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.

翻译：无线传感器网络是当今时代最有希望的技术之一,因为其规模小、成本低、部署方便。随着无线传感器数量的增加,生成缺失数据的概率也上升。如果将这种不完整的数据用于决策,可能导致灾难性后果。有丰富的文献处理这一问题。然而,大多数方法显示,当大量数据丢失时,性能会退化。在图形信号处理新领域的指导下,本文对无线传感器网络的索波列夫重建算法进行了新的研究。对几个公开提供的数据集的实验性比较表明,算法在最大54%的幅度内超过了多种最先进的技术。我们进一步表明,即使在大量数据丢失的情况下,这种算法也一贯检索缺失的数据。