended, using the example of integrating artificial intelligence (AI) concepts and show how annotation is done by adding persistent concept identifiers in the metadata of training materials. This platform allows us to share online information with clarified semantics. A prolonged use necessitates the incentivisation of an active expert community and a further adoption of infrastructure standards. |
Quezada-Gaibor, Darwin; Klus, Lucie; Torres-Sospedra, Joaquín; Lohan, Elena Simona; Nurmi, Jari; Granell-Canut, Carlos; Huerta-Guijarro, Joaquín Data Cleansing for Indoor Positioning Wi-Fi Fingerprinting Datasets Inproceedings 2022 23rd IEEE International Conference on Mobile Data Management (MDM), pp. 349-354, IEEE, 2022, ISBN: 978-1-6654-5176-5. Abstract | Links | BibTeX | Tags: Data science, Indoor positioning, Wi-Fi fingerprint @inproceedings{Quezada2022c, title = {Data Cleansing for Indoor Positioning Wi-Fi Fingerprinting Datasets}, author = {Darwin Quezada-Gaibor and Lucie Klus and Joaquín Torres-Sospedra and Elena Simona Lohan and Jari Nurmi and Carlos Granell-Canut and Joaquín Huerta-Guijarro}, doi = {https://doi.org/10.1109/MDM55031.2022.00079}, isbn = {978-1-6654-5176-5}, year = {2022}, date = {2022-06-10}, booktitle = {2022 23rd IEEE International Conference on Mobile Data Management (MDM)}, pages = {349-354}, publisher = {IEEE}, abstract = {Wearable and IoT devices requiring positioning and localisation services grow in number exponentially every year. This rapid growth also produces millions of data entries that need to be pre-processed prior to being used in any indoor positioning system to ensure the data quality and provide a high Quality of Service (QoS) to the end-user. In this paper, we offer a novel and straightforward data cleansing algorithm for WLAN fingerprinting radio maps. This algorithm is based on the correlation among fingerprints using the Received Signal Strength (RSS) values and the Access Points (APs)'s identifier. We use those to compute the correlation among all samples in the dataset and remove fingerprints with low level of correlation from the dataset. We evaluated the proposed method on 14 independent publicly-available datasets. As a result, an average of 14% of fingerprints were removed from the datasets. The 2D positioning error was reduced by 2.7% and 3D positioning error by 5.3% with a slight increase in the floor hit rate by 1.2% on average. Consequently, the average speed of position prediction was also increased by 14%.}, keywords = {Data science, Indoor positioning, Wi-Fi fingerprint}, pubstate = {published}, tppubtype = {inproceedings} } Wearable and IoT devices requiring positioning and localisation services grow in number exponentially every year. This rapid growth also produces millions of data entries that need to be pre-processed prior to being used in any indoor positioning system to ensure the data quality and provide a high Quality of Service (QoS) to the end-user. In this paper, we offer a novel and straightforward data cleansing algorithm for WLAN fingerprinting radio maps. This algorithm is based on the correlation among fingerprints using the Received Signal Strength (RSS) values and the Access Points (APs)'s identifier. We use those to compute the correlation among all samples in the dataset and remove fingerprints with low level of correlation from the dataset. We evaluated the proposed method on 14 independent publicly-available datasets. As a result, an average of 14% of fingerprints were removed from the datasets. The 2D positioning error was reduced by 2.7% and 3D positioning error by 5.3% with a slight increase in the floor hit rate by 1.2% on average. Consequently, the average speed of position prediction was also increased by 14%. |
Pascacio-de-los-Santos, Pavel; Torres-Sospedra, Joaquín; Jiménez, Antonio R; Casteleyn, Sven Mobile device-based Bluetooth Low Energy Database for range estimation in indoor environments Journal Article Scientific Data, 9 (281), 2022, ISSN: 2052-4463. Abstract | Links | BibTeX | Tags: Bluetooth Low Energy, Indoor positioning @article{Pascacio2022a, title = {Mobile device-based Bluetooth Low Energy Database for range estimation in indoor environments}, author = {Pavel Pascacio-de-los-Santos and Joaquín Torres-Sospedra and Antonio R Jiménez and Sven Casteleyn }, doi = {https://doi.org/10.1038/s41597-022-01406-2}, issn = {2052-4463}, year = {2022}, date = {2022-06-08}, journal = {Scientific Data}, volume = {9}, number = {281}, abstract = {The demand to enhance distance estimation and location accuracy in a variety of Non-Line-of-Sight (NLOS) indoor environments has boosted investigation into infrastructure-less ranging and collaborative positioning approaches. Unfortunately, capturing the required measurements to support such systems is tedious and time-consuming, as it requires simultaneous measurements using multiple mobile devices, and no such database are available in literature. This article presents a Bluetooth Low Energy (BLE) database, including Received-Signal-Strength (RSS) and Ground-Truth (GT) positions, for indoor positioning and ranging applications, using mobile devices as transmitters and receivers. The database is composed of three subsets: one devoted to the calibration in an indoor scenario; one for ranging and collaborative positioning under Non-Line-of-Sight conditions; and one for ranging and collaborative positioning in real office conditions. As a validation of the dataset, a baseline analysis for data visualization, data filtering and collaborative distance estimation applying a path-loss based on the Levenberg-Marquardt Least Squares Trilateration method are included.}, keywords = {Bluetooth Low Energy, Indoor positioning}, pubstate = {published}, tppubtype = {article} } The demand to enhance distance estimation and location accuracy in a variety of Non-Line-of-Sight (NLOS) indoor environments has boosted investigation into infrastructure-less ranging and collaborative positioning approaches. Unfortunately, capturing the required measurements to support such systems is tedious and time-consuming, as it requires simultaneous measurements using multiple mobile devices, and no such database are available in literature. This article presents a Bluetooth Low Energy (BLE) database, including Received-Signal-Strength (RSS) and Ground-Truth (GT) positions, for indoor positioning and ranging applications, using mobile devices as transmitters and receivers. The database is composed of three subsets: one devoted to the calibration in an indoor scenario; one for ranging and collaborative positioning under Non-Line-of-Sight conditions; and one for ranging and collaborative positioning in real office conditions. As a validation of the dataset, a baseline analysis for data visualization, data filtering and collaborative distance estimation applying a path-loss based on the Levenberg-Marquardt Least Squares Trilateration method are included. |
Lemmens, Rob; Lang, Stefan; Albrecht, Florian; Augustijn, Ellen-Wien; Granell-Canut, Carlos; Olijslagers, Marc; Pathe, Carsten; Dubois, Clemence; Stelmaszczuk-Górska, Martyna Integrating concepts of artificial intelligence in the EO4GEO Body of Knowledge Inproceedings The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (XXIV ISPRS Congress), pp. 53-59, Copernicus Publications, 2022, ISSN: 2194-9034. Abstract | Links | BibTeX | Tags: Body of Knowledge, education, EO4GEO, GIScience @inproceedings{Lemmens2022b, title = {Integrating concepts of artificial intelligence in the EO4GEO Body of Knowledge}, author = {Rob Lemmens and Stefan Lang and Florian Albrecht and Ellen-Wien Augustijn and Carlos Granell-Canut and Marc Olijslagers and Carsten Pathe and Clemence Dubois and Martyna Stelmaszczuk-Górska}, doi = {10.5194/isprs-archives-XLIII-B4-2022-53-2022}, issn = {2194-9034}, year = {2022}, date = {2022-06-01}, booktitle = {The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (XXIV ISPRS Congress)}, volume = {XLIII-B4-2022}, pages = {53-59}, publisher = {Copernicus Publications}, abstract = {The EO4GEO Body of Knowledge (BoK) forms a structure of concepts and relationships between them, describing the domain of Earth Observation and Geo-Information (EO/GI). Each concept carries a short description, a list of key literature references and a set of associated skills which are used for job profiling and curriculum building. As the EO/GI domain is evolving continuously, the BoK needs regular updates with new concepts embodying new trends, and deprecating concepts which are not relevant anymore. This paper presents the inclusion of BoK concepts related to Artificial Intelligence. This broad field of knowledge has links to several applications in EO/GI. Its connection to concepts, already existing in the BoK, needs special attention. To perform a clean and structural integration of the cross-cutting domain of AI, first a separate cluster of AI concepts was created, which was then merged with the existing BoK. The paper provides examples of this integration with specific concepts and examples of training resources in which AI-related concepts are used. Although the presented structure already provides a good starting point, the positioning of AI within the EO/GI-focussed BoK needs to be further enhanced with the help of expert calls as part of the BoK update cycle.}, keywords = {Body of Knowledge, education, EO4GEO, GIScience}, pubstate = {published}, tppubtype = {inproceedings} } The EO4GEO Body of Knowledge (BoK) forms a structure of concepts and relationships between them, describing the domain of Earth Observation and Geo-Information (EO/GI). Each concept carries a short description, a list of key literature references and a set of associated skills which are used for job profiling and curriculum building. As the EO/GI domain is evolving continuously, the BoK needs regular updates with new concepts embodying new trends, and deprecating concepts which are not relevant anymore. This paper presents the inclusion of BoK concepts related to Artificial Intelligence. This broad field of knowledge has links to several applications in EO/GI. Its connection to concepts, already existing in the BoK, needs special attention. To perform a clean and structural integration of the cross-cutting domain of AI, first a separate cluster of AI concepts was created, which was then merged with the existing BoK. The paper provides examples of this integration with specific concepts and examples of training resources in which AI-related concepts are used. Although the presented structure already provides a good starting point, the positioning of AI within the EO/GI-focussed BoK needs to be further enhanced with the help of expert calls as part of the BoK update cycle. |
Iskandaryan, Ditsuhi; Ramos-Romero, Francisco; Trilles-Oliver, Sergio Bidirectional convolutional LSTM for the prediction of nitrogen dioxide in the city of Madrid Journal Article PLOS ONE, 17 (6), pp. e0269295, 2022, ISSN: 932-6203. Abstract | Links | BibTeX | Tags: air quality prediction, machine learning @article{Iskandaryan2022b, title = {Bidirectional convolutional LSTM for the prediction of nitrogen dioxide in the city of Madrid}, author = {Ditsuhi Iskandaryan and Francisco Ramos-Romero and Sergio Trilles-Oliver}, doi = {https://doi.org/10.1371/journal.pone.0269295}, issn = {932-6203}, year = {2022}, date = {2022-05-01}, journal = {PLOS ONE}, volume = {17}, number = {6}, pages = {e0269295}, abstract = {Nitrogen dioxide is one of the pollutants with the most significant health effects. Advanced information on its concentration in the air can help to monitor and control further consequences more effectively, while also making it easier to apply preventive and mitigating measures. Machine learning technologies with available methods and capabilities, combined with the geospatial dimension, can perform predictive analyses with higher accuracy and, as a result, can serve as a supportive tool for productive management. One of the most advanced machine learning algorithms, Bidirectional convolutional LSTM, is being used in ongoing work to predict the concentration of nitrogen dioxide. The model has been validated to perform more accurate spatiotemporal analysis based on the integration of temporal and geospatial factors. The analysis was carried out according to two scenarios developed on the basis of selected features using data from the city of Madrid for the periods January-June 2019 and January-June 2020. Evaluation of the model’s performance was conducted using the Root Mean Square Error and the Mean Absolute Error which emphasises the superiority of the proposed model over the reference models. In addition, the significance of a feature selection technique providing improved accuracy was underlined. In terms of execution time, due to the complexity of the Bidirectional convolutional LSTM architecture, convergence and generalisation of the data took longer, resulting in the superiority of the reference models.}, keywords = {air quality prediction, machine learning}, pubstate = {published}, tppubtype = {article} } Nitrogen dioxide is one of the pollutants with the most significant health effects. Advanced information on its concentration in the air can help to monitor and control further consequences more effectively, while also making it easier to apply preventive and mitigating measures. Machine learning technologies with available methods and capabilities, combined with the geospatial dimension, can perform predictive analyses with higher accuracy and, as a result, can serve as a supportive tool for productive management. One of the most advanced machine learning algorithms, Bidirectional convolutional LSTM, is being used in ongoing work to predict the concentration of nitrogen dioxide. The model has been validated to perform more accurate spatiotemporal analysis based on the integration of temporal and geospatial factors. The analysis was carried out according to two scenarios developed on the basis of selected features using data from the city of Madrid for the periods January-June 2019 and January-June 2020. Evaluation of the model’s performance was conducted using the Root Mean Square Error and the Mean Absolute Error which emphasises the superiority of the proposed model over the reference models. In addition, the significance of a feature selection technique providing improved accuracy was underlined. In terms of execution time, due to the complexity of the Bidirectional convolutional LSTM architecture, convergence and generalisation of the data took longer, resulting in the superiority of the reference models. |
Trilles-Oliver, Sergio; Monfort-Muriach, Aida; Gómez-Cambronero, Águeda; Granell-Canut, Carlos Sucre4Stem: Collaborative Projects Based on IoT Devices for Students in Secondary and Pre-University Education Journal Article IEEE Revista Iberoamericana de Tecnologias del Aprendizaje, 17 (2), pp. 150-159, 2022, ISSN: 1932-8540. Abstract | Links | BibTeX | Tags: computer science, education, SUCRE, sucre4stem @article{Trilles2022a, title = {Sucre4Stem: Collaborative Projects Based on IoT Devices for Students in Secondary and Pre-University Education}, author = {Sergio Trilles-Oliver and Aida Monfort-Muriach and Águeda Gómez-Cambronero and Carlos Granell-Canut}, doi = {https://doi.org/10.1109/RITA.2022.3166854}, issn = {1932-8540}, year = {2022}, date = {2022-05-01}, journal = {IEEE Revista Iberoamericana de Tecnologias del Aprendizaje}, volume = {17}, number = {2}, pages = {150-159}, abstract = {This paper describes a new technological evolution of the Sucre project, which aims to foster a vocation for science and develop computational thinking and programming skills in pre-university students. This improved version is called Sucre4Stem and has been designed from the Internet of Things perspective. At a technological level, we differentiate two main tools, SucreCore and SucreCode . SucreCore provides a new, more compact design, encapsulates an advanced microcontroller and supports wireless connectivity with the ability to create online variables and functions. SucreCode , the block-based visual programming tool, has a revamped interface and allows wireless communication with SucreCore . At the pedagogical level, Sucre4Stem makes it easier to implement new group dynamics and to create novel types of collaborative projects between groups of students. In this article, we also explore how these collaborative projects can be carried out by taking advantage of the different types of communications between SucreCore and the server-side platform using shared online variables and functions.}, keywords = {computer science, education, SUCRE, sucre4stem}, pubstate = {published}, tppubtype = {article} } This paper describes a new technological evolution of the Sucre project, which aims to foster a vocation for science and develop computational thinking and programming skills in pre-university students. This improved version is called Sucre4Stem and has been designed from the Internet of Things perspective. At a technological level, we differentiate two main tools, SucreCore and SucreCode . SucreCore provides a new, more compact design, encapsulates an advanced microcontroller and supports wireless connectivity with the ability to create online variables and functions. SucreCode , the block-based visual programming tool, has a revamped interface and allows wireless communication with SucreCore . At the pedagogical level, Sucre4Stem makes it easier to implement new group dynamics and to create novel types of collaborative projects between groups of students. In this article, we also explore how these collaborative projects can be carried out by taking advantage of the different types of communications between SucreCore and the server-side platform using shared online variables and functions. |
Iskandaryan, Ditsuhi; Ramos-Romero, Francisco; Trilles-Oliver, Sergio Application of deep learning and machine learning in air quality modeling Book Chapter Marques, Gonçalo; Ighalo, Joshua (Ed.): pp. 11-23, Elsevier, 2022, ISBN: 9780323855976. Links | BibTeX | Tags: air quality prediction, deep learning, machine learning @inbook{Iskandaryan2022a, title = {Application of deep learning and machine learning in air quality modeling}, author = {Ditsuhi Iskandaryan and Francisco Ramos-Romero and Sergio Trilles-Oliver}, editor = {Gonçalo Marques and Joshua Ighalo }, doi = {https://doi.org/10.1016/B978-0-323-85597-6.00018-5}, isbn = {9780323855976}, year = {2022}, date = {2022-03-30}, pages = {11-23}, publisher = {Elsevier}, keywords = {air quality prediction, deep learning, machine learning}, pubstate = {published}, tppubtype = {inbook} } |
