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:
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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
Conference article
Open Access
Agricultural Data Space: the METRIQA platform and a case study in the CODECS project
Bacco M., Dimitri G. M., Kocian A., Barsocchi P., Crivello A., Brunori G., Gori M., Chessa S.
This work describes the ongoing design and devel- opment of the METRIQA platform, hosting the Italian agrifood data space. Both are key components that the Italian National Research Centre for Agricultural Technologies is putting forward in its activities. We present a high-level description of the platform, which is designed to provide web-like access to digital resources and services following an approach called Web of Agri-Food, to support the digital transformation of the sector in Italy. To show its potential, we also present a real case study demonstrating both the benefits and impacts of the proposed architecture, connecting stakeholders and authorities at different levels.Source: ANNALS OF COMPUTER SCIENCE AND INFORMATION SYSTEMS, vol. 39, pp. 543-548. Belgrade, Serbia, 8-11/09/2024
DOI: 10.15439/2024f5291
Metrics:
Bacco M., Dimitri G. M., Kocian A., Barsocchi P., Crivello A., Brunori G., Gori M., Chessa S.
This work describes the ongoing design and devel- opment of the METRIQA platform, hosting the Italian agrifood data space. Both are key components that the Italian National Research Centre for Agricultural Technologies is putting forward in its activities. We present a high-level description of the platform, which is designed to provide web-like access to digital resources and services following an approach called Web of Agri-Food, to support the digital transformation of the sector in Italy. To show its potential, we also present a real case study demonstrating both the benefits and impacts of the proposed architecture, connecting stakeholders and authorities at different levels.Source: ANNALS OF COMPUTER SCIENCE AND INFORMATION SYSTEMS, vol. 39, pp. 543-548. Belgrade, Serbia, 8-11/09/2024
DOI: 10.15439/2024f5291
Metrics:
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annals-csis.org | Annals of computer science and information systems | 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:
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CNR IRIS | ieeexplore.ieee.org | CNR IRIS | CNR IRIS
2023
Conference article
Open Access
A TinyML-approach to detect the proximity of people based on bluetooth low energy beacons
Girolami M, Fattori F, Chessa S
Proximity detection is the process of estimating the closeness between a target and a point of interest, and it can be estimated with different technologies and techniques. In this paper we focus on how detecting proximity between people with a TinyML-based approach. We analyze RSS values (Received Signal Strength) estimated by a micro-controller and propagated by Bluetooth's tags. To this purpose, we collect a dataset of Bluetooth RSS signals by considering different postures of the involved people. The dataset is adopted to train and test two neural networks: a fully-connected and an LSTM model that we compress to be executed directly on-board of the micro-controller. Experimental results conducted over the dataset show an average precision and recall metrics of 0.8 with both of the models, and with an inference time less than 1 ms.DOI: 10.1109/ie57519.2023.10179090
Metrics:
Girolami M, Fattori F, Chessa S
Proximity detection is the process of estimating the closeness between a target and a point of interest, and it can be estimated with different technologies and techniques. In this paper we focus on how detecting proximity between people with a TinyML-based approach. We analyze RSS values (Received Signal Strength) estimated by a micro-controller and propagated by Bluetooth's tags. To this purpose, we collect a dataset of Bluetooth RSS signals by considering different postures of the involved people. The dataset is adopted to train and test two neural networks: a fully-connected and an LSTM model that we compress to be executed directly on-board of the micro-controller. Experimental results conducted over the dataset show an average precision and recall metrics of 0.8 with both of the models, and with an inference time less than 1 ms.DOI: 10.1109/ie57519.2023.10179090
Metrics:
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CNR IRIS | ieeexplore.ieee.org | ISTI Repository | CNR IRIS | CNR IRIS
2023
Conference article
Open Access
A VNF-chaining approach for enhancing ground network with UAVs in a crowd-based environment
Bozzone Montagno D, Chessa S, Girolami M, Paganelli F
In the context of a 5G and beyond network operating in a smart city, in which the fixed network infrastructure is supported by a flock of unmanned aerial vehicles (UAV) operating as carriers of Virtual Network Functions (VNF), we propose a Mixed Integer Linear Programming (MILP) model to place chains of VNFs on a hybrid UAV-terrestrial infrastructure so to maximize the UAV lifetime while considering resource constraints and by taking into account the network traffic originated by crowds of people assembling in the city at given hotpoints. We formalize the UAV deployment problem and we test our solution with a practical scenario based on DoS detection system. The experimental results assess the deployment in a practical scenario of a DoS detection system and show that the proposed solution can effectively enhance the capability of the system to process the input flows under a DoS attack.Source: PROCEEDINGS - IEEE SYMPOSIUM ON COMPUTERS AND COMMUNICATIONS. Gammarth, Tunisia, 9-12/07/2023
DOI: 10.1109/iscc58397.2023.10217879
Metrics:
