2024
Marozzo, Fabrizio; Vinci, Andrea
Design of Platform-Independent IoT Applications in the Edge-Cloud Continuum Proceedings Article Forthcoming
In: Proceedings of 3rd DISCOLI Workshop on DIStributed COLlective Intelligence (DISCOLI 2024), Forthcoming.
BibTeX | Tags: Edge and cloud computing, insider
@inproceedings{marozzo2024,
title = {Design of Platform-Independent IoT Applications in the Edge-Cloud Continuum},
author = {Fabrizio Marozzo and Andrea Vinci},
year = {2024},
date = {2024-12-31},
booktitle = {Proceedings of 3rd DISCOLI Workshop on DIStributed COLlective Intelligence (DISCOLI 2024)},
keywords = {Edge and cloud computing, insider},
pubstate = {forthcoming},
tppubtype = {inproceedings}
}
Cesario, Eugenio; Lindia, Paolo; Vinci, Andrea
Multi-Density Crime Predictor: an approach to forecast criminal activities in multi-density crime hotspots Journal Article
In: Journal of Big Data, 2024, ISSN: 21961115.
Links | BibTeX | Tags: insider, sobigdata.it
@article{ceslinvin2024jbd,
title = {Multi-Density Crime Predictor: an approach to forecast criminal activities in multi-density crime hotspots},
author = {Eugenio Cesario and Paolo Lindia and Andrea Vinci},
doi = {https://doi.org/10.1186/s40537-024-00935-4},
issn = {21961115},
year = {2024},
date = {2024-05-09},
urldate = {2024-05-09},
journal = {Journal of Big Data},
keywords = {insider, sobigdata.it},
pubstate = {published},
tppubtype = {article}
}
Mastroianni, Carlo; Plastina, Francesco; Settino, Jacopo; Vinci, Andrea
Quantum Variational Algorithms for the Allocation of Resources in a Cloud/Edge Architecture Journal Article
In: IEEE Transactions on Quantum Engineering, 2024, ISSN: 26891808.
Links | BibTeX | Tags: Edge and cloud computing, insider, quantum computing, sobigdata.it
@article{mastroianni2024quantum,
title = {Quantum Variational Algorithms for the Allocation of Resources in a Cloud/Edge Architecture},
author = {Carlo Mastroianni and Francesco Plastina and Jacopo Settino and Andrea Vinci},
url = {https://ieeexplore.ieee.org/document/10522849},
doi = {https://doi.org/10.1109/TQE.2024.3398410},
issn = {26891808},
year = {2024},
date = {2024-05-08},
urldate = {2024-05-08},
journal = {IEEE Transactions on Quantum Engineering},
keywords = {Edge and cloud computing, insider, quantum computing, sobigdata.it},
pubstate = {published},
tppubtype = {article}
}
Franco Cicirelli Irfanullah Khan, Emilio Greco
Leveraging Distributed AI for Multi-Occupancy Prediction in Cognitive Buildings Journal Article
In: Internet Of Things, 2024, ISSN: 2542-6605.
Links | BibTeX | Tags: Edge and cloud computing, insider, sobigdata.it
@article{Khan2024,
title = {Leveraging Distributed AI for Multi-Occupancy Prediction in Cognitive Buildings},
author = {Irfanullah Khan, Franco Cicirelli, Emilio Greco, Antonio Guerrieri, Carlo Mastroianni, Luigi Scarcello, Giandomenico Spezzano, Andrea Vinci},
doi = {10.1016/j.iot.2024.101181},
issn = {2542-6605},
year = {2024},
date = {2024-04-07},
urldate = {2024-04-07},
journal = {Internet Of Things},
publisher = {Elsevier},
keywords = {Edge and cloud computing, insider, sobigdata.it},
pubstate = {published},
tppubtype = {article}
}
Cesario, Eugenio; Lindia, Paolo; Vinci, Andrea
A scalable multi-density clustering approach to detect city hotspots in a smart city Journal Article
In: Future Generation Computer Systems, vol. 157, pp. 226-236, 2024, ISSN: 0167-739X.
