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.Source: IE 2023 - 19th International Conference on Intelligent Environments, Island of Mauritius, 29-30/06/2023
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.Source: IE 2023 - 19th International Conference on Intelligent Environments, Island of Mauritius, 29-30/06/2023
DOI: 10.1109/ie57519.2023.10179090
Metrics:
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ISTI Repository 
| ieeexplore.ieee.org 
| CNR ExploRA
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: ISCC 2023 - 28th 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: ISCC 2023 - 28th IEEE Symposium on Computers and Communications, Gammarth, Tunisia, 9-12/07/2023
DOI: 10.1109/iscc58397.2023.10217879
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ISTI Repository | ieeexplore.ieee.org | CNR ExploRA
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.Source: PerCom Workshops 2022 - IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, pp. 22–25, Pisa, Italy, 21-25/03/2022
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.Source: PerCom Workshops 2022 - IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, pp. 22–25, Pisa, Italy, 21-25/03/2022
DOI: 10.1109/percomworkshops53856.2022.9767356
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ISTI Repository | ieeexplore.ieee.org | CNR ExploRA
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.Source: ICC 2022 - IEEE International Conference on Communications, pp. 5011–5016, Seoul, Republic of Korea, 16-20/05/2022
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.Source: ICC 2022 - IEEE International Conference on Communications, pp. 5011–5016, Seoul, Republic of Korea, 16-20/05/2022
DOI: 10.1109/icc45855.2022.9838963
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ISTI Repository | ieeexplore.ieee.org | CNR ExploRA
2021
Report
Restricted
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%Source: ISTI-TR-2021/010, pp.1–14, 2021
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%Source: ISTI-TR-2021/010, pp.1–14, 2021
DOI: 10.32079/isti-tr-2021/010
Metrics:
See at: CNR ExploRA
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) 21 (2021). doi:10.3390/s21196397
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) 21 (2021). doi:10.3390/s21196397
DOI: 10.3390/s21196397
Metrics:
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ISTI Repository | www.mdpi.com | CNR ExploRA
2020
Journal article
Closed 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 7 (2020): 2421–2431. doi:10.1109/JIOT.2019.2957835
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 7 (2020): 2421–2431. doi:10.1109/JIOT.2019.2957835
DOI: 10.1109/jiot.2019.2957835
Metrics:
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doi.org | Archivio istituzionale della ricerca - Alma Mater Studiorum Università di Bologna | ieeexplore.ieee.org | CNR ExploRA
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 157 (2020): 132–142. doi:10.1016/j.comcom.2020.04.006
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 157 (2020): 132–142. doi:10.1016/j.comcom.2020.04.006
DOI: 10.1016/j.comcom.2020.04.006
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Archivio istituzionale della ricerca - Alma Mater Studiorum Università di Bologna | Computer Communications | www.sciencedirect.com | CNR ExploRA
2020
Master thesis
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.
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.