Bozzone Montagno D, Chessa S, Girolami M, Paganelli F
In the context of a 5G and beyond network operating in a smart city, in which the fixed network infrastructure is supported by a flock of unmanned aerial vehicles (UAV) operating as carriers of Virtual Network Functions (VNF), we propose a Mixed Integer Linear Programming (MILP) model to place chains of VNFs on a hybrid UAV-terrestrial infrastructure so to maximize the UAV lifetime while considering resource constraints and by taking into account the network traffic originated by crowds of people assembling in the city at given hotpoints. We formalize the UAV deployment problem and we test our solution with a practical scenario based on DoS detection system. The experimental results assess the deployment in a practical scenario of a DoS detection system and show that the proposed solution can effectively enhance the capability of the system to process the input flows under a DoS attack.Source: PROCEEDINGS - IEEE SYMPOSIUM ON COMPUTERS AND COMMUNICATIONS. Gammarth, Tunisia, 9-12/07/2023
DOI: 10.1109/iscc58397.2023.10217879
Metrics:
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CNR IRIS | ieeexplore.ieee.org | ISTI Repository | CNR IRIS | CNR IRIS
2022
Conference article
Open Access
Encrypted data aggregation in mobile crowdsensing based on differential privacy
Girolami M., Urselli E., Chessa S.
The increasing sensing capabilities of mobile devices enable the collection of sensing-based data sets, by exploiting the active participation of the crowd. Often, it is not required to disclose the identity of the owners of the data, as the sensing information are analyzed only on an aggregated form. In this work we propose a privacy-preserving schema based on differential privacy which offers data integrity and fault tolerance properties. In our schema, data providers firstly add a noise component to the sensed data and, secondly, they encrypt and send the cryptogram to the aggregator. The data aggregator is in charge of only decrypting the cryptograms, by preserving the identify of the data owners. We extend such schema by enabling data providers to submit multiple cryptograms in a time window, by using time-varying encryption keys. We evaluate the impact of the noise component to the generated cryptograms so that to evaluate the data loss during the encryption process.DOI: 10.1109/percomworkshops53856.2022.9767356
Metrics:
Girolami M., Urselli E., Chessa S.
The increasing sensing capabilities of mobile devices enable the collection of sensing-based data sets, by exploiting the active participation of the crowd. Often, it is not required to disclose the identity of the owners of the data, as the sensing information are analyzed only on an aggregated form. In this work we propose a privacy-preserving schema based on differential privacy which offers data integrity and fault tolerance properties. In our schema, data providers firstly add a noise component to the sensed data and, secondly, they encrypt and send the cryptogram to the aggregator. The data aggregator is in charge of only decrypting the cryptograms, by preserving the identify of the data owners. We extend such schema by enabling data providers to submit multiple cryptograms in a time window, by using time-varying encryption keys. We evaluate the impact of the noise component to the generated cryptograms so that to evaluate the data loss during the encryption process.DOI: 10.1109/percomworkshops53856.2022.9767356
Metrics:
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CNR IRIS | ieeexplore.ieee.org | ISTI Repository | CNR IRIS | CNR IRIS
2022
Conference article
Open Access
Evaluation of a location coverage model for mobile edge computing
Girolami M, Pacini T, Chessa S
The Mobile Edge Computing paradigm shifts the computation back to places where it is required. A traditional MEC architecture comprises a number of Edge Data Centers (EDC) in charge of seamlessly providing services to users with wireless network technologies. In this scenario, it becomes crucial to deploy the EDCs in strategic locations, such as highly visited places. In this paper we focus on the deployment phase of an EDC. In particular, we propose a probabilistic model designed to measure the location converge, namely the probability that a candidate location for an EDC is visited by users. Our model is based on the analysis of user's trajectories and on the probability of detouring towards the target locations for the EDS. The information returned by our model offers the possibility of implementing mobility-aware deployment strategies in urban environments. We test the model with two real-world mobility data sets, evaluating its applicability of realistic settings.DOI: 10.1109/icc45855.2022.9838963
Metrics:
Girolami M, Pacini T, Chessa S
The Mobile Edge Computing paradigm shifts the computation back to places where it is required. A traditional MEC architecture comprises a number of Edge Data Centers (EDC) in charge of seamlessly providing services to users with wireless network technologies. In this scenario, it becomes crucial to deploy the EDCs in strategic locations, such as highly visited places. In this paper we focus on the deployment phase of an EDC. In particular, we propose a probabilistic model designed to measure the location converge, namely the probability that a candidate location for an EDC is visited by users. Our model is based on the analysis of user's trajectories and on the probability of detouring towards the target locations for the EDS. The information returned by our model offers the possibility of implementing mobility-aware deployment strategies in urban environments. We test the model with two real-world mobility data sets, evaluating its applicability of realistic settings.DOI: 10.1109/icc45855.2022.9838963
