2026
Journal article
Open Access
An experimental dataset for indoor localization using angle of arrival and RSS measurements
Lombardi Giuseppe, Mavilia Fabio, Girolami Michele, Barsocchi Paolo, Furfari Francesco
Indoor positioning systems based on Bluetooth 5.1 Direction Finding technology have recently attracted significant attention due to their capability to estimate the Angle of Arrival (AoA) of radio signals using commercial off-the-shelf devices. Despite this progress, the availability of large-scale, well-annotated experimental datasets collected under realistic conditions remains limited. This paper presents a comprehensive experimental dataset for indoor localization based on BLE 5.1 devices, providing synchronized azimuth and elevation AoA measurements together with Received Signal Strength (RSS) values and precise ground-truth annotations. Data were acquired in a 110 indoor environment with an adjacent corridor, deploying nine anchors in two configurations (wall-mounted and ceiling-mounted) and two wearable BLE tags. The campaign covers three scenarios: calibration (187 static reference points), static measurements with body orientation variations, and multiple mobility use-cases reproducing realistic walking patterns and posture changes. Overall, more than 4.5 million samples were collected. A preliminary analysis highlights the impact of anchor geometry, field of view, and body shadowing on angular accuracy. The dataset is publicly available and provides a comprehensive benchmark for evaluating angle-based localization, fingerprinting, filtering, and machine-learning approaches in realistic indoor environments.Source: AD HOC NETWORKS, vol. 193 (issue 104364)
DOI: 10.1016/j.adhoc.2026.104364
Project(s): Project Age-IT
Metrics:
Lombardi Giuseppe, Mavilia Fabio, Girolami Michele, Barsocchi Paolo, Furfari Francesco
Indoor positioning systems based on Bluetooth 5.1 Direction Finding technology have recently attracted significant attention due to their capability to estimate the Angle of Arrival (AoA) of radio signals using commercial off-the-shelf devices. Despite this progress, the availability of large-scale, well-annotated experimental datasets collected under realistic conditions remains limited. This paper presents a comprehensive experimental dataset for indoor localization based on BLE 5.1 devices, providing synchronized azimuth and elevation AoA measurements together with Received Signal Strength (RSS) values and precise ground-truth annotations. Data were acquired in a 110 indoor environment with an adjacent corridor, deploying nine anchors in two configurations (wall-mounted and ceiling-mounted) and two wearable BLE tags. The campaign covers three scenarios: calibration (187 static reference points), static measurements with body orientation variations, and multiple mobility use-cases reproducing realistic walking patterns and posture changes. Overall, more than 4.5 million samples were collected. A preliminary analysis highlights the impact of anchor geometry, field of view, and body shadowing on angular accuracy. The dataset is publicly available and provides a comprehensive benchmark for evaluating angle-based localization, fingerprinting, filtering, and machine-learning approaches in realistic indoor environments.Source: AD HOC NETWORKS, vol. 193 (issue 104364)
DOI: 10.1016/j.adhoc.2026.104364
Project(s): Project Age-IT
Metrics:
See at:
CNR IRIS 
| www.sciencedirect.com | CNR IRIS 
2026
Other
Open Access
Ogni secondo conta - Nuove prospettive per la ricerca dei dispersi in valanga
Ferro Erina, Barsocchi Paolo, Girolami Michele
Lo scopo dell’attività LoRa Snow è realizzare un prototipo portatile, a bassi consumi e integrabile su droni, che consenta alle squadre di soccorso di individuare, in tempi rapidi e da grande distanza, persone sepolte da valanghe. Le squadre di soccorso possono così stabilire un primo contatto radio con la persona sepolta a distanze molto maggiori da quelle attualmente consentite dal solo ARTVA, facilitando le fasi di localizzazione e di disseppellimento.
Ferro Erina, Barsocchi Paolo, Girolami Michele
Lo scopo dell’attività LoRa Snow è realizzare un prototipo portatile, a bassi consumi e integrabile su droni, che consenta alle squadre di soccorso di individuare, in tempi rapidi e da grande distanza, persone sepolte da valanghe. Le squadre di soccorso possono così stabilire un primo contatto radio con la persona sepolta a distanze molto maggiori da quelle attualmente consentite dal solo ARTVA, facilitando le fasi di localizzazione e di disseppellimento.
