2016
Software
Unknown
CC2530 Firmware for environmental monitoring in smart building application
Mavilia F.
Source files, drivers and hex files for energy and environmental monitoring of buildings in distributed wireless sensor networks.
Mavilia F.
Source files, drivers and hex files for energy and environmental monitoring of buildings in distributed wireless sensor networks.
See at: CNR ExploRA
2016
Software
Unknown
Project files of two electronic boards for environmental monitoring in smart building application
Mavilia F.
Schematic, footprints and tracks design, gerber files of two electronic boards for energy and environmental monitoring of buildings in distributed wireless sensor networks
Mavilia F.
Schematic, footprints and tracks design, gerber files of two electronic boards for energy and environmental monitoring of buildings in distributed wireless sensor networks
See at: CNR ExploRA
2013
Report
Unknown
Progettazione e analisi di una piattaforma hardware software open per applicazioni Smart Energy: studio preliminare
Mavilia F., Lenzi S.
In questo documento descriviamo l'esperienza acquisita durante la fase di progettazione e programmazione di una piattaforma aperta per il monitoraggio energetico basata su reti di sensori wireless. In particolare, l'obiettivo finale e? quello di effettuare uno studio ed analisi dei costi energetici di un complesso di edifici come quello dell'area di ricerca del CNR di Pisa, individuando la collocazione ottimale di sensori wireless all'interno degli immobili per monitorarne il consumo di energia elettrica, sviluppando sensori ad hoc. L'attivita? effettuata, dunque, si e? articolata nei seguenti temi principali che, nel proseguo del documento, saranno esplicati in dettaglio: la scelta della piattaforma hardware, la raccolta dei dati energetici, lo sviluppo di device ZigBee.Source: ISTI Technical reports, 2013
Mavilia F., Lenzi S.
In questo documento descriviamo l'esperienza acquisita durante la fase di progettazione e programmazione di una piattaforma aperta per il monitoraggio energetico basata su reti di sensori wireless. In particolare, l'obiettivo finale e? quello di effettuare uno studio ed analisi dei costi energetici di un complesso di edifici come quello dell'area di ricerca del CNR di Pisa, individuando la collocazione ottimale di sensori wireless all'interno degli immobili per monitorarne il consumo di energia elettrica, sviluppando sensori ad hoc. L'attivita? effettuata, dunque, si e? articolata nei seguenti temi principali che, nel proseguo del documento, saranno esplicati in dettaglio: la scelta della piattaforma hardware, la raccolta dei dati energetici, lo sviluppo di device ZigBee.Source: ISTI Technical reports, 2013
See at: CNR ExploRA
2015
Report
Unknown
Enhanced accelerometer for structural health monitoring
Cassarà P., Mavilia F., Pellegrini D.
Structural Health Monitoring (SHM) plays a crucial role in conserving and safeguarding the world's patrimony of historical buildings. Indeed, it allows, on the one hand, understanding the dynamic behavior of the structures in question, and on the other, checking their health status in real time. Obviously, application of such systems in the field of cultural heritage calls for employing low-cost technologies that enable non-invasive monitoring under in-service conditions. This paper describes a wireless SHM system based on a low-cost 3-axis accelerometer and the frame structure used for its calibration.Source: ISTI Technical reports, 2015
Cassarà P., Mavilia F., Pellegrini D.
Structural Health Monitoring (SHM) plays a crucial role in conserving and safeguarding the world's patrimony of historical buildings. Indeed, it allows, on the one hand, understanding the dynamic behavior of the structures in question, and on the other, checking their health status in real time. Obviously, application of such systems in the field of cultural heritage calls for employing low-cost technologies that enable non-invasive monitoring under in-service conditions. This paper describes a wireless SHM system based on a low-cost 3-axis accelerometer and the frame structure used for its calibration.Source: ISTI Technical reports, 2015
See at: CNR ExploRA
2017
Conference article
Restricted
Sensing the cities with social-aware unmanned aerial vehicles
Chessa S., Girolami M., Mavilia F., Dini G., Perazzo P., Rasori M.
