earticle

영어

Correlation of wireless sensor network data is the foundation and premise of using compressive sensing theory to reconstruct the network data as well as the main basis for the design of network data reconstruction algorithm. However, various interferences of network data in practical transmission would result in consistent changes of correlation, thus lead to sharply aggravation of reconstruction performances in the existing compressive sensing theory. In order to improve the reliability and efficiency of compressive sensing of wireless sensor network data, a correlation adaptive reconstruction algorithm for network data is proposed. This algorithm first estimates the correlation of data to be reconstructed through iteration and then restores the data to be restructured using the two-step correlation test method of support elements. Compared to the previous reconstruction methods, correlation adaptive reconstruction method can greatly improve the reconstruction accuracy when the correlation of network data is changing.