摘要
为提高无线传感器网络(WSN)数据融合效率,减少网络的通信量以及降低传感网的能量消耗,提出一种基于粒子群优化BP神经网络的无线传感器网络数据融合算法;该算法将粒子群算法优化BP神经网络的权值和阈值后,与传感器网络分簇路由协议有机结合,将无线传感器网络中簇头和节点等同于BP神经网络里的神经元,利用优化后的BP神经网络有效地提取WSN数据融合原始数据之中的少量特征数据,之后把提取的特征数据发送到汇聚节点,进而提升数据融合效率,延长网络生存周期;仿真实验证明,与LEACH算法、BP神经网络和GABP算法相比,该算法可有效减少网络通信量,降低节点总能耗的15%,延长网络生存时间。
To improve wireless sensor network （WSN） data fusion efficiency and reduce network traffic and the energy consumption of the sensor network, proposed the particle swarm optimization BP neural network algorithm for wireless sensor network data fusion. The al- gorithm of particle swarm optimization BP neural network weights and threshold value, and the sensor network clustering routing protocols combine the wireless sensor network cluster head and nodes equivalent to the BP neural network in the neurons, the use of optimized the BP Neural Network data Fusion WSN effectively extract the raw data into a small number of feature data , then the extracted feature data is sent to the sink node , and thus enhance the efficiency of data fusion , to extend the network lifetime . Simulation results show that compared with LEACH algorithm, BP neural network and GABP algorithm, this algorithm can effectively reduce network traffic, reduce 15% of the node energy consumption and prolong network lifetime.
出处
《计算机测量与控制》
北大核心
2014年第4期1212-1214,1216,共4页
Computer Measurement &Control
基金
河南省科技厅基金项目(102102210020)