The increasing diffusion of smart devices opens to a new era for collecting large quantities of data from urban areas. Sensing information can be collected by using existing network infrastructures, but also by adopting small, cheap and configurable aerial vehicles, namely drones. Our work focusses on studying how to optimize their adoption for smart city applications designed to gather sensing data from user's devices roaming on the ground. To this purpose, we used HUMsim, a tool which generates realistic human traces, to mimic pedestrian mobility. From this dataset, we extract some sociality features that we exploit to plan a social-aware drone trajectory with the goal of maximizing the opportunities of interaction between drone and devices. Our experiments compare social-aware and social-oblivious trajectories showing that knowing the way people move and interact boosts the amount of retrievable data.Source: IEEE Symposium on Computers and Communications, Heraklion, Greece, 3-6 July 2017
DOI: 10.1109/iscc.2017.8024542
Metrics:
Chessa S., Girolami M., Mavilia F., Dini G., Perazzo P., Rasori M.
The increasing diffusion of smart devices opens to a new era for collecting large quantities of data from urban areas. Sensing information can be collected by using existing network infrastructures, but also by adopting small, cheap and configurable aerial vehicles, namely drones. Our work focusses on studying how to optimize their adoption for smart city applications designed to gather sensing data from user's devices roaming on the ground. To this purpose, we used HUMsim, a tool which generates realistic human traces, to mimic pedestrian mobility. From this dataset, we extract some sociality features that we exploit to plan a social-aware drone trajectory with the goal of maximizing the opportunities of interaction between drone and devices. Our experiments compare social-aware and social-oblivious trajectories showing that knowing the way people move and interact boosts the amount of retrievable data.Source: IEEE Symposium on Computers and Communications, Heraklion, Greece, 3-6 July 2017
DOI: 10.1109/iscc.2017.8024542
Metrics:
See at:
doi.org 
| ieeexplore.ieee.org | CNR ExploRA
2017
Report
Unknown
Sensing the health state of a city structural monitoring system by IoT wireless sensing devices
Barsocchi P., Cassarà P., Mavilia F., Pellegrini D.
We are witnesses of technological ascent accomplished by the Internet of Things (IoT) in the new era of the shared informatization. Smart Cities make up certainly one of the major areas of IoT applications. Despite the several defi- nitions for this new concept, the aim is achieve a better use of public resources, increasing the quality of services and decreasing the costs of public services. Some initiatives of Smart Cities around the world have become reality [1]. Many components compose a smart city, e.g. smart transportation, smart energy, smart education, and smart building surely. A smart building consists in services offered to the occupants, resources distributed to the city and, in case of historical heritage, informations about its "health state" to municipalities. The core of a smart building, and more in general of a smart city, is represented by the IoT devices [2]. Physical objects connected over a network take part in Internet sharing information about themselves at any time. The marketplace offers several solutions for IoT developments, both in hardware and software. Developers have proposed simply and low-cost embedded systems, that allowed users to develop on their own monitoring and actuation systems [3]. The research in the structural health monitoring field (SHM) [4] takes advantage by the IoT paradigm. Indeed, the goal of SHM is to obtain informa- tion about the characteristics of a structure, its constituent materials, and the different parts that compose itself. The SHM needs of observations of a structure over the time, using measurements obtained with different kind of sensors (accelerometers, thermometers, hygrometer, extensometers) deployed along the whole structure to be monitored. Such procedures are recognized as a good way to test the state of conservation of a structure, and are also important aids in identifying when interventions are necessary. Exploiting low-invasive and aesthetically acceptable sensors becomes essential to widespread SHM applications. IoT technolo- gies have invaded SHM field providing an economical and relatively non-invasive instruments for real-time structural monitoring of buildings and monuments. Indeed, IoT-based sensors are able to monitor wide areas and transmit data to a remote server. We envisage their employment in a not too distant future in the monitoring of entire areas within a city, facilitating the management of maintenance operations and prompt interventions in the case of an emergency. The main issues that must be taken into account when an IoT-based sensor network is used for SHM applications are: the number and location of the sensors used to ac- quire data, the synchronization of the acquired data and the energy consumption of the nodes. The optimization of the number and location of sensors is a new challenge in these technologies, which typically involve a large number of redundant sensors. Moreover, the synchronization of the sampled data is essential in order to correlate data coming from different sensors deployed in the ancient monuments. Finally, adopting energy-efficiency policies becomes essen- tial for long term monitoring systems. In this paper we show how a long term monitoring infras- tructure based on the IoT paradigm can be achieved with off-the-shelf devices, where each sensing device is able of autonomously transferring data (3-axial acceleration, tem- perature and humidity) to a server. We deployed the sensing devices on the San Frediano bell tower in Lucca, collecting a large amount of data. Finally, we studied the structure behavior under external stresses, showing results in three particular conditions: environmental noise in a weekday, during the ringing of the bells and during a large event taken place in the city.Source: ISTI Working papers, 2017
Barsocchi P., Cassarà P., Mavilia F., Pellegrini D.
