A Push-Based Probabilistic Method for Source Location Privacy Protection in Underwater Acoustic Sensor Networks
TL;DR: In this article , a push-based probabilistic method for source location privacy protection (PP-SLPP) is proposed to counter passive attacks in underwater acoustic sensor networks (UASNs).
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Abstract: As the research topics in ocean emerge, underwater acoustic sensor networks (UASNs) have become ever more relevant. Consequently, challenges arise with the security and privacy of the UASNs. Compared to the active attacks, the characteristics of passive attacks are more difficult to discriminate. Thus, the focus of this study is on the passive attacks in UASNs, where a push-based probabilistic method for source location privacy protection (PP-SLPP) is proposed. The fake packet technology and the multipath technology are utilized in the PP-SLPP scheme to counter the passive attacks, so as to protect the source location privacy in UASNs. Moreover, the Ekman drift current model is employed to simulate the underwater environment. And the mean shift algorithm and the k-means algorithm are adopted in the dynamic layer and static layer of the Ekman drift current model, respectively, to increase the stability of the clusters. Finally, an autonomous underwater vehicle (AUV) swarm is implemented to collect data in clusters. Through the comparison with existing data collection schemes in UASNs, the simulation results have demonstrated that the PP-SLPP scheme can achieve a longer safety period, with a minor compromise of energy consumption and delay.
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Citations
Position-Independent and Section-Based Source Location Privacy Protection in WSN
TL;DR: In this paper , a new privacy protection technique named position independent and section-based source location privacy (PSSLP) is developed, where a biased random walk and greedy routing using a three- or four-phase routing strategy is employed.
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A Multi-Round Game-based Source Location Privacy Protection Scheme with AUV enabled in Underwater Acoustic Sensor Networks
TL;DR: In this paper , a multi-round game between the source and adversary is proposed to protect the location privacy of the static source first and then initial attempts to protect dynamic source location privacy by fuzzing the remaining semantic information after the mobile source passes by.
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A Deep Learning-based Multi-Path Routing Protocol for Improving Security using Encryption in Underwater Wireless Sensor Networks
K. Prakash,S. Sathya +1 more
- 06 Jul 2023
TL;DR: This study proposes a deep learning-based multi-path routing protocol, MRP, for improving security in Underwater Wireless Sensor Networks (UWSN) using encryption, leveraging Spectral Social Spider optimization for feature selection and Improved Data Encryption Standard for secure data transmission.
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LSLPR: A Layering and Source-Location-Privacy-Based Routing Protocol for Underwater Acoustic Sensor Networks
Xiaojing Tian,Xiujuan Du,Lijuan Wang,Lei Zhao,Duoliang Han +4 more
TL;DR: Simulation results show the superiority and validity of the LSLPR protocol in terms of safety period, delay, and energy efficiency compared with the Push-based probabilistic method for source location privacy protection (PP-SLPP), stratification-based SLP (SSLP), and 2hop-AHH-VBF.
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AUV-Assisted Stratified Source Location Privacy Protection Scheme Based on Network Coding in UASNs
15 Jun 2023
TL;DR: In this paper , the authors proposed a stratified source location privacy protection scheme (SSLP-NC) with autonomous underwater vehicle (AUV) for a strong adversary that can decode data.
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Complete Coverage Autonomous Underwater Vehicles Path Planning Based on Glasius Bio-Inspired Neural Network Algorithm for Discrete and Centralized Programming
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