no code implementations • 30 May 2023 • Saira Bano, Pietro Cassarà, Nicola Tonellotto, Alberto Gotta
The paper proposes a data-driven approach to air-to-ground channel estimation in a millimeter-wave wireless network on an unmanned aerial vehicle.
no code implementations • 30 May 2023 • Saira Bano, Achilles Machumilane, Pietro Cassarà, Alberto Gotta
Specifically, the authors show that generative models can be trained with a small available dataset to generate large datasets that can be used to train ML models for LOS estimation.
no code implementations • 1 May 2022 • Saira Bano, Achilles Machumilane, Lorenzo Valerio, Pietro Cassarà, Alberto Gotta
The federated gateways of 3D network help to enhance the global knowledge of network traffic to improve the accuracy of anomaly and intrusion detection and service identification of a new traffic flow.
no code implementations • 28 Apr 2022 • Achilles Machumilane, Alberto Gotta, Pietro Cassarà, Claudio Gennaro, Giuseppe Amato
The simulation results show that our scheduler can target a very low loss rate at the receiver by dynamically adapting in real-time the scheduling policy to the path conditions without performing training or relying on prior knowledge of network channel models.
no code implementations • 3 Feb 2022 • Valerio De Caro, Saira Bano, Achilles Machumilane, Alberto Gotta, Pietro Cassará, Antonio Carta, Rudy Semola, Christos Sardianos, Christos Chronis, Iraklis Varlamis, Konstantinos Tserpes, Vincenzo Lomonaco, Claudio Gallicchio, Davide Bacciu
This paper presents a proof-of-concept implementation of the AI-as-a-Service toolkit developed within the H2020 TEACHING project and designed to implement an autonomous driving personalization system according to the output of an automatic driver's stress recognition algorithm, both of them realizing a Cyber-Physical System of Systems.
no code implementations • IEEE Transactions on Vehicular Technology 2022 • Pietro Cassarà, Alberto Gotta, Lorenzo Valerio
In this work, we address such a problem by proposing a federated feature selection algorithm where all the AVs collaborate to filter out, iteratively, the redundant or irrelevant attributes in a distributed manner, without any exchange of raw data.
no code implementations • 14 Jul 2021 • Davide Bacciu, Siranush Akarmazyan, Eric Armengaud, Manlio Bacco, George Bravos, Calogero Calandra, Emanuele Carlini, Antonio Carta, Pietro Cassara, Massimo Coppola, Charalampos Davalas, Patrizio Dazzi, Maria Carmela Degennaro, Daniele Di Sarli, Jürgen Dobaj, Claudio Gallicchio, Sylvain Girbal, Alberto Gotta, Riccardo Groppo, Vincenzo Lomonaco, Georg Macher, Daniele Mazzei, Gabriele Mencagli, Dimitrios Michail, Alessio Micheli, Roberta Peroglio, Salvatore Petroni, Rosaria Potenza, Farank Pourdanesh, Christos Sardianos, Konstantinos Tserpes, Fulvio Tagliabò, Jakob Valtl, Iraklis Varlamis, Omar Veledar
This paper discusses the perspective of the H2020 TEACHING project on the next generation of autonomous applications running in a distributed and highly heterogeneous environment comprising both virtual and physical resources spanning the edge-cloud continuum.