2026
Journal article
Open Access
Navigation solutions for blind and visually impaired persons: a state-of-the-art survey
Belli Dimitri, Barsocchi Paolo, Lombardi Giuseppe, Coffrini Alberto, Furfari Francesco, Crivello Antonino
This survey provides a comprehensive overview of navigation solutions designed for Blind and Visually Impaired (BVI) individuals, analyzing 75 systematically selected papers and focusing on three critical aspects. First, it identifies a significant gap in the literature, highlighting the lack of seamless indoor/outdoor navigation systems that can support uninterrupted mobility for users in different environments. Secondly, it highlights the limited attention given to inclusivity factors such as usability, accessibility, user experience, and co-design when developing these solutions. Finally, it assesses the technological readiness of current navigation systems by evaluating their ability to effectively meet the needs of BVI persons in real-world scenarios. Furthermore, this study provides a complete open-access repository of the analyzed data to support reproducibility. The results of this study are intended to guide future research and development efforts toward creating more comprehensive, user-centered navigation solutions.Source: INTERNATIONAL JOURNAL OF HUMAN-COMPUTER INTERACTION, pp. 1-30
DOI: 10.1080/10447318.2026.2643358
Metrics:
Belli Dimitri, Barsocchi Paolo, Lombardi Giuseppe, Coffrini Alberto, Furfari Francesco, Crivello Antonino
This survey provides a comprehensive overview of navigation solutions designed for Blind and Visually Impaired (BVI) individuals, analyzing 75 systematically selected papers and focusing on three critical aspects. First, it identifies a significant gap in the literature, highlighting the lack of seamless indoor/outdoor navigation systems that can support uninterrupted mobility for users in different environments. Secondly, it highlights the limited attention given to inclusivity factors such as usability, accessibility, user experience, and co-design when developing these solutions. Finally, it assesses the technological readiness of current navigation systems by evaluating their ability to effectively meet the needs of BVI persons in real-world scenarios. Furthermore, this study provides a complete open-access repository of the analyzed data to support reproducibility. The results of this study are intended to guide future research and development efforts toward creating more comprehensive, user-centered navigation solutions.Source: INTERNATIONAL JOURNAL OF HUMAN-COMPUTER INTERACTION, pp. 1-30
DOI: 10.1080/10447318.2026.2643358
Metrics:
See at:
International Journal of Human-Computer Interaction 
| CNR IRIS | www.tandfonline.com | CNR IRIS 
2026
Journal article
Open Access
Evaluation of underfloor accelerometers for enabling location-based services in intelligent environments
Belli Dimitri, Crivello Antonino, La Rosa Davide, Barsocchi Paolo
Device-free indoor localization systems play a pivotal role in enhancing the functionality and intelligence of modern environments. They can effectively monitor people’s movements in their everyday environment without the constraints of invasive or wearable devices, and are open to a wide range of application domains. Through a systematic experimental approach, in this work we investigate the performance of underfloor accelerometers in accurately detecting and tracking user movements. The collected data, augmented with ground truth information, are analyzed using fingerprint maps and k-Nearest Neighbor (k-NN) algorithms to estimate the user’s position within the environment. In the literature, this work represents a first attempt to apply the fingerprint technique in this context. The results show promising capabilities of underfloor accelerometers in facilitating location-based services, while the short time required for installation, data pre-processing and calibration indicate this approach as an easy-to-deploy location-based system. In this regard, intra-user tests show that the variability of the error exceeds 1 m regardless of k-values or time windows, inter-user tests show that the time window does not affect the variability of distance estimation with 2-NN, which outperforms other k-configurations, while 3-NN performs better as the time window increases. The cumulative distribution function over the entire test set shows that more than 75% of the predictions are less than 1 m.Source: INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS, vol. 19 (issue 1)
DOI: 10.1007/s44196-026-01205-2
Project(s): PE8 - Conseguenze e sfide dell'invecchiamento
Metrics:
Belli Dimitri, Crivello Antonino, La Rosa Davide, Barsocchi Paolo
Device-free indoor localization systems play a pivotal role in enhancing the functionality and intelligence of modern environments. They can effectively monitor people’s movements in their everyday environment without the constraints of invasive or wearable devices, and are open to a wide range of application domains. Through a systematic experimental approach, in this work we investigate the performance of underfloor accelerometers in accurately detecting and tracking user movements. The collected data, augmented with ground truth information, are analyzed using fingerprint maps and k-Nearest Neighbor (k-NN) algorithms to estimate the user’s position within the environment. In the literature, this work represents a first attempt to apply the fingerprint technique in this context. The results show promising capabilities of underfloor accelerometers in facilitating location-based services, while the short time required for installation, data pre-processing and calibration indicate this approach as an easy-to-deploy location-based system. In this regard, intra-user tests show that the variability of the error exceeds 1 m regardless of k-values or time windows, inter-user tests show that the time window does not affect the variability of distance estimation with 2-NN, which outperforms other k-configurations, while 3-NN performs better as the time window increases. The cumulative distribution function over the entire test set shows that more than 75% of the predictions are less than 1 m.Source: INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS, vol. 19 (issue 1)
DOI: 10.1007/s44196-026-01205-2
Project(s): PE8 - Conseguenze e sfide dell'invecchiamento
Metrics:
See at:
CNR IRIS | link.springer.com | CNR IRIS
2026
Journal article
Open Access
Effectiveness of changing stimulus frequency and intensity of sound for evaluating hearing by monitoring pupil responses
Tramonti Fantozzi Mara Paola, Crivello Antonino, La Rosa Davide, Marconi Ottavia, Milazzo Mario, Manzoni Diego, Fiacchini Giacomo, Bruschini Luca
Assessing hearing through the pupil dilation response (PDR) to sound offers a non-invasive alternative to traditional audiometry, particularly useful in individuals unable to provide behavioral responses. However, reliable detection of sound-evoked PDRs is limited by variability in pupil size and the risk of false positives (Type I Errors). This study investigated whether pseudo-random variation of sound frequency and amplitude enhances the correspondence between PDR detection and sound perception in normal-hearing subjects. To improve reliability, epochs with different pre-stimulus pupil sizes were analyzed separately, as baseline pupil diameter affects PDR amplitude. Pupil responses to auditory stimuli were recorded, averaged, and compared with baseline data obtained in silence. Additional averages were computed for specific frequencies, amplitudes, and pre-stimulus pupil size ranges. Sub-threshold PDR traces were further processed by averaging all possible differences between sound and baseline frames. The probability of Type I Errors was estimated using randomly sampled baseline data. Results show that using stimuli with varying frequency and amplitude markedly increased PDR retrieval compared with standard constant-tone paradigms. Restricting analysis to defined pre-stimulus pupil size ranges or specific stimulus parameters further improved detection rates. The difference-averaging method confirmed several low-amplitude PDRs as genuine responses, though some may still occur by chance. Overall, significant PDRs were observed in 80% of participants, indicating that while this approach enhances sensitivity, further refinement is needed before clinical application. More efficient paradigms are required to establish reliable, objective hearing assessment “by looking into the eyes.”Source: BIOMEDICAL SIGNAL PROCESSING AND CONTROL, vol. 120-A (issue 110035)
DOI: 10.1016/j.bspc.2026.110035
Project(s): Audiometry with Pupil Response
