A novel predictive localization algorithm for underwater wireless sensor networks

Liu, Haiming; Xu, Bo; Liu, Bin

doi:10.1007/s11276-022-03107-5

Cited by 6 publications

(13 citation statements)

References 32 publications

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“…In the last few years, many researchers have carried out research on localization in UASN and reported their performance results based on different parameters such as localization error, ranging error, convergence time, RMSE, NLE, execution time, and inverted coverability in the literature. 11,12,14,15,[17][18][19] The comparative study of various underwater localization algorithms proposed by authors are classified and presented in Table 2. The classifications are shown based on The LASP (localization algorithm for compensating stratification effect based on improved PSO in UASN) has been proposed which calculates the location of the 2D coordinate point of target nodes in three-dimensional networks using range free techniques and got the location using ray theory method, the proposed algorithm gives an improvement in localization accuracy, ranging accuracy and convergence time.…”

Section: Related Workmentioning

confidence: 99%

“…Also, it calculates the location of the target node in a closed surface (Sphere) using a two-point (TB signal), and the accuracy of location is computed using the minimum mean square error (MMSE) method. 12 The prediction-based (MP-LBG) range-based algorithm was introduced for UWSN which provides high localization accuracy and coverage compared with the existing algorithm. In order to achieve high accuracy in localization, they have applied TOA, GWA-HSS (Gray wolf optimizer-Hunting Step Size), and the clock synchronization technique.…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…[6][7][8] In the range-based localization technique, localization of target nodes calculation is based on Time of Arrival (ToA), Time difference of arrival (TDoA), RSSI-based, and range-free localization techniques of target nodes analysis is performed using centroid-based, DV-hop, and amorphous algorithm. [9][10][11][12] A novel localization strategy is needed to improve localization accuracy, which can be achieved by transforming a location problem with target nodes into a problem with an ideal solution in UASN. PSO (Particle Swarm Optimization) is a metaheuristic algorithm inspired by the social behavior of birds flocking.…”

Section: Introductionmentioning

confidence: 99%

“…Localization techniques are classified into range‐free and range‐based localization, and the basic difference between both localization techniques is listed in Table 1 6‐8 . In the range‐based localization technique, localization of target nodes calculation is based on Time of Arrival (ToA), Time difference of arrival (TDoA), RSSI‐based, and range‐free localization techniques of target nodes analysis is performed using centroid‐based, DV‐hop, and amorphous algorithm 9‐12 . A novel localization strategy is needed to improve localization accuracy, which can be achieved by transforming a location problem with target nodes into a problem with an ideal solution in UASN.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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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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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Wireless network topology optimization algorithm based on discrete variables

Ran

2024

Systems and Soft Computing

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A novel predictive localization algorithm for underwater wireless sensor networks

Cited by 6 publications

References 32 publications

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

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

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

Wireless network topology optimization algorithm based on discrete variables

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