Díaz-Sanahuja, Laura; Miralles, Ignacio; Granell-Canut, Carlos; Mira, Adriana; González-Pérez, Alberto; Casteleyn, Sven; García-Palacios, Azucena; Bretón-López, Juana Client’s Experiences Using a Location-Based Technology ICT System during Gambling Treatments’ Crucial Components: A Qualitative Study Journal Article International Journal of Environmental Research and Public Health, 19 (7), pp. 3769, 2022, ISSN: 1660-4601. Abstract | Links | BibTeX | Tags: gambling, geolocation, mental health, symptoms @article{diazsanchez2022a, title = {Client’s Experiences Using a Location-Based Technology ICT System during Gambling Treatments’ Crucial Components: A Qualitative Study}, author = {Laura Díaz-Sanahuja and Ignacio Miralles and Carlos Granell-Canut and Adriana Mira and Alberto González-Pérez and Sven Casteleyn and Azucena García-Palacios and Juana Bretón-López }, doi = {https://doi.org/10.3390/ijerph19073769}, issn = {1660-4601}, year = {2022}, date = {2022-03-22}, journal = {International Journal of Environmental Research and Public Health}, volume = {19}, number = {7}, pages = {3769}, abstract = {Cognitive Behavioral Therapy is the treatment of choice for Gambling Disorder (GD), with stimulus control (SC) and exposure with response prevention (ERP) being its two core components. Despite their efficacy, SC and ERP are not easy to deliver, so it is important to explore new ways to enhance patient compliance regarding SC and ERP. The aim of this study is to describe and assess the opinion of two patients diagnosed with problem gambling and GD that used the Symptoms app, a location-based ICT system, during SC and ERP. A consensual qualitative research study was conducted. We used a semi-structured interview, developed ad-hoc based on the Expectation and Satisfaction Scale and System Usability Scale. A total of 20 categories were identified within six domains: usefulness, improvements, recommendation to other people, safety, usability, and opinion regarding the use of the app after completing the intervention. The patients considered the app to be useful during the SC and ERP components and emphasized that feeling observed and supported at any given time helped them avoid lapses. This work can offer a starting point that opens up new research paths regarding psychological interventions for gambling disorder, such as assessing whether location-based ICT tools enhance commitment rates.}, keywords = {gambling, geolocation, mental health, symptoms}, pubstate = {published}, tppubtype = {article} } Cognitive Behavioral Therapy is the treatment of choice for Gambling Disorder (GD), with stimulus control (SC) and exposure with response prevention (ERP) being its two core components. Despite their efficacy, SC and ERP are not easy to deliver, so it is important to explore new ways to enhance patient compliance regarding SC and ERP. The aim of this study is to describe and assess the opinion of two patients diagnosed with problem gambling and GD that used the Symptoms app, a location-based ICT system, during SC and ERP. A consensual qualitative research study was conducted. We used a semi-structured interview, developed ad-hoc based on the Expectation and Satisfaction Scale and System Usability Scale. A total of 20 categories were identified within six domains: usefulness, improvements, recommendation to other people, safety, usability, and opinion regarding the use of the app after completing the intervention. The patients considered the app to be useful during the SC and ERP components and emphasized that feeling observed and supported at any given time helped them avoid lapses. This work can offer a starting point that opens up new research paths regarding psychological interventions for gambling disorder, such as assessing whether location-based ICT tools enhance commitment rates. |
Renaudin, Valerie; Potorti, Francesco; Torres-Sospedra, Joaquín; Knauth, Stefan; O’keefe, Kyle; Park, Chan Gook; Sugimoto, Masanori; Wei, Dongyan; Nurmi, Jari Guest Editorial Special Issue on Advanced Sensors and Sensing Technologies for Indoor Positioning and Navigation Journal Article IEEE Sensors Journal, 22 (6), pp. 4754-4754, 2022, ISBN: 1558-1748. Abstract | Links | BibTeX | Tags: Indoor localization @article{Renaudin2022a, title = {Guest Editorial Special Issue on Advanced Sensors and Sensing Technologies for Indoor Positioning and Navigation}, author = {Valerie Renaudin and Francesco Potorti and Joaquín Torres-Sospedra and Stefan Knauth and Kyle O’keefe and Chan Gook Park and Masanori Sugimoto and Dongyan Wei and Jari Nurmi}, doi = {https://doi.org/10.1109/JSEN.2022.3150130}, isbn = {1558-1748}, year = {2022}, date = {2022-03-22}, journal = {IEEE Sensors Journal}, volume = {22}, number = {6}, pages = {4754-4754}, abstract = {Indoor localization is a growing research field and interest is expanding in many application fields, including services, measurement, mapping, security, and standardization. The quest for appropriate tracking technologies for COVID-19 pandemic control has shown us the importance of identifying the sensors data and processing that are suitable, accurate, reliable, and respectful of privacy. A prominent area is, therefore, that of sensors, where both improved hardware solutions and more powerful data analysis are required.}, keywords = {Indoor localization}, pubstate = {published}, tppubtype = {article} } Indoor localization is a growing research field and interest is expanding in many application fields, including services, measurement, mapping, security, and standardization. The quest for appropriate tracking technologies for COVID-19 pandemic control has shown us the importance of identifying the sensors data and processing that are suitable, accurate, reliable, and respectful of privacy. A prominent area is, therefore, that of sensors, where both improved hardware solutions and more powerful data analysis are required. |
Potorti, Francesco; Torres-Sospedra, Joaquín; Quezada-Gaibor, Darwin; Jiménez, Antonio Ramón; Seco, Fernando; Pérez-Navarro, Antoni; Ortiz, Miguel; Zhu, Ni; Renaudin, Valerie; Ichikari, Ryosuke; Shimomura, Ryo; Ohta, Nozomu; Nagae, Satsuki; Kurata, Takeshi; Wei, Dongyan; Ji, Xinchun; Zhang, Wenchao; Kram, Sebastian; Stahlke, Maximilian; Mutschler, Christopher; Crivello, Antonino; Barsocchi, Paolo; Girolami, Michele; Palumbo, Filippo; Chen, Ruizhi; Wu, Yuan; Li, Wei; Yu, Yue; Xu, Shihao; Huang, Lixiong; Liu, Tao; Kuang, Jian; Niu, Xiaoji; Yoshida, Takuto; Nagata, Yoshiteru; Fukushima, Yuto; Fukatani, Nobuya; Hayashida, Nozomi; Asai, Yusuke; Urano, Kenta; Ge, Wenfei; Lee, Nien-Ting; Fang, Shih-Hau; Jie, You-Cheng; Young, Shawn-Rong; Chien, Ying-Ren; Yu, Chih-Chieh; Ma, Chengqi; Wu, Bang; Zhang, Wei; Wang, Yankun; Fan, Yonglei; Poslad, Stefan; Selviah, David R; Wang, Weixi; Yuan, Hong; Yonamoto, Yoshitomo; Yamaguchi, Masahiro; Kaichi, Tomoya; Zhou, Baoding; Liu, Xu; Gu, Zhining; Yang, Chengjing; Wu, Zhiqian; Xie, Doudou; Huang, Can; Zheng, Lingxiang; Peng, Ao; Jin, Ge; Wang, Qu; Xiong, Haiyong Luo Hao; Bao, Linfeng; Zhang, Pushuo; Zhao, Fang; Yuj, Chia-An; Hung, Chun-Hao; Antsfeld, Leonid; Chidlovskii, Boris; Jiang, Haitao; Xia, Ming; Yan, Dayu; Li, Yuhang; Dong, Yitong; Silva, Ivo; Pendão, Cristiano; Meneses, Filipe; Nicolau, Maria João; Costa, António; Moreira, Adriano; Cock, Cedric De; Plets, David; Opiela, Miroslav; Dzama, Jakub; Zhang, Liqiang; Li, Hu; Chen, Boxuan; Liu, Yu; Yean, Seanglidet; Lim, Bo Zhi; Teo, Wei Jie; Leep, Bu Sung; Oh, Hong Lye Off-Line Evaluation of Indoor Positioning Systems in Different Scenarios: The Experiences From IPIN 2020 Competition Journal Article IEEE Sensors Journal, 22 (6), pp. 5011-5054, 2022, ISSN: 1558-1748. Abstract | Links | BibTeX | Tags: Indoor positioning @article{Potorsi2022a, title = {Off-Line Evaluation of Indoor Positioning Systems in Different Scenarios: The Experiences From IPIN 2020 Competition}, author = {Francesco Potorti and Joaquín Torres-Sospedra and Darwin Quezada-Gaibor and Antonio Ramón Jiménez and Fernando Seco and Antoni Pérez-Navarro and Miguel Ortiz and Ni Zhu and Valerie Renaudin and Ryosuke Ichikari and Ryo Shimomura and Nozomu Ohta and Satsuki Nagae and Takeshi Kurata and Dongyan Wei and Xinchun Ji and Wenchao Zhang and Sebastian Kram and Maximilian Stahlke and Christopher Mutschler and Antonino Crivello and Paolo Barsocchi and Michele Girolami and Filippo Palumbo and Ruizhi Chen and Yuan Wu and Wei Li and Yue Yu and Shihao Xu and Lixiong Huang and Tao Liu and Jian Kuang and Xiaoji Niu and Takuto Yoshida and Yoshiteru Nagata and Yuto Fukushima and Nobuya Fukatani and Nozomi Hayashida and Yusuke Asai and Kenta Urano and Wenfei Ge and Nien-Ting Lee and Shih-Hau Fang and You-Cheng Jie and Shawn-Rong Young and Ying-Ren Chien and Chih-Chieh Yu and Chengqi Ma and Bang Wu and Wei Zhang and Yankun Wang and Yonglei Fan and Stefan Poslad and David R. Selviah and Weixi Wang and Hong Yuan and Yoshitomo Yonamoto and Masahiro Yamaguchi and Tomoya Kaichi and Baoding Zhou and Xu Liu and Zhining Gu and Chengjing Yang and Zhiqian Wu and Doudou Xie and Can Huang and Lingxiang Zheng and Ao Peng and Ge Jin and Qu Wang and Haiyong Luo Hao Xiong and Linfeng Bao and Pushuo Zhang and Fang Zhao and Chia-An Yuj and Chun-Hao Hung and Leonid Antsfeld and Boris Chidlovskii and Haitao Jiang and Ming Xia and Dayu Yan and Yuhang Li and Yitong Dong and Ivo Silva and Cristiano Pendão and Filipe Meneses and Maria João Nicolau and António Costa and Adriano Moreira and Cedric De Cock and David Plets and Miroslav Opiela and Jakub Dzama and Liqiang Zhang and Hu Li and Boxuan Chen and Yu Liu and Seanglidet Yean and Bo Zhi Lim and Wei Jie Teo and Bu Sung Leep and Hong Lye Oh}, doi = {https://doi.org/10.1109/JSEN.2021.3083149}, issn = {1558-1748}, year = {2022}, date = {2022-03-22}, journal = {IEEE Sensors Journal}, volume = {22}, number = {6}, pages = {5011-5054}, abstract = {Every year, for ten years now, the IPIN competition has aimed at evaluating real-world indoor localisation systems by testing them in a realistic environment, with realistic movement, using the EvAAL framework. The competition provided a unique overview of the state-of-the-art of systems, technologies, and methods for indoor positioning and navigation purposes. Through fair comparison of the performance achieved by each system, the competition was able to identify the most promising approaches and to pinpoint the most critical working conditions. In 2020, the competition included 5 diverse off-site off-site Tracks, each resembling real use cases and challenges for indoor positioning. The results in terms of participation and accuracy of the proposed systems have been encouraging. The best performing competitors obtained a third quartile of error of 1 m for the Smartphone Track and 0.5 m for the Foot-mounted IMU Track. While not running on physical systems, but only as algorithms, these results represent impressive achievements.}, keywords = {Indoor positioning}, pubstate = {published}, tppubtype = {article} } Every year, for ten years now, the IPIN competition has aimed at evaluating real-world indoor localisation systems by testing them in a realistic environment, with realistic movement, using the EvAAL framework. The competition provided a unique overview of the state-of-the-art of systems, technologies, and methods for indoor positioning and navigation purposes. Through fair comparison of the performance achieved by each system, the competition was able to identify the most promising approaches and to pinpoint the most critical working conditions. In 2020, the competition included 5 diverse off-site off-site Tracks, each resembling real use cases and challenges for indoor positioning. The results in terms of participation and accuracy of the proposed systems have been encouraging. The best performing competitors obtained a third quartile of error of 1 m for the Smartphone Track and 0.5 m for the Foot-mounted IMU Track. While not running on physical systems, but only as algorithms, these results represent impressive achievements. |
Chukhno, Nadezhda; Trilles-Oliver, Sergio; Torres-Sospedra, Joaquín; Iera, Antonio; Araniti, Giuseppe D2D-Based Cooperative Positioning Paradigm for Future Wireless Systems: A Survey Journal Article IEEE Sensors Journal, 22 (6), pp. 5101-5112, 2022, ISSN: 1558-1748. Abstract | Links | BibTeX | Tags: cellular positioning, device-to-device communication @article{Chukhno2022a, title = {D2D-Based Cooperative Positioning Paradigm for Future Wireless Systems: A Survey}, author = {Nadezhda Chukhno and Sergio Trilles-Oliver and Joaquín Torres-Sospedra and Antonio Iera and Giuseppe Araniti}, doi = {10.1109/JSEN.2021.3096730}, issn = {1558-1748}, year = {2022}, date = {2022-03-15}, journal = {IEEE Sensors Journal}, volume = {22}, number = {6}, pages = {5101-5112}, abstract = {Emerging communication network applications require a location accuracy of less than 1 m in more than 95% of the service area. For this purpose, 5G New Radio (NR) technology is designed to facilitate high-accuracy continuous localization. In 5G systems, the existence of high-density small cells and the possibility of the device-to-device (D2D) communication between mobile terminals paves the way for cooperative positioning applications. From the standardization perspective, D2D technology is already under consideration (5G NR Release 16) for ultra-dense networks enabling cooperative positioning and is expected to achieve the ubiquitous positioning of below one-meter accuracy, thereby fulfilling the 5G requirements. In this survey, the strengths and weaknesses of D2D as an enabling technology for cooperative cellular positioning are analyzed (including two D2D approaches to perform cooperative positioning); lessons learned and open issues are highlighted to serve as guidelines for future research.}, keywords = {cellular positioning, device-to-device communication}, pubstate = {published}, tppubtype = {article} } Emerging communication network applications require a location accuracy of less than 1 m in more than 95% of the service area. For this purpose, 5G New Radio (NR) technology is designed to facilitate high-accuracy continuous localization. In 5G systems, the existence of high-density small cells and the possibility of the device-to-device (D2D) communication between mobile terminals paves the way for cooperative positioning applications. From the standardization perspective, D2D technology is already under consideration (5G NR Release 16) for ultra-dense networks enabling cooperative positioning and is expected to achieve the ubiquitous positioning of below one-meter accuracy, thereby fulfilling the 5G requirements. In this survey, the strengths and weaknesses of D2D as an enabling technology for cooperative cellular positioning are analyzed (including two D2D approaches to perform cooperative positioning); lessons learned and open issues are highlighted to serve as guidelines for future research. |
Mendoza-Silva, Germán Martin; Costa, Ana Cristina; Torres-Sospedra, Joaquín; Painho, Marco; Huerta-Guijarro, Joaquín Environment-Aware Regression for Indoor Localization Based on WiFi Fingerprinting Journal Article IEEE Sensors Journal, 22 (6), pp. 4978-4988, 2022, ISSN: 1558-1748. Abstract | Links | BibTeX | Tags: Indoor localization, Wi-Fi fingerprint @article{Mendoza-Silva2022a, title = {Environment-Aware Regression for Indoor Localization Based on WiFi Fingerprinting}, author = {Germán Martin Mendoza-Silva and Ana Cristina Costa and Joaquín Torres-Sospedra and Marco Painho and Joaquín Huerta-Guijarro}, doi = {https://doi.org/10.1109/JSEN.2021.3073878}, issn = {1558-1748}, year = {2022}, date = {2022-03-15}, journal = {IEEE Sensors Journal}, volume = {22}, number = {6}, pages = {4978-4988}, abstract = {Data enrichment through interpolation or regression is a common approach to deal with sample collection for Indoor Localization with WiFi fingerprinting. This paper provides guidelines on where to collect WiFi samples and proposes a new model for received signal strength regression. The new model creates vectors that describe the presence of obstacles between an access point and the collected samples. The vectors, the distance between the access point and the positions of the samples, and the collected, are used to train a Support Vector Regression. The experiments included some relevant analyses and showed that the proposed model improves received signal strength regression in terms of regression residuals and positioning accuracy.}, keywords = {Indoor localization, Wi-Fi fingerprint}, pubstate = {published}, tppubtype = {article} } Data enrichment through interpolation or regression is a common approach to deal with sample collection for Indoor Localization with WiFi fingerprinting. This paper provides guidelines on where to collect WiFi samples and proposes a new model for received signal strength regression. The new model creates vectors that describe the presence of obstacles between an access point and the collected samples. The vectors, the distance between the access point and the positions of the samples, and the collected, are used to train a Support Vector Regression. The experiments included some relevant analyses and showed that the proposed model improves received signal strength regression in terms of regression residuals and positioning accuracy. |
López-Vázquez, Carlos; Gonzalez-Campos, María Ester; Bernabé-Poveda, Miguel A; Moctezuma, Daniela; Hochsztain, Esther; Barrera, María A; Granell-Canut, Carlos; León-Pazmiño, María F; López-Ramírez, Pablo; adn Moya-Honduvilla, Villie Morocho-Zurita Javier; Manrique-Sancho, María T; Montiveros, Marcela E; Narváez-Benalcázar, Rocío; de Pérez-Alcázar, José Jesús; Resnichenko, Yuri; Seco., Diego Building a Gold Standard Dataset to Identify Articles About Geographic Information Science Journal Article IEEE Access, 10 , pp. 19926-19936, 2022, ISSN: 2169-3536. Abstract | Links | BibTeX | Tags: Geographic Information Systems (GIS), GIScience @article{LopezVazquez2022a, title = {Building a Gold Standard Dataset to Identify Articles About Geographic Information Science}, author = {Carlos López-Vázquez and María Ester Gonzalez-Campos and Miguel A. Bernabé-Poveda and Daniela Moctezuma and Esther Hochsztain and María A. Barrera and Carlos Granell-Canut and María F. León-Pazmiño and Pablo López-Ramírez and Villie Morocho-Zurita adn Javier Moya-Honduvilla and María T. Manrique-Sancho and Marcela E. Montiveros and Rocío Narváez-Benalcázar and José de Jesús Pérez-Alcázar and Yuri Resnichenko and Diego Seco. }, doi = {https://doi.org/10.1109/ACCESS.2022.3150869}, issn = {2169-3536}, year = {2022}, date = {2022-02-10}, journal = {IEEE Access}, volume = {10}, pages = {19926-19936}, abstract = {To know the overall regional or international scientific production is of vital importance to many areas of knowledge. Nevertheless, in interdisciplinary areas such as Geographic Information Science (GISc) it is not enough to just count papers published in specific journals. Most of them, as is the case of the International Journal of Remote Sensing (IJRS), welcome GISc papers but are not exclusive to that area so the production assignable to authors in the region must consider not only affiliation but also whether or not each paper falls into the theme of GISc. IJRS publishes far more papers than any other GISc journal, so it is important to assess quantitatively how many of them are of GISc. In this work, a representative sample of IJRS articles published over a period of almost 30 years was analyzed using a specific GISc definition. With these data, a manual classification methodology through a set of experts was carried out, and a dataset was built, analyzed, and statistically tested. As a result we estimate that between 47 and 76% of the IJRS articles can be considered from GISc, with a confidence level of 95%. Aside from the primary goal, this set could be used as a gold standard for future classification tasks. It constitutes the first GISc dataset of this kind, that may be used to train artificial intelligence systems capable of performing the same classification automatically and in a massive way. A similar procedure could be applied to other interdisciplinary fields of knowledge as well.}, keywords = {Geographic Information Systems (GIS), GIScience}, pubstate = {published}, tppubtype = {article} } To know the overall regional or international scientific production is of vital importance to many areas of knowledge. Nevertheless, in interdisciplinary areas such as Geographic Information Science (GISc) it is not enough to just count papers published in specific journals. Most of them, as is the case of the International Journal of Remote Sensing (IJRS), welcome GISc papers but are not exclusive to that area so the production assignable to authors in the region must consider not only affiliation but also whether or not each paper falls into the theme of GISc. IJRS publishes far more papers than any other GISc journal, so it is important to assess quantitatively how many of them are of GISc. In this work, a representative sample of IJRS articles published over a period of almost 30 years was analyzed using a specific GISc definition. With these data, a manual classification methodology through a set of experts was carried out, and a dataset was built, analyzed, and statistically tested. As a result we estimate that between 47 and 76% of the IJRS articles can be considered from GISc, with a confidence level of 95%. Aside from the primary goal, this set could be used as a gold standard for future classification tasks. It constitutes the first GISc dataset of this kind, that may be used to train artificial intelligence systems capable of performing the same classification automatically and in a massive way. A similar procedure could be applied to other interdisciplinary fields of knowledge as well. |
