摘要
在无线传感器网络(WSNs)中,一般采用电池供电,节能是WSNs设计的研究重点。为了提高测量结果的准确度和降低网络的能耗,提出了一种两层模式数据融合方案。在传感器节点上用格林贝斯准则和顺序加权算法进行低层次数据融合,在簇头节点上用神经网络算法进行高层次数据融合。仿真实验结果表明:两层模式数据融合方案有效减少了网络中的数据传输量,提高了融合数据的精度,降低传感器节点的能耗。
Wireless sensor networks （WSNs）generally adopts battery as power, research of WSNs focus on energy- saving. To improve precision of measurement result and reduce network energy consumption, a two-layer mode datafusion program is proposed. Low-level data fusion based on Grubbs criterion and the order of weighted algorithm is used on sensor nodes, high-level data fusion based on neural network algorithm is used on duster head nodes. The simulation experimental results show that the two-layer mode data fusion scheme effectively reduces amount of data transmission, improves precision of the fusion data and reduces energy consumption of sensor nodes.
出处
《传感器与微系统》
CSCD
北大核心
2014年第1期147-149,160,共4页
Transducer and Microsystem Technologies
基金
国家自然科学基金资助项目(61163063
50764005)
关键词
无线传感器网络
数据融合
格林贝斯准则
神经网络
wireless sensor networks （WSNs）
data fusion
Grubbs verify check
neural network
