无线传感器网络中一种基于时空相关性的数据聚合方法
首发时间:2016-01-04
摘要:本文基于分簇结构的无线传感器网络,利用节点采集数据的时间、空间相关性,结合线性回归分析与压缩感知技术提出了基于时空间相关性的簇内数据聚合(STICDA)方法,在数据采集传输中进行数据压缩聚合处理,降低节点能耗。对于簇内成员节点,通过构建动态预测模型抑制节点的数据传输,降低通信开销。压缩感知(CS)理论表明,稀疏信号可以从少量线性投影中以高概率被精确恢复。因此在簇头节点上,STICDA提出了适用于资源受限设备的数据压缩方法,对簇内部同节点的数据进行联合编码压缩处理。仿真、分析表明,STICDA能有效降低簇内通信开销从而节省能量。
关键词: 无线传感器网络 数据聚合 线性回归 压缩感知 分簇
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A Spatial and Temporal Based Data Aggregation Method In Wireless Sensor Network
Abstract:On the basis of clustering structure wireles sensor network and in order to reduce energy consumption of each node, this paper presents Spatial-Temporal In-Cluster Data Aggregation(STICDA) method to carry out data aggregation processing during data acquisition and transmission. STICDA take advantage of the spatial and temporal correlation of data selected by each node and combine linear regression analysis with compressed sensing technique. For cluster member node in network, STICDA bulds dynamic forecasting model to supress its data transmission and reduce commmunication consumption. Compressed sensing theory shows that sparse signal can be recovered with high probability from its linear projection. Therefore, on the cluster head node, STICDA presents a data compression method for resource-constrained decice to carry out date compression process jointly for the date of each node in the cluster. Simulation results show that STICDA can effectively reduce the intra-cluster communication cost and save energy.
Keywords: Wireless Sensor Network Data Aggregation Linear Regression Compressed Sensing Clustering
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