摘要
为降低无线传感网络的能量消耗,提出了一种基于神经网络的数据融合改进算法(NBPNA),该算法将无线传感网络的分簇路由协议与BP神经网络结合起来,通过神经网络方法对簇内节点采集到的信息进行数据拟合,提取训练拟合好的权值与阈值,把其作为信息融合值传输;同时再通过将上一次拟合好的权值与阈值赋予下一次网络训练来减少神经网络的训练步数,减少网络训练所需的耗能;通过实验验证,该算法可有效减少网络通信量,降低节点能耗,延长网络寿命,同时还验证了本算法在环境监测等方面的可行性和有效性。
To save energy for wireless sensor networks （WSNs）, NBPNA, a new data aggregation algorithm based on back--propaga- tion networks was proposed, which integrates a three--layer BP neural network with clustering routing protocol. We use it for data fusion in WSNs, and then send the weight and threshold rather than the raw data monitored from sensors to the sink, at the same time, using the weight and threshold in the last fitting as the input of the new fitting, the number of Neural Network training steps can be reduced greatly. Simulation results show that the proposed algorithm can effectively reduce data transmissions, so as to achieve energy efficiency in WSNs, and the lifetime of the network is prolonged. At the same time, this algorithm is also verified the feasibility and effectiveness of environmen- tal monitoring, etc.
出处
《计算机测量与控制》
北大核心
2014年第2期476-479,共4页
Computer Measurement &Control
基金
国家自然科学基金赞助项目(61072087)
山西省科技攻关项(20120313013-6)
关键词
无线传感网络
数据融合
神经网络
权值
阈值
wireless sensor networks
data aggregation
artificial neural networks
weight and threshold
