2022
Canino, M. P.; Cesario, E.; Vinci, A.; Zarin, S.
Epidemic forecasting based on mobility patterns: an approach and experimental evaluation on COVID-19 Data Journal Article
In: Social Network Analysis and Mining, vol. 12, iss. 1, 2022, ISSN: 18695469.
Abstract | Links | BibTeX | Tags: COVID-19, Epidemic forecasting, Predictive models
@article{Canino2022,
title = {Epidemic forecasting based on mobility patterns: an approach and experimental evaluation on COVID-19 Data},
author = {M. P. Canino and E. Cesario and A. Vinci and S. Zarin},
doi = {10.1007/s13278-022-00932-6},
issn = {18695469},
year = {2022},
date = {2022-01-01},
journal = {Social Network Analysis and Mining},
volume = {12},
issue = {1},
abstract = {During an epidemic, decision-makers in public health need accurate predictions of the future case numbers, in order to control the spread of new cases and allow efficient resource planning for hospital needs and capacities. In particular, considering that infectious diseases are spread through human-human transmissions, the analysis of spatio-temporal mobility data can play a fundamental role to enable epidemic forecasting. This paper presents the design and implementation of a predictive approach, based on spatial analysis and regressive models, to discover spatio-temporal predictive epidemic patterns from mobility and infection data. The experimental evaluation, performed on mobility and COVID-19 data collected in the city of Chicago, is aimed to assess the effectiveness of the approach in a real-world scenario.},
keywords = {COVID-19, Epidemic forecasting, Predictive models},
pubstate = {published},
tppubtype = {article}
}
During an epidemic, decision-makers in public health need accurate predictions of the future case numbers, in order to control the spread of new cases and allow efficient resource planning for hospital needs and capacities. In particular, considering that infectious diseases are spread through human-human transmissions, the analysis of spatio-temporal mobility data can play a fundamental role to enable epidemic forecasting. This paper presents the design and implementation of a predictive approach, based on spatial analysis and regressive models, to discover spatio-temporal predictive epidemic patterns from mobility and infection data. The experimental evaluation, performed on mobility and COVID-19 data collected in the city of Chicago, is aimed to assess the effectiveness of the approach in a real-world scenario.
Cesario, E.; Uchubilo, P. I.; Vinci, A.; Zhu, X.
Multi-density urban hotspots detection in smart cities: A data-driven approach and experiments Journal Article
In: Pervasive and Mobile Computing, vol. 86, 2022, ISSN: 15741192.
Abstract | Links | BibTeX | Tags: Multi-density city hotspots, smart city, Urban computing
@article{Cesario2022,
title = {Multi-density urban hotspots detection in smart cities: A data-driven approach and experiments},
author = {E. Cesario and P. I. Uchubilo and A. Vinci and X. Zhu},
doi = {10.1016/j.pmcj.2022.101687},
issn = {15741192},
year = {2022},
date = {2022-01-01},
journal = {Pervasive and Mobile Computing},
volume = {86},
abstract = {The detection of city hotspots from geo-referenced urban data is a valuable knowledge support for planners, scientists, and policymakers. However, the application of classic density-based clustering algorithms on multi-density data can produce inaccurate results. Since metropolitan cities are heavily characterized by variable densities, multi-density clustering seems to be more appropriate to discover city hotspots. This paper presents CHD (City Hotspot Detector), a multi-density approach to discover urban hotspots in a city, by reporting an extensive comparative analysis with three classic density-based clustering algorithms, on both state-of-the-art and real-world datasets. The comparative experimental evaluation in an urban scenario shows that the proposed multi-density algorithm, enhanced by an additional rolling moving average technique, detects higher quality city hotspots than other classic density-based approaches proposed in literature.},
keywords = {Multi-density city hotspots, smart city, Urban computing},
pubstate = {published},
tppubtype = {article}
}
The detection of city hotspots from geo-referenced urban data is a valuable knowledge support for planners, scientists, and policymakers. However, the application of classic density-based clustering algorithms on multi-density data can produce inaccurate results. Since metropolitan cities are heavily characterized by variable densities, multi-density clustering seems to be more appropriate to discover city hotspots. This paper presents CHD (City Hotspot Detector), a multi-density approach to discover urban hotspots in a city, by reporting an extensive comparative analysis with three classic density-based clustering algorithms, on both state-of-the-art and real-world datasets. The comparative experimental evaluation in an urban scenario shows that the proposed multi-density algorithm, enhanced by an additional rolling moving average technique, detects higher quality city hotspots than other classic density-based approaches proposed in literature.
Cicirelli, F.; Guerrieri, A.; Vinci, A.
Smart monitoring and control in the future internet of things Journal Article
In: Sensors, vol. 22, iss. 1, 2022, ISSN: 14248220.
@article{Cicirelli2022,
title = {Smart monitoring and control in the future internet of things},
author = {F. Cicirelli and A. Guerrieri and A. Vinci},
doi = {10.3390/s22010027},
issn = {14248220},
year = {2022},
date = {2022-01-01},
journal = {Sensors},
volume = {22},
issue = {1},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
2021
Cicirelli, F.; Guerrieri, A.; Mastroianni, C.; Scarcello, L.; Spezzano, G.; Vinci, A.
Balancing Energy Consumption and Thermal Comfort with Deep Reinforcement Learning Inproceedings
In: 2021, ISBN: 9781665401708.
Abstract | Links | BibTeX | Tags: Cognitive Buildings, Deep Reinforcement Learning, smart environments, Thermal Comfort
@inproceedings{Cicirelli2021,
title = {Balancing Energy Consumption and Thermal Comfort with Deep Reinforcement Learning},
author = {F. Cicirelli and A. Guerrieri and C. Mastroianni and L. Scarcello and G. Spezzano and A. Vinci},
doi = {10.1109/ICHMS53169.2021.9582638},
isbn = {9781665401708},
year = {2021},
date = {2021-01-01},
journal = {Proceedings of the 2021 IEEE International Conference on Human-Machine Systems, ICHMS 2021},
abstract = {The management of thermal comfort in a building is a challenging and multi-faced problem because it requires considering both objective and subjective parameters that are often in contrast. Subjective parameters are tied to reaching and maintaining an adequate user comfort by considering human preferences and behaviours, while objective parameters can be related to other important aspects like the reduction of energy consumption. This paper exploits cognitive technologies, based on Deep Reinforcement Learning (DRL), for automatically learning how to control the HVAC system in an office. The goal is to develop a cyber-controller able to minimize both the perceived thermal discomfort and the needed energy. The learning process is driven through the definition of a cumulative reward, which includes and combines two reward components that consider, respectively, user comfort and energy consumption. Simulation experiments show that the adopted approach is able to affect the behaviour of the DRL controller and the learning process and therefore to balance the two objectives by weighing the two components of the reward.},
keywords = {Cognitive Buildings, Deep Reinforcement Learning, smart environments, Thermal Comfort},
pubstate = {published},
tppubtype = {inproceedings}
}
The management of thermal comfort in a building is a challenging and multi-faced problem because it requires considering both objective and subjective parameters that are often in contrast. Subjective parameters are tied to reaching and maintaining an adequate user comfort by considering human preferences and behaviours, while objective parameters can be related to other important aspects like the reduction of energy consumption. This paper exploits cognitive technologies, based on Deep Reinforcement Learning (DRL), for automatically learning how to control the HVAC system in an office. The goal is to develop a cyber-controller able to minimize both the perceived thermal discomfort and the needed energy. The learning process is driven through the definition of a cumulative reward, which includes and combines two reward components that consider, respectively, user comfort and energy consumption. Simulation experiments show that the adopted approach is able to affect the behaviour of the DRL controller and the learning process and therefore to balance the two objectives by weighing the two components of the reward.
Cesario, E.; Vinci, A.; Zarin, S.
Towards Parallel Multi-density Clustering for Urban Hotspots Detection Inproceedings
In: 2021, ISBN: 9781665414555.
Abstract | Links | BibTeX | Tags: Multi density clustering, smart city, Urban computing
@inproceedings{Cesario2021,
title = {Towards Parallel Multi-density Clustering for Urban Hotspots Detection},
author = {E. Cesario and A. Vinci and S. Zarin},
doi = {10.1109/PDP52278.2021.00046},
isbn = {9781665414555},
year = {2021},
date = {2021-01-01},
journal = {Proceedings - 29th Euromicro International Conference on Parallel, Distributed and Network-Based Processing, PDP 2021},
abstract = {Detecting city hotspots in urban environments is a valuable organization methodology for framing detailed knowledge of a metropolitan area, providing high-level summaries for spatial urban datasets. Such knowledge is a valuable support for planner, scientist and policy-maker's decisions. Classic density-based clustering algorithms show to be suitable to discover hotspots characterized by homogeneous density, but their application on multi-density data can produce inaccurate results. For such a reason, since metropolitan cities are heavily characterized by variable densities, multi-density clustering approaches show higher effectiveness to discover city hotspots. Moreover, the growing volumes of data collected in urban environments require high-performance computing solutions, to guarantee efficient, scalable and elastic task executions. This paper describes the design and implementation of a parallel multi-density clustering algorithm, aimed at analyzing high volume of urban data in an efficient way. The experimental evaluation shows that the proposed parallel clustering approach takes out encouraging advantages in terms of execution time and speedup.},
keywords = {Multi density clustering, smart city, Urban computing},
pubstate = {published},
tppubtype = {inproceedings}
}
Detecting city hotspots in urban environments is a valuable organization methodology for framing detailed knowledge of a metropolitan area, providing high-level summaries for spatial urban datasets. Such knowledge is a valuable support for planner, scientist and policy-maker's decisions. Classic density-based clustering algorithms show to be suitable to discover hotspots characterized by homogeneous density, but their application on multi-density data can produce inaccurate results. For such a reason, since metropolitan cities are heavily characterized by variable densities, multi-density clustering approaches show higher effectiveness to discover city hotspots. Moreover, the growing volumes of data collected in urban environments require high-performance computing solutions, to guarantee efficient, scalable and elastic task executions. This paper describes the design and implementation of a parallel multi-density clustering algorithm, aimed at analyzing high volume of urban data in an efficient way. The experimental evaluation shows that the proposed parallel clustering approach takes out encouraging advantages in terms of execution time and speedup.
Cicirelli, F.; Guerrieri, A.; Mastroianni, C.; Vinci, A.
Emerging internet of things solutions and technologies Journal Article
In: Electronics (Switzerland), vol. 10, iss. 16, 2021, ISSN: 20799292.
@article{Cicirelli2021b,
title = {Emerging internet of things solutions and technologies},
author = {F. Cicirelli and A. Guerrieri and C. Mastroianni and A. Vinci},
doi = {10.3390/electronics10161928},
issn = {20799292},
year = {2021},
date = {2021-01-01},
journal = {Electronics (Switzerland)},
volume = {10},
issue = {16},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
2020
Maiolo, M.; Palermo, S. A.; Brusco, A. C.; Pirouz, B.; Turco, M.; Vinci, A.; Spezzano, G.; Piro, P.
On the use of a real-time control approach for urban stormwater management Journal Article
In: Water (Switzerland), vol. 12, iss. 10, 2020, ISSN: 20734441.
Abstract | Links | BibTeX | Tags: Distributed real-time system, Gossip-based algorithm, multi-agent systems, PID, Rainfall-runoff, Sewer system, SWMM
@article{Maiolo2020,
title = {On the use of a real-time control approach for urban stormwater management},
author = {M. Maiolo and S. A. Palermo and A. C. Brusco and B. Pirouz and M. Turco and A. Vinci and G. Spezzano and P. Piro},
doi = {10.3390/w12102842},
issn = {20734441},
year = {2020},
date = {2020-01-01},
journal = {Water (Switzerland)},
volume = {12},
issue = {10},
abstract = {The real-time control (RTC) system is a valid and cost-effective solution for urban stormwater management. This paper aims to evaluate the beneficial effect on urban flooding risk mitigation produced by applying RTC techniques to an urban drainage network by considering different control configuration scenarios. To achieve the aim, a distributed real-time system, validated in previous studies, was considered. This approach uses a smart moveable gates system, controlled by software agents, managed by a swarm intelligence algorithm. By running the different scenarios by a customized version of the Storm Water Management Model (SWMM), the findings obtained show a redistribution of conduits filling degrees, exploiting the whole system storage capacity, with a significant reduction of node flooding and total flood volume.},
keywords = {Distributed real-time system, Gossip-based algorithm, multi-agent systems, PID, Rainfall-runoff, Sewer system, SWMM},
pubstate = {published},
tppubtype = {article}
}
The real-time control (RTC) system is a valid and cost-effective solution for urban stormwater management. This paper aims to evaluate the beneficial effect on urban flooding risk mitigation produced by applying RTC techniques to an urban drainage network by considering different control configuration scenarios. To achieve the aim, a distributed real-time system, validated in previous studies, was considered. This approach uses a smart moveable gates system, controlled by software agents, managed by a swarm intelligence algorithm. By running the different scenarios by a customized version of the Storm Water Management Model (SWMM), the findings obtained show a redistribution of conduits filling degrees, exploiting the whole system storage capacity, with a significant reduction of node flooding and total flood volume.
