@article {DEVITA202030,
	title = {On the use of a full stack hardware/software infrastructure for sensor data fusion and fault prediction in industry 4.0},
	journal = {Pattern Recognition Letters},
	volume = {138},
	year = {2020},
	pages = {30 - 37},
	abstract = {Aspects related to prognostics are becoming a crucial part in the industrial sector. In this sense, Industry 4.0 is considered as a new paradigm that leverages on the IoT to propose increasingly more solutions to provide an estimate on the working conditions of an industrial plant. However, in context like the industrial sector where the number and heterogeneity of sensors can be very large, and the time requirements are very stringent, emerges the challenge to design effective infrastructures to interact with these complex systems. In this paper, we propose a full stack hardware/software infrastructure to collect, manage, and analyze the data gathered from a set of heterogeneous sensors attached to a real scale replica industrial plant available in our laboratory. On top of the proposed infrastructure we designed and implemented a fault prediction algorithm which exploits sensors data fusion with the aim to assess the working conditions of the industrial plant. The result section shows the obtained results in terms of accuracy from testing our proposed model and provides a comparison with a traditional Deep Neural Network (DNN) topology.},
	keywords = {Data fusion, Deep Learning, IIoT industrial testbed, Industry4.0},
	issn = {0167-8655},
	doi = {https://doi.org/10.1016/j.patrec.2020.06.028},
	url = {http://www.sciencedirect.com/science/article/pii/S0167865520302464},
	author = {Fabrizio [De Vita] and Dario Bruneo and Sajal 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}
}
@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}
}