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 ExploRA
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.Source: GLOBECOM 2020 - IEEE Global Communications Conference, Taipei, Taiwan, December 07-11, 2020
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.Source: GLOBECOM 2020 - IEEE Global Communications Conference, Taipei, Taiwan, December 07-11, 2020
DOI: 10.1109/globecom42002.2020.9348085
Metrics:
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Archivio istituzionale della ricerca - Alma Mater Studiorum Università di Bologna | ieeexplore.ieee.org | xplorestaging.ieee.org | CNR ExploRA
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.Source: GLOBECOM 2020 - 2020 IEEE Global Communications Conference, Taipei, Taiwan, 07-11/12/2020
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.Source: GLOBECOM 2020 - 2020 IEEE Global Communications Conference, Taipei, Taiwan, 07-11/12/2020
DOI: 10.1109/globecom42002.2020.9322541
Metrics:
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ISTI Repository | doi.org | Archivio istituzionale della ricerca - Alma Mater Studiorum Università di Bologna | ieeexplore.ieee.org | CNR ExploRA
2019
Journal article
Restricted
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 (Dordr., Online) 27 (2019): 233–268. doi:10.1007/s10922-018-9465-0
DOI: 10.1007/s10922-018-9465-0
Metrics:
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 (Dordr., Online) 27 (2019): 233–268. doi:10.1007/s10922-018-9465-0
DOI: 10.1007/s10922-018-9465-0
Metrics:
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Journal of Network and Systems Management | link.springer.com | CNR ExploRA
2019
Conference article
Closed Access
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%.Source: IE 2019 - 15th International Conference on Intelligent Environments, pp. 16–21, Rabat, Morocco, June 24-27, 2109
DOI: 10.1109/ie.2019.000-1
Project(s): NESTORE
Metrics:
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%.Source: IE 2019 - 15th International Conference on Intelligent Environments, pp. 16–21, Rabat, Morocco, June 24-27, 2109
DOI: 10.1109/ie.2019.000-1
Project(s): NESTORE
Metrics:
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doi.org | ieeexplore.ieee.org | CNR ExploRA
2019
Conference article
Restricted
Selection of mobile edges for a hybrid crowdsensing architecture
Belli D., Chessa S., Corradi, 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.Source: ISCC 2019 - IEEE Symposium on Computers and Communications, Barcelona, Spain, 29 June - 3 July, 2019
DOI: 10.1109/iscc47284.2019.8969597
Metrics:
Belli D., Chessa S., Corradi, 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.Source: ISCC 2019 - IEEE Symposium on Computers and Communications, Barcelona, Spain, 29 June - 3 July, 2019
DOI: 10.1109/iscc47284.2019.8969597
Metrics:
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doi.org | Archivio istituzionale della ricerca - Alma Mater Studiorum Università di Bologna | ieeexplore.ieee.org | CNR ExploRA
2019
Journal article
Closed Access
Personalized real-time anomaly detection and health feedback for older adults
Parvin P., Chessa S., Kaptein M., Paterno 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) 11 (2019): 453–469. doi:10.3233/AIS-190536
DOI: 10.3233/ais-190536
Metrics:
Parvin P., Chessa S., Kaptein M., Paterno 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) 11 (2019): 453–469. doi:10.3233/AIS-190536
DOI: 10.3233/ais-190536
Metrics:
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content.iospress.com | Journal of Ambient Intelligence and Smart Environments | NARCIS | CNR ExploRA
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: WiSATS 2017 - International Conference on Wireless and Satellite Systems, pp. 115–124, Oxford, UK, 14-15 September 2017
DOI: 10.1007/978-3-319-76571-6_12
Metrics:
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: WiSATS 2017 - International Conference on Wireless and Satellite Systems, pp. 115–124, Oxford, UK, 14-15 September 2017
DOI: 10.1007/978-3-319-76571-6_12
Metrics:
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ISTI Repository | doi.org | link.springer.com | CNR ExploRA
2018
Journal article
Open Access
Real-time anomaly detection in elderly behavior with the support of task models
Parvin P., Chessa S., Manca M., Paternò F.
With today's technology, elderly can be supported in living independently in their own homes for a prolonged period of time. Monitoring and analyzing their behavior in order to find possible unusual situation helps to provide the elderly with health warnings at the proper time. Current studies are focusing on the elderly daily activity and the detection of anomalous behaviors aiming to provide the older people with remote support. To this aim, we propose a real-time solution which models the user daily routine using a task model specification and detects relevant contextual events occurred in their life through a context manager. In addition, by a systematic validation through a system that automatically generates wrong sequences of tasks, we show that our algorithm is able to find behavioral deviations from the expected behavior at different times by considering the extended classification of the possible deviations with good accuracy.Source: Proceedings of the ACM on human-computer interaction 2 (2018). doi:10.1145/3229097
DOI: 10.1145/3229097
Metrics:
Parvin P., Chessa S., Manca M., Paternò F.