We are witnesses of technological ascent accomplished by the Internet of Things (IoT) in the new era of the shared informatization. Smart Cities make up certainly one of the major areas of IoT applications. Despite the several defi- nitions for this new concept, the aim is achieve a better use of public resources, increasing the quality of services and decreasing the costs of public services. Some initiatives of Smart Cities around the world have become reality [1]. Many components compose a smart city, e.g. smart transportation, smart energy, smart education, and smart building surely. A smart building consists in services offered to the occupants, resources distributed to the city and, in case of historical heritage, informations about its "health state" to municipalities. The core of a smart building, and more in general of a smart city, is represented by the IoT devices [2]. Physical objects connected over a network take part in Internet sharing information about themselves at any time. The marketplace offers several solutions for IoT developments, both in hardware and software. Developers have proposed simply and low-cost embedded systems, that allowed users to develop on their own monitoring and actuation systems [3]. The research in the structural health monitoring field (SHM) [4] takes advantage by the IoT paradigm. Indeed, the goal of SHM is to obtain informa- tion about the characteristics of a structure, its constituent materials, and the different parts that compose itself. The SHM needs of observations of a structure over the time, using measurements obtained with different kind of sensors (accelerometers, thermometers, hygrometer, extensometers) deployed along the whole structure to be monitored. Such procedures are recognized as a good way to test the state of conservation of a structure, and are also important aids in identifying when interventions are necessary. Exploiting low-invasive and aesthetically acceptable sensors becomes essential to widespread SHM applications. IoT technolo- gies have invaded SHM field providing an economical and relatively non-invasive instruments for real-time structural monitoring of buildings and monuments. Indeed, IoT-based sensors are able to monitor wide areas and transmit data to a remote server. We envisage their employment in a not too distant future in the monitoring of entire areas within a city, facilitating the management of maintenance operations and prompt interventions in the case of an emergency. The main issues that must be taken into account when an IoT-based sensor network is used for SHM applications are: the number and location of the sensors used to ac- quire data, the synchronization of the acquired data and the energy consumption of the nodes. The optimization of the number and location of sensors is a new challenge in these technologies, which typically involve a large number of redundant sensors. Moreover, the synchronization of the sampled data is essential in order to correlate data coming from different sensors deployed in the ancient monuments. Finally, adopting energy-efficiency policies becomes essen- tial for long term monitoring systems. In this paper we show how a long term monitoring infras- tructure based on the IoT paradigm can be achieved with off-the-shelf devices, where each sensing device is able of autonomously transferring data (3-axial acceleration, tem- perature and humidity) to a server. We deployed the sensing devices on the San Frediano bell tower in Lucca, collecting a large amount of data. Finally, we studied the structure behavior under external stresses, showing results in three particular conditions: environmental noise in a weekday, during the ringing of the bells and during a large event taken place in the city.Source: ISTI Working papers, 2017
See at: CNR ExploRA
2018
Journal article
Open Access
Sensing a city's state of health: structural monitoring system by Internet-of-Things wireless sensing devices
Barsocchi P., Cassarà P., Mavilia F., Pellegrini D.
We are the witnesses of a technological ascent accomplished by the Internet of Things (IoT) in the new era of shared informatization. Smart cities make up one of the major areas of IoT applications. Despite several definitions for this new concept, the aim is to achieve a better use of public resources by increasing the quality of services and decreasing the costs of public services. Some initiatives of smart cities around the world have become reality, such as in [1]. Many smart components compose a smart city, such as transportation, energy, education, and buildings. A smart building includes services offered to the occupants, resources distributed to the city, and, in the case of historical buildings, information provided to municipalities about their state of health.Source: IEEE Consumer Electronics Society newsletter 7 (2018): 22–31. doi:10.1109/MCE.2017.2717198
DOI: 10.1109/mce.2017.2717198
Metrics:
Barsocchi P., Cassarà P., Mavilia F., Pellegrini D.
