## 4 Case Study: Embraer E2 Model Aircraft Flag Track
### Implementation Following the Methodology
The official test of the component had an estimated duration of nine months. However, the measurements performed with the network were limited to an eight-hour working day, considering that this time interval is long enough to record an adequate number of measurements and check the correct operation of the system. Since the case study involves a fatigue test, the conditions to which the specimen is subjected are repeated periodically. In this case, the specific period was 4 min. For practical purposes, extending the data recording time further did not add value to the study in assessing the performance of the AIoT system. During the measurements performed, it was not necessary to change the batteries of the edge modules. The batteries were sized to last 8 h, a time that was extended by the energy-saving procedures implemented in the edge node boards. The time during which  
Figure 4: Integrated modules used in the aeronautical tests under real conditions: (**a**) module 1 provided with the position sensor, (**b**) module 2 provided with the inclinometer and the thermistor, (**c**) module 3 provided with the position sensor and a webcam. measurements were recorded was sufficient for the batteries to avoid reaching the 10% level without installing the upcoming battery replacement. In addition, the battery replacement alert was sent to the computing center and the mobile application, verifying the correct operation of the service. Replacing the battery should not alter the course of the test and, of course, should not cause downtime. This is easily achieved by means of a double battery system connected in parallel with a relay activated by a voltage drop, where the battery with the highest charge maintains the operation of the device while the discharged battery is replaced. The official data acquisition frequency in the test was 1 Hz. Although the time required to reach successive load conditions was several seconds, in order to have a dataset sufficiently representative of the process followed by the electric motor and hydraulic actuators, measurements were taken every second. The data measurement rate of the AloT network sensors was adjusted to this same frequency in order to adequately compare the accuracies of the values taken by the two types of sensors. The smart sensor node allows for much higher acquisition speeds, and the MQTT protocol achieves transmission times of up to 80 ms under specific conditions. The real-time analysis of the test data (online interpretation) included data processing and interpretation using advanced edge and fog computing technologies. For this purpose, a desktop computer (Intel Core i5, 4 GB RAM) and an additional RPi 4B device were used in the fog layer to handle communication and computation. The Eclipse Mosquitto MQTT broker, installed in the fog layer, managed the connectivity among the IoT devices and the fog devices. MQTT clients were implemented in Python (v.2.1.0) using the Paho MQTT library. Data received at the fog layer were processed in real-time using machine learning models. Algorithms for prediction and anomaly detection were implemented using TensorFlow and Scikit-learn libraries. These models enabled the estimation of future values for key parameters and facilitated proactive decision-making. Finally, a Graphical User Interface (GUI), built with Python's Klinter framework, allowed the real-time visualization of test data in the control room. Several actions were taken to leverage the results of the real-time analysis so that the analyzed data supported various operational decisions and enhanced accessibility. A smartphone application notified the operator responsible for the test about anomalies or critical events in real time, by means of an MQTT client. The safety system monitored the test zone and issued alerts if unauthorized personnel entered the area. A private cloud, deployed using a Western Digital My Cloud Pro Series PR4100 NAS, enabled persistent storage of test data for further post-processing and historical analysis.