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 
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
Optimized forest framework with a binary multineighborhood artificial bee colony for enhanced diabetes mellitus detection
Pradhan G., Thapa G., Pradhan R., Khandelwal B., Panigrahi R., Bhoi A. K., Barsocchi P.
Diabetes mellitus (DM) is a common chronic condition that mainly affects older adults. It's important to identify it early to prevent complications. Machine learning is essential for early detection of DM. This article introduces a new method for detecting DM using a random forest ensemble within an optimized framework. The optimized forest framework depends on finding the best DM features, which are identified using the binary multineighborhood artificial bee colony (BMNABC) technique. During preprocessing, the BMNABC algorithm efficiently identifies important features and then inputs them into the random forest within the optimized forest framework for accurate classification. Five modern DM datasets were used to validate the suggested model. The comparison of the proposed model with other leading models revealed significant insights. The BMNABC + ODF(RFE) model demonstrated exceptional proficiency in detecting diabetes mellitus (DM) across various datasets. It achieved an accuracy of 96.36% and a sensitivity of 99.95% on the merged dataset (130 US and PIMA images). The Iranian Ministry of Health dataset showed an accuracy of 97.28% and a sensitivity of 97.12%. In the Sylhet Diabetes Hospital dataset, the accuracy and sensitivity were 96.81% and 98.07% respectively. However, on the PIMA dataset, the model displayed a nuanced performance, with an accuracy of 77.21% and a sensitivity of 68.83%. Lastly, on the questionnaire dataset, the BMNABC + ODF(RFE) model achieved an accuracy of 96.43% and a sensitivity of 97.15%. These findings emphasize the model's ability to adapt and perform effectively in different clinical environments, outperforming other models in terms of accuracy and sensitivity in detecting DM.Source: INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS, vol. 17 (issue 119 (article number))
DOI: 10.1007/s44196-024-00598-2
Project(s): Smart and high-efficient technologies for fruits and vegetable production in greenhouse and plant factory
Metrics:
Pradhan G., Thapa G., Pradhan R., Khandelwal B., Panigrahi R., Bhoi A. K., Barsocchi P.
Diabetes mellitus (DM) is a common chronic condition that mainly affects older adults. It's important to identify it early to prevent complications. Machine learning is essential for early detection of DM. This article introduces a new method for detecting DM using a random forest ensemble within an optimized framework. The optimized forest framework depends on finding the best DM features, which are identified using the binary multineighborhood artificial bee colony (BMNABC) technique. During preprocessing, the BMNABC algorithm efficiently identifies important features and then inputs them into the random forest within the optimized forest framework for accurate classification. Five modern DM datasets were used to validate the suggested model. The comparison of the proposed model with other leading models revealed significant insights. The BMNABC + ODF(RFE) model demonstrated exceptional proficiency in detecting diabetes mellitus (DM) across various datasets. It achieved an accuracy of 96.36% and a sensitivity of 99.95% on the merged dataset (130 US and PIMA images). The Iranian Ministry of Health dataset showed an accuracy of 97.28% and a sensitivity of 97.12%. In the Sylhet Diabetes Hospital dataset, the accuracy and sensitivity were 96.81% and 98.07% respectively. However, on the PIMA dataset, the model displayed a nuanced performance, with an accuracy of 77.21% and a sensitivity of 68.83%. Lastly, on the questionnaire dataset, the BMNABC + ODF(RFE) model achieved an accuracy of 96.43% and a sensitivity of 97.15%. These findings emphasize the model's ability to adapt and perform effectively in different clinical environments, outperforming other models in terms of accuracy and sensitivity in detecting DM.Source: INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS, vol. 17 (issue 119 (article number))
DOI: 10.1007/s44196-024-00598-2
Project(s): Smart and high-efficient technologies for fruits and vegetable production in greenhouse and plant factory
Metrics:
See at:
bmcmedinformdecismak.biomedcentral.com | International Journal of Computational Intelligence Systems | International Journal of Computational Intelligence Systems | CNR IRIS | CNR IRIS
2024
Journal article
Open Access
Machine learning-empowered sleep staging classification using multi-modality signals
Satapathy S. K., Brahma B., Panda B., Barsocchi P., Bhoi A. K.
The goal is to enhance an automated sleep staging system's performance by leveraging the diverse signals captured through multi-modal polysomnography recordings. Three modalities of PSG signals, namely electroencephalogram (EEG), electrooculogram (EOG), and electromyogram (EMG), were considered to obtain the optimal fusions of the PSG signals, where 63 features were extracted. These include frequency-based, time-based, statistical-based, entropy-based, and non-linear-based features. We adopted the ReliefF (ReF) feature selection algorithms to find the suitable parts for each signal and superposition of PSG signals. Twelve top features were selected while correlated with the extracted feature sets' sleep stages. The selected features were fed into the AdaBoost with Random Forest (ADB + RF) classifier to validate the chosen segments and classify the sleep stages. This study's experiments were investigated by obtaining two testing schemes: epoch-wise testing and subject-wise testing. The suggested research was conducted using three publicly available datasets: ISRUC-Sleep subgroup1 (ISRUC-SG1), sleep-EDF(S-EDF), Physio bank CAP sleep database (PB-CAPSDB), and S-EDF-78 respectively. This work demonstrated that the proposed fusion strategy overestimates the common individual usage of PSG signals.Source: BMC MEDICAL INFORMATICS AND DECISION MAKING, vol. 24 (issue 1)
DOI: 10.1186/s12911-024-02522-2
Project(s): Scotland's Net Zero Infrastructure (SNZI)
Metrics:
Satapathy S. K., Brahma B., Panda B., Barsocchi P., Bhoi A. K.
