An accurate RSS/AoA-based localization method for internet of underwater things

Pourkabirian, Azadeh; Kooshki, Fereshteh; Anisi, Mohammad Hossein; Jindal, Anish

doi:10.1016/j.adhoc.2023.103177

Cited by 11 publications

(5 citation statements)

References 35 publications

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“…Xu et al developed an efficient underwater localization method based on semi-definite programming (SDP) [40]. Pourkabirian et al integrated RSS measurements and angle information to create a hybrid localization scheme using SDP efficiently [46]. Gao et al proposed a similar hybrid scheme that combines RSS-based localization with time-of-flight information, introducing a consensus fusion estimator to mitigate falsification attacks [47].…”

Section: Related Workmentioning

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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Section: Related Workmentioning

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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“…In ISAC-based IoUT networks, localization and sensing play pivotal roles as they rely on techniques to precisely determine the positions of IoUT nodes, enabling effective communication and data collection in oceanographic and environmental monitoring applications. As mentioned earlier, various techniques, including RSS/AOA-based localization, have been developed to address the challenges posed by the underwater environment, including signal propagation delays, multi-path effects, and uncertainties in anchor positions due to currents [48]- [50]. As highlighted in various studies, research efforts focus on enhancing the efficiency and accuracy of localization algorithms to overcome these obstacles and improve the overall performance of ISACbased IoUT networks.…”

Section: Related Workmentioning

confidence: 99%

ISAC-Enabled Underwater IoT Network Localization: Overcoming Asynchrony, Mobility, and Stratification Issues

Jehangir,

Majid Ashraf,

Amin Khalil

et al. 2024

IEEE Open J. Commun. Soc.

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In oceanographic and environmental monitoring, achieving precise localization and sensing through Integrated Sensing and Communication (ISAC) within the Internet of Underwater Things (IoUT) networks is paramount. However, ISAC-based IoUT systems present distinctive challenges, including depth-dependent propagation speed, asynchronous clock synchronization, and node mobility. This paper introduces an efficient asynchronous localization method explicitly tailored for ISAC-based IoUT networks, which effectively addresses both the stratification effect and node mobility. Our approach centers on an iterative least squares (LS) algorithm designed to localize Autonomous Underwater Vehicles (AUVs) while carefully considering propagation delay and location estimation. Furthermore, we introduce a mobility model grounded in target sensing mechanisms that rely on AUVs' spatial coordinates and propulsion velocities, thereby enhancing the accuracy of target position estimation. We propose a novel precoding design for sensing using random acoustic signals within IoUT networks. To validate the effectiveness of our method, we conduct comprehensive Monte Carlo simulations and benchmark the results against stateof-the-art techniques. The findings demonstrate a significant reduction in estimation errors, confirming the superior efficiency of our approach compared to existing methods.

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“…To name a few, Mei et al (2022a) investigated the optimal deployment of autonomous surface vehicles (ASVs) to minimize underwater localization errors. Pourkabirian et al (2023) have proposed a semidefinite programming (SDP) method to mitigate errors caused by path loss. Mei et al (2020a) studied a localization scheme with hybrid loss, including path and absorption losses.…”

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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An accurate RSS/AoA-based localization method for internet of underwater things

Cited by 11 publications

References 35 publications

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

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

ISAC-Enabled Underwater IoT Network Localization: Overcoming Asynchrony, Mobility, and Stratification Issues

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

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