Metrics:
Tramonti Fantozzi Mara Paola, Crivello Antonino, La Rosa Davide, Marconi Ottavia, Milazzo Mario, Manzoni Diego, Fiacchini Giacomo, Bruschini Luca
Assessing hearing through the pupil dilation response (PDR) to sound offers a non-invasive alternative to traditional audiometry, particularly useful in individuals unable to provide behavioral responses. However, reliable detection of sound-evoked PDRs is limited by variability in pupil size and the risk of false positives (Type I Errors). This study investigated whether pseudo-random variation of sound frequency and amplitude enhances the correspondence between PDR detection and sound perception in normal-hearing subjects. To improve reliability, epochs with different pre-stimulus pupil sizes were analyzed separately, as baseline pupil diameter affects PDR amplitude. Pupil responses to auditory stimuli were recorded, averaged, and compared with baseline data obtained in silence. Additional averages were computed for specific frequencies, amplitudes, and pre-stimulus pupil size ranges. Sub-threshold PDR traces were further processed by averaging all possible differences between sound and baseline frames. The probability of Type I Errors was estimated using randomly sampled baseline data. Results show that using stimuli with varying frequency and amplitude markedly increased PDR retrieval compared with standard constant-tone paradigms. Restricting analysis to defined pre-stimulus pupil size ranges or specific stimulus parameters further improved detection rates. The difference-averaging method confirmed several low-amplitude PDRs as genuine responses, though some may still occur by chance. Overall, significant PDRs were observed in 80% of participants, indicating that while this approach enhances sensitivity, further refinement is needed before clinical application. More efficient paradigms are required to establish reliable, objective hearing assessment “by looking into the eyes.”Source: BIOMEDICAL SIGNAL PROCESSING AND CONTROL, vol. 120-A (issue 110035)
DOI: 10.1016/j.bspc.2026.110035
Project(s): Audiometry with Pupil Response
Metrics:
See at:
Biomedical Signal Processing and Control | CNR IRIS | www.sciencedirect.com | CNR IRIS
2025
Conference article
Open Access
Reducing training data for indoor positioning through physics-informed neural networks
Lombardi G., Crivello A., Barsocchi P., Chessa S., Furfari F.
In this work, we propose a novel framework based on Physics-Informed Neural Networks (PINNs) for directly estimating indoor positions, a method that, to the best of our knowledge, has not been previously explored. Training is performed on a public BLE dataset that includes a variety of indoor scenarios, including Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) conditions caused by human body signal attenuation. The integration of physics-compliant synthetic data during the training phase significantly reduces dependence on large-scale real-world datasets, enabling the use of a simple Multilayer Perceptron (MLP) architecture. Our results demonstrate that combining PINNs with real-world measurements enhances model generalization without compromising accuracy.DOI: 10.1109/ipin66788.2025.11213454
Project(s): A novel public-private alliance to generate socioeconomic, biomedical and technological solutions for an inclusive Italian ageing society
Metrics:
Lombardi G., Crivello A., Barsocchi P., Chessa S., Furfari F.
In this work, we propose a novel framework based on Physics-Informed Neural Networks (PINNs) for directly estimating indoor positions, a method that, to the best of our knowledge, has not been previously explored. Training is performed on a public BLE dataset that includes a variety of indoor scenarios, including Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) conditions caused by human body signal attenuation. The integration of physics-compliant synthetic data during the training phase significantly reduces dependence on large-scale real-world datasets, enabling the use of a simple Multilayer Perceptron (MLP) architecture. Our results demonstrate that combining PINNs with real-world measurements enhances model generalization without compromising accuracy.DOI: 10.1109/ipin66788.2025.11213454
Project(s): A novel public-private alliance to generate socioeconomic, biomedical and technological solutions for an inclusive Italian ageing society
Metrics:
See at:
CNR IRIS | ieeexplore.ieee.org | CNR IRIS | CNR IRIS
2025
Journal article
Open Access
Comprehensive assessment of open science practices in indoor positioning: open data, code, and material
Anagnostopoulos G. G., Barsocchi P., Crivello A., Pendão C., Silva I., Torres-Sospedra J.
Transparency and verifiability have long been regarded as cornerstones of the scientific ethos and practice. However, persistent reproducibility challenges across numerous disciplines have brought renewed attention to the imperative for widespread adoption of open science practices. These considerations are particularly relevant to the research field of indoor positioning. Open data and open code sharing are gradually gaining traction in the field, but are still far from standard practice. This study comprehensively evaluates the extent of the adoption of open science practices within the community of the International Conference on Indoor Positioning and Indoor Navigation (IPIN), by systematically analyzing all reference papers from the 2019 to 2024 editions of the IPIN. The work thoroughly examines the open data and code usage, and the use of other types of open materials while performing a particular close-up review of the open data that are leveraged in these studies. Our findings reveal that 21.7% of papers use open research data, 8.3% utilize open code, and 20.2% incorporate other open materials. However, only 6.8% of papers provide both open data and code. Moreover, emerging patterns and intuitive best practices are highlighted. The complete characterization of all reviewed publications is publicly available. This study brings to light the need for wider adoption of open science practices, to enhance the transparency, reproducibility, replicability, and reliability of research outcomes in the field of indoor positioning.Source: IEEE JOURNAL OF INDOOR AND SEAMLESS POSITIONING AND NAVIGATION, vol. 3, pp. 175-194
DOI: 10.1109/jispin.2025.3570258
DOI: 10.5281/zenodo.14931103
DOI: 10.5281/zenodo.14931104
Metrics:
Anagnostopoulos G. G., Barsocchi P., Crivello A., Pendão C., Silva I., Torres-Sospedra J.
Transparency and verifiability have long been regarded as cornerstones of the scientific ethos and practice. However, persistent reproducibility challenges across numerous disciplines have brought renewed attention to the imperative for widespread adoption of open science practices. These considerations are particularly relevant to the research field of indoor positioning. Open data and open code sharing are gradually gaining traction in the field, but are still far from standard practice. This study comprehensively evaluates the extent of the adoption of open science practices within the community of the International Conference on Indoor Positioning and Indoor Navigation (IPIN), by systematically analyzing all reference papers from the 2019 to 2024 editions of the IPIN. The work thoroughly examines the open data and code usage, and the use of other types of open materials while performing a particular close-up review of the open data that are leveraged in these studies. Our findings reveal that 21.7% of papers use open research data, 8.3% utilize open code, and 20.2% incorporate other open materials. However, only 6.8% of papers provide both open data and code. Moreover, emerging patterns and intuitive best practices are highlighted. The complete characterization of all reviewed publications is publicly available. This study brings to light the need for wider adoption of open science practices, to enhance the transparency, reproducibility, replicability, and reliability of research outcomes in the field of indoor positioning.Source: IEEE JOURNAL OF INDOOR AND SEAMLESS POSITIONING AND NAVIGATION, vol. 3, pp. 175-194
DOI: 10.1109/jispin.2025.3570258
DOI: 10.5281/zenodo.14931103
DOI: 10.5281/zenodo.14931104
Metrics:
See at:
IEEE Journal of Indoor and Seamless Positioning and Navigation | CNR IRIS | ieeexplore.ieee.org | ZENODO | ZENODO | ZENODO | CNR IRIS
2025
Journal article
Open Access
Challenges in using pupil dilation responses to sounds as a reliable alternative to standard audiometric tests
Tramonti Fantozzi M. P., Crivello A., La Rosa D., Milazzo M., Danti S., De Cicco V., Orsini P., Manzoni D., Lazzerini F., Canelli R., Fiacchini G., Bruschini L.