The goal is to enhance an automated sleep staging system's performance by leveraging the diverse signals captured through multi-modal polysomnography recordings. Three modalities of PSG signals, namely electroencephalogram (EEG), electrooculogram (EOG), and electromyogram (EMG), were considered to obtain the optimal fusions of the PSG signals, where 63 features were extracted. These include frequency-based, time-based, statistical-based, entropy-based, and non-linear-based features. We adopted the ReliefF (ReF) feature selection algorithms to find the suitable parts for each signal and superposition of PSG signals. Twelve top features were selected while correlated with the extracted feature sets' sleep stages. The selected features were fed into the AdaBoost with Random Forest (ADB + RF) classifier to validate the chosen segments and classify the sleep stages. This study's experiments were investigated by obtaining two testing schemes: epoch-wise testing and subject-wise testing. The suggested research was conducted using three publicly available datasets: ISRUC-Sleep subgroup1 (ISRUC-SG1), sleep-EDF(S-EDF), Physio bank CAP sleep database (PB-CAPSDB), and S-EDF-78 respectively. This work demonstrated that the proposed fusion strategy overestimates the common individual usage of PSG signals.Source: BMC MEDICAL INFORMATICS AND DECISION MAKING, vol. 24 (issue 1)
DOI: 10.1186/s12911-024-02522-2
Project(s): Scotland's Net Zero Infrastructure (SNZI)
Metrics:
See at:
bmcmedinformdecismak.biomedcentral.com | BMC Medical Informatics and Decision Making | PubMed Central | CNR IRIS | 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
2024
Contribution to book
Restricted
A review of automated sleep stage scoring using machine learning techniques based on physiological signals
Satapathy S. K., Agrawal P., Shah N., Panigrahi R., Khandelwal B., Barsocchi P., Bhoi A. K.
The background and goal of this research are to address the importance of sleep in our lifestyle and health. To analyze sleep problems, legitimate scoring of sleep stages is fundamental, and this is usually finished through a tedious visual survey of, for the time being, polysomnograms by a human expert. However, this process can be improved with artificial intelligence algorithms. To accurately interpret the physiological signals associated with sleep disorders, it is essential to understand how changes in sleep stages are reflected in the signal waveform. With this knowledge, automated sleep stage scoring systems can be developed, making the diagnosis of sleep disorders more efficient and providing insight into the amount of information about sleep stages that can be gleaned from a particular physiological signal. The review study thoroughly examines automated sleep stage rating systems developed since 2000. These systems were created to analyze electrocardiograms (ECGs), electroencephalograms (EEGs), electDOI: 10.1002/9781394227990.ch5
Metrics:
Satapathy S. K., Agrawal P., Shah N., Panigrahi R., Khandelwal B., Barsocchi P., Bhoi A. K.
The background and goal of this research are to address the importance of sleep in our lifestyle and health. To analyze sleep problems, legitimate scoring of sleep stages is fundamental, and this is usually finished through a tedious visual survey of, for the time being, polysomnograms by a human expert. However, this process can be improved with artificial intelligence algorithms. To accurately interpret the physiological signals associated with sleep disorders, it is essential to understand how changes in sleep stages are reflected in the signal waveform. With this knowledge, automated sleep stage scoring systems can be developed, making the diagnosis of sleep disorders more efficient and providing insight into the amount of information about sleep stages that can be gleaned from a particular physiological signal. The review study thoroughly examines automated sleep stage rating systems developed since 2000. These systems were created to analyze electrocardiograms (ECGs), electroencephalograms (EEGs), electDOI: 10.1002/9781394227990.ch5
Metrics:
See at:
CNR IRIS | CNR IRIS | CNR IRIS | onlinelibrary.wiley.com
2024
Journal article
Open Access
SSO-CCNN: a correlation-based optimized deep CNN for brain tumor classification using sampled PGGAN
Sahoo S., Mishra S., Brahma B., Barsocchi P., Bhoi A. K.
Recently, new advancements in technologies have promoted the classification of brain tumors at the early stages to reduce mortality and disease severity. Hence, there is a need for an automatic classification model to automatically segment and classify the tumor regions, which supports researchers and medical practitioners without the need for any expert knowledge. Thus, this research proposes a novel framework called the scatter sharp optimization-based correlation-driven deep CNN model (SSO-CCNN) for classifying brain tumors. The implication of this research is based on the growth of the optimized correlation-enabled deep model, which classifies the tumors using the optimized segments acquired through the developed sampled progressively growing generative adversarial networks (sampled PGGANs). The hyperparameter training is initiated through the designed SSO optimization that is developed by combining the features of the global and local searching phase of flower pollination optimization as well as the adaptive automatic solution convergence of sunflower optimization for precise consequences. The recorded accuracy, sensitivity, and specificity of the SSO-CCNN classification scheme are 97.41%, 97.89%, and 96.93%, respectively, using the brain tumor dataset. In addition, the execution latency was found to be 1.6 s. Thus, the proposed framework can be beneficial to medical experts in tracking and assessing symptoms of brain tumors reliably.Source: INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS, vol. 17 (issue 179 (n. articolo))
DOI: 10.1007/s44196-024-00574-w
Metrics:
Sahoo S., Mishra S., Brahma B., Barsocchi P., Bhoi A. K.