We are the witnesses of a technological ascent accomplished by the Internet of Things (IoT) in the new era of shared informatization. Smart cities make up one of the major areas of IoT applications. Despite several definitions for this new concept, the aim is to achieve a better use of public resources by increasing the quality of services and decreasing the costs of public services. Some initiatives of smart cities around the world have become reality, such as in [1]. Many smart components compose a smart city, such as transportation, energy, education, and buildings. A smart building includes services offered to the occupants, resources distributed to the city, and, in the case of historical buildings, information provided to municipalities about their state of health.Source: IEEE Consumer Electronics Society newsletter 7 (2018): 22–31. doi:10.1109/MCE.2017.2717198
DOI: 10.1109/mce.2017.2717198
Metrics:
See at:
ISTI Repository 
| IEEE Consumer Electronics Magazine | ieeexplore.ieee.org | CNR ExploRA
2017
Contribution to book
Restricted
Energy and environmental long-term monitoring system for inhabitants' well-being
Barsocchi P., Crivello A., Mavilia F., Palumbo F.
The increasing demand for building services and comfort levels, together with the increased time spent inside buildings, assures an upward trend in long-term monitoring system demand for the future. In this paper, we present the work done for designing added value human well-being services starting from a state-of-the-art continuous data gathering infrastructure. The paper presents the proposed energy and environmental long-term monitoring system that is able to measure both the energy consumed by end users and the environmental parameters in office environments. The paper shortly describes the general idea, then it focuses on the work done to create a well-being service on the top of the data gathering layer. In particular, it dwells on the deployment approach focusing on the description of the long-term monitoring system and providing preliminary results of the proposed real-time social interactions algorithm.Source: State of the Art in AI Applied to Ambient Intelligence, pp. 91–108. Amsterdam: IOS Press, 2017
DOI: 10.3233/978-1-61499-804-4-91
Metrics:
Barsocchi P., Crivello A., Mavilia F., Palumbo F.
The increasing demand for building services and comfort levels, together with the increased time spent inside buildings, assures an upward trend in long-term monitoring system demand for the future. In this paper, we present the work done for designing added value human well-being services starting from a state-of-the-art continuous data gathering infrastructure. The paper presents the proposed energy and environmental long-term monitoring system that is able to measure both the energy consumed by end users and the environmental parameters in office environments. The paper shortly describes the general idea, then it focuses on the work done to create a well-being service on the top of the data gathering layer. In particular, it dwells on the deployment approach focusing on the description of the long-term monitoring system and providing preliminary results of the proposed real-time social interactions algorithm.Source: State of the Art in AI Applied to Ambient Intelligence, pp. 91–108. Amsterdam: IOS Press, 2017
DOI: 10.3233/978-1-61499-804-4-91
Metrics:
See at:
ebooks.iospress.nl | CNR ExploRA
2018
Conference article
Open Access
Detecting social interactions through commercial mobile devices
Girolami M., Mavilia F., Delmastro F., Distefano E.
Social interactions represent an important factor in the human society and it presents different issues depending on the user category involved. In this paper, we present technological issues of using exclusively commercial mobile devices of the users to detect social interactions. Then, we propose a solution based on Bluetooth wearable tags, minimally invasive and low-cost. This solution is based on the analysis of the RSSI emitted by BLE beacon messages and received by the user personal device through a dedicated mobile app. We collected such information during a calibration campaign. To this purpose, we recruited volunteer students from a high school who mimic a number of interactions with class-mates. We compared the results of our algorithm with a diary of the interactions provided by the students, obtaining an overall accuracy of 81% and F-Score measure of 84%.Source: IEEE PerCom 2018 - HCCS Workshop, pp. 125–130, Athens, Greece, 19/03/2018
DOI: 10.1109/percomw.2018.8480397
Metrics:
Girolami M., Mavilia F., Delmastro F., Distefano E.
Social interactions represent an important factor in the human society and it presents different issues depending on the user category involved. In this paper, we present technological issues of using exclusively commercial mobile devices of the users to detect social interactions. Then, we propose a solution based on Bluetooth wearable tags, minimally invasive and low-cost. This solution is based on the analysis of the RSSI emitted by BLE beacon messages and received by the user personal device through a dedicated mobile app. We collected such information during a calibration campaign. To this purpose, we recruited volunteer students from a high school who mimic a number of interactions with class-mates. We compared the results of our algorithm with a diary of the interactions provided by the students, obtaining an overall accuracy of 81% and F-Score measure of 84%.Source: IEEE PerCom 2018 - HCCS Workshop, pp. 125–130, Athens, Greece, 19/03/2018
DOI: 10.1109/percomw.2018.8480397
Metrics:
See at:
ISTI Repository | doi.org | sites.google.com | CNR ExploRA
2020
Journal article
Open Access
A bluetooth low energy dataset for the analysis of social interactions with commercial devices
Girolami M., Mavilia F., Delmastro F.
