Due to data loss and sparse sampling methods utilized inWSNs to reduce energy consumption, reconstructing the raw sensed data\nfrompartial data is an indispensable operation. In this paper, a real-time data recovery method is proposed using the spatiotemporal\ncorrelation among WSN data. Specifically, by introducing the historical data, joint low-rank constraint and temporal stability are\nutilized to further exploit the data spatiotemporal correlation. Furthermore, an algorithmbased on the alternating directionmethod\nof multipliers is described to solve the resultant optimization problem efficiently. The simulation results show that the proposed\nmethod outperforms the state-of-the-artmethods for different types of signal in the network.
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