Network intelligence vs. jamming in underwater networks: how learning can cope with misbehavior - Mertens, J. S.; Panebianco, A.; Surudhi, A.; Prabagarane, N.; Galluccio, L. (Frontiers in Communications and Networks, 2023)
In this paper, we present a machine learning technique to counteract jamming attacks in underwater networks. Indeed this is relevant in security applications where sensor devices are located in critical regions, for example in case of national borders surveillance or for identifying any unauthorized intrusion. To this aim, a multi-hop routing protocol which relies on the exploitation of a Q-Learning methodology is presented with the focus on increasing reliability in data communication and network lifetime. Performance results assess the effectiveness of the proposed solution as compared to other efficient state of the art approaches.