# The Current Development of Structural Health Monitoring for Bridges: A Review
## 2 Monitoring Technology
### Sensor Monitoring Technology in BHM
#### 2.1.2 Wireless Sensor Technology
[PERSON] et al. [34] developed a reservation-based protocol based on the existing IEEE 802.15.4 standard, which provides a selectable solution for addressing the additional energy consumption of ZigBee. In addition, a medium access control (MAC) protocol can reduce the energy consumption of sensor nodes during idle time, but it has the disadvantage of communication delay, which is solved well by a B-MAC protocol. [PERSON] et al. [35] introduces an Energy Harvesting System in wireless sensor nodes and reduces unnecessary energy consumption that may occur through the B-MAC protocol. The combination of the two technologies greatly reduces the energy consumption of sensor nodes. [PERSON] et al. [36] developed sensor more hardware to trigger a piezoelectric sensor with lead zirconate titanate material, which had excellent fracture detection performance. They used the network interface of the system to remotely analyze the data collected after triggering the piezoelectric sensor. The results showed that the newly developed system can successfully detect cracks that threaten the safety of the structure. [PERSON] et al. [37] proposed a method based on WSNs to measure the micro-vibration of a bridge piers during  
Figure 2: WSN-based bridge health monitoring system [31]. extreme weather conditions and frequent natural disasters. They first used a WSN to collect data signals at different locations on a pier. Then, the Fast Fourier Transform (FFT) and Welch methods were used to process the signals to obtain the pier vibration frequency data. At the same time, the measured natural frequency was used as a calibration parameter to establish a finite element model to estimate the scouring depth. This method provides a practical tool for dealing with the risk of bridge collapse caused by pier scouring. In a study on the method of estimating bridge cable tension, [PERSON] et al. [38] proposed a fully automated and robust identification method based on an Xnode wireless sensor system. The method was validated by Jindo Bridge data. The numerical results showed that the natural frequency and order of the bridge cable obtained by this method and the Matlab method were basically consistent, and the predicted tension results matched the actual data very well, which can effectively realize the automatic estimation of cable tension. Wireless sensor networks are widely used in civil SHM because of their excellent data collection capability, convenient installation, and low cost. To realize their long-term operation, some scholars have studied the power reduction methods of WSNs. Figure 3 illustrates a simple WSN model in which sensor nodes are linearly arranged and each sensor collects data within the distance between adjacent nodes. Assuming that the model considers transmission power as the only parameter, the energy \(E\) required to transmit a data packet at a rate of \(R\) bits/s along the distance \(d\) can be expressed as [39]  
\[E=\epsilon_{amp}Rd^{k} \tag{1}\]  
where \(\epsilon_{amp}\) is the energy dissipated in the transmitting radio-frequency (RF) amplifier and \(k\) is the path loss exponent, which normally ranges from 2 to 6 [40]. It is apparent from the formula that, under certain environmental conditions, we can control the energy consumption of the WSN by altering the type of RF amplifier or changing the layout of the sensor network, including modifying the position and spacing of the sensors. As more innovative sensing technologies, including WSNs, are continuously being applied to SHM, it is worth noting how to provide an optimized solution for determining the number and placement of sensors to maximize their information collection capability and address energy consumption issues. Generally speaking, the sensor node localization methods in WSNs can be classified into non-distance-based and distance-based methods. In non-distance-based methods, a validated and effective approach is to identify the high-connectivity anchors for each sensor and use the centroid of the anchor points as its location [41].