## 1 Introduction
Although using physical cabling has advantages in terms of security and data transfer speed, it presents some drawbacks: (1) it is costly and troublesome, especially as the number of sensors increases; (2) cables are susceptible to break during installation and test rig assembly tasks, leading to possible loss of data during tests and downtime for repair work; (3) in tests with a large number of sensors, the weight of the cabling may require designing and manufacturing counterweight systems to hold it. In this scenario, wireless technologies may introduce several benefits in terms of flexibility to modify the layout of the nodes and the scaling of the number of devices. Actually, they are being introduced in industrial applications even for time-critical tasks [10]. Some works (e.g., [11; 12]) explore the use of IoT technologies for structural health monitoring applications. They provide several benefits, such as increasing flexibility, reducing cabling and easing the deployment of the sensors. However, these technologies are still not common in aeronautical structural tests. For example, it is frequent to increase the number of sensors after analyzing the first recorded data, but installing new sensors can take several hours, delaying the campaign and the component certification process. The introduction of wireless technologies may drastically reduce the sensor installation time. Due to the strict regulations in the aerospace industry, aeronautical structural tests are performed under very specific and controlled conditions. In addition, test results are typically confidential. Although there is scarce literature on this subject, the introduction of AI and IoT technologies for this kind of test is still incipient. This work presents an AIoTarchitecture that eases the deployment of structural tests of aeronautical components by combining the use of wireless IoT technologies for data acquisition tasks with AI techniques to interpret the test results. The major contributions of this work are as follows:  
* Present a generic AIoT architecture for implementing structural testing in aeronautical applications. This architecture allows the integration of wired and wireless IoT devices with different AI algorithms for data processing. * Promote the integration of different AI-based algorithms for analyzing structural testing results both in run-time and off-line. * Present a methodology to deploy the AIoT architecture in structural testing applications, with guidelines that may help developers. * Validate the presented approach experimentally in the certification test of a real aerospace component by analyzing test data obtained by means of IoT devices in real-time. In order to illustrate and validate experimentally the feasibility of the proposed approach, this study implemented the proposed AIoT architecture in an actual certification test of one aeronautical component, namely a flag track component of the Embraer E2 model aircraft. In this rigorous and standardized certification test, the recorded data were compared with the data obtained from conventional wired equipment. In order to illustrate the approach, acquired data were analyzed in run-time with two AI algorithms frequently used for these tasks: LSTM and ARIMA. This use case allows for confronting the genuine challenges and complexities found in the deployment of AIoT applications aimed at aeronautical testing. The obtained results demonstrate the technical feasibility of using wireless IoT technologies to complement and, occasionally, replace conventional wired technologies in the structural testing of aeronautical components. IoT technologies achieve a significant reduction in cabling and deployment time and improve flexibility and scalability. As a result, the efficiency and competitiveness of the aeronautical testing laboratories improve. The presented approach paves the way to introduce AI-based algorithms for analyzing structural testing results. This article is organized as follows: Section 2 analyses diverse related works considering several key subjects.