# 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
Additionally, reliable sensor localization can be achieved by measuring various parameters such as the pairwise time-of-arrival (TOA), the time-difference-of-arrival (TDOA), the received signal strength (RSS), and the angle-of-arrival using distance-based algorithms [42]. In their research on sensor location optimization, [PERSON] and [PERSON] [43] proposed two comparative standards using vibration displacement as the dataset. The first standard is the mean square error between the FE model and the cubic spline-interpolated mode shapes, and the second standard is the information content of the sensors, which shows the signal strength obtained by different placement schemes and their noise resistance ability. Figure 3: A linear network model comprising n relay nodes (from \(S_{1}\) to \(S_{n}\)). [PERSON] et al. [44] studied two optimization schemes of node layout. They compared the life of WSNs under the two schemes with the results obtained by the common equidistant layout method, and obtained the optimal node layout scheme of the sensor network, which can reduce energy consumption by 20% and extend life by 140%. [PERSON] et al. [45] designed an event-based WSN to reduce the power consumption of sensor nodes that capture random events. In addition, the system can be easily configured to existing wireless sensor platforms. The current consumption before and after activating the system on a commercial wireless platform verified the ultra-low power consumption of the system. Considering WSNs with multi-type sensor clusters, [PERSON] et al. [46] developed a cluster-based network optimization algorithm to improve the energy utilization efficiency of the network and extend the network lifetime. On this basis, they obtained the coefficient of variation of the estimated parameters based on Bayesian inference, which can be used as a global measure to evaluate the accuracy of the sensor network. In another study, [PERSON] et al. [47] added the genetic algorithm (GA) strategy based on the previous research to improve the computational efficiency of this method. The performance of the proposed method was verified in two WSNs. In a study on the application of WSNs, [PERSON] [48] studied the network layout of wireless sensors and applied a WSN to the dynamic response test of bridges under train effects. They tested the layout effect and detection capability of the WSN through a case study comparing wired sensors. The results showed that the WSN had a reliable dynamic response detection performance and an effective network layout. In practical applications, wireless measurement is often limited by transmission distance. [PERSON] et al. [49] introduced a low-power Internet of Things method to detect bridge displacement in wireless sensor systems. This system can greatly reduce the limit of the transmission distance, accurately collect displacement data, and send it to the server for remote analysis and visual operation via a web interface. Based on the experimental and field test results, the effectiveness of the system was verified. In most cases, monitoring data in BHM systems are sensitive and confidential. Therefore, more secure routing protocols are needed for data transmission. [PERSON] et al. [50] proposed an improved encryption algorithm, called the R-XOR algorithm, to address data security issues in WSNs. The security performance of the proposed algorithm was validated using Brute Force attacks, and it has a higher throughput and lower overhead. To address the issue of WSN node failures, [PERSON] and [PERSON] [51] proposed an early prevention method (EPM). They studied various aggregation functions and models in a WSN and found that weighted bucketing has a higher working level, significantly filtering out irrelevant information collected by sensors, and effectively solving the problem of node data loss. The main challenge in applying WSNs to BHM is the high energy consumption during the wireless long-distance transmission of sensor data.