Data aggregation of WSN based on neighborhood support and BP neural network

LIANG Jun-qing; ZHAO Jian-shi; LYU Xiao-lin

Chinese

您当前的位置：

首页 >

文章列表页 >

Data aggregation of WSN based on neighborhood support and BP neural network

更新时间：2026-09-04

- Data aggregation of WSN based on neighborhood support and BP neural network
- Vol. 36, Issue 8, Pages: 87-91(2019)
- 作者机构：
  
  青岛理工大学机构信息与控制工程学院,山东,青岛,266033
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2019
- 稿件说明：
移动端阅览
梁俊卿, 赵建视, 吕笑琳. 基于邻里支持和神经网络的WSN数据融合算法研究[J]. 微电子学与计算机, 2019, 36(8): 87-91.

LIANG Jun-qing, ZHAO Jian-shi, LYU Xiao-lin. Data aggregation of WSN based on neighborhood support and BP neural network[J]. 2019, 36(8): 87-91.
梁俊卿, 赵建视, 吕笑琳. 基于邻里支持和神经网络的WSN数据融合算法研究[J]. 微电子学与计算机, 2019, 36(8): 87-91. DOI：

LIANG Jun-qing, ZHAO Jian-shi, LYU Xiao-lin. Data aggregation of WSN based on neighborhood support and BP neural network[J]. 2019, 36(8): 87-91. DOI：

摘要

为了提高无线传感器网络(wireless sensor network

WSN)的可靠性、稳健性、减少错误及冗余信息的传输

提出了一种基于邻里支持度的动态分簇BP(Back Propagation)神经网络数据融合算法(Neighborhood-SupportBack-Propagation NetworksData Aggregation

NBNDA).其中动态分簇以及簇首的选择基于邻里支持度的大小以及节点的剩余能量等.同时

为了减少WSN中的通信量

在簇首处使用三层神经网络进行监测数据的特征提取

然后将特征值发送至汇聚节点.仿真实验表明

与LEACH协议相比

本文算法不仅能够提高WSN的可靠性和稳健性

又能减少数据冗余

延长网络寿命.

Abstract

To improve wireless sensor network (WSN) reliability、robustness and reduce error and redundant information transmission. A dynamic clustering BP neural network Data Aggregation algorithm based on neighborhood support is proposed. The dynamic clustering and the selection of cluster head are based on the state of the node

the neighborhood support and the residual energy of the node. At the same time

in order to reduce communications in WSN

the cluster head uses three-layer neural network for feature extraction of monitoring data

and then sends the feature value to the sink node. Simulation results show that this algorithm can not only improve the reliability and robustness of WSN

but also reduce data redundancy and extend the network life compared with the classic clustering protocol LEACH.

关键词

Keywords

references

范时平, 何超杰.基于NARX神经网络的分簇数据融合算法[J].计算机工程与设计, 2018, 39(4):938-942. DOI: 938

孙凌逸, 黄先祥, 蔡伟, 等.基于神经网络的无线传感器网络数据融合算法[J].传感技术学报, 2011, 24(1):122-127. DOI: 122

杨永健, 刘帅.基于SOFM神经网络的无线传感器网络数据融合算法[J].传感技术学报, 2013, 26(12):1757-1763. DOI: 1757

谭德坤, 付雪峰, 赵嘉, 等.基于异常数据驱动的WSN簇内数据融合方法[J].传感技术学报.2017, 30(2):306-312. DOI: 306

DING S F, SU C Y, YU J Z.An optimizing BP neuralnetwork algorithm based on genetic algorithm[J].Artificial Intelligence Review, 2011, 36(2):153-162. DOI: 153

余修武, 张枫, 周利兴, 等.WSN中事件驱动与信任分配加权的层次数据融合[J].传感技术学报, 2017, 30(12):1948-1953. DOI: 1948

张浩, 张静静.无线传感器网络数据融合算法综述[J].软件, 2017, 38(12):296-304. DOI: 296

ACHARYA S, TRIPATHY C R. A reliable fault-tolerantANFIS model based data aggregation scheme for Wireless Sensor Networks[J]. Journal of King Saud University - Computer and Information Sciences, 2017, 11(1):1319-1322. DOI: 1319

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Research on SDT Security Scheme for Wireless Sensor Network

Simulation on the DC characteristics of double heterojunction p-GaN HEMT with AlN/GaN hybrid buffer layer

A feature-oriented context understanding and slice analysis network for tuberculosis detection

Design of ultra-low power real-time clock circuits

High gain stability high temperature operational amplifier based on reverse body bias

Related Author

LIU Ning

Related Institution

Department of Information & Engineering, Liuzhou Vocational & Technical College

AI问答

Postal code：100079
Tel：（010）53879206 Email：tmw@bjxintong.com.cn
Technical support is provided by Beijing Founder electronics co., LTD 京ICP备09082226号-64 京公网安备11010602201714号
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