摘要
以应用于隧道结构健康监测的无线传感器网络为基础,针对长线形的隧道结构和分布式的节点布置,提出了超长线状多跳非均匀分簇结构.通过考虑节点剩余能量和优化簇头分簇半径,降低并平衡节点能耗.针对传感器数据冗余量大的问题,提出了基于超长线状分簇结构的分布式卡尔曼滤波融合算法.利用单节点不同时刻的数据,通过卡尔曼滤波器得到局部估计值,降低数据时间冗余度.在簇头节点端和汇聚节点端分别实现分布式卡尔曼滤波融合算法,降低数据空间冗余度,达到具有一致性的网络数据估计值.实验结果表明:该方法能有效实现超长线状分簇结构下的分布式数据融合,具有高可靠性和准确性.
Based on wireless sensor network used in the tunnel structure health monitoring,a long and linear topology with a multi-hop and uneven clustering structure was proposed to adapt to long and linear tunnel structure and distributed node deployment.Energy consumption of nodes was reduced and balanced by considering the residual energy of nodes and optimizing the clustering radius of cluster heads.Aimed at large redundancy of sensor data,a distributed Kalman filtering fusion algorithm based on long and linear clustering structure was proposed.Using sensor data of single node at different time,local estimates were obtained by using Kalman filter to reduce the temporal redundancy of data.The distributed Kalman filtering fusion algorithm was implemented in the cluster heads and the sink node separately to reduce the spatial redundancy of data and achieve a consistent estimation of the network data.Experiment results show that this method can effectively achieve distributed data fusion based on long and linear clustering structure with high reliability and accuracy.
作者
何斌
李刚
唐利敏
许点红
He Bin Li Gang Tang Limin Xu Dianhong(School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China)
出处
《华中科技大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2016年第11期70-74,共5页
Journal of Huazhong University of Science and Technology(Natural Science Edition)
基金
国家重点基础研究发展计划资助项目(2011CB013803)
赛尔网络下一代互联网技术创新项目(NGII20151205)
关键词
无线传感器网络
分簇
卡尔曼滤波
数据融合
结构健康监测
wireless sensor networks
clustering
Kalman filtering
data fusion
structural health monitoring
