A push-based probabilistic method for source location privacy protection in underwater acoustic sensor networks
journal contributionposted on 2021-08-03, 13:06 authored by Hao Wang, Guangjie Han, Eve ZhangEve Zhang, Ling Xie
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 multi-path 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.
National Natural Science Foundation of China under Grant No. 62072072, No. 62072155 and No. 62002099
Open fund of State Key Laboratory of Acoustics under Grant SKLA202102
- Aeronautical, Automotive, Chemical and Materials Engineering
- Aeronautical and Automotive Engineering