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CNR IRIS | ieeexplore.ieee.org | ISTI Repository | CNR IRIS | CNR IRIS
2021
Other
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A deployment strategy for UAV based on a probabilistic data coverage model in mobile crowd-sensing
Girolami M, Cipullo E, Colella T, Chessa S
Mobile CrowdSensing (MCS) is a computational paradigm designed to gather sensing data by using the personal devices of the MCS platform users. However, being the mobility of the devices tightly correlated with mobility of their owners, the covered area might be limited to specific sub-regions. We extend the coverage capability of a 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 by analysing the user's trajectories and the detouring capability of MCS users towards a location of interest. Our model provides a coverage used revealing low-covered locations. These are used as targets for StationPositioning, our proposed algorithm optimizing the deployment of k UAV stations. We analyze the performance of StationPositioning by comparing the ratio of the covered locations against Random, DBSCAN and KMeasn 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%DOI: 10.32079/isti-tr-2021/010
Metrics:
Girolami M, Cipullo E, Colella T, Chessa S
Mobile CrowdSensing (MCS) is a computational paradigm designed to gather sensing data by using the personal devices of the MCS platform users. However, being the mobility of the devices tightly correlated with mobility of their owners, the covered area might be limited to specific sub-regions. We extend the coverage capability of a 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 by analysing the user's trajectories and the detouring capability of MCS users towards a location of interest. Our model provides a coverage used revealing low-covered locations. These are used as targets for StationPositioning, our proposed algorithm optimizing the deployment of k UAV stations. We analyze the performance of StationPositioning by comparing the ratio of the covered locations against Random, DBSCAN and KMeasn 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%DOI: 10.32079/isti-tr-2021/010
Metrics:
2021
Journal article
Open Access
How mobility and sociality reshape the context: a decade of experience in mobile crowdsensing
Girolami M, Belli D, Chessa S, Foschini L
The possibility of understanding the dynamics of human mobility and sociality creates the opportunity to re-design the way data are collected by exploiting the crowd. We survey the last decade of experimentation and research in the field of mobile CrowdSensing, a paradigm centred on users' devices as the primary source for collecting data from urban areas. To this purpose, we report the methodologies aimed at building information about users' mobility and sociality in the form of ties among users and communities of users. We present two methodologies to identify communities: spatial and co-location-based. We also discuss some perspectives about the future of mobile CrowdSensing and its impact on four investigation areas: contact tracing, edge-based MCS architectures, digitalization in Industry 5.0 and community detection algorithms.Source: SENSORS (BASEL), vol. 21 (issue 19)
DOI: 10.3390/s21196397
Metrics:
Girolami M, Belli D, Chessa S, Foschini L
The possibility of understanding the dynamics of human mobility and sociality creates the opportunity to re-design the way data are collected by exploiting the crowd. We survey the last decade of experimentation and research in the field of mobile CrowdSensing, a paradigm centred on users' devices as the primary source for collecting data from urban areas. To this purpose, we report the methodologies aimed at building information about users' mobility and sociality in the form of ties among users and communities of users. We present two methodologies to identify communities: spatial and co-location-based. We also discuss some perspectives about the future of mobile CrowdSensing and its impact on four investigation areas: contact tracing, edge-based MCS architectures, digitalization in Industry 5.0 and community detection algorithms.Source: SENSORS (BASEL), vol. 21 (issue 19)
DOI: 10.3390/s21196397
Metrics:
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CNR IRIS | ISTI Repository | www.mdpi.com | CNR IRIS
2020
Journal article
Open Access
A probabilistic model for the deployment of human-enabled edge computing in massive sensing scenarios
Belli D., Chessa S., Foschini L., Girolami M.
Human-enabled Edge Computing (HEC) is a recent smart city technology designed to combine the advantages of massive Mobile CrowdSensing (MCS) techniques with the potential of Multi-access Edge Computing (MEC). In this context, the architectural hierarchy of the network shifts the management of sensing information close to terminal nodes through the use of intermediate entities (edges) bridging the direct Cloud-Device communication channel. Recent proposals suggest the implementation of those edges, not only employing fixed MEC nodes, but also opportunistically using as edge nodes mobile devices selected among the terminal ones. However, inappropriate selection techniques may lead to an overestimation or an underestimation of the number of nodes to be used in such a layer. In this work, we propose a probabilistic model for the estimation of the number of mobile nodes to be selected as substitutes of fixed ones. The effectiveness of our model is verified with tests performed on real-world mobility traces.Source: IEEE INTERNET OF THINGS JOURNAL, vol. 7 (issue 3), pp. 2421-2431
DOI: 10.1109/jiot.2019.2957835
Metrics:
Belli D., Chessa S., Foschini L., Girolami M.