Assessing hearing in neonates and uncooperative patients can be challenging. Pupil dilation response (PDR) as an objective physiological measure may offer a solution. To test its feasibility, PDRs were averaged in response to a sequence of 60–100 audible tones (same frequency and amplitude). This was performed in subjects with normal hearing and communication abilities, who were exposed to two different lighting levels. We evaluated whether averaged post-stimulus intervals of PDRs were significantly different from randomly generated averages of pupil traces recorded in the absence of sound stimulation from the same subject. This analysis was repeated for the first, second, and third block of PDRs to account for possible adaptation phenomena. Although all the participants clearly perceived the tones, significant PDRs in response to sound were only detected in a fraction of subjects, primarily in the low luminance condition. Consequently, only in the low luminance group, the grand average of individual PDRs was significantly larger than that obtained for traces recorded in the absence of sound input. In this most favorable condition, when the three blocks of PDRs were averaged separately, significant PDRs were observed in 40 % of the subjects in at least one of the blocks. Therefore, the PDR to sound input is not a reliable indicator of hearing perception when standard audiometric stimuli of the same amplitude and frequency are used. Possible modifications to sound input and stimulation protocols for obtaining reliable PDRs in diagnosing and treating hearing impairments are discussed.Source: HELIYON, vol. 11 (issue 4)
DOI: 10.1016/j.heliyon.2025.e42666
Project(s): APURE - Audiometry with PUpil REsponse
Metrics:
Tramonti Fantozzi M. P., Crivello A., La Rosa D., Milazzo M., Danti S., De Cicco V., Orsini P., Manzoni D., Lazzerini F., Canelli R., Fiacchini G., Bruschini L.
Assessing hearing in neonates and uncooperative patients can be challenging. Pupil dilation response (PDR) as an objective physiological measure may offer a solution. To test its feasibility, PDRs were averaged in response to a sequence of 60–100 audible tones (same frequency and amplitude). This was performed in subjects with normal hearing and communication abilities, who were exposed to two different lighting levels. We evaluated whether averaged post-stimulus intervals of PDRs were significantly different from randomly generated averages of pupil traces recorded in the absence of sound stimulation from the same subject. This analysis was repeated for the first, second, and third block of PDRs to account for possible adaptation phenomena. Although all the participants clearly perceived the tones, significant PDRs in response to sound were only detected in a fraction of subjects, primarily in the low luminance condition. Consequently, only in the low luminance group, the grand average of individual PDRs was significantly larger than that obtained for traces recorded in the absence of sound input. In this most favorable condition, when the three blocks of PDRs were averaged separately, significant PDRs were observed in 40 % of the subjects in at least one of the blocks. Therefore, the PDR to sound input is not a reliable indicator of hearing perception when standard audiometric stimuli of the same amplitude and frequency are used. Possible modifications to sound input and stimulation protocols for obtaining reliable PDRs in diagnosing and treating hearing impairments are discussed.Source: HELIYON, vol. 11 (issue 4)
DOI: 10.1016/j.heliyon.2025.e42666
Project(s): APURE - Audiometry with PUpil REsponse
Metrics:
See at:
Heliyon | Archivio della Ricerca - Università di Pisa | CNR IRIS | Heliyon | www.sciencedirect.com | Archivio della Ricerca - Università di Pisa | CNR IRIS
2025
Journal article
Open Access
Beyond prototypes: what is missing to fill the gaps in IoT-enabled hydroponics platforms
Sportelli M., La Rosa D., Crivello A., Pineda-Medina Dunia, Bacco M., Barsocchi P.
Hydroponic agriculture, when combined with Internet of Things (IoT) technologies, provides a promising pathway to sustainable and efficient food production. This paper aims to systematically review and analyze recent advancements in IoT-based management for hydroponic systems, with a particular focus on assessing the technological maturity of current solutions, identifying existing gaps, and outlining promising directions for future research and development. Based on a review of 74 recent studies, the findings reveal a fragmented landscape characterized by custom-built solutions, predominantly relying on open-source microcontrollers and WiFi connectivity, but with limited adoption of standardized protocols and interoperable platforms. The majority of applications emphasize monitoring of core hydroponic parameters such as pH, EC, and temperature, while emerging uses of machine learning remain at an early stage. Few systems demonstrate readiness for commercial deployment or integration within broader smart agriculture ecosystems. By clarifying the current state of IoT-enabled hydroponics, this review highlights both the opportunities and the challenges in advancing from isolated prototypes toward robust, scalable systems capable of real-world application.Source: HORTICULTURAE, vol. 11 (issue 11)
DOI: 10.3390/horticulturae11111322
Metrics:
Sportelli M., La Rosa D., Crivello A., Pineda-Medina Dunia, Bacco M., Barsocchi P.
Hydroponic agriculture, when combined with Internet of Things (IoT) technologies, provides a promising pathway to sustainable and efficient food production. This paper aims to systematically review and analyze recent advancements in IoT-based management for hydroponic systems, with a particular focus on assessing the technological maturity of current solutions, identifying existing gaps, and outlining promising directions for future research and development. Based on a review of 74 recent studies, the findings reveal a fragmented landscape characterized by custom-built solutions, predominantly relying on open-source microcontrollers and WiFi connectivity, but with limited adoption of standardized protocols and interoperable platforms. The majority of applications emphasize monitoring of core hydroponic parameters such as pH, EC, and temperature, while emerging uses of machine learning remain at an early stage. Few systems demonstrate readiness for commercial deployment or integration within broader smart agriculture ecosystems. By clarifying the current state of IoT-enabled hydroponics, this review highlights both the opportunities and the challenges in advancing from isolated prototypes toward robust, scalable systems capable of real-world application.Source: HORTICULTURAE, vol. 11 (issue 11)
DOI: 10.3390/horticulturae11111322
Metrics:
See at:
Horticulturae | CNR IRIS | www.mdpi.com | CNR IRIS
2025
Journal article
Open Access
StepLogger and EvaalScore: the software suite of the IPIN onsite indoor localization competition
Girolami M., Baronti P., Potortì F., Crivello A., Palumbo F.
