SHM With Low-Cost, Low-Energy, and Low-Rate IoT Devices: Reducing Transmission Burden With Compressive Sensing

This is the authors' abstract. We don't add key points for this paper.

Structural Health Monitoring (SHM) is a process aimed at studying variations in the expected behavior of a structure in order to locate damage, material deterioration and other abnormalities. To this aim, SHM is usually performed continuously, thus generating large amounts of data, often by employing wired, expensive and proprietary systems. Introducing low-cost, low-energy consumption and low-rate IoT devices allows for cheaper and easier installations also in scenarios where computation and transmission resources are limited. Since many structural signals (e.g., vibrations) are sparse in the frequency domain, it is possible to apply well-known Compressive Sensing (CS) techniques to limit the amount of information to be transmitted. CS allows recovering a vector using a reduced amount of entries, thus being able to perform sub-Nyquist sampling. This paper shows the results obtained by applying CS to inertial signals coming from wireless IoT devices, developed as laboratory prototypes, applied to real structures (specifically, a bridge). Such findings are further expanded by discussing the efficiency of CS with respect to the number of used samples and its feasibility for IoT applications, from the transmission burden and energy consumption standpoints.