Signal Integrity Enhancement and Sparse Recovery Techniques for Reliable Wireless Sensor Network Communications

Abstract

Wireless Sensor Networks (WSNs) are crucial in various applications of monitoring and communication, such as environmental surveillance, industrial automation, healthcare, and smart infrastructure. Yet, to convey the data collected by the sensors in a reliable way is still a challenge because of noise, interference, channel impairments, or limited communication resources. All of these can affect the quality of the signal and have a negative impact on the accuracy of information received at the destination. To overcome these difficulties, in this research a signal integrity improvement and sparse recovery method for reliable wireless sensor network communication is suggested. The proposed method combines signal preprocessing methods such as noise suppression, normalization and adaptive thresholding with sparse signal representation and recovery methods to enhance the reconstruction quality. The principles of compressive sensing are used to gather and send information from sensors efficiently without a lot of overhead in communication. The receiver side performs a sparse recovery algorithm to reconstruct the signal from the measurements at a high fidelity. Using the widely accepted signal reconstruction metrics, the performance of the proposed framework is assessed as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Peak Signal-to-Noise Ratio (PSNR), Signal-to-Noise Ratio (SNR) and Normalized Mean Squared Error (NMSE) metrics. The experimental results show that the proposed method can effectively reduce the reconstruction error and enhance the quality of the signal and the accuracy of signal reconstruction under different environments of noise. The results demonstrate that the combination of signal integrity enhancement and sparse recovery approaches is an effective method to enhance the reliability of communications and reconstruction performance in wireless sensor network settings. The proposed framework is a promising approach that can be used for energy-efficient and robust sensor data transmission applications.