This paper illustrates the software suite developed for Track 1 of the IPIN competition, which evaluates smartphone apps for indoor localization. Competitors have one day before the trial day to survey the competition area. On the trial day, an independent “actor” carries the competing system on smartphone and walks a predefined path. Competing systems provide continuous location estimates, which are later compared to a ground truth. We describe the software suite used to gather and present the results: the StepLogger Android application for real-time logging of position estimates and the EvaalScore tool for performance evaluation. StepLogger collects location estimating data from competitors with a timestamp, while EvaalScore calculates the accuracy of the competing systems. The competition ranking is based on the third quartile of point localization error. The presented software suite ensures a standardized and fair assessment of competing systems, thus promoting reproducibility and transparency in indoor localization research.Source: SOFTWAREX, vol. 30
DOI: 10.1016/j.softx.2025.102115
Project(s): Age-IT
Metrics:
Girolami M., Baronti P., Potortì F., Crivello A., Palumbo F.
This paper illustrates the software suite developed for Track 1 of the IPIN competition, which evaluates smartphone apps for indoor localization. Competitors have one day before the trial day to survey the competition area. On the trial day, an independent “actor” carries the competing system on smartphone and walks a predefined path. Competing systems provide continuous location estimates, which are later compared to a ground truth. We describe the software suite used to gather and present the results: the StepLogger Android application for real-time logging of position estimates and the EvaalScore tool for performance evaluation. StepLogger collects location estimating data from competitors with a timestamp, while EvaalScore calculates the accuracy of the competing systems. The competition ranking is based on the third quartile of point localization error. The presented software suite ensures a standardized and fair assessment of competing systems, thus promoting reproducibility and transparency in indoor localization research.Source: SOFTWAREX, vol. 30
DOI: 10.1016/j.softx.2025.102115
Project(s): Age-IT
Metrics:
See at:
CNR IRIS | www.sciencedirect.com | CNR IRIS | CNR IRIS
2025
Journal article
Open Access
ORDIP: Principle, practice and guidelines for open research data in indoor positioning
Anagnostopoulos G. G., Barsocchi P., Crivello A., Pendão C., Silva I., Torres-Sospedra J.
The community of indoor positioning research has identified the need for a paradigm shift towards more reproducible and open research dissemination. Despite recent efforts to openly share data and code, accompanying research results with Open Research Data (ORD) is far from being the de facto standard option for publications in the indoor positioning field. The lack of recognized public benchmarks and the rather slow adoption of ORD, set a great volume of astute contributions in the field to remain irreproducible. Performance comparisons may often be made on experiments performed in different settings, hindering their consistency, and eventually slowing down progress and the evolution of knowledge in the field. In this work, we systematically review the landscape of Open Research Data in Indoor Positioning, enlisting, presenting, and analyzing the characteristic features of the relevant available open datasets of the field. As a result of our systematic review, the statistical analysis of the 119 identified open datasets, highlights the tendencies and the missing elements, such as underrepresented technologies (such as Ultra-Wideband) and measurement types (such as Angle of Arrival, Time Difference of Arrival). A result that stands out is the frequency of crucial metadata information that remains undefined, such as the size of the area of collection (50% of the datasets), the ground truth collection protocol (21%), or the environment type (13%). As a fruit of the systematic analysis, we discuss potential shortcomings, and we share lessons learned and observed good practices regarding the provision of a new ORD and the reuse of existing ones. A significant practical contribution of this work is a list of guidelines that researchers aiming to collect and share a new ORD can follow as a simple checklist. In a broader context, we consider that ORDIP can help measure the future progress of the Indoor Positioning field in the ORD front through the snapshot of the current landscape that it provides. The Open provision of our full systematic analysis of the ORDs (Anagnostopoulos et al., 2024) can serve as a look-up table for easy access to the ORDs containing the most relevant features for each interested researcher, while our guidelines aim to support the community and spark the discussion towards a consensus-based standard for ORD of the field.Source: INTERNET OF THINGS
DOI: 10.1016/j.iot.2024.101485
Project(s): European Union - Next Generation EU, in the context of The National Recovery and Resilience Plan, Investment Partenariato Esteso PE8 “Conseguenze e sfide dell’invecchiamento”, Project Age-IT, CUP: B83C22004880006
Metrics:
Anagnostopoulos G. G., Barsocchi P., Crivello A., Pendão C., Silva I., Torres-Sospedra J.
The community of indoor positioning research has identified the need for a paradigm shift towards more reproducible and open research dissemination. Despite recent efforts to openly share data and code, accompanying research results with Open Research Data (ORD) is far from being the de facto standard option for publications in the indoor positioning field. The lack of recognized public benchmarks and the rather slow adoption of ORD, set a great volume of astute contributions in the field to remain irreproducible. Performance comparisons may often be made on experiments performed in different settings, hindering their consistency, and eventually slowing down progress and the evolution of knowledge in the field. In this work, we systematically review the landscape of Open Research Data in Indoor Positioning, enlisting, presenting, and analyzing the characteristic features of the relevant available open datasets of the field. As a result of our systematic review, the statistical analysis of the 119 identified open datasets, highlights the tendencies and the missing elements, such as underrepresented technologies (such as Ultra-Wideband) and measurement types (such as Angle of Arrival, Time Difference of Arrival). A result that stands out is the frequency of crucial metadata information that remains undefined, such as the size of the area of collection (50% of the datasets), the ground truth collection protocol (21%), or the environment type (13%). As a fruit of the systematic analysis, we discuss potential shortcomings, and we share lessons learned and observed good practices regarding the provision of a new ORD and the reuse of existing ones. A significant practical contribution of this work is a list of guidelines that researchers aiming to collect and share a new ORD can follow as a simple checklist. In a broader context, we consider that ORDIP can help measure the future progress of the Indoor Positioning field in the ORD front through the snapshot of the current landscape that it provides. The Open provision of our full systematic analysis of the ORDs (Anagnostopoulos et al., 2024) can serve as a look-up table for easy access to the ORDs containing the most relevant features for each interested researcher, while our guidelines aim to support the community and spark the discussion towards a consensus-based standard for ORD of the field.Source: INTERNET OF THINGS
DOI: 10.1016/j.iot.2024.101485
Project(s): European Union - Next Generation EU, in the context of The National Recovery and Resilience Plan, Investment Partenariato Esteso PE8 “Conseguenze e sfide dell’invecchiamento”, Project Age-IT, CUP: B83C22004880006
Metrics:
See at:
CNR IRIS | www.sciencedirect.com | CNR IRIS | CNR IRIS
2025
Conference article
Open Access
AI-Empowered IoT data collection via UAV in rural areas
Vo P. T., Giambene G., Barsocchi P., Crivello A.