Recently, new advancements in technologies have promoted the classification of brain tumors at the early stages to reduce mortality and disease severity. Hence, there is a need for an automatic classification model to automatically segment and classify the tumor regions, which supports researchers and medical practitioners without the need for any expert knowledge. Thus, this research proposes a novel framework called the scatter sharp optimization-based correlation-driven deep CNN model (SSO-CCNN) for classifying brain tumors. The implication of this research is based on the growth of the optimized correlation-enabled deep model, which classifies the tumors using the optimized segments acquired through the developed sampled progressively growing generative adversarial networks (sampled PGGANs). The hyperparameter training is initiated through the designed SSO optimization that is developed by combining the features of the global and local searching phase of flower pollination optimization as well as the adaptive automatic solution convergence of sunflower optimization for precise consequences. The recorded accuracy, sensitivity, and specificity of the SSO-CCNN classification scheme are 97.41%, 97.89%, and 96.93%, respectively, using the brain tumor dataset. In addition, the execution latency was found to be 1.6 s. Thus, the proposed framework can be beneficial to medical experts in tracking and assessing symptoms of brain tumors reliably.Source: INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS, vol. 17 (issue 179 (n. articolo))
DOI: 10.1007/s44196-024-00574-w
Metrics:
See at:
International Journal of Computational Intelligence Systems | International Journal of Computational Intelligence Systems | CNR IRIS | link.springer.com | CNR IRIS
2024
Journal article
Open Access
Integrating indoor localization systems through a handoff protocol
Furfari F., Girolami M., Barsocchi P.
The increasing adoption of location-based services drives the pervasive adoption of localization systems available anywhere. Environments equipped with multiple indoor localization systems (ILSs) require managing the transition from one ILS to another in order to continue localizing the user's device even when moving indoor or outdoor. In this article, we focus on the handoff procedure, whose goal is to enable a device to trigger the transition between ILSs when specific conditions are verified. We distinguish between the triggering and managing operations, each requiring specific actions. We describe the activation of the handoff procedure by considering three types of ILSs design, each with increasing complexity. Moreover, we define five handoff algorithms-based RSSI signal analysis and we test them in a realistic environment with two nearby ILSs. We establish a set of evaluation metrics to measure the performance of the handoff procedure.Source: IEEE JOURNAL OF INDOOR AND SEAMLESS POSITIONING AND NAVIGATION, vol. 2, pp. 130-142
DOI: 10.1109/jispin.2024.3377146
Project(s): Investment Partenariato Esteso PE8 “Conseguenze e sfide dell'invecchiamento”
Metrics:
Furfari F., Girolami M., Barsocchi P.
The increasing adoption of location-based services drives the pervasive adoption of localization systems available anywhere. Environments equipped with multiple indoor localization systems (ILSs) require managing the transition from one ILS to another in order to continue localizing the user's device even when moving indoor or outdoor. In this article, we focus on the handoff procedure, whose goal is to enable a device to trigger the transition between ILSs when specific conditions are verified. We distinguish between the triggering and managing operations, each requiring specific actions. We describe the activation of the handoff procedure by considering three types of ILSs design, each with increasing complexity. Moreover, we define five handoff algorithms-based RSSI signal analysis and we test them in a realistic environment with two nearby ILSs. We establish a set of evaluation metrics to measure the performance of the handoff procedure.Source: IEEE JOURNAL OF INDOOR AND SEAMLESS POSITIONING AND NAVIGATION, vol. 2, pp. 130-142
DOI: 10.1109/jispin.2024.3377146
Project(s): Investment Partenariato Esteso PE8 “Conseguenze e sfide dell'invecchiamento”
Metrics:
2024
Journal article
Open Access
A review of the electric vehicle charging technology, impact on grid integration, policy consequences, challenges and future trends
Sarda J., Patel N., Patel H., Vaghela R., Brahma B., Bhoi A. K., Barsocchi P.
The effectiveness of electric vehicles (EVs) in mitigating petrol emissions and diminishing reliance on oil for transportation is well recognized. The increasing popularity of EVs has resulted in a proportional increase in the number of charging stations, so significantly affecting the energy grid. In order to promote the general implementation of EVs worldwide, it is crucial to develop a strong charging infrastructure that can satisfy rural and urban areas, especially those that have an inconsistent or non-existent electrical grid. However, the incorporation of EVs into the power grid has posed several challenges in terms of power grid management, network planning, and safety. These issues stem from the rising demand for electricity, negative impacts on the quality of power, and higher power losses. This article offers a comprehensive analysis of the infrastructure of EV charging stations, emphasizing the advantages and consequences associated with it. Moreover, it provides a review of the impact of EVs on the integration of power grids, as well as a thorough study of the standards and rules that regulate the operation of EV charging stations.Source: ENERGY REPORTS, vol. 12, pp. 5671-5692
DOI: 10.1016/j.egyr.2024.11.047
Metrics:
Sarda J., Patel N., Patel H., Vaghela R., Brahma B., Bhoi A. K., Barsocchi P.