This paper describes a data collection campaign and a dataset of BLE beacons for detecting and analysing human social interactions. The dataset has been collected by involving 15 volunteers that interacted in indoor environments for a total of 11 hours of activity. The dataset is released as a collection of CSV files with a timestamp, RSSI (Received Signal Strength Indicator) and a unique identifier of the emitting and of the receiving devices. Volunteers wear a wristband equipped with BLE tags emitting beacons at a fixed rate, and a mobile application able to collect and to store beacons. We organized 6 interaction sessions, designed to reproduce the three common stages of an interaction (Non Interaction, Approaching and Interaction). Moreover, we reproduced interactions by varying the volunteer's posture as well as the position of the receiving device. The dataset is released with a ground truth annotation reporting the exact time intervals during which volunteers actually interacted. The combination of such factors, provides a rich dataset useful to experiment algorithms for detecting interactions and for analyzing dynamics of interactions in a real-world setting. We present in detail the dataset and its evaluation in "Sensing Social Interactions through BLE Beacons and Commercial Mobile Devices", in which we focus on two orthogonal analysis: quality of the dataset and RSSI symmetry of the channel during the interaction stage between pairs of users.Source: Data in brief 32 (2020). doi:10.1016/j.dib.2020.106102
DOI: 10.1016/j.dib.2020.106102
Metrics:
Girolami M., Mavilia F., Delmastro F.
This paper describes a data collection campaign and a dataset of BLE beacons for detecting and analysing human social interactions. The dataset has been collected by involving 15 volunteers that interacted in indoor environments for a total of 11 hours of activity. The dataset is released as a collection of CSV files with a timestamp, RSSI (Received Signal Strength Indicator) and a unique identifier of the emitting and of the receiving devices. Volunteers wear a wristband equipped with BLE tags emitting beacons at a fixed rate, and a mobile application able to collect and to store beacons. We organized 6 interaction sessions, designed to reproduce the three common stages of an interaction (Non Interaction, Approaching and Interaction). Moreover, we reproduced interactions by varying the volunteer's posture as well as the position of the receiving device. The dataset is released with a ground truth annotation reporting the exact time intervals during which volunteers actually interacted. The combination of such factors, provides a rich dataset useful to experiment algorithms for detecting interactions and for analyzing dynamics of interactions in a real-world setting. We present in detail the dataset and its evaluation in "Sensing Social Interactions through BLE Beacons and Commercial Mobile Devices", in which we focus on two orthogonal analysis: quality of the dataset and RSSI symmetry of the channel during the interaction stage between pairs of users.Source: Data in brief 32 (2020). doi:10.1016/j.dib.2020.106102
DOI: 10.1016/j.dib.2020.106102
Metrics:
See at:
Data in Brief | Data in Brief | ISTI Repository | Data in Brief | CNR ExploRA
2020
Journal article
Open Access
Sensing social interactions through BLE beacons and commercial mobile devices
Girolami M., Mavilia F., Delmastro F.
Wearable sensing devices can provide high-resolution data useful to characterise and identify complex human behaviours. Sensing human social interactions through wearable devices represents one of the emerging field in mobile social sensing, considering their impact on different user categories and on different social contexts. However, it is important to limit the collection and use of sensitive information characterising individual users and their social interactions in order to maintain the user compliance. For this reason, we decided to focus mainly on physical proximity and, specifically, on the analysis of BLE wireless signals commonly used by commercial mobile devices. In this work, we present the SocializeME framework designed to collect proximity information and to detect social interactions through heterogeneous personal mobile devices. We also present the results of an experimental data collection campaign conducted with real users, highlighting technical limitations and performances in terms of quality of RSS, packet loss, and channel symmetry, and how they are influenced by different configurations of the user's body and the position of the personal device. Specifically, we obtained a dataset with more than 820.000 Bluetooth signals (BLE beacons) collected, with a total monitoring of over 11?h. The dataset collected reproduces 4 different configurations by mixing two user posture's layouts (standing and sitting) and different positions of the receiver device (in hand, in the front pocket and in the back pocket). The large number of experiments in those different configurations, well cover the common way of holding a mobile device, and the layout of a dyad involved in a social interaction. We also present the results obtained by SME-D algorithm, designed to automatically detect social interactions based on the collected wireless signals, which obtained an overall accuracy of 81.56% and F-score 84.7%. The collected and labelled dataset is also released to the mobile social sensing community in order to evaluate and compare new algorithms.Source: Pervasive and mobile computing (Print) 67 (2020). doi:10.1016/j.pmcj.2020.101198
DOI: 10.1016/j.pmcj.2020.101198
Metrics:
Girolami M., Mavilia F., Delmastro F.
