Vibration Signal Reconstruction Based on K-SVD and Compressed Sensing in Wireless Sensor Network

In order to tackle the significant shortage of storage and the extensive latency in transmitting substantial vibration data in Wireless Sensor Networks (WSN). A refined method that integrates Compressed Sensing (CS) with K-singular Value Decomposition (K-SVD) is suggested to boost the precision of reconstruction. Firstly, CS is employed for signal sampling, which surpasses the limitation of the Nyquist sampling theorem and acquires signals from a lesser quantity of samples. Then, the Alternating Direction of the Multipliers Method (ADMM) algorithm is implemented to attain a faster convergence speed. Moreover, to enhance the adaptability of the dictionary, the K-SVD method is employed for updating the dictionary in a way that is both adaptive and segmented. Comprehensive experimen-tal outcomes show that the suggested approach exhibits superior reconstruction precision and velocity compared to alternative methods.