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Recovery of Missing Sensor Data by Reconstructing Time-varying Graph Signals

Anindya MondalMayukhmali DasAditi ChatterjeePalaniandavar Venkateswaran
Mar 2022
摘要
Wireless sensor networks are among the most promising technologies of thecurrent era because of their small size, lower cost, and ease of deployment.With the increasing number of wireless sensors, the probability of generatingmissing data also rises. This incomplete data could lead to disastrousconsequences if used for decision-making. There is rich literature dealing withthis problem. However, most approaches show performance degradation when asizable amount of data is lost. Inspired by the emerging field of graph signalprocessing, this paper performs a new study of a Sobolev reconstructionalgorithm in wireless sensor networks. Experimental comparisons on severalpublicly available datasets demonstrate that the algorithm surpasses multiplestate-of-the-art techniques by a maximum margin of 54%. We further show thatthis algorithm consistently retrieves the missing data even during massive dataloss situations.
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