Abstract | Links | BibTeX | Tags: insider, Multi density-based clustering, Parallel data mining, smart city, sobigdata.it
@article{CESARIO2024,
title = {A scalable multi-density clustering approach to detect city hotspots in a smart city},
author = {Eugenio Cesario and Paolo Lindia and Andrea Vinci},
url = {https://www.sciencedirect.com/science/article/pii/S0167739X24001122},
doi = {https://doi.org/10.1016/j.future.2024.03.042},
issn = {0167-739X},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {Future Generation Computer Systems},
volume = {157},
pages = {226-236},
abstract = {In the field of Smart City applications, the analysis of urban data to detect city hotspots, i.e., regions where urban events (such as pollution peaks, virus infections, traffic spikes, and crimes) occur at a higher density than in the rest of the dataset, is becoming a common task. The detection of such hotspots can serve as a valuable organizational technique for framing detailed information about a metropolitan area, providing high-level spatial knowledge for planners, scientists, and policymakers. From the algorithmic viewpoint, classic density-based clustering algorithms are very effective in discovering hotspots characterized by homogeneous density; however, their application on multi-density data can produce inaccurate results. For such a reason, since metropolitan cities are characterized by areas with significantly variable densities, multi-density clustering approaches are more effective in discovering city hotspots. Moreover, the growing volumes of data collected in urban environments require the development of parallel approaches, in order to take advantage of scalable executions offered by Edge and Cloud environments. This paper describes the design and implementation of a parallel multi-density clustering algorithm aimed at analyzing high volumes 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, speedup, and efficiency.},
keywords = {insider, Multi density-based clustering, Parallel data mining, smart city, sobigdata.it},
pubstate = {published},
tppubtype = {article}
}
Mastroianni, Carlo; Plastina, Francesco; Scarcello, Luigi; Settino, Jacopo; Vinci, Andrea
Assessing Quantum Computing Performance for Energy Optimization in a Prosumer Community Journal Article
In: IEEE Trans. Smart Grid, vol. 15, no. 1, pp. 444–456, 2024, ISSN: 1949-3061.
Links | BibTeX | Tags: quantum computing, sobigdata.it
@article{Mastroianni2024,
title = {Assessing Quantum Computing Performance for Energy Optimization in a Prosumer Community},
author = {Carlo Mastroianni and Francesco Plastina and Luigi Scarcello and Jacopo Settino and Andrea Vinci},
doi = {10.1109/tsg.2023.3286106},
issn = {1949-3061},
year = {2024},
date = {2024-01-00},
urldate = {2024-01-00},
journal = {IEEE Trans. Smart Grid},
volume = {15},
number = {1},
pages = {444–456},
publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
keywords = {quantum computing, sobigdata.it},
pubstate = {published},
tppubtype = {article}
}
2023
Amadeo, Marica; Cicirelli, Franco; Guerrieri, Antonio; Ruggeri, Giuseppe; Spezzano, Giandomenico; Vinci, Andrea
When edge intelligence meets cognitive buildings: The COGITO platform Journal Article
In: Internet of Things, vol. 24, 2023, ISSN: 2542-6605.
Links | BibTeX | Tags: Artificial Intelligence, cogito, Computer Science (miscellaneous), Computer Science Applications, Engineering (miscellaneous), Hardware and Architecture, Information Systems, Management of Technology and Innovation, Software
@article{Amadeo2023,
title = {When edge intelligence meets cognitive buildings: The COGITO platform},
author = {Marica Amadeo and Franco Cicirelli and Antonio Guerrieri and Giuseppe Ruggeri and Giandomenico Spezzano and Andrea Vinci},
doi = {10.1016/j.iot.2023.100908},
issn = {2542-6605},
year = {2023},
date = {2023-12-00},
urldate = {2023-12-00},
journal = {Internet of Things},
volume = {24},
publisher = {Elsevier BV},
keywords = {Artificial Intelligence, cogito, Computer Science (miscellaneous), Computer Science Applications, Engineering (miscellaneous), Hardware and Architecture, Information Systems, Management of Technology and Innovation, Software},
pubstate = {published},
tppubtype = {article}
}
Cesario, Eugenio; Lindia, Paolo; Vinci, Andrea
Detecting Multi-Density Urban Hotspots in a Smart City: Approaches, Challenges and Applications Journal Article
In: Big Data and Cognitive Computing, vol. 7, no. 1, 2023, ISSN: 2504-2289.