González-Pérez, Alberto; Matey-Sanz, Miguel; Granell-Canut, Carlos; Casteleyn, Sven Using Mobile Devices as Scientific Measurements Instruments: Reliable Android Task Scheduling Journal Article Pervasive and Mobile Computing, 81 (101550), 2022, ISBN: 1574-1192. Abstract | Links | BibTeX | Tags: Mobile apps, mobile computing, symptoms @article{Gonzalez-Perez2022a, title = {Using Mobile Devices as Scientific Measurements Instruments: Reliable Android Task Scheduling}, author = {Alberto González-Pérez and Miguel Matey-Sanz and Carlos Granell-Canut and Sven Casteleyn}, doi = {https://doi.org/10.1016/j.pmcj.2022.101550}, isbn = {1574-1192}, year = {2022}, date = {2022-02-01}, journal = {Pervasive and Mobile Computing}, volume = {81}, number = {101550}, abstract = {In various usage scenarios, smartphones are used as measuring instruments to systematically and unobtrusively collect data measurements (e.g., sensor data, user activity, phone usage data). Unfortunately, in the race towards extending battery life and improving privacy, mobile phone manufacturers are gradually restricting developers in (frequently) scheduling background (sensing) tasks and impede the exact scheduling of their execution time (i.e., Android’s “best effort” approach). This evolution hampers successful deployment of smartphones in sensing applications in scientific contexts, with unreliable and incomplete sampling rates frequently reported in literature. In this article, we discuss the ins and outs of Android’s background tasks scheduling mechanism, and formulate guidelines for developers to successfully implement reliable task scheduling. Implementing these guidelines, we present a software library, agnostic from the underlying Android scheduling mechanisms and restrictions, that allows Android developers to reliably schedule tasks with a maximum sampling rate of one minute. Our evaluation demonstrates the use and versatility of our task scheduler, and experimentally confirms its reliability and acceptable energy usage.}, keywords = {Mobile apps, mobile computing, symptoms}, pubstate = {published}, tppubtype = {article} } In various usage scenarios, smartphones are used as measuring instruments to systematically and unobtrusively collect data measurements (e.g., sensor data, user activity, phone usage data). Unfortunately, in the race towards extending battery life and improving privacy, mobile phone manufacturers are gradually restricting developers in (frequently) scheduling background (sensing) tasks and impede the exact scheduling of their execution time (i.e., Android’s “best effort” approach). This evolution hampers successful deployment of smartphones in sensing applications in scientific contexts, with unreliable and incomplete sampling rates frequently reported in literature. In this article, we discuss the ins and outs of Android’s background tasks scheduling mechanism, and formulate guidelines for developers to successfully implement reliable task scheduling. Implementing these guidelines, we present a software library, agnostic from the underlying Android scheduling mechanisms and restrictions, that allows Android developers to reliably schedule tasks with a maximum sampling rate of one minute. Our evaluation demonstrates the use and versatility of our task scheduler, and experimentally confirms its reliability and acceptable energy usage. |
González-Pérez, Alberto; Casteleyn, Sven Hypnos: A Hardware and Software Toolkit for Energy-Aware Sensing in Low-Cost IoT Nodes Journal Article IEEE Internet of Things Journal, 9 (15), pp. 13524-13541, 2022, ISBN: 2327-4662. Abstract | Links | BibTeX | Tags: Internet of things @article{Gonzalez-Perez2022b, title = {Hypnos: A Hardware and Software Toolkit for Energy-Aware Sensing in Low-Cost IoT Nodes}, author = {Alberto González-Pérez and Sven Casteleyn}, doi = {https://doi.org/10.1109/JIOT.2022.3145338}, isbn = {2327-4662}, year = {2022}, date = {2022-01-21}, journal = {IEEE Internet of Things Journal}, volume = {9}, number = {15}, pages = {13524-13541}, abstract = {Through the Internet of Things (IoT), autonomous sensing devices can be deployed to regularly capture environmental and other sensor measurements for a variety of usage scenarios. However, for the market segment of stand-alone, self-sustaining small IoT nodes, long-term deployment remains problematic due to the energy-constrained nature of these devices, requiring frequent maintenance. This article introduces Hypnos, an open hardware and software toolkit that aims to balance energy intake and usage through the adaptive sensing rate for low-cost Internet-connected IoT nodes. We describe the hardware architecture of the IoT node, an open hardware board based on the Arduino Uno form-factor packing the energy measurement circuitry, and the associated opensource software library, which interfaces with the sensing node’s microcontroller and provides access to the low-level energy measurements. Hypnos comes equipped with a built-in, configurable, modified sigmoid function to regulate duty-cycle frequency based on energy intake and usage, yet developers may also plug in their custom duty/sleep balancing function. An experiment was set up, whereby two identical boards ran for two months: one with the Hypnos software framework and built-in energy-balancing function to regulate sensing rate and the other with fixed sensing rate. The experiment showed that Hypnos is able to successfully balance energy usage and sensing frequency within configurable energy ranges. Hereby, it increases the reliability by avoiding the complete shutdown, while, at the same time, optimizing performance in terms of the average amount of sensor measurements.}, keywords = {Internet of things}, pubstate = {published}, tppubtype = {article} } Through the Internet of Things (IoT), autonomous sensing devices can be deployed to regularly capture environmental and other sensor measurements for a variety of usage scenarios. However, for the market segment of stand-alone, self-sustaining small IoT nodes, long-term deployment remains problematic due to the energy-constrained nature of these devices, requiring frequent maintenance. This article introduces Hypnos, an open hardware and software toolkit that aims to balance energy intake and usage through the adaptive sensing rate for low-cost Internet-connected IoT nodes. We describe the hardware architecture of the IoT node, an open hardware board based on the Arduino Uno form-factor packing the energy measurement circuitry, and the associated opensource software library, which interfaces with the sensing node’s microcontroller and provides access to the low-level energy measurements. Hypnos comes equipped with a built-in, configurable, modified sigmoid function to regulate duty-cycle frequency based on energy intake and usage, yet developers may also plug in their custom duty/sleep balancing function. An experiment was set up, whereby two identical boards ran for two months: one with the Hypnos software framework and built-in energy-balancing function to regulate sensing rate and the other with fixed sensing rate. The experiment showed that Hypnos is able to successfully balance energy usage and sensing frequency within configurable energy ranges. Hereby, it increases the reliability by avoiding the complete shutdown, while, at the same time, optimizing performance in terms of the average amount of sensor measurements. |
Quezada-Gaibor, Darwin; Torres-Sospedra, Joaquín; Nurmi, Jari; Koucheryavy, Yevgeni; Huerta-Guijarro, Joaquín Cloud Platforms for Context-Adaptive Positioning and Localisation in GNSS-Denied Scenarios—A Systematic Review Journal Article Sensors, 22 (1), pp. 110, 2022, ISSN: 1424-8220. Abstract | Links | BibTeX | Tags: Cloud computing, Indoor positioning @article{Quezada2022a, title = {Cloud Platforms for Context-Adaptive Positioning and Localisation in GNSS-Denied Scenarios—A Systematic Review}, author = {Darwin Quezada-Gaibor and Joaquín Torres-Sospedra and Jari Nurmi and Yevgeni Koucheryavy and Joaquín Huerta-Guijarro}, doi = {https://doi.org/10.3390/s22010110}, issn = {1424-8220}, year = {2022}, date = {2022-01-15}, journal = {Sensors}, volume = {22}, number = {1}, pages = {110}, abstract = {Cloud Computing and Cloud Platforms have become an essential resource for businesses, due to their advanced capabilities, performance, and functionalities. Data redundancy, scalability, and security, are among the key features offered by cloud platforms. Location-Based Services (LBS) often exploit cloud platforms to host positioning and localisation systems. This paper introduces a systematic review of current positioning platforms for GNSS-denied scenarios. We have undertaken a comprehensive analysis of each component of the positioning and localisation systems, including techniques, protocols, standards, and cloud services used in the state-of-the-art deployments. Furthermore, this paper identifies the limitations of existing solutions, outlining shortcomings in areas that are rarely subjected to scrutiny in existing reviews of indoor positioning, such as computing paradigms, privacy, and fault tolerance. We then examine contributions in the areas of efficient computation, interoperability, positioning, and localisation. Finally, we provide a brief discussion concerning the challenges for cloud platforms based on GNSS-denied scenarios.}, keywords = {Cloud computing, Indoor positioning}, pubstate = {published}, tppubtype = {article} } Cloud Computing and Cloud Platforms have become an essential resource for businesses, due to their advanced capabilities, performance, and functionalities. Data redundancy, scalability, and security, are among the key features offered by cloud platforms. Location-Based Services (LBS) often exploit cloud platforms to host positioning and localisation systems. This paper introduces a systematic review of current positioning platforms for GNSS-denied scenarios. We have undertaken a comprehensive analysis of each component of the positioning and localisation systems, including techniques, protocols, standards, and cloud services used in the state-of-the-art deployments. Furthermore, this paper identifies the limitations of existing solutions, outlining shortcomings in areas that are rarely subjected to scrutiny in existing reviews of indoor positioning, such as computing paradigms, privacy, and fault tolerance. We then examine contributions in the areas of efficient computation, interoperability, positioning, and localisation. Finally, we provide a brief discussion concerning the challenges for cloud platforms based on GNSS-denied scenarios. |
Granell-Canut, Carlos; Mooney, Peter; Jirka, Simon; Rieke, Matthes; Ostermann, Frank O; Broecke, Just Van Den; Sarretta, Alessandro; Verhulst, Stefaan; Dencik, Lina; Oost, Hillen; Micheli, Marina; Minghini, Marco; Kotsev, Alexander; Schade, Sven Emerging approaches for data-driven innovation in Europe Book Publications Office of the European Union, Luxemburg, 2022, ISBN: 978-92-76-46937-7. Abstract | Links | BibTeX | Tags: Data science, Data spaces, Europe, Geographic data @book{Granell2022a, title = {Emerging approaches for data-driven innovation in Europe}, author = {Carlos Granell-Canut and Peter Mooney and Simon Jirka and Matthes Rieke and Frank O Ostermann and Just Van Den Broecke and Alessandro Sarretta and Stefaan Verhulst and Lina Dencik and Hillen Oost and Marina Micheli and Marco Minghini and Alexander Kotsev and Sven Schade}, url = {https://publications.jrc.ec.europa.eu/repository/handle/JRC127730}, doi = {https://doi.org/10.2760/630723}, isbn = {978-92-76-46937-7}, year = {2022}, date = {2022-01-15}, number = {JRC127730}, publisher = {Publications Office of the European Union}, address = {Luxemburg}, abstract = {Europe’s digital transformation of the economy and society is one of the priorities of the current Commission and is framed by the European strategy for data. This strategy aims at creating a single market for data through the establishment of a common European data space, based in turn on domain-specific data spaces in strategic sectors such as environment, agriculture, industry, health and transportation. Acknowledging the key role that emerging technologies and innovative approaches for data sharing and use can play to make European data spaces a reality, this document presents a set of experiments that explore emerging technologies and tools for data-driven innovation, and also deepen in the socio-technical factors and forces that occur in data-driven innovation. Experimental results shed some light in terms of lessons learned and practical recommendations towards the establishment of European data spaces.}, keywords = {Data science, Data spaces, Europe, Geographic data}, pubstate = {published}, tppubtype = {book} } Europe’s digital transformation of the economy and society is one of the priorities of the current Commission and is framed by the European strategy for data. This strategy aims at creating a single market for data through the establishment of a common European data space, based in turn on domain-specific data spaces in strategic sectors such as environment, agriculture, industry, health and transportation. Acknowledging the key role that emerging technologies and innovative approaches for data sharing and use can play to make European data spaces a reality, this document presents a set of experiments that explore emerging technologies and tools for data-driven innovation, and also deepen in the socio-technical factors and forces that occur in data-driven innovation. Experimental results shed some light in terms of lessons learned and practical recommendations towards the establishment of European data spaces. |
Blanford, Justine I; Bowlick, Forrest; Gidudu, Anthony; Gould, Michael; Griffin, Amy L; Kar, Bandana; Kemp, Karen; de Róiste, Mairéad; deSabbata, Stefano; Sinton, Diana; Strobl, Josef; Tate, Nicholas; Toppen, Fred; Unwin, David Lockdown lessons: an international conversation on resilient GI science teaching Journal Article Journal of Geography in Higher Education, 46 (1), pp. 7-19, 2022, ISSN: 0309-8265. Abstract | Links | BibTeX | Tags: GI teaching, GIScience @article{Blanford2022a, title = {Lockdown lessons: an international conversation on resilient GI science teaching}, author = {Justine I. Blanford and Forrest Bowlick and Anthony Gidudu and Michael Gould and Amy L. Griffin and Bandana Kar and Karen Kemp and Mairéad de Róiste and Stefano deSabbata and Diana Sinton and Josef Strobl and Nicholas Tate and Fred Toppen and David Unwin }, doi = {https://doi.org/10.1080/03098265.2021.1986687}, issn = {0309-8265}, year = {2022}, date = {2022-01-15}, journal = {Journal of Geography in Higher Education}, volume = {46}, number = {1}, pages = {7-19}, abstract = {We report the findings from two global panel “conversations” that, stimulated by the exceptional coronavirus pandemic of 2020/21, explored the concept of resilience in geographic science teaching and learning. Characteristics of resilient teaching, both in general and with reference to GISc, are listed and shown to be essentially what might in the past have been called good teaching. Similarly, barriers to resilient teaching are explored and strategies for overcoming them listed. Perhaps the most important conclusion is a widespread desire not to “bounce back” to pre-COVID ways, but to use the opportunity to “bounce forward” towards better teaching and learning practices.}, keywords = {GI teaching, GIScience}, pubstate = {published}, tppubtype = {article} } We report the findings from two global panel “conversations” that, stimulated by the exceptional coronavirus pandemic of 2020/21, explored the concept of resilience in geographic science teaching and learning. Characteristics of resilient teaching, both in general and with reference to GISc, are listed and shown to be essentially what might in the past have been called good teaching. Similarly, barriers to resilient teaching are explored and strategies for overcoming them listed. Perhaps the most important conclusion is a widespread desire not to “bounce back” to pre-COVID ways, but to use the opportunity to “bounce forward” towards better teaching and learning practices. |
2021 |