Wearable sensing devices can provide high-resolution data useful to characterise and identify complex human behaviours. Sensing human social interactions through wearable devices represents one of the emerging field in mobile social sensing, considering their impact on different user categories and on different social contexts. However, it is important to limit the collection and use of sensitive information characterising individual users and their social interactions in order to maintain the user compliance. For this reason, we decided to focus mainly on physical proximity and, specifically, on the analysis of BLE wireless signals commonly used by commercial mobile devices. In this work, we present the SocializeME framework designed to collect proximity information and to detect social interactions through heterogeneous personal mobile devices. We also present the results of an experimental data collection campaign conducted with real users, highlighting technical limitations and performances in terms of quality of RSS, packet loss, and channel symmetry, and how they are influenced by different configurations of the user's body and the position of the personal device. Specifically, we obtained a dataset with more than 820.000 Bluetooth signals (BLE beacons) collected, with a total monitoring of over 11?h. The dataset collected reproduces 4 different configurations by mixing two user posture's layouts (standing and sitting) and different positions of the receiver device (in hand, in the front pocket and in the back pocket). The large number of experiments in those different configurations, well cover the common way of holding a mobile device, and the layout of a dyad involved in a social interaction. We also present the results obtained by SME-D algorithm, designed to automatically detect social interactions based on the collected wireless signals, which obtained an overall accuracy of 81.56% and F-score 84.7%. The collected and labelled dataset is also released to the mobile social sensing community in order to evaluate and compare new algorithms.Source: Pervasive and mobile computing (Print) 67 (2020). doi:10.1016/j.pmcj.2020.101198
DOI: 10.1016/j.pmcj.2020.101198
Metrics:
See at:
Pervasive and Mobile Computing | Pervasive and Mobile Computing | ISTI Repository | www.sciencedirect.com | 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:
See at:
doi.org | ieeexplore.ieee.org | CNR ExploRA
2020
Conference article
Open Access
On the analysis of human posture for detecting social interactions with wearable devices
Baronti P., Girolami M., Mavilia F., Palumbo F., Luisetto G.
Detecting the dynamics of the social interaction represents a difficult task also with the adoption of sensing devices able to collect data with a high-Temporal resolution. Under this context, this work focuses on the effect of the body posture for the purpose of detecting a face-To-face interactions between individuals. To this purpose, we describe the NESTORE sensing kit that we used to collect a significant dataset that mimics some common postures of subjects while interacting. Our experimental results distinguish clearly those postures that negatively affect the quality of the signals used for detecting an interactions, from those postures that do not have such a negative impact. We also show the performance of the SID (Social Interaction Detector) algorithm with different settings, and we present its performance in terms of accuracy during the classification of interaction and non-interaction events.Source: ICHMS 2020 - IEEE International Conference on Human-Machine Systems, Online Conference, September 07-09, 2020
DOI: 10.1109/ichms49158.2020.9209510
Project(s): NESTORE
Metrics:
Baronti P., Girolami M., Mavilia F., Palumbo F., Luisetto G.
Detecting the dynamics of the social interaction represents a difficult task also with the adoption of sensing devices able to collect data with a high-Temporal resolution. Under this context, this work focuses on the effect of the body posture for the purpose of detecting a face-To-face interactions between individuals. To this purpose, we describe the NESTORE sensing kit that we used to collect a significant dataset that mimics some common postures of subjects while interacting. Our experimental results distinguish clearly those postures that negatively affect the quality of the signals used for detecting an interactions, from those postures that do not have such a negative impact. We also show the performance of the SID (Social Interaction Detector) algorithm with different settings, and we present its performance in terms of accuracy during the classification of interaction and non-interaction events.Source: ICHMS 2020 - IEEE International Conference on Human-Machine Systems, Online Conference, September 07-09, 2020
DOI: 10.1109/ichms49158.2020.9209510
Project(s): NESTORE
Metrics:
See at:
ISTI Repository | doi.org | ieeexplore.ieee.org | ZENODO | CNR ExploRA
2021
Contribution to book
Embargo
Monitoring in the physical domain to support active ageing
Denna E., Civiello M., Porcelli S., Crivello A., Mavilia F., Palumbo F.
