Recovery of Missing Sensor Data by Reconstructing Time-varying Graph Signals
This addresses the critical issue of incomplete sensor data for decision-making in wireless sensor networks, representing an incremental improvement over existing methods.
The paper tackles the problem of missing data in wireless sensor networks by proposing a Sobolev reconstruction algorithm based on graph signal processing, which surpasses state-of-the-art techniques by up to 54% and consistently retrieves missing data even during massive data loss.
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.