Cesario, E.; Uchubilo, P. I.; Vinci, A.; Zhu, X.
Discovering Multi-density Urban Hotspots in a Smart City Inproceedings
In: 2020, ISBN: 9781728169972.
Abstract | Links | BibTeX | Tags: Crime Data Analysis, Data Mining, smart city
@inproceedings{Cesario2020,
title = {Discovering Multi-density Urban Hotspots in a Smart City},
author = {E. Cesario and P. I. Uchubilo and A. Vinci and X. Zhu},
doi = {10.1109/SMARTCOMP50058.2020.00073},
isbn = {9781728169972},
year = {2020},
date = {2020-01-01},
journal = {Proceedings - 2020 IEEE International Conference on Smart Computing, SMARTCOMP 2020},
abstract = {Leveraged by a large-scale diffusion of sensing networks and scanning devices in modern cities, huge volumes of geo-referenced urban data are collected every day. Such amount of information is analyzed to discover data-driven models, which can be exploited to tackle the major issues that cities face, including air pollution, virus diffusion, human mobility, traffic flows. In particular, the detection of city hotspots is becoming a valuable organization technique for framing detailed knowledge of a metropolitan area, providing high-level summaries for spatial datasets, which are valuable for planners, scientists, and policymakers. However, while classic density-based clustering algorithms show to be suitable to discover hotspots characterized by homogeneous density, their application on multi-density data can produce inaccurate results. For such a reason, since metropolitan cities are heavily characterized by variable densities, multi-density clustering seems to be more appropriate to discover city hotspots. This paper presents a study about how density-based clustering algorithms are suitable for discovering urban hotspots in a city, by showing a comparative analysis of single-density and multi-density clustering on both state-of-the-art data and real-world data. The experimental evaluation shows that, in an urban scenario, multi-density clustering achieves higher quality hotspots than a single-density approach.},
keywords = {Crime Data Analysis, Data Mining, smart city},
pubstate = {published},
tppubtype = {inproceedings}
}
Leveraged by a large-scale diffusion of sensing networks and scanning devices in modern cities, huge volumes of geo-referenced urban data are collected every day. Such amount of information is analyzed to discover data-driven models, which can be exploited to tackle the major issues that cities face, including air pollution, virus diffusion, human mobility, traffic flows. In particular, the detection of city hotspots is becoming a valuable organization technique for framing detailed knowledge of a metropolitan area, providing high-level summaries for spatial datasets, which are valuable for planners, scientists, and policymakers. However, while classic density-based clustering algorithms show to be suitable to discover hotspots characterized by homogeneous density, their application on multi-density data can produce inaccurate results. For such a reason, since metropolitan cities are heavily characterized by variable densities, multi-density clustering seems to be more appropriate to discover city hotspots. This paper presents a study about how density-based clustering algorithms are suitable for discovering urban hotspots in a city, by showing a comparative analysis of single-density and multi-density clustering on both state-of-the-art data and real-world data. The experimental evaluation shows that, in an urban scenario, multi-density clustering achieves higher quality hotspots than a single-density approach.
Cicirelli, F.; Guerrieri, A.; Mastroianni, C.; Spezzano, G.; Vinci, A.
Thermal comfort management leveraging deep reinforcement learning and human-in-The-loop Inproceedings
In: 2020, ISBN: 9781728158716.
Abstract | Links | BibTeX | Tags: Cognitive Building., Deep Reinforcement Learning, smart environments, Thermal Comfort
@inproceedings{Cicirelli2020,
title = {Thermal comfort management leveraging deep reinforcement learning and human-in-The-loop},
author = {F. Cicirelli and A. Guerrieri and C. Mastroianni and G. Spezzano and A. Vinci},
doi = {10.1109/ICHMS49158.2020.9209555},
isbn = {9781728158716},
year = {2020},
date = {2020-01-01},
journal = {Proceedings of the 2020 IEEE International Conference on Human-Machine Systems, ICHMS 2020},
abstract = {The design and implementation of effective systems devoted to the thermal comfort management in a building is a challenging task because they require to consider both objective and subjective parameters, tied for instance to human profile and behavior. This paper presents a novel approach for the management of thermal comfort in buildings by leveraging cognitive technologies, namely the Deep Reinforcement Learning paradigm. The approach is able to learn how to automatically control the HVAC system and improve people's comfort. The learning process is driven by a reward that includes and combines an environmental reward, related to objective environmental parameters, with a human reward, related to subjective human perceptions that are implicitly inferred by the way people interact with the HVAC system. Simulation results aim to assess the impact of the two types of reward on the achieved comfort level.},
keywords = {Cognitive Building., Deep Reinforcement Learning, smart environments, Thermal Comfort},
pubstate = {published},
tppubtype = {inproceedings}
}
The design and implementation of effective systems devoted to the thermal comfort management in a building is a challenging task because they require to consider both objective and subjective parameters, tied for instance to human profile and behavior. This paper presents a novel approach for the management of thermal comfort in buildings by leveraging cognitive technologies, namely the Deep Reinforcement Learning paradigm. The approach is able to learn how to automatically control the HVAC system and improve people's comfort. The learning process is driven by a reward that includes and combines an environmental reward, related to objective environmental parameters, with a human reward, related to subjective human perceptions that are implicitly inferred by the way people interact with the HVAC system. Simulation results aim to assess the impact of the two types of reward on the achieved comfort level.
Cicirelli, F.; Guerrieri, A.; Pizzuti, C.; Socievole, A.; Spezzano, G.; Vinci, A.
Preface Book
2020, ISSN: 18650937.
BibTeX | Tags:
@book{Cicirelli2020b,
title = {Preface},
author = {F. Cicirelli and A. Guerrieri and C. Pizzuti and A. Socievole and G. Spezzano and A. Vinci},
issn = {18650937},
year = {2020},
date = {2020-01-01},
journal = {Communications in Computer and Information Science},
volume = {1200 CCIS},
keywords = {},
pubstate = {published},
tppubtype = {book}
}
Cesario, E.; Vinci, A.; Zhu, X.
Hierarchical Clustering of Spatial Urban Data Book
2020, ISSN: 16113349.
Abstract | Links | BibTeX | Tags:
@book{Cesario2020b,
title = {Hierarchical Clustering of Spatial Urban Data},
author = {E. Cesario and A. Vinci and X. Zhu},
doi = {10.1007/978-3-030-39081-5_20},
issn = {16113349},
year = {2020},
date = {2020-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {11973 LNCS},
abstract = {The growth of data volume collected in urban contexts opens up to their exploitation for improving citizens’ quality-of-life and city management issues, like resource planning (water, electricity), traffic, air and water quality, public policy and public safety services. Moreover, due to the large-scale diffusion of GPS and scanning devices, most of the available data are geo-referenced. Considering such an abundance of data, a very desirable and common task is to identify homogeneous regions in spatial data by partitioning a city into uniform regions based on pollution density, mobility spikes, crimes, or on other characteristics. Density-based clustering algorithms have been shown to be very suitable to detect density-based regions, i.e. areas in which urban events occur with higher density than the remainder of the dataset. Nevertheless, an important issue of such algorithms is that, due to the adoption of global parameters, they fail to identify clusters with varied densities, unless the clusters are clearly separated by sparse regions. In this paper we provide a preliminary analysis about how hierarchical clustering can be used to discover spatial clusters of different densities, in spatial urban data. The algorithm can automatically estimate the area of data having different densities, it can automatically estimate parameters for each cluster so as to reduce the requirement for human intervention or domain knowledge.},
keywords = {},
pubstate = {published},
tppubtype = {book}
}
The growth of data volume collected in urban contexts opens up to their exploitation for improving citizens’ quality-of-life and city management issues, like resource planning (water, electricity), traffic, air and water quality, public policy and public safety services. Moreover, due to the large-scale diffusion of GPS and scanning devices, most of the available data are geo-referenced. Considering such an abundance of data, a very desirable and common task is to identify homogeneous regions in spatial data by partitioning a city into uniform regions based on pollution density, mobility spikes, crimes, or on other characteristics. Density-based clustering algorithms have been shown to be very suitable to detect density-based regions, i.e. areas in which urban events occur with higher density than the remainder of the dataset. Nevertheless, an important issue of such algorithms is that, due to the adoption of global parameters, they fail to identify clusters with varied densities, unless the clusters are clearly separated by sparse regions. In this paper we provide a preliminary analysis about how hierarchical clustering can be used to discover spatial clusters of different densities, in spatial urban data. The algorithm can automatically estimate the area of data having different densities, it can automatically estimate parameters for each cluster so as to reduce the requirement for human intervention or domain knowledge.
Cicirelli, F.; Gentile, A. F.; Greco, E.; Guerrieri, A.; Spezzano, G.; Vinci, A.
An Energy Management System at the Edge based on Reinforcement Learning Inproceedings
In: 2020, ISBN: 9781728173436.
Abstract | Links | BibTeX | Tags: Edge computing, Energy Management Systems, internet of things, multi-agent systems, Reinforcement Learning
@inproceedings{Cicirelli2020c,
title = {An Energy Management System at the Edge based on Reinforcement Learning},
author = {F. Cicirelli and A. F. Gentile and E. Greco and A. Guerrieri and G. Spezzano and A. Vinci},
doi = {10.1109/DS-RT50469.2020.9213697},
isbn = {9781728173436},
year = {2020},
date = {2020-01-01},
journal = {Proceedings of the 2020 IEEE/ACM 24th International Symposium on Distributed Simulation and Real Time Applications, DS-RT 2020},
abstract = {In this work, we propose an IoT edge-based energy management system devoted to minimizing the energy cost for the daily-use of in-home appliances. The proposed approach employs a load scheduling based on a load shifting technique, and it is designed to operate in an edge-computing environment naturally. The scheduling considers all together time-variable profiles for energy cost, energy production, and energy consumption for each shiftable appliance. Deadlines for load termination can also be expressed. In order to address these goals, the scheduling problem is formulated as a Markov decision process and then processed through a reinforcement learning technique. The approach is validated by the development of an agent-based real-world test case deployed in an edge context.},
keywords = {Edge computing, Energy Management Systems, internet of things, multi-agent systems, Reinforcement Learning},
pubstate = {published},
tppubtype = {inproceedings}
}
In this work, we propose an IoT edge-based energy management system devoted to minimizing the energy cost for the daily-use of in-home appliances. The proposed approach employs a load scheduling based on a load shifting technique, and it is designed to operate in an edge-computing environment naturally. The scheduling considers all together time-variable profiles for energy cost, energy production, and energy consumption for each shiftable appliance. Deadlines for load termination can also be expressed. In order to address these goals, the scheduling problem is formulated as a Markov decision process and then processed through a reinforcement learning technique. The approach is validated by the development of an agent-based real-world test case deployed in an edge context.
2019
Cicirelli, F.; Guerrieri, A.; Mastroianni, C.; Palopoli, F.; Spezzano, G.; Vinci, A.
Comfort-aware Cognitive Buildings Leveraging Deep Reinforcement Learning Inproceedings
In: 2019, ISBN: 9781728129235.