Soil monitoring is essential for smart agriculture in remote rural areas with limited connectivity. It helps forecast regional runoff, soil erosion, and weather impacts while promoting more efficient irrigation. Current artificial intelligence (AI) methods often struggle to adapt to heterogeneous environments and limited connectivity. This study presents a vertical federated architecture called multi-head split learning (MHSL), utilizing AI-powered devices onboard Unmanned Aerial Vehicles (UAVs) mission that is designed to increase awareness of in-situ soil moisture collected data to forecast environmental trends for enhanced monitoring in rural areas. Our architecture connects the local convolutional neural network (CNN) head model of multiple worker UAVs to the long-short-term memory (LSTM) tail model of a central master UAV, creating a global model. This is made possible by adopting GPUs onboard and WiFi connectivity among UAVs. To validate our approach, we have used the real datasets of the TERENO-Wüstebach network. The numerical results show that our CNN-LSTM approach can forecast the SSM data for the next days with sufficient accuracy measured in terms of mean square error (MSE). The good performance of CNN-LSTM has been supported by comparisons with other schemes in the literature.DOI: 10.1109/infocomwkshps65812.2025.11152890
Metrics:
Vo P. T., Giambene G., Barsocchi P., Crivello A.
Soil monitoring is essential for smart agriculture in remote rural areas with limited connectivity. It helps forecast regional runoff, soil erosion, and weather impacts while promoting more efficient irrigation. Current artificial intelligence (AI) methods often struggle to adapt to heterogeneous environments and limited connectivity. This study presents a vertical federated architecture called multi-head split learning (MHSL), utilizing AI-powered devices onboard Unmanned Aerial Vehicles (UAVs) mission that is designed to increase awareness of in-situ soil moisture collected data to forecast environmental trends for enhanced monitoring in rural areas. Our architecture connects the local convolutional neural network (CNN) head model of multiple worker UAVs to the long-short-term memory (LSTM) tail model of a central master UAV, creating a global model. This is made possible by adopting GPUs onboard and WiFi connectivity among UAVs. To validate our approach, we have used the real datasets of the TERENO-Wüstebach network. The numerical results show that our CNN-LSTM approach can forecast the SSM data for the next days with sufficient accuracy measured in terms of mean square error (MSE). The good performance of CNN-LSTM has been supported by comparisons with other schemes in the literature.DOI: 10.1109/infocomwkshps65812.2025.11152890
Metrics:
See at:
CNR IRIS | ieeexplore.ieee.org | CNR IRIS
2025
Journal article
Open Access
MCSim: A multi-access edge computing mobile crowdsensing simulator
Belli D., Barsocchi P., Crivello A., La Rosa D., Girolami M.
This paper introduces MCSim, a modular and extensible simulator designed to support the planning and evaluation of Mobile CrowdSensing (MCS) campaigns in urban environments. MCSim integrates a useful approximation of urban mobility patterns based on real-world street networks, as well as the simulation of task execution effectiveness within configurable data transmission ranges. Unlike other simulators, MCSim is built to accommodate future extensions, such as edge/fog computing architectures. The current version of the software offers a user-friendly interface, customizable configuration options, and robust output analysis. By combining realistic mobility modeling, configurable task logic, and architectural flexibility, MCSim provides researchers and practitioners with a powerful tool for optimizing MCS strategies while minimizing deployment costs and risks.Source: SOFTWAREX, vol. 31 (issue 102229)
DOI: 10.1016/j.softx.2025.102229
Project(s): Cyber and Human Intelligence for Physical Systems
Metrics:
Belli D., Barsocchi P., Crivello A., La Rosa D., Girolami M.
This paper introduces MCSim, a modular and extensible simulator designed to support the planning and evaluation of Mobile CrowdSensing (MCS) campaigns in urban environments. MCSim integrates a useful approximation of urban mobility patterns based on real-world street networks, as well as the simulation of task execution effectiveness within configurable data transmission ranges. Unlike other simulators, MCSim is built to accommodate future extensions, such as edge/fog computing architectures. The current version of the software offers a user-friendly interface, customizable configuration options, and robust output analysis. By combining realistic mobility modeling, configurable task logic, and architectural flexibility, MCSim provides researchers and practitioners with a powerful tool for optimizing MCS strategies while minimizing deployment costs and risks.Source: SOFTWAREX, vol. 31 (issue 102229)
DOI: 10.1016/j.softx.2025.102229
Project(s): Cyber and Human Intelligence for Physical Systems
Metrics:
See at:
SoftwareX | SoftwareX | CNR IRIS | www.sciencedirect.com | CNR IRIS | CNR IRIS
2025
Conference article
Open Access
Performing audiometry using pupillometry: state-of-the-market and sensor selection
Crivello A., La Rosa D., Belli D., Milazzo M., Palumbo F.
Hearing impairment poses a significant global health challenge, impacting millions of individuals across all age groups. Early detection and intervention are paramount, especially in infants and young children, to mitigate the adverse effects on speech, language, and cognitive development. Traditional audiometry methods, however, rely on subjective patient responses, rendering them unsuitable for non-collaborative individuals such as infants, newborns, and those with cognitive impairments. To address this limitation, the APURE (Audiometry with PUpil REsponse) project seeks to develop an objective audiometer leveraging pupillometry, the measurement of pupil size and reactivity. This paper presents a comprehensive state-of-the-market survey of eye-tracking systems, a crucial step in identifying the most suitable sensors for the APURE project.Source: LECTURE NOTES OF THE INSTITUTE FOR COMPUTER SCIENCES, SOCIAL INFORMATICS AND TELECOMMUNICATIONS ENGINEERING, vol. 612, pp. 159-169. Heraklion, Crete, Greece, 17–18/09/2024
DOI: 10.1007/978-3-031-85575-7_9
Metrics:
Crivello A., La Rosa D., Belli D., Milazzo M., Palumbo F.
Hearing impairment poses a significant global health challenge, impacting millions of individuals across all age groups. Early detection and intervention are paramount, especially in infants and young children, to mitigate the adverse effects on speech, language, and cognitive development. Traditional audiometry methods, however, rely on subjective patient responses, rendering them unsuitable for non-collaborative individuals such as infants, newborns, and those with cognitive impairments. To address this limitation, the APURE (Audiometry with PUpil REsponse) project seeks to develop an objective audiometer leveraging pupillometry, the measurement of pupil size and reactivity. This paper presents a comprehensive state-of-the-market survey of eye-tracking systems, a crucial step in identifying the most suitable sensors for the APURE project.Source: LECTURE NOTES OF THE INSTITUTE FOR COMPUTER SCIENCES, SOCIAL INFORMATICS AND TELECOMMUNICATIONS ENGINEERING, vol. 612, pp. 159-169. Heraklion, Crete, Greece, 17–18/09/2024
DOI: 10.1007/978-3-031-85575-7_9
Metrics:
See at:
CNR IRIS | link.springer.com | CNR IRIS | CNR IRIS
2025
Conference article
Open Access
LLM-Guided indoor navigation with multimodal map understanding
Coffrini A., Barsocchi P., Furfari F., Crivello A., Ferrari A.
