2020
Contribution to journal
Open Access
Editorial of Sensors and Sensing Technologies for Indoor Positioning and Indoor Navigation
Potortì F., Palumbo F., Crivello A.
Source: Sensors (Basel) 20 (2020). doi:10.3390/s20205924
DOI: 10.3390/s20205924
Metrics:
Potortì F., Palumbo F., Crivello A.
Source: Sensors (Basel) 20 (2020). doi:10.3390/s20205924
DOI: 10.3390/s20205924
Metrics:
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Sensors 
| Sensors | ISTI Repository | Sensors | Sensors | CNR ExploRA
2020
Conference article
Restricted
A privacy-by-design architecture for indoor localization systems
Barsocchi P., Calabro A., Crivello A., Daoudagh S., Furfari F., Girolami M., Marchetti E.
The availability of mobile devices has led to an arising development of indoor location services collecting a large amount of sensitive information. However, without accurate and verified management, such information could become severe back-doors for security and privacy issues. We propose in this paper a novel Location-Based Service (LBS) architecture in line with the GDPR's provisions. For feasibility purposes and considering a representative use-case, a reference implementation, based on the popular Telegram app, is also presented.Source: 13th International Conference on the Quality of Information and Communications Technology (QUATIC 2020), pp. 358–366, Faro, Portugal, September 9-11, 2020
DOI: 10.1007/978-3-030-58793-2_29
Project(s): CyberSec4Europe
Metrics:
Barsocchi P., Calabro A., Crivello A., Daoudagh S., Furfari F., Girolami M., Marchetti E.
The availability of mobile devices has led to an arising development of indoor location services collecting a large amount of sensitive information. However, without accurate and verified management, such information could become severe back-doors for security and privacy issues. We propose in this paper a novel Location-Based Service (LBS) architecture in line with the GDPR's provisions. For feasibility purposes and considering a representative use-case, a reference implementation, based on the popular Telegram app, is also presented.Source: 13th International Conference on the Quality of Information and Communications Technology (QUATIC 2020), pp. 358–366, Faro, Portugal, September 9-11, 2020
DOI: 10.1007/978-3-030-58793-2_29
Project(s): CyberSec4Europe
Metrics:
See at:
Communications in Computer and Information Science 
| link-springer-com-443.webvpn.fjmu.edu.cn | CNR ExploRA
2020
Conference article
Closed Access
Detecting Social Interactions in Indoor Environments with the Red-HuP Algorithm
Barsocchi P., Crivello A., Girolami M., Mavilia F.
Detecting social interactions among people represents a challenging task. In this study we evaluate the performance of the ReD-HuP algorithm. We study a real-world and useful experimental dataset and we provide a comparison with some classification methods. Interactions are inferred from co-location of people by exploiting Bluetooth Low Energy (BLE) beacons. Our analysis investigates how the different transmission powers affect the overall performance, we also analyze the results by varying the width of the time window used to analyze BLE beacons. Results obtained with the ReD-HuP algorithm have been compared against two well known and wide adopted machine learning classification methods.Source: 2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), Austin, TX, USA, USA, 23-27 March 2020
DOI: 10.1109/percomworkshops48775.2020.9156095
Metrics:
Barsocchi P., Crivello A., Girolami M., Mavilia F.
Detecting social interactions among people represents a challenging task. In this study we evaluate the performance of the ReD-HuP algorithm. We study a real-world and useful experimental dataset and we provide a comparison with some classification methods. Interactions are inferred from co-location of people by exploiting Bluetooth Low Energy (BLE) beacons. Our analysis investigates how the different transmission powers affect the overall performance, we also analyze the results by varying the width of the time window used to analyze BLE beacons. Results obtained with the ReD-HuP algorithm have been compared against two well known and wide adopted machine learning classification methods.Source: 2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), Austin, TX, USA, USA, 23-27 March 2020
DOI: 10.1109/percomworkshops48775.2020.9156095
Metrics:
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doi.org | ieeexplore.ieee.org | CNR ExploRA
2020
Journal article
Open Access
Remote detection of social interactions in indoor environments through bluetooth low energy beacons
Baronti P., Barsocchi P., Chessa S., Crivello A., Girolami M., Mavilia F., Palumbo F.