See at:
CNR IRIS | www.collegioingegneripadova.it | CNR IRIS
2025
Journal article
Open Access
An experimental dataset using UAVs and LoRa technology in avalanche scenarios
Mavilia F., La Rosa D., Berton A., Girolami M.
Wireless communication technologies play a critical role in the effectiveness of Search and Rescue (SaR) operations, especially in avalanche scenarios where rapid localization of victims is essential. Traditional systems like ARTVA research beacons have been widely adopted for this purpose, but their performance is strongly affected by environmental factors such as snow depth and snowpack characteristics. The dataset presented in this article explores the feasibility and the performance of LoRa (Long Range) technology on board of a UAV for use in SaR scenarios. The transmitter was buried in snow across a wide area in the Dolomites, simulating the scale and conditions of a typical human-triggered avalanche, while the receiver is mounted on a commercial UAV following different flight trajectories. Specifically, we vary the flying path, duration, covered area and antenna type. For each experiment, we record key communication metrics such as the Received Signal Strength Indicator (RSSI) and the Signal-to-Noise Ratio (SNR), together with precise ground truth transmitter and receiver positions obtained via GPS-RTK. The tests covered both dry and wet snow conditions, allowing evaluation of how snow characteristics impact LoRa performance. This dataset provides strong reuse potential for researchers aiming to improve UAV-assisted localization algorithms in extreme snow environments. It can support the development and benchmarking of positioning methods based on LoRa signal strength and, more broadly, the design of resilient SaR communication systems for avalanche-prone areasBy releasing the data and contextual documentation publicly, we seek to encourage innovation in disaster response technologies and promote safer mountain rescue practices.Source: DATA IN BRIEF, vol. 63 (issue 112243)
DOI: 10.1016/j.dib.2025.112243
Metrics:
Mavilia F., La Rosa D., Berton A., Girolami M.
Wireless communication technologies play a critical role in the effectiveness of Search and Rescue (SaR) operations, especially in avalanche scenarios where rapid localization of victims is essential. Traditional systems like ARTVA research beacons have been widely adopted for this purpose, but their performance is strongly affected by environmental factors such as snow depth and snowpack characteristics. The dataset presented in this article explores the feasibility and the performance of LoRa (Long Range) technology on board of a UAV for use in SaR scenarios. The transmitter was buried in snow across a wide area in the Dolomites, simulating the scale and conditions of a typical human-triggered avalanche, while the receiver is mounted on a commercial UAV following different flight trajectories. Specifically, we vary the flying path, duration, covered area and antenna type. For each experiment, we record key communication metrics such as the Received Signal Strength Indicator (RSSI) and the Signal-to-Noise Ratio (SNR), together with precise ground truth transmitter and receiver positions obtained via GPS-RTK. The tests covered both dry and wet snow conditions, allowing evaluation of how snow characteristics impact LoRa performance. This dataset provides strong reuse potential for researchers aiming to improve UAV-assisted localization algorithms in extreme snow environments. It can support the development and benchmarking of positioning methods based on LoRa signal strength and, more broadly, the design of resilient SaR communication systems for avalanche-prone areasBy releasing the data and contextual documentation publicly, we seek to encourage innovation in disaster response technologies and promote safer mountain rescue practices.Source: DATA IN BRIEF, vol. 63 (issue 112243)
DOI: 10.1016/j.dib.2025.112243
Metrics:
See at:
CNR IRIS | www.sciencedirect.com | CNR IRIS | CNR IRIS
2025
Conference article
Open Access
Evaluating angle of arrival and distance with Ultra WideBand technology for indoor localization
Mavilia F., Furfari F., Barsocchi P., Girolami M.
