论文标题
通过重建时间变化的图形信号恢复缺失的传感器数据
Recovery of Missing Sensor Data by Reconstructing Time-varying Graph Signals
论文作者
论文摘要
无线传感器网络是当前时代最有前途的技术之一，因为它们的尺寸较小，成本较低和易于部署。随着无线传感器数量的增加，生成丢失数据的概率也会上升。如果用于决策，这种不完整的数据可能会导致灾难性后果。有很多关于这个问题的文献。但是，大多数方法显示出大量数据丢失时性能降解。受图形信号处理的新兴领域的启发，本文对无线传感器网络中的Sobolev重建算法进行了一项新研究。几个公开可用数据集的实验比较表明，该算法超过了多个最新技术的最大利润率为54％。我们进一步表明，即使在大规模数据丢失情况下，该算法也能始终检索丢失的数据。
Wireless sensor networks are among the most promising technologies of the current era because of their small size, lower cost, and ease of deployment. With the increasing number of wireless sensors, the probability of generating missing data also rises. This incomplete data could lead to disastrous consequences if used for decision-making. There is rich literature dealing with this problem. However, most approaches show performance degradation when a sizable amount of data is lost. Inspired by the emerging field of graph signal processing, this paper performs a new study of a Sobolev reconstruction algorithm in wireless sensor networks. Experimental comparisons on several publicly available datasets demonstrate that the algorithm surpasses multiple state-of-the-art techniques by a maximum margin of 54%. We further show that this algorithm consistently retrieves the missing data even during massive data loss situations.