The effectiveness of electric vehicles (EVs) in mitigating petrol emissions and diminishing reliance on oil for transportation is well recognized. The increasing popularity of EVs has resulted in a proportional increase in the number of charging stations, so significantly affecting the energy grid. In order to promote the general implementation of EVs worldwide, it is crucial to develop a strong charging infrastructure that can satisfy rural and urban areas, especially those that have an inconsistent or non-existent electrical grid. However, the incorporation of EVs into the power grid has posed several challenges in terms of power grid management, network planning, and safety. These issues stem from the rising demand for electricity, negative impacts on the quality of power, and higher power losses. This article offers a comprehensive analysis of the infrastructure of EV charging stations, emphasizing the advantages and consequences associated with it. Moreover, it provides a review of the impact of EVs on the integration of power grids, as well as a thorough study of the standards and rules that regulate the operation of EV charging stations.Source: ENERGY REPORTS, vol. 12, pp. 5671-5692
DOI: 10.1016/j.egyr.2024.11.047
Metrics:
See at:
Energy Reports | CNR IRIS | www.sciencedirect.com | CNR IRIS
2024
Journal article
Open Access
Multi-scale based approach for denoising real-world noisy image using curvelet thresholding: scope and beyond
Panigrahi S. K., Tripathy S. K., Bhowmick A., Satapathy S. K., Barsocchi P., Bhoi A. K.
Naive simulated additive white Gaussian noise (AWGN) may not fully characterize the complexity of real world noisy images. Owing to optimal sparsity in image representation, we propose a curvelet based model for denoising real-world RGB images. Initially, the image is decomposed in three curvelet scales, namely: the approximation scale (that retains low-frequency information), the coarser scale and the finest scale (that preserves high-frequency components). Coefficients in the approximation and finest scale are estimated using NLM filter, while a scale dependent threshold is adopted for signal estimation in the coarser scale. The reconstructed image in spatial domain is further processed using Guided Image Filter (GIF) to suppress the ringing artifacts due to curvelet thresholding. The proposed approach known as CTuNLM method is extended for color image denoising using uncorrelated YUV color space. Extensive experiments on multi-channel real noisy images are conducted in comparison with eight sate-ofthe-art methods. With four encouraging qualitative and quantitative measures including PSNR and SSIM, we found that CTuNLM method achieves better denoising performance in terms of noise reduction and detail preservation. We further examined the potential of proposed approach by focusing only on the Finest scale curvelet Coefficients (FC). Features like small details, edges and textures always add up to improve the overall denoising performance, while minimizing spurious details. We studied ''The Curious Case of the Finest Scale'' and constructed ''Deep Curvelet-Net'': an encoder-decoder-based CNN architecture, as a pilot work. The encoder uses multiscale spatial characteristics from noisy FC, while the decoder processes de-noised FC under the supervision of encoder's multiscale spatial attention map. The ''Deep CurveletNet'' links encoder multiscale feature modeling with decoder spatial attention supervision to learn the most essential features for denoising. The CNN-based architecture only estimates FC, while all other CTuNLM stages are left unchanged to produce the denoised output. Results presented in this article validated the design of proposed CNN architecture in curvelet domain and motivated us to search beyond classical thresholding and/or filtering approaches.Source: IEEE ACCESS, vol. 12, pp. 25090-25105
DOI: 10.1109/access.2024.3364397
Metrics:
Panigrahi S. K., Tripathy S. K., Bhowmick A., Satapathy S. K., Barsocchi P., Bhoi A. K.
Naive simulated additive white Gaussian noise (AWGN) may not fully characterize the complexity of real world noisy images. Owing to optimal sparsity in image representation, we propose a curvelet based model for denoising real-world RGB images. Initially, the image is decomposed in three curvelet scales, namely: the approximation scale (that retains low-frequency information), the coarser scale and the finest scale (that preserves high-frequency components). Coefficients in the approximation and finest scale are estimated using NLM filter, while a scale dependent threshold is adopted for signal estimation in the coarser scale. The reconstructed image in spatial domain is further processed using Guided Image Filter (GIF) to suppress the ringing artifacts due to curvelet thresholding. The proposed approach known as CTuNLM method is extended for color image denoising using uncorrelated YUV color space. Extensive experiments on multi-channel real noisy images are conducted in comparison with eight sate-ofthe-art methods. With four encouraging qualitative and quantitative measures including PSNR and SSIM, we found that CTuNLM method achieves better denoising performance in terms of noise reduction and detail preservation. We further examined the potential of proposed approach by focusing only on the Finest scale curvelet Coefficients (FC). Features like small details, edges and textures always add up to improve the overall denoising performance, while minimizing spurious details. We studied ''The Curious Case of the Finest Scale'' and constructed ''Deep Curvelet-Net'': an encoder-decoder-based CNN architecture, as a pilot work. The encoder uses multiscale spatial characteristics from noisy FC, while the decoder processes de-noised FC under the supervision of encoder's multiscale spatial attention map. The ''Deep CurveletNet'' links encoder multiscale feature modeling with decoder spatial attention supervision to learn the most essential features for denoising. The CNN-based architecture only estimates FC, while all other CTuNLM stages are left unchanged to produce the denoised output. Results presented in this article validated the design of proposed CNN architecture in curvelet domain and motivated us to search beyond classical thresholding and/or filtering approaches.Source: IEEE ACCESS, vol. 12, pp. 25090-25105
DOI: 10.1109/access.2024.3364397
Metrics:
See at:
IEEE Access | IEEE Access | CNR IRIS | ieeexplore.ieee.org | CNR IRIS
2024
Conference article
Open Access
Design of postural analysis and indoor localization services in AAL scenarios
Barsocchi P., Girolami M., Palumbo F.