Abstract | Links | BibTeX | Tags: Cognitive Systems, Deep Reinforcement Learning, Energy Saving, Simulation, Smart Buildings
@inproceedings{Cicirelli2019,
title = {Comfort-aware Cognitive Buildings Leveraging Deep Reinforcement Learning},
author = {F. Cicirelli and A. Guerrieri and C. Mastroianni and F. Palopoli and G. Spezzano and A. Vinci},
doi = {10.1109/DS-RT47707.2019.8958661},
isbn = {9781728129235},
year = {2019},
date = {2019-01-01},
journal = {Proceedings - 2019 IEEE/ACM 23rd International Symposium on Distributed Simulation and Real Time Applications, DS-RT 2019},
abstract = {This paper presents a novel approach for the management of buildings by leveraging cognitive technologies. The proposed approach exploits the Deep Reinforcement Learning paradigm to learn from both a physical and a simulated environment so as to optimize people comfort and energy consumption.},
keywords = {Cognitive Systems, Deep Reinforcement Learning, Energy Saving, Simulation, Smart Buildings},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper presents a novel approach for the management of buildings by leveraging cognitive technologies. The proposed approach exploits the Deep Reinforcement Learning paradigm to learn from both a physical and a simulated environment so as to optimize people comfort and energy consumption.
Cesario, E.; Vinci, A.
A comparative analysis of classification and regression models for energy-efficient clouds Inproceedings
In: 2019, ISBN: 9781728100838.
Abstract | Links | BibTeX | Tags: Data Mining for Energy Efficiency, Energy-aware Clouds, Green Computing
@inproceedings{Cesario2019,
title = {A comparative analysis of classification and regression models for energy-efficient clouds},
author = {E. Cesario and A. Vinci},
doi = {10.1109/ICNSC.2019.8743292},
isbn = {9781728100838},
year = {2019},
date = {2019-01-01},
journal = {Proceedings of the 2019 IEEE 16th International Conference on Networking, Sensing and Control, ICNSC 2019},
abstract = {Consolidation of virtual machines (VM) is one of the key strategies used to reduce the power consumption of Cloud servers. For this reason, it is extensively studied. Consolidation has the goal of allocating virtual machines on a few physical servers as possible while satisfying the Service Level Agreement established with users. Nevertheless, the effectiveness of a con-solidation strategy strongly depends on the forecast of the VMs resource needs. This paper presents the experimental evaluation of a system for energy-aware allocation of virtual machines, driven by predictive data mining models. Migrations are driven by the forecast of the future computational needs (CPU, RAM) of each virtual machine, in order to efficiently allocate those on the available servers. The experimental evaluation, performed on real-world Cloud data traces, reports a comparison of performance achieved by exploiting classification and regression models and shows good benefits in terms of energy saving.},
keywords = {Data Mining for Energy Efficiency, Energy-aware Clouds, Green Computing},
pubstate = {published},
tppubtype = {inproceedings}
}
Consolidation of virtual machines (VM) is one of the key strategies used to reduce the power consumption of Cloud servers. For this reason, it is extensively studied. Consolidation has the goal of allocating virtual machines on a few physical servers as possible while satisfying the Service Level Agreement established with users. Nevertheless, the effectiveness of a con-solidation strategy strongly depends on the forecast of the VMs resource needs. This paper presents the experimental evaluation of a system for energy-aware allocation of virtual machines, driven by predictive data mining models. Migrations are driven by the forecast of the future computational needs (CPU, RAM) of each virtual machine, in order to efficiently allocate those on the available servers. The experimental evaluation, performed on real-world Cloud data traces, reports a comparison of performance achieved by exploiting classification and regression models and shows good benefits in terms of energy saving.
Cicirelli, F.; Guerrieri, A.; Spezzano, G.; Vinci, A.
A Cognitive Enabled, Edge-Computing Architecture for Future Generation IoT Environments Inproceedings
In: 2019, ISBN: 9781538649800.
Abstract | Links | BibTeX | Tags: Architectures, Cognitive Internet of Things, Edge computing, smart environments
@inproceedings{Cicirelli2019b,
title = {A Cognitive Enabled, Edge-Computing Architecture for Future Generation IoT Environments},
author = {F. Cicirelli and A. Guerrieri and G. Spezzano and A. Vinci},
doi = {10.1109/WF-IoT.2019.8767246},
isbn = {9781538649800},
year = {2019},
date = {2019-01-01},
journal = {IEEE 5th World Forum on Internet of Things, WF-IoT 2019 - Conference Proceedings},
abstract = {Nowadays, Smart Environments (SEs) are pervasively deployed in buildings (e.g., houses, schools, and offices) and outdoor environments with the goal of improving the quality of life of their inhabitants. SEs are usually designed and developed by using well-suited architectures and platforms having the aim of simplifying and making straightforward the SE implementation. Up to now, SEs are mostly reactive and, in some ways, proactive. Current research efforts are devoted to making such environments cognitive, i.e., able to automatically adapt and adhere to the possible changes in users' needs and behaviors. Anyway, in this field, the development of SEs is still in its infancy. In this direction, the paper proposes a novel Cognitive-enabled, Edge-based Internet of Things (CEIoT) architecture, purposely designed to develop cognitive IoT-based SEs. Such architecture wants to overcome some limitations arising during the usage of common SE platforms and architectures. CEIoT introduces some abstractions ranging from the "in-platform" implementation of decentralized cognitive algorithms to the realization of smart data aggregations.},
keywords = {Architectures, Cognitive Internet of Things, Edge computing, smart environments},
pubstate = {published},
tppubtype = {inproceedings}
}
Nowadays, Smart Environments (SEs) are pervasively deployed in buildings (e.g., houses, schools, and offices) and outdoor environments with the goal of improving the quality of life of their inhabitants. SEs are usually designed and developed by using well-suited architectures and platforms having the aim of simplifying and making straightforward the SE implementation. Up to now, SEs are mostly reactive and, in some ways, proactive. Current research efforts are devoted to making such environments cognitive, i.e., able to automatically adapt and adhere to the possible changes in users' needs and behaviors. Anyway, in this field, the development of SEs is still in its infancy. In this direction, the paper proposes a novel Cognitive-enabled, Edge-based Internet of Things (CEIoT) architecture, purposely designed to develop cognitive IoT-based SEs. Such architecture wants to overcome some limitations arising during the usage of common SE platforms and architectures. CEIoT introduces some abstractions ranging from the "in-platform" implementation of decentralized cognitive algorithms to the realization of smart data aggregations.
Altomare, A.; Cesario, E.; Vinci, A.
Data analytics for energy-efficient clouds: design, implementation and evaluation Journal Article
In: International Journal of Parallel, Emergent and Distributed Systems, vol. 34, iss. 6, 2019, ISSN: 17445779.
Abstract | Links | BibTeX | Tags: Data Mining for Energy Efficiency, Energy-aware Clouds, Green Computing
@article{Altomare2019,
title = {Data analytics for energy-efficient clouds: design, implementation and evaluation},
author = {A. Altomare and E. Cesario and A. Vinci},
doi = {10.1080/17445760.2018.1448931},
issn = {17445779},
year = {2019},
date = {2019-01-01},
journal = {International Journal of Parallel, Emergent and Distributed Systems},
volume = {34},
issue = {6},
abstract = {The success of Cloud Computing and the resulting ever growing of large data centers is causing a huge rise in electrical power consumption by hardware facilities and cooling systems. This results in an increment of operational costs of data centres, that is becoming a crucial issue to deal with. Consolidation of virtual machines (VM) is one of the key strategies used to reduce the power consumption of Cloud servers. For this reason, it is extensively studied. Consolidation has the goal of allocating virtual machines on a few physical servers as possible while satisfying the Service Level Agreement established with users. Nevertheless, the effectiveness of a consolidation strategy strongly depends on the forecast of the VM resource needs. Predictive data mining models can be exploited for this purpose. This paper describes the design and development of a system for energy-aware allocation of virtual machines, driven by predictive data mining models. In particular, migrations are driven by the forecast of the future computational needs (CPU, RAM) of each virtual machine, in order to efficiently allocate those on the available servers. The experimental evaluation, performed on real-world Cloud data traces, reports a comparison of performance achieved by exploiting several classification models and shows good benefits in terms of energy saving.},
keywords = {Data Mining for Energy Efficiency, Energy-aware Clouds, Green Computing},
pubstate = {published},
tppubtype = {article}
}
The success of Cloud Computing and the resulting ever growing of large data centers is causing a huge rise in electrical power consumption by hardware facilities and cooling systems. This results in an increment of operational costs of data centres, that is becoming a crucial issue to deal with. Consolidation of virtual machines (VM) is one of the key strategies used to reduce the power consumption of Cloud servers. For this reason, it is extensively studied. Consolidation has the goal of allocating virtual machines on a few physical servers as possible while satisfying the Service Level Agreement established with users. Nevertheless, the effectiveness of a consolidation strategy strongly depends on the forecast of the VM resource needs. Predictive data mining models can be exploited for this purpose. This paper describes the design and development of a system for energy-aware allocation of virtual machines, driven by predictive data mining models. In particular, migrations are driven by the forecast of the future computational needs (CPU, RAM) of each virtual machine, in order to efficiently allocate those on the available servers. The experimental evaluation, performed on real-world Cloud data traces, reports a comparison of performance achieved by exploiting several classification models and shows good benefits in terms of energy saving.
Catlett, C.; Cesario, E.; Talia, D.; Vinci, A.
Spatio-temporal crime predictions in smart cities: A data-driven approach and experiments Journal Article
In: Pervasive and Mobile Computing, vol. 53, 2019, ISSN: 15741192.
Abstract | Links | BibTeX | Tags: Crime prediction, Data analytics, smart city, Urban computing
@article{Catlett2019,
title = {Spatio-temporal crime predictions in smart cities: A data-driven approach and experiments},
author = {C. Catlett and E. Cesario and D. Talia and A. Vinci},
doi = {10.1016/j.pmcj.2019.01.003},
issn = {15741192},
year = {2019},
date = {2019-01-01},
journal = {Pervasive and Mobile Computing},
volume = {53},
abstract = {Steadily increasing urbanization is causing significant economic and social transformations in urban areas, posing several challenges related to city management and services. In particular, in cities with higher crime rates, effectively providing for public safety is an increasingly complex undertaking. To handle this complexity, new technologies are enabling police departments to access growing volumes of crime-related data that can be analyzed to understand patterns and trends. These technologies have potentially to increase the efficient deployment of police resources within a given territory and ultimately support more effective crime prevention. This paper presents a predictive approach based on spatial analysis and auto-regressive models to automatically detect high-risk crime regions in urban areas and to reliably forecast crime trends in each region. The algorithm result is a spatio-temporal crime forecasting model, composed of a set of crime-dense regions with associated crime predictors, each one representing a predictive model for estimating the number of crimes likely to occur in its associated region. The experimental evaluation was performed on two real-world datasets collected in the cities of Chicago and New York City. This evaluation shows that the proposed approach achieves good accuracy in spatial and temporal crime forecasting over rolling time horizons.},
keywords = {Crime prediction, Data analytics, smart city, Urban computing},
pubstate = {published},
tppubtype = {article}
}
Steadily increasing urbanization is causing significant economic and social transformations in urban areas, posing several challenges related to city management and services. In particular, in cities with higher crime rates, effectively providing for public safety is an increasingly complex undertaking. To handle this complexity, new technologies are enabling police departments to access growing volumes of crime-related data that can be analyzed to understand patterns and trends. These technologies have potentially to increase the efficient deployment of police resources within a given territory and ultimately support more effective crime prevention. This paper presents a predictive approach based on spatial analysis and auto-regressive models to automatically detect high-risk crime regions in urban areas and to reliably forecast crime trends in each region. The algorithm result is a spatio-temporal crime forecasting model, composed of a set of crime-dense regions with associated crime predictors, each one representing a predictive model for estimating the number of crimes likely to occur in its associated region. The experimental evaluation was performed on two real-world datasets collected in the cities of Chicago and New York City. This evaluation shows that the proposed approach achieves good accuracy in spatial and temporal crime forecasting over rolling time horizons.
Cicirelli, F.; Guerrieri, A.; Mercuri, A.; Spezzano, G.; Vinci, A.
ITEMa: A methodological approach for cognitive edge computing IoT ecosystems Journal Article
In: Future Generation Computer Systems, vol. 92, 2019, ISSN: 0167739X.
