Localization in Underwater Acoustic IoT Networks: Dealing With Perturbed Anchors and Stratification

Mei, Xiaojun; Han, Dezhi; Saeed, Nasir; Wu, Huafeng; Han, Bing; Li, Kuan-Ching

doi:10.1109/jiot.2024.3360245

Cited by 9 publications

(2 citation statements)

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“…Consequently, various underwater localization techniques have been developed, typically comprising two main procedures: range acquisition and location computation. The range acquisition phase employs various ranging techniques, such as time of arrival (TOA), time difference of arrival (TDOA), angle of arrival (AOA), and received signal strength (RSS)-based schemes [12][13][14][15]. The location computation phase then utilizes algorithms to calculate the precise position.…”

Section: Introductionmentioning

confidence: 99%

“…Despite the development of several RSS-based localization techniques, accurately determining location information remains a challenge [22][23][24]. 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].…”

Section: Introductionmentioning

confidence: 99%

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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: Introductionmentioning

confidence: 99%