Advancements in Ambient Assisted Living (AAL) technology have enabled innovative solutions to enhance the quality of life for older adults. The AA@THE project focuses on personalized coaching and monitoring systems to prevent risky conditions and promote healthy ageing. Two crucial domains, sedentariness and stability, are addressed through advanced technologies such as proximity detection and postural analysis services. By analyzing these specific health and behavioural aspects, personalized feedback is provided to improve overall well-being.Source: LECTURE NOTES IN BIOENGINEERING, pp. 157-160. Bari, Italy, 14-16/06/2023
DOI: 10.1007/978-3-031-63913-5_14
Project(s): Tuscany Health Ecosystem
Metrics:
Barsocchi P., Girolami M., Palumbo F.
Advancements in Ambient Assisted Living (AAL) technology have enabled innovative solutions to enhance the quality of life for older adults. The AA@THE project focuses on personalized coaching and monitoring systems to prevent risky conditions and promote healthy ageing. Two crucial domains, sedentariness and stability, are addressed through advanced technologies such as proximity detection and postural analysis services. By analyzing these specific health and behavioural aspects, personalized feedback is provided to improve overall well-being.Source: LECTURE NOTES IN BIOENGINEERING, pp. 157-160. Bari, Italy, 14-16/06/2023
DOI: 10.1007/978-3-031-63913-5_14
Project(s): Tuscany Health Ecosystem
Metrics:
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CNR IRIS | link.springer.com | doi.org | CNR IRIS | CNR IRIS
2024
Journal article
Open Access
Alzheimer’s disease detection via multiscale feature modelling using improved spatial attention guided depth separable CNN
Tripathy S. K., Nayak R. K., Gadupa K. S., Mishra R. D., Patel A. K., Satapathy S. K., Bhoi A. K., Barsocchi P.
Early detection of Alzheimer's disease (AD) is critical due to its rising prevalence. AI-aided AD diagnosis has grown for decades. Most of these systems use deep learning using CNN. However, a few concerns must be addressed to identify AD: a. there is a lack of attention paid to spatial features; b. there is a lack of scale-invariant feature modelling; and c. the convolutional spatial attention block (C-SAB) mechanism is available in the literature, but it exploits limited feature sets from its input features to obtain a spatial attention map, which needs to be enhanced. The suggested model addresses these issues in two ways: through a backbone of multilayers of depth-separable CNN. Firstly, we propose an improved spatial convolution attention block (I-SAB) to generate an enhanced spatial attention map for the multilayer features of the backbone. The I-SAB, a modified version of the C-SAB, generates a spatial attention map by combining multiple cues from input feature maps. Such a map is forwarded to a multilayer of depth-separable CNN for further feature extraction and employs a skip connection to produce an enhanced spatial attention map. Second, we combine multilayer spatial attention features to make scale-invariant spatial attention features that can fix scale issues in MRI images. We demonstrate extensive experimentation and ablation studies using two open-source datasets, OASIS and AD-Dataset. The recommended model outperforms existing best practices with 99.75% and 96.20% accuracy on OASIS and AD-Dataset. This paper also performed a domain adaptation test on the OASIS dataset, which obtained 83.25% accuracy.Source: INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS, vol. 17 (issue 1)
DOI: 10.1007/s44196-024-00502-y
Metrics:
Tripathy S. K., Nayak R. K., Gadupa K. S., Mishra R. D., Patel A. K., Satapathy S. K., Bhoi A. K., Barsocchi P.
Early detection of Alzheimer's disease (AD) is critical due to its rising prevalence. AI-aided AD diagnosis has grown for decades. Most of these systems use deep learning using CNN. However, a few concerns must be addressed to identify AD: a. there is a lack of attention paid to spatial features; b. there is a lack of scale-invariant feature modelling; and c. the convolutional spatial attention block (C-SAB) mechanism is available in the literature, but it exploits limited feature sets from its input features to obtain a spatial attention map, which needs to be enhanced. The suggested model addresses these issues in two ways: through a backbone of multilayers of depth-separable CNN. Firstly, we propose an improved spatial convolution attention block (I-SAB) to generate an enhanced spatial attention map for the multilayer features of the backbone. The I-SAB, a modified version of the C-SAB, generates a spatial attention map by combining multiple cues from input feature maps. Such a map is forwarded to a multilayer of depth-separable CNN for further feature extraction and employs a skip connection to produce an enhanced spatial attention map. Second, we combine multilayer spatial attention features to make scale-invariant spatial attention features that can fix scale issues in MRI images. We demonstrate extensive experimentation and ablation studies using two open-source datasets, OASIS and AD-Dataset. The recommended model outperforms existing best practices with 99.75% and 96.20% accuracy on OASIS and AD-Dataset. This paper also performed a domain adaptation test on the OASIS dataset, which obtained 83.25% accuracy.Source: INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS, vol. 17 (issue 1)
DOI: 10.1007/s44196-024-00502-y
Metrics:
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International Journal of Computational Intelligence Systems | International Journal of Computational Intelligence Systems | CNR IRIS | link.springer.com | CNR IRIS
2024
Journal article
Open Access
Indoor localization algorithms based on Angle of Arrival with a benchmark comparison
Furfari F., Girolami M., Mavilia F., Barsocchi P.
