摘要
针对某些特定场合无线传感器网络中传感器产生的数据时间和空间上的冗余和高度相关性,提出了一种面向数据相关性及权重的传感器网络采样优化算法DCACW。它基于聚合树结构连接整个网络,在各节点根据样本的相关性和节点权重进行数据融合。仿真实验结果表明,本算法采集的样本覆盖度更广,而且在聚合树中去除了冗余和相关的数据,保证了最终收集的样本差异性较强。
In consideration of the spatial and temporal redundancy and correlation in some wireless sensor networks,a novel data collection Algorithm based on correlation and weight （DCACW） is presented. Based on the aggregation tree network structure the data aggregates in the tree nodes according to the correlation of samples and the weight of nodes. The simulations results indicate that this data collection algorithm makes the degree of coverage even broader,and eliminates the spatial and temporal redundancy and correlation in the aggregation tree, ensures the variation in the ultimate sample.
出处
《通信技术》
2009年第12期122-124,共3页
Communications Technology
基金
国家自然科学基金资助项目(No.40671145)
广东省自然科学基金(No.06025838
No.8151064201000037)
关键词
无线传感器网络
数据融合
相关性
权重
wireless sensor networks
data aggregation
correlation
weight
