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The most effective way to reduce the energy consumption of energy-limited wireless sensor networks is to reduce the amount of data collected. However, this will increase the difficulty of data recovery. At the same time, most of the data collection and recovery algorithms based on matrix completion are optimized by matrix decomposition. Therefore, the mathematical model and corresponding optimization algorithm will be more complicated and take a lot of running time. In order to deal with the above problems, we propose a data recovery method based on low rank and short-term stability in wireless sensor networks in this paper. The low-rank and short-term stability features of the sensor data are fused in the nuclear norm regularization minimization model, and the proposed method is used to optimize the model. The simulation results show that the mathematical model and data recovery method constructed in this paper outperform the state-of-the-art methods in terms of recovery performance and reconstruction accuracy.

Data Recovery in Wireless Sensor Networks Based on Low Rank and Short-Term Stability