Abstract | Links | BibTeX | Tags: activity recognition, Cognitive Systems, Edge and cloud computing, IoT-based ecosystems, Smart Office
@article{Cicirelli2019c,
title = {ITEMa: A methodological approach for cognitive edge computing IoT ecosystems},
author = {F. Cicirelli and A. Guerrieri and A. Mercuri and G. Spezzano and A. Vinci},
doi = {10.1016/j.future.2018.10.003},
issn = {0167739X},
year = {2019},
date = {2019-01-01},
journal = {Future Generation Computer Systems},
volume = {92},
abstract = {The ever-increasing spread of Internet of Things (IoT)-based technologies paired with the diffusion of the edge-based computing boosts the development of pervasive cyber ecosystems having the goal of improving the life quality of people and assisting them in daily activities. In this context, cognitive behaviors are purposely required to make such ecosystems able to adapt to people needs and to envisage their behaviors. Despite the growing interest in cognitive ecosystems, still there is a lack of methodological approaches devoted to supporting the design and implementation of such complex systems. This paper proposes ITEMa, an Iot-based smarT Ecosystem Modeling Approach based on a three-layered architecture offering some well-suited abstractions tailored to the development of IoT-based ecosystems which exhibit cognitive behaviors and are able to exploit computational resources located either at the edge of the network or in the Cloud. The effectiveness of the approach is demonstrated through a case study concerning the development of a Smart Office devoted to forecast some usual office activities and to properly adapt the office environmental conditions to them.},
keywords = {activity recognition, Cognitive Systems, Edge and cloud computing, IoT-based ecosystems, Smart Office},
pubstate = {published},
tppubtype = {article}
}
The ever-increasing spread of Internet of Things (IoT)-based technologies paired with the diffusion of the edge-based computing boosts the development of pervasive cyber ecosystems having the goal of improving the life quality of people and assisting them in daily activities. In this context, cognitive behaviors are purposely required to make such ecosystems able to adapt to people needs and to envisage their behaviors. Despite the growing interest in cognitive ecosystems, still there is a lack of methodological approaches devoted to supporting the design and implementation of such complex systems. This paper proposes ITEMa, an Iot-based smarT Ecosystem Modeling Approach based on a three-layered architecture offering some well-suited abstractions tailored to the development of IoT-based ecosystems which exhibit cognitive behaviors and are able to exploit computational resources located either at the edge of the network or in the Cloud. The effectiveness of the approach is demonstrated through a case study concerning the development of a Smart Office devoted to forecast some usual office activities and to properly adapt the office environmental conditions to them.
Cicirelli, F.; Guerrieri, A.; Mastroianni, C.; Spezzano, G.; Vinci, A.
Preface Book
2019, ISSN: 21991081.
BibTeX | Tags:
@book{Cicirelli2019d,
title = {Preface},
author = {F. Cicirelli and A. Guerrieri and C. Mastroianni and G. Spezzano and A. Vinci},
issn = {21991081},
year = {2019},
date = {2019-01-01},
journal = {Internet of Things},
keywords = {},
pubstate = {published},
tppubtype = {book}
}
Briante, O.; Cicirelli, F.; Guerrieri, A.; Iera, A.; Mercuri, A.; Ruggeri, G.; Spezzano, G.; Vinci, A.
A social and pervasive IoT platform for developing smart environments Book
2019, ISSN: 21991081.
Abstract | Links | BibTeX | Tags: Edge computing, IoT development platforms, IoT-based applications, Multi agent systems, smart environments, social internet of things
@book{Briante2019,
title = {A social and pervasive IoT platform for developing smart environments},
author = {O. Briante and F. Cicirelli and A. Guerrieri and A. Iera and A. Mercuri and G. Ruggeri and G. Spezzano and A. Vinci},
doi = {10.1007/978-3-319-96550-5_1},
issn = {21991081},
year = {2019},
date = {2019-01-01},
journal = {Internet of Things},
abstract = {Nowadays, the increasing in the use of Internet of Things (IoT) devices is growing the realization of pervasive Smart Environments (SEs) and Smart Urban Ecosystems, where all the data gathered by the “Things” can be elaborated and used to improve the livability, the safety and the security of the environment, and to make inhabitants lives easier. Many efforts have been already done in the direction of SEs development and in the implementation of platforms specifically designed for SE realization. Anyway, such efforts miss of solutions regarding the interoperability among the realized SEs and other third-part “Things”. This chapter gives an overview of iSapiens, which is a Java-based platform specifically designed for the development and implementation of SEs. iSapiens tries to overcome the interoperability issue by leveraging the Social Internet of Things (SIoT) paradigm that allows to dynamically include in an SE the new “Things” that can appear in an environment without requiring interventions from humans. iSapiens provides tools for the realization of pervasive SEs and relies on the edge computing paradigm. Such paradigm is extremely important in a distributed system since it allows to use distributed storage and computation at the edge of a network, so reducing latencies with respect to move all the executions and storages in the cloud. Moreover, the chapter will review some SE applications realized by exploiting iSapiens concepts.},
keywords = {Edge computing, IoT development platforms, IoT-based applications, Multi agent systems, smart environments, social internet of things},
pubstate = {published},
tppubtype = {book}
}
Nowadays, the increasing in the use of Internet of Things (IoT) devices is growing the realization of pervasive Smart Environments (SEs) and Smart Urban Ecosystems, where all the data gathered by the “Things” can be elaborated and used to improve the livability, the safety and the security of the environment, and to make inhabitants lives easier. Many efforts have been already done in the direction of SEs development and in the implementation of platforms specifically designed for SE realization. Anyway, such efforts miss of solutions regarding the interoperability among the realized SEs and other third-part “Things”. This chapter gives an overview of iSapiens, which is a Java-based platform specifically designed for the development and implementation of SEs. iSapiens tries to overcome the interoperability issue by leveraging the Social Internet of Things (SIoT) paradigm that allows to dynamically include in an SE the new “Things” that can appear in an environment without requiring interventions from humans. iSapiens provides tools for the realization of pervasive SEs and relies on the edge computing paradigm. Such paradigm is extremely important in a distributed system since it allows to use distributed storage and computation at the edge of a network, so reducing latencies with respect to move all the executions and storages in the cloud. Moreover, the chapter will review some SE applications realized by exploiting iSapiens concepts.
2018
Cicirelli, Franco; Fortino, Giancarlo; Guerrieri, Antonio; Mercuri, Alessandro; Spezzano, Giandomenico; Vinci, Andrea
A Metamodel Framework for Edge-Based Smart Environments Inproceedings
In: 2018 IEEE International Conference on Cloud Engineering (IC2E), pp. 286-291, 2018.
Abstract | Links | BibTeX | Tags: Computational modeling, Edge computing, internet of things, Metamodeling, modeling, Object oriented modeling, Sensors, smart environments, Smart Office, Timing, Unified modeling language
@inproceedings{8360343,
title = {A Metamodel Framework for Edge-Based Smart Environments},
author = { Franco Cicirelli and Giancarlo Fortino and Antonio Guerrieri and Alessandro Mercuri and Giandomenico Spezzano and Andrea Vinci},
url = {https://ieeexplore.ieee.org/document/8360343/},
doi = {10.1109/IC2E.2018.00067},
year = {2018},
date = {2018-04-01},
booktitle = {2018 IEEE International Conference on Cloud Engineering (IC2E)},
pages = {286-291},
abstract = {Smart Environments (SEs) are pervasive systems usually built on top of IoT-based sensing and actuation devices which are spread in an environment. The increase of the on-board computational capacity of the used devices opens to the possibility of naturally exploiting the edge computing paradigm in which the computation is pushed at the edge of the network. Anyway, despite the huge interest towards SEs, there is a lack of approaches for their design. This paper proposes an enhancement of the existing Smart Environment Metamodel (SEM) framework suited for designing SEs. The provided extension aims at taking into account issues related to edge computing, management of timing information and definition of the data types involved in data sources. The effectiveness of the whole proposal is assessed through a case study describing the development of a Smart Office.},
keywords = {Computational modeling, Edge computing, internet of things, Metamodeling, modeling, Object oriented modeling, Sensors, smart environments, Smart Office, Timing, Unified modeling language},
pubstate = {published},
tppubtype = {inproceedings}
}
Smart Environments (SEs) are pervasive systems usually built on top of IoT-based sensing and actuation devices which are spread in an environment. The increase of the on-board computational capacity of the used devices opens to the possibility of naturally exploiting the edge computing paradigm in which the computation is pushed at the edge of the network. Anyway, despite the huge interest towards SEs, there is a lack of approaches for their design. This paper proposes an enhancement of the existing Smart Environment Metamodel (SEM) framework suited for designing SEs. The provided extension aims at taking into account issues related to edge computing, management of timing information and definition of the data types involved in data sources. The effectiveness of the whole proposal is assessed through a case study describing the development of a Smart Office.
Spezzano, G.; Vinci, A.
A Nature-Inspired, Anytime and Parallel Algorithm for Data Stream Clustering Book
2018, ISSN: 1879808X.
Abstract | Links | BibTeX | Tags: Anytime Algorithm, Clustering, CUDA, Data Stream, general purpose GPU computing
@book{Spezzano2018,
title = {A Nature-Inspired, Anytime and Parallel Algorithm for Data Stream Clustering},
author = {G. Spezzano and A. Vinci},
doi = {10.3233/978-1-61499-843-3-317},
issn = {1879808X},
year = {2018},
date = {2018-01-01},
journal = {Advances in Parallel Computing},
volume = {32},
abstract = {In the context of time-critical applications there exists the need of clustering data streams so as to provide approximated solutions in the shortest possible time, in order to capture in real-time the evolution of physical or social phenomena. In this work, a nature-inspired algorithm for clustering of evolving big data stream is presented, which is designed to be executed on many-core GPU architectures.},
keywords = {Anytime Algorithm, Clustering, CUDA, Data Stream, general purpose GPU computing},
pubstate = {published},
tppubtype = {book}
}
In the context of time-critical applications there exists the need of clustering data streams so as to provide approximated solutions in the shortest possible time, in order to capture in real-time the evolution of physical or social phenomena. In this work, a nature-inspired algorithm for clustering of evolving big data stream is presented, which is designed to be executed on many-core GPU architectures.
Cicirelli, F.; Fortino, G.; Guerrieri, A.; Spezzano, G.; Vinci, A.
A Scalable Agent-Based Smart Environment for Edge-Based Urban IoT Systems Book
2018, ISSN: 18678211.
Abstract | Links | BibTeX | Tags: Edge computing, Intelligent agents, IoT, smart environments, Urban computing
@book{Cicirelli2018,
title = {A Scalable Agent-Based Smart Environment for Edge-Based Urban IoT Systems},
author = {F. Cicirelli and G. Fortino and A. Guerrieri and G. Spezzano and A. Vinci},
doi = {10.1007/978-3-319-93797-7_7},
issn = {18678211},
year = {2018},
date = {2018-01-01},
journal = {Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST},
volume = {242},
abstract = {New Internet of Things (IoT) applications are encouraging Smart City and Smart Environments initiatives all over the world, by leveraging big data and ubiquitous connectivity. This new technology enables systems to monitor, manage and control devices, and to create new knowledge and actionable information, by the real-time analysis of data streams. In order to develop applications in the depicted scenario, the adoption of new paradigms is required. This paper suggests combining the emergent concept of edge/fog computing with the agent metaphor, so as to enable designing systems based on the decentralization of control functions over distributed autonomous and cooperative entities, which run at the edge of the network. Moreover, we suggest the adoption of the iSapiens platform as a reference, as it was designed specifically for the mentioned purposes. Multi-agent applications running on top of iSapiens can create smart services using adaptive and decentralized algorithms which exploit the principles of cognitive IoT.},
keywords = {Edge computing, Intelligent agents, IoT, smart environments, Urban computing},
pubstate = {published},
tppubtype = {book}
}
New Internet of Things (IoT) applications are encouraging Smart City and Smart Environments initiatives all over the world, by leveraging big data and ubiquitous connectivity. This new technology enables systems to monitor, manage and control devices, and to create new knowledge and actionable information, by the real-time analysis of data streams. In order to develop applications in the depicted scenario, the adoption of new paradigms is required. This paper suggests combining the emergent concept of edge/fog computing with the agent metaphor, so as to enable designing systems based on the decentralization of control functions over distributed autonomous and cooperative entities, which run at the edge of the network. Moreover, we suggest the adoption of the iSapiens platform as a reference, as it was designed specifically for the mentioned purposes. Multi-agent applications running on top of iSapiens can create smart services using adaptive and decentralized algorithms which exploit the principles of cognitive IoT.
Cicirelli, F.; Fortino, G.; Guerrieri, A.; Mercuri, A.; Spezzano, G.; Vinci, A.
Exploiting the sem framework for modeling smart cities Book
2018, ISSN: 16113349.