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Computer Science Applications, Information Systems, Management Information Systems, sobigdata.it
@article{Cesario2023,
title = {Detecting Multi-Density Urban Hotspots in a Smart City: Approaches, Challenges and Applications},
author = {Eugenio Cesario and Paolo Lindia and Andrea Vinci},
doi = {10.3390/bdcc7010029},
issn = {2504-2289},
year = {2023},
date = {2023-03-00},
urldate = {2023-03-00},
journal = {Big Data and Cognitive Computing},
volume = {7},
number = {1},
publisher = {MDPI AG},
abstract = {<jats:p>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 an 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, crime forecasting, traffic flows, etc. In particular, the detection of city hotspots is de facto a valuable organization technique for framing detailed knowledge of a metropolitan area, providing high-level summaries for spatial datasets, which are a valuable support for planners, scientists, and policymakers. However, while classic density-based clustering algorithms show to be suitable for discovering hotspots characterized by homogeneous density, their application on multi-density data can produce inaccurate results. In fact, a proper threshold setting is very difficult when clusters in different regions have considerably different densities, or clusters with different density levels are nested. For such a reason, since metropolitan cities are heavily characterized by variable densities, multi-density clustering seems to be more appropriate for discovering city hotspots. Indeed, such algorithms rely on multiple minimum threshold values and are able to detect multiple pattern distributions of different densities, aiming at distinguishing between several density regions, which may or may not be nested and are generally of a non-convex shape. This paper discusses the research issues and challenges for analyzing urban data, aimed at discovering multi-density hotspots in urban areas. In particular, the study compares the four approaches (DBSCAN, OPTICS-xi, HDBSCAN, and CHD) proposed in the literature for clustering urban data and analyzes their performance on both state-of-the-art and real-world datasets. Experimental results show that multi-density clustering algorithms generally achieve better results on urban data than classic density-based algorithms.</jats:p>},
keywords = {Artificial Intelligence, Computer Science Applications, Information Systems, Management Information Systems, sobigdata.it},
pubstate = {published},
tppubtype = {article}
}
Scarcello, Luigi; Cicirelli, Franco; Guerrieri, Antonio; Mastroianni, Carlo; Spezzano, Giandomenico; Vinci, Andrea
Pursuing Energy Saving and Thermal Comfort With a Human-Driven DRL Approach Journal Article
In: IEEE Transactions on Human-Machine Systems, vol. 53, no. 4, pp. 707-719, 2023.
Links | BibTeX | Tags: cogito, HVAC;Energy consumption;Thermal management;Process control;Behavioral sciences;Training;Temperature distribution;Cognitive buildings;deep reinforcement learning (DRL);energy saving;human-in-the-loop;thermal comfort
@article{9940565,
title = {Pursuing Energy Saving and Thermal Comfort With a Human-Driven DRL Approach},
author = {Luigi Scarcello and Franco Cicirelli and Antonio Guerrieri and Carlo Mastroianni and Giandomenico Spezzano and Andrea Vinci},
doi = {10.1109/THMS.2022.3216365},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
journal = {IEEE Transactions on Human-Machine Systems},
volume = {53},
number = {4},
pages = {707-719},
keywords = {cogito, HVAC;Energy consumption;Thermal management;Process control;Behavioral sciences;Training;Temperature distribution;Cognitive buildings;deep reinforcement learning (DRL);energy saving;human-in-the-loop;thermal comfort},
pubstate = {published},
tppubtype = {article}
}
Mastroianni, Carlo; Scarcello, Luigi; Vinci, Andrea
Quantum Computing Management of a Cloud/Edge Architecture Proceedings Article
In: Bartolini, Andrea; Rietveld, Kristian F. D.; Schuman, Catherine D.; Moreira, Jose (Ed.): Proceedings of the 20th ACM International Conference on Computing Frontiers, CF 2023, pp. 193–196, ACM, 2023.