Several radio-frequency technologies have been investigated to develop accurate indoor localization systems, each offering distinct techniques for estimating a target’s position in indoor environments. Among them, the Ultra-WideBand (UWB) technology is a promising approach because it can estimate the distance and angle between a tag and an anchor. In this work, we evaluate the performance of a commercial UWB kit with a systematic data collection campaign. We gather data in a realistic setting, comparing estimated and actual Angles of Arrival (AoA) and distances. Results highlight that, while the system performs reliably in most scenarios, a few instances reveal noticeable deviations from the Ground Truth (GT) data.DOI: 10.1109/iscc65549.2025.11326376
Project(s): Age-IT, STRIVE
Metrics:
Mavilia F., Furfari F., Barsocchi P., Girolami M.
Several radio-frequency technologies have been investigated to develop accurate indoor localization systems, each offering distinct techniques for estimating a target’s position in indoor environments. Among them, the Ultra-WideBand (UWB) technology is a promising approach because it can estimate the distance and angle between a tag and an anchor. In this work, we evaluate the performance of a commercial UWB kit with a systematic data collection campaign. We gather data in a realistic setting, comparing estimated and actual Angles of Arrival (AoA) and distances. Results highlight that, while the system performs reliably in most scenarios, a few instances reveal noticeable deviations from the Ground Truth (GT) data.DOI: 10.1109/iscc65549.2025.11326376
Project(s): Age-IT, STRIVE
Metrics:
See at:
CNR IRIS | ieeexplore.ieee.org | CNR IRIS | CNR IRIS
2025
Journal article
Open Access
StepLogger and EvaalScore: the software suite of the IPIN onsite indoor localization competition
Girolami M., Baronti P., Potortì F., Crivello A., Palumbo F.
This paper illustrates the software suite developed for Track 1 of the IPIN competition, which evaluates smartphone apps for indoor localization. Competitors have one day before the trial day to survey the competition area. On the trial day, an independent “actor” carries the competing system on smartphone and walks a predefined path. Competing systems provide continuous location estimates, which are later compared to a ground truth. We describe the software suite used to gather and present the results: the StepLogger Android application for real-time logging of position estimates and the EvaalScore tool for performance evaluation. StepLogger collects location estimating data from competitors with a timestamp, while EvaalScore calculates the accuracy of the competing systems. The competition ranking is based on the third quartile of point localization error. The presented software suite ensures a standardized and fair assessment of competing systems, thus promoting reproducibility and transparency in indoor localization research.Source: SOFTWAREX, vol. 30
DOI: 10.1016/j.softx.2025.102115
Project(s): Age-IT
Metrics:
Girolami M., Baronti P., Potortì F., Crivello A., Palumbo F.
This paper illustrates the software suite developed for Track 1 of the IPIN competition, which evaluates smartphone apps for indoor localization. Competitors have one day before the trial day to survey the competition area. On the trial day, an independent “actor” carries the competing system on smartphone and walks a predefined path. Competing systems provide continuous location estimates, which are later compared to a ground truth. We describe the software suite used to gather and present the results: the StepLogger Android application for real-time logging of position estimates and the EvaalScore tool for performance evaluation. StepLogger collects location estimating data from competitors with a timestamp, while EvaalScore calculates the accuracy of the competing systems. The competition ranking is based on the third quartile of point localization error. The presented software suite ensures a standardized and fair assessment of competing systems, thus promoting reproducibility and transparency in indoor localization research.Source: SOFTWAREX, vol. 30
DOI: 10.1016/j.softx.2025.102115
Project(s): Age-IT
Metrics:
See at:
CNR IRIS | www.sciencedirect.com | CNR IRIS | CNR IRIS
2025
Conference article
Open Access
Whispers in the snow: exploring LoRa technology for avalanche search and rescue scenarios
Girolami M., Bianco G. M., Mavilia F., Marrocco G.
This contribution outlines an experimental setup and methodology employed to extensively characterize LoRa propagation in the demanding scenario of avalanche search and rescue (SaR), where the transmitter is buried under snow. The considered scenario presents challenges, including the absence of line-of-sight between the transmitter and receiver, as well as signal attenuation due to environmental factors such as temperature, humidity, and snow conditions. We analyze the variations in Received Signal Strength (RSS) and Signal-to-Noise Ratio (SNR) with increasing distance between the transmitter and receiver. Our data collection campaign completely characterizes, for the first time, the snow type during wireless communication tests. Finally, we test the maximum distance at which the LoRa signal can be received from the buried transmitter, demonstrating the technology's potential in challenging environments.Source: IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE. Milano, Italy, 24-27/03/2025
DOI: 10.1109/wcnc61545.2025.10978500
Project(s): Le Scienze per le TRansizioni Industriale, Verde ed Energetica
Metrics:
Girolami M., Bianco G. M., Mavilia F., Marrocco G.
