Large scale WSNs node localization based on salp swarm algorithm using density peak clustering strategy

To improve the node localization accuracy of large-scale wireless sensor networks (WSNs), a node localization method for WSNs using density peak clustering to optimize the Salp Swarm Algorithm is proposed. Firstly, the block-based non-ranging WSNs node localization model is established, adaptively d...

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Veröffentlicht in:Wireless networks Jg. 31; H. 7; S. 4451 - 4463
Hauptverfasser: Liu, Zhouzhou, Jin, Cong, Liu, Chao, Jiang, Guangyi, Jia, Nan, Chen, Nan, Peng, Han
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.10.2025
Springer Nature B.V
Schlagworte:
Accuracy
Algorithms
Clustering
Communication
Communications Engineering
Computer Communication Networks
Density
Electrical Engineering
Energy consumption
Engineering
Extreme values
Global positioning systems
GPS
IT in Business
Localization
Localization method
Networks
Nodes
Optimization
Optimization algorithms
Original Paper
Sensors
Wireless sensor networks
Salp swarm algorithm
Density peak clustering algorithm
Wireless sensor networks
Accuracy
Location
ISSN:1022-0038, 1572-8196
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Zusammenfassung:To improve the node localization accuracy of large-scale wireless sensor networks (WSNs), a node localization method for WSNs using density peak clustering to optimize the Salp Swarm Algorithm is proposed. Firstly, the block-based non-ranging WSNs node localization model is established, adaptively determines the number of WSNs subregion divisions, and the location problem is abstracted as the optimal extreme value solution problem. Secondly, the improved density peak clustering (IDPC) algorithm and the improved salp swarm algorithm (ISSA) algorithm are designed for adaptive determination of hyper-parameters by defining the disparity truncation distance judgment index and two-stage approximation computation to improve the effectiveness of IDPC clustering. The IDPC is used to cluster the spatial characteristics of bottlenose sea squirt populations, adaptively determine leader and follower groups, and redefine the individual evolutionary approach to improve the global convergence accuracy of ISSA. Finally, ISSA is employed to solve the optimal extreme value problem of node location. The simulation results show that compared with the existing node location algorithm, the localization errors of the proposed method are reduced by about 65.83% and 23.93%.
Bibliographie:ObjectType-Article-1
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ISSN:1022-0038
1572-8196
DOI:10.1007/s11276-025-04011-4