WSN(Wireless Sensor Network), as the front-end of data acquisition of IoT(Internet of Things), its data always has the properties of massive, dynamic, correlated, and polymorphic, causing big troubles for the subsequent series of signal processing. Dense sensor nodes are randomly deployed in the monitoring field, so their perceived information cannot be independent so that there must be data redundancy. Therefore, considering the properties of complicated correlation in data acquisition, we propose a mixed matrix decomposition approach based on NMF(Non-negative Matrix Factorization) and 2-SVD-QR(Double-Singular-Value-QR) to optimize WSN: 1. Turn off redundant sensor nodes and retain a few nodes collectively to approximate the raw data output of WSN; 2. Explicit this method to largely eliminate the collected redundant data by the sensor in a coherent time. This approach not only reduces the amount of data that needs to be collected, but also significantly saves energy consumption and prolongs network lifetime. Experimental results verify that this method can effectively eliminate the correlation between the raw collected sensor data and highly improve the data (CR)compression ratio under the premise of ensuring the data reconstruction accuracy, and it is also better than existed WSN data compression approaches in terms of CR and reconstruction accuracy, which demonstrates the effectiveness of this mixed matrix decomposition.

WSN（Wireless Sensor Network，无线传感器网络）作为物联网数据采集的前端，其数据始终具有海量，动态，关联和多态的特性，给后续的一系列信号处理带来很大麻烦。密集的传感器节点随机部署在监视区域中，因此它们的感知信息不能独立，因此必须具有数据冗余性。因此，考虑到数据采集中复杂相关的特性，我们提出了一种基于NMF（非负矩阵分解）和2-SVD-QR（Double-SingularValue-QR）的混合矩阵分解方法来优化WSN：1。关闭冗余传感器节点并集中保留几个节点，以近似WSN的原始数据输出；2。显式此方法可在相干时间内极大地消除传感器收集的冗余数据。这种方法不仅减少了需要收集的数据量，而且还大大节省了能耗并延长了网络寿命。实验结果证明，该方法在保证数据重构精度的前提下，可以有效消除原始采集的传感器数据之间的相关性，大大提高了数据（CR）的压缩率，在性能上也优于现有的WSN数据压缩方法。 CR和重构精度的比较，证明了这种混合矩阵分解的有效性。