Human-enabled Edge Computing (HEC) is a recent smart city technology designed to combine the advantages of massive Mobile CrowdSensing (MCS) techniques with the potential of Multi-access Edge Computing (MEC). In this context, the architectural hierarchy of the network shifts the management of sensing information close to terminal nodes through the use of intermediate entities (edges) bridging the direct Cloud-Device communication channel. Recent proposals suggest the implementation of those edges, not only employing fixed MEC nodes, but also opportunistically using as edge nodes mobile devices selected among the terminal ones. However, inappropriate selection techniques may lead to an overestimation or an underestimation of the number of nodes to be used in such a layer. In this work, we propose a probabilistic model for the estimation of the number of mobile nodes to be selected as substitutes of fixed ones. The effectiveness of our model is verified with tests performed on real-world mobility traces.Source: IEEE INTERNET OF THINGS JOURNAL, vol. 7 (issue 3), pp. 2421-2431
DOI: 10.1109/jiot.2019.2957835
Metrics:
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CNR IRIS | ieeexplore.ieee.org | doi.org | Archivio istituzionale della ricerca - Alma Mater Studiorum Università di Bologna | CNR IRIS | CNR IRIS
2020
Journal article
Open Access
Optimization strategies for the selection of mobile edges in hybrid crowdsensing architectures
Belli D, Chessa S, Corradi A, Foschini L, Girolami M
Communication infrastructures are rapidly evolving to support 5G enabling lower latency, high reliability, and scalability of the network and of the service provisioning. An important element of the 5G vision is Multi- access Edge Computing (MEC), that leverages the availability of powerful and low-cost middle boxes, i.e., MEC nodes, statically deployed at suitable edges of the network to extend the centralized cloud backbone. At the same time, after almost a decade of research, Mobile CrowdSensing (MCS) has established the technology able to collect sensing data on the environment by using personal devices, usually smartphones, as powerful sensing-and-communication platforms. Even though, mutual benefits due to the integration of MEC and Mobile CrowdSensing (MCS) are still largely unexplored. In this paper, we address and analyze the potential of the synergic use of MCS and MEC by thoroughly assessing various strategies for the selection of both traditional Fixed MEC (FMEC) edges as well as human-enabled Mobile MEC (M2EC) edges to support the collection of mobile CrowdSensing data. Collected results quantitatively show the effectiveness of the proposed optimization strategies in elastically scaling the load at edge nodes according to runtime provisioning needs.Source: COMPUTER COMMUNICATIONS, vol. 157, pp. 132-142
DOI: 10.1016/j.comcom.2020.04.006
Metrics:
Belli D, Chessa S, Corradi A, Foschini L, Girolami M
Communication infrastructures are rapidly evolving to support 5G enabling lower latency, high reliability, and scalability of the network and of the service provisioning. An important element of the 5G vision is Multi- access Edge Computing (MEC), that leverages the availability of powerful and low-cost middle boxes, i.e., MEC nodes, statically deployed at suitable edges of the network to extend the centralized cloud backbone. At the same time, after almost a decade of research, Mobile CrowdSensing (MCS) has established the technology able to collect sensing data on the environment by using personal devices, usually smartphones, as powerful sensing-and-communication platforms. Even though, mutual benefits due to the integration of MEC and Mobile CrowdSensing (MCS) are still largely unexplored. In this paper, we address and analyze the potential of the synergic use of MCS and MEC by thoroughly assessing various strategies for the selection of both traditional Fixed MEC (FMEC) edges as well as human-enabled Mobile MEC (M2EC) edges to support the collection of mobile CrowdSensing data. Collected results quantitatively show the effectiveness of the proposed optimization strategies in elastically scaling the load at edge nodes according to runtime provisioning needs.Source: COMPUTER COMMUNICATIONS, vol. 157, pp. 132-142
DOI: 10.1016/j.comcom.2020.04.006
Metrics:
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Archivio istituzionale della ricerca - Alma Mater Studiorum Università di Bologna | CNR IRIS | www.sciencedirect.com | Computer Communications | CNR IRIS | CNR IRIS
2020
Other
Restricted
A survey on the use of 802.11 Channel State Information in device-free applications: indoor localization and human activity and gesture recognition
Uccheddu M. C., Chessa S., Palumbo F.
