Optimization of WSN localization algorithm based on improved multi-strategy seagull algorithm

Yu, Xiuwu; Liu, Yinhao; Liu, Yong

doi:10.1007/s11235-024-01137-2

Telecommun Syst

2024

DOI: 10.1007/s11235-024-01137-2

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Optimization of WSN localization algorithm based on improved multi-strategy seagull algorithm

Xiuwu Yu,

Yinhao Liu,

Yong Liu

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2024

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Cited by 3 publications

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JAYA NL-WSN: Jaya Algorithm for Node Localization Issue in Wireless Sensor Network

Mohar,

Goyal,

Kaur

2024

Wireless Pers Commun

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JAYA NL-WSN: Jaya Algorithm for Node Localization Issue in Wireless Sensor Network

Mohar,

Goyal,

Kaur

2024

Wireless Pers Commun

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Enhancing sand cat swarm optimization based on multi-strategy mixing for solving engineering optimization problems

Wang,

Han,

Zhang

et al. 2024

Evol. Intel.

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An improved DVHop localization algorithm using a novel angle based node reduction and optimization technique

Bhat,

Venkata

2024

Discov Appl Sci

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Wireless sensor networks are becoming increasingly popular across a range of applications. One notable use is in seismic exploration and monitoring for oil and gas reservoirs. This application involves deploying numerous sensor nodes across outdoor fields to measure backscattered waves, which are then used to create an image of the subsurface. These sensor nodes remain active in the field for several days and must be accurately localized to ensure efficient reservoir detection. However, the Distance Vector-Hop (DVHop) algorithm, despite its simplicity, is not suitable for accurate node localization in exploration fields due to obstructions. In this paper, we propose a modified DVHop algorithm specifically designed for precise localization in such environments. Proposed algorithm uses angles between intermediate nodes to identify and circumvent nodes affected by obstructions. Distance estimation is performed using this reduced set of nodes. The estimated distances between these nodes are subsequently solved using Velocity Pausing Particle Swarm Optimization to determine the nodes’ locations. When evaluated in environments resembling exploration fields, our algorithm demonstrated an improvement of 25% to 63% in Average Localization Accuracy compared to other hop-based localization algorithms under similar conditions.

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Optimization of WSN localization algorithm based on improved multi-strategy seagull algorithm

Cited by 3 publications

References 11 publications

JAYA NL-WSN: Jaya Algorithm for Node Localization Issue in Wireless Sensor Network

JAYA NL-WSN: Jaya Algorithm for Node Localization Issue in Wireless Sensor Network

Enhancing sand cat swarm optimization based on multi-strategy mixing for solving engineering optimization problems

An improved DVHop localization algorithm using a novel angle based node reduction and optimization technique

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