## 2 Related Work
The analysis of vibration data, obtained with ad-hoc low-cost IoT devices, is used to predict damages in real-time [30]. In addition, IoT technologies allow new approaches. For example, the work in [31] presents a methodological framework aimed at integrating the components of a structural health monitoring system. In this case, a Digital Twin predicts the deformation of a mechanical structure. A sensor-to-cloud architecture aimed at monitoring industrial plants and civil structures is presented in [32]. Other studies use wireless communications for data transmission between transducers and data acquisition devices in structural health of components [33]. However, previous studies mostly address civil structures, not aerospace applications. These proposals are not designed for moving devices and they are not valid for certification of parts in aeronautics. Modern computers and new sensors allow the adoption of more sophisticated algorithms in structural testing. For example, the performance of diverse AI-assisted models that use ultrasonic pulse waves to assess early-stage concrete damage is compared in [34]. Some approaches implement AIoT solutions for structural testing. As a matter of example, the approach in [35], allows using several algorithms for monitoring structures in real time. AloT also allows for identifying damage and detecting corrosion in hydropower plants [36]. AI techniques are starting to be used for structural testing in aeronautics: A system to detect faults in the structural components of aircraft is presented in [37]. The study in [38], discusses diverse algorithms aimed at achieving prognosis and fail detection of dynamic rotors in mechanical or aeronautical structures. Their approach introduces a model to make predictions and to evaluate the load-shape specificities of the training set. The use of IoT technologies and AI algorithms for structural health monitoring in real applications suggests that they might be used for testing aeronautical components: Machine learning vision techniques were used for surface defect detection in [39]; the structural health monitoring of a bridge was carried out in [40]; the structural health of a large roller coaster was monitored by means of several IoT devices [41]. From the analysis of the works summarized in Table 1, the authors conclude that AIoT is a powerful combination. On one side, IoT offers great advantages in terms of increasing the flexibility and scalability of locating the sensors and reducing the deployment time. On the other side, AI techniques may extract information in real-time and execute accurate predictive models. However, AIoT architectures are needed in order to ease the creation of complex applications. The combination of AI and IoT has proven its efficacy in structural health monitoring applications, mostly in the field of civil engineering. However, after examining the literature, to the best of the authors' knowledge, the introduction of AIoT for structural testing in aeronautics is an innovative topic. The aeronautical industries tend to be conservative, mainly due to the strict regulations required to ensure safety. In particular, aeronautical components are certified by means of structured, normalized and very rigorous tests in which data must be recorded for future auditing and treated confidentially. In this scenario, the definition of an AIoT architecture, aimed at deploying and executing structural tests of aeronautical components, may help test engineers to deploy this kind of test, increasing the flexibility and reducing the deployment time. Also, engineers may benefit from some guidelines to implement structural testing applications.