Data fusion algorithm of wireless sensor network based on BP neural network optimized by improved grey wolf optimizer

doi:10.7523/j.ucas.2020.0004

Abstract

Abstract: In order to improve the accuracy of the fusion data in wireless sensor network, reduce the energy consumption, and extend the network lifetime, data fusion algorithm of wireless sensor network based on improved grey wolf optimizer to optimize BP neural network (IGWOBPDA) is proposed in this paper. Firstly to balance the global and local search capabilties, the improved considering the actual energy consumption and clustering of the wireless sensor network's transmitting nodes, a clustering scheme based on node residual energy parameters and node density parameters is proposed,which adjusts the weighting factors to adapt to the actual situation of the network data fusion transmission process. Compared with BPNDA algorithm and GAPSOBP algorithm, simulation results show that IGWOBPDA algorithm has better data fusion accuracy and convergence in different data sets, which can effectively reduce the amount of data transmission and node energy consumption, extend network survival time, and maintain stability under different network scales. control parameter and the method of dynamic weight update position is proposed in the paper, which aims at the problems that the initial value of BP neural network in wireless sensor network data fusion algorithm is sensitive and the result can easily be the local optimal solution. Secondly,

Key words: wireless sensor network, data fusion algorithm, BP neural network, grey wolf optimizer, control parameter, weighting factor

CLC Number:

TN919

CAO Ke, TAN Chong, LIU Hong, ZHENG Min. Data fusion algorithm of wireless sensor network based on BP neural network optimized by improved grey wolf optimizer[J]. Journal of University of Chinese Academy of Sciences, 2022, 39(2): 232-239.

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