Journal of Jilin University(Engineering and Technology Edition) ›› 2023, Vol. 53 ›› Issue (8): 2358-2363.doi: 10.13229/j.cnki.jdxbgxb.20220748

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Simulation of segmentation wavelet noise reduction algorithm for large⁃scale IoT terminal ciphertext data

Shou-qi CAO1(),Fan-hui KONG1,Zheng ZHANG1,Ru-yi XIONG2   

  1. 1.School of Engineering，Shanghai Ocean University，Shanghai 201306，China
    2.Sino-German School，Chongqing Vocational College of Transportation，Chongqing 402260，China
  • Received:2022-06-15 Online:2023-08-01 Published:2023-08-21

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Abstract:

In order to effectively filter the noise in terminal ciphertext data， a segmentation wavelet noise reduction algorithm for large-scale IoT terminal ciphertext data is proposed. The continuous spectral components and line spectral components of different types of data are extracted by the periodogram estimation feature extraction method， and the noise features of the ciphertext data are obtained. The maximum projection of noise data in the scale space is used to construct the energy matching criterion， and the structured wavelet filter bank is used to establish the optimal energy matching wavelet consistent with the signal energy. Through the waveform matching criterion， the optimal waveform matching wavelet which is the same as the signal waveform is established by using the optimization function to complete the segmented wavelet de-noising of the ciphertext data of the large-scale IOT terminal. The experimental test results show that the proposed algorithm can effectively reduce the delay of data noise reduction， and can also obtain satisfactory noise reduction effect.

Key words: large-scale internet of things, terminal ciphertext data, segmentation, wavelet noise reduction

CLC Number: 

  • TM933

Fig.1

Flow chart of wavelet threshold denoising"

Fig.2

Operation flow chart of optimal waveform matching algorithm"

Fig.3

Comparative analysis of the results of segmental wavelet noise reduction for large-scale IoT terminal ciphertext data with different algorithms"

Table 1

Comparison of noise reduction delay results of different algorithms"

测试组次降噪时延/ms
本文算法文献［3］算法文献［4］算法
10.2540.2640.271
20.2830.2990.300
30.3010.3150.334
40.3260.3340.359
50.3450.3580.376
60.3660.3700.389
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