It presents a survey on device-free applications using 802.11n Channel State Information (CSI). The survey analyzed device-free indoor localization works, human activity recognition works and gesture recognition works. For each work are described the system setting, the experimental environments and finally the evaluation. There is also the description of my personal implementation of a device-free indoor signal-based system setting, that was deployed at Consiglio Nazionale delle Ricerche (CNR) in Pisa.
Uccheddu M. C., Chessa S., Palumbo F.
It presents a survey on device-free applications using 802.11n Channel State Information (CSI). The survey analyzed device-free indoor localization works, human activity recognition works and gesture recognition works. For each work are described the system setting, the experimental environments and finally the evaluation. There is also the description of my personal implementation of a device-free indoor signal-based system setting, that was deployed at Consiglio Nazionale delle Ricerche (CNR) in Pisa.
See at:
etd.adm.unipi.it | CNR IRIS | CNR IRIS
2020
Conference article
Restricted
Impact of evolutionary community detection algorithms for edge selection strategies
Barsocchi P, Belli D, Chessa S, Foschini L, Girolami M
The combination of the edge computing paradigm with Mobile CrowdSensing (MCS) is a promising approach. However, the selection of the proper edge nodes is a crucial aspect that greatly affects the performance of the extended architecture. This work studies the performance of an edge-based MCS architecture with ParticipAct, a real-word experimental dataset. We present a community-based edge selection strategy and we measure two key metrics, namely latency and the number of requests satisfied. We show how they vary by adopting three evolutionary community detection algorithms, TILES, Infomap and iLCD configured by changing several configuration settings. We also study the two metrics, by varying the number of edge nodes selected so that to show its benefit.DOI: 10.1109/globecom42002.2020.9348085
Metrics:
Barsocchi P, Belli D, Chessa S, Foschini L, Girolami M
The combination of the edge computing paradigm with Mobile CrowdSensing (MCS) is a promising approach. However, the selection of the proper edge nodes is a crucial aspect that greatly affects the performance of the extended architecture. This work studies the performance of an edge-based MCS architecture with ParticipAct, a real-word experimental dataset. We present a community-based edge selection strategy and we measure two key metrics, namely latency and the number of requests satisfied. We show how they vary by adopting three evolutionary community detection algorithms, TILES, Infomap and iLCD configured by changing several configuration settings. We also study the two metrics, by varying the number of edge nodes selected so that to show its benefit.DOI: 10.1109/globecom42002.2020.9348085
Metrics:
See at:
Archivio istituzionale della ricerca - Alma Mater Studiorum Università di Bologna | CNR IRIS | ieeexplore.ieee.org | CNR IRIS | CNR IRIS | xplorestaging.ieee.org
2020
Conference article
Open Access
Understanding human mobility for CrowdSensing strategies with the ParticipAct data set
Chessa S, Foschini L, Girolami M
The Mobile CrowdSensing (MCS) paradigm has been increasingly adopted in the last years. Its adoption has been proved as beneficial for different scenarios, such as environmental monitoring and mobility analysis. However, one of the major barriers of the MCS initiatives, is the difficulty in recruiting users for the purpose of collecting data. We focus in this work to such limitation, and we analyze the mobility traces collected with a real-world MCS experiment, namely ParticipAct. Our goal is to discuss how to exploit the mobility features of the recruited users, as grounding information to plan and optimize a MCS data collection campaign. In detail, we analyze the quality of the data set, its accuracy and several features of human mobility such as radius of gyration and the real entropy of the locations visited. We discuss the impact of such metrics on the task scheduling, allocation and how to obtain a certain Tcoverage of data from visited locations.DOI: 10.1109/globecom42002.2020.9322541
Metrics:
Chessa S, Foschini L, Girolami M
The Mobile CrowdSensing (MCS) paradigm has been increasingly adopted in the last years. Its adoption has been proved as beneficial for different scenarios, such as environmental monitoring and mobility analysis. However, one of the major barriers of the MCS initiatives, is the difficulty in recruiting users for the purpose of collecting data. We focus in this work to such limitation, and we analyze the mobility traces collected with a real-world MCS experiment, namely ParticipAct. Our goal is to discuss how to exploit the mobility features of the recruited users, as grounding information to plan and optimize a MCS data collection campaign. In detail, we analyze the quality of the data set, its accuracy and several features of human mobility such as radius of gyration and the real entropy of the locations visited. We discuss the impact of such metrics on the task scheduling, allocation and how to obtain a certain Tcoverage of data from visited locations.DOI: 10.1109/globecom42002.2020.9322541
Metrics:
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CNR IRIS | ieeexplore.ieee.org | ISTI Repository | doi.org | Archivio istituzionale della ricerca - Alma Mater Studiorum Università di Bologna | CNR IRIS | CNR IRIS