Abstract | Links | BibTeX | Tags: Implementation, internet of things, Meta-modeling, modeling, Smart Cities, Smart Street Cosenza
@book{Cicirelli2018b,
title = {Exploiting the sem framework for modeling smart cities},
author = {F. Cicirelli and G. Fortino and A. Guerrieri and A. Mercuri and G. Spezzano and A. Vinci},
doi = {10.1007/978-3-319-97795-9_9},
issn = {16113349},
year = {2018},
date = {2018-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {10794 LNCS},
abstract = {Smart Cities are smart environments extending over a wide geographical area having the aim of improving the quality of life of the citizens and optimizing the management of city resources. Despite the paramount interest towards these systems, there is a lack of approaches for their design. The Smart Environment Metamodel (SEM) is a framework which is well suited for the development of smart environments in general, and Smart Cities in particular. SEM allows the design of such systems by offering two different perspective focusing on functional and data requirements. This paper aims at showing the effectiveness of SEM by exploiting the framework for the design of a case study referring to a realized Smart City application developed in the city of Cosenza, Italy.},
keywords = {Implementation, internet of things, Meta-modeling, modeling, Smart Cities, Smart Street Cosenza},
pubstate = {published},
tppubtype = {book}
}
Smart Cities are smart environments extending over a wide geographical area having the aim of improving the quality of life of the citizens and optimizing the management of city resources. Despite the paramount interest towards these systems, there is a lack of approaches for their design. The Smart Environment Metamodel (SEM) is a framework which is well suited for the development of smart environments in general, and Smart Cities in particular. SEM allows the design of such systems by offering two different perspective focusing on functional and data requirements. This paper aims at showing the effectiveness of SEM by exploiting the framework for the design of a case study referring to a realized Smart City application developed in the city of Cosenza, Italy.
Cicirelli, F.; Guerrieri, A.; Mercuri, A.; Spezzano, G.; Vinci, A.
Cognitive smart environment: An approach based on concept hierarchies and sensor data fusion Journal Article
In: International Journal of Simulation and Process Modelling, vol. 13, iss. 5, 2018, ISSN: 17402131.
Abstract | Links | BibTeX | Tags: Cognitive Internet of Things, Context awareness, multi-agent systems, Sensor data fusion, smart city, smart environments, Smart museum, Statecharts
@article{Cicirelli2018c,
title = {Cognitive smart environment: An approach based on concept hierarchies and sensor data fusion},
author = {F. Cicirelli and A. Guerrieri and A. Mercuri and G. Spezzano and A. Vinci},
doi = {10.1504/IJSPM.2018.094741},
issn = {17402131},
year = {2018},
date = {2018-01-01},
journal = {International Journal of Simulation and Process Modelling},
volume = {13},
issue = {5},
abstract = {Sensor data fusion gathers technological solutions for collecting, classifying and combining data from multiple sensors in a smart environment for augmenting knowledge about the system and realising cognitive behaviours. The goal is to make effective the management of acquired data and to promote the realisation of cognitive systems which can sense the environment, reason, and properly (re)act for reaching some purposes. Anyway, the development of such systems asks for suitable approaches able to deal with issues like heterogeneity, sensor/actuator management, system reactivity, behavioural modelling, and context awareness. This paper proposes a multi-tier approach, based on concept hierarchies and sensor data fusion, dealing with the above aspects. The approach favours separation of concerns and abstractions. It relies on the use of the agent metaphor and statecharts. The iSapiens platform is suggested for implementation purposes. As a significant case study, the development of a smart museum located in Cosenza (Italy) is proposed.},
keywords = {Cognitive Internet of Things, Context awareness, multi-agent systems, Sensor data fusion, smart city, smart environments, Smart museum, Statecharts},
pubstate = {published},
tppubtype = {article}
}
Sensor data fusion gathers technological solutions for collecting, classifying and combining data from multiple sensors in a smart environment for augmenting knowledge about the system and realising cognitive behaviours. The goal is to make effective the management of acquired data and to promote the realisation of cognitive systems which can sense the environment, reason, and properly (re)act for reaching some purposes. Anyway, the development of such systems asks for suitable approaches able to deal with issues like heterogeneity, sensor/actuator management, system reactivity, behavioural modelling, and context awareness. This paper proposes a multi-tier approach, based on concept hierarchies and sensor data fusion, dealing with the above aspects. The approach favours separation of concerns and abstractions. It relies on the use of the agent metaphor and statecharts. The iSapiens platform is suggested for implementation purposes. As a significant case study, the development of a smart museum located in Cosenza (Italy) is proposed.
Catlett, C.; Cesario, E.; Talia, D.; Vinci, A.
A data-driven approach for spatio-Temporal crime predictions in smart cities Inproceedings
In: 2018, ISBN: 9781538647059.
Abstract | Links | BibTeX | Tags: Crime prediction, smart city, Urban computing
@inproceedings{Catlett2018,
title = {A data-driven approach for spatio-Temporal crime predictions in smart cities},
author = {C. Catlett and E. Cesario and D. Talia and A. Vinci},
doi = {10.1109/SMARTCOMP.2018.00069},
isbn = {9781538647059},
year = {2018},
date = {2018-01-01},
journal = {Proceedings - 2018 IEEE International Conference on Smart Computing, SMARTCOMP 2018},
abstract = {The steadily increasing urbanization is causing significant economic and social transformations in urban areas and it will be posing several challenges in city management issues. In particular, given that the larger cities the higher crime rates, crime spiking is becoming one of the most important social problems in large urban areas. To handle with the increase in crimes, new technologies are enabling police departments to access growing volumes of crime-related data that can be analyzed to understand patterns and trends, finalized to an efficient deployment of police officers over the territory and more effective crime prevention. This paper presents an approach based on spatial analysis and auto-regressive models to automatically detect high-risk crime regions in urban areas and reliably forecast crime trends in each region. The final result of the algorithm is a spatio-Temporal crime forecasting model, composed of a set of crime dense regions and a set of associated crime predictors, each one representing a predictive model for forecasting the number of crimes that will happen in its specific region. The experimental evaluation, performed on real-world data collected in a big area of Chicago, shows that the proposed approach achieves good accuracy in spatial and temporal crime forecasting over rolling time horizons.},
keywords = {Crime prediction, smart city, Urban computing},
pubstate = {published},
tppubtype = {inproceedings}
}
The steadily increasing urbanization is causing significant economic and social transformations in urban areas and it will be posing several challenges in city management issues. In particular, given that the larger cities the higher crime rates, crime spiking is becoming one of the most important social problems in large urban areas. To handle with the increase in crimes, new technologies are enabling police departments to access growing volumes of crime-related data that can be analyzed to understand patterns and trends, finalized to an efficient deployment of police officers over the territory and more effective crime prevention. This paper presents an approach based on spatial analysis and auto-regressive models to automatically detect high-risk crime regions in urban areas and reliably forecast crime trends in each region. The final result of the algorithm is a spatio-Temporal crime forecasting model, composed of a set of crime dense regions and a set of associated crime predictors, each one representing a predictive model for forecasting the number of crimes that will happen in its specific region. The experimental evaluation, performed on real-world data collected in a big area of Chicago, shows that the proposed approach achieves good accuracy in spatial and temporal crime forecasting over rolling time horizons.
Cicirelli, F.; Guerrieri, A.; Mercuri, A.; Spezzano, G.; Vinci, A.
IoT-centric edge computing for context-aware smart environments Inproceedings
In: 2018, ISBN: 9781538672440.
Abstract | Links | BibTeX | Tags: Context awareness, Edge computing, internet of things, Methodological approaches, smart environments
@inproceedings{Cicirelli2018d,
title = {IoT-centric edge computing for context-aware smart environments},
author = {F. Cicirelli and A. Guerrieri and A. Mercuri and G. Spezzano and A. Vinci},
doi = {10.1109/ICIOT.2018.00031},
isbn = {9781538672440},
year = {2018},
date = {2018-01-01},
journal = {Proceedings - 2018 IEEE International Congress on Internet of Things, ICIOT 2018 - Part of the 2018 IEEE World Congress on Services},
abstract = {The ever increasing diffusion of the Internet of Things is currently promoting the development of pervasive Smart Environments. The effectiveness of such systems is highly related to the capability of dealing with possible changes in users' habits, adapting the system to people needs and envisaging people behaviors. For this purposes, it becomes important to have methodological approaches and technologies favoring the development of cognitive systems aware of what is happening inside them. In this paper a methodological approach for the development of context-aware IoT-based Smart Environments is proposed. Such approach relies on a three-layered architecture offering some well suited abstractions taking also into account that computational resources in a system can be located either at the edge of the network or in the Cloud. A case study is proposed which concerns the development of a Smart Office devoted to forecast workers' presence and to adapt the office environmental conditions to them.},
keywords = {Context awareness, Edge computing, internet of things, Methodological approaches, smart environments},
pubstate = {published},
tppubtype = {inproceedings}
}
The ever increasing diffusion of the Internet of Things is currently promoting the development of pervasive Smart Environments. The effectiveness of such systems is highly related to the capability of dealing with possible changes in users' habits, adapting the system to people needs and envisaging people behaviors. For this purposes, it becomes important to have methodological approaches and technologies favoring the development of cognitive systems aware of what is happening inside them. In this paper a methodological approach for the development of context-aware IoT-based Smart Environments is proposed. Such approach relies on a three-layered architecture offering some well suited abstractions taking also into account that computational resources in a system can be located either at the edge of the network or in the Cloud. A case study is proposed which concerns the development of a Smart Office devoted to forecast workers' presence and to adapt the office environmental conditions to them.
2017
Cicirelli, Franco; Fortino, Giancarlo; Guerrieri, Antonio; Spezzano, Giandomenico; Vinci, Andrea
Metamodeling of Smart Environments: from Design to Implementation Journal Article
In: Advanced engineering informatics, 2017, ISSN: 1474-0346.
Abstract | Links | BibTeX | Tags: cyber physical systems, development methodology, internet of things, modeling, smart environments
@article{cic:advei:2017,
title = {Metamodeling of Smart Environments: from Design to Implementation},
author = {Franco Cicirelli and Giancarlo Fortino and Antonio Guerrieri and Giandomenico Spezzano and Andrea Vinci},
url = {www.sciencedirect.com/science/article/pii/S1474034616302063},
doi = {10.1016/j.aei.2016.11.005 },
issn = {1474-0346},
year = {2017},
date = {2017-08-01},
journal = {Advanced engineering informatics},
abstract = { A smart environment is a physical environment enriched with sensing, actuation, communication and computation capabilities aiming at acquiring and exploiting knowledge about the environment so as to adapt itself to its inhabitants' preferences and requirements. In this domain, there is the need of tools supporting the design and analysis of applications. In this paper, the Smart Environment Metamodel (SEM) framework is proposed. The framework allows to model applications by exploiting concepts specific to the smart environment domain. SEM approaches the modeling from two different points of view, namely the functional and data perspectives. The application of the framework is supported by a set of general guidelines to drive the analysis, the design and the implementation of smart environments. The effectiveness of the framework is shown by applying it to the modeling of a real smart office scenario that has been developed, deployed and analyzed.},
keywords = {cyber physical systems, development methodology, internet of things, modeling, smart environments},
pubstate = {published},
tppubtype = {article}
}
A smart environment is a physical environment enriched with sensing, actuation, communication and computation capabilities aiming at acquiring and exploiting knowledge about the environment so as to adapt itself to its inhabitants' preferences and requirements. In this domain, there is the need of tools supporting the design and analysis of applications. In this paper, the Smart Environment Metamodel (SEM) framework is proposed. The framework allows to model applications by exploiting concepts specific to the smart environment domain. SEM approaches the modeling from two different points of view, namely the functional and data perspectives. The application of the framework is supported by a set of general guidelines to drive the analysis, the design and the implementation of smart environments. The effectiveness of the framework is shown by applying it to the modeling of a real smart office scenario that has been developed, deployed and analyzed.
Cicirelli, Franco; Guerrieri, Antonio; Spezzano, Giandomenico; Vinci, Andrea
An edge-based platform for dynamic smart city applications Journal Article
In: Future Generation Computer Systems, pp. -, 2017, ISSN: 0167-739X.