With today's technology, elderly can be supported in living independently in their own homes for a prolonged period of time. Monitoring and analyzing their behavior in order to find possible unusual situation helps to provide the elderly with health warnings at the proper time. Current studies are focusing on the elderly daily activity and the detection of anomalous behaviors aiming to provide the older people with remote support. To this aim, we propose a real-time solution which models the user daily routine using a task model specification and detects relevant contextual events occurred in their life through a context manager. In addition, by a systematic validation through a system that automatically generates wrong sequences of tasks, we show that our algorithm is able to find behavioral deviations from the expected behavior at different times by considering the extended classification of the possible deviations with good accuracy.Source: Proceedings of the ACM on human-computer interaction 2 (2018). doi:10.1145/3229097
DOI: 10.1145/3229097
Metrics:
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ISTI Repository | dl.acm.org | Proceedings of the ACM on Human-Computer Interaction | CNR ExploRA
2018
Journal article
Open Access
Indoor bluetooth low energy dataset for localization, tracking, occupancy, and social interaction
Baronti P., Barsocchi P., Chessa S., Mavilia F., Palumbo F.
Indoor localization has become a mature research area, but further scientific developments are limited due to the lack of open datasets and corresponding frameworks suitable to compare and evaluate specialized localization solutions. Although several competitions provide datasets and environments for comparing different solutions, they hardly consider novel technologies such as Bluetooth Low Energy (BLE), which is gaining more and more importance in indoor localization due to its wide availability in personal and environmental devices and to its low costs and flexibility. This paper contributes to cover this gap by: (i) presenting a new indoor BLE dataset; (ii) reviewing several, meaningful use cases in different application scenarios; and (iii) discussing alternative uses of the dataset in the evaluation of different positioning and navigation applications, namely localization, tracking, occupancy and social interaction.Source: Sensors (Basel) 18 (2018). doi:10.3390/s18124462
DOI: 10.3390/s18124462
Project(s): NESTORE
Metrics:
Baronti P., Barsocchi P., Chessa S., Mavilia F., Palumbo F.
Indoor localization has become a mature research area, but further scientific developments are limited due to the lack of open datasets and corresponding frameworks suitable to compare and evaluate specialized localization solutions. Although several competitions provide datasets and environments for comparing different solutions, they hardly consider novel technologies such as Bluetooth Low Energy (BLE), which is gaining more and more importance in indoor localization due to its wide availability in personal and environmental devices and to its low costs and flexibility. This paper contributes to cover this gap by: (i) presenting a new indoor BLE dataset; (ii) reviewing several, meaningful use cases in different application scenarios; and (iii) discussing alternative uses of the dataset in the evaluation of different positioning and navigation applications, namely localization, tracking, occupancy and social interaction.Source: Sensors (Basel) 18 (2018). doi:10.3390/s18124462
DOI: 10.3390/s18124462
Project(s): NESTORE
Metrics:
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Sensors | Sensors | ISTI Repository | Sensors | Sensors | CNR ExploRA
2018
Conference article
Open Access
A Social-Based Approach to Mobile Edge Computing
Belli D., Chessa S., Foschini L., Girolami M.
Mobile Edge Computing (MEC) opens to the opportunity of moving high-volumes of data from the cloud to locations where the information is actually accessed. In turn, the combination of MEC with the Mobile Crowdsensing approach, using a restricted number of devices with respect the number of base stations, matches the performance of the conventional MEC middleware layer ensuring the same spatial coverage. In this work, we envision a MEC architecture composed by mobile and fixed edges. Their goal is to optimize the share of contents among users by exploiting their mobility and sociality. We first present an algorithm to identify a suitable set of mobile edges and we show how such selection increases the performance of a content-sharing scenario. Our experiments are based on the ParticipAct dataset, which captures the mobility of about 170 users for 10 months. The experiments show that the number of requests that can be served mobile edges is similar to that of requests served by fixed edges, and then that mobile edges can be considered a viable (and lowcost) alternative to fixed edges.Source: IEEE Symposium on Computers and Communications (ISCC), pp. 00292–00297, 25/06/2018, 28/06/2016
DOI: 10.1109/iscc.2018.8538763
Metrics:
Belli D., Chessa S., Foschini L., Girolami M.
