A practical approach for missing wireless sensor networks data recovery

In wireless sensor networks (WSNs), the performance of related applications is highly dependent on the quality of data collected. Unfortunately, missing data is almost inevitable in the process of data acquisition and transmission. Existing methods often rely on prior information such as low-rank characteristics or spatiotemporal correlation when recovering missing WSNs data. However, in realistic application scenarios, it is very difficult to obtain these prior information from incomplete data sets. Therefore, we aim to recover the missing WSNs data effectively while getting rid of the perplexity of prior information. By designing the corresponding measurement matrix that can capture the position of missing data and sparse representation matrix, a compressive sensing (CS) based missing data recovery model is established. Then, we design a comparison standard to select the best sparse representation basis and introduce average cross-correlation to examine the rationality of the established model. Furthermore, an improved fast matching pursuit algorithm is proposed to solve the model. Simulation results show that the proposed method can effectively recover the missing WSNs data.