Indoor navigation presents unique challenges due to complex layouts and the unavailability of GNSS signals. Existing solutions often struggle with contextual adaptation, and typically require dedicated hardware. In this work, we explore the potential of a Large Language Model (LLM), i.e., ChatGPT, to generate natural, context-aware navigation instructions from indoor map images. We design and evaluate test cases across different real-world environments, analyzing the effectiveness of LLMs in interpreting spatial layouts, handling user constraints, and planning efficient routes. Our findings demonstrate the potential of LLMs for supporting personalized indoor navigation, with an average of 86.59% correct indications and a maximum of 97.14%. The proposed system achieves high accuracy and reasoning performance. These results have key implications for AI-driven navigation and assistive technologies.DOI: 10.1109/ipin66788.2025.11212934
DOI: 10.48550/arxiv.2503.11702
Metrics:
Coffrini A., Barsocchi P., Furfari F., Crivello A., Ferrari A.
Indoor navigation presents unique challenges due to complex layouts and the unavailability of GNSS signals. Existing solutions often struggle with contextual adaptation, and typically require dedicated hardware. In this work, we explore the potential of a Large Language Model (LLM), i.e., ChatGPT, to generate natural, context-aware navigation instructions from indoor map images. We design and evaluate test cases across different real-world environments, analyzing the effectiveness of LLMs in interpreting spatial layouts, handling user constraints, and planning efficient routes. Our findings demonstrate the potential of LLMs for supporting personalized indoor navigation, with an average of 86.59% correct indications and a maximum of 97.14%. The proposed system achieves high accuracy and reasoning performance. These results have key implications for AI-driven navigation and assistive technologies.DOI: 10.1109/ipin66788.2025.11212934
DOI: 10.48550/arxiv.2503.11702
Metrics:
See at:
arXiv.org e-Print Archive | CNR IRIS | ieeexplore.ieee.org | doi.org | doi.org | CNR IRIS | CNR IRIS
2025
Conference article
Open Access
A bluetooth proximity-based IoT approach for continuous monitoring of indoor sedentariness
Girolami M., Baronti P., Crivello A., La Rosa D., Barsocchi P.
Sedentary behavior is a critical factor influencing overall health and well-being, particularly in aging populations. This work presents an indoor monitoring solution leveraging Bluetooth-Based proximity estimation to infer users’ location and movement patterns across different home environments. The objective is to generate a Sedentary Behavior Index (SBI) that quantifies the duration individuals spend in specific domestic spaces without requiring active user input. This index, derived through passive and pervasive sensing, provides healthcare professionals and researchers with insights into users’ lifestyle and activity levels within the context of their daily living environment. The proposed system, deployed in 45 houses, monitors 55 users and operates as a proximity-based IoT service that seamlessly integrates with broader health monitoring studies, enabling context-aware analysis when cross-referenced with clinical outcomes or other observational data. This approach aims to support continuous, unobtrusive, and personalized well-being assessments, laying the groundwork for adaptive interventions in remote healthcare and aging-in-place scenarios.DOI: 10.1109/wf-iot64238.2025.11270510
Project(s): Project Tuscany Health Ecosystem
Metrics:
Girolami M., Baronti P., Crivello A., La Rosa D., Barsocchi P.
Sedentary behavior is a critical factor influencing overall health and well-being, particularly in aging populations. This work presents an indoor monitoring solution leveraging Bluetooth-Based proximity estimation to infer users’ location and movement patterns across different home environments. The objective is to generate a Sedentary Behavior Index (SBI) that quantifies the duration individuals spend in specific domestic spaces without requiring active user input. This index, derived through passive and pervasive sensing, provides healthcare professionals and researchers with insights into users’ lifestyle and activity levels within the context of their daily living environment. The proposed system, deployed in 45 houses, monitors 55 users and operates as a proximity-based IoT service that seamlessly integrates with broader health monitoring studies, enabling context-aware analysis when cross-referenced with clinical outcomes or other observational data. This approach aims to support continuous, unobtrusive, and personalized well-being assessments, laying the groundwork for adaptive interventions in remote healthcare and aging-in-place scenarios.DOI: 10.1109/wf-iot64238.2025.11270510
Project(s): Project Tuscany Health Ecosystem
Metrics:
See at:
CNR IRIS | ieeexplore.ieee.org | CNR IRIS | CNR IRIS
2025
Journal article
Open Access
Web components for late blight (Phytophthora infestans (Mont.) De Bary) and early blight (Alternaria solani Sor.) outbreaks forecast on Solanum tuberosum L. in Cuba under future climate scenarios
Pineda Medina D., Crivello A., Sportelli M., Bianchini M., Miranda Cabrera I.
The invasive nature of late blight (Phytophthora infestans [Mont.] De Bary) and early blight (Alternaria solani Sor.) has caused important losses in the potato crop, and studies point to climatic variability as one of the most significant causes. The objective of this work was to predict the probability of outbreak of late blight and early blight epiphytotic diseases through weather patterns during the potato harvest season. Future meteorological data for the years 2024 to 2075 obtained from the National Institute of Meteorology in Cuba were used. For the late blight forecasting model development, disease behavior rules and a Random Forest model were used and validated on a small real-filed dataset. For early blight prediction model a framework based on disease behavior rules and phenological age of the crop (P-Days) was developed. The web system was implemented using the Python programming language, the Flask and Bootstrap frameworks, and the necessary libraries, and PyCharm as the development environment. Likewise, the PostgreSQL manager and PgAdmin were used for data management and as a tool for information administration. A web system was obtained that alerts on the probability of outbreak of late blight and early blight in the provinces of Mayabeque, Villa Clara and Ciego de Ávila, important potato-producing regions in Cuba. The forecast for the year 2025 was analyzed and it was found that Ciego de Avila and Mayabeque show a higher number of critical days for each of the diseases. In the month of March, late blight proliferation reached its peak, while early blight outbreak appeared to be more intense in January. The web system is an intelligent tool to guide farmers in making the necessary decisions and prevent an epidemic outbreak.Source: SMART AGRICULTURAL TECHNOLOGY, vol. 11 (issue 101003)
DOI: 10.1016/j.atech.2025.101003
Metrics:
Pineda Medina D., Crivello A., Sportelli M., Bianchini M., Miranda Cabrera I.