2020
Journal article
Open Access
The rhythm of the crowd: Properties of evolutionary community detection algorithms for mobile edge selection
Belli D, Chessa S, Foschini L, Girolami M
The Multi-access Edge Computing (MEC) paradigm increases the computational capabilities of distributed sensing architectures, such as Mobile CrowdSensing platforms, which are designed to collect heterogeneous data from the crowd by exploiting mobile devices. In this context, our work focusses on the impact of three community detection algorithms to our edge selection strategy. In particular, we study TILES, Infomap, and iLCD which are specifically designed to identify evolving communities of users in dynamic networks. Our analysis is based on the ParticipAct data set that offers real human mobility data. We first measure the quality of the data set during an observation period of 1 year, during which the data set provides the 75% of the expected traces collected by approximately 170 users. We then compare some structural properties of the communities detected, namely Similarity, Forward Stability, Cohesion and Coverage. We conclude our study with a performance analysis of the selected Mobile MECs by varying the community detection algorithms adopted. In particular, we measure the latency and the number of satisfied requests and we show that the average latency obtained with Infomap is slightly lower than that of the other algorithms, while the average number of satisfied requests is higher when we adopt the TILES algorithm.Source: PERVASIVE AND MOBILE COMPUTING (PRINT), vol. 67
DOI: 10.1016/j.pmcj.2020.101231
Metrics:
Belli D, Chessa S, Foschini L, Girolami M
The Multi-access Edge Computing (MEC) paradigm increases the computational capabilities of distributed sensing architectures, such as Mobile CrowdSensing platforms, which are designed to collect heterogeneous data from the crowd by exploiting mobile devices. In this context, our work focusses on the impact of three community detection algorithms to our edge selection strategy. In particular, we study TILES, Infomap, and iLCD which are specifically designed to identify evolving communities of users in dynamic networks. Our analysis is based on the ParticipAct data set that offers real human mobility data. We first measure the quality of the data set during an observation period of 1 year, during which the data set provides the 75% of the expected traces collected by approximately 170 users. We then compare some structural properties of the communities detected, namely Similarity, Forward Stability, Cohesion and Coverage. We conclude our study with a performance analysis of the selected Mobile MECs by varying the community detection algorithms adopted. In particular, we measure the latency and the number of satisfied requests and we show that the average latency obtained with Infomap is slightly lower than that of the other algorithms, while the average number of satisfied requests is higher when we adopt the TILES algorithm.Source: PERVASIVE AND MOBILE COMPUTING (PRINT), vol. 67
DOI: 10.1016/j.pmcj.2020.101231
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Archivio istituzionale della ricerca - Alma Mater Studiorum Università di Bologna | Archivio istituzionale della ricerca - Alma Mater Studiorum Università di Bologna | CNR IRIS | www.sciencedirect.com | Archivio della Ricerca - Università di Pisa | Pervasive and Mobile Computing | Pervasive and Mobile Computing | Archivio della Ricerca - Università di Pisa | IRIS Cnr | CNR IRIS | CNR IRIS | Archivio della Ricerca - Università di Pisa | IRIS Cnr
2019
Journal article
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Collaborative service discovery in mobile social networks
Girolami M, Belli D, Chessa S
Mobile social networking is a recent paradigm arisen from the wide spread of mobile and wearable devices. Based on the short-range communication interfaces of these devices it is possible to establish opportunistic communications among them and build networks independent to the global one. Challenges introduced by this new type of networks are related to the sharing of resources and services and to the exploitation of the communication opportunities among devices. Limit of existing algorithms, that have sought to fill these shortages, is the lack of attention on the main actor of this service-oriented chain, the user. To this purpose, we introduce the COllaborative seRvice DIscovery ALgorithm (CORDIAL) that leverages both mobility and sociality of the users. We evaluate the performance of CORDIAL combined with different routing protocols for opportunistic networks, and we compare it with a benchmark algorithm (S-Flood) based on flooding and another service discovery algorithm designed to leverage mobile social network features, namely, ServIce DiscovEry in Mobile sociAl Networks (SIDEMAN). Our results show that the performance of CORDIAL remains stable with the different routing algorithms and that, in function of the query forwarding strategy triggered, CORDIAL matches the performance of S-Flood in terms of Query Response Time, achieving a better proactivity score with respect S-Flood and SIDEMAN as well.Source: JOURNAL OF NETWORK AND SYSTEMS MANAGEMENT, vol. 27 (issue 1), pp. 233-268
DOI: 10.1007/s10922-018-9465-0
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Girolami M, Belli D, Chessa S
