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Optimization of wireless sensor network node localization based on improved multi-population genetic algorithm
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AI
DOI：10.1007/s11227-026-08799-6.png)
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Abstract
En 中文
To address the low accuracy and poor stability of the traditional DV-Hop algorithm, this paper proposes an improved DV-Hop algorithm based on a multi-population genetic algorithm (MPGA). Based on the minimum mean square error criterion, the algorithm introduces a weighting factor to correct the average hop distance of anchor nodes. By incorporating a weighting matrix, the least squares method is modified to estimate the coordinates of unknown nodes. To further optimize the coordinates of unknown nodes while reducing the number of iterations, a multi-population genetic algorithm is employed. To evaluate the localization accuracy of the algorithm in anisotropic networks, a radio irregularity model is also considered. The simulation results show that compared with traditional DV-Hop algorithms and common heuristic optimization DV-Hop algorithms, this algorithm significantly reduces positioning errors and improves positioning stability. It can provide algorithmic foundation for subsequent implementation on parallel or distributed computing platforms.
Keywords:
Wireless sensor networks
Localization
DV-Hop
Weighting matrix
Multi-population genetic algorithm
Journal
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2.7
Papers:
990
Citations:
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