Osma, Jorge; Martínez-García, Laura; Perís-Baquero, Óscar; Navarro-Haro, María Vicenta; González-Pérez, Alberto; Suso-Ribera, Carlos BMJ Open, 11 (e054286), 2021, ISSN: 2044-6055. Abstract | Links | BibTeX | Tags: mental health, symptoms @article{Osma2021, title = {Implementation, efficacy and cost effectiveness of the unified protocol in a blended format for the transdiagnostic treatment of emotional disorders: a study protocol for a multicentre, randomised, superiority controlled trial in the Spanish National Health System }, author = {Jorge Osma and Laura Martínez-García and Óscar Perís-Baquero and María Vicenta Navarro-Haro and Alberto González-Pérez and Carlos Suso-Ribera}, doi = {10.1136/bmjopen-2021-054286}, issn = {2044-6055}, year = {2021}, date = {2021-12-31}, journal = {BMJ Open}, volume = {11}, number = {e054286}, abstract = {Introduction: Emotional disorders (EDs) have become the most prevalent psychological disorders in the general population, which has boosted the economic burden associated with their management. Approximately half of the individuals do not receive adequate treatment. Consequently, finding solutions to deliver cost-effective treatments for EDs has become a key goal of today’s clinical psychology. Blended treatments, a combination of face-to-face and online interventions, have emerged as a potential solution to the previous. The Unified Protocol for the Transdiagnostic Treatment of EDs (UP) might serve this purpose, as it can be applied to a variety of disorders simultaneously and its manualised format makes it suitable for blended interventions. Methods and analysis: The study is a multicentre, randomised, superiority, clinical trial. Participants will be 310 individuals with a diagnosis of an ED. They will be randomised to a treatment as usual (individual cognitive behavioural therapy) or a UP condition in a blended format (face-to-face individual UP +online, app-based UP). Primary outcomes will be ED diagnostic criteria and depression and anxiety symptoms. Cost efficiency of the intervention, app usability, as well as opinion and confidence in the treatment will also be evaluated. Assessment points will include baseline and 3 months, 6 months and 12 months after UP treatment. Ethics and dissemination: The study has received approvals by the Ethics Research Committee of Navarra, Castellón, Euskadi, Castilla y León, Extremadura, Lleida and Aragón. The study is currently under an approval process by the Ethics Research Committees of all the remaining collaborating centres. Outcomes will be disseminated through publication in peer-reviewed journals and presentations at international conference meetings.}, keywords = {mental health, symptoms}, pubstate = {published}, tppubtype = {article} } Introduction: Emotional disorders (EDs) have become the most prevalent psychological disorders in the general population, which has boosted the economic burden associated with their management. Approximately half of the individuals do not receive adequate treatment. Consequently, finding solutions to deliver cost-effective treatments for EDs has become a key goal of today’s clinical psychology. Blended treatments, a combination of face-to-face and online interventions, have emerged as a potential solution to the previous. The Unified Protocol for the Transdiagnostic Treatment of EDs (UP) might serve this purpose, as it can be applied to a variety of disorders simultaneously and its manualised format makes it suitable for blended interventions. Methods and analysis: The study is a multicentre, randomised, superiority, clinical trial. Participants will be 310 individuals with a diagnosis of an ED. They will be randomised to a treatment as usual (individual cognitive behavioural therapy) or a UP condition in a blended format (face-to-face individual UP +online, app-based UP). Primary outcomes will be ED diagnostic criteria and depression and anxiety symptoms. Cost efficiency of the intervention, app usability, as well as opinion and confidence in the treatment will also be evaluated. Assessment points will include baseline and 3 months, 6 months and 12 months after UP treatment. Ethics and dissemination: The study has received approvals by the Ethics Research Committee of Navarra, Castellón, Euskadi, Castilla y León, Extremadura, Lleida and Aragón. The study is currently under an approval process by the Ethics Research Committees of all the remaining collaborating centres. Outcomes will be disseminated through publication in peer-reviewed journals and presentations at international conference meetings. |
Martín, Ana Jiménez; Gordo, Ismael Miranda; Domínguez, Juan Jesús García; Torres-Sospedra, Joaquín; Plaza, Sergio Lluva; Gómez, David Gualda Affinity Propagation Clustering for Older Adults Daily Routine Estimation Inproceedings Proceedings of the Eleventh International Conference on Indoor Positioning and Indoor Navigation, IEEE, 2021. Abstract | Links | BibTeX | Tags: Indoor positioning @inproceedings{Jimenez2021a, title = {Affinity Propagation Clustering for Older Adults Daily Routine Estimation}, author = {Ana Jiménez Martín and Ismael Miranda Gordo and Juan Jesús García Domínguez and Joaquín Torres-Sospedra and Sergio Lluva Plaza and David Gualda Gómez}, doi = {https://doi.org/10.1109/IPIN51156.2021.9662579}, year = {2021}, date = {2021-12-15}, booktitle = {Proceedings of the Eleventh International Conference on Indoor Positioning and Indoor Navigation}, publisher = {IEEE}, abstract = {This work proposes a system that allows estimating and monitoring daily routine changes in a sensorized home through Machine Learning and Affinity Propagation clustering techniques. Older adults often have low-activity and rather routine lives, which means that these routines can be an indicator of their physical and cognitive state in order to lead an independent life and healthy ageing. Therefore, it is important to be able to generate precise routines, as well as to monitor them, to trigger alarms in case of significant variations. This proposal defines routines based on the time spent in each of the monitored rooms. The daily time in each room is estimated trough a Bluetooth Low Energy-based indoor localization system. The localization is obtained through the Bluetooth received signal strength, which is processed with different supervised algorithms and fused with the acceleration measured by the mobile receiver, obtaining an accuracy above 96 %. From these data, the sample has been synthetically expanded to generate four different routines, on which the proposed algorithm based on Principal Component Analysis and Affinity Propagation clustering has been tested, obtaining very promising results.}, keywords = {Indoor positioning}, pubstate = {published}, tppubtype = {inproceedings} } This work proposes a system that allows estimating and monitoring daily routine changes in a sensorized home through Machine Learning and Affinity Propagation clustering techniques. Older adults often have low-activity and rather routine lives, which means that these routines can be an indicator of their physical and cognitive state in order to lead an independent life and healthy ageing. Therefore, it is important to be able to generate precise routines, as well as to monitor them, to trigger alarms in case of significant variations. This proposal defines routines based on the time spent in each of the monitored rooms. The daily time in each room is estimated trough a Bluetooth Low Energy-based indoor localization system. The localization is obtained through the Bluetooth received signal strength, which is processed with different supervised algorithms and fused with the acceleration measured by the mobile receiver, obtaining an accuracy above 96 %. From these data, the sample has been synthetically expanded to generate four different routines, on which the proposed algorithm based on Principal Component Analysis and Affinity Propagation clustering has been tested, obtaining very promising results. |
Pendão, Cristiano; Silva, Ivo; Moreira, Adriano; Torres-Sospedra, Joaquín Dioptra – A Data Generation Application for Indoor Positioning Systems Inproceedings Proceedings of the Eleventh International Conference on Indoor Positioning and Indoor Navigation, IEEE, 2021. Abstract | Links | BibTeX | Tags: Indoor positioning @inproceedings{Pendao2021a, title = {Dioptra – A Data Generation Application for Indoor Positioning Systems}, author = {Cristiano Pendão and Ivo Silva and Adriano Moreira and Joaquín Torres-Sospedra}, doi = {https://doi.org/10.1109/IPIN51156.2021.9662585}, year = {2021}, date = {2021-12-15}, booktitle = {Proceedings of the Eleventh International Conference on Indoor Positioning and Indoor Navigation}, publisher = {IEEE}, abstract = {Indoor Positioning Systems (IPSs) based on different approaches and technologies have been proposed to support localization and navigation applications in indoor environments. The fair benchmarking and comparison of these IPSs is a difficult task since each IPS is usually evaluated in very specific and controlled conditions and using private data sets, not allowing reproducibility and direct comparison between the reported results and other competing solutions. In addition, testing and evaluating an IPS in the real world is difficult and time-consuming, especially when considering evaluation in multiple environments and conditions. To enhance IPS assessment, we propose Dioptra, an open access and user-friendly application to support research, development and evaluation of IPSs through simulation. To the best of our knowledge, Dioptra is the first application specially developed to generate synthetic datasets to promote reproducibility and fair benchmarking between IPSs.}, keywords = {Indoor positioning}, pubstate = {published}, tppubtype = {inproceedings} } Indoor Positioning Systems (IPSs) based on different approaches and technologies have been proposed to support localization and navigation applications in indoor environments. The fair benchmarking and comparison of these IPSs is a difficult task since each IPS is usually evaluated in very specific and controlled conditions and using private data sets, not allowing reproducibility and direct comparison between the reported results and other competing solutions. In addition, testing and evaluating an IPS in the real world is difficult and time-consuming, especially when considering evaluation in multiple environments and conditions. To enhance IPS assessment, we propose Dioptra, an open access and user-friendly application to support research, development and evaluation of IPSs through simulation. To the best of our knowledge, Dioptra is the first application specially developed to generate synthetic datasets to promote reproducibility and fair benchmarking between IPSs. |
Aranda, Fernando J; Parralejo, Felipe; Aguilera, Teodoro; Álvarez, Fernando J; Torres-Sospedra, Joaquín Finding Optimal BLE Configuration for Indoor Positioning with Consumption Restrictions Inproceedings Proceedings of the Eleventh International Conference on Indoor Positioning and Indoor Navigation, IEEE, 2021. Abstract | Links | BibTeX | Tags: Indoor positioning @inproceedings{Aranda2021a, title = {Finding Optimal BLE Configuration for Indoor Positioning with Consumption Restrictions}, author = {Fernando J. Aranda and Felipe Parralejo and Teodoro Aguilera and Fernando J. Álvarez and Joaquín Torres-Sospedra}, doi = {https://doi.org/10.1109/IPIN51156.2021.9662563}, year = {2021}, date = {2021-12-15}, booktitle = {Proceedings of the Eleventh International Conference on Indoor Positioning and Indoor Navigation}, publisher = {IEEE}, abstract = {Bluetooth Low Energy (BLE) fingerprinting has gained a lot of research effort in recent years due to flexibility in both beacons placement and configuration. Different works have addressed the effect of the configuration parameters, mainly the transmission power (Tx) and period (Ts), over positioning accuracy but not on the system lifespan and the trade-off between these two. In this work, different configurations of one, three and six slots have been tested over the same experimental setup. Positioning accuracy was obtained using different variations of the Weighted k-Nearest Neighbours (Wk-NN) algorithm, and the system lifespan was estimated using the actual current consumption and transmission mechanism for each configuration. Experimental results have shown that Tx and the number of slots can be adjusted to optimize this trade-off; meanwhile, changes in Ts worsen Wk-NN results more than in the other parameters, showing that the minimum Ts is always the best option.}, keywords = {Indoor positioning}, pubstate = {published}, tppubtype = {inproceedings} } Bluetooth Low Energy (BLE) fingerprinting has gained a lot of research effort in recent years due to flexibility in both beacons placement and configuration. Different works have addressed the effect of the configuration parameters, mainly the transmission power (Tx) and period (Ts), over positioning accuracy but not on the system lifespan and the trade-off between these two. In this work, different configurations of one, three and six slots have been tested over the same experimental setup. Positioning accuracy was obtained using different variations of the Weighted k-Nearest Neighbours (Wk-NN) algorithm, and the system lifespan was estimated using the actual current consumption and transmission mechanism for each configuration. Experimental results have shown that Tx and the number of slots can be adjusted to optimize this trade-off; meanwhile, changes in Ts worsen Wk-NN results more than in the other parameters, showing that the minimum Ts is always the best option. |
Quezada-Gaibor, Darwin; Torres-Sospedra, Joaquín; Nurmi, Jari; Koucheryavy, Yevgeni; Huerta-Guijarro, Joaquín Lightweight Wi-Fi Fingerprinting with a Novel RSS Clustering Algorithm Inproceedings Proceedings of the Eleventh International Conference on Indoor Positioning and Indoor Navigation, IEEE, 2021. Abstract | Links | BibTeX | Tags: Indoor positioning @inproceedings{Quezada2021a, title = {Lightweight Wi-Fi Fingerprinting with a Novel RSS Clustering Algorithm}, author = {Darwin Quezada-Gaibor and Joaquín Torres-Sospedra and Jari Nurmi and Yevgeni Koucheryavy and Joaquín Huerta-Guijarro}, doi = {https://doi.org/10.1109/IPIN51156.2021.9662612}, year = {2021}, date = {2021-12-15}, booktitle = {Proceedings of the Eleventh International Conference on Indoor Positioning and Indoor Navigation}, publisher = {IEEE}, abstract = {Nowadays, several indoor positioning solutions sup-port Wi-Fi and use this technology to estimate the user position. It is characterized by its low cost, availability in indoor and outdoor environments, and a wide variety of devices support Wi-Fi technology. However, this technique suffers from scalability problems when the radio map has a large number of reference fingerprints because this might increase the time response in the operational phase. In order to minimize the time response, many solutions have been proposed along the time. The most common solution is to divide the data set into clusters. Thus, the incoming fingerprint will be compared with a specific number of samples grouped by, for instance similarity (clusters). Many of the current studies have proposed a variety of solutions based on the modification of traditional clustering algorithms in order to provide a better distribution of samples and reduce the computational load. This work proposes a new clustering method based on the maximum Received Signal Strength (RSS) values to join similar fingerprints. As a result, the proposed fingerprinting clustering method outperforms three of the most well-known clustering algorithms in terms of processing time at the operational phase of fingerprinting.}, keywords = {Indoor positioning}, pubstate = {published}, tppubtype = {inproceedings} } Nowadays, several indoor positioning solutions sup-port Wi-Fi and use this technology to estimate the user position. It is characterized by its low cost, availability in indoor and outdoor environments, and a wide variety of devices support Wi-Fi technology. However, this technique suffers from scalability problems when the radio map has a large number of reference fingerprints because this might increase the time response in the operational phase. In order to minimize the time response, many solutions have been proposed along the time. The most common solution is to divide the data set into clusters. Thus, the incoming fingerprint will be compared with a specific number of samples grouped by, for instance similarity (clusters). Many of the current studies have proposed a variety of solutions based on the modification of traditional clustering algorithms in order to provide a better distribution of samples and reduce the computational load. This work proposes a new clustering method based on the maximum Received Signal Strength (RSS) values to join similar fingerprints. As a result, the proposed fingerprinting clustering method outperforms three of the most well-known clustering algorithms in terms of processing time at the operational phase of fingerprinting. |
Bellavista-Parent, Vladimir; Torres-Sospedra, Joaquín; Perez-Navarro, Antoni New trends in indoor positioning based on WiFi and machine learning: A systematic review Inproceedings Proceedings of the Eleventh International Conference on Indoor Positioning and Indoor Navigation, IEEE, 2021. Abstract | Links | BibTeX | Tags: Indoor positioning, machine learning @inproceedings{Bellavista2021a, title = {New trends in indoor positioning based on WiFi and machine learning: A systematic review}, author = {Vladimir Bellavista-Parent and Joaquín Torres-Sospedra and Antoni Perez-Navarro}, doi = {https://doi.org/10.1109/IPIN51156.2021.9662521}, year = {2021}, date = {2021-12-15}, booktitle = {Proceedings of the Eleventh International Conference on Indoor Positioning and Indoor Navigation}, publisher = {IEEE}, abstract = {Currently there is no standard indoor positioning system, similar to outdoor GPS. However, WiFi signals have been used in a large number of proposals to achieve the above positioning, many of which use machine learning to do so. But what are the most commonly used techniques in machine learning? What accuracy do they achieve? Where have they been tested? This article presents a systematic review of works between 2019 and 2021 that use WiFi as the signal for positioning and machine learning models to estimate indoor position. 64 papers have been identified as relevant, which have been systematically analyzed for a better understanding of the current situation in different aspects. The results show that indoor positioning based on WiFi trends use neural network-based models, evaluated in empirical experiments. Despite this, many works still conduct an assessment in small areas, which can influence the goodness of the results presented.}, keywords = {Indoor positioning, machine learning}, pubstate = {published}, tppubtype = {inproceedings} } Currently there is no standard indoor positioning system, similar to outdoor GPS. However, WiFi signals have been used in a large number of proposals to achieve the above positioning, many of which use machine learning to do so. But what are the most commonly used techniques in machine learning? What accuracy do they achieve? Where have they been tested? This article presents a systematic review of works between 2019 and 2021 that use WiFi as the signal for positioning and machine learning models to estimate indoor position. 64 papers have been identified as relevant, which have been systematically analyzed for a better understanding of the current situation in different aspects. The results show that indoor positioning based on WiFi trends use neural network-based models, evaluated in empirical experiments. Despite this, many works still conduct an assessment in small areas, which can influence the goodness of the results presented. |