Mobile Edge Computing (MEC) opens to the opportunity of moving high-volumes of data from the cloud to locations where the information is actually accessed. In turn, the combination of MEC with the Mobile Crowdsensing approach, using a restricted number of devices with respect the number of base stations, matches the performance of the conventional MEC middleware layer ensuring the same spatial coverage. In this work, we envision a MEC architecture composed by mobile and fixed edges. Their goal is to optimize the share of contents among users by exploiting their mobility and sociality. We first present an algorithm to identify a suitable set of mobile edges and we show how such selection increases the performance of a content-sharing scenario. Our experiments are based on the ParticipAct dataset, which captures the mobility of about 170 users for 10 months. The experiments show that the number of requests that can be served mobile edges is similar to that of requests served by fixed edges, and then that mobile edges can be considered a viable (and lowcost) alternative to fixed edges.Source: IEEE Symposium on Computers and Communications (ISCC), pp. 00292–00297, 25/06/2018, 28/06/2016
DOI: 10.1109/iscc.2018.8538763
Metrics:
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ISTI Repository | doi.org | Archivio istituzionale della ricerca - Alma Mater Studiorum Università di Bologna | ieeexplore.ieee.org | CNR ExploRA
2018
Conference article
Restricted
Enhancing mobile edge computing architecture with human-driven edge computing model
Belli D., Chessa S., Foschini L., Girolami M.
In an increasingly interconnected world, mobile and wearable devices, through short range communication interfaces and sensors, become needful tools for collecting and disseminating information in high population density environments. In this context Mobile Crowdsensing (MCS), leveraging people's roaming and their devices' resources, raised the citizen from mere walk-on parts to active participant in the knowledge building and data dissemination process. At the same time, Mobile Edge Computing (MEC) architecture has recently enhanced the two-layer cloud-device architectural model easing the exchange of information and shifting most computational cost from devices towards middle-layer proxies, namely, network edges. We introduce Human-driven Edge Computing, a new model which melts together the power of MEC platform and the large-scale sensing of MCS to realize a better data spreading and environmental coverage in smart cities. In addition, it will be briefly discussed the main sociological aspects related to human behavior and how they can influence the exchange of data in large-scale sensor networks.Source: 14th International Conference on Intelligent Environments (IE), pp. 95–98, Rome, Italy, 25-28 June, 2018
DOI: 10.1109/ie.2018.00023
Metrics:
Belli D., Chessa S., Foschini L., Girolami M.
In an increasingly interconnected world, mobile and wearable devices, through short range communication interfaces and sensors, become needful tools for collecting and disseminating information in high population density environments. In this context Mobile Crowdsensing (MCS), leveraging people's roaming and their devices' resources, raised the citizen from mere walk-on parts to active participant in the knowledge building and data dissemination process. At the same time, Mobile Edge Computing (MEC) architecture has recently enhanced the two-layer cloud-device architectural model easing the exchange of information and shifting most computational cost from devices towards middle-layer proxies, namely, network edges. We introduce Human-driven Edge Computing, a new model which melts together the power of MEC platform and the large-scale sensing of MCS to realize a better data spreading and environmental coverage in smart cities. In addition, it will be briefly discussed the main sociological aspects related to human behavior and how they can influence the exchange of data in large-scale sensor networks.Source: 14th International Conference on Intelligent Environments (IE), pp. 95–98, Rome, Italy, 25-28 June, 2018
DOI: 10.1109/ie.2018.00023
Metrics:
See at:
doi.org | Archivio istituzionale della ricerca - Alma Mater Studiorum Università di Bologna | ieeexplore.ieee.org | CNR ExploRA