An Impact Localization Solution Using Embedded Intelligence—Methodology and Experimental Verification via a Resource-Constrained IoT Device

Abstract

Recent advances both in hardware and software have facilitated the embedded intelligence (EI) research field, and enabled machine learning and decision-making integration in resource-scarce IoT devices and systems, realizing “conscious” and self-explanatory objects (smart objects). In the context of the broad use of WSNs in advanced IoT applications, this is the first work to provide an extreme-edge system, to address structural health monitoring (SHM) on polymethyl methacrylate (PPMA) thin-plate. To the best of our knowledge, state-of-the-art solutions primarily utilize impact positioning methods based on the time of arrival of the stress wave, while in the last decade machine learning data analysis has been performed, by more expensive and resource-abundant equipment than general/development purpose IoT devices, both for the collection and the inference stages of the monitoring system. In contrast to the existing systems, we propose a methodology and a system, implemented by a low-cost device, with the benefit of performing an online and on-device impact localization service from an agnostic perspective, regarding the material and the sensors’ location (as none of those attributes are used). Thus, a design of experiments and the corresponding methodology to build an experimental time-series dataset for impact detection and localization is proposed, using ceramic piezoelectric transducers (PZTs). The system is excited with a steel ball, varying the height from which it is released. Based on TinyML technology for embedding intelligence in low-power devices, we implement and validate random forest and shallow neural network models to localize in real-time (less than 400 ms latency) any occurring impacts on the structure, achieving higher than 90% accuracy.

An Edge Computing Application of Fundamental Frequency Extraction for Ocean Currents and Waves

“…Approaches in the literature that are implemented on microcontrollers rely on CNNs, RNNs, LSTM and their variations [ 24 ]. These applications are related, for example, to the estimation of Ion-Li battery parameters [ 5 ], structural health monitoring [ 28 ], rail vehicle running states [ 29 ], ECG Monitoring [ 30 ] or the eye blink detection [ 31 ]. All of those approaches use at least one ARM Cortex-M4 that includes FPU and specific DSP instructions.…”