@conference {9097595,
	title = {A Novel Data Collection Framework for Telemetry and Anomaly Detection in Industrial IoT Systems},
	booktitle = {2020 IEEE/ACM Fifth International Conference on Internet-of-Things Design and Implementation (IoTDI)},
	year = {2020},
	pages = {245-251},
	author = {F. De Vita and D. Bruneo and S. K. Das}
}
@conference {8784023,
	title = {Towards trustless prediction-as-a-service},
	booktitle = {2019 IEEE International Conference on Smart Computing (SMARTCOMP)},
	year = {2019},
	month = {June},
	pages = {317-322},
	keywords = {application program interfaces, Blockchain, cloud computing, cloud provider infrastructure, Computational modeling, Cryptography, Deep Learning, deep learning models, deep neural network models, inference model, inference service, infrastructure acquisition, Machine learning, malicious behaviors, neural nets, neural networks, operation costs, peculiar threat models, prediction API provider, prediction API providers, prediction APIs, prediction-as-a-service, Predictive models, Protocols, security of data, self-hosting costs, software-as-a-service business model, Tendermint},
	issn = {null},
	doi = {10.1109/SMARTCOMP.2019.00068},
	author = {G. Santhosh and F. De Vita and D. Bruneo and F. Longo and A. Puliafito}
}
@conference {8784003,
	title = {On the Use of LSTM Networks for Predictive Maintenance in Smart Industries},
	booktitle = {2019 IEEE International Conference on Smart Computing (SMARTCOMP)},
	year = {2019},
	month = {June},
	pages = {241-248},
	keywords = {Deep Learning, Engines, History, Industry 4.0, Keras, learning (artificial intelligence), Logic gates, LSTM networks hyperparameters, Machine learning, machine learning approach, machine learning techniques, maintenance engineering, maintenance scheduling, predictive maintenance, production engineering computing, recurrent neural nets, remaining life assessment, remaining useful life, RUL, Sensor systems, smart industries, Smart Industry, TensorFlow},
	issn = {null},
	doi = {10.1109/SMARTCOMP.2019.00059},
	author = {D. Bruneo and F. De Vita}
}
@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}
}
@conference {8421336,
	title = {A Deep Learning Approach for Indoor User Localization in Smart Environments},
	booktitle = {2018 IEEE International Conference on Smart Computing (SMARTCOMP)},
	year = {2018},
	month = {June},
	pages = {89-96},
	keywords = {Buildings, cloud computing, Deep Learning, deep learning approach, Global Positioning System, Indoor environments, indoor localization, indoor navigation, indoor user localization, indoor user location, integral part, learning (artificial intelligence), Machine learning, radionavigation, smart environments, smart services, telecommunication computing, TensorFlow, user position, Wi-Fi fingerprint, Wireless communication, Wireless fidelity, wireless LAN},
	doi = {10.1109/SMARTCOMP.2018.00078},
	author = {F. De Vita and D. Bruneo}
}