This contribution outlines an experimental setup and methodology employed to extensively characterize LoRa propagation in the demanding scenario of avalanche search and rescue (SaR), where the transmitter is buried under snow. The considered scenario presents challenges, including the absence of line-of-sight between the transmitter and receiver, as well as signal attenuation due to environmental factors such as temperature, humidity, and snow conditions. We analyze the variations in Received Signal Strength (RSS) and Signal-to-Noise Ratio (SNR) with increasing distance between the transmitter and receiver. Our data collection campaign completely characterizes, for the first time, the snow type during wireless communication tests. Finally, we test the maximum distance at which the LoRa signal can be received from the buried transmitter, demonstrating the technology's potential in challenging environments.Source: IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE. Milano, Italy, 24-27/03/2025
DOI: 10.1109/wcnc61545.2025.10978500
Project(s): Le Scienze per le TRansizioni Industriale, Verde ed Energetica
Metrics:
See at:
CNR IRIS | ieeexplore.ieee.org | CNR IRIS | CNR IRIS
2025
Journal article
Open Access
MCSim: A multi-access edge computing mobile crowdsensing simulator
Belli D., Barsocchi P., Crivello A., La Rosa D., Girolami M.
This paper introduces MCSim, a modular and extensible simulator designed to support the planning and evaluation of Mobile CrowdSensing (MCS) campaigns in urban environments. MCSim integrates a useful approximation of urban mobility patterns based on real-world street networks, as well as the simulation of task execution effectiveness within configurable data transmission ranges. Unlike other simulators, MCSim is built to accommodate future extensions, such as edge/fog computing architectures. The current version of the software offers a user-friendly interface, customizable configuration options, and robust output analysis. By combining realistic mobility modeling, configurable task logic, and architectural flexibility, MCSim provides researchers and practitioners with a powerful tool for optimizing MCS strategies while minimizing deployment costs and risks.Source: SOFTWAREX, vol. 31 (issue 102229)
DOI: 10.1016/j.softx.2025.102229
Project(s): Cyber and Human Intelligence for Physical Systems
Metrics:
Belli D., Barsocchi P., Crivello A., La Rosa D., Girolami M.
This paper introduces MCSim, a modular and extensible simulator designed to support the planning and evaluation of Mobile CrowdSensing (MCS) campaigns in urban environments. MCSim integrates a useful approximation of urban mobility patterns based on real-world street networks, as well as the simulation of task execution effectiveness within configurable data transmission ranges. Unlike other simulators, MCSim is built to accommodate future extensions, such as edge/fog computing architectures. The current version of the software offers a user-friendly interface, customizable configuration options, and robust output analysis. By combining realistic mobility modeling, configurable task logic, and architectural flexibility, MCSim provides researchers and practitioners with a powerful tool for optimizing MCS strategies while minimizing deployment costs and risks.Source: SOFTWAREX, vol. 31 (issue 102229)
DOI: 10.1016/j.softx.2025.102229
Project(s): Cyber and Human Intelligence for Physical Systems
Metrics:
See at:
SoftwareX | SoftwareX | CNR IRIS | www.sciencedirect.com | CNR IRIS | CNR IRIS
2025
Conference article
Open Access
A bluetooth proximity-based IoT approach for continuous monitoring of indoor sedentariness
Girolami M., Baronti P., Crivello A., La Rosa D., Barsocchi P.
