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A trust update mechanism based on reinforcement learning in underwater acoustic sensor networks
journal contributionposted on 2020-10-01, 12:35 authored by Yu He, Guangjie Han, Jinfang Jiang, Hao Wang, Miguel Martinez-GarciaMiguel Martinez-Garcia
Underwater acoustic sensor networks (UASNs) have been widely applied in marine scenarios, such as offshore exploration, auxiliary navigation and marine military. Due to the limitations in communication, computation, and storage of underwater sensor nodes, traditional security mechanisms are not applicable to UASNs. Recently, various trust models have been investigated as effective tools towards improving the security of UASNs. However, the existing trust models lack flexible trust update rules, particularly when facing the inevitable dynamic fluctuations in the underwater environment and a wide spectrum of potential attack modes. In this study, a novel trust update mechanism for UASNs based on reinforcement learning (TUMRL) is proposed. The scheme is developed in three phases. First, an environment model is designed to quantify the impact of underwater fluctuations in the sensor data, which assists in updating the trust scores. Then, the definition of key degree is given; in the process of trust update, nodes with higher key degree react more sensitively to malicious attacks, thereby better protecting important nodes in the network. Finally, a novel trust update mechanism based on reinforcement learning is presented, to withstand changing attack modes while achieving efficient trust update. The experimental results prove that our proposed scheme has satisfactory performance in improving trust update efficiency and network security.
National Key Research and Development Program, No.2018YFC0407900
National Natural Science Foundation of China under Grant No. 61971206
Open fund of State Key Laboratory of Acoustics under Grant SKLA201901
- Mechanical, Electrical and Manufacturing Engineering
Published inIEEE Transactions on Mobile Computing
Pages811 - 821
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
- AM (Accepted Manuscript)
Rights holder© IEEE
Publisher statementPersonal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
DepositorDr Miguel Martinez Garcia. Deposit date: 30 September 2020