## 4 Case Study: Embraer E2 Model Aircraft Flag Track
### Implementation Following the Methodology  
The implementation of the proposed AloT architecture for structural testing follows the structure of the generic flowchart outlined in the previous subsection (Figure 2). Several issues were identified in the capture of requirements stage to address the specific needs of this particular aeronautical structural test: variables to be controlled, namely position, inclination and temperature; precision requirements, such as distance sensors with sub-centimeter accuracy, and temperature sensors with \(\pm\)0.5 \({}^{\circ}\)C accuracy; sampling frequency of 1 Hz and duration of the test up to several months, with sampling rates aligned with the dynamic characteristics of the tests, and the duration defined based on the variability of test types and load conditions. Safety margins for power requirements were also established to avoid data loss during the test. The design of the AloT acquisition system leveraged both commercial standard components and customized configurations. Three types of sensors were used: the HC-SR04 ultrasonic sensor  
Measured distances between 2 and 450 cm with high precision; the MPU-605 IMU tilt sensor offered 6-degree-of-freedom movement detection and the KY-013 temperature sensor captured ambient temperature in a range of \(-\)55 \({}^{\circ}\)C to +125 \({}^{\circ}\)C with an accuracy of \(\pm\)0.5 \({}^{\circ}\)C. There are alternative sensors, based on the same or different measurement technologies, that meet the measurement requirements and have a comparable cost. Any of them could have been used; however, these sensors were selected over other alternatives because their technical specifications met the test's measurement requirements and because there was prior experience with their use. The prototypes of the IoT sensor nodes were built with Arduino MKR 1010 boards. This platform was selected for its flexibility, technical capabilities and ease of use. The MQTT protocol, implemented through the PubSubClient library, facilitated the communication among the edge and fog layers over WiFi and TCP/IP technologies. Additionally, a  
Figure 3: Implementation diagram of the proposed monitoring network. prototype of a system aimed at guaranteeing the safety of the operators was deployed. This system was intended to ensure that no person could enter the test zone when in operation. This system included a Raspberry Pi (RPi) 4B equipped with a Spedal MF9220 PRO webcam. All sensor devices were powered by batteries. Their capacity was analyzed in order to achieve uninterrupted operation of the smart sensors during the test operation with some safety margins. A procedure was also designed to change the batteries while the test was in operation, if necessary. In order to illustrate the design of the AIoT acquisition system, the authors developed ad-hoc, low-cost smart sensors aimed at measuring selected variables of the structural test. Namely, an ultrasonic position sensor (Figure 4a), an IoT device to measure the inclination and the temperature of the specimen (Figure 4b) and a third module aimed at monitoring invasions of the test area (Figure 4c). The validity of the sensors used in the measurements was demonstrated by means of the AIoT architecture implementation methodology, where the selection of the sensors is preceded by a requirements analysis phase, in which the necessary technical characteristics for the sensors and acquisition devices are identified. Subsequently, in the same design stage of the AIoT acquisition system, a calibration of the sensors is carried out in order to obtain the real accuracy of the measurements, verifying in all cases that the sensors and acquisition devices used comply with the technical requirements imposed for the test. Furthermore, it was found that for tests with faster data reading requirements, above 1 Hz, the acquisition systems selected for this test would not be valid. Instead, systems with greater data recording and transfer capacity would need to be used.