A Computationally Efficient Target Localization Algorithm in Underwater Wireless Sensor Networks

Mei, Xiaojun; Wu, Huafeng; Han, Dong‐Guk; Chen, Xinqiang; Xian, Jiangfeng; Han, Bing

doi:10.1109/icccs57501.2023.10150512

Cited by 3 publications

(1 citation statement)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Compared to other methods, RSS-based schemes can obtain location information without requiring additional facilities or time synchronization [17]. Therefore, there has been a surge of interest in RSS-based underwater target localization [18][19][20][21].…”

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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

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

Self Cite

View full text Add to dashboard Cite

show abstract

An ameliorated localization algorithm for compensating stratification effect based on improved underwater salp swarm optimization technique

Yadav,

Mohan Khilar,

Sharma

2024

Int J Communication

View full text Add to dashboard Cite

SummaryThe underwater acoustic sensor network is a fundamental source for ocean exploration. The potential applications of underwater acoustic sensor network (UASN) include seismic imaging, disaster prevention, mine reconnaissance, pollution monitoring, exploration of natural resources, and military surveillance. To acquire accurate results, implementing all applications of underwater sensor networks requires an adequate network connection and communication technology. The precise placement of underwater sensor nodes needs to be identified in order to achieve effective communication. To accomplish the requirement, the paper presents an efficient localization algorithm to compensate the stratification effect based on an improved underwater salp swarm optimization technique (LAS‐IUSSOT). To compute the location of sensor nodes with high accuracy, the nodes are initially randomly deployed in three‐dimensional underwater acoustic sensor network (3D‐UASN). After that, the hybridization of centroid‐based localization and the ray theory technique is used, and then, the degree of coplanarity is analyzed among the underwater sensor nodes. Then, the computation of the location of unknown nodes is performed using improved underwater salp swarm optimization technique (IUSSOT) to obtain the optimized location and compensate the impact of the stratification. The comparison of the simulation results of the existing algorithm and the proposed algorithm is performed. The LAS‐IUSSOT achieves 40.46% and 28.00% accuracy in terms of localization of underwater sensor nodes for both the sparse and dense regions in 3D‐UASN. The LAS‐IUSSOT achieves 49.39% and 62.57% accuracy in terms of ranging of underwater sensor nodes for both the sparse and dense regions in 3D‐UASN. Simulation results illustrate that the proposed algorithm outperforms the existing algorithm in terms of localization and ranging accuracy in both sparse and dense regions in 3D‐UASN, root mean square error (RMSE), normalized localization error (NLE), computation time, and convergence rate.

show abstract

An Energy-Efficient and Distributed Self-Adjusting Deployment Approach Based on Virtual Force for Underwater Sensor Networks

Shen,

Yin

2024

2024 9th International Conference on Computer and Communication Systems (ICCCS)

View full text Add to dashboard Cite

A Computationally Efficient Target Localization Algorithm in Underwater Wireless Sensor Networks

Cited by 3 publications

References 34 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

An ameliorated localization algorithm for compensating stratification effect based on improved underwater salp swarm optimization technique

An Energy-Efficient and Distributed Self-Adjusting Deployment Approach Based on Virtual Force for Underwater Sensor Networks

Contact Info

Product

Resources

About