Sedentary behavior is a critical factor influencing overall health and well-being, particularly in aging populations. This work presents an indoor monitoring solution leveraging Bluetooth-Based proximity estimation to infer users’ location and movement patterns across different home environments. The objective is to generate a Sedentary Behavior Index (SBI) that quantifies the duration individuals spend in specific domestic spaces without requiring active user input. This index, derived through passive and pervasive sensing, provides healthcare professionals and researchers with insights into users’ lifestyle and activity levels within the context of their daily living environment. The proposed system, deployed in 45 houses, monitors 55 users and operates as a proximity-based IoT service that seamlessly integrates with broader health monitoring studies, enabling context-aware analysis when cross-referenced with clinical outcomes or other observational data. This approach aims to support continuous, unobtrusive, and personalized well-being assessments, laying the groundwork for adaptive interventions in remote healthcare and aging-in-place scenarios.DOI: 10.1109/wf-iot64238.2025.11270510
Project(s): Project Tuscany Health Ecosystem
Metrics:
Girolami M., Baronti P., Crivello A., La Rosa D., Barsocchi P.
Sedentary behavior is a critical factor influencing overall health and well-being, particularly in aging populations. This work presents an indoor monitoring solution leveraging Bluetooth-Based proximity estimation to infer users’ location and movement patterns across different home environments. The objective is to generate a Sedentary Behavior Index (SBI) that quantifies the duration individuals spend in specific domestic spaces without requiring active user input. This index, derived through passive and pervasive sensing, provides healthcare professionals and researchers with insights into users’ lifestyle and activity levels within the context of their daily living environment. The proposed system, deployed in 45 houses, monitors 55 users and operates as a proximity-based IoT service that seamlessly integrates with broader health monitoring studies, enabling context-aware analysis when cross-referenced with clinical outcomes or other observational data. This approach aims to support continuous, unobtrusive, and personalized well-being assessments, laying the groundwork for adaptive interventions in remote healthcare and aging-in-place scenarios.DOI: 10.1109/wf-iot64238.2025.11270510
Project(s): Project Tuscany Health Ecosystem
Metrics:
See at:
CNR IRIS | ieeexplore.ieee.org | CNR IRIS | CNR IRIS
2024
Journal article
Open Access
Distributed versus centralized computing of coverage in mobile crowdsensing
Girolami M., Kocian A., Chessa S.
The expected spatial coverage of a crowdsensing platform is an important parameter that derives from the mobility data of the crowdsensing platform users. We tackle the challenge of estimating the anticipated coverage while adhering to privacy constraints, where the platform is restricted from accessing detailed mobility data of individual users. Specifically, we model the coverage as the probability that a user detours to a point of interest if the user is present in a certain region around that point. Following this approach, we propose and evaluate a centralized as well as a distributed implementation model. We examine real-world mobility data employed for assessing the coverage performance of the two models, and we show that the two implementation models provide different privacy requirements but are equivalent in terms of their outputs.Source: JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING, vol. 15 (issue 6), pp. 2941-2951
DOI: 10.1007/s12652-024-04788-w
Metrics:
Girolami M., Kocian A., Chessa S.
The expected spatial coverage of a crowdsensing platform is an important parameter that derives from the mobility data of the crowdsensing platform users. We tackle the challenge of estimating the anticipated coverage while adhering to privacy constraints, where the platform is restricted from accessing detailed mobility data of individual users. Specifically, we model the coverage as the probability that a user detours to a point of interest if the user is present in a certain region around that point. Following this approach, we propose and evaluate a centralized as well as a distributed implementation model. We examine real-world mobility data employed for assessing the coverage performance of the two models, and we show that the two implementation models provide different privacy requirements but are equivalent in terms of their outputs.Source: JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING, vol. 15 (issue 6), pp. 2941-2951
DOI: 10.1007/s12652-024-04788-w
Metrics:
See at:
Journal of Ambient Intelligence and Humanized Computing | IRIS Cnr | IRIS Cnr | Archivio della Ricerca - Università di Pisa | Archivio della Ricerca - Università di Pisa | CNR IRIS | CNR IRIS
2024
Journal article
Open Access
An experimental dataset for search and rescue operations in avalanche scenarios based on LoRa technology
Girolami M., Mavilia F., Berton A., Marrocco G., Bianco G. M.