Mobile social networking is a recent paradigm arisen from the wide spread of mobile and wearable devices. Based on the short-range communication interfaces of these devices it is possible to establish opportunistic communications among them and build networks independent to the global one. Challenges introduced by this new type of networks are related to the sharing of resources and services and to the exploitation of the communication opportunities among devices. Limit of existing algorithms, that have sought to fill these shortages, is the lack of attention on the main actor of this service-oriented chain, the user. To this purpose, we introduce the COllaborative seRvice DIscovery ALgorithm (CORDIAL) that leverages both mobility and sociality of the users. We evaluate the performance of CORDIAL combined with different routing protocols for opportunistic networks, and we compare it with a benchmark algorithm (S-Flood) based on flooding and another service discovery algorithm designed to leverage mobile social network features, namely, ServIce DiscovEry in Mobile sociAl Networks (SIDEMAN). Our results show that the performance of CORDIAL remains stable with the different routing algorithms and that, in function of the query forwarding strategy triggered, CORDIAL matches the performance of S-Flood in terms of Query Response Time, achieving a better proactivity score with respect S-Flood and SIDEMAN as well.Source: JOURNAL OF NETWORK AND SYSTEMS MANAGEMENT, vol. 27 (issue 1), pp. 233-268
DOI: 10.1007/s10922-018-9465-0
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Journal of Network and Systems Management | CNR IRIS | CNR IRIS | link.springer.com
2019
Conference article
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Remote detection of indoor human proximity using bluetooth low energy beacons
Mavilia F, Palumbo F, Barsocchi P, Chessa S, Girolami M
The way people interact in daily life is a challenging phenomenon to capture and to study without altering the natural rhythm of interactions. Our work investigates the possibility of automatically detecting proximity among people, the first mandatory condition before a dyad starts interacting. We present Remote Detection of Human Proximity (ReD-HuP), an algorithm based on the analysis of Bluetooth Low Energy beacons emitted by commercial wearable tags. We validate ReD-HuP with real-world indoor settings and we compare its performance with respect to detailed ground truth data collected from a number of volunteers. Experimental results show an accuracy and F-Score metric up to 95%.DOI: 10.1109/ie.2019.000-1
Project(s): NESTORE
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Mavilia F, Palumbo F, Barsocchi P, Chessa S, Girolami M
The way people interact in daily life is a challenging phenomenon to capture and to study without altering the natural rhythm of interactions. Our work investigates the possibility of automatically detecting proximity among people, the first mandatory condition before a dyad starts interacting. We present Remote Detection of Human Proximity (ReD-HuP), an algorithm based on the analysis of Bluetooth Low Energy beacons emitted by commercial wearable tags. We validate ReD-HuP with real-world indoor settings and we compare its performance with respect to detailed ground truth data collected from a number of volunteers. Experimental results show an accuracy and F-Score metric up to 95%.DOI: 10.1109/ie.2019.000-1
Project(s): NESTORE
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doi.org | CNR IRIS | ieeexplore.ieee.org | CNR IRIS
2019
Conference article
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Selection of mobile edges for a hybrid crowdsensing architecture
Belli D., Chessa S., Corradi A., Di Paolo G., Foschini L., Girolami M.
Mobile crowdsensing aims at the collection of sensor data on the environment by leveraging personal devices, usually smartphones. Its popularity is due to the ability of reaching capillary even the most remote areas (provided humans live there), with no infrastructure costs. This is possible because it leverages on existing 4G/5G communication infrastructures that are now rapidly evolving towards edge computing models. In this work we address the synergy between mobile crowdsensing and multi-access edge computing by analysing and assessing strategies for the selection of fixed and mobile edges to support the collection of mobile crowdsensing data.DOI: 10.1109/iscc47284.2019.8969597
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Belli D., Chessa S., Corradi A., Di Paolo G., Foschini L., Girolami M.
Mobile crowdsensing aims at the collection of sensor data on the environment by leveraging personal devices, usually smartphones. Its popularity is due to the ability of reaching capillary even the most remote areas (provided humans live there), with no infrastructure costs. This is possible because it leverages on existing 4G/5G communication infrastructures that are now rapidly evolving towards edge computing models. In this work we address the synergy between mobile crowdsensing and multi-access edge computing by analysing and assessing strategies for the selection of fixed and mobile edges to support the collection of mobile crowdsensing data.DOI: 10.1109/iscc47284.2019.8969597
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doi.org | Archivio istituzionale della ricerca - Alma Mater Studiorum Università di Bologna | CNR IRIS | ieeexplore.ieee.org | CNR IRIS
2019
Journal article
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Personalized real-time anomaly detection and health feedback for older adults
Parvin P., Chessa S., Kaptein M., Paternò F.