Abstract | Links | BibTeX | Tags: cyber physical systems, Edge computing, internet of things, multi-agent systems, smart city, Urban computing
@article{Cicirelli2017,
title = {An edge-based platform for dynamic smart city applications},
author = {Franco Cicirelli and Antonio Guerrieri and Giandomenico Spezzano and Andrea Vinci},
url = {http://www.sciencedirect.com/science/article/pii/S0167739X16308342},
doi = {10.1016/j.future.2017.05.034},
issn = {0167-739X},
year = {2017},
date = {2017-06-15},
journal = {Future Generation Computer Systems},
pages = {-},
abstract = {Abstract A Smart City is a cyber-physical system improving urban behavior and capabilities by providing ICT-based functionalities. An infrastructure for Smart City has to be geographically and functionally extensible, as it requires both to grow up with the physical environment and to meet the increasing in needs and demands of city users/inhabitants. In this paper, we propose iSapiens, an IoT-based platform for the development of general cyber-physical systems suitable for the design and implementation of smart city services and applications. As distinguishing features, the iSapiens platform implements the edge computing paradigm through both the exploitation of the agent metaphor and a distributed network of computing nodes directly scattered in the urban environment. The platform promotes the dynamic deployment of new computing nodes as well as software agents for addressing geographical and functional extensibility. iSapiens provides a set of abstractions suitable to hide the heterogeneity of the physical sensing/actuator devices embedded in the system, and to support the development of complex applications. The paper also furnishes a set of methodological guidelines exploitable for the design and implementation of smart city applications by properly using iSapiens. As a significant case study, the design and implementation of a real Smart Street in the city of Cosenza (Italy) are shown, which provides decentralized urban intelligence services to citizens.},
keywords = {cyber physical systems, Edge computing, internet of things, multi-agent systems, smart city, Urban computing},
pubstate = {published},
tppubtype = {article}
}
Abstract A Smart City is a cyber-physical system improving urban behavior and capabilities by providing ICT-based functionalities. An infrastructure for Smart City has to be geographically and functionally extensible, as it requires both to grow up with the physical environment and to meet the increasing in needs and demands of city users/inhabitants. In this paper, we propose iSapiens, an IoT-based platform for the development of general cyber-physical systems suitable for the design and implementation of smart city services and applications. As distinguishing features, the iSapiens platform implements the edge computing paradigm through both the exploitation of the agent metaphor and a distributed network of computing nodes directly scattered in the urban environment. The platform promotes the dynamic deployment of new computing nodes as well as software agents for addressing geographical and functional extensibility. iSapiens provides a set of abstractions suitable to hide the heterogeneity of the physical sensing/actuator devices embedded in the system, and to support the development of complex applications. The paper also furnishes a set of methodological guidelines exploitable for the design and implementation of smart city applications by properly using iSapiens. As a significant case study, the design and implementation of a real Smart Street in the city of Cosenza (Italy) are shown, which provides decentralized urban intelligence services to citizens.
Garofalo, Giuseppina; Giordano, Andrea; Piro, Patrizia; Spezzano, Giandomenico; Vinci, Andrea
A distributed real-time approach for mitigating CSO and flooding in urban drainage systems Journal Article
In: Journal of network and computer applications, vol. 78, pp. 30–42, 2017, ISSN: 1084-8045.
Abstract | Links | BibTeX | Tags: combined sewer overflow, cyber physical systems, flooding, multi-agent systems, real-time control, urban drainage system
@article{garJnca2016,
title = {A distributed real-time approach for mitigating CSO and flooding in urban drainage systems},
author = {Giuseppina Garofalo and Andrea Giordano and Patrizia Piro and Giandomenico Spezzano and Andrea Vinci},
url = {http://www.sciencedirect.com/science/article/pii/S1084804516302752},
doi = {10.1016/j.jnca.2016.11.004},
issn = {1084-8045},
year = {2017},
date = {2017-01-15},
journal = {Journal of network and computer applications},
volume = {78},
pages = {30--42},
publisher = {Academic Press},
address = {New York},
abstract = {In an urban environment, sewer flooding and combined sewer overflows (CSOs) are a potential risk to human life, economic assets and the environment. To mitigate such phenomena, real time control systems represent a valid and cost-effective solution. This paper proposes an urban drainage network equipped by sensors and a series of electronically movable gates controlled by a decentralized real-time system based on a gossip-based algorithm which exhibits good performance and fault tolerance properties. The proposal aims to exploit effectively the storage capacity of the urban drainage network so as to reduce flooding and CSO. The approach is validated by considering the urban drainage system of the city of Cosenza (Italy) and a set of extreme rainfall events as a testbed. Experiments are conducted by using a customized version of the SWMM simulation software and show that the CSO and local flooding volumes are significantly reduced.},
keywords = {combined sewer overflow, cyber physical systems, flooding, multi-agent systems, real-time control, urban drainage system},
pubstate = {published},
tppubtype = {article}
}
In an urban environment, sewer flooding and combined sewer overflows (CSOs) are a potential risk to human life, economic assets and the environment. To mitigate such phenomena, real time control systems represent a valid and cost-effective solution. This paper proposes an urban drainage network equipped by sensors and a series of electronically movable gates controlled by a decentralized real-time system based on a gossip-based algorithm which exhibits good performance and fault tolerance properties. The proposal aims to exploit effectively the storage capacity of the urban drainage network so as to reduce flooding and CSO. The approach is validated by considering the urban drainage system of the city of Cosenza (Italy) and a set of extreme rainfall events as a testbed. Experiments are conducted by using a customized version of the SWMM simulation software and show that the CSO and local flooding volumes are significantly reduced.
Cicirelli, Franco; Guerrieri, Antonio; Spezzano, Giandomenico; Vinci, Andrea; Briante, Orazio; Iera, Antonio; Ruggeri, Giuseppe
An edge-based approach to develop large-scale smart environments by leveraging SIoT Inproceedings
In: Networking, Sensing and Control (ICNSC), 2017 IEEE 14th International Conference on, pp. 738–743, IEEE 2017, ISBN: 978-1-5090-4428-3.
Abstract | Links | BibTeX | Tags: Edge computing, internet of things, multi-agent systems, smart environments, social internet of things
@inproceedings{cicirelli2017edge,
title = {An edge-based approach to develop large-scale smart environments by leveraging SIoT},
author = {Franco Cicirelli and Antonio Guerrieri and Giandomenico Spezzano and Andrea Vinci and Orazio Briante and Antonio Iera and Giuseppe Ruggeri},
url = {http://ieeexplore.ieee.org/document/8000182/},
doi = {10.1109/ICNSC.2017.8000182},
isbn = {978-1-5090-4428-3},
year = {2017},
date = {2017-01-01},
booktitle = {Networking, Sensing and Control (ICNSC), 2017 IEEE 14th International Conference on},
pages = {738--743},
organization = {IEEE},
abstract = {Abstract—Large-scale Smart Environments (LSEs) are open and dynamic systems where issues related to scalability and interoperability require to be carefully addressed. Moreover, as such systems typically extend on a wide area and include a huge number of interacting devices, aspects concerning services and objects discovery and reputation assessment require being managed. Despite the increasing interest in this topic, there is a lack of approaches for developing LSEs. This paper proposes an agent-based approach for the development of LSEs which leverages Edge Computing and Social Internet of Things paradigms in order to address the above mentioned issues. The effectiveness of such an approach is assessed through a case study involving a Smart School District environment.
},
keywords = {Edge computing, internet of things, multi-agent systems, smart environments, social internet of things},
pubstate = {published},
tppubtype = {inproceedings}
}
Abstract—Large-scale Smart Environments (LSEs) are open and dynamic systems where issues related to scalability and interoperability require to be carefully addressed. Moreover, as such systems typically extend on a wide area and include a huge number of interacting devices, aspects concerning services and objects discovery and reputation assessment require being managed. Despite the increasing interest in this topic, there is a lack of approaches for developing LSEs. This paper proposes an agent-based approach for the development of LSEs which leverages Edge Computing and Social Internet of Things paradigms in order to address the above mentioned issues. The effectiveness of such an approach is assessed through a case study involving a Smart School District environment.
Cicirelli, Franco; Guerrieri, Antonio; Spezzano, Giandomenico; Vinci, Andrea; Briante, Orazio; Iera, Antonio; Ruggeri, Giuseppe
Edge Computing and Social Internet of Things for large-scale smart environments development Journal Article
In: IEEE Internet of Things Journal, vol. PP, no. 99, pp. 1-1, 2017, ISSN: 2327-4662.
Abstract | Links | BibTeX | Tags: Edge computing, internet of things, multi-agent systems, smart city, smart environments
@article{iotj2017,
title = {Edge Computing and Social Internet of Things for large-scale smart environments development},
author = {Franco Cicirelli and Antonio Guerrieri and Giandomenico Spezzano and Andrea Vinci and Orazio Briante and Antonio Iera and Giuseppe Ruggeri},
doi = {10.1109/JIOT.2017.2775739},
issn = {2327-4662},
year = {2017},
date = {2017-01-01},
journal = {IEEE Internet of Things Journal},
volume = {PP},
number = {99},
pages = {1-1},
abstract = {Large-scale Smart Environments (LSEs) are open and dynamic systems typically extending over a wide area and including a huge number of interacting devices with a heterogeneous nature. Thus, during their deployment scalability and interoperability are key requirements to be definitely taken into account. To these, discovery and reputation assessment of services and objects have to be added, given that new devices and functionalities continuously join LSEs. In spite of the increasing interest in this topic, effective approaches to develop LSEs are still missing. This paper proposes an agent-based approach that leverages Edge Computing and Social Internet of Things paradigms in order to address the above mentioned issues. The effectiveness of such an approach is assessed through a sample case study involving a commercial road environment.},
keywords = {Edge computing, internet of things, multi-agent systems, smart city, smart environments},
pubstate = {published},
tppubtype = {article}
}
Large-scale Smart Environments (LSEs) are open and dynamic systems typically extending over a wide area and including a huge number of interacting devices with a heterogeneous nature. Thus, during their deployment scalability and interoperability are key requirements to be definitely taken into account. To these, discovery and reputation assessment of services and objects have to be added, given that new devices and functionalities continuously join LSEs. In spite of the increasing interest in this topic, effective approaches to develop LSEs are still missing. This paper proposes an agent-based approach that leverages Edge Computing and Social Internet of Things paradigms in order to address the above mentioned issues. The effectiveness of such an approach is assessed through a sample case study involving a commercial road environment.
Fortino, Giancarlo; Zhou, MengChu; Lukszo, Zofia; Vasilakos, Athanasios V.; Basile, Francesco; Palau, Carlos E.; Liotta, Antonio; Fanti, Maria Pia; Guerrieri, Antonio; Vinci, Andrea (Ed.)
IEEE, 2017, ISBN: 978-1-5090-4429-0.
@proceedings{icnscProc2017,
title = {14th IEEE International Conference on Networking, Sensing and Control,
ICNSC 2017, Calabria, Italy, May 16-18, 2017},
editor = {Giancarlo Fortino and
MengChu Zhou and
Zofia Lukszo and
Athanasios V. Vasilakos and
Francesco Basile and
Carlos E. Palau and
Antonio Liotta and
Maria Pia Fanti and
Antonio Guerrieri and
Andrea Vinci},
url = {http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7990365},
isbn = {978-1-5090-4429-0},
year = {2017},
date = {2017-01-01},
publisher = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {proceedings}
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Cicirelli, F.; Guerrieri, A.; Spezzano, G.; Vinci, A.; Briante, O.; Ruggeri, G.
ISapiens: A platform for social and pervasive smart environments Inproceedings
In: 2017, ISBN: 9781509041305.
