Research on network data transmission and compression based on compression-aware model

Abstract

Abstract This paper first analyzes the theory of compressive sensing for wireless sensors and constructs a mathematical model of compressive sensing. Secondly, the sparse representation and observation of the signal in the compressed sensing technique are analyzed to provide the theoretical basis for the new model of P-tensor product compressed sensing based on the digital signature encryption algorithm in the later paper. Finally, the recovery performance of the model and the encryption effect is analyzed by simulation experiments. The results show that when the CR is 0.8, the PSNR values of the Lena image, Peppers image and Cameraman image are 37.608dB, 37.32884dB and 37.3428dB, respectively. 512×512 network data fragment has the shortest encryption time of 2.0156s. This shows that the compression-aware model can guarantee network data transmission security and guarantee compression quality.