## 1 Introduction  
Bridges constitute a vital component of modern transportation infrastructure, facilitating the efficient movement of people and goods and thereby contributing significantly to economic growth and societal development. However, over time, bridges are subjected to various deteriorative factors, including material aging, environmental conditions, excessive traffic loads, and natural hazards. These factors collectively contribute to the structural degradation of bridges, potentially compromising their safety, functionality,and serviceability. In extreme cases, such deterioration may culminate in catastrophic failures, such as partial or total collapse [1; 2]. To mitigate these risks and ensure structural integrity throughout the bridge's service life, continuous and reliable monitoring is essential [3; 4; 5]. Structural Health Monitoring (SHM) systems have emerged as a key tool in this regard, enabling real-time assessment of structural responses to both operational and environmental loads [6]. Traditionally, wired sensor networks have been employed in SHM applications [7; 8]. However, such systems are often constrained by complex installation procedures, high maintenance costs, and limited scalability due to extensive cabling requirements. In response to these limitations, wireless sensor networks (WSNs) have gained increasing attention in the field of SHM. Wireless monitoring systems offer significant advantages in terms of cost efficiency, ease of deployment, and scalability. Moreover, recent advancements in wireless communication technologies and low-power embedded systems have further enhanced the feasibility of implementing real-time, high-resolution data acquisition systems for large-scale civil infrastructure. Consequently, wireless SHM has become a promising alternative to conventional wired systems for bridge monitoring applications. [PERSON] and [PERSON] (1998) [9] were among the first to explore the application of Wireless Sensor Networks (WSNs) in SHM, proposing a modular SHM system based on wireless sensor nodes. Their seminal work laid the foundation for a broad range of subsequent research and experimental validation efforts. By decoupling the system into distinct modules--namely, sensors, wireless transmitters, and data processing units--they introduced a flexible architecture capable of adapting to diverse structural configurations and environmental conditions. This modular approach has since been recognized for its practical advantages, particularly in terms of deployment scalability and maintenance efficiency. Despite its pioneering contributions, the study was limited to small-scale structures and short-range communication scenarios. Critical challenges, such as wireless signal attenuation, multipath interference, and a restricted communication range, remained unresolved. Moreover, the issue of time synchronization across multiple wireless nodes--an essential requirement for accurate vibration characterization and modal comparison--was insufficiently addressed. Although sensor data were transmitted wirelessly, the system architecture relied predominantly on offline data storage and post-processing, thereby falling short of achieving true real-time SHM capabilities. Consequently, while the proposed system represented a significant conceptual advancement, its practical implementation revealed notable limitations in scalability, synchronization fidelity, and real-time responsiveness. From the early 2000s through the subsequent decade, significant research efforts were directed toward addressing the inherent limitations of early wireless communication in SHM systems. These efforts included the adoption of low-power wireless communication protocols, such as LoRa, Bluetooth Low Energy (BLE), and Narrowband Internet of Things (NB-IoT), as well as the integration of energy harvesting technologies and the development of intelligent sensor nodes equipped with embedded signal processing and data analysis capabilities [10; 11; 12; 13; 14; 15]. [PERSON] et al.