Adaptive-Wavelet-Threshold-Function-Based M2M Gaussian Noise Removal Method

With little or no human intervention, almost every object in Internet of Things (IoT) has ability to communicate, sense, and process information to make everything connected. Noise in complex environment has a great impact on machine-to-machine (M2M) interaction in IoT. Adaptive wavelet threshold function (AWTF)-based M2M Gaussian Noise Removal Method is proposed in this article. First, a bilateral enhanced wavelet threshold function is derived based on the adjustable zeroing window. Further, threshold is used to construct a bilateral enhanced wavelet threshold function, which can eliminate the oscillations in the existing wavelet threshold function. This ensures that there is no break in wavelet coefficients during the reconstruction process and that stable wavelet decomposition and reconstruction can be achieved. The signal-to-noise ratio (SNR) of a signal is estimated based on the variance of the noise-containing signal, and the zeroing window parameters are adjusted adaptively according to the SNR value to eliminate the noisy wavelet coefficients and improve the denoising performance. In addition, when the wavelet coefficients are reconstructed, the proposed algorithm can select a suitable threshold function according to a particular threshold value, which improves the robustness of the algorithm. “Doppler” and “Bumps” standard test signals are used as interactive signals to simulate the proposed algorithms. For “Doppler” signals, the SNR, root mean square error (RMSE), and noise suppression ratio (NSR) of AWTF increased by 8.48%, 42.59%, and 1.69%, respectively, compared with the GDES+ABC algorithm. For “Bumps” signal, the SNR, RMSE, and NSR of AWTF are improved by 11.67%, 24.46%, and 2.99%, respectively, compared with GDES+ABC algorithm. In addition, we also use different modulated signals to carry out real field experiments in Qingdao cruise ship home port, which proves the effectiveness of the proposed algorithm.