Wireless technologies suitable for Search and Rescue (SaR) operations are becoming crucial for the success of such missions. In avalanche scenarios, the snow depth and the snowpack profile significantly influence the wireless propagation of technologies used to locate victims, such as ARVA (in French: appareil de recherche de victimes d’avalanche) systems. In this work, we explore the potential of LoRa technology under challenging realistic conditions. For the first time, we collect radiopropagation data and the contextual snow profile when the transmitter is buried over a 50×50 m area resembling a typical human-triggered avalanche. Specifically, we detail the methodology adopted to collect data through three test types: cross, maximum distance, and drone flyover. The data are annotated with accurate ground truth which allows evaluating localization algorithms based on the RSSI (received signal strength indicator) and SNR (signal-to-noise ratio) of LoRa units. We conducted tests under various environmental conditions, ranging from dry to wet snowpacks. Our results demonstrate the high quality of the LoRa channel, even when the target is buried at a depth of 1 meter in snow with a high liquid water content. At the same time, we quantify the effects of two main degrading factors for the LoRa propagation: the amount of the snow and the liquid water content existing in the snowpack profiles.Source: IEEE ACCESS, vol. 12, pp. 171015-171035
DOI: 10.1109/access.2024.3497654
Metrics:
Girolami M., Mavilia F., Berton A., Marrocco G., Bianco G. M.
Wireless technologies suitable for Search and Rescue (SaR) operations are becoming crucial for the success of such missions. In avalanche scenarios, the snow depth and the snowpack profile significantly influence the wireless propagation of technologies used to locate victims, such as ARVA (in French: appareil de recherche de victimes d’avalanche) systems. In this work, we explore the potential of LoRa technology under challenging realistic conditions. For the first time, we collect radiopropagation data and the contextual snow profile when the transmitter is buried over a 50×50 m area resembling a typical human-triggered avalanche. Specifically, we detail the methodology adopted to collect data through three test types: cross, maximum distance, and drone flyover. The data are annotated with accurate ground truth which allows evaluating localization algorithms based on the RSSI (received signal strength indicator) and SNR (signal-to-noise ratio) of LoRa units. We conducted tests under various environmental conditions, ranging from dry to wet snowpacks. Our results demonstrate the high quality of the LoRa channel, even when the target is buried at a depth of 1 meter in snow with a high liquid water content. At the same time, we quantify the effects of two main degrading factors for the LoRa propagation: the amount of the snow and the liquid water content existing in the snowpack profiles.Source: IEEE ACCESS, vol. 12, pp. 171015-171035
DOI: 10.1109/access.2024.3497654
Metrics:
See at:
IEEE Access | IEEE Access | CNR IRIS | ieeexplore.ieee.org | 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
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:
See at:
CNR IRIS | link.springer.com | doi.org | CNR IRIS | CNR IRIS
2024
Conference article
Open Access
Evaluating the impact of injected mobility data on measuring data coverage in crowdsensing scenarios
Kocian A., Girolami M., Capoccia S., Foschini L., Chessa S.
A major weakness of Mobile CrowdSensing Platforms (MCS) is the willingness of users to participate, as this implies disclosing their private data (for example, concerning mobility) to the MCS platform. In the effort to enforce data privacy in the creation of mobility coverage maps using an MCS platform, recent work proposes the use of a spatially distributed approach that, however, is vulnerable to data injection attacks. In this contribution, we define and implement a progressive attacker model following a statistical approach. We propose a novel mitigation strategy based on unsupervised anomaly detection. Accessing the coverage performance with real-world mobility data indicates that the mean value of the attacker's profile determines the probability of being revealed. In particular, we are able to identify the attacker and filter out the data injected by the attackers with high precision.Source: ... IEEE GLOBAL COMMUNICATIONS CONFERENCE, pp. 4672-4677. Cape Town, South Africa, 2024
DOI: 10.1109/globecom52923.2024.10901097
Metrics:
Kocian A., Girolami M., Capoccia S., Foschini L., Chessa S.
