To Hide Private Position Information in Localization for Internet of Underwater Things

Yan, Jing; Meng, Yuan; Luo, Xiaoyuan; Guan, Xinping

doi:10.1109/jiot.2021.3068298

Cited by 17 publications

(6 citation statements)

References 50 publications

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“…Poursheikhali and Zamiri-Jafarian (2021) developed an array-RSS localization method based on an iterative algorithm to mitigate the errors caused by inhomogeneous underwater media. Yan et al (2021) investigated underwater localization using RSS information with a malicious anchor detection strategy under a stratification effect. (Zhang et al, 2016) have verified the stratified propagation impacts on localization accuracy via simulation results.…”

Section: Related Workmentioning

confidence: 99%

Navigating the depths: a stratification-aware coarse-to-fine received signal strength-based localization for internet of underwater things

Mei,

Han,

Saeed

et al. 2023

Front. Mar. Sci.

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Underwater wireless sensor networks (UWSNs) are the primary enabling technology for the Internet of underwater things (IoUT), with which all underwater objects can interact and communicate. In UWSNs, localization is vital for military or civilized applications since data collected without location are meaningless. However, accurate localization using acoustic signals in UWSNs is challenging, especially for received signal strength (RSS)-based techniques. The adverse effect of hybrid loss (path and absorption loss) and stratified propagation may severely impact localization accuracy. Even though some schemes have been proposed in the literature, the accuracy is unsatisfactory. To this end, this study proposes a coarse-to-fine localization method (CFLM). The problem is reformed into an alternating nonnegative constrained least squares (ANCLS) framework, where a constrained ellipse adjustment (CEA) using block principal pivoting is proposed to obtain the coarse estimation. A refined step using a Taylor series expansion is then further presented, in which a corrected solution is acquired by iteration. Additionally, this study derives the Cramér-Rao lower bound (CRLB) to evaluate the proposed method. Simulation results show that the proposed CFLM improves the localization accuracy by up to 66 percent compared with weighted least squares (WLS), privacy-preserving localization (PPSL), two-step linearization localization approach (TLLA), particle swarm optimization-based (PSO) localization, and differential evolution-based (DE) localization under different scenarios.

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Section: Related Workmentioning

confidence: 99%

Navigating the depths: a stratification-aware coarse-to-fine received signal strength-based localization for internet of underwater things

Mei,

Han,

Saeed

et al. 2023

Front. Mar. Sci.

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“…One contributing factor is the highly dynamic underwater environment, which introduces significant measurement noise [14,25]. Another challenge arises from the heterogeneous underwater environment, which causes stratified signal propagation and signal absorption [26,27]. These factors can severely impact localization accuracy [28].…”

Section: Introductionmentioning

confidence: 99%

Enhanced Target Localization in the Internet of Underwater Things through Quantum-Behaved Metaheuristic Optimization with Multi-Strategy Integration

Mei,

Miao,

Wang

et al. 2024

JMSE

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Underwater localization is considered a critical technique in the Internet of Underwater Things (IoUTs). However, acquiring accurate location information is challenging due to the heterogeneous underwater environment and the hostile propagation of acoustic signals, especially when using received signal strength (RSS)-based techniques. Additionally, most current solutions rely on strict mathematical expressions, which limits their effectiveness in certain scenarios. To address these challenges, this study develops a quantum-behaved meta-heuristic algorithm, called quantum enhanced Harris hawks optimization (QEHHO), to solve the localization problem without requiring strict mathematical assumptions. The algorithm builds on the original Harris hawks optimization (HHO) by integrating four strategies into various phases to avoid local minima. The initiation phase incorporates good point set theory and quantum computing to enhance the population quality, while a random nonlinear technique is introduced in the transition phase to expand the exploration region in the early stages. A correction mechanism and exploration enhancement combining the slime mold algorithm (SMA) and quasi-oppositional learning (QOL) are further developed to find an optimal solution. Furthermore, the RSS-based Cramér–Raolower bound (CRLB) is derived to evaluate the effectiveness of QEHHO. Simulation results demonstrate the superior performance of QEHHO under various conditions compared to other state-of-the-art closed-form-expression- and meta-heuristic-based solutions.

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“…The accurate and effective recognition of bionic robots will facilitate the secure navigation of AUVs and the safe transmission of underwater signals. 6,7 Camouflage is a vital skill for many creatures in nature. The lion disguises itself in the grass, waiting for its prey.…”

Section: Introductionmentioning

confidence: 99%

“…However, the proliferation of bionic robots and their unsupervised navigation will have a serious impact on marine information security. The accurate and effective recognition of bionic robots will facilitate the secure navigation of AUVs and the safe transmission of underwater signals 6 , 7 …”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Adversarial learning-based method for recognition of bionic and highly contextual underwater targets

Zhao

Meng

et al. 2023

J. Electron. Imag.

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.Underwater bionic robots are very similar to real underwater creatures in appearance and features. This makes it difficult for autonomous underwater vehicles (AUVs) to distinguish their appearance features. Moreover, the existing underwater target detection methods have large model sizes and many parameters. This leads to difficulty in practical application of such UAV methods. To solve this, we propose an underwater camouflaged target recognition algorithm based on an adversarial graph structure. First, the salient features and environmental features of underwater images are fully explored by a custom salient feature extraction network and a related environmental feature extraction network. Second, the camouflaged target graph structure and the natural target graph structure of the fusion environment feature are constructed to obtain the dependency between the target and environment features. Finally, an adaptive frame regression camouflaged target detection network is constructed based on adversarial learning to recognize camouflaged targets and natural targets. In contrast to the existing target recognition model in the simulation of the four datasets of concealed target detection, quantitative underwater image dataset, underwater image enhancement benchmark dataset, and underwater image dataset, the proposed model gets higher average recognition accuracy of 0.820, which is an improvement of 3.4%, and the number of parameters is only 1.2 m. Proved by the experiments, the proposed target recognition algorithm is more effective and superior to the existing algorithms.

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To Hide Private Position Information in Localization for Internet of Underwater Things

Cited by 17 publications

References 50 publications

Navigating the depths: a stratification-aware coarse-to-fine received signal strength-based localization for internet of underwater things

Navigating the depths: a stratification-aware coarse-to-fine received signal strength-based localization for internet of underwater things

Enhanced Target Localization in the Internet of Underwater Things through Quantum-Behaved Metaheuristic Optimization with Multi-Strategy Integration

Adversarial learning-based method for recognition of bionic and highly contextual underwater targets

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