Silva, Ivo; Pendão, Cristiano; Torres-Sospedra, Joaquín; Moreira, Adriano Quantifying the Degradation of Radio Maps in Wi-Fi Fingerprinting Inproceedings Proceedings of the Eleventh International Conference on Indoor Positioning and Indoor Navigation, IEEE, 2021. Abstract | Links | BibTeX | Tags: Wi-Fi fingerprint, Wi-Fi mapping @inproceedings{Silva2021ab, title = {Quantifying the Degradation of Radio Maps in Wi-Fi Fingerprinting}, author = {Ivo Silva and Cristiano Pendão and Joaquín Torres-Sospedra and Adriano Moreira}, doi = {https://doi.org/10.1109/IPIN51156.2021.9662558}, year = {2021}, date = {2021-12-15}, booktitle = {Proceedings of the Eleventh International Conference on Indoor Positioning and Indoor Navigation}, publisher = {IEEE}, abstract = {One of the most common assumptions regarding indoor positioning systems based on Wi-Fi fingerprinting is that the Radio Map (RM) becomes outdated and has to be updated to maintain the positioning performance. It is known that propagation effects, the addition/removal of Access Points (APs), changes in the indoor layout, among others, cause RMs to become outdated. However, there is a lack of studies that show how the RM degrades over time. In this paper, we describe an empirical study, based on real-world experiments, to evaluate how and why RMs degrade over time. We conducted site surveys and deployed monitoring devices to analyse the radio environment of one building over 2+ years, which allowed us to identify significant changes/events that caused the degradation of RMs. To quantify the RM degradation, we use the positioning error and propose the RM degradation ratio, a metric to directly compare two RMs and measure how different they are. Obtained results show that the positioning performance is much better when RMs are collected on the same day as the test data, and although RM degradation tends to increase over time, it only leads to large positioning errors when significant changes occur in the Wi-Fi infrastructure, making previous RMs outdated.}, keywords = {Wi-Fi fingerprint, Wi-Fi mapping}, pubstate = {published}, tppubtype = {inproceedings} } One of the most common assumptions regarding indoor positioning systems based on Wi-Fi fingerprinting is that the Radio Map (RM) becomes outdated and has to be updated to maintain the positioning performance. It is known that propagation effects, the addition/removal of Access Points (APs), changes in the indoor layout, among others, cause RMs to become outdated. However, there is a lack of studies that show how the RM degrades over time. In this paper, we describe an empirical study, based on real-world experiments, to evaluate how and why RMs degrade over time. We conducted site surveys and deployed monitoring devices to analyse the radio environment of one building over 2+ years, which allowed us to identify significant changes/events that caused the degradation of RMs. To quantify the RM degradation, we use the positioning error and propose the RM degradation ratio, a metric to directly compare two RMs and measure how different they are. Obtained results show that the positioning performance is much better when RMs are collected on the same day as the test data, and although RM degradation tends to increase over time, it only leads to large positioning errors when significant changes occur in the Wi-Fi infrastructure, making previous RMs outdated. |
Rodriguez-Martinez, Cristina; Torres-Sospedra, Joaquín Revisiting the Analysis of Hyperparameters in k-NN for Wi-Fi and BLE Fingerprinting: Current Status and General Results Inproceedings Proceedings of the Eleventh International Conference on Indoor Positioning and Indoor Navigation, IEEE, 2021. Abstract | Links | BibTeX | Tags: machine learning, Wi-Fi fingerprint @inproceedings{Rodriguez2021a, title = {Revisiting the Analysis of Hyperparameters in k-NN for Wi-Fi and BLE Fingerprinting: Current Status and General Results}, author = {Cristina Rodriguez-Martinez and Joaquín Torres-Sospedra}, doi = {https://doi.org/10.1109/IPIN51156.2021.9662542}, year = {2021}, date = {2021-12-15}, booktitle = {Proceedings of the Eleventh International Conference on Indoor Positioning and Indoor Navigation}, publisher = {IEEE}, abstract = {Wi-Fi Fingerprinting is a very popular technique in the field of indoor positioning, since the release of Microsoft RADAR system back in 2000. Since that milestone, the vast majority of studies and improvements in this field keep using the same base algorithm, an adaptation of the k-NN algorithm to treat geospatial data (e.g., x/y or lat/lon). One of the most relevant drawbacks of k-NN algorithm resides in its initial design, focused on resolving general classification problems. Wi-Fi fingerprinting technique is based on the measurement of the signal strength emitted by close and available Wi-Fi networks. However, the nature of signal propagation is not linear, and it is impacted by the fixed and dynamic obstacles present in the environment. This work consists in the study of k-NN algorithm parameters, k value, distance metric and data representation, to improve the efficiency of this prediction model. The evaluation will be conducted over several different heterogeneous databases and propose a model to automatically set the value of k.}, keywords = {machine learning, Wi-Fi fingerprint}, pubstate = {published}, tppubtype = {inproceedings} } Wi-Fi Fingerprinting is a very popular technique in the field of indoor positioning, since the release of Microsoft RADAR system back in 2000. Since that milestone, the vast majority of studies and improvements in this field keep using the same base algorithm, an adaptation of the k-NN algorithm to treat geospatial data (e.g., x/y or lat/lon). One of the most relevant drawbacks of k-NN algorithm resides in its initial design, focused on resolving general classification problems. Wi-Fi fingerprinting technique is based on the measurement of the signal strength emitted by close and available Wi-Fi networks. However, the nature of signal propagation is not linear, and it is impacted by the fixed and dynamic obstacles present in the environment. This work consists in the study of k-NN algorithm parameters, k value, distance metric and data representation, to improve the efficiency of this prediction model. The evaluation will be conducted over several different heterogeneous databases and propose a model to automatically set the value of k. |
Torres-Sospedra, Joaquín; Silva, Ivo; Klus, Lucie; Quezada-Gaibor, Darwin; Crivello, Antonino; Barsocchi, Paolo; Pendão, Cristiano; Lohan, Elena Simona; Nurmi, Jari; Moreira, Adriano Towards Ubiquitous Indoor Positioning: Comparing Systems across Heterogeneous Datasets Inproceedings Proceedings of the Eleventh International Conference on Indoor Positioning and Indoor Navigation, IEEE, 2021. Abstract | Links | BibTeX | Tags: Indoor positioning @inproceedings{Torres-Sospedra2021c, title = {Towards Ubiquitous Indoor Positioning: Comparing Systems across Heterogeneous Datasets}, author = {Joaquín Torres-Sospedra and Ivo Silva and Lucie Klus and Darwin Quezada-Gaibor and Antonino Crivello and Paolo Barsocchi and Cristiano Pendão and Elena Simona Lohan and Jari Nurmi and Adriano Moreira}, doi = {https://doi.org/10.1109/IPIN51156.2021.9662560}, year = {2021}, date = {2021-12-15}, booktitle = {Proceedings of the Eleventh International Conference on Indoor Positioning and Indoor Navigation}, publisher = {IEEE}, abstract = {The evaluation of Indoor Positioning Systems (IPSs) mostly relies on local deployments in the researchers' or partners' facilities. The complexity of preparing comprehensive experiments, collecting data, and considering multiple scenarios usually limits the evaluation area and, therefore, the assessment of the proposed systems. The requirements and features of controlled experiments cannot be generalized since the use of the same sensors or anchors density cannot be guaranteed. The dawn of datasets is pushing IPS evaluation to a similar level as machine-learning models, where new proposals are evaluated over many heterogeneous datasets. This paper proposes a way to evaluate IPSs in multiple scenarios, that is validated with three use cases. The results prove that the proposed aggregation of the evaluation metric values is a useful tool for high-level comparison of IPSs.}, keywords = {Indoor positioning}, pubstate = {published}, tppubtype = {inproceedings} } The evaluation of Indoor Positioning Systems (IPSs) mostly relies on local deployments in the researchers' or partners' facilities. The complexity of preparing comprehensive experiments, collecting data, and considering multiple scenarios usually limits the evaluation area and, therefore, the assessment of the proposed systems. The requirements and features of controlled experiments cannot be generalized since the use of the same sensors or anchors density cannot be guaranteed. The dawn of datasets is pushing IPS evaluation to a similar level as machine-learning models, where new proposals are evaluated over many heterogeneous datasets. This paper proposes a way to evaluate IPSs in multiple scenarios, that is validated with three use cases. The results prove that the proposed aggregation of the evaluation metric values is a useful tool for high-level comparison of IPSs. |
Klus, Roman; Klus, Lucie; Talvitie, Jukka; Pihlajasalo, Jaakko; Torres-Sospedra, Joaquín; Valkama, Mikko Transfer Learning for Convolutional Indoor Positioning Systems Inproceedings Proceedings of the Eleventh International Conference on Indoor Positioning and Indoor Navigation, IEEE, 2021. Abstract | Links | BibTeX | Tags: Indoor positioning @inproceedings{Klus2021a, title = {Transfer Learning for Convolutional Indoor Positioning Systems}, author = {Roman Klus and Lucie Klus and Jukka Talvitie and Jaakko Pihlajasalo and Joaquín Torres-Sospedra and Mikko Valkama}, doi = {https://doi.org/10.1109/IPIN51156.2021.9662544}, year = {2021}, date = {2021-12-15}, booktitle = {Proceedings of the Eleventh International Conference on Indoor Positioning and Indoor Navigation}, publisher = {IEEE}, abstract = {Fingerprinting is a widely used technique in indoor positioning, mainly due to its simplicity. Usually, this technique is used with the deterministic k - Nearest Neighbors (k-NN) algorithm. Utilizing a neural network model for fingerprinting positioning purposes can greatly improve the prediction speed compared to the k-NN approach, but requires a voluminous training dataset to achieve comparable performance. In many indoor positioning datasets, the number of samples is only at a level of hundreds, which results in poor performance of the neural network solution. In this work, we develop a novel algorithm based on a transfer learning approach, which combines samples from 15 different Wi-Fi RSS indoor positioning datasets, to train a single convolutional neural network model, which learns the common patterns in the combined data. The proposed model is then fine-tuned to optimally fit the individual databases. We show that the proposed solution reduces the positioning error by up to 25% compared to the benchmark model while reducing the number of outlier predictions.}, keywords = {Indoor positioning}, pubstate = {published}, tppubtype = {inproceedings} } Fingerprinting is a widely used technique in indoor positioning, mainly due to its simplicity. Usually, this technique is used with the deterministic k - Nearest Neighbors (k-NN) algorithm. Utilizing a neural network model for fingerprinting positioning purposes can greatly improve the prediction speed compared to the k-NN approach, but requires a voluminous training dataset to achieve comparable performance. In many indoor positioning datasets, the number of samples is only at a level of hundreds, which results in poor performance of the neural network solution. In this work, we develop a novel algorithm based on a transfer learning approach, which combines samples from 15 different Wi-Fi RSS indoor positioning datasets, to train a single convolutional neural network model, which learns the common patterns in the combined data. The proposed model is then fine-tuned to optimally fit the individual databases. We show that the proposed solution reduces the positioning error by up to 25% compared to the benchmark model while reducing the number of outlier predictions. |
Trilles-Oliver, Sergio; Juan-Verdoy, Pablo; Chaudhuric, Somnath; Fortea, Ana Belen Vicente Data on CO2, temperature and air humidity records in Spanish classrooms during the reopening of schools in the COVID-19 pandemic Journal Article Data in Brief, 39 , pp. 107489, 2021, ISSN: 2352-3409. Abstract | Links | BibTeX | Tags: Air quality sensors, Internet of things @article{Trilles2021a, title = {Data on CO2, temperature and air humidity records in Spanish classrooms during the reopening of schools in the COVID-19 pandemic}, author = {Sergio Trilles-Oliver and Pablo Juan-Verdoy and Somnath Chaudhuric and Ana Belen Vicente Fortea}, doi = {https://doi.org/10.1016/j.dib.2021.107489}, issn = {2352-3409}, year = {2021}, date = {2021-12-01}, journal = {Data in Brief}, volume = {39}, pages = {107489}, abstract = {In order to reduce the advance of the pandemic produced by COVID-19, many actions and restrictions have been applied and the field of education has been no exception. In Spain, during the academic year 2020–2021, face-to-face teaching generally continued in both primary and secondary schools. Throughout the year, different measures have been taken to reduce the likelihood of contagion in classrooms, one of which was to improve ventilation by opening windows and doors. One of the most commonly used techniques to check for good ventilation has been CO2 monitoring. This work provides a set of 80,000 CO2 concentration records collected by low-cost Internet of Things nodes, primarily located within twelve classrooms in two primary schools. The published observations were collected between 1 May 2020 and 23 June 2021. Additionally, the same dataset includes temperature, air humidity and battery level observations.}, keywords = {Air quality sensors, Internet of things}, pubstate = {published}, tppubtype = {article} } In order to reduce the advance of the pandemic produced by COVID-19, many actions and restrictions have been applied and the field of education has been no exception. In Spain, during the academic year 2020–2021, face-to-face teaching generally continued in both primary and secondary schools. Throughout the year, different measures have been taken to reduce the likelihood of contagion in classrooms, one of which was to improve ventilation by opening windows and doors. One of the most commonly used techniques to check for good ventilation has been CO2 monitoring. This work provides a set of 80,000 CO2 concentration records collected by low-cost Internet of Things nodes, primarily located within twelve classrooms in two primary schools. The published observations were collected between 1 May 2020 and 23 June 2021. Additionally, the same dataset includes temperature, air humidity and battery level observations. |
Tang, Vicente; Acedo-Sánchez, Albert; Painho, Marco Sense of place and the city: the case of non-native residents in Lisbon Journal Article Journal of Spatial Information Science, (23), pp. 125-155, 2021, ISSN: 1948-660X. Abstract | Links | BibTeX | Tags: sense of place @article{Tang2021a, title = {Sense of place and the city: the case of non-native residents in Lisbon}, author = {Vicente Tang and Albert Acedo-Sánchez and Marco Painho}, doi = {https://doi.org/10.5311/JOSIS.2021.23.165}, issn = {1948-660X}, year = {2021}, date = {2021-11-01}, journal = {Journal of Spatial Information Science}, number = {23}, pages = {125-155}, abstract = {When immigrants move to a new city, they tend to develop distinct relationships with the urban landscape, which in turn becomes the new setting of their routine-based activities that evolve over time. Previous works in environmental psychology have quantitatively examined non-native residents' development of sense of place towards their new environment. In this paper, we introduce the spatial perspective into studying the sense of place experienced by non-natives in an urban context. We study the person-place bonds, relationships, and feelings cultivated by non-native residents living in the city of Lisbon (Portugal) through an online map-based survey. Then, we carried out spatial analysis aimed at distinguishing and visualizing the different facets of sense of place developed by two participant groups: short-term residents and long-term residents. Results showed that while short-term residents reported bonds with places, long-term residents' senses of place were more intense and broader throughout the city. The correlations, associations, and relationships between participant groups and the dimensions of sense of place allowed us to observe features and patterns that were previously described in the literature, although adding the spatial lenses can potentially provide better insights for urban planning, community development, and inclusive policies.}, keywords = {sense of place}, pubstate = {published}, tppubtype = {article} } When immigrants move to a new city, they tend to develop distinct relationships with the urban landscape, which in turn becomes the new setting of their routine-based activities that evolve over time. Previous works in environmental psychology have quantitatively examined non-native residents' development of sense of place towards their new environment. In this paper, we introduce the spatial perspective into studying the sense of place experienced by non-natives in an urban context. We study the person-place bonds, relationships, and feelings cultivated by non-native residents living in the city of Lisbon (Portugal) through an online map-based survey. Then, we carried out spatial analysis aimed at distinguishing and visualizing the different facets of sense of place developed by two participant groups: short-term residents and long-term residents. Results showed that while short-term residents reported bonds with places, long-term residents' senses of place were more intense and broader throughout the city. The correlations, associations, and relationships between participant groups and the dimensions of sense of place allowed us to observe features and patterns that were previously described in the literature, although adding the spatial lenses can potentially provide better insights for urban planning, community development, and inclusive policies. |
Pascacio-de-los-Santos, Pavel; Torres-Sospedra, Joaquín; Casteleyn, Sven A Lateration Method based on Effective Combinatorial Beacon Selection for Bluetooth Low Energy Indoor Positioning Inproceedings 2021 17th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), pp. 397-402, IEEE, 2021, ISBN: 978-1-6654-2854-5. Abstract | Links | BibTeX | Tags: Indoor positioning @inproceedings{Pascacio2021c, title = {A Lateration Method based on Effective Combinatorial Beacon Selection for Bluetooth Low Energy Indoor Positioning}, author = {Pavel Pascacio-de-los-Santos and Joaquín Torres-Sospedra and Sven Casteleyn}, doi = {10.1109/WiMob52687.2021.9606419}, isbn = {978-1-6654-2854-5}, year = {2021}, date = {2021-10-15}, booktitle = {2021 17th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)}, pages = {397-402}, publisher = {IEEE}, abstract = {Nowadays, the Bluetooth Low Energy (BLE) technology joined with the Received Signal Strength Indicator technique has became a popular approach in Indoor Positioning System, thanks to the wide availability of BLE in anchors and wearable devices and the straightforward implementation of both. Consequently, methods based on geometric properties of anchors, as lateration, are capable of enhancing the positioning accuracy exploiting the growing availability of anchors and their rich geometric distribution in indoor environments. On the downside, an inappropriate selection of anchors decreases the positioning accuracy estimation. Therefore, integrating an effective beacon selection method can enhance the reliability and accuracy of these methods. In this paper, we present a novel and straightforward Lateration indoor positioning method based on effective combinatorial BLE beacon selection. The combinatorial BLE selection approach relies on a geometrical analysis (difference of triangle areas), of each beacon combination, considering the reference beacons’ position with the estimated position using lateration, and with a globally calculated virtual target position as reference. The real-world experiment demonstrated that the proposed method improves the traditional lateration with 5% to 16%, considering different evaluation metrics.}, keywords = {Indoor positioning}, pubstate = {published}, tppubtype = {inproceedings} } Nowadays, the Bluetooth Low Energy (BLE) technology joined with the Received Signal Strength Indicator technique has became a popular approach in Indoor Positioning System, thanks to the wide availability of BLE in anchors and wearable devices and the straightforward implementation of both. Consequently, methods based on geometric properties of anchors, as lateration, are capable of enhancing the positioning accuracy exploiting the growing availability of anchors and their rich geometric distribution in indoor environments. On the downside, an inappropriate selection of anchors decreases the positioning accuracy estimation. Therefore, integrating an effective beacon selection method can enhance the reliability and accuracy of these methods. In this paper, we present a novel and straightforward Lateration indoor positioning method based on effective combinatorial BLE beacon selection. The combinatorial BLE selection approach relies on a geometrical analysis (difference of triangle areas), of each beacon combination, considering the reference beacons’ position with the estimated position using lateration, and with a globally calculated virtual target position as reference. The real-world experiment demonstrated that the proposed method improves the traditional lateration with 5% to 16%, considering different evaluation metrics. |