Links | BibTeX | Tags: Edge and cloud computing, insider, quantum computing
@inproceedings{MastroianniSV23,
title = {Quantum Computing Management of a Cloud/Edge Architecture},
author = {Carlo Mastroianni and Luigi Scarcello and Andrea Vinci},
editor = {Andrea Bartolini and Kristian F. D. Rietveld and Catherine D. Schuman and Jose Moreira},
url = {https://doi.org/10.1145/3587135.3592190},
doi = {10.1145/3587135.3592190},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
booktitle = {Proceedings of the 20th ACM International Conference on Computing Frontiers, CF 2023},
pages = {193–196},
publisher = {ACM},
keywords = {Edge and cloud computing, insider, quantum computing},
pubstate = {published},
tppubtype = {inproceedings}
}
Cicirelli, Franco; Greco, Emilio; Guerrieri, Antonio; Spezzano, Giandomenico; Vinci, Andrea
Collaborative Learning over Cellular Automata Book Chapter
In: Communications in Computer and Information Science, pp. 3–14, Springer Nature Switzerland, 2023, ISBN: 9783031311833.
@inbook{Cicirelli2023,
title = {Collaborative Learning over Cellular Automata},
author = {Franco Cicirelli and Emilio Greco and Antonio Guerrieri and Giandomenico Spezzano and Andrea Vinci},
doi = {10.1007/978-3-031-31183-3_1},
isbn = {9783031311833},
year = {2023},
date = {2023-00-00},
booktitle = {Communications in Computer and Information Science},
pages = {3–14},
publisher = {Springer Nature Switzerland},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
Cicirelli, Franco; Guerrieri, Antonio; Vinci, Andrea; Spezzano, Giandomenico (Ed.)
IoT Edge Solutions for Cognitive Buildings Book
Springer International Publishing, 2023, ISBN: 9783031151606.
@book{2023,
title = {IoT Edge Solutions for Cognitive Buildings},
editor = {Franco Cicirelli and Antonio Guerrieri and Andrea Vinci and Giandomenico Spezzano},
doi = {10.1007/978-3-031-15160-6},
isbn = {9783031151606},
year = {2023},
date = {2023-00-00},
urldate = {2023-00-00},
publisher = {Springer International Publishing},
keywords = {cogito},
pubstate = {published},
tppubtype = {book}
}
2022
Khan, Irfanullah; Guerrieri, Antonio; Spezzano, Giandomenico; Vinci, Andrea
Occupancy Prediction in Buildings: An approach leveraging LSTM and Federated Learning Proceedings Article
In: 2022 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech), IEEE, 2022.
@inproceedings{Khan2022,
title = {Occupancy Prediction in Buildings: An approach leveraging LSTM and Federated Learning},
author = {Irfanullah Khan and Antonio Guerrieri and Giandomenico Spezzano and Andrea Vinci},
doi = {10.1109/dasc/picom/cbdcom/cy55231.2022.9927838},
year = {2022},
date = {2022-09-12},
urldate = {2022-09-12},
booktitle = {2022 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech)},
publisher = {IEEE},
keywords = {cogito},
pubstate = {published},
tppubtype = {inproceedings}
}
Canino, Maria Pia; Cesario, Eugenio; Vinci, Andrea; Zarin, Shabnam
Exploiting mobility data to forecast Covid-19 spread Proceedings Article
In: 2022 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech), IEEE, 2022.
