Framework for the Identification of Rare Events via MAchine learning and IoT Networks

FIREMAN - Framework for the Identification of Rare Events via MAchine learning and IoT Networks

Start:	01 May 2019
End:	01 May 2022
Funding:	European
Status:	Ongoing
Division:	Communication Technologies
Department:	Machine to Machine Communications (M2M)
Web:	https://fireman-project.eu/

T. Alexopoulos, C. Kalalas, G. Korres, On the Imputation of Power System Measurement Streams with Imperfect Wireless Communication , in Proceedings of IEEE Global Power, Energy and Communication Conference 2020 (IEEE GPECOM '20), 20-23 October 2020, virtual event.

C. Kalalas, J. Alonso-Zarate, Lightweight and Space-efficient Vehicle Authentication based on Cuckoo Filter , in Proceedings of 2020 IEEE 5G World Forum (IEEE 5G-WF '20), 10-12 September 2020, virtual event.

D. Gutierrez-Rojas, M. Ullah, I. T. Christou, G. Almeida, P. H. J. Nardelli, D. Carrillo, J. M. Sant’Ana, H. Alves, M. Dzaferagic, A. Chiumento, C. Kalalas, Three-layer Approach to Detect Anomalies in Industrial Environments based on Machine Learning , in Proceedings of IEEE International Conference on Industrial Cyber-Physical Systems (ICPS 2020), 9-12 June 2020, virtual event.

R. Sedar, C. Kalalas, F. Vázquez-Gallego, J. Alonso-Zarate, Intelligent Transport System as an Example of a Wireless IoT System , Chapter in Wireless Networks and Industrial IoT, published by Springer International. October 2020. ISBN 978-3-030-51473-0.

C. Kalalas, J. Alonso-Zarate, Sensor Data Reconstruction in Industrial Environments with Cellular Connectivity , in Proceedings of IEEE International Symposium on Personal, Indoor and Mobile Radio Communications 2020 (IEEE PIMRC ’20), virtual event, 31 August-3 September 2020.

D. Carrillo-Melgarejo, C. Kalalas, A. S. Sena, P. Nardelli, G. Fraidenraich, Reconfigurable Intelligent Surface-Aided Grant-Free Access for Uplink URLLC , in Proceedings of 6G Wireless Summit 2020 (6G Summit), 17 March 2020, virtual event.

FIREMAN - Framework for the Identification of Rare Events via MAchine learning and IoT Networks