Gómez-Cambronero, Águeda A Serious Game to Battle Depression Inproceedings Extended Abstracts of the 2021 Annual Symposium on Computer-Human Interaction in Play (CHI PLAY '21), pp. 401-402, ACM, 2021, ISBN: 9781450383561. Abstract | Links | BibTeX | Tags: depression, mental health, serious games, symptoms @inproceedings{GomezCambronero2021b, title = {A Serious Game to Battle Depression}, author = {Águeda Gómez-Cambronero}, doi = {https://doi.org/10.1145/3450337.3483520}, isbn = {9781450383561}, year = {2021}, date = {2021-10-01}, booktitle = {Extended Abstracts of the 2021 Annual Symposium on Computer-Human Interaction in Play (CHI PLAY '21)}, pages = {401-402}, publisher = {ACM}, abstract = {This multidisciplinary project aims to develop a mobile serious game – Horizon: Resilience – as an intervention for patient suffering from depression, the most common mental disorder globally. The game is based on Cognitive Behavioral Therapy (CBT), and intends to map therapeutic principles – such as Behavioral Activation (BA), motivation for change and cognitive flexibility – to game mechanics and gameplay. As such, as players progress in the game, they undergo an ecological momentary intervention in a playful way, which teaches them coping strategies, stimulates behavioral change and an active lifestyle. Once the game is fully developed, a validation with real patients under guidance of a therapist is foreseen.}, keywords = {depression, mental health, serious games, symptoms}, pubstate = {published}, tppubtype = {inproceedings} } This multidisciplinary project aims to develop a mobile serious game – Horizon: Resilience – as an intervention for patient suffering from depression, the most common mental disorder globally. The game is based on Cognitive Behavioral Therapy (CBT), and intends to map therapeutic principles – such as Behavioral Activation (BA), motivation for change and cognitive flexibility – to game mechanics and gameplay. As such, as players progress in the game, they undergo an ecological momentary intervention in a playful way, which teaches them coping strategies, stimulates behavioral change and an active lifestyle. Once the game is fully developed, a validation with real patients under guidance of a therapist is foreseen. |
Gómez-Cambronero, Águeda; Casteleyn, Sven; Mira, Adriana Horizon: Resilience – Design of a Serious Game for Ecological Momentary Intervention for Depression Inproceedings Extended Abstracts of the 2021 Annual Symposium on Computer-Human Interaction in Play (CHI PLAY '21), pp. 236–241, ACM, 2021, ISBN: 9781450383561. Abstract | Links | BibTeX | Tags: depression, mental health, serious games, symptoms @inproceedings{GomezCambronero2021a, title = {Horizon: Resilience – Design of a Serious Game for Ecological Momentary Intervention for Depression}, author = {Águeda Gómez-Cambronero and Sven Casteleyn and Adriana Mira}, doi = {https://doi.org/10.1145/3450337.3483500}, isbn = {9781450383561}, year = {2021}, date = {2021-10-01}, booktitle = {Extended Abstracts of the 2021 Annual Symposium on Computer-Human Interaction in Play (CHI PLAY '21)}, pages = {236–241}, publisher = {ACM}, abstract = {Depression is the world’s most prevalent mental disorder and the primary source of disability adjusted life years (DALY). While traditional face-to-face therapies have been shown to be effective, alternative delivery methods, e.g. internet-based therapies, have been investigated to overcome barriers to access, such as lack of availability of therapists and infrastructure. This article presents the design of a mobile serious game as a novel psychological momentary ecological intervention for depressive symptoms. We discuss how selected principles and techniques of common psychological frameworks used to tackle depression, namely Cognitive Behavioral Therapy (including Behavioral Activation) and Positive Psychotherapy, were integrated in the game concept, gameplay and game mechanics of ”Horizon: Resilience”, a City Building and Decision Making serious game. The selected techniques are put central in the game design by introducing ”the Power R(esilience)”, which groups the psychological principles of motivation for change, cognitive flexibility, activation and positivity. While identifying with game characters and maintaining high levels of the Power R, the players are introduced to and learn to use Cognitive Behavioral Therapy and Positive Psychotherapy strategies, which they can ultimately apply in their real-life depressive symptomatology}, keywords = {depression, mental health, serious games, symptoms}, pubstate = {published}, tppubtype = {inproceedings} } Depression is the world’s most prevalent mental disorder and the primary source of disability adjusted life years (DALY). While traditional face-to-face therapies have been shown to be effective, alternative delivery methods, e.g. internet-based therapies, have been investigated to overcome barriers to access, such as lack of availability of therapists and infrastructure. This article presents the design of a mobile serious game as a novel psychological momentary ecological intervention for depressive symptoms. We discuss how selected principles and techniques of common psychological frameworks used to tackle depression, namely Cognitive Behavioral Therapy (including Behavioral Activation) and Positive Psychotherapy, were integrated in the game concept, gameplay and game mechanics of ”Horizon: Resilience”, a City Building and Decision Making serious game. The selected techniques are put central in the game design by introducing ”the Power R(esilience)”, which groups the psychological principles of motivation for change, cognitive flexibility, activation and positivity. While identifying with game characters and maintaining high levels of the Power R, the players are introduced to and learn to use Cognitive Behavioral Therapy and Positive Psychotherapy strategies, which they can ultimately apply in their real-life depressive symptomatology |
Ostermann, Frank; Nüst, Daniel; Granell-Canut, Carlos; Hofer, Barbara; Konkol, Markus Reproducible Research and GIScience: an evaluation using GIScience conference papers Inproceedings Janowicz, K; Verstegen, J A (Ed.): Proceedings of the 11th International Conference on Geographic Information Science - Part II, pp. 2:1–2:16, LIPIcs, 2021, ISBN: 78-3-95977-208-2. Abstract | Links | BibTeX | Tags: Geographic Information Systems (GIS), Replicability, Reproducibility @inproceedings{Ostermann2021a, title = {Reproducible Research and GIScience: an evaluation using GIScience conference papers}, author = {Frank Ostermann and Daniel Nüst and Carlos Granell-Canut and Barbara Hofer and Markus Konkol }, editor = {K. Janowicz and J.A. Verstegen }, doi = {https://doi.org/10.4230/LIPIcs.GIScience.2021.II.2}, isbn = {78-3-95977-208-2}, year = {2021}, date = {2021-09-30}, booktitle = {Proceedings of the 11th International Conference on Geographic Information Science - Part II}, volume = {208}, pages = {2:1--2:16}, publisher = {LIPIcs}, abstract = {GIScience conference authors and researchers face the same computational reproducibility challenges as authors and researchers from other disciplines who use computers to analyse data. Here, to assess the reproducibility of GIScience research, we apply a rubric for assessing the reproducibility of 75 conference papers published at the GIScience conference series in the years 2012-2018. Since the rubric and process were previously applied to the publications of the AGILE conference series, this paper itself is an attempt to replicate that analysis, however going beyond the previous work by evaluating and discussing proposed measures to improve reproducibility in the specific context of the GIScience conference series. The results of the GIScience paper assessment are in line with previous findings: although descriptions of workflows and the inclusion of the data and software suffice to explain the presented work, in most published papers they do not allow a third party to reproduce the results and findings with a reasonable effort. We summarise and adapt previous recommendations for improving this situation and propose the GIScience community to start a broad discussion on the reusability, quality, and openness of its research. Further, we critically reflect on the process of assessing paper reproducibility, and provide suggestions for improving future assessments. The code and data for this article are published at https://doi.org/10.5281/zenodo.4032875.}, keywords = {Geographic Information Systems (GIS), Replicability, Reproducibility}, pubstate = {published}, tppubtype = {inproceedings} } GIScience conference authors and researchers face the same computational reproducibility challenges as authors and researchers from other disciplines who use computers to analyse data. Here, to assess the reproducibility of GIScience research, we apply a rubric for assessing the reproducibility of 75 conference papers published at the GIScience conference series in the years 2012-2018. Since the rubric and process were previously applied to the publications of the AGILE conference series, this paper itself is an attempt to replicate that analysis, however going beyond the previous work by evaluating and discussing proposed measures to improve reproducibility in the specific context of the GIScience conference series. The results of the GIScience paper assessment are in line with previous findings: although descriptions of workflows and the inclusion of the data and software suffice to explain the presented work, in most published papers they do not allow a third party to reproduce the results and findings with a reasonable effort. We summarise and adapt previous recommendations for improving this situation and propose the GIScience community to start a broad discussion on the reusability, quality, and openness of its research. Further, we critically reflect on the process of assessing paper reproducibility, and provide suggestions for improving future assessments. The code and data for this article are published at https://doi.org/10.5281/zenodo.4032875. |
Zaragozí, Benito; Trilles-Oliver, Sergio; Gutiérrez, Aaron; Miravet, Daniel Development of a Common Framework for Analysing Public Transport Smart Card Data Journal Article Energies, 14 (19), pp. 6083, 2021, ISSN: 1996-1073. Abstract | Links | BibTeX | Tags: Open science, public transport, smart data card @article{Zaragozi2021c, title = {Development of a Common Framework for Analysing Public Transport Smart Card Data}, author = {Benito Zaragozí and Sergio Trilles-Oliver and Aaron Gutiérrez and Daniel Miravet}, doi = {https://doi.org/10.3390/en14196083}, issn = {1996-1073}, year = {2021}, date = {2021-09-24}, journal = {Energies}, volume = {14}, number = {19}, pages = {6083}, abstract = {The data generated in public transport systems have proven to be of great importance in improving knowledge of public transport systems, being very valuable in promoting the sustainability of public transport through rational management. However, the analysis of this data involves numerous tasks, so that when the value of analysing the data is finally verified, the effort has already been very great. The management and analysis of the collected data face some difficulties. This is the case of the data collected by the current automated fare collection systems. These systems do not follow any open standards and are not usually designed with a multipurpose nature, so they do not facilitate the data analysis workflow (i.e., acquisition, storage, quality control, integration and quantitative analysis). Intending to reduce this workload, we propose a conceptual framework for analysing data from automated fare collection systems in mobility studies. The main components of this framework are (1) a simple data model, (2) scripts for creating and querying the database and (3) a system for reusing the most useful queries. This framework has been tested in a real public transport consortium in a Spanish region shaped by tourism. The outcomes of this research work could be reused and applied, with a lower initial effort, in other areas that have data recorded by an automated fare collection system but are not sure if it is worth investing in exploiting the data. After this experience, we consider that, even with the legal limitations applicable to the analysis of this type of data, the use of open standards by automated fare collection systems would facilitate the use of this type of data to its full potential. Meanwhile, the use of a common framework may be enough to start analysing the data.}, keywords = {Open science, public transport, smart data card}, pubstate = {published}, tppubtype = {article} } The data generated in public transport systems have proven to be of great importance in improving knowledge of public transport systems, being very valuable in promoting the sustainability of public transport through rational management. However, the analysis of this data involves numerous tasks, so that when the value of analysing the data is finally verified, the effort has already been very great. The management and analysis of the collected data face some difficulties. This is the case of the data collected by the current automated fare collection systems. These systems do not follow any open standards and are not usually designed with a multipurpose nature, so they do not facilitate the data analysis workflow (i.e., acquisition, storage, quality control, integration and quantitative analysis). Intending to reduce this workload, we propose a conceptual framework for analysing data from automated fare collection systems in mobility studies. The main components of this framework are (1) a simple data model, (2) scripts for creating and querying the database and (3) a system for reusing the most useful queries. This framework has been tested in a real public transport consortium in a Spanish region shaped by tourism. The outcomes of this research work could be reused and applied, with a lower initial effort, in other areas that have data recorded by an automated fare collection system but are not sure if it is worth investing in exploiting the data. After this experience, we consider that, even with the legal limitations applicable to the analysis of this type of data, the use of open standards by automated fare collection systems would facilitate the use of this type of data to its full potential. Meanwhile, the use of a common framework may be enough to start analysing the data. |
Sánchez-Pozo, Nadia N; Trilles-Oliver, Sergio; Solé-Ribalta, Albert; Lorente-Leyva, Leandro L; Mayorca-Torres, Dagoberto; Peluffo-Ordóñez, Diego H Algorithms Air Quality Estimation: A Comparative Study of Stochastic and Heuristic Predictive Inproceedings Hybrid Artificial Intelligent Systems (International Conference on Hybrid Artificial Intelligence Systems), pp. 293-304, Springer, Cham, 2021, ISBN: 978-3-030-86271-8. Abstract | Links | BibTeX | Tags: air quality prediction, machine learning @inproceedings{SanchezPozo2021a, title = {Algorithms Air Quality Estimation: A Comparative Study of Stochastic and Heuristic Predictive}, author = {Nadia N Sánchez-Pozo and Sergio Trilles-Oliver and Albert Solé-Ribalta and Leandro L. Lorente-Leyva and Dagoberto Mayorca-Torres and Diego H Peluffo-Ordóñez}, doi = {https://doi.org/10.1007/978-3-030-86271-8_25}, isbn = {978-3-030-86271-8}, year = {2021}, date = {2021-09-15}, booktitle = {Hybrid Artificial Intelligent Systems (International Conference on Hybrid Artificial Intelligence Systems)}, pages = {293-304}, publisher = {Springer, Cham}, abstract = {This paper presents a comparative analysis of predictive models applied to air quality estimation. Currently, among other global issues, there is a high concern about air pollution, for this reason, there are several air quality indicators, with carbon monoxide (CO), sulfur dioxide (SO2), nitrogen dioxide (NO2) and ozone (O3) being the main ones. When the concentration level of an indicator exceeds an established air quality safety threshold, it is considered harmful to human health, therefore, in cities like London, there are monitoring systems for air pollutants. This study aims to compare the efficiency of stochastic and heuristic predictive models for forecasting ozone (O3) concentration to estimate London's air quality by analyzing an open dataset retrieved from the London Datastore portal. Models based on data analysis have been widely used in air quality forecasting. This paper develops four predictive models (autoregressive integrated moving average - ARIMA, support vector regression - SVR, neural networks (specifically, long-short term memory - LSTM) and Facebook Prophet). Experimentally, ARIMA models and LSTM are proved to reach the highest accuracy in predicting the concentration of air pollutants among the considered models. As a result, the comparative analysis of the loss function (root-mean-square error) reveled that ARIMA and LSTM are the most suitable, accomplishing a low error rate of 0.18 and 0.20, respectively.}, keywords = {air quality prediction, machine learning}, pubstate = {published}, tppubtype = {inproceedings} } This paper presents a comparative analysis of predictive models applied to air quality estimation. Currently, among other global issues, there is a high concern about air pollution, for this reason, there are several air quality indicators, with carbon monoxide (CO), sulfur dioxide (SO2), nitrogen dioxide (NO2) and ozone (O3) being the main ones. When the concentration level of an indicator exceeds an established air quality safety threshold, it is considered harmful to human health, therefore, in cities like London, there are monitoring systems for air pollutants. This study aims to compare the efficiency of stochastic and heuristic predictive models for forecasting ozone (O3) concentration to estimate London's air quality by analyzing an open dataset retrieved from the London Datastore portal. Models based on data analysis have been widely used in air quality forecasting. This paper develops four predictive models (autoregressive integrated moving average - ARIMA, support vector regression - SVR, neural networks (specifically, long-short term memory - LSTM) and Facebook Prophet). Experimentally, ARIMA models and LSTM are proved to reach the highest accuracy in predicting the concentration of air pollutants among the considered models. As a result, the comparative analysis of the loss function (root-mean-square error) reveled that ARIMA and LSTM are the most suitable, accomplishing a low error rate of 0.18 and 0.20, respectively. |