Indoor localization is crucial for developing intelligent environments capable of understanding user contexts and adapting to environmental changes. Bluetooth 5.1 Direction Finding is a recent specification that leverages the angle of departure (AoD) and angle of arrival (AoA) of radio signals to locate objects or people indoors. This paper presents a set of algorithms that estimate user positions using AoA values and the concept of the Confidence Region (CR), which defines the expected position uncertainty and helps to remove outlier measurements, thereby improving performance compared to traditional triangulation algorithms. We validate the algorithms with a publicly available dataset, and analyze the impact of body orientation relative to receiving units. The experimental results highlight the limitations and potential of the proposed solutions. From our experiments, we observe that the Conditional All-in algorithm presented in this work, achieves the best performance across all configuration settings in both line-of-sight and non-line-of-sight conditions.Source: AD HOC NETWORKS, vol. 166
DOI: 10.1016/j.adhoc.2024.103691
DOI: 10.2139/ssrn.4876021
Project(s): Age-IT, ChAALenge
Metrics:
Furfari F., Girolami M., Mavilia F., Barsocchi P.
Indoor localization is crucial for developing intelligent environments capable of understanding user contexts and adapting to environmental changes. Bluetooth 5.1 Direction Finding is a recent specification that leverages the angle of departure (AoD) and angle of arrival (AoA) of radio signals to locate objects or people indoors. This paper presents a set of algorithms that estimate user positions using AoA values and the concept of the Confidence Region (CR), which defines the expected position uncertainty and helps to remove outlier measurements, thereby improving performance compared to traditional triangulation algorithms. We validate the algorithms with a publicly available dataset, and analyze the impact of body orientation relative to receiving units. The experimental results highlight the limitations and potential of the proposed solutions. From our experiments, we observe that the Conditional All-in algorithm presented in this work, achieves the best performance across all configuration settings in both line-of-sight and non-line-of-sight conditions.Source: AD HOC NETWORKS, vol. 166
DOI: 10.1016/j.adhoc.2024.103691
DOI: 10.2139/ssrn.4876021
Project(s): Age-IT, ChAALenge
Metrics:
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Ad Hoc Networks | IRIS Cnr | IRIS Cnr | doi.org | CNR IRIS
2024
Journal article
Open Access
Bluetooth dataset for proximity detection in indoor environments collected with smartphones
Girolami M., La Rosa D., Barsocchi P.
This paper describes a data collection experiment and the resulting dataset based on Bluetooth beacon messages collected in an indoor museum. The goal of this dataset is to study algorithms and techniques for proximity detection between people and points of interest (POI). To this purpose, we release the data we collected during 32 museum's visits, in which we vary the adopted smartphones and the visiting paths. The smartphone is used to collect Bluetooth beacons emitted by Bluetooth tags positioned nearby each POI. The visiting layout defines the order of visit of 10 artworks. The combination of different smartphones, the visiting paths and features of the indoor museum allow experiencing with realistic environmental conditions. The dataset comprises RSS (Received Signal Strength) values, timestamp and artwork identifiers, as long as a detailed ground truth, reporting the starting and ending time of each artwork's visit. The dataset is addressed to researchers and industrial players interested in further investigating how to automatically detect the location or the proximity between people and specific points of interest, by exploiting commercial technologies available with smartphone. The dataset is designed to speed up the prototyping process, by releasing an accurate ground truth annotation and details concerning the adopted hardware.Source: DATA IN BRIEF, vol. 53
DOI: 10.1016/j.dib.2024.110215
Project(s): Project Tuscany Health Ecosystem, Recupero di Sistemi Informativi STOrico-artistici per una rinnovata comunicazione del patrimonio
Metrics:
Girolami M., La Rosa D., Barsocchi P.
This paper describes a data collection experiment and the resulting dataset based on Bluetooth beacon messages collected in an indoor museum. The goal of this dataset is to study algorithms and techniques for proximity detection between people and points of interest (POI). To this purpose, we release the data we collected during 32 museum's visits, in which we vary the adopted smartphones and the visiting paths. The smartphone is used to collect Bluetooth beacons emitted by Bluetooth tags positioned nearby each POI. The visiting layout defines the order of visit of 10 artworks. The combination of different smartphones, the visiting paths and features of the indoor museum allow experiencing with realistic environmental conditions. The dataset comprises RSS (Received Signal Strength) values, timestamp and artwork identifiers, as long as a detailed ground truth, reporting the starting and ending time of each artwork's visit. The dataset is addressed to researchers and industrial players interested in further investigating how to automatically detect the location or the proximity between people and specific points of interest, by exploiting commercial technologies available with smartphone. The dataset is designed to speed up the prototyping process, by releasing an accurate ground truth annotation and details concerning the adopted hardware.Source: DATA IN BRIEF, vol. 53
DOI: 10.1016/j.dib.2024.110215
Project(s): Project Tuscany Health Ecosystem, Recupero di Sistemi Informativi STOrico-artistici per una rinnovata comunicazione del patrimonio
Metrics:
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Data in Brief | Data in Brief | IRIS Cnr | IRIS Cnr | CNR IRIS
2023
Journal article
Open Access
Privacy by design in systems for assisted living, personalized care and well-being: a stakeholder analysis
Carboni A, Russo D, Moroni D, Barsocchi P
