基于结构化噪声矩阵补全的WSNs收集数据重建方法

Data Reconstruction in WSNs via Matrix Completion with Structural Noise

许多科学研究都需要对环境数据进行分析,这些环境数据通常是通过部署在研究区域内的无线传感器网络(Wireless sensor networks,WSNs)来收集的。收集数据的完整性和准确性决定了科研结果的可靠性。然而,在数据收集过程中普遍存在的数据丢失和错误影响了收集数据的可用性,为此需要利用收集到的数据重建完整的环境数据。基于环境数据低秩特性,将数据重建问题建模为L2,1范数正则化矩阵补全模型,提出一种基于结构化噪声矩阵补全的WSNs收集数据重建方法(Data reconstruction approach via matrix completion with structural noise,DRMCSN)。真实数据集上的实验结果表明,该方法性能优于现有算法,不仅能以较高的精度恢复缺失的环境数据,而且能辨识出收集到错误数据的传感器节点。

Many scientific work needs to analyze the environmental data which are usually collected by wireless sensor networks(WSNs)deployed in research areas.The integrity and accuracy of the collected data determine the reliability of the research results.However,data loss and error usually occur during the process of data collection,which affect the availability of collected data.Therefore,it is necessary to reconstruct the environmental data from the incomplete and erroneous sensory data.Based on the lowrank feature of the environmental data,an efficient data reconstruction approach via matrix completion with structural noise(DRMCSN)is proposed by formulating data reconstruction problem as a L2,1-norm regularized matrix completion model.Finally,experimental results on a real dataset demonstrate that the proposed approach can not only effectively reconstruct the environmental data,but also recognize the sensor nodes that collect erroneous data.