Rapid population aging and the availability of sensors and intelligent objects motivate the development of healthcare systems; these systems, in turn, meet the needs of older adults by supporting them to accomplish their day-to-day activities. Collecting information regarding older adults daily activity potentially helps to detect abnormal behavior. Anomaly detection can subsequently be combined with real-time, continuous and personalized interventions to help older adults actively enjoy a healthy lifestyle. This paper introduces a system that uses a novel approach to generate personalized health feedback. The proposed system models user's daily behavior in order to detect anomalous behaviors and strategically generates interventions to encourage behaviors conducive to a healthier lifestyle. The system uses a Mamdani-type fuzzy rule-based component to predict the level of intervention needed for each detected anomaly and a sequential decision-making algorithm, Contextual Multi-armed Bandit, to generate suggestions to minimize anomalous behavior. We describe the system's architecture in detail and we provide example implementations for the anomaly detection and corresponding health feedback.Source: JOURNAL OF AMBIENT INTELLIGENCE AND SMART ENVIRONMENTS (PRINT), vol. 11 (issue 5), pp. 453-469
DOI: 10.3233/ais-190536
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Parvin P., Chessa S., Kaptein M., Paternò F.
Rapid population aging and the availability of sensors and intelligent objects motivate the development of healthcare systems; these systems, in turn, meet the needs of older adults by supporting them to accomplish their day-to-day activities. Collecting information regarding older adults daily activity potentially helps to detect abnormal behavior. Anomaly detection can subsequently be combined with real-time, continuous and personalized interventions to help older adults actively enjoy a healthy lifestyle. This paper introduces a system that uses a novel approach to generate personalized health feedback. The proposed system models user's daily behavior in order to detect anomalous behaviors and strategically generates interventions to encourage behaviors conducive to a healthier lifestyle. The system uses a Mamdani-type fuzzy rule-based component to predict the level of intervention needed for each detected anomaly and a sequential decision-making algorithm, Contextual Multi-armed Bandit, to generate suggestions to minimize anomalous behavior. We describe the system's architecture in detail and we provide example implementations for the anomaly detection and corresponding health feedback.Source: JOURNAL OF AMBIENT INTELLIGENCE AND SMART ENVIRONMENTS (PRINT), vol. 11 (issue 5), pp. 453-469
DOI: 10.3233/ais-190536
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content.iospress.com | Journal of Ambient Intelligence and Smart Environments | CNR IRIS | CNR IRIS | NARCIS
2018
Conference article
Open Access
UAVs and UAV swarms for civilian applications: communications and image processing in the SCIADRO project
Bacco M, Chessa S, Di Benedetto M, Fabbri D, Girolami M, Gotta A, Moroni D, Pascali M A, Pellegrini V
The use of Unmanned Aerial Vehicles (UAVs), or drones, is increasingly common in both research and industrial fields. Nowadays, the use of single UAVs is quite established and several products are already available to consumers, while UAV swarms are still subject of research and development. This position paper describes the objectives of a research project, namely SCIADRO2, which deals with innovative applications and network architectures based on the use of UAVs and UAV swarms in several civilian fields.Source: LECTURE NOTES OF THE INSTITUTE FOR COMPUTER SCIENCES, SOCIAL INFORMATICS AND TELECOMMUNICATIONS ENGINEERING, pp. 115-124. Oxford, UK, 14-15 September 2017
DOI: 10.1007/978-3-319-76571-6_12
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Bacco M, Chessa S, Di Benedetto M, Fabbri D, Girolami M, Gotta A, Moroni D, Pascali M A, Pellegrini V
The use of Unmanned Aerial Vehicles (UAVs), or drones, is increasingly common in both research and industrial fields. Nowadays, the use of single UAVs is quite established and several products are already available to consumers, while UAV swarms are still subject of research and development. This position paper describes the objectives of a research project, namely SCIADRO2, which deals with innovative applications and network architectures based on the use of UAVs and UAV swarms in several civilian fields.Source: LECTURE NOTES OF THE INSTITUTE FOR COMPUTER SCIENCES, SOCIAL INFORMATICS AND TELECOMMUNICATIONS ENGINEERING, pp. 115-124. Oxford, UK, 14-15 September 2017
DOI: 10.1007/978-3-319-76571-6_12
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CNR IRIS | link.springer.com | ISTI Repository | doi.org | CNR IRIS | CNR IRIS