2022
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 | Tag: 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.
2021
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 | Tag: 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}
}
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.
2019
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 | Tag: 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.
2018
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 | Tag: 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}
}
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.
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 | Tag: 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}
}
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.
2017
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 | Tag: 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}
}
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.