The concept of privacy-by-design within a system for assisted living, personalized care and well-being is crucial to protect users from misuse of the data collected about their health. Especially if the information is collected through audio-video devices, the question is even more delicate due to the nature of this data. In fact, in addition to guaranteeing a high level of privacy, it is necessary to reassure end-users about the correct use of these streams. The evolution of data analysis techniques began to take In review on an important role and increasingly defined characteristics in recent years. In this article, with reference to European projects in the AHA/AAL domain, we will see a differentiation of the concept of privacy-by-design according to different dimensions (Technical, Contextual, Business) and to the Stakeholders involved. The analysis is intended to cover technical aspects, legislative and policies-related aspects also regarding the point of view of the municipalities and aspects related to the acceptance and, therefore, to the perception of the safety of these technologies by the final end-users.Source: FRONTIERS IN DIGITAL HEALTH
DOI: 10.3389/fdgth.2022.934609
Project(s): PlatformUptake.eu
Metrics:
Carboni A, Russo D, Moroni D, Barsocchi P
The concept of privacy-by-design within a system for assisted living, personalized care and well-being is crucial to protect users from misuse of the data collected about their health. Especially if the information is collected through audio-video devices, the question is even more delicate due to the nature of this data. In fact, in addition to guaranteeing a high level of privacy, it is necessary to reassure end-users about the correct use of these streams. The evolution of data analysis techniques began to take In review on an important role and increasingly defined characteristics in recent years. In this article, with reference to European projects in the AHA/AAL domain, we will see a differentiation of the concept of privacy-by-design according to different dimensions (Technical, Contextual, Business) and to the Stakeholders involved. The analysis is intended to cover technical aspects, legislative and policies-related aspects also regarding the point of view of the municipalities and aspects related to the acceptance and, therefore, to the perception of the safety of these technologies by the final end-users.Source: FRONTIERS IN DIGITAL HEALTH
DOI: 10.3389/fdgth.2022.934609
Project(s): PlatformUptake.eu
Metrics:
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CNR IRIS | ISTI Repository | ISTI Repository | www.frontiersin.org | CNR IRIS | CNR IRIS
2023
Conference article
Open Access
Evaluating the impact of anchors deployment for an AoA-based indoor localization system
Mavilia F, Barsocchi P, Furfari F, Girolami M
Indoor localization techniques are rapidly moving toward the combination of multiple source of information. Among these, RSS, Time of Flight (ToF), Angle of Arrival (AoA) and of Departure (AoD) represent effective solutions for indoor environments. In this work, we propose an on-going activity investigating the performance of an indoor localization system based on the AoA-Bluetooth 5.1 specification, namely Direction Finding. We evaluate the effect of two anchor deployments and we test our localization algorithm by varying the orientation of the target according to four postures: North, West, South and East. From our study, we observe that anchor nodes deployed on the ceiling provide the best performance in terms of localization error. We conclude this work with a discussion of two further lines of investigation potentially increasing the performance of AoA-based indoor localization systems.DOI: 10.23919/wons57325.2023.10061949
Metrics:
Mavilia F, Barsocchi P, Furfari F, Girolami M
Indoor localization techniques are rapidly moving toward the combination of multiple source of information. Among these, RSS, Time of Flight (ToF), Angle of Arrival (AoA) and of Departure (AoD) represent effective solutions for indoor environments. In this work, we propose an on-going activity investigating the performance of an indoor localization system based on the AoA-Bluetooth 5.1 specification, namely Direction Finding. We evaluate the effect of two anchor deployments and we test our localization algorithm by varying the orientation of the target according to four postures: North, West, South and East. From our study, we observe that anchor nodes deployed on the ceiling provide the best performance in terms of localization error. We conclude this work with a discussion of two further lines of investigation potentially increasing the performance of AoA-based indoor localization systems.DOI: 10.23919/wons57325.2023.10061949
Metrics:
See at:
CNR IRIS | ieeexplore.ieee.org | ISTI Repository | CNR IRIS | CNR IRIS
2023
Other
Restricted
THE D.3.2.1 - AA@THE User needs, technical requirements and specifications
Pratali L, Campana M G, Delmastro F, Di Martino F, Pescosolido L, Barsocchi P, Broccia G, Ciancia V, Gennaro C, Girolami M, Lagani G, La Rosa D, Latella D, Magrini M, Manca M, Massink M, Mattioli A, Moroni D, Palumbo F, Paradisi P, Paternò F, Santoro C, Sebastiani L, Vairo C
Deliverable D3.2.1 del progetto PNRR Ecosistemi ed innovazione - THE
Pratali L, Campana M G, Delmastro F, Di Martino F, Pescosolido L, Barsocchi P, Broccia G, Ciancia V, Gennaro C, Girolami M, Lagani G, La Rosa D, Latella D, Magrini M, Manca M, Massink M, Mattioli A, Moroni D, Palumbo F, Paradisi P, Paternò F, Santoro C, Sebastiani L, Vairo C
Deliverable D3.2.1 del progetto PNRR Ecosistemi ed innovazione - THE
2023
Journal article
Open Access
A Bluetooth 5.1 dataset based on angle of arrival and RSS for indoor localization
Girolami M, Furfari F, Barsocchi P, Mavilia F
Several Radio-Frequency technologies have been explored to evaluate the efficacy of localization algorithms in indoor environments, including Received Signal Strength (RSS), Time of Flight (ToF), and Angle of Arrival (AoA). Among these, AoA technique has been gaining interest when adopted with the Bluetooth protocol. In this work, we describe a data collection measurement campaign of AoA and RSS values collected from Bluetooth 5.1 compliant tags and a set of anchor nodes deployed in the environment. We detail the adopted methodology to collect the dataset and we report all the technical details to reproduce the data collection process. The resulting dataset and the adopted software is publicly available to the community. To collect the dataset, we deploy four anchor nodes and four Bluetooth tags and we reproduce some representative scenarios for indoor localization: calibration, static, mobility, and proximity. Each scenario is annotated with an accurate ground truth (GT). We also assess the quality of the collected data. Specifically, we compute the Mean Absolute Error (MAE) between the AoA estimated by the anchors and the corresponding GT. Additionally, we investigate the packet loss metric which measures the percentage of Bluetooth beacons lost by the anchors.Source: IEEE ACCESS, vol. 11, pp. 81763-81776
