Abstract
Due to the limit spectrum resource in the underwater acoustic networks, underwater cognitive acoustic communication is a promising technique. The channel sharing mechanism in cognitive networks can improve the communication capacity efficiently. Jamming attack is a common deny of service attack in cognitive networks. In the underwater cognitive acoustic networks, the anti-jamming problem is quite different from cognitive radio networks. It calls for an effective anti-jamming strategy in the cognitive acoustic channel access. In this article, we propose an online learning anti-jamming algorithm called multi-armed bandit–based acoustic channel access algorithm to achieve the jamming-resilient cognitive acoustic communication. The imperfect channel sensing and the constraints of underwater acoustic communication are considered in the anti-jamming game. Under different kinds of jamming attacks, the channel utilization can be improved with our jamming-resilient approach.
Highlights
During the past decade, underwater wireless sensor networks (UWSNs) have attracted significant interests due to a variety of applications such as ocean environment monitoring, underwater target tracking, oceanography data collection
Since multi-armed bandit (MAB)-ACA algorithm just gives a heuristic solution to the jamming-resilient acoustic channel access problem, it is expected that our strategy can track the optimal strategy
Cognitive acoustic communication is a promising technique for the underwater acoustic networks
Summary
Underwater wireless sensor networks (UWSNs) have attracted significant interests due to a variety of applications such as ocean environment monitoring, underwater target tracking, oceanography data collection. Most of jamming-resilient methods for CRNs employ opportunistic channel sensing and utilization techniques.[13,14,15,16,17,18,19,20] In the works,[14,15,16,17,18,19] the opportunistic channel access strategy is designed through online learning algorithms. The existing anti-jamming approaches for CRNs are not suitable in the underwater environment It calls for a novel jamming-resilient approach for UCANs. A multi-armed bandit–based acoustic channel access (MAB-ACA) algorithm is proposed in this article. The probability is determined through online learning based on the current reward This approach shows some similarities with the anti-jamming algorithms in the CRNs, the properties of UCANs are considered in our approach. ‘‘Performance evaluation and discussions’’; in section ‘‘Conclusion,’’ we conclude the article
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