The way people interact in daily life is a challenging phenomenon to be captured and studied without altering the natural rhythm of the interactions. We investigate the development of automated tools that may provide information to the researchers that analyse interactions among humans. One important requirement of these tools is that should not interfere with the subjects under observation, in order to avoid any alteration in the subject's normal behaviour. Our approach is based on the detection of proximity among groups of people that is obtained using commercial wearable wireless tags based on Bluetooth Low Energy (BLE) and a novel algorithm called Remote Detection of Human Proximity (ReD-HuP) that analyses the wireless signal of tags and produce the proximity information. The algorithm, which has been validated against the ground truth of an experimental dataset, achieves an accuracy of 95.91% and an F-Score of 95.79%.Source: Journal of ambient intelligence and smart environments (Print) 12 (2020): 203–217. doi:10.3233/AIS-200560
DOI: 10.3233/ais-200560
Project(s): NESTORE
Metrics:
Baronti P., Barsocchi P., Chessa S., Crivello A., Girolami M., Mavilia F., Palumbo F.
The way people interact in daily life is a challenging phenomenon to be captured and studied without altering the natural rhythm of the interactions. We investigate the development of automated tools that may provide information to the researchers that analyse interactions among humans. One important requirement of these tools is that should not interfere with the subjects under observation, in order to avoid any alteration in the subject's normal behaviour. Our approach is based on the detection of proximity among groups of people that is obtained using commercial wearable wireless tags based on Bluetooth Low Energy (BLE) and a novel algorithm called Remote Detection of Human Proximity (ReD-HuP) that analyses the wireless signal of tags and produce the proximity information. The algorithm, which has been validated against the ground truth of an experimental dataset, achieves an accuracy of 95.91% and an F-Score of 95.79%.Source: Journal of ambient intelligence and smart environments (Print) 12 (2020): 203–217. doi:10.3233/AIS-200560
DOI: 10.3233/ais-200560
Project(s): NESTORE
Metrics:
See at:
ISTI Repository | content.iospress.com | Journal of Ambient Intelligence and Smart Environments | CNR ExploRA
2020
Journal article
Open Access
Accurate indoor positioning using temporal-spatial constraints based on wi-fi fine time measurements
Shao W., Luo H., Zhao F., Tian H., Yan S. ., Crivello A.
The IEEE 802.11mc-2016 protocol enables certified devices to obtain precise ranging information using time-of-flight based techniques. The ranging error increases in indoor environments due to the multipath effect. Traditional methods utilize only the ranging measurements of the current location, thus limiting the abilities to reduce the influence of multi-path problems. This paper introduces a robust positioning method that leverages the constraints of multiple positioning nodes at different positions. We transfer a sequence of temporal ranging measurements into multiple virtual positioning clients in the spatial domain by considering their spatial constraints. Defining an objective function and the spatial constraints of the virtual positioning clients as Karush-Kuhn-Tucker conditions, we solve the positioning estimation with non-convex optimization. We propose an iterative weight estimation method for the time of flight ranging and the virtual positioning client to optimize the positioning model. An extensive experimental campaign demonstrates that our proposal is able to remarkably improve the positioning accuracy in complex indoor environments.Source: IEEE Internet of Things Journal 7 (2020): 11006–11019. doi:10.1109/JIOT.2020.2992069
DOI: 10.1109/jiot.2020.2992069
Metrics:
Shao W., Luo H., Zhao F., Tian H., Yan S. ., Crivello A.
