IoT-Based Compressive Sensing for Real-World Infrastructural Monitoring Application

Compressive Sensing (CS) is a sampling technique that challenges the traditional sampling scheme introduced by the Whittaker-Shannon theorem. Under certain conditions, a signal can be sampled at rates lower than the Nyquist rate, introducing a different kind of approach to signal handling, both in the acquisition and in the reconstruction phases. CS relies on the property of sparsity, the idea that a signal possesses an amount of information which is smaller than the amount of data required to store it. This paper employs the CS approach to inertial signals sensed by innovative IoT devices by showing applications of real-world infrastructure monitoring. Numerical results show that our approach is able to efficiently estimate the infrastructures modal frequencies with an innovative inertial IoT prototype by achieving a compression level around 20 times below the Nyquist rate.