A major weakness of Mobile CrowdSensing Platforms (MCS) is the willingness of users to participate, as this implies disclosing their private data (for example, concerning mobility) to the MCS platform. In the effort to enforce data privacy in the creation of mobility coverage maps using an MCS platform, recent work proposes the use of a spatially distributed approach that, however, is vulnerable to data injection attacks. In this contribution, we define and implement a progressive attacker model following a statistical approach. We propose a novel mitigation strategy based on unsupervised anomaly detection. Accessing the coverage performance with real-world mobility data indicates that the mean value of the attacker's profile determines the probability of being revealed. In particular, we are able to identify the attacker and filter out the data injected by the attackers with high precision.Source: ... IEEE GLOBAL COMMUNICATIONS CONFERENCE, pp. 4672-4677. Cape Town, South Africa, 2024
DOI: 10.1109/globecom52923.2024.10901097
Metrics:
See at:
CNR IRIS | ieeexplore.ieee.org | CNR IRIS | CNR IRIS
2024
Journal article
Open Access
PRORL: Proactive Resource Orchestrator for Open RANs Using Deep Reinforcement Learning
Staffolani A., Darvariu V., Foschini L., Girolami M., Bellavista P., Musolesi M.
Open Radio Access Network (O-RAN) is an emerging paradigm proposed for enhancing the 5G network infrastructure. O-RAN promotes open vendor-neutral interfaces and virtualized network functions that enable the decoupling of network components and their optimization through intelligent controllers. The decomposition of base station functions enables better resource usage, but also opens new technical challenges concerning their efficient orchestration and allocation. In this paper, we propose Proactive Resource Orchestrator based on Reinforcement Learning (PRORL), a novel solution for the efficient and dynamic allocation of resources in O-RAN infrastructures. We frame the problem as a Markov Decision Process and solve it using Deep Reinforcement Learning; one relevant feature of PRORL is that it learns demand patterns from experience for proactive resource allocation. We extensively evaluate our proposal by using both synthetic and real-world data, showing that we can significantly outperform the existing algorithms, which are typically based on the analysis of static demands. More specifically, we achieve an improvement of 90% over greedy baselines and deal with complex trade-offs in terms of competing objectives such as demand satisfaction, resource utilization, and the inherent cost associated with allocating resources.Source: IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT
DOI: 10.1109/tnsm.2024.3373606
Project(s): The Alan Turing Institute
Metrics:
Staffolani A., Darvariu V., Foschini L., Girolami M., Bellavista P., Musolesi M.
Open Radio Access Network (O-RAN) is an emerging paradigm proposed for enhancing the 5G network infrastructure. O-RAN promotes open vendor-neutral interfaces and virtualized network functions that enable the decoupling of network components and their optimization through intelligent controllers. The decomposition of base station functions enables better resource usage, but also opens new technical challenges concerning their efficient orchestration and allocation. In this paper, we propose Proactive Resource Orchestrator based on Reinforcement Learning (PRORL), a novel solution for the efficient and dynamic allocation of resources in O-RAN infrastructures. We frame the problem as a Markov Decision Process and solve it using Deep Reinforcement Learning; one relevant feature of PRORL is that it learns demand patterns from experience for proactive resource allocation. We extensively evaluate our proposal by using both synthetic and real-world data, showing that we can significantly outperform the existing algorithms, which are typically based on the analysis of static demands. More specifically, we achieve an improvement of 90% over greedy baselines and deal with complex trade-offs in terms of competing objectives such as demand satisfaction, resource utilization, and the inherent cost associated with allocating resources.Source: IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT
DOI: 10.1109/tnsm.2024.3373606
Project(s): The Alan Turing Institute
Metrics:
See at:
UCL Discovery | doi.org | Archivio istituzionale della ricerca - Alma Mater Studiorum Università di Bologna | IRIS Cnr | Archivio istituzionale della ricerca - Alma Mater Studiorum Università di Bologna | GitHub | CNR IRIS
2024
Journal article
Open Access
A UAV deployment strategy based on a probabilistic data coverage model for mobile CrowdSensing applications
Girolami M., Cipullo E., Colella T., Chessa S.