The invasive nature of late blight (Phytophthora infestans [Mont.] De Bary) and early blight (Alternaria solani Sor.) has caused important losses in the potato crop, and studies point to climatic variability as one of the most significant causes. The objective of this work was to predict the probability of outbreak of late blight and early blight epiphytotic diseases through weather patterns during the potato harvest season. Future meteorological data for the years 2024 to 2075 obtained from the National Institute of Meteorology in Cuba were used. For the late blight forecasting model development, disease behavior rules and a Random Forest model were used and validated on a small real-filed dataset. For early blight prediction model a framework based on disease behavior rules and phenological age of the crop (P-Days) was developed. The web system was implemented using the Python programming language, the Flask and Bootstrap frameworks, and the necessary libraries, and PyCharm as the development environment. Likewise, the PostgreSQL manager and PgAdmin were used for data management and as a tool for information administration. A web system was obtained that alerts on the probability of outbreak of late blight and early blight in the provinces of Mayabeque, Villa Clara and Ciego de Ávila, important potato-producing regions in Cuba. The forecast for the year 2025 was analyzed and it was found that Ciego de Avila and Mayabeque show a higher number of critical days for each of the diseases. In the month of March, late blight proliferation reached its peak, while early blight outbreak appeared to be more intense in January. The web system is an intelligent tool to guide farmers in making the necessary decisions and prevent an epidemic outbreak.Source: SMART AGRICULTURAL TECHNOLOGY, vol. 11 (issue 101003)
DOI: 10.1016/j.atech.2025.101003
Metrics:
See at:
Smart Agricultural Technology | Smart Agricultural Technology | Usiena air - Università di Siena | CNR IRIS | www.sciencedirect.com | Usiena air - Università di Siena | GitHub | CNR IRIS
2024
Journal article
Open Access
Offsite evaluation of localization systems: criteria, systems and results from IPIN 2021-22 competitions
Potortì F., Crivello A.
Indoor positioning is a thriving research area which is slowly gaining market momentum. Its applications are mostly customised, ad hoc installations; ubiquitous applications analogous to GNSS for outdoors are not available because of the lack of generic platforms, widely accepted standards and interoperability protocols. In this context, the Indoor Positioning and Indoor Navigation (IPIN) competition is the only long-term, technically sound initiative to monitor the state of the art of real systems by measuring their performance in a realistic environment. Most competing systems are pedestrian-oriented and based on the use of smartphones, but several competing Tracks were set up, enabling comparison of an array of technologies. The two IPIN competitions described here include only off-site Tracks. In contrast with on-site Tracks where competitors bring their systems on site -- which were impossible to organise during 2021 and 2022 -- in off-site Tracks competitors download pre-recorded data from multiple sensors and process them using the EvaalAPI, a real-time, web-based emulation interface. As usual with IPIN competitions, Tracks were compliant with the EvAAL framework, ensuring consistency of the measurement procedure and reliability of results. The main contribution of this work is to show a compilation of possible indoor positioning scenarios and different indoor positioning solutions to the same problem.Source: IEEE JOURNAL OF INDOOR AND SEAMLESS POSITIONING AND NAVIGATION, vol. 2, pp. 92-129
DOI: 10.1109/jispin.2024.3355840
Metrics:
Potortì F., Crivello A.
Indoor positioning is a thriving research area which is slowly gaining market momentum. Its applications are mostly customised, ad hoc installations; ubiquitous applications analogous to GNSS for outdoors are not available because of the lack of generic platforms, widely accepted standards and interoperability protocols. In this context, the Indoor Positioning and Indoor Navigation (IPIN) competition is the only long-term, technically sound initiative to monitor the state of the art of real systems by measuring their performance in a realistic environment. Most competing systems are pedestrian-oriented and based on the use of smartphones, but several competing Tracks were set up, enabling comparison of an array of technologies. The two IPIN competitions described here include only off-site Tracks. In contrast with on-site Tracks where competitors bring their systems on site -- which were impossible to organise during 2021 and 2022 -- in off-site Tracks competitors download pre-recorded data from multiple sensors and process them using the EvaalAPI, a real-time, web-based emulation interface. As usual with IPIN competitions, Tracks were compliant with the EvAAL framework, ensuring consistency of the measurement procedure and reliability of results. The main contribution of this work is to show a compilation of possible indoor positioning scenarios and different indoor positioning solutions to the same problem.Source: IEEE JOURNAL OF INDOOR AND SEAMLESS POSITIONING AND NAVIGATION, vol. 2, pp. 92-129
DOI: 10.1109/jispin.2024.3355840
Metrics:
See at:
CNR IRIS | ieeexplore.ieee.org | CNR IRIS
2024
Journal article
Open Access
MOC: wi-fi FTM with motion observation chain for pervasive indoor positioning
Wenhua Shao, Haiyong Luo, Fang Zhao, Yunhan Hong, Yaqi Li, Chen Zhang, Bingzheng Sun, Antonino Crivello
The IEEE 802.11-2016 standard enables devices to gather precise ranging information through the time-of-flight evaluation, facilitating the development of accurate indoor location-based services. Researchers have indicated that the protocol’s most effective performance is in scenarios with direct line-of-sight, despite providing meter-level ranging accuracy. In real indoor environments, the accuracy diminishes considerably due to random errors caused by interference such as multipath effects and non-line-of-sight signal propagation. Therefore, it is essential to accurately evaluate the reliability of each ranging measurement and effectively leverage neighboring highquality observations to improve positioning accuracy. This study presents a novel optimization algorithm that relies on the motion observation series by incorporating adjacent ranging observations and a priori motion knowledge into a factor graph model, resulting in a unified optimization objective. Consequently, our system can dynamically estimate the confidence of fine time measurements ranging measurements. It optimizes the position estimation of the current user by maximizing the probability of not only the current ranging measurements but also the adjacent historical measurements and a priori motion. Additionally, to enable real-time positioning, a fast-solving procedure employing an adaptive gradient is proposed, capable of providing evaluations in under 10ms. The system has been tested in real indoor environments, showing improved performance compared to existing methods. It achieves meter-level realtime positioning accuracy at 1 sigma without requiring a specific device pose, additional sensor, or expensive site survey. This makes our proposal highly applicable for wide adoption and readiness for the market.Source: IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
DOI: 10.1109/tii.2024.3413342
Metrics:
Wenhua Shao, Haiyong Luo, Fang Zhao, Yunhan Hong, Yaqi Li, Chen Zhang, Bingzheng Sun, Antonino Crivello
The IEEE 802.11-2016 standard enables devices to gather precise ranging information through the time-of-flight evaluation, facilitating the development of accurate indoor location-based services. Researchers have indicated that the protocol’s most effective performance is in scenarios with direct line-of-sight, despite providing meter-level ranging accuracy. In real indoor environments, the accuracy diminishes considerably due to random errors caused by interference such as multipath effects and non-line-of-sight signal propagation. Therefore, it is essential to accurately evaluate the reliability of each ranging measurement and effectively leverage neighboring highquality observations to improve positioning accuracy. This study presents a novel optimization algorithm that relies on the motion observation series by incorporating adjacent ranging observations and a priori motion knowledge into a factor graph model, resulting in a unified optimization objective. Consequently, our system can dynamically estimate the confidence of fine time measurements ranging measurements. It optimizes the position estimation of the current user by maximizing the probability of not only the current ranging measurements but also the adjacent historical measurements and a priori motion. Additionally, to enable real-time positioning, a fast-solving procedure employing an adaptive gradient is proposed, capable of providing evaluations in under 10ms. The system has been tested in real indoor environments, showing improved performance compared to existing methods. It achieves meter-level realtime positioning accuracy at 1 sigma without requiring a specific device pose, additional sensor, or expensive site survey. This makes our proposal highly applicable for wide adoption and readiness for the market.Source: IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
DOI: 10.1109/tii.2024.3413342
Metrics:
See at:
IRIS Cnr | IRIS Cnr | IRIS Cnr | IEEE Transactions on Industrial Informatics | CNR IRIS
2024
Journal article
Open Access
What are data spaces? Systematic survey and future outlook
Bacco M., Kocian A., Chessa S., Crivello A., Barsocchi P.