The IEEE 802.11mc-2016 protocol enables certified devices to obtain precise ranging information using time-of-flight based techniques. The ranging error increases in indoor environments due to the multipath effect. Traditional methods utilize only the ranging measurements of the current location, thus limiting the abilities to reduce the influence of multi-path problems. This paper introduces a robust positioning method that leverages the constraints of multiple positioning nodes at different positions. We transfer a sequence of temporal ranging measurements into multiple virtual positioning clients in the spatial domain by considering their spatial constraints. Defining an objective function and the spatial constraints of the virtual positioning clients as Karush-Kuhn-Tucker conditions, we solve the positioning estimation with non-convex optimization. We propose an iterative weight estimation method for the time of flight ranging and the virtual positioning client to optimize the positioning model. An extensive experimental campaign demonstrates that our proposal is able to remarkably improve the positioning accuracy in complex indoor environments.Source: IEEE Internet of Things Journal 7 (2020): 11006–11019. doi:10.1109/JIOT.2020.2992069
DOI: 10.1109/jiot.2020.2992069
Metrics:
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ISTI Repository | doi.org | ieeexplore.ieee.org | CNR ExploRA
2020
Contribution to journal
Unknown
Sensors and sensing technologies for indoor positioning and indoor navigation
Potortì F., Palumbo F., Crivello A.
The last ten years have seen enormous technical progress in the field of indoor positioning and indoor navigation; yet, in contrast with outdoor well-established GNSS solutions, no technology exists that is cheap and accurate enough for the general market. The potential applications of indoor localization are all-encompassing, from home to wide public areas, from IoT and personal devices to surveillance and crowd behavior applications, and from casual use to mission-critical systems. This Special Issue encourages authors, from academia and industry, to submit new research results about innovations for indoor positioning and navigation. The Special Issue topics include but are not limited to: Location-based services and applications; Benchmarking, assessment, evaluation, standards; User requirements; UI, indoor maps, and 3D building models; Human motion monitoring and modeling; Robotics and UAV; Indoor navigation and tracking methods; Self-contained sensors; Wearable and multisensor systems; Privacy and security for ILS.Source: Basel: MDPI AG, 2020
Potortì F., Palumbo F., Crivello A.
The last ten years have seen enormous technical progress in the field of indoor positioning and indoor navigation; yet, in contrast with outdoor well-established GNSS solutions, no technology exists that is cheap and accurate enough for the general market. The potential applications of indoor localization are all-encompassing, from home to wide public areas, from IoT and personal devices to surveillance and crowd behavior applications, and from casual use to mission-critical systems. This Special Issue encourages authors, from academia and industry, to submit new research results about innovations for indoor positioning and navigation. The Special Issue topics include but are not limited to: Location-based services and applications; Benchmarking, assessment, evaluation, standards; User requirements; UI, indoor maps, and 3D building models; Human motion monitoring and modeling; Robotics and UAV; Indoor navigation and tracking methods; Self-contained sensors; Wearable and multisensor systems; Privacy and security for ILS.Source: Basel: MDPI AG, 2020
See at: CNR ExploRA | www.mdpi.com
2020
Journal article
Open Access
"Hi This Is NESTORE, Your Personal Assistant": Design of an Integrated IoT System for a Personalized Coach for Healthy Aging
Palumbo F., Crivello A., Furfari F., Girolami M., Mastropietro A., Manferdelli G., Röcke C., Guye S., Salvá Casanovas A., Caon M., Carrino F., Khaled O. A., Mugellini E., Denna E., Mauri M., Ward D., Subías-Beltrán P., Orte S., Candea C., Candea G., Rizzo G.
