传感技术学报Issue(12)：1757-1763,7.DOI:10.3969/j.issn.1004-1699.2013.12.026
基于SOFM神经网络的无线传感器网络数据融合算法
Data Aggregation in WSN Based on SOFM Neural Network
摘要
Abstract
In order to reduce the communication traffic in wireless sensor networks and reduce energy consumption and increase the network lifetime,a data fusion algorithm based on SOFM( Self-Organizing Feature Mapping) neural network( SOFMDA) was proposed, which combined self-organizing neural networks and wireless sensor network clustering routing protocol. Each node in WSN performed is a neuron. According to the data characteristics, SOFMDA made classification,in which the data with same characteristic was classified into the same class,then got the feature data which is sent to the Sink node. Thereby reducing the amount of data sent,to extend the lifetime of the network. Simulation results show that compared with conventional data fusion methods,SOFMDA can guarantee the accuracy of the data under the premise of effectively reducing network traffic,extending the network lifetime. During the simulation,the performance of SOFMDA reached 150% of LEACH's.关键词
无线传感器网络/数据融合算法/自组织映射神经网络/特征提取Key words
wireless sensor networks/data aggregation/SOFM neural network/feature extraction分类
信息技术与安全科学引用本文复制引用
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,(12):1757-1763,7.基金项目
国家自然科学基金项目(61272412) （61272412）
吉林省科技发展计划项目(20120303) （20120303）
教育部博士点基金项目(20120061110044) （20120061110044）