Abstract | Links | BibTeX | Tags: Agent Computing, Edge computing, smart environments, social internet of things
@inproceedings{Cicirelli2017c,
title = {ISapiens: A platform for social and pervasive smart environments},
author = {F. Cicirelli and A. Guerrieri and G. Spezzano and A. Vinci and O. Briante and G. Ruggeri},
doi = {10.1109/WF-IoT.2016.7845502},
isbn = {9781509041305},
year = {2017},
date = {2017-01-01},
journal = {2016 IEEE 3rd World Forum on Internet of Things, WF-IoT 2016},
abstract = {The advent of Internet of Things (IoT) has fueled the implementation of Smart Environments (SEs) where the information made available by the 'Things' is processed and utilized to increase the livability, the safety and the security of the environment, and to make easier the life of its inhabitants. In literature there are many attempts to create specific SEs or platform for the SEs realization. Although effective, these solutions lack of interoperability with other third-part 'Things'. In this paper we propose iSapiens, a Java-based platform for the design and the implementation of SEs. iSapiens deals with the above mentioned limitation by leveraging the Social Internet of Things (SIoT) paradigm to dynamically include in a SE newly available 'Things' without requiring any human intervention. iSapiens allows the creation of pervasive smart environments by exploiting the edge computing paradigm, that makes locally available distributed storage and computational resources without introducing latency and causing bandwidth shortage.},
keywords = {Agent Computing, Edge computing, smart environments, social internet of things},
pubstate = {published},
tppubtype = {inproceedings}
}
The advent of Internet of Things (IoT) has fueled the implementation of Smart Environments (SEs) where the information made available by the 'Things' is processed and utilized to increase the livability, the safety and the security of the environment, and to make easier the life of its inhabitants. In literature there are many attempts to create specific SEs or platform for the SEs realization. Although effective, these solutions lack of interoperability with other third-part 'Things'. In this paper we propose iSapiens, a Java-based platform for the design and the implementation of SEs. iSapiens deals with the above mentioned limitation by leveraging the Social Internet of Things (SIoT) paradigm to dynamically include in a SE newly available 'Things' without requiring any human intervention. iSapiens allows the creation of pervasive smart environments by exploiting the edge computing paradigm, that makes locally available distributed storage and computational resources without introducing latency and causing bandwidth shortage.
Cicirelli, F.; Fortino, G.; Guerrieri, A.; Spezzano, G.; Vinci, A.
Edge enabled development of Smart Cyber-Physical Environments Inproceedings
In: 2017, ISBN: 9781509018970.
Abstract | Links | BibTeX | Tags: Agent Computing, Cyber-Physical Systems, Edge computing, internet of things, smart environments, Smart Office
@inproceedings{Cicirelli2017d,
title = {Edge enabled development of Smart Cyber-Physical Environments},
author = {F. Cicirelli and G. Fortino and A. Guerrieri and G. Spezzano and A. Vinci},
doi = {10.1109/SMC.2016.7844769},
isbn = {9781509018970},
year = {2017},
date = {2017-01-01},
journal = {2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings},
abstract = {Smart Cyber-Physical Environments are augmented physical environments whose behaviours are enhanced through the use of ICT technologies. The goal is to offer new services and functionalities devoted to meet people's needs and preferences, and to better exploit existing services and infrastructures. The use of IoT technologies, paired with the edge computing, fosters the development of Smart Environment applications having the important features of reliability, scalability and extensibility. This paper proposes an approach for the design and the implementation of Smart Cyber Physical Environment applications having the aforementioned features. The approach relies on the use of isapiens which is an IoT platform enabling edge computing through the exploitation of the agent metaphor. Such platform provides effective abstractions which are able to hide heterogeneity of both the adopted hardware devices and communication protocols. The approach is validated through a case study involving the realization of a Smart Office prototype for profiling and monitoring daily working activities and performing actuations in the environment on the basis of the obtained information.},
keywords = {Agent Computing, Cyber-Physical Systems, Edge computing, internet of things, smart environments, Smart Office},
pubstate = {published},
tppubtype = {inproceedings}
}
Smart Cyber-Physical Environments are augmented physical environments whose behaviours are enhanced through the use of ICT technologies. The goal is to offer new services and functionalities devoted to meet people's needs and preferences, and to better exploit existing services and infrastructures. The use of IoT technologies, paired with the edge computing, fosters the development of Smart Environment applications having the important features of reliability, scalability and extensibility. This paper proposes an approach for the design and the implementation of Smart Cyber Physical Environment applications having the aforementioned features. The approach relies on the use of isapiens which is an IoT platform enabling edge computing through the exploitation of the agent metaphor. Such platform provides effective abstractions which are able to hide heterogeneity of both the adopted hardware devices and communication protocols. The approach is validated through a case study involving the realization of a Smart Office prototype for profiling and monitoring daily working activities and performing actuations in the environment on the basis of the obtained information.
2016
Cicirelli, Franco; Fortino, Giancarlo; Giordano, Andrea; Guerrieri, Antonio; Spezzano, Giandomenico; Vinci, Andrea
On the Design of Smart Homes: A Framework for Activity Recognition in Home Environment Journal Article
In: Journal of medical systems, vol. 40, no. 9, pp. 200, 2016, ISSN: 0148-5598.
Abstract | Links | BibTeX | Tags: activity recognition, analytics, cloud computing, internet of things, multi-agent systems, smart homes, wearable wireless body sensor networks, wireless sensor and actuator networks
@article{cicirelli2016design,
title = {On the Design of Smart Homes: A Framework for Activity Recognition in Home Environment},
author = {Franco Cicirelli and Giancarlo Fortino and Andrea Giordano and Antonio Guerrieri and Giandomenico Spezzano and Andrea Vinci},
doi = {doi:10.1007/s10916-016-0549-7},
issn = {0148-5598},
year = {2016},
date = {2016-07-28},
journal = {Journal of medical systems},
volume = {40},
number = {9},
pages = {200},
publisher = {Springer US},
abstract = {A smart home is a home environment enriched with sensing, actuation, communication and computation capabilities which permits to adapt it to inhabitants preferences and requirements. Establishing a proper strategy of actuation on the home environment can require complex computational tasks on the sensed data. This is the case of activity recognition, which consists in retrieving high-level knowledge about what occurs in the home environment and about the behaviour of the inhabitants. The inherent complexity of this application domain asks for tools able to properly support the design and implementation phases. This paper proposes a framework for the design and implementation of smart home applications focused on activity recognition in home environments. The framework mainly relies on the Cloud-assisted Agent-based Smart home Environment (CASE) architecture offering basic abstraction entities which easily allow to design and implement Smart Home applications. CASE is a three layered architecture which exploits the distributed multi-agent paradigm and the cloud technology for offering analytics services. Details about how to implement activity recognition onto the CASE architecture are supplied focusing on the low-level technological issues as well as the algorithms and the methodologies useful for the activity recognition. The effectiveness of the framework is shown through a case study consisting of a daily activity recognition of a person in a home environment.},
keywords = {activity recognition, analytics, cloud computing, internet of things, multi-agent systems, smart homes, wearable wireless body sensor networks, wireless sensor and actuator networks},
pubstate = {published},
tppubtype = {article}
}
A smart home is a home environment enriched with sensing, actuation, communication and computation capabilities which permits to adapt it to inhabitants preferences and requirements. Establishing a proper strategy of actuation on the home environment can require complex computational tasks on the sensed data. This is the case of activity recognition, which consists in retrieving high-level knowledge about what occurs in the home environment and about the behaviour of the inhabitants. The inherent complexity of this application domain asks for tools able to properly support the design and implementation phases. This paper proposes a framework for the design and implementation of smart home applications focused on activity recognition in home environments. The framework mainly relies on the Cloud-assisted Agent-based Smart home Environment (CASE) architecture offering basic abstraction entities which easily allow to design and implement Smart Home applications. CASE is a three layered architecture which exploits the distributed multi-agent paradigm and the cloud technology for offering analytics services. Details about how to implement activity recognition onto the CASE architecture are supplied focusing on the low-level technological issues as well as the algorithms and the methodologies useful for the activity recognition. The effectiveness of the framework is shown through a case study consisting of a daily activity recognition of a person in a home environment.
Giordano, Andrea; Spezzano, Giandomenico; Vinci, Andrea
A Smart Platform for Large-Scale Networked Cyber-Physical Systems Book Chapter
In: Management of Cyber Physical Objects in the Future Internet of Things Methods, Architectures and Applications, Springer, 2016.
BibTeX | Tags: cyber physical systems, internet of things, multi-agent systems
@inbook{giordano2016smart,
title = {A Smart Platform for Large-Scale Networked Cyber-Physical Systems},
author = {Andrea Giordano and Giandomenico Spezzano and Andrea Vinci},
year = {2016},
date = {2016-01-01},
booktitle = {Management of Cyber Physical Objects in the Future Internet of Things Methods, Architectures and Applications},
publisher = {Springer},
keywords = {cyber physical systems, internet of things, multi-agent systems},
pubstate = {published},
tppubtype = {inbook}
}
Giordano, Andrea; Spezzano, Giandomenico; Vinci, Andrea
Smart Agents and Fog Computing for Smart City Applications Inproceedings
In: International Conference on Smart Cities, pp. 137–146, Springer International Publishing 2016.
BibTeX | Tags: cyber physical systems, fog computing, internet of things, multi-agent systems, smart city
@inproceedings{giordano2016smartb,
title = {Smart Agents and Fog Computing for Smart City Applications},
author = {Andrea Giordano and Giandomenico Spezzano and Andrea Vinci},
year = {2016},
date = {2016-01-01},
booktitle = {International Conference on Smart Cities},
pages = {137--146},
organization = {Springer International Publishing},
keywords = {cyber physical systems, fog computing, internet of things, multi-agent systems, smart city},
pubstate = {published},
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}
Cicirelli, Franco; Fortino, Giancarlo; Guerrieri, Antonio; Spezzano, Giandomenico; Vinci, Andrea
A meta-model framework for the design and analysis of smart cyber-physical environments Inproceedings
In: 20th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2016, Nanchang, China, May 4-6, 2016, pp. 687–692, IEEE, 2016.
Links | BibTeX | Tags: cyber physical systems, development methodology, internet of things, modeling, smart environments, uml
@inproceedings{CicirelliFGSV16,
title = {A meta-model framework for the design and analysis of smart cyber-physical environments},
author = {Franco Cicirelli and
Giancarlo Fortino and
Antonio Guerrieri and
Giandomenico Spezzano and
Andrea Vinci},
url = {http://dx.doi.org/10.1109/CSCWD.2016.7566072},
doi = {10.1109/CSCWD.2016.7566072},
year = {2016},
date = {2016-01-01},
booktitle = {20th IEEE International Conference on Computer Supported Cooperative
Work in Design, CSCWD 2016, Nanchang, China, May 4-6, 2016},
pages = {687--692},
publisher = {IEEE},
keywords = {cyber physical systems, development methodology, internet of things, modeling, smart environments, uml},
pubstate = {published},
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}
2015
Spezzano, Giandomenico; Vinci, Andrea
Pattern Detection in Cyber-Physical Systems Journal Article
In: Procedia Computer Science, vol. 52, pp. 1016–1021, 2015.
BibTeX | Tags: cyber physical systems, data stream mining, monitor applications, stream clustering
@article{spezzano2015pattern,
title = {Pattern Detection in Cyber-Physical Systems},
author = {Giandomenico Spezzano and Andrea Vinci},
year = {2015},
date = {2015-01-01},
journal = {Procedia Computer Science},
volume = {52},
pages = {1016--1021},
publisher = {Elsevier},
keywords = {cyber physical systems, data stream mining, monitor applications, stream clustering},
pubstate = {published},
tppubtype = {article}
}
Giordano, Andrea; Spezzano, Giandomenico; Sunarsa, Hanry; Vinci, Andrea
Twitter to integrate human and Smart Objects by a Web of Things architecture Inproceedings
In: Computer Supported Cooperative Work in Design (CSCWD), 2015 IEEE 19th International Conference on, pp. 355–361, IEEE 2015.
BibTeX | Tags: internet of things, smart objects, twitter
@inproceedings{giordano2015twitter,
title = {Twitter to integrate human and Smart Objects by a Web of Things architecture},
author = {Andrea Giordano and Giandomenico Spezzano and Hanry Sunarsa and Andrea Vinci},
year = {2015},
date = {2015-01-01},
booktitle = {Computer Supported Cooperative Work in Design (CSCWD), 2015 IEEE 19th International Conference on},
pages = {355--361},
organization = {IEEE},
keywords = {internet of things, smart objects, twitter},
pubstate = {published},
tppubtype = {inproceedings}
}
Fortino, Giancarlo; Giordano, Andrea; Guerrieri, Antonio; Spezzano, Giandomenico; Vinci, Andrea
A Data Analytics Schema for Activity Recognition in Smart Home Environments Inproceedings
In: Ubiquitous Computing and Ambient Intelligence. Sensing, Processing, and Using Environmental Information: 9th International Conference, UCAmI 2015, Puerto Varas, Chile, December 1-4, 2015, Proceedings, pp. 91, Springer 2015.
