  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.
