<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</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.

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

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

This paper first analyzes the theory of compressive sensing for wireless sensors and constructs a mathematical model of compressive sensing. Secondly, the...

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

Online veröffentlicht: 15. Okt. 2023

Seitenbereich: -

Eingereicht: 22. Nov. 2022

Akzeptiert: 25. Apr. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00638

Schlüsselwörter
Network data transmission, Compression-aware model, P-tensor product, Digital signature encryption, Encryption compression

© 2023 Zhijian Mao et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

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

Online veröffentlicht: 15. Okt. 2023

Seitenbereich: -

Eingereicht: 22. Nov. 2022

Akzeptiert: 25. Apr. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00638

SchlüsselwörterNetwork data transmission, Compression-aware model, P-tensor product, Digital signature encryption, Encryption compression

© 2023 Zhijian Mao et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Network data transmission, Compression-aware model, P-tensor product, Digital signature encryption, Encryption compression