@article {8928171,
	title = {Using Deep Reinforcement Learning for Application Relocation in Multi-Access Edge Computing},
	journal = {IEEE Communications Standards Magazine},
	volume = {3},
	number = {3},
	year = {2019},
	month = {Sep.},
	pages = {71-78},
	keywords = {5G mobile communication, Base stations, Computer architecture, Edge computing, Long Term Evolution, Quality of service, Reinforcement learning},
	issn = {2471-2833},
	doi = {10.1109/MCOMSTD.2019.1900011},
	author = {F. De Vita and G. Nardini and A. Virdis and D. Bruneo and A. Puliafito and G. Stea}
}
@article {Dautov201829822,
	title = {Data Processing in Cyber-Physical-Social Systems Through Edge Computing},
	journal = {IEEE Access - IEEE},
	volume = {6},
	year = {2018},
	note = {cited By 0},
	pages = {29822-29835},
	publisher = {Institute of Electrical and Electronics Engineers Inc.},
	abstract = {<p>Cloud and Fog computing have established a convenient and widely adopted approach for computation offloading, where raw data generated by edge devices in the Internet of Things (IoT) context is collected and processed remotely. This vertical offloading pattern, however, typically does not take into account increasingly pressing time constraints of the emerging IoT scenarios, in which numerous data sources, including human agents (i.e., Social IoT), continuously generate large amounts of data to be processed in a timely manner. Big data solutions could be applied in this respect, provided that networking issues and limitations related to connectivity of edge devices are properly addressed. Although edge devices are traditionally considered to be resource-constrained, main limitations refer to energy, networking, and memory capacities, whereas their ever-growing processing capabilities are already sufficient to be effectively involved in actual (big data) processing. In this context, the role of human agents is no longer limited to passive data generation, but can also include their voluntary involvement in relatively complex computations. This way, users can share their personal computational resources (i.e., mobile phones) to support collaborative data processing, thereby turning the existing IoT into a global cyber-physical-social system (CPSS). To this extent, this paper proposes a novel IoT/CPSS data processing pattern based on the stream processing technology, aiming to distribute the workload among a cluster of edge devices, involving mobile nodes shared by contributors on a voluntary basis, and paving the way for cluster computing at the edge. Experiments on an intelligent surveillance system deployed on an edge device cluster demonstrate the feasibility of the proposed approach, illustrating how its distributed in-memory data processing architecture can be effective. {\textcopyright} 2013 IEEE.</p>
},
	keywords = {Apache NiFi, Big Data, Cameras, Cellular telephone systems, cloud computing, Cluster computing, Computer architecture, Cyber physical social systems, Cyber Physical System, Edge computing, Fog computing, Horizontal and Vertical Offloading, Internet of Things, Media streaming, Network security, Servers, Stream processing, Streaming media},
	issn = {21693536},
	doi = {10.1109/ACCESS.2018.2839915},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85047619039\&doi=10.1109\%2fACCESS.2018.2839915\&partnerID=40\&md5=48b52a73084c2f6396c4ce1dd6a690f4},
	author = {Rustem Dautov and Salvatore Distefano and Dario Bruneo and Francesco Longo and Giovanni Merlino and Antonio Puliafito}
}
@article {7060517,
	title = {Cloud4sens: a cloud-based architecture for sensor controlling and monitoring},
	journal = {IEEE Communications Magazine},
	volume = {53},
	number = {3},
	year = {2015},
	month = {March},
	pages = {41-47},
	keywords = {cloud clients, cloud computing, cloud framework, cloud service, cloud-based architecture, Cloud4Sens, Communication standards, Computer architecture, computerised instrumentation, Data models, data-centric model, Databases, device-centric model, environmental data, extensible messaging and presence protocol, Geospatial analysis, IEEE standards, open geospatial consortium, sensing resources management, sensor control, sensor monitoring, sensor web enablement, Sensors, software architecture, Standards, SWE specifications, Temperature sensors, virtual devices, virtual sensing infrastructure, Web services, X509 certificates, XMPP},
	issn = {0163-6804},
	doi = {10.1109/MCOM.2015.7060517},
	author = {M. Fazio and A. Puliafito}
}
@conference {6726193,
	title = {Using Virtualization and Guacamole/VNC to Provide Adaptive User Interfaces to Disabled People in Cloud Computing},
	booktitle = {Ubiquitous Intelligence and Computing, 2013 IEEE 10th International Conference on and 10th International Conference on Autonomic and Trusted Computing (UIC/ATC)},
	year = {2013},
	month = {Dec},
	pages = {72-79},
	keywords = {adaptive user interfaces, Adaptive User Interfaces and Tools, assisted living, Assistive Technology, AT software, AT tools, Browsers, CLEVER, cloud computing, Computer architecture, Computers, disabled people, graphical user interfaces, Guacamole, handicapped aids, HTML5, HTML5 remote desktop gateway, HTML5-based Web interface, hypermedia markup languages, hypervisor, Internet, internetworking, Keyboards, middleware, networked computer, online front-ends, personalized virtual desktops, Psychological Factors, Remote desktop, SaaS, Servers, software as a service, VIM middleware, Virtual Box, virtual infrastructure manager middleware, virtual machine monitor, virtual machines, virtual network computing, virtualisation, Virtualization, VNC, Web browser, Web-accessed VM},
	doi = {10.1109/UIC-ATC.2013.42},
	author = {Mulfari, D. and Celesti, A. and Villari, M. and Puliafito, A.}
}
