A performance analysis of various compressive sensing techniques in IoT-based WSNs and its applications

Compressive sensing is a way to deal with signals. The most efficient method for lowering latency and energy usage in IoT-based WSNs is compression sensing (CS). CS is used to lower the quantity and size of transmitted data packets via the IoTnetwork. The compressive sensing (CS) technique lowers the network’s energy consumption and end-to-end latency. The practiceof compressive sensing is one of the signal-processing methods that find solutions to underdetermined linear systems by efficientlycollecting and recreating data. To recover a signal, a significant number of samples is required, as stated by the Nyquist-Shannon sampling theorem; however, this number can be lowered through optimization by taking advantage of the signal’s intrinsic sparsity. It gives us an easy-to-use framework that lets us gather data and figure out what the signal is from fewer observations. In this paper, we compare CS reconstruction algorithms for overall system performance, data processing complexity values, reconstruction errors, and time for various compression techniques. This paper is useful for future work to compare CS reconstruction techniques to find a new optimized solution method.