摘要
在无线传感器网络中，压缩感知是一种新兴的数据融合方法，能利用少量数据采样进行数据恢复。由于具有较好的节省能耗的性质，压缩感知受到研究人员越来越多的关注。然而，传统的应用于无线传感器网络中压缩感知方法是在汇聚节点得到所有节点的加权和。然后利用重构算法对整个网络中各节点的数据进行恢复，并没有考虑到网络节点的分布式的特性。因此，当网络拓扑较复杂时．应用压缩感知时数据需要传输的次数并不会低于利用最短路径树时数据需要传输的次数。在该文中，我们考虑如何将压缩感知技术更好的和网络节点的分布式结构相结合．使得该技术的更加符合无线传感器网络的需求。
In wireless sensor network, compressive sensing is a novel idea of data fusion, which only uses a small amount of data sampling for data recovery. As its advantage of energy saving, compressive sensing is attracting more and more attention of researchers. However, traditional compressive sensing in wireless sensor network is to get a weighted sum in the sink node and then recover the data of the whole network by reconstruction algorithm, which do not consider the feature of distributed in the network. Thus, the transmission times in compressive sensing will not be less than that in the SPT, when the topology of the network is complex. In this paper, we consider how to combine the compressive sensing and the distribution of nodes, which will make this technology more fit for wireless sensor network.
作者
潘澔
PAN Hao （Soochow University, Suzhou 215009, China）
出处
《电脑知识与技术》
2011年第7期4650-4653,共4页
Computer Knowledge and Technology
关键词
无线传感器
压缩感知
Wireless sensor
Comoressive sensing
