Publications

@inproceedings{marozzo2024,

title = {Design of Platform-Independent IoT Applications in the Edge-Cloud Continuum},

author = {Fabrizio Marozzo and Andrea Vinci},

doi = {10.1109/DCOSS-IoT61029.2024.00092},

year  = {2024},

date = {2024-08-12},

urldate = {2024-12-31},

booktitle = {2024 20th International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT)},

pages = {589-594},

keywords = {Edge and cloud computing, insider},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1145/3625549.3660508,

title = {Tutorial on Variational Quantum Algorithms for Resource Management in Cloud/Edge Architectures},

author = {Carlo Mastroianni and Andrea Vinci},

url = {https://doi.org/10.1145/3625549.3660508},

doi = {10.1145/3625549.3660508},

isbn = {9798400704130},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

booktitle = {Proceedings of the 33rd International Symposium on High-Performance Parallel and Distributed Computing},

pages = {350–351},

publisher = {Association for Computing Machinery},

address = {Pisa, Italy},

series = {HPDC '24},

keywords = {Edge and cloud computing, insider, quantum computing},

pubstate = {published},

tppubtype = {inproceedings}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}