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基于改进萤火虫优化神经网络的WSNs分簇路由协议
引用本文:戴剑勇,邓先红,王彬,汪恒浩.基于改进萤火虫优化神经网络的WSNs分簇路由协议[J].北京邮电大学学报,2020,43(3):131-137.
作者姓名:戴剑勇  邓先红  王彬  汪恒浩
作者单位:1. 南华大学 资源环境与安全工程学院, 衡阳 421001;
2. 湖南省铀尾矿库退役治理技术工程技术研究中心, 衡阳 421001
基金项目:国家自然科学基金项目(51174116);湖南省教育厅重点资助科研项目(18A235);铀矿冶放射性控制技术湖南省工程研究中心、湖南省铀尾矿库退役治理技术工程技术研究中心联合开放课题重点项目(2018YKZX1001)
摘    要:针对无线传感器网络中能耗不均衡问题,提出了一种基于改进萤火虫算法优化反向传播神经网络的非均匀分簇路由协议.通过在萤火虫算法中引进权重因子并增加4个评价指标,来平衡簇内负载和减少簇间的通信距离.结合BP神经网络,优化路径选择和簇首选举方式,达到最佳成簇效果.仿真结果表明,改进萤火虫算法优化BP神经网络的非均匀分簇路由协议能有效延长网络生命周期,节省能量,并均衡能耗.

关 键 词:BP神经网络  萤火虫算法  非均匀分簇  
收稿时间:2019-07-14

Clustering Routing Protocol for WSNs Based on Neural Network Optimization by Improved Firefly Algorithm
DAI Jian-yong,DENG Xian-hong,WANG Bin,WANG Heng-hao.Clustering Routing Protocol for WSNs Based on Neural Network Optimization by Improved Firefly Algorithm[J].Journal of Beijing University of Posts and Telecommunications,2020,43(3):131-137.
Authors:DAI Jian-yong  DENG Xian-hong  WANG Bin  WANG Heng-hao
Affiliation:1. School of Resources, Environment and Safety Engineering, University of South China, Hengyang 421001, China;
2. Hunan Province Engineering Technology Research Center of Uranium Tailings Treatment Technology, Hengyang 421001, China
Abstract:Aiming at solving the problem of uneven energy consumption in wireless sensor networks (WSNs),an uneven clustering routing protocol based on the improved firefly algorithm optimized back propagation(BP) neural network (IFABPUC) is proposed. To balance the intra-cluster load and reduce the inter-cluster communication distances,a weighting factor which takes into account four more evaluation indexes than the conventional firefly algorithm is embedded in the improved firefly algorithm. To achieve the best clustering, BP neural network is combined to optimize the way to path selection and cluster head election. Simulations show that IFABPUC can effectively extend the lifecycle of networks,save energy and balance energy consumption.
Keywords:back propagation neural network  firefly algorithm  uneven clustering  
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