Data spaces, a novel concept pushing data sharing and exchange, are experi- encing momentum because of recent developments motivated by the increas- ing need for interoperability and data sovereignty. After an initial phase, dating back to approximately twenty years ago, in which this concept has been tentatively explored in different scenarios, it is presently going through a consolidation phase in which both specifications and implementations con- verge towards a common reference for standardisation. In this context, we offer our view on data spaces by presenting a systematic literature survey, a description of the components needed to build them, how they work, and of existing mature software implementations. We thoroughly present the architectural vision behind the concept and we analyse the Reference Archi- tectural Model by IDS. We provide practical pointers to readers interested in experimenting with software components used in data spaces, and we con- clude by highlighting open challenges for their success.Source: DATA IN BRIEF
DOI: 10.1016/j.dib.2024.110969
Project(s): CODECS
Metrics:
Bacco M., Kocian A., Chessa S., Crivello A., Barsocchi P.
Data spaces, a novel concept pushing data sharing and exchange, are experi- encing momentum because of recent developments motivated by the increas- ing need for interoperability and data sovereignty. After an initial phase, dating back to approximately twenty years ago, in which this concept has been tentatively explored in different scenarios, it is presently going through a consolidation phase in which both specifications and implementations con- verge towards a common reference for standardisation. In this context, we offer our view on data spaces by presenting a systematic literature survey, a description of the components needed to build them, how they work, and of existing mature software implementations. We thoroughly present the architectural vision behind the concept and we analyse the Reference Archi- tectural Model by IDS. We provide practical pointers to readers interested in experimenting with software components used in data spaces, and we con- clude by highlighting open challenges for their success.Source: DATA IN BRIEF
DOI: 10.1016/j.dib.2024.110969
Project(s): CODECS
Metrics:
See at:
Data in Brief | IRIS Cnr | IRIS Cnr | Archivio della Ricerca - Università di Pisa | Archivio della Ricerca - Università di Pisa | CNR IRIS
2024
Journal article
Open Access
Public irrigation decision support systems (IDSS) in Italy: description, evaluation and national context overview
Sportelli M., Crivello A., Bacco M., Rallo G., Brunori G.
This survey comprehensively examines the public irrigation decision support systems (IDSS) in Italy, offering a detailed description, analysis and evaluation of their features. The study investigates the agrometeorological networks and infrastructures that support Italian IDSS, providing a clearer understanding of the national context. The evaluation criteria include relevant factors such as soil moisture monitoring, crop water requirements (CWR) estimation models, biophysical parameters along with their spatial and temporal resolutions, irrigation planning and decision support visualization. Additionally, the assessment covers accessibility, scalability and interoperability of these systems. The survey also highlights the strengths and weaknesses of various IDSS, such as IRRIFRAME, IRRISIAS and IRTO, discussing their operational methodologies, data integration and regional coverage. The aim is to provide insights that facilitate advancements in sustainable irrigation management practices and address key challenges for future developments at both regional and national levels. This comprehensive evaluation seeks to enhance the effectiveness of IDSS in promoting sustainable water management in agriculture across Italy.Source: SMART AGRICULTURAL TECHNOLOGY, vol. 9
DOI: 10.1016/j.atech.2024.100564
Metrics:
Sportelli M., Crivello A., Bacco M., Rallo G., Brunori G.
This survey comprehensively examines the public irrigation decision support systems (IDSS) in Italy, offering a detailed description, analysis and evaluation of their features. The study investigates the agrometeorological networks and infrastructures that support Italian IDSS, providing a clearer understanding of the national context. The evaluation criteria include relevant factors such as soil moisture monitoring, crop water requirements (CWR) estimation models, biophysical parameters along with their spatial and temporal resolutions, irrigation planning and decision support visualization. Additionally, the assessment covers accessibility, scalability and interoperability of these systems. The survey also highlights the strengths and weaknesses of various IDSS, such as IRRIFRAME, IRRISIAS and IRTO, discussing their operational methodologies, data integration and regional coverage. The aim is to provide insights that facilitate advancements in sustainable irrigation management practices and address key challenges for future developments at both regional and national levels. This comprehensive evaluation seeks to enhance the effectiveness of IDSS in promoting sustainable water management in agriculture across Italy.Source: SMART AGRICULTURAL TECHNOLOGY, vol. 9
DOI: 10.1016/j.atech.2024.100564
Metrics:
See at:
Smart Agricultural Technology | Smart Agricultural Technology | IRIS Cnr | IRIS Cnr | Archivio della Ricerca - Università di Pisa | Archivio della Ricerca - Università di Pisa | CNR IRIS
2024
Conference article
Open Access
Agricultural Data Space: the METRIQA platform and a case study in the CODECS project
Bacco M., Dimitri G. M., Kocian A., Barsocchi P., Crivello A., Brunori G., Gori M., Chessa S.
This work describes the ongoing design and devel- opment of the METRIQA platform, hosting the Italian agrifood data space. Both are key components that the Italian National Research Centre for Agricultural Technologies is putting forward in its activities. We present a high-level description of the platform, which is designed to provide web-like access to digital resources and services following an approach called Web of Agri-Food, to support the digital transformation of the sector in Italy. To show its potential, we also present a real case study demonstrating both the benefits and impacts of the proposed architecture, connecting stakeholders and authorities at different levels.Source: ANNALS OF COMPUTER SCIENCE AND INFORMATION SYSTEMS, vol. 39, pp. 543-548. Belgrade, Serbia, 8-11/09/2024
DOI: 10.15439/2024f5291
Metrics:
Bacco M., Dimitri G. M., Kocian A., Barsocchi P., Crivello A., Brunori G., Gori M., Chessa S.
This work describes the ongoing design and devel- opment of the METRIQA platform, hosting the Italian agrifood data space. Both are key components that the Italian National Research Centre for Agricultural Technologies is putting forward in its activities. We present a high-level description of the platform, which is designed to provide web-like access to digital resources and services following an approach called Web of Agri-Food, to support the digital transformation of the sector in Italy. To show its potential, we also present a real case study demonstrating both the benefits and impacts of the proposed architecture, connecting stakeholders and authorities at different levels.Source: ANNALS OF COMPUTER SCIENCE AND INFORMATION SYSTEMS, vol. 39, pp. 543-548. Belgrade, Serbia, 8-11/09/2024
DOI: 10.15439/2024f5291
Metrics:
See at:
annals-csis.org | Annals of computer science and information systems | CNR IRIS | CNR IRIS