# Cloud-Based Internet-of-Things System for Long-Term Bridge Bearing Monitoring Using Computer Vision
## 5 Conclusions  
This paper proposes a cloud-based real-time monitoring system that integrates computer vision and low-power IoT sensors to enable long-term continuous monitoring of bridge bearings. The system incorporates wireless communication, low-power design, and simplified installation and maintenance processes, allowing for more general monitoring of bearing structures. Specifically, the EDS-based low-power design with RTC and latch mechanisms ensures long-term operation with minimal energy consumption. Captured images are processed in the cloud for 3D displacement estimation and are systematically stored in a database for long-term analysis. Laboratory testing confirmed the reliability of the displacement measurements, with an error rate of less than 3%. Power consumption tests demonstrated the system's ability to operate for up to 60 days using a 10,000 mAh power source. The system was successfully field tested for nine months on the Dongbu Highway in Seoul, South Korea, effectively capturing 3D displacement data. Maximum displacements of 62.5 mm, 15 mm, and 11 mm were observed along the X, Y, and Z axes, respectively, with a high linear correlation (0.84) between displacement and temperature along the \(X\)-axis. Statistical anomaly detection using GPR revealed no abnormal behavior during the monitoring period, further validating the system's reliability. The system's versatile design supports deployment across various types of bridge bearings, enabling data-driven decision-making and reducing maintenance burdens. By providing a practical and automated solution, this system represents a significant advancement in structural health monitoring (SHM).  
Although the proposed system demonstrated promising results during the field tests, further investigation of the data analysis is necessary. Future research will focus on analyzing the effects of traffic loads on bridge bearings to establish a deeper understanding of the relationship between traffic-induced dynamics and bearing displacements. Integrating various sensors for vibration measurement could enable more precise monitoring of traffic loads and their impact on bearing behavior. Collecting and analyzing long-term data related to traffic loads would contribute to developing predictive maintenance strategies and optimizing load management practices.  
All authors contributed to the main idea of this paper. Conceptualization, [PERSON] and J.W.; Software, G.K.; Validation, G.K.; Formal analysis, J.W. and J.S.; Investigation, G.K. and J.S.; Data curation, G.K.; Writing--original draft, G.K.; Writing--review & editing, J.W., J.S., and J.P.; Visualization, G.K., J.S. and J.W.; Project administration, J.W. and J.P.; Funding acquisition, J.P. All authors have read and agreed to the published version of the manuscript.  
Funding: This research was supported by the Chung-Ang University Research Scholarship Grants in 2023 and by the National R&D Project for Smart Construction Technology (Grant RS-2020-KA156887; RS-2020-KA156007), funded by the Korea Agency for Infrastructure Technology Advancement under the Ministry of Land, Infrastructure, and Transport managed by the Korea Expressway Corporation. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: All data are presented in the article. Conflicts of Interest: The authors declare no conflicts of interest. Abbreviations The following abbreviations are used in this manuscript: LVDT Linear Variable Differential Transformer FBG Fiber Bragg Grating MEMSs Micro-Electro-Mechanical Systems CV Computer vision SHM Structural health monitoring GPR Gaussian Process Regression EDS Event-driven system RTC Real-Time Clock RPI Raspberry Pi PCB Printed circuit board Vbat Battery voltage PnP Perspective-n-Point URIs Uniform Resource Identifiers ARD Automatic relevance determination UCL Upper control limit LCL Lower control limit MR Moving range SPC Statistical process control