BibTeX | Tags: activity recognition, analytics, cloud computing, internet of things, multi-agent systems, smart homes, wireless sensor and actuator networks
@inproceedings{fortino2015data,
title = {A Data Analytics Schema for Activity Recognition in Smart Home Environments},
author = {Giancarlo Fortino and Andrea Giordano and Antonio Guerrieri and Giandomenico Spezzano and Andrea Vinci},
year = {2015},
date = {2015-01-01},
booktitle = {Ubiquitous Computing and Ambient Intelligence. Sensing, Processing, and Using Environmental Information: 9th International Conference, UCAmI 2015, Puerto Varas, Chile, December 1-4, 2015, Proceedings},
volume = {9454},
pages = {91},
organization = {Springer},
keywords = {activity recognition, analytics, cloud computing, internet of things, multi-agent systems, smart homes, wireless sensor and actuator networks},
pubstate = {published},
tppubtype = {inproceedings}
}
Garofalo, Giuseppina; Giordano, Andrea; Spezzano, Giandomenico; Vinci, Andrea
Definizione e sperimentazione di un sistema per la gestione dei deflussi nella rete idrica tramite smart object Technical Report
2015.
BibTeX | Tags: combined sewer overflow, flooding, real-time control, swarm intelligence, urban drainage system
@techreport{CNRPRODOTTI342299,
title = {Definizione e sperimentazione di un sistema per la gestione dei deflussi nella rete idrica tramite smart object},
author = {Giuseppina Garofalo and Andrea Giordano and Giandomenico Spezzano and Andrea Vinci},
year = {2015},
date = {2015-01-01},
keywords = {combined sewer overflow, flooding, real-time control, swarm intelligence, urban drainage system},
pubstate = {published},
tppubtype = {techreport}
}
Spezzano, Giandomenico; Vinci, Andrea
Realizzazione di un algoritmo bio-ispirato per il clustering di stream di dati evolventi su architettura GPU Technical Report
2015.
BibTeX | Tags: data stream mining, general purpose GPU computing, swarm intelligence
@techreport{CNRPRODOTTI342302,
title = {Realizzazione di un algoritmo bio-ispirato per il clustering di stream di dati evolventi su architettura GPU},
author = {Giandomenico Spezzano and Andrea Vinci},
year = {2015},
date = {2015-01-01},
keywords = {data stream mining, general purpose GPU computing, swarm intelligence},
pubstate = {published},
tppubtype = {techreport}
}
2014
Giordano, Andrea; Spezzano, Giandomenico; Vinci, Andrea
Rainbow: an Intelligent Platform for Large-Scale Networked Cyber-Physical Systems. Inproceedings
In: Fortino, Giancarlo; Karnouskos, Stamatis; Marrón, Pedro José (Ed.): Proceedings of the 5th International Workshop on Networks of Cooperating Objects for Smart Cities 2014 (UBICITEC 2014) co-located with CPS 2014, Berlin, Germany, Apr 14, 2014., pp. 70–85, CEUR-WS.org, 2014, ISSN: 1613-0073.
Abstract | Links | BibTeX | Tags: cloud computing, cyber physical systems, multi-agent systems, smart city, swarm intelligence
@inproceedings{giordano2014rainbow,
title = {Rainbow: an Intelligent Platform for Large-Scale Networked Cyber-Physical Systems.},
author = {Andrea Giordano and Giandomenico Spezzano and Andrea Vinci},
editor = {Giancarlo Fortino and
Stamatis Karnouskos and
Pedro José Marrón},
url = {http://ceur-ws.org/Vol-1156/paper6.pdf},
issn = {1613-0073},
year = {2014},
date = {2014-01-01},
booktitle = {Proceedings of the 5th International Workshop on Networks of Cooperating Objects for Smart Cities 2014 (UBICITEC 2014) co-located with CPS 2014, Berlin, Germany, Apr 14, 2014.},
volume = {1156},
pages = {70--85},
publisher = {CEUR-WS.org},
series = {CEUR Workshop Proceedings},
abstract = {Recent advancements in the fields of embedded systems,communication technologies and computer science, have laid the foundations for new kinds of applications in which a plethora of physical devices are interconnected and immersed in an environment together with human beings. These so-called Cyber-Physical Systems (CPS) issue a design challenge for new architecture in order to cope with problems such as the heterogeneity of devices, the intrinsically distributed nature of these systems, the lack of reliability in communications, etc. In this paper we introduce Rainbow, an architecture designed to address CPS issues. Rainbow hides heterogeneity by providing a Virtual Object (VO) concept, and addresses the distributed nature of CPS introducing a distributed multi-agent system on top of the physical part. Rainbow aims to get the computation close to the sources of information (i.e., the physical devices) and addresses the dynamic adaptivity requirements of CPS by using Swarm Intelligence algorithms.},
keywords = {cloud computing, cyber physical systems, multi-agent systems, smart city, swarm intelligence},
pubstate = {published},
tppubtype = {inproceedings}
}
Recent advancements in the fields of embedded systems,communication technologies and computer science, have laid the foundations for new kinds of applications in which a plethora of physical devices are interconnected and immersed in an environment together with human beings. These so-called Cyber-Physical Systems (CPS) issue a design challenge for new architecture in order to cope with problems such as the heterogeneity of devices, the intrinsically distributed nature of these systems, the lack of reliability in communications, etc. In this paper we introduce Rainbow, an architecture designed to address CPS issues. Rainbow hides heterogeneity by providing a Virtual Object (VO) concept, and addresses the distributed nature of CPS introducing a distributed multi-agent system on top of the physical part. Rainbow aims to get the computation close to the sources of information (i.e., the physical devices) and addresses the dynamic adaptivity requirements of CPS by using Swarm Intelligence algorithms.
Giordano, Andrea; Spezzano, Giandomenico; Vinci, Andrea; Garofalo, Giuseppina; Piro, Patrizia
A cyber-physical system for distributed real-time control of urban drainage networks in smart cities Inproceedings
In: Fortino, Giancarlo; Fatta, Giuseppe Di; Li, Wenfeng; Ochoa, Sergio F.; Cuzzocrea, Alfredo; Pathan, Mukaddim (Ed.): Internet and Distributed Computing Systems - 7h International Conference on Internet and Distributed Computing Systems, IDCS 2014, Calabria, Italy, September 22-24, 2014. Proceedings, pp. 87–98, Springer International Publishing Springher, 2014, ISBN: 978-3-319-11691-4.
Abstract | Links | BibTeX | Tags: combined sewer overflow, cyber physical systems, flooding, multi-agent systems, real-time control, smart city, swarm intelligence, urban drainage system, wireless sensor and actuator networks
@inproceedings{giordano2014cyber,
title = {A cyber-physical system for distributed real-time control of urban drainage networks in smart cities},
author = {Andrea Giordano and Giandomenico Spezzano and Andrea Vinci and Giuseppina Garofalo and Patrizia Piro},
editor = {Giancarlo Fortino and
Giuseppe Di Fatta and
Wenfeng Li and
Sergio F. Ochoa and
Alfredo Cuzzocrea and
Mukaddim Pathan},
doi = {10.1007/978-3-319-11692-1_8},
isbn = {978-3-319-11691-4},
year = {2014},
date = {2014-01-01},
booktitle = {Internet and Distributed Computing Systems - 7h International Conference on Internet and Distributed Computing Systems, IDCS 2014, Calabria, Italy, September 22-24, 2014. Proceedings},
volume = {8729},
pages = {87--98},
publisher = {Springher},
organization = {Springer International Publishing},
series = {Lecture Notes in Computer Science},
abstract = {This paper focuses on a distributed real time control approach applied to drainage networks. The increasing of urbanization and climate change heightens the challenge for new technologies to be developed for drainage networks. Higher runoff volume, produced by the increase in impervious surfaces and intense rain events, overwhelms the existing urban drainage systems. Recent technical improvements have enabled the exploitation of real-time control on drainage networks. The novelty in this paper regards the use of a totally decentralized approach based on a proper combination of a Gossip-based algorithm, which ensures a global correct behaviour even if local faults occur, and a classic controlling technique (PID) used for local actuations.},
keywords = {combined sewer overflow, cyber physical systems, flooding, multi-agent systems, real-time control, smart city, swarm intelligence, urban drainage system, wireless sensor and actuator networks},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper focuses on a distributed real time control approach applied to drainage networks. The increasing of urbanization and climate change heightens the challenge for new technologies to be developed for drainage networks. Higher runoff volume, produced by the increase in impervious surfaces and intense rain events, overwhelms the existing urban drainage systems. Recent technical improvements have enabled the exploitation of real-time control on drainage networks. The novelty in this paper regards the use of a totally decentralized approach based on a proper combination of a Gossip-based algorithm, which ensures a global correct behaviour even if local faults occur, and a classic controlling technique (PID) used for local actuations.
Giordano, Andrea; Spezzano, Giandomenico; Vinci, Andrea
Designing Cyber-Physical Systems for Smart Infrastructures: The Rainbow Platform. Journal Article
In: ERCIM News, vol. 2014, no. 97, 2014, ISSN: 09264981.
Abstract | Links | BibTeX | Tags: cyber physical systems, multi-agent systems, smart city
@article{giordano2014designing,
title = {Designing Cyber-Physical Systems for Smart Infrastructures: The Rainbow Platform.},
author = {Andrea Giordano and Giandomenico Spezzano and Andrea Vinci},
url = {http://ercim-news.ercim.eu/images/stories/EN97/EN97-web.pdf},
issn = {09264981},
year = {2014},
date = {2014-01-01},
journal = {ERCIM News},
volume = {2014},
number = {97},
publisher = {ERCIM},
series = {ERCIM news},
abstract = {Although Cyber-Physical System (CPS) technologies are essential for the creation of smart infrastructures, enabling the optimization and management of resources and facilities, they
represent a design challenge. The Rainbow platform has been developed to facilitate the development of new CPS architectures.},
keywords = {cyber physical systems, multi-agent systems, smart city},
pubstate = {published},
tppubtype = {article}
}
Although Cyber-Physical System (CPS) technologies are essential for the creation of smart infrastructures, enabling the optimization and management of resources and facilities, they
represent a design challenge. The Rainbow platform has been developed to facilitate the development of new CPS architectures.
represent a design challenge. The Rainbow platform has been developed to facilitate the development of new CPS architectures.
Garofalo, Giuseppina; Giordano, Andrea; Vinci, Andrea
Applicazione di un sistema distribuito di controllo in tempo reale di una rete di drenaggio urbano Inproceedings
In: XXXIV Congresso Nazionale di Idraulica e Costruzioni Idrauliche (IDRA 2014), Zaccaria Editore 2014.
BibTeX | Tags: cyber physical systems, flooding, multi-agent systems, real-time control, smart city, urban drainage system
@inproceedings{garofalo2014applicazione,
title = {Applicazione di un sistema distribuito di controllo in tempo reale di una rete di drenaggio urbano},
author = {Giuseppina Garofalo and Andrea Giordano and Andrea Vinci},
year = {2014},
date = {2014-01-01},
booktitle = {XXXIV Congresso Nazionale di Idraulica e Costruzioni Idrauliche (IDRA 2014)},
organization = {Zaccaria Editore},
keywords = {cyber physical systems, flooding, multi-agent systems, real-time control, smart city, urban drainage system},
pubstate = {published},
tppubtype = {inproceedings}
}
Giordano, Andrea; Spezzano, Giandomenico; Vinci, Andrea
Analisi e progettazione di algoritmi di data mining streaming per l'analisi online dei dati. Technical Report
2014.
BibTeX | Tags: data stream mining, data stream processing
@techreport{CNRPRODOTTI342297,
title = {Analisi e progettazione di algoritmi di data mining streaming per l'analisi online dei dati.},
author = {Andrea Giordano and Giandomenico Spezzano and Andrea Vinci},
year = {2014},
date = {2014-01-01},
keywords = {data stream mining, data stream processing},
pubstate = {published},
tppubtype = {techreport}
}
Giordano, Andrea; Spezzano, Giandomenico; Vinci, Andrea
Definizione di algoritmi per la gestione di eventi provenienti da Smart Object in ambito Cloud Technical Report
2014.
BibTeX | Tags: cloud computing, complex event processing, multi-agent systems
@techreport{CNRPRODOTTI342298,
title = {Definizione di algoritmi per la gestione di eventi provenienti da Smart Object in ambito Cloud},
author = {Andrea Giordano and Giandomenico Spezzano and Andrea Vinci},
year = {2014},
date = {2014-01-01},
keywords = {cloud computing, complex event processing, multi-agent systems},
pubstate = {published},
tppubtype = {techreport}
}