Monitoring system have been customized to collect data and to analyse several aspects of the users' life, the reason of this custom solution came from the needs to join physical activity of the user, life usage, social interaction and mind activities, all these features are not present in standard devices all together, so we arrived to a new system architecture where the monitoring system is the first front end versus the user. This chapter describes the general monitoring system architecture and provides insight into the contribution and role of sensors. Such sensing solutions are not only designed to match the needs and requirements of the user but also to reduce intrusiveness and usage complexity. By doing so the system is designed around the life of its users and maximizes the effectiveness of data collection. Example from NESTORE project are taken as reference.Source: Digital Health Technology for Better Aging. A multidisciplinary approach, edited by G. Andreoni, C. Mambretti, pp. 55–76, 2021
DOI: 10.1007/978-3-030-72663-8_4
Project(s): NESTORE
Metrics:
Denna E., Civiello M., Porcelli S., Crivello A., Mavilia F., Palumbo F.
Monitoring system have been customized to collect data and to analyse several aspects of the users' life, the reason of this custom solution came from the needs to join physical activity of the user, life usage, social interaction and mind activities, all these features are not present in standard devices all together, so we arrived to a new system architecture where the monitoring system is the first front end versus the user. This chapter describes the general monitoring system architecture and provides insight into the contribution and role of sensors. Such sensing solutions are not only designed to match the needs and requirements of the user but also to reduce intrusiveness and usage complexity. By doing so the system is designed around the life of its users and maximizes the effectiveness of data collection. Example from NESTORE project are taken as reference.Source: Digital Health Technology for Better Aging. A multidisciplinary approach, edited by G. Andreoni, C. Mambretti, pp. 55–76, 2021
DOI: 10.1007/978-3-030-72663-8_4
Project(s): NESTORE
Metrics:
See at:
link.springer.com | CNR ExploRA
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.Source: WONS 2023 - 18th Wireless On-Demand Network Systems and Services Conference, pp. 20–23, Madonna di Campiglio, Italy, 30/01/2023-01/02/2023
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.Source: WONS 2023 - 18th Wireless On-Demand Network Systems and Services Conference, pp. 20–23, Madonna di Campiglio, Italy, 30/01/2023-01/02/2023
DOI: 10.23919/wons57325.2023.10061949
Metrics:
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ISTI Repository | ieeexplore.ieee.org | CNR ExploRA
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 11 (2023): 81763–81776. doi:10.1109/ACCESS.2023.3301126
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 11 (2023): 81763–81776. doi:10.1109/ACCESS.2023.3301126
DOI: 10.1109/access.2023.3301126
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ieeexplore.ieee.org | ISTI Repository | CNR ExploRA
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.Source: IE 2023 - 19th International Conference on Intelligent Environments, Island of Mauritius, 29-30/06/2023
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.Source: IE 2023 - 19th International Conference on Intelligent Environments, Island of Mauritius, 29-30/06/2023
DOI: 10.1109/ie57519.2023.10179094
Metrics:
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ISTI Repository | ieeexplore.ieee.org | CNR ExploRA
2023
Journal article
Open Access
An experimental evaluation based on direction finding specification for indoor localization and proximity detection
Girolami M., Mavilia F., Furfari F., Barsocchi P.
