Network intelligence vs. jamming in underwater networks: how learning can cope with misbehavior

Frontiers in Communications and Networks

Communications and Networks

A. Panebianco¹  N. Prabagarane²  L. Galluccio³  J. S. Mertens³  A. Surudhi⁴ 

[1] CNIT Research Unit at University of Catania, Catania, Italy;Department of Engineering, University of Palermo, Italy;Department of ECE Sri Sivasubramaniya Nadar College of Engineering, Chennai, India;Department of Electrical Electronic and Computer Engineering, University of Catania, Italy;CNIT Research Unit at University of Catania, Catania, Italy;Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, United States;

关键词: underwater networks;    jamming attacks;    Q-learning;    routing;    Markov model;   

DOI  :  10.3389/frcmn.2023.1179626

 received in 2023-03-04, accepted in 2023-05-15,  发布年份 2023

来源: Frontiers

【 摘 要 】

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 the case of national border surveillance or for identifying any unauthorized intrusion. To this aim, a multi-hop routing protocol that relies on the exploitation of a Q-learning methodology is presented with a 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.

【 授权许可】

Unknown   
Copyright © 2023 Mertens, Panebianco, Surudhi, Prabagarane and Galluccio.