@inproceedings{Canino2022b,
title = {Exploiting mobility data to forecast Covid-19 spread},
author = {Maria Pia Canino and Eugenio Cesario and Andrea Vinci and Shabnam Zarin},
doi = {10.1109/dasc/picom/cbdcom/cy55231.2022.9927898},
year = {2022},
date = {2022-09-12},
urldate = {2022-09-12},
booktitle = {2022 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech)},
publisher = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Buono, Michele De; Gullo, Nicola; Spezzano, Giandomenico; Vennera, Andrea; Vinci, Andrea
Development of Indoor Smart Environments Leveraging the Internet of Things and Artificial Intelligence: A Case Study Book Chapter
In: Internet of Things, pp. 263–284, Springer International Publishing, 2022, ISBN: 9783031151606.
@inbook{Buono2022,
title = {Development of Indoor Smart Environments Leveraging the Internet of Things and Artificial Intelligence: A Case Study},
author = {Michele De Buono and Nicola Gullo and Giandomenico Spezzano and Andrea Vennera and Andrea Vinci},
doi = {10.1007/978-3-031-15160-6_12},
isbn = {9783031151606},
year = {2022},
date = {2022-08-09},
urldate = {2022-08-09},
booktitle = {Internet of Things},
pages = {263–284},
publisher = {Springer International Publishing},
keywords = {cogito},
pubstate = {published},
tppubtype = {inbook}
}
Colace, Simone; Laurita, Sara; Spezzano, Giandomenico; Vinci, Andrea
Room Occupancy Prediction Leveraging LSTM: An Approach for Cognitive and Self-Adapting Buildings Book Chapter
In: Internet of Things, pp. 197–219, Springer International Publishing, 2022, ISBN: 9783031151606.
@inbook{Colace2022,
title = {Room Occupancy Prediction Leveraging LSTM: An Approach for Cognitive and Self-Adapting Buildings},
author = {Simone Colace and Sara Laurita and Giandomenico Spezzano and Andrea Vinci},
doi = {10.1007/978-3-031-15160-6_9},
isbn = {9783031151606},
year = {2022},
date = {2022-08-09},
urldate = {2022-08-09},
booktitle = {Internet of Things},
pages = {197–219},
publisher = {Springer International Publishing},
keywords = {cogito},
pubstate = {published},
tppubtype = {inbook}
}
Amadeo, Marica; Cicirelli, Franco; Guerrieri, Antonio; Ruggeri, Giuseppe; Spezzano, Giandomenico; Vinci, Andrea
COGITO: A Platform for Developing Cognitive Environments Book Chapter
In: Internet of Things, pp. 1–22, Springer International Publishing, 2022, ISBN: 9783031151606.
@inbook{Amadeo2022,
title = {COGITO: A Platform for Developing Cognitive Environments},
author = {Marica Amadeo and Franco Cicirelli and Antonio Guerrieri and Giuseppe Ruggeri and Giandomenico Spezzano and Andrea Vinci},
doi = {10.1007/978-3-031-15160-6_1},
isbn = {9783031151606},
year = {2022},
date = {2022-08-09},
booktitle = {Internet of Things},
pages = {1–22},
publisher = {Springer International Publishing},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
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}
}
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}
}
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 Proceedings Article
In: 2021 IEEE 2nd International Conference on Human-Machine Systems (ICHMS), IEEE, 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},
urldate = {2021-01-01},
booktitle = {2021 IEEE 2nd International Conference on Human-Machine Systems (ICHMS)},
journal = {Proceedings of the 2021 IEEE International Conference on Human-Machine Systems, ICHMS 2021},
publisher = {IEEE},
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}
}
Cesario, E.; Vinci, A.; Zarin, S.
Towards Parallel Multi-density Clustering for Urban Hotspots Detection Proceedings Article
In: 2021 29th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP), IEEE, 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},
urldate = {2021-01-01},
booktitle = {2021 29th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP)},
journal = {Proceedings - 29th Euromicro International Conference on Parallel, Distributed and Network-Based Processing, PDP 2021},
publisher = {IEEE},
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}
}
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}
}
Cesario, E.; Uchubilo, P. I.; Vinci, A.; Zhu, X.
