计算机应用研究Issue(1)：185-188,4.DOI:10.3969/j.issn.1001-3695.2016.01.043
基于深度学习的无线传感器网络数据融合
Data aggregation in wireless sensor networks based on deep learning
摘要
Abstract
Data fusion algorithms widely used BP neural network to extract and classify the node data features in wireless sen-sor networks.In order to overcome the shortcomings of BP neural network leading to poor performance for data fusion,such as low convergence speed,local optimal and bad generalization ability,this paper proposed a data fusion algorithm SAESMDA combined with deep learning technology and wireless sensor network clustering routing protocol.SAESMDA used deep learning model SAESMbased on stacked autoencoder(SAE)instead of the BP neural network,algorithm firstly trained SAESMin sink node and generated clusters for network,then used SAESMto exacted node data features in cluster nodes,finally the data fea-tures in the same class would be fused and sent to sink node by cluster heads.Simulation experiments show that compared with BPNDA based on the BP neural network ,SAESMDA has a higher feature extraction and classification accuracy with the simi-lar network energy consumption.关键词
无线传感器网络/数据融合/深度学习/自动编码器Key words
wireless sensor networks/data fusion/deep learning/autoencoder分类
信息技术与安全科学引用本文复制引用
邱立达,刘天键,傅平..基于深度学习的无线传感器网络数据融合[J].计算机应用研究,2016,(1):185-188,4.基金项目
国家自然科学基金资助项目（51277091）；福建省科技计划重点项目（2011H0017）；福建省教育厅科技计划项目（JA12263）；福州市科技计划项目 （）
