# Wireless Accelerometer Architecture for Bridge SHM: From Sensor Design to System Deployment
## 4 Laboratory Tests Results
### Energy Management  
Effective energy management is paramount to ensure the long-term reliability and sustainability of the monitoring system. With sensor nodes typically deployed in inaccessible locations, reliable power sources are essential for uninterrupted operation. Therefore, prioritizing energy management is crucial for achieving the long-term effectiveness of the sensor network. This section is aimed at presenting the testing activity carried out in order to assess the capability of the node to operate while keeping the battery level within a safe region. To devise an effective energy management strategy, it is essential to start by estimating the power requirements for every state of the node. This involves analyzing the energy consumption patterns associated with each operational state: deep sleep, synchronization, acquisition, and transmission. This analysis was carried out in a laboratory. The next paragraph presents the methodologies and the obtained results.  
The experimental setup used for this test is very simple and consists of a sensor node, a power supply and a multimeter to measure the current consumption. The power supply is set to be in constant voltage mode and provide 3.6 V to the battery connector pins, while the multimeter is placed in series with the positive pole of the power supply. Finally, the sensor node is programmed to follow the previously presented acquisition procedure. For each of the states, the current is measured and recorded; the obtained results are collected in Table 1.  
Given this information, the energy consumption of the sensor node for one day of operation can be estimated with the following equation:  
\[E_{\text{day}}=\sum_{i=\{\text{sl, sy, ac, tr}\}}P_{i}\cdot t_{i} \tag{1}\]  
where  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**State** & **Current [mA]** & **Voltage [V]** & **Power [mW]** \\ \hline Deep-Sleep & 0.05 & 3.6 & 0.18 \\ Syncronization & 95 & 3.6 & 342 \\ Acquisition & 95 & 3.6 & 342 \\ Transmission & 150 & 3.6 & 540 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Power consumption table.  
* \(E_{\text{day}}\): Total energy consumed in one day.
* \(P_{i}\): Power consumption for each operational state \(i\).
* \(t_{i}\): Time duration for each operational state \(i\).
* sl: Sleep mode.
* sy: Synchronization phase.
* ac: Data acquisition phase.
* tr: Transmission phase.  
Equation (1) provides important information to predict the evolution of the charge of the battery. Moreover, accurately estimating the energy consumed by the sensor node serves as a crucial factor in selecting the frequency of data acquisition throughout the day, ensuring that the battery remains operational without depletion. Dynamically adjusting the frequency of data acquisition based on estimated energy consumption becomes particularly pertinent during periods of limited solar energy availability, such as overcast days.  
Following laboratory tests, an on-field test was conducted to evaluate the sensor node's performance in real-world conditions. During the field test, the sensor node collected data twice daily, with each data collection session lasting for 30 min. The sensor node was powered by a 4500 mAh, 3.7 V lithium-polymer (LiPo) battery and supplemented by a 6 V (open circuit voltage) solar panel with a nominal output power of 1.1 W. Throughout the test period, the sensor node successfully transmitted collected data to a Raspberry Pi gateway. The node operated continuously for 50 days, and at the end of the test, the LiPo battery remained fully charged, demonstrating the effectiveness of the solar-powered setup in sustaining prolonged operation.