Recover Corrupted Data in Sensor Networks: A Matrix Completion Solution
Recover Corrupted Data in Sensor Networks: A Matrix Completion Solution
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DOI：
10.1109/tmc.2016.2595569
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发表时间：
2017-05
影响因子：
7.9
通讯作者：
Kun Xie;Xueping Ning;Xin Wang;Dongliang Xie;Jiannong Cao;Gaogang Xie;Jigang Wen
中科院分区：
文献类型：
--
作者：
Kun Xie;Xueping Ning;Xin Wang;Dongliang Xie;Jiannong Cao;Gaogang Xie;Jigang Wen
受无线传感器网络中硬件和无线条件的影响，原始传感器数据通常会有显著的数据丢失和损坏。现有的研究主要考虑在不存在数据损坏的情况下对随机缺失数据的插值。也没有处理连续缺失数据的策略。为了解决这些问题，本文提出了一种新的方法，基于矩阵完成（MC）恢复连续丢失和损坏的数据。通过对中国株洲地区196个气象传感器采集的大量气象数据进行分析，验证了气象数据具有低秩、时间稳定性和空间相关性等特征。此外，通过对真实的气象数据的仿真，我们还发现，在传统的矩阵补全方法中，连续数据损坏不仅严重影响了丢失数据和损坏数据的恢复精度，甚至污染了正常数据。出于这些观察，我们提出了一种新的主成分分析（PCA）为基础的计划，以有效地识别数据损坏的存在。我们进一步提出了一个两阶段的MC为基础的数据恢复方案，命名为MC两阶段，它适用于矩阵完成技术，充分利用环境数据的固有特性，恢复数据矩阵，由于数据丢失或损坏。最后，大量的模拟与现实世界的传感器数据表明，所提出的MC-两阶段的方法可以实现非常高的恢复精度在连续丢失和损坏的数据存在。
Affected by hardware and wireless conditions in WSNs, raw sensory data usually have notable data loss and corruption. Existing studies mainly consider the interpolation of random missing data in the absence of the data corruption. There is also no strategy to handle the successive missing data. To address these problems, this paper proposes a novel approach based on matrix completion (MC) to recover the successive missing and corrupted data. By analyzing a large set of weather data collected from 196 sensors in Zhu Zhou, China, we verify that weather data have the features of low-rank, temporal stability, and spatial correlation. Moreover, from simulations on the real weather data, we also discover that successive data corruption not only seriously affects the accuracy of missing and corrupted data recovery but even pollutes the normal data when applying the matrix completion in a traditional way. Motivated by these observations, we propose a novel Principal Component Analysis (PCA)-based scheme to efficiently identify the existence of data corruption. We further propose a two-phase MC-based data recovery scheme, named MC-Two-Phase, which applies the matrix completion technique to fully exploit the inherent features of environmental data to recover the data matrix due to either data missing or corruption. Finally, the extensive simulations with real-world sensory data demonstrate that the proposed MC-Two-Phase approach can achieve very high recovery accuracy in the presence of successively missing and corrupted data.