In the context of the fourth revolution in healthcare technologies, leveraging monitoring and personalization across different domains becomes a key factor for providing useful services to maintain and promote well-being. This is even more crucial for older people, with aging being a complex multi-dimensional and multi-factorial process which can lead to frailty. The NESTORE project was recently funded by the EU Commission with the aim of supporting healthy older people to sustain their well-being and capacity to live independently. It is based on a multi-dimensional model of the healthy aging process that covers physical activity, nutrition, cognition, and social activity. NESTORE is based on the paradigm of the human-in-the-loop cyber-physical system that, exploiting the availability of Internet of Things technologies combined with analytics in the cloud, provides a virtual coaching system to support healthy aging. This work describes the design of the NESTORE methodology and its IoT architecture. We first model the end-user under several domains, then we present the NESTORE system that, analyzing relevant key-markers, provides coaching activities and personalized feedback to the user. Finally, we describe the validation strategy to assess the effectiveness of NESTORE as a coaching platform for healthy aging.Source: Frontiers in digital health (2020). doi:10.3389/fdgth.2020.545949
DOI: 10.3389/fdgth.2020.545949
Project(s): NESTORE
Metrics:
Palumbo F., Crivello A., Furfari F., Girolami M., Mastropietro A., Manferdelli G., Röcke C., Guye S., Salvá Casanovas A., Caon M., Carrino F., Khaled O. A., Mugellini E., Denna E., Mauri M., Ward D., Subías-Beltrán P., Orte S., Candea C., Candea G., Rizzo G.
In the context of the fourth revolution in healthcare technologies, leveraging monitoring and personalization across different domains becomes a key factor for providing useful services to maintain and promote well-being. This is even more crucial for older people, with aging being a complex multi-dimensional and multi-factorial process which can lead to frailty. The NESTORE project was recently funded by the EU Commission with the aim of supporting healthy older people to sustain their well-being and capacity to live independently. It is based on a multi-dimensional model of the healthy aging process that covers physical activity, nutrition, cognition, and social activity. NESTORE is based on the paradigm of the human-in-the-loop cyber-physical system that, exploiting the availability of Internet of Things technologies combined with analytics in the cloud, provides a virtual coaching system to support healthy aging. This work describes the design of the NESTORE methodology and its IoT architecture. We first model the end-user under several domains, then we present the NESTORE system that, analyzing relevant key-markers, provides coaching activities and personalized feedback to the user. Finally, we describe the validation strategy to assess the effectiveness of NESTORE as a coaching platform for healthy aging.Source: Frontiers in digital health (2020). doi:10.3389/fdgth.2020.545949
DOI: 10.3389/fdgth.2020.545949
Project(s): NESTORE
Metrics:
See at:
Diposit Digital de Documents de la UAB | Frontiers in Digital Health | Frontiers in Digital Health | ISTI Repository | DOAJ-Articles | www.frontiersin.org | Frontiers in Digital Health | Zurich Open Repository and Archive | CNR ExploRA
2020
Journal article
Open Access
The IPIN 2019 indoor localisation competition - Description and results
Potortì F., Park S., Crivello A., Palumbo F., Girolami M., Barsocchi P., Lee S., Torres-Sospedra J., Jimenez A. R., Pérez-Navarro A., Mendoza-Silva G. M., Seco F., Ortiz M., Perul J., Renaudin V., Kang H., Hong Lee J., Park C. G., Ha J., Han J., Park C., Kim K., Lee Y., Gye S., Lee K., Kim E., Choi J., Choi Y. S., Talwar S., Cho S. Y., Ben-Moshe B., Scherbakov A., Antsfeld L., Sansano-Sansano E., Chidlovskii B., Kronenwett N., Prophet S., Landau Y., Marbel R., Zheng L., Peng A., Lin Z., Wu B., Ma C., Poslad S., Selviah D. R., Wu W., Ma Z., Zhang W., Wei D., Yuan H., Jiang J. B., Huang S. Y., Liu J. W., Su K. W., Leu J. S., Nishiguchi K., Bousselham W., Uchiyama H., Thomas D., Shimada A., Taniguchi R. I., Cortés V., Lungenstrass T., Ashraf I., Lee C., Usman Ali M., Im Y., Kim G., Eom J., Hur S., Park Y., Opiela M., Moreira A., João Nicolau M., Pendão C., Silva I., Meneses F., Costa A., Trogh J., Plets D., Chien Y. R., Chang T. Y., Fang S. H, Tsao Y.