Discovering Multi-density Urban Hotspots in a Smart City Proceedings Article
In: 2020 IEEE International Conference on Smart Computing (SMARTCOMP), IEEE, 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},
urldate = {2020-01-01},
booktitle = {2020 IEEE International Conference on Smart Computing (SMARTCOMP)},
journal = {Proceedings - 2020 IEEE International Conference on Smart Computing, SMARTCOMP 2020},
publisher = {IEEE},
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}
}
Cicirelli, F.; Guerrieri, A.; Mastroianni, C.; Spezzano, G.; Vinci, A.
Thermal comfort management leveraging deep reinforcement learning and human-in-The-loop Proceedings Article
In: 2020 IEEE International Conference on Human-Machine Systems (ICHMS), IEEE, 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},
urldate = {2020-01-01},
booktitle = {2020 IEEE International Conference on Human-Machine Systems (ICHMS)},
journal = {Proceedings of the 2020 IEEE International Conference on Human-Machine Systems, ICHMS 2020},
publisher = {IEEE},
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}
}
Cesario, E.; Vinci, A.; Zhu, X.
Hierarchical Clustering of Spatial Urban Data Proceedings Article
In: Y. Sergeyev, Kvasov (Ed.): Numerical Computations: Theory and Algorithms. NUMTA 2019., Springer, 2020, ISSN: 16113349.
Abstract | Links | BibTeX | Tags:
@inproceedings{Cesario2020b,
title = {Hierarchical Clustering of Spatial Urban Data},
author = {E. Cesario and A. Vinci and X. Zhu},
editor = {Sergeyev, Y., Kvasov, D. },
doi = {10.1007/978-3-030-39081-5_20},
issn = {16113349},
year = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
booktitle = {Numerical Computations: Theory and Algorithms. NUMTA 2019.},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {11973 LNCS},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
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 = {inproceedings}
}
Cicirelli, F.; Gentile, A. F.; Greco, E.; Guerrieri, A.; Spezzano, G.; Vinci, A.
An Energy Management System at the Edge based on Reinforcement Learning Best Paper Proceedings Article
In: 2020 IEEE/ACM 24th International Symposium on Distributed Simulation and Real Time Applications (DS-RT), IEEE, 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},
urldate = {2020-01-01},
booktitle = {2020 IEEE/ACM 24th International Symposium on Distributed Simulation and Real Time Applications (DS-RT)},
journal = {Proceedings of the 2020 IEEE/ACM 24th International Symposium on Distributed Simulation and Real Time Applications, DS-RT 2020},
publisher = {IEEE},
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}
}
Cicirelli, Franco; Guerrieri, Antonio; Pizzuti, Clara; Socievole, Annalisa; Spezzano, Giandomenico; Vinci, Andrea (Ed.)
Springer, vol. 1200, 2020, ISBN: 978-3-030-45015-1.
@proceedings{wivace/2019,
title = {Artificial Life and Evolutionary Computation - 14th Italian Workshop, WIVACE 2019, Rende, Italy, September 18-20, 2019, Revised Selected Papers},
editor = {Franco Cicirelli and Antonio Guerrieri and Clara Pizzuti and Annalisa Socievole and Giandomenico Spezzano and Andrea Vinci},
url = {https://doi.org/10.1007/978-3-030-45016-8},
doi = {10.1007/978-3-030-45016-8},
isbn = {978-3-030-45015-1},
year = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
volume = {1200},
publisher = {Springer},
series = {Communications in Computer and Information Science},
keywords = {},
pubstate = {published},
tppubtype = {proceedings}
}
2019
Cicirelli, F.; Guerrieri, A.; Mastroianni, C.; Palopoli, F.; Spezzano, G.; Vinci, A.
Comfort-aware Cognitive Buildings Leveraging Deep Reinforcement Learning Proceedings Article
In: 2019 IEEE/ACM 23rd International Symposium on Distributed Simulation and Real Time Applications (DS-RT), IEEE, 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},
urldate = {2019-01-01},
booktitle = {2019 IEEE/ACM 23rd International Symposium on Distributed Simulation and Real Time Applications (DS-RT)},
journal = {Proceedings - 2019 IEEE/ACM 23rd International Symposium on Distributed Simulation and Real Time Applications, DS-RT 2019},
publisher = {IEEE},
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}
}
Cesario, E.; Vinci, A.