Belmonte-Fernández, Óscar; Sansano-Sansano, Emilio; Trilles-Oliver, Sergio; Caballer-Miedes, Antonio Artificial Intelligence, Machine Learning, and Optimization Tools for Smart Cities, 186 , pp. 155-175, Springer, Cham, 2021, ISBN: 978-3-030-84459-2. Abstract | Links | BibTeX | Tags: deep learning, Internet of things @inbook{Belmonte2021a, title = {A Reactive Architectural Proposal for Fog/Edge Computing in the Internet of Things Paradigm with Application in Deep Learning}, author = {Óscar Belmonte-Fernández and Emilio Sansano-Sansano and Sergio Trilles-Oliver and Antonio Caballer-Miedes}, doi = {https://doi.org/10.1007/978-3-030-84459-2_9}, isbn = {978-3-030-84459-2}, year = {2021}, date = {2021-09-01}, booktitle = {Artificial Intelligence, Machine Learning, and Optimization Tools for Smart Cities}, volume = {186}, pages = {155-175}, publisher = {Springer, Cham}, series = {Springer Optimization and Its Applications}, abstract = {The fog/edge computing paradigm has been proposed to tackle the challenges inherent to the Internet of Things realm. Timely response, bandwidth efficiency, context awareness, data privacy and safety, and mobility support are some of the requirements that are only partially covered by cloud computing. A collaboration of both paradigms when developing deep learning solutions for the Internet of Things can be seen as a win–win approach. Time-consuming and hardware demanding deep learning models are built in the cloud with data provided by the fog/edge, and then these models are returned to the fog/edge for use. This work proposes a new architecture, based on the principles of reactive systems, for building responsive, resilient and elastic systems, where all components interact with one another through asynchronous message passing. As a proof of concept, two particular applications of this architecture in the realms of e-health and precision agriculture are presented.}, keywords = {deep learning, Internet of things}, pubstate = {published}, tppubtype = {inbook} } The fog/edge computing paradigm has been proposed to tackle the challenges inherent to the Internet of Things realm. Timely response, bandwidth efficiency, context awareness, data privacy and safety, and mobility support are some of the requirements that are only partially covered by cloud computing. A collaboration of both paradigms when developing deep learning solutions for the Internet of Things can be seen as a win–win approach. Time-consuming and hardware demanding deep learning models are built in the cloud with data provided by the fog/edge, and then these models are returned to the fog/edge for use. This work proposes a new architecture, based on the principles of reactive systems, for building responsive, resilient and elastic systems, where all components interact with one another through asynchronous message passing. As a proof of concept, two particular applications of this architecture in the realms of e-health and precision agriculture are presented. |
Casanova-Marqués, Raúl; Pascacio-de-los-Santos, Pavel; Hajny, Jan; Torres-Sospedra, Joaquín Anonymous Attribute-based Credentials in Collaborative Indoor Positioning Systems Inproceedings Proceedings of the 18th International Conference on Security and Cryptography (SECRYPT 2021), pp. 791-797, SciTePress, 2021, ISBN: 978-989-758-524-1. Abstract | Links | BibTeX | Tags: geoprivacy, Indoor positioning @inproceedings{CasanovaMarques2021a, title = {Anonymous Attribute-based Credentials in Collaborative Indoor Positioning Systems}, author = {Raúl Casanova-Marqués and Pavel Pascacio-de-los-Santos and Jan Hajny and Joaquín Torres-Sospedra }, doi = {http://dx.doi.org/10.5220/0010582507910797}, isbn = {978-989-758-524-1}, year = {2021}, date = {2021-09-01}, booktitle = {Proceedings of the 18th International Conference on Security and Cryptography (SECRYPT 2021)}, pages = {791-797}, publisher = {SciTePress}, abstract = {Collaborative Indoor Positioning Systems (CIPSs) have recently received considerable attention, mainly because they address some existing limitations of traditional Indoor Positioning Systems (IPSs). In CIPSs, Bluetooth Low Energy (BLE) can be used to exchange positioning data and provide information (the Received Signal Strength Indicator (RSSI)) to establish the relative distance between the actors. The collaborative models exploit the position of actors and the relative position among them to allow positioning to external actors or improve the accuracy of the existing actors. However, the traditional protocols (e.g., iBeacon) are not yet ready for providing sufficient privacy protection. This paper deals with privacy-enhancing technologies and their application in CIPS. In particular, we focus on cryptographic schemes which allow the verification of users without their identification, so-called Anonymous Attribute-based Credential (ABC) schemes. As the main contribution, we presen t a cryptographic scheme that allows security and privacy-friendly sharing of location information sent through BLE advertising packets. In order to demonstrate the practicality of our scheme, we also present the results from our implementation and benchmarks on different devices.}, keywords = {geoprivacy, Indoor positioning}, pubstate = {published}, tppubtype = {inproceedings} } Collaborative Indoor Positioning Systems (CIPSs) have recently received considerable attention, mainly because they address some existing limitations of traditional Indoor Positioning Systems (IPSs). In CIPSs, Bluetooth Low Energy (BLE) can be used to exchange positioning data and provide information (the Received Signal Strength Indicator (RSSI)) to establish the relative distance between the actors. The collaborative models exploit the position of actors and the relative position among them to allow positioning to external actors or improve the accuracy of the existing actors. However, the traditional protocols (e.g., iBeacon) are not yet ready for providing sufficient privacy protection. This paper deals with privacy-enhancing technologies and their application in CIPS. In particular, we focus on cryptographic schemes which allow the verification of users without their identification, so-called Anonymous Attribute-based Credential (ABC) schemes. As the main contribution, we presen t a cryptographic scheme that allows security and privacy-friendly sharing of location information sent through BLE advertising packets. In order to demonstrate the practicality of our scheme, we also present the results from our implementation and benchmarks on different devices. |
Chukhno, Nadezhda; Chukhno, Olga; Pizzi, Sara; Molinaro, Antonella; Iera, Antonio; Araniti, Giuseppe Efficient Management of Multicast Traffic in Directional mmWave Networks Journal Article IEEE Transactions on Broadcasting, 67 (3), pp. 593-605, 2021, ISSN: 1557-9611. Abstract | Links | BibTeX | Tags: A-wear, wearables @article{Chukhno2021a, title = {Efficient Management of Multicast Traffic in Directional mmWave Networks}, author = {Nadezhda Chukhno and Olga Chukhno and Sara Pizzi and Antonella Molinaro and Antonio Iera and Giuseppe Araniti}, doi = {10.1109/TBC.2021.3061979}, issn = {1557-9611}, year = {2021}, date = {2021-09-01}, journal = {IEEE Transactions on Broadcasting}, volume = {67}, number = {3}, pages = {593-605}, abstract = {Multicasting is becoming more and more important in the Internet of Things (IoT) and wearable applications (e.g., high definition video streaming, virtual reality gaming, public safety, among others) that require high bandwidth efficiency and low energy consumption. In this regard, millimeter wave (mmWave) communications can play a crucial role to efficiently disseminate large volumes of data as well as to enhance the throughput gain in fifth-generation (5G) and beyond networks. There are, however, challenges to face in view of providing multicast services with high data rates under the conditions of short propagation range caused by high path loss at mmWave frequencies. Indeed, the strong directionality required at extremely high frequency bands excludes the possibility of serving all multicast users via a single transmission. Therefore, multicasting in directional systems consists of a sequence of beamformed transmissions to serve all multicast group members, subgroup by subgroup. This paper focuses on multicast data transmission optimization in terms of throughput and, hence, of the energy efficiency of resource-constrained devices such as wearables, running their resource-hungry applications. In particular, we provide a means to perform the beam switching and propose a radio resource management (RRM) policy that can determine the number and width of the beams required to deliver the multicast content to all interested users. Achieved simulation results show that the proposed RRM policy significantly improves network throughput with respect to benchmark approaches. It also achieves a high gain in energy efficiency over unicast and multicast with fixed predefined beams.}, keywords = {A-wear, wearables}, pubstate = {published}, tppubtype = {article} } Multicasting is becoming more and more important in the Internet of Things (IoT) and wearable applications (e.g., high definition video streaming, virtual reality gaming, public safety, among others) that require high bandwidth efficiency and low energy consumption. In this regard, millimeter wave (mmWave) communications can play a crucial role to efficiently disseminate large volumes of data as well as to enhance the throughput gain in fifth-generation (5G) and beyond networks. There are, however, challenges to face in view of providing multicast services with high data rates under the conditions of short propagation range caused by high path loss at mmWave frequencies. Indeed, the strong directionality required at extremely high frequency bands excludes the possibility of serving all multicast users via a single transmission. Therefore, multicasting in directional systems consists of a sequence of beamformed transmissions to serve all multicast group members, subgroup by subgroup. This paper focuses on multicast data transmission optimization in terms of throughput and, hence, of the energy efficiency of resource-constrained devices such as wearables, running their resource-hungry applications. In particular, we provide a means to perform the beam switching and propose a radio resource management (RRM) policy that can determine the number and width of the beams required to deliver the multicast content to all interested users. Achieved simulation results show that the proposed RRM policy significantly improves network throughput with respect to benchmark approaches. It also achieves a high gain in energy efficiency over unicast and multicast with fixed predefined beams. |
Weerapanpisit, Ponlawat; Trilles-Oliver, Sergio; Huerta-Guijarro, Joaquín; Painho, Marco Enabling Geospatial Context in an IoT Decentralised Reputation Management System using Ethereum Smart Contracts Inproceedings 2021 IEEE International Conference on Omni-Layer Intelligent Systems (COINS), pp. 148-153, IEEE, 2021, ISBN: 978-1-6654-3156-9. Abstract | Links | BibTeX | Tags: Internet of things, Smart contracts @inproceedings{Weerapanpisit2021a, title = {Enabling Geospatial Context in an IoT Decentralised Reputation Management System using Ethereum Smart Contracts}, author = {Ponlawat Weerapanpisit and Sergio Trilles-Oliver and Joaquín Huerta-Guijarro and Marco Painho}, doi = {https://doi.org/10.1109/COINS51742.2021.9524217}, isbn = {978-1-6654-3156-9}, year = {2021}, date = {2021-09-01}, booktitle = {2021 IEEE International Conference on Omni-Layer Intelligent Systems (COINS)}, pages = {148-153}, publisher = {IEEE}, abstract = {Social Internet of Things (SIoT) is a concept that integrates the Internet of Things and human social networks. An SIoT system has to store and manage device reputation values, which are used by end devices to determine the trustworthiness of another one. This device trustworthiness can also be affected by its geographical location. In this work, we introduced an architecture that includes the geospatial context in the part concerned with reputation management. The proposed architecture is based on the cloud-fog-edge architecture and uses the fog layer as the management system. The devices in the fog layer form an Ethereum Blockchain network and store the Smart Contracts. These in turn allow the management functionalities to be carried out in a decentralised, transparent and secure way, which are the advantages of Blockchain. To enable the characteristics with a geospatial component, it is necessary to apply a geocoding technique. This work shows how geocoding techniques can be adapted to cover the main geospatial functionalities and compares two geocoding options (Geohash or S2). The results showed that it is possible to include the geospatial context in a decentralised reputation management system by using hierarchical geocoding techniques, and the experiments showed that both Geohash and S2 can offer a similar performance in the proposed architecture.}, keywords = {Internet of things, Smart contracts}, pubstate = {published}, tppubtype = {inproceedings} } Social Internet of Things (SIoT) is a concept that integrates the Internet of Things and human social networks. An SIoT system has to store and manage device reputation values, which are used by end devices to determine the trustworthiness of another one. This device trustworthiness can also be affected by its geographical location. In this work, we introduced an architecture that includes the geospatial context in the part concerned with reputation management. The proposed architecture is based on the cloud-fog-edge architecture and uses the fog layer as the management system. The devices in the fog layer form an Ethereum Blockchain network and store the Smart Contracts. These in turn allow the management functionalities to be carried out in a decentralised, transparent and secure way, which are the advantages of Blockchain. To enable the characteristics with a geospatial component, it is necessary to apply a geocoding technique. This work shows how geocoding techniques can be adapted to cover the main geospatial functionalities and compares two geocoding options (Geohash or S2). The results showed that it is possible to include the geospatial context in a decentralised reputation management system by using hierarchical geocoding techniques, and the experiments showed that both Geohash and S2 can offer a similar performance in the proposed architecture. |
Chukhno, Nadezhda; Chukhno, Olga; Pizzi, Sara; Molinaro, Antonella; Iera, Antonio; Araniti, Giuseppe Unsupervised Learning for D2D-Assisted Multicast Scheduling in mmWave Networks Inproceedings 2021 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, pp. 1-6, IEEE, 2021, ISBN: 978-1-6654-4909-0. Abstract | Links | BibTeX | Tags: A-wear, machine learning, wearables @inproceedings{Chukhno2021b, title = {Unsupervised Learning for D2D-Assisted Multicast Scheduling in mmWave Networks}, author = {Nadezhda Chukhno and Olga Chukhno and Sara Pizzi and Antonella Molinaro and Antonio Iera and Giuseppe Araniti}, doi = {10.1109/BMSB53066.2021.9547189}, isbn = {978-1-6654-4909-0}, year = {2021}, date = {2021-08-08}, booktitle = {2021 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting}, pages = {1-6}, publisher = {IEEE}, abstract = {The combination of multicast and directional mmWave communication paves the way for solving spectrum crunch problems, increasing spectrum efficiency, ensuring reliability, and reducing access point load. Furthermore, multi-hop relaying is considered as one of the key interest areas in future 5G+ systems to achieve enhanced system performance. Based on this approach, users located close to the base station may serve as relays towards cell-edge users in their proximity by using more robust device-to-device (D2D) links, which is essential, e.g., to reduce the power consumption for wearable devices. In this paper, we account for the limitations and capabilities of directional mmWave multicast systems by proposing a low-complexity heuristic solution that leverages an unsupervised machine learning algorithm for multicast group formation and by exploiting the D2D technology to deal with the blockage problem.}, keywords = {A-wear, machine learning, wearables}, pubstate = {published}, tppubtype = {inproceedings} } The combination of multicast and directional mmWave communication paves the way for solving spectrum crunch problems, increasing spectrum efficiency, ensuring reliability, and reducing access point load. Furthermore, multi-hop relaying is considered as one of the key interest areas in future 5G+ systems to achieve enhanced system performance. Based on this approach, users located close to the base station may serve as relays towards cell-edge users in their proximity by using more robust device-to-device (D2D) links, which is essential, e.g., to reduce the power consumption for wearable devices. In this paper, we account for the limitations and capabilities of directional mmWave multicast systems by proposing a low-complexity heuristic solution that leverages an unsupervised machine learning algorithm for multicast group formation and by exploiting the D2D technology to deal with the blockage problem. |
Stéphenne, Nathalie; Riedler, Barbara; Aguilar-Moreno, Estefanía; Jagaille, Marie; Monfort-Muriach, Aida; Fiore, Grazia; Antoniou, Natassa Women in Copernicus: Recommendations from Women Testimonials Inproceedings 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, pp. 33-36, IEEE, 2021, ISBN: 978-1-6654-4762-1. Abstract | Links | BibTeX | Tags: Women in Copernicus @inproceedings{Stephenne2021a, title = {Women in Copernicus: Recommendations from Women Testimonials}, author = {Nathalie Stéphenne and Barbara Riedler and Estefanía Aguilar-Moreno and Marie Jagaille and Aida Monfort-Muriach and Grazia Fiore and Natassa Antoniou}, doi = {https://doi.org/10.1109/IGARSS47720.2021.9554567}, isbn = {978-1-6654-4762-1}, year = {2021}, date = {2021-08-01}, booktitle = {2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS}, pages = {33-36}, publisher = {IEEE}, abstract = {Women are an important part of the Copernicus experience. Although they are active and present in the production flow of the Copernicus / Earth Observation (EO) / Geoinformation (GI) domains, they are not always visible. Numerous studies have been carried out in the past on gender inequality in Science, Technology, Engineering and Mathematics (STEM), underlining the need to attract more women/girls into these disciplines. Nevertheless, little information exists on women working in a transversal and relatively new sector like the EO domain, and especially in the current Copernicus ecosystem. The project “Women in Copernicus” (WIC) shed some light into the gender subject from the point of view of women active in the Copernicus field. The WiC project included the implementation of a survey to which 460 women replied. Far from being fully representative of the whole ecosystem, these replies provide a first insight into a subject that deserves further consideration and actions in the future. One of the main conclusions drawn from the analysis of the survey is the need to establish cooperation and bridges between the networks aiming at empowering women in the EO/GI domain that already exist.}, keywords = {Women in Copernicus}, pubstate = {published}, tppubtype = {inproceedings} } Women are an important part of the Copernicus experience. Although they are active and present in the production flow of the Copernicus / Earth Observation (EO) / Geoinformation (GI) domains, they are not always visible. Numerous studies have been carried out in the past on gender inequality in Science, Technology, Engineering and Mathematics (STEM), underlining the need to attract more women/girls into these disciplines. Nevertheless, little information exists on women working in a transversal and relatively new sector like the EO domain, and especially in the current Copernicus ecosystem. The project “Women in Copernicus” (WIC) shed some light into the gender subject from the point of view of women active in the Copernicus field. The WiC project included the implementation of a survey to which 460 women replied. Far from being fully representative of the whole ecosystem, these replies provide a first insight into a subject that deserves further consideration and actions in the future. One of the main conclusions drawn from the analysis of the survey is the need to establish cooperation and bridges between the networks aiming at empowering women in the EO/GI domain that already exist. |
