An evolved algorithm for underwater acoustic sensor node localization enhancement using reference node

Saha, Souvik; Arya, Rajeev

doi:10.1016/j.phycom.2022.101827

Cited by 6 publications

(7 citation statements)

References 23 publications

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“…In addition, to enhance the accuracy and robustness of Range-free localization, some modern research have proposed various improvements, such as incorporating localization algorithms with mobility prediction [64], deploying mobile anchor nodes [65] using directional antennas [66], and integrating additional contextual information, such as RSSIs and AOA measurements [67].…”

Section: ) Range-free Strategymentioning

confidence: 99%

Optimization Algorithms for Wireless Sensor Networks Node Localization: An Overview

Ahmad,

Alhasan,

Wazirali

et al. 2024

IEEE Access

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Wireless Sensor Networks (WSNs) play a critical role in numerous applications, and the accurate localization of sensor nodes is vital for their effective operation. In recent years, optimization algorithms have garnered significant attention as a means to enhance WSN node localization. This paper presents an in-depth exploration of the necessity of localization in WSN nodes and offers a comprehensive review of optimization algorithms used for this purpose. The review encompasses a diverse range of optimization techniques, including evolutionary algorithms, swarm intelligence, and metaheuristic approaches. Key factors, such as localization accuracy, scalability, computational complexity, and robustness, are systematically evaluated and compared across various optimization algorithms. Additionally, the paper sheds light on the strengths and limitations of each optimization approach and discusses their applicability in different WSN deployment scenarios. The insights provided in this review serve as a valuable resource for researchers and practitioners seeking to optimize WSN node localization, thus promoting the efficient and reliable operation of WSNs in diverse real-world applications.

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Section: ) Range-free Strategymentioning

confidence: 99%

Optimization Algorithms for Wireless Sensor Networks Node Localization: An Overview

Ahmad,

Alhasan,

Wazirali

et al. 2024

IEEE Access

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“…The modified DV‐Hop localization approach considers the mobility effect of the static underwater TSNs. The algorithm computed the location of the target node with high localization accuracy, low convergence rate and high localization coverage as compared to that of the existing algorithm 24 . An improved underwater acoustical localization (UWAL) method based on TDOA is proposed.…”

Section: Related Workmentioning

confidence: 99%

“…The algorithm computed the location of the target node with high localization accuracy, low convergence rate and high localization coverage as compared to that of the existing algorithm. 24 An improved underwater acoustical localization (UWAL) method based on TDOA is proposed. This algorithm computes the location of sensors floating underwater.…”

Section: Related Workmentioning

confidence: 99%

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

Yadav,

Mohan Khilar,

Sharma

2024

Int J Communication

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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.

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“…The minimum value of the objective function leads to the more accurate location of the target nodes. Thus, computing the minimum value of the objective function depends on the value of the distance D ju and D jv which is calculated using Equations ( 25) and (26). In this paper enhanced version of IPSO is used to calculate the optimal position coordinates of target nodes.…”

Section: Improved-lasp Algorithmmentioning

confidence: 99%

“…Authors have focused on clock synchronization and node localization, and the simulation result shows that the algorithm outperforms the existing algorithm. The proposed 26 algorithm calculates the localization of target nodes based on improved DV‐Hop technique and school topper optimization (STO), considering the underwater mobility effect. The simulation result shows better localization accuracy and less computational time than the existing algorithm.…”

Section: Related Workmentioning

confidence: 99%

An efficient 3D localization algorithm for compensating stratification effect in underwater acoustic sensor network

Yadav

Khilar

2023

Trans Emerging Tel Tech

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Communication and networking plays a crucial role in data transmission in UASN. The sensor nodes in UASN are intermittently deployed randomly in the three‐dimension scenario. As a consequence, the three‐dimension based localization algorithm is the need for an hour. To fulfill the objective, we have proposed a localization algorithm I‐LASP (Improvement of localization algorithm for compensating stratification effect based on extended improved particle swarm optimization technique) in three‐dimensional UASN, based on compensation of stratification effect for the improvement of the performance parameters such as localization accuracy, ranging accuracy, convergence rate and execution time. To compute the accurate position of target nodes, EIPSO (Extended improved PSO) technique is applied, and the degree of coplanarity is checked before the calculation of distance among nodes in order to get the accurate location of target nodes. The Centroid method is used to initialize the position of sensor nodes, and the ray theory method is used to compensate the stratification effect on the layered ocean water. The proposed algorithm is compared to the existing LASP, Std PSO, and GNA‐ESSP (Gauss‐newton algorithm‐extended sound speed profile) localization algorithm. The proposed algorithm provides 34.50%, 38.87%, and 42.66% of high accuracy in terms of localization with low density of target sensor nodes and 37.96%, 29.58%, and 50.77% high accuracy in terms of localization with a high density of target sensor nodes respectively. The proposed algorithm is compared with LASP, GNA‐ESSP, and TDOA to obtain 66.84%, 71.14%, and 86.13% of high accuracy in terms of ranging with low density of target sensor nodes and 42.34%, 89.00%, and 95.08% high accuracy in terms of ranging with a high density of target sensor nodes respectively. Experimental results represents that the proposed algorithm obtains better performance in terms of localization accuracy, ranging accuracy, root mean square error, normalized localization error, execution time and convergence rate.

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An evolved algorithm for underwater acoustic sensor node localization enhancement using reference node

Cited by 6 publications

References 23 publications

Optimization Algorithms for Wireless Sensor Networks Node Localization: An Overview

Optimization Algorithms for Wireless Sensor Networks Node Localization: An Overview

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

An efficient 3D localization algorithm for compensating stratification effect in underwater acoustic sensor network

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