@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 {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}
}