Ometov, Aleksandr; Shubina, Viktoriia; Klus, Lucie; Skibińska, Justyna; Saafi, Salwa; Pascacio-de-los-Santos, Pavel; Flueratoru, Laura; Quezada-Gaibor, Darwin; Chukhno, Nadezhda; Chukhno, Olga; Ali, Asad; Channa, Asma; Svertoka, Ekaterina; Qaim, Waleed Bin; Casanova-Marqués, Raúl; Holcer, Sylvia; Torres-Sospedra, Joaquín; Casteleyn, Sven; Ruggeri, Giuseppe; Araniti, Giuseppe; Burget, Radim; Hosek, Jiri; Lohan, Elena Simona A Survey on Wearable Technology: History, State-of-the-Art and Current Challenges Journal Article Computer Networks, 193 , pp. 108074, 2021, ISSN: 1389-1286. Abstract | Links | BibTeX | Tags: A-wear, interoperability, Standards, wearables @article{Ometov2021, title = {A Survey on Wearable Technology: History, State-of-the-Art and Current Challenges}, author = {Aleksandr Ometov and Viktoriia Shubina and Lucie Klus and Justyna Skibińska and Salwa Saafi and Pavel Pascacio-de-los-Santos and Laura Flueratoru and Darwin Quezada-Gaibor and Nadezhda Chukhno and Olga Chukhno and Asad Ali and Asma Channa and Ekaterina Svertoka and Waleed Bin Qaim and Raúl Casanova-Marqués and Sylvia Holcer and Joaquín Torres-Sospedra and Sven Casteleyn and Giuseppe Ruggeri and Giuseppe Araniti and Radim Burget and Jiri Hosek and Elena Simona Lohan}, doi = {https://doi.org/10.1016/j.comnet.2021.108074}, issn = {1389-1286}, year = {2021}, date = {2021-07-05}, journal = {Computer Networks}, volume = {193}, pages = {108074}, abstract = {Technology is continually undergoing a constituent development caused by the appearance of billions new interconnected “things” and their entrenchment in our daily lives. One of the underlying versatile technologies, namely wearables, is able to capture rich contextual information produced by such devices and use it to deliver a legitimately personalized experience. The main aim of this paper is to shed light on the history of wearable devices and provide a state-of-the-art review on the wearable market. Moreover, the paper provides an extensive and diverse classification of wearables, based on various factors, a discussion on wireless communication technologies, architectures, data processing aspects, and market status, as well as a variety of other actual information on wearable technology. Finally, the survey highlights the critical challenges and existing/future solutions.}, keywords = {A-wear, interoperability, Standards, wearables}, pubstate = {published}, tppubtype = {article} } Technology is continually undergoing a constituent development caused by the appearance of billions new interconnected “things” and their entrenchment in our daily lives. One of the underlying versatile technologies, namely wearables, is able to capture rich contextual information produced by such devices and use it to deliver a legitimately personalized experience. The main aim of this paper is to shed light on the history of wearable devices and provide a state-of-the-art review on the wearable market. Moreover, the paper provides an extensive and diverse classification of wearables, based on various factors, a discussion on wireless communication technologies, architectures, data processing aspects, and market status, as well as a variety of other actual information on wearable technology. Finally, the survey highlights the critical challenges and existing/future solutions. |
González-Pérez, Alberto; Cárdenas, Ramón Mollineda A; Piñana, David Llorens Aprendizaje basado en metodologías ágiles centradas en diseño evolutivo dirigido por pruebas de aceptación Inproceedings Actas de las XXVII Jornadas sobre la Enseñanza universitaria de la Informática (Jenui 2021), pp. 99-106, AENUI, 2021, ISSN: 2531-0607. Abstract | Links | BibTeX | Tags: education, software models @inproceedings{Gonzalez-Perez2021, title = {Aprendizaje basado en metodologías ágiles centradas en diseño evolutivo dirigido por pruebas de aceptación}, author = {Alberto González-Pérez and Ramón A. Mollineda Cárdenas and David Llorens Piñana}, url = {http://bioinfo.uib.es/~joemiro/aenui/procJenui/Jen2021/EC0040.pdf}, issn = {2531-0607}, year = {2021}, date = {2021-07-01}, booktitle = {Actas de las XXVII Jornadas sobre la Enseñanza universitaria de la Informática (Jenui 2021)}, journal = {Actas de las JENUI}, volume = {6}, pages = {99-106}, publisher = {AENUI}, abstract = {Este artículo presenta una experiencia de aprendizaje basado en proyecto a partir de la coordinación docente entre dos asignaturas del Grado en Ingeniería Informática de la Universitat Jaume I, con el objetivo principal de mejorar competencias prácticas en el uso de metodologías ágiles de desarrollo de software muy difíciles de adquirir en asignaturas aisladas. La propuesta consiste en un proyecto de prácticas compartido entre las asignaturas Diseño de software y Paradigmas de software, las cuales se imparten en el primer cuatrimestre del cuarto curso en la intensificación en Ingeniería de Software. La primera asignatura introduce fundamentos de diseño de software, mientras que la segunda estudia la metodología ágil Desarrollo Dirigido por Pruebas de Aceptación (ATDD, de Acceptance Test Driven Development). El proyecto fue concebido para promover estrategias de diseño evolutivo de arriba a abajo centradas en la gestión eficiente de dependencias, según necesidades de usuarios formuladas en términos de pruebas de aceptación ejecutables escritas antes de diseñar el código objetivo. La especificación incluyó el uso de tecnologías de desarrollo web, aplicaciones móviles y servicios en la nube, contexto en el que se generaron escenarios ricos en gestión de dependencias desde la doble perspectiva del diseño y de la validación del software. Además de fomentar valores de la cultura ágil, la propuesta pretendía eliminar tareas redundantes (presentes en proyectos diferentes) y ofrecer una experiencia más cercana al desarrollo de soluciones profesionales. Los resultados de una encuesta revelaron un alumnado motivado con un proyecto realista, así como la percepción mayoritaria de haber experimentado principios claves del diseño y desarrollo ágil bajo condiciones de incertidumbres.}, keywords = {education, software models}, pubstate = {published}, tppubtype = {inproceedings} } Este artículo presenta una experiencia de aprendizaje basado en proyecto a partir de la coordinación docente entre dos asignaturas del Grado en Ingeniería Informática de la Universitat Jaume I, con el objetivo principal de mejorar competencias prácticas en el uso de metodologías ágiles de desarrollo de software muy difíciles de adquirir en asignaturas aisladas. La propuesta consiste en un proyecto de prácticas compartido entre las asignaturas Diseño de software y Paradigmas de software, las cuales se imparten en el primer cuatrimestre del cuarto curso en la intensificación en Ingeniería de Software. La primera asignatura introduce fundamentos de diseño de software, mientras que la segunda estudia la metodología ágil Desarrollo Dirigido por Pruebas de Aceptación (ATDD, de Acceptance Test Driven Development). El proyecto fue concebido para promover estrategias de diseño evolutivo de arriba a abajo centradas en la gestión eficiente de dependencias, según necesidades de usuarios formuladas en términos de pruebas de aceptación ejecutables escritas antes de diseñar el código objetivo. La especificación incluyó el uso de tecnologías de desarrollo web, aplicaciones móviles y servicios en la nube, contexto en el que se generaron escenarios ricos en gestión de dependencias desde la doble perspectiva del diseño y de la validación del software. Además de fomentar valores de la cultura ágil, la propuesta pretendía eliminar tareas redundantes (presentes en proyectos diferentes) y ofrecer una experiencia más cercana al desarrollo de soluciones profesionales. Los resultados de una encuesta revelaron un alumnado motivado con un proyecto realista, así como la percepción mayoritaria de haber experimentado principios claves del diseño y desarrollo ágil bajo condiciones de incertidumbres. |
IF Journal
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2022 |
Instrumented Timed Up and Go Test Using Inertial Sensors from Consumer Wearable Devices Inproceedings Artificial Intelligence in Medicine. AIME 2022, pp. 144-154, Springer, Cham, 2022, ISBN: 978-3031093418. |
TrackInFactory: A Tight Coupling Particle Filter for Industrial Vehicle Tracking in Indoor Environments Journal Article IEEE Transactions on Systems, Man, and Cybernetics: Systems, 52 (7), pp. 4151 - 4162, 2022, ISSN: 2168-2232. |
Comparison of Nitrogen Dioxide Predictions During a Pandemic and Non-pandemic Scenario in the City of Madrid using a Convolutional LSTM Network Journal Article International Journal of Computational Intelligence and Applications, 21 (2), pp. 2250014, 2022, ISSN: 1757-5885. |
Lightweight Hybrid CNN-ELM Model for Multi-building and Multi-floor Classification Inproceedings 2022 International Conference on Localization and GNSS (ICL-GNSS), pp. 1-6, IEEE, 2022. |
Towards Accelerated Localization Performance Across Indoor Positioning Datasets Inproceedings 2022 International Conference on Localization and GNSS (ICL-GNSS), pp. 1-7, IEEE, 2022. |
Exploratory Analysis and Feature Selection for the Prediction of Nitrogen Dioxide Inproceedings AGILE GIScience Series (Proceedings of the 25th AGILE Conference on Geographic Information Science), Copernicus Publications, 2022. |
Updating and using the EO4GEO Body of Knowledge for (AI) concept annotation Inproceedings AGILE GIScience Series (Proceedings of the 25th AGILE Conference on Geographic Information Science), Copernicus Publications, 2022. |
Data Cleansing for Indoor Positioning Wi-Fi Fingerprinting Datasets Inproceedings 2022 23rd IEEE International Conference on Mobile Data Management (MDM), pp. 349-354, IEEE, 2022, ISBN: 978-1-6654-5176-5. |
Mobile device-based Bluetooth Low Energy Database for range estimation in indoor environments Journal Article Scientific Data, 9 (281), 2022, ISSN: 2052-4463. |
Integrating concepts of artificial intelligence in the EO4GEO Body of Knowledge Inproceedings The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (XXIV ISPRS Congress), pp. 53-59, Copernicus Publications, 2022, ISSN: 2194-9034. |
Bidirectional convolutional LSTM for the prediction of nitrogen dioxide in the city of Madrid Journal Article PLOS ONE, 17 (6), pp. e0269295, 2022, ISSN: 932-6203. |
Sucre4Stem: Collaborative Projects Based on IoT Devices for Students in Secondary and Pre-University Education Journal Article IEEE Revista Iberoamericana de Tecnologias del Aprendizaje, 17 (2), pp. 150-159, 2022, ISSN: 1932-8540. |
Application of deep learning and machine learning in air quality modeling Book Chapter Marques, Gonçalo; Ighalo, Joshua (Ed.): pp. 11-23, Elsevier, 2022, ISBN: 9780323855976. |
Client’s Experiences Using a Location-Based Technology ICT System during Gambling Treatments’ Crucial Components: A Qualitative Study Journal Article International Journal of Environmental Research and Public Health, 19 (7), pp. 3769, 2022, ISSN: 1660-4601. |
Guest Editorial Special Issue on Advanced Sensors and Sensing Technologies for Indoor Positioning and Navigation Journal Article IEEE Sensors Journal, 22 (6), pp. 4754-4754, 2022, ISBN: 1558-1748. |
Off-Line Evaluation of Indoor Positioning Systems in Different Scenarios: The Experiences From IPIN 2020 Competition Journal Article IEEE Sensors Journal, 22 (6), pp. 5011-5054, 2022, ISSN: 1558-1748. |
D2D-Based Cooperative Positioning Paradigm for Future Wireless Systems: A Survey Journal Article IEEE Sensors Journal, 22 (6), pp. 5101-5112, 2022, ISSN: 1558-1748. |
Environment-Aware Regression for Indoor Localization Based on WiFi Fingerprinting Journal Article IEEE Sensors Journal, 22 (6), pp. 4978-4988, 2022, ISSN: 1558-1748. |
Building a Gold Standard Dataset to Identify Articles About Geographic Information Science Journal Article IEEE Access, 10 , pp. 19926-19936, 2022, ISSN: 2169-3536. |
Using Mobile Devices as Scientific Measurements Instruments: Reliable Android Task Scheduling Journal Article Pervasive and Mobile Computing, 81 (101550), 2022, ISBN: 1574-1192. |
Hypnos: A Hardware and Software Toolkit for Energy-Aware Sensing in Low-Cost IoT Nodes Journal Article IEEE Internet of Things Journal, 9 (15), pp. 13524-13541, 2022, ISBN: 2327-4662. |
Cloud Platforms for Context-Adaptive Positioning and Localisation in GNSS-Denied Scenarios—A Systematic Review Journal Article Sensors, 22 (1), pp. 110, 2022, ISSN: 1424-8220. |
Emerging approaches for data-driven innovation in Europe Book Publications Office of the European Union, Luxemburg, 2022, ISBN: 978-92-76-46937-7. |
Lockdown lessons: an international conversation on resilient GI science teaching Journal Article Journal of Geography in Higher Education, 46 (1), pp. 7-19, 2022, ISSN: 0309-8265. |
2021 |
BMJ Open, 11 (e054286), 2021, ISSN: 2044-6055. |
Affinity Propagation Clustering for Older Adults Daily Routine Estimation Inproceedings Proceedings of the Eleventh International Conference on Indoor Positioning and Indoor Navigation, IEEE, 2021. |
Dioptra – A Data Generation Application for Indoor Positioning Systems Inproceedings Proceedings of the Eleventh International Conference on Indoor Positioning and Indoor Navigation, IEEE, 2021. |
Finding Optimal BLE Configuration for Indoor Positioning with Consumption Restrictions Inproceedings Proceedings of the Eleventh International Conference on Indoor Positioning and Indoor Navigation, IEEE, 2021. |
Lightweight Wi-Fi Fingerprinting with a Novel RSS Clustering Algorithm Inproceedings Proceedings of the Eleventh International Conference on Indoor Positioning and Indoor Navigation, IEEE, 2021. |
New trends in indoor positioning based on WiFi and machine learning: A systematic review Inproceedings Proceedings of the Eleventh International Conference on Indoor Positioning and Indoor Navigation, IEEE, 2021. |
Quantifying the Degradation of Radio Maps in Wi-Fi Fingerprinting Inproceedings Proceedings of the Eleventh International Conference on Indoor Positioning and Indoor Navigation, IEEE, 2021. |
Revisiting the Analysis of Hyperparameters in k-NN for Wi-Fi and BLE Fingerprinting: Current Status and General Results Inproceedings Proceedings of the Eleventh International Conference on Indoor Positioning and Indoor Navigation, IEEE, 2021. |
Towards Ubiquitous Indoor Positioning: Comparing Systems across Heterogeneous Datasets Inproceedings Proceedings of the Eleventh International Conference on Indoor Positioning and Indoor Navigation, IEEE, 2021. |
Transfer Learning for Convolutional Indoor Positioning Systems Inproceedings Proceedings of the Eleventh International Conference on Indoor Positioning and Indoor Navigation, IEEE, 2021. |
Data on CO2, temperature and air humidity records in Spanish classrooms during the reopening of schools in the COVID-19 pandemic Journal Article Data in Brief, 39 , pp. 107489, 2021, ISSN: 2352-3409. |
Sense of place and the city: the case of non-native residents in Lisbon Journal Article Journal of Spatial Information Science, (23), pp. 125-155, 2021, ISSN: 1948-660X. |
A Lateration Method based on Effective Combinatorial Beacon Selection for Bluetooth Low Energy Indoor Positioning Inproceedings 2021 17th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), pp. 397-402, IEEE, 2021, ISBN: 978-1-6654-2854-5. |
A Serious Game to Battle Depression Inproceedings Extended Abstracts of the 2021 Annual Symposium on Computer-Human Interaction in Play (CHI PLAY '21), pp. 401-402, ACM, 2021, ISBN: 9781450383561. |
Horizon: Resilience – Design of a Serious Game for Ecological Momentary Intervention for Depression Inproceedings Extended Abstracts of the 2021 Annual Symposium on Computer-Human Interaction in Play (CHI PLAY '21), pp. 236–241, ACM, 2021, ISBN: 9781450383561. |
Reproducible Research and GIScience: an evaluation using GIScience conference papers Inproceedings Janowicz, K; Verstegen, J A (Ed.): Proceedings of the 11th International Conference on Geographic Information Science - Part II, pp. 2:1–2:16, LIPIcs, 2021, ISBN: 78-3-95977-208-2. |
Development of a Common Framework for Analysing Public Transport Smart Card Data Journal Article Energies, 14 (19), pp. 6083, 2021, ISSN: 1996-1073. |
Algorithms Air Quality Estimation: A Comparative Study of Stochastic and Heuristic Predictive Inproceedings Hybrid Artificial Intelligent Systems (International Conference on Hybrid Artificial Intelligence Systems), pp. 293-304, Springer, Cham, 2021, ISBN: 978-3-030-86271-8. |
Artificial Intelligence, Machine Learning, and Optimization Tools for Smart Cities, 186 , pp. 155-175, Springer, Cham, 2021, ISBN: 978-3-030-84459-2. |
Anonymous Attribute-based Credentials in Collaborative Indoor Positioning Systems Inproceedings Proceedings of the 18th International Conference on Security and Cryptography (SECRYPT 2021), pp. 791-797, SciTePress, 2021, ISBN: 978-989-758-524-1. |
Efficient Management of Multicast Traffic in Directional mmWave Networks Journal Article IEEE Transactions on Broadcasting, 67 (3), pp. 593-605, 2021, ISSN: 1557-9611. |
Enabling Geospatial Context in an IoT Decentralised Reputation Management System using Ethereum Smart Contracts Inproceedings 2021 IEEE International Conference on Omni-Layer Intelligent Systems (COINS), pp. 148-153, IEEE, 2021, ISBN: 978-1-6654-3156-9. |
Unsupervised Learning for D2D-Assisted Multicast Scheduling in mmWave Networks Inproceedings 2021 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, pp. 1-6, IEEE, 2021, ISBN: 978-1-6654-4909-0. |
Women in Copernicus: Recommendations from Women Testimonials Inproceedings 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, pp. 33-36, IEEE, 2021, ISBN: 978-1-6654-4762-1. |
A Survey on Wearable Technology: History, State-of-the-Art and Current Challenges Journal Article Computer Networks, 193 , pp. 108074, 2021, ISSN: 1389-1286. |
Aprendizaje basado en metodologías ágiles centradas en diseño evolutivo dirigido por pruebas de aceptación Inproceedings Actas de las XXVII Jornadas sobre la Enseñanza universitaria de la Informática (Jenui 2021), pp. 99-106, AENUI, 2021, ISSN: 2531-0607. |