A comparative analysis of classification and regression models for energy-efficient clouds Proceedings Article
In: 2019 IEEE 16th International Conference on Networking, Sensing and Control (ICNSC), IEEE, 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},
urldate = {2019-01-01},
booktitle = {2019 IEEE 16th International Conference on Networking, Sensing and Control (ICNSC)},
journal = {Proceedings of the 2019 IEEE 16th International Conference on Networking, Sensing and Control, ICNSC 2019},
publisher = {IEEE},
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}
}
Cicirelli, F.; Guerrieri, A.; Spezzano, G.; Vinci, A.
A Cognitive Enabled, Edge-Computing Architecture for Future Generation IoT Environments Proceedings Article
In: 2019 IEEE 5th World Forum on Internet of Things (WF-IoT), IEEE, 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},
urldate = {2019-01-01},
booktitle = {2019 IEEE 5th World Forum on Internet of Things (WF-IoT)},
journal = {IEEE 5th World Forum on Internet of Things, WF-IoT 2019 - Conference Proceedings},
publisher = {IEEE},
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}
}
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}
}
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}
}
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, domus, 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},
urldate = {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, domus, Edge and cloud computing, IoT-based ecosystems, Smart Office},
pubstate = {published},
tppubtype = {article}
}
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 Chapter
In: 2019, ISSN: 21991081.
Abstract | Links | BibTeX | Tags: Edge computing, IoT development platforms, IoT-based applications, Multi agent systems, smart environments, social internet of things
@inbook{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},
urldate = {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 = {inbook}
}
Cicirelli, Franco; Guerrieri, Antonio; Mastroianni, Carlo; Spezzano, Giandomenico; Vinci, Andrea (Ed.)
The Internet of Things for Smart Urban Ecosystems Book
Springer, 2019, ISBN: 978-3-319-96549-9.
@book{bookCGMSV2019,
title = {The Internet of Things for Smart Urban Ecosystems},
editor = {Franco Cicirelli and Antonio Guerrieri and Carlo Mastroianni and Giandomenico Spezzano and Andrea Vinci},
url = {https://doi.org/10.1007/978-3-319-96550-5},
doi = {10.1007/978-3-319-96550-5},
isbn = {978-3-319-96549-9},
year = {2019},
date = {2019-01-01},
urldate = {2019-01-01},
publisher = {Springer},
keywords = {domus},
pubstate = {published},
tppubtype = {book}
}
2018
Cicirelli, Franco; Fortino, Giancarlo; Guerrieri, Antonio; Mercuri, Alessandro; Spezzano, Giandomenico; Vinci, Andrea
A Metamodel Framework for Edge-Based Smart Environments Proceedings Article
In: 2018 IEEE International Conference on Cloud Engineering (IC2E), pp. 286-291, IEEE, 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},
urldate = {2018-04-01},
booktitle = {2018 IEEE International Conference on Cloud Engineering (IC2E)},
pages = {286-291},
publisher = {IEEE},
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}
}
Spezzano, G.; Vinci, A.
A Nature-Inspired, Anytime and Parallel Algorithm for Data Stream Clustering Proceedings Article
In: Marco Danelutto Sanzio Bassini, Patrizio Dazzi (Ed.): Parallel Computing is Everywhere. Preceedings of Parallel Computing Conference (ParCo2017), IOS Press, 2018, ISSN: 1879808X.
Abstract | Links | BibTeX | Tags: Anytime Algorithm, Clustering, CUDA, Data Stream, general purpose GPU computing
@inproceedings{Spezzano2018,
title = {A Nature-Inspired, Anytime and Parallel Algorithm for Data Stream Clustering},
author = {G. Spezzano and A. Vinci},
editor = {Sanzio Bassini, Marco Danelutto, Patrizio Dazzi, Gerhard R. Joubert, Frans Peters},
doi = {10.3233/978-1-61499-843-3-317},
issn = {1879808X},
year = {2018},
date = {2018-01-01},
urldate = {2018-01-01},
booktitle = {Parallel Computing is Everywhere. Preceedings of Parallel Computing Conference (ParCo2017)},
journal = {Advances in Parallel Computing},
volume = {32},
publisher = {IOS Press},
series = {Advances in Parallel Computing},
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 = {inproceedings}
}
Cicirelli, F.; Fortino, G.; Guerrieri, A.; Spezzano, G.; Vinci, A.