IPIN 2019 Competition, sixth in a series of IPIN competitions, was held at the CNR Research Area of Pisa (IT), integrated into the program of the IPIN 2019 Conference. It included two on-site real-time Tracks and three off-site Tracks. The four Tracks presented in this paper were set in the same environment, made of two buildings close together for a total usable area of 1000 m² outdoors and and 6000 m² indoors over three floors, with a total path length exceeding 500 m. IPIN competitions, based on the EvAAL framework, have aimed at comparing the accuracy performance of personal positioning systems in fair and realistic conditions: past editions of the competition were carried in big conference settings, university campuses and a shopping mall. Positioning accuracy is computed while the person carrying the system under test walks at normal walking speed, uses lifts and goes up and down stairs or briefly stops at given points. Results presented here are a showcase of state-of-the-art systems tested side by side in real-world settings as part of the on-site real-time competition Tracks. Results for off-site Tracks allow a detailed and reproducible comparison of the most recent positioning and tracking algorithms in the same environment as the on-site Tracks.Source: IEEE access 8 (2020): 206674–206718. doi:10.1109/ACCESS.2020.3037221
DOI: 10.1109/access.2020.3037221
DOI: 10.5445/ir/1000130123
Metrics:
Potortì F., Park S., Crivello A., Palumbo F., Girolami M., Barsocchi P., Lee S., Torres-Sospedra J., Jimenez A. R., Pérez-Navarro A., Mendoza-Silva G. M., Seco F., Ortiz M., Perul J., Renaudin V., Kang H., Hong Lee J., Park C. G., Ha J., Han J., Park C., Kim K., Lee Y., Gye S., Lee K., Kim E., Choi J., Choi Y. S., Talwar S., Cho S. Y., Ben-Moshe B., Scherbakov A., Antsfeld L., Sansano-Sansano E., Chidlovskii B., Kronenwett N., Prophet S., Landau Y., Marbel R., Zheng L., Peng A., Lin Z., Wu B., Ma C., Poslad S., Selviah D. R., Wu W., Ma Z., Zhang W., Wei D., Yuan H., Jiang J. B., Huang S. Y., Liu J. W., Su K. W., Leu J. S., Nishiguchi K., Bousselham W., Uchiyama H., Thomas D., Shimada A., Taniguchi R. I., Cortés V., Lungenstrass T., Ashraf I., Lee C., Usman Ali M., Im Y., Kim G., Eom J., Hur S., Park Y., Opiela M., Moreira A., João Nicolau M., Pendão C., Silva I., Meneses F., Costa A., Trogh J., Plets D., Chien Y. R., Chang T. Y., Fang S. H, Tsao Y.
IPIN 2019 Competition, sixth in a series of IPIN competitions, was held at the CNR Research Area of Pisa (IT), integrated into the program of the IPIN 2019 Conference. It included two on-site real-time Tracks and three off-site Tracks. The four Tracks presented in this paper were set in the same environment, made of two buildings close together for a total usable area of 1000 m² outdoors and and 6000 m² indoors over three floors, with a total path length exceeding 500 m. IPIN competitions, based on the EvAAL framework, have aimed at comparing the accuracy performance of personal positioning systems in fair and realistic conditions: past editions of the competition were carried in big conference settings, university campuses and a shopping mall. Positioning accuracy is computed while the person carrying the system under test walks at normal walking speed, uses lifts and goes up and down stairs or briefly stops at given points. Results presented here are a showcase of state-of-the-art systems tested side by side in real-world settings as part of the on-site real-time competition Tracks. Results for off-site Tracks allow a detailed and reproducible comparison of the most recent positioning and tracking algorithms in the same environment as the on-site Tracks.Source: IEEE access 8 (2020): 206674–206718. doi:10.1109/ACCESS.2020.3037221
DOI: 10.1109/access.2020.3037221
DOI: 10.5445/ir/1000130123
Metrics:
See at:
Ghent University Academic Bibliography | IEEE Access | doi.org | Recolector de Ciencia Abierta, RECOLECTA | Universidade do Minho: RepositoriUM | ieeexplore.ieee.org | IEEE Access | ISTI Repository | KITopen | Hyper Article en Ligne | CNR ExploRA