Radio-frequency technologies have been largely explored to deliver reliable indoor localization systems. However, at the current stage, none of the proposed technologies represent a de-facto standard. Although RSS-based (Received Signal Strength) techniques have been extensively studied, they suffer of a number of side-effects mainly caused by the complexity of radio propagation in indoor environments. A possible solution is designing systems exploiting multiple techniques, so that to compensate weaknesses of a specific source of information. Under this respect, Bluetooth represents an interesting technology, combining multiple techniques for indoor localization. In particular, the BT5.1 direction finding specification includes the possibility of estimating the angle between an emitting device and an antenna array. The Angle of Arrival (AoA) provides interesting features for the localization purpose, as it allows estimating the direction from which a signal is propagated. In this work, we detail our experimental setting based on a BT5.1-compliant kit to quantitatively measure the performance in three scenarios: static positioning, mobility and proximity detection. Scenarios provide a robust benchmark allowing us to identify and discuss features of AoA values also in comparison with respect to traditional RSS-based approaches.Source: IEEE journal of indoor and seamless positioning and navigation 2 (2023): 36–50. doi:10.1109/JISPIN.2023.3345268
DOI: 10.1109/jispin.2023.3345268
Metrics:
Girolami M., Mavilia F., Furfari F., Barsocchi P.
Radio-frequency technologies have been largely explored to deliver reliable indoor localization systems. However, at the current stage, none of the proposed technologies represent a de-facto standard. Although RSS-based (Received Signal Strength) techniques have been extensively studied, they suffer of a number of side-effects mainly caused by the complexity of radio propagation in indoor environments. A possible solution is designing systems exploiting multiple techniques, so that to compensate weaknesses of a specific source of information. Under this respect, Bluetooth represents an interesting technology, combining multiple techniques for indoor localization. In particular, the BT5.1 direction finding specification includes the possibility of estimating the angle between an emitting device and an antenna array. The Angle of Arrival (AoA) provides interesting features for the localization purpose, as it allows estimating the direction from which a signal is propagated. In this work, we detail our experimental setting based on a BT5.1-compliant kit to quantitatively measure the performance in three scenarios: static positioning, mobility and proximity detection. Scenarios provide a robust benchmark allowing us to identify and discuss features of AoA values also in comparison with respect to traditional RSS-based approaches.Source: IEEE journal of indoor and seamless positioning and navigation 2 (2023): 36–50. doi:10.1109/JISPIN.2023.3345268
DOI: 10.1109/jispin.2023.3345268
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ieeexplore.ieee.org | ISTI Repository | ISTI Repository | CNR ExploRA
2014
Report
Unknown
EMS@CNR: an energy monitoring sensor network infrastructure for in-building location-based services
Barsocchi P., Ferro E., Fortunati L., Mavilia F., Palumbo F.
The increasing demand for building services and comfort levels, together with the rise in time spent inside buildings, assure an upward trend in energy demand for the future. In this paper we present a long term energy monitoring system called EMS@CNR that is able to measure the energy consumed by end users in office environments. The proposed infrastructure stands as an enabling technology for future in- building location-based services.Source: ISTI Technical reports, 2014
Barsocchi P., Ferro E., Fortunati L., Mavilia F., Palumbo F.
The increasing demand for building services and comfort levels, together with the rise in time spent inside buildings, assure an upward trend in energy demand for the future. In this paper we present a long term energy monitoring system called EMS@CNR that is able to measure the energy consumed by end users in office environments. The proposed infrastructure stands as an enabling technology for future in- building location-based services.Source: ISTI Technical reports, 2014
See at: CNR ExploRA
2014
Conference article
Restricted
EMS@CNR: an energy monitoring sensor network infrastructure for in-building location-based services
Barsocchi P., Ferro E., Fortunati L., Mavilia F., Palumbo F.
The increasing demand for building services and comfort levels, together with the rise in time spent inside buildings, assure an upward trend in energy demand for the future. In this paper we present a long term energy monitoring system called EMS@CNR that is able to measure the energy consumed by end users in office environments. The proposed infrastructure stands as an enabling technology for future in- building location-based services.Source: 2014 International Conference on High Performance Computing & Simulation (HPCS 2014), pp. 857–862, Bologna, Italy, 21-25 July 2014
DOI: 10.1109/hpcsim.2014.6903779
Metrics:
Barsocchi P., Ferro E., Fortunati L., Mavilia F., Palumbo F.
The increasing demand for building services and comfort levels, together with the rise in time spent inside buildings, assure an upward trend in energy demand for the future. In this paper we present a long term energy monitoring system called EMS@CNR that is able to measure the energy consumed by end users in office environments. The proposed infrastructure stands as an enabling technology for future in- building location-based services.Source: 2014 International Conference on High Performance Computing & Simulation (HPCS 2014), pp. 857–862, Bologna, Italy, 21-25 July 2014
DOI: 10.1109/hpcsim.2014.6903779
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doi.org | ieeexplore.ieee.org | CNR ExploRA