A Scalable Agent-Based Smart Environment for Edge-Based Urban IoT Systems Proceedings Article
In: Interoperability, Safety and Security in IoT. InterIoT SaSeIoT 2017., Springer International Publishing, 2018, ISSN: 18678211.
Abstract | Links | BibTeX | Tags: Edge computing, Intelligent agents, IoT, smart environments, Urban computing
@inproceedings{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},
urldate = {2018-01-01},
booktitle = {Interoperability, Safety and Security in IoT. InterIoT SaSeIoT 2017.},
journal = {Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST},
volume = {242},
publisher = {Springer International Publishing},
series = {Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering},
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 = {inproceedings}
}
Cicirelli, F.; Fortino, G.; Guerrieri, A.; Mercuri, A.; Spezzano, G.; Vinci, A.
Exploiting the sem framework for modeling smart cities Proceedings Article
In: G. Fortino, Ali (Ed.): Internet and Distributed Computing Systems. IDCS 2017., Springer, 2018, ISSN: 16113349.
Abstract | Links | BibTeX | Tags: Implementation, internet of things, Meta-modeling, modeling, Smart Cities, Smart Street Cosenza
@inproceedings{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},
editor = {Fortino, G., Ali, A., Pathan, M., Guerrieri, A., Di Fatta, G.},
doi = {10.1007/978-3-319-97795-9_9},
issn = {16113349},
year = {2018},
date = {2018-01-01},
urldate = {2018-01-01},
booktitle = {Internet and Distributed Computing Systems. IDCS 2017.},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {10794 LNCS},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
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 = {inproceedings}
}
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, res-novae, 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},
urldate = {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, res-novae, Sensor data fusion, smart city, smart environments, Smart museum, Statecharts},
pubstate = {published},
tppubtype = {article}
}
Catlett, C.; Cesario, E.; Talia, D.; Vinci, A.
A data-driven approach for spatio-Temporal crime predictions in smart cities Best Paper Proceedings Article
In: 2018 IEEE International Conference on Smart Computing (SMARTCOMP), IEEE, 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},
urldate = {2018-01-01},
booktitle = {2018 IEEE International Conference on Smart Computing (SMARTCOMP)},
journal = {Proceedings - 2018 IEEE International Conference on Smart Computing, SMARTCOMP 2018},
publisher = {IEEE},
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}
}
Cicirelli, F.; Guerrieri, A.; Mercuri, A.; Spezzano, G.; Vinci, A.
IoT-centric edge computing for context-aware smart environments Proceedings Article
In: 2018 IEEE International Congress on Internet of Things (ICIOT), IEEE, 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},
urldate = {2018-01-01},
booktitle = {2018 IEEE International Congress on Internet of Things (ICIOT)},
journal = {Proceedings - 2018 IEEE International Congress on Internet of Things, ICIOT 2018 - Part of the 2018 IEEE World Congress on Services},
publisher = {IEEE},
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}
}
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, domus, 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},
urldate = {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, domus, internet of things, modeling, smart environments},
pubstate = {published},
tppubtype = {article}
}
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, res-novae, 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},
urldate = {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, res-novae, smart city, Urban computing},
pubstate = {published},
tppubtype = {article}
}
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, res-novae, 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},
urldate = {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, res-novae, urban drainage system},
pubstate = {published},
tppubtype = {article}
}
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 Proceedings Article
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}
}
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: domus, 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},
urldate = {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 = {domus, Edge computing, internet of things, multi-agent systems, smart city, smart environments},
pubstate = {published},
tppubtype = {article}
}
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}
}