Data fusion of WSN based on fireworks algorithm optimization

被引：0
|
作者：
Yu X. [1 ]
Li P. [1 ]
Liu Y. [1 ]
Huang L. [1 ]
机构：
[1] School of Resource Environment and Safety Engineering, University of South China, Hunan, Hengyang
来源：
Huazhong Keji Daxue Xuebao (Ziran Kexue Ban)/Journal of Huazhong University of Science and Technology (Natural Science Edition) | 2023年 / 51卷 / 05期
关键词：
BP neural network; chaos search strategy; data fusion; fireworks algorithm; wireless sensor network (WSN);
D O I：
10.13245/j.hust.230518
中图分类号：
学科分类号：
摘要：
To reduce the redundancy of data in wireless sensor network (WSN)，prolong the network life cycle and overcome the disadvantages of slow convergence，low parameter accuracy and easy falling into local optimal values of data fusion algorithm based on BP (back propagation) neural network，a data fusion algorithm for wireless sensor network optimized by fireworks algorithm (IFWABP) was proposed．First，Tent chaotic map was used to improve the initial position distribution of fireworks population in fireworks algorithm，and make the initial fireworks distribution more uniform．Then，the improved fireworks algorithm was used to optimize the parameters of BP neural network such as weight matrix and threshold matrix for data fusion．Finally，the performance of the algorithm was tested and analyzed through simulation experiments． Simulation results show that compared with other algorithms，IFWABP algorithm improves the accuracy of WSN data fusion，reduces the network energy consumption，and prolongs the network life cycle. © 2023 Huazhong University of Science and Technology. All rights reserved.
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页码：112 / 118
页数：6
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