DOI: 10.1109/access.2023.3301126
Metrics:
Girolami M, Furfari F, Barsocchi P, Mavilia F
Several Radio-Frequency technologies have been explored to evaluate the efficacy of localization algorithms in indoor environments, including Received Signal Strength (RSS), Time of Flight (ToF), and Angle of Arrival (AoA). Among these, AoA technique has been gaining interest when adopted with the Bluetooth protocol. In this work, we describe a data collection measurement campaign of AoA and RSS values collected from Bluetooth 5.1 compliant tags and a set of anchor nodes deployed in the environment. We detail the adopted methodology to collect the dataset and we report all the technical details to reproduce the data collection process. The resulting dataset and the adopted software is publicly available to the community. To collect the dataset, we deploy four anchor nodes and four Bluetooth tags and we reproduce some representative scenarios for indoor localization: calibration, static, mobility, and proximity. Each scenario is annotated with an accurate ground truth (GT). We also assess the quality of the collected data. Specifically, we compute the Mean Absolute Error (MAE) between the AoA estimated by the anchors and the corresponding GT. Additionally, we investigate the packet loss metric which measures the percentage of Bluetooth beacons lost by the anchors.Source: IEEE ACCESS, vol. 11, pp. 81763-81776
DOI: 10.1109/access.2023.3301126
Metrics:
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CNR IRIS | ieeexplore.ieee.org | ISTI Repository | CNR IRIS | CNR IRIS
2023
Conference article
Open Access
Modelling the localization error of an AoA-based localization system
Furfari F, Barsocchi P, Girolami M, Mavilia F
Indoor localization provides important context information to develop Intelligent Environments able to understand user situations, to react and adapt to changes in the surrounding environment. Bluetooth 5.1 Direction Finding (DF) is a recent specification based on angle of departure (AoD) and arrival (AoA) of radio signals and it is addressed to localize objects or people in indoor scenarios. In this work, we study the error propagation of an indoor localization system based on AoA technique and on multiple anchor receivers.DOI: 10.1109/ie57519.2023.10179094
Metrics:
Furfari F, Barsocchi P, Girolami M, Mavilia F
Indoor localization provides important context information to develop Intelligent Environments able to understand user situations, to react and adapt to changes in the surrounding environment. Bluetooth 5.1 Direction Finding (DF) is a recent specification based on angle of departure (AoD) and arrival (AoA) of radio signals and it is addressed to localize objects or people in indoor scenarios. In this work, we study the error propagation of an indoor localization system based on AoA technique and on multiple anchor receivers.DOI: 10.1109/ie57519.2023.10179094
Metrics:
See at:
CNR IRIS | ieeexplore.ieee.org | ISTI Repository | CNR IRIS | CNR IRIS
2023
Conference article
Open Access
On the analysis of body orientation for indoor positioning with BLE 5.1 direction finding
Mavilia F, Barsocchi P, Furfari F, La Rosa D, Girolami M
The last decade showed a clear technological trend toward the adoption of heterogeneous source of information, combined with data-fusion strategies to increase the performance of indoor localization systems. In this respect, the adoption of short-range network protocols such as WiFi and Bluetooth represent a common approach. We investigate, in this work, the use of Bluetooth 5.1 Direction Finding specification to test an indoor localization system solely based on the estimated Angle of Arrival (AoA) between an anchor and a receiver. We first detail our experimental data collection campaign and the adopted hardware. Then, we study not only the accuracy of the estimated angles on two reference planes but also the localization error introduced with the proposed algorithm by varying the body orientation of the target user, namely North, South, West, Est. Experimental results in a real-world indoor environment show an average localization error of 2.08m with only 1 anchor node and 5° of AoA' error for all 28 monitored locations. We also identify regions in which the AoA estimation rapidly decreases, giving rise to the possibility of identifying the boundaries of the adopted technology.DOI: 10.1109/icc45041.2023.10279029
Metrics:
Mavilia F, Barsocchi P, Furfari F, La Rosa D, Girolami M
The last decade showed a clear technological trend toward the adoption of heterogeneous source of information, combined with data-fusion strategies to increase the performance of indoor localization systems. In this respect, the adoption of short-range network protocols such as WiFi and Bluetooth represent a common approach. We investigate, in this work, the use of Bluetooth 5.1 Direction Finding specification to test an indoor localization system solely based on the estimated Angle of Arrival (AoA) between an anchor and a receiver. We first detail our experimental data collection campaign and the adopted hardware. Then, we study not only the accuracy of the estimated angles on two reference planes but also the localization error introduced with the proposed algorithm by varying the body orientation of the target user, namely North, South, West, Est. Experimental results in a real-world indoor environment show an average localization error of 2.08m with only 1 anchor node and 5° of AoA' error for all 28 monitored locations. We also identify regions in which the AoA estimation rapidly decreases, giving rise to the possibility of identifying the boundaries of the adopted technology.DOI: 10.1109/icc45041.2023.10279029
Metrics:
See at:
CNR IRIS | ieeexplore.ieee.org | ISTI Repository | CNR IRIS | CNR IRIS