## 4 Asset Digitalization Use Case: IoT and AHI App for a Bridge Maintenance
### Bridge Monitoring Model  
The functional block diagram of the proposed architecture is shown in Figure 5. On the one hand, at the sensor node level, we can distinguish the acquisition layer (in orange), the local processing and storage layer (in red), and the wireless communication layer (in purple). On the other hand, at the cloud processing server level, d \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data Type** & **Description** & **Platform** & **Superstructure** \\ \hline \multirow{4}{*}{Functional Location Data} & Distant to the coast Altitude & 160 km, 86 m & 160 km \\  & Average outdoor temperature & 22.5 \({}^{\circ}\)C & 86 m \\  & Corrosive atmosphere & No & 22.5 \({}^{\circ}\)C \\  & Dust in suspension & No & No \\ \hline \multirow{4}{*}{O\&M Cost Data} & Corrective maintenance cost & EUR 10,000 & EUR 20,000 \\  & Preventive maintenance cost & EUR 3000 & EUR 2000 \\ \cline{1-1}  & Other operational costs & EUR 1000 & EUR 1000 \\ \cline{1-1}  & Major maintenance cost & EUR 60,000 & EUR 100,000 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Platform and superstructure functional location data. are included for role management, data reception, management and processing of the models of each asset, and the presentation of the Graphical User Interface (GUI). To digitize the asset's condition monitoring, it is imperative to establish integration with information sourced from the IoT/cloud network strategically positioned at various points across the bridge. For instance, Figure 6 illustrates the selected mesh for data acquisition at each point, highlighting specific locations in a roadway section measuring 42 m in length and 10.5 m in width. These nodes serve the dual purpose of capturing information from the bridge and offering data not only exclusive to the roadway due to their location but also insights into the condition of other bridge components, as delineated in Figure 6. According to the structural modal analysis community [60], the interval of measurements must be greater than 1000 times the fundamental period of the structure, so a periodic monitoring duration of 8 min with an accuracy of 3.8 \(\upmu\)g in a range of \(\pm\)2 g is chosen. The dynamics of change and degradation of the structures are slow enough to select a monitoring periodicity of 24 h, which guarantees the autonomy of the nodes for up to 10 years. From a perceptual standpoint, the solution integrates hardware and firmware design in order to face the challenges presented by the application framework. On the one hand, since these structures are usually located in rural environments that are difficult to access, a low-energy-consumption design is created in order to maximize their useful lifetime and reduce battery replacement actions on the devices. To this end, the hardware design adopts a modular structure consisting of a base module and a sensor module. The base module manages local processing, device control, NB-IoT wireless communications, data storage, and power supply, housing crucial components like the microcontroller (STM32L1), the NB-IoT communications transceiver (SIM7080G), and the SD memory interface. The sensor module, connected to the base module, includes sensors for real-time structure monitoring, such as accelerometers (ADXL355) and analog interfaces for strain gauges, which are not used in this application case, but allow the solution to be scaled to other frameworks. The firmware design, structured modularly, involves real-time operating system tasks like FreeRTOS with tasks for sensor monitoring, local information processing, and wireless transceiver control. This allows simple events such as collapses or high-intensity impacts to be detected in real time and transmitted wirelessly to the central server. The modularity allows flexibility in adapting the sensor node to various structures, ensuring efficient and  
Figure 5: The IoT network. customized monitoring. In addition, time synchronization between nodes is carried out via the A2235H GPS module.