Mobile CrowdSensing (MCS) is a computational paradigm designed to gather sensing data by using personal devices of MCS platform users. However, being the mobility of devices tightly correlated with mobility of their owners, the locations from which data are collected might be limited to specific sub-regions. We extend the data coverage capability of a traditional MCS platform by exploiting unmanned aerial vehicles (UAV) as mobile sensors gathering data from low covered locations. We present a probabilistic model designed to measure the coverage of a location. The model analyses the user’s trajectories and the detouring capability of users towards locations of interest. Our model provides a coverage probability for each of the target locations, so that to identify low-covered locations. In turn, these locations are used as targets for the StationPositioning algorithms which optimizes the deployment of k UAV stations. We analyze the performance of StationPositioning by comparing the ratio of the covered locations against Random, DBSCAN and KMeans deployment algorithm. We explore the performance by varying the time period, the deployment regions and the existence of areas where it is not possible to deploy any station. Our experimental results show that StationPositioning is able to optimize the selected target location for a number of UAV stations with a maximum covered ratio up to 60%.Source: JOURNAL OF AMBIENT INTELLIGENCE AND SMART ENVIRONMENTS, vol. 16 (issue 2), pp. 241-268
DOI: 10.3233/ais-220601
Metrics:
Girolami M., Cipullo E., Colella T., Chessa S.
Mobile CrowdSensing (MCS) is a computational paradigm designed to gather sensing data by using personal devices of MCS platform users. However, being the mobility of devices tightly correlated with mobility of their owners, the locations from which data are collected might be limited to specific sub-regions. We extend the data coverage capability of a traditional MCS platform by exploiting unmanned aerial vehicles (UAV) as mobile sensors gathering data from low covered locations. We present a probabilistic model designed to measure the coverage of a location. The model analyses the user’s trajectories and the detouring capability of users towards locations of interest. Our model provides a coverage probability for each of the target locations, so that to identify low-covered locations. In turn, these locations are used as targets for the StationPositioning algorithms which optimizes the deployment of k UAV stations. We analyze the performance of StationPositioning by comparing the ratio of the covered locations against Random, DBSCAN and KMeans deployment algorithm. We explore the performance by varying the time period, the deployment regions and the existence of areas where it is not possible to deploy any station. Our experimental results show that StationPositioning is able to optimize the selected target location for a number of UAV stations with a maximum covered ratio up to 60%.Source: JOURNAL OF AMBIENT INTELLIGENCE AND SMART ENVIRONMENTS, vol. 16 (issue 2), pp. 241-268
DOI: 10.3233/ais-220601
Metrics:
See at:
IRIS Cnr | IRIS Cnr | IRIS Cnr | Archivio della Ricerca - Università di Pisa | Journal of Ambient Intelligence and Smart Environments | Archivio della Ricerca - Università di Pisa | Archivio della Ricerca - Università di Pisa | CNR IRIS | 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:
See at:
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:
See at:
Data in Brief | Data in Brief | IRIS Cnr | IRIS Cnr | CNR IRIS
2024
Contribution to book
Open Access
HCCS 2024: 4th Workshop on Human-Centered Computational Sensing - Welcome and Committees
Delmastro F., Girolami M., Theoleyre F.
We are pleased to present the proceedings of the Fourth edition of the International IEEE Workshop on Human-Centered Computational Sensing (HCCS'24) held in conjunction with IEEE PerCom 2024. The HCCS workshop aims to advance and promote research about how unobtrusive observation of human cognitive, behavioral, physiological, and contextual data increasingly enables new computing experiences and effective intervention opportunities. The workshop also seeks to stimulate dialogue about the implications of human-centered computational sensing for societyDOI: 10.1109/percomworkshops59983.2024.10503049
Metrics:
Delmastro F., Girolami M., Theoleyre F.
We are pleased to present the proceedings of the Fourth edition of the International IEEE Workshop on Human-Centered Computational Sensing (HCCS'24) held in conjunction with IEEE PerCom 2024. The HCCS workshop aims to advance and promote research about how unobtrusive observation of human cognitive, behavioral, physiological, and contextual data increasingly enables new computing experiences and effective intervention opportunities. The workshop also seeks to stimulate dialogue about the implications of human-centered computational sensing for societyDOI: 10.1109/percomworkshops59983.2024.10503049
Metrics:
See at:
CNR IRIS | ieeexplore.ieee.org | doi.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