## 1 Introduction
These systems, capable of operating without external power sources, enabled long-term deployment and real-time detection of structural anomalies. While they contributed meaningfully by minimizing data transmission requirements and reducing communication costs, significant limitations remain. In particular, the lack of standardized data formats and communication protocols impede interoperability across heterogeneous sensing platforms, and the long-term durability and reliability of the sensor units require further empirical validation. [PERSON] et al. (2018) [PERSON] et al. (2018) [PERSON] et al. (2018) addressed performance bottlenecks of conventional wireless sensors by developing a high-sensitivity, energy-efficient wireless accelerometer system for real-time SHM. By integrating the Seiko Epson M-A351 digital MEMS accelerometer into the Xnode platform, they achieved precise measurements of low-amplitude vibrations, with a resolution of 0.06 \(\upmu\)g/LSB and a noise density of 0.5 \(\upmu\)g/\(\surd\)Hz. Comparative testing with a high-precision wired reference sensor (PCB393B12, PCB Piezotronics) under laboratory and ambient vibration conditions demonstrated the feasibility of capturing both high- and ultra-low-amplitude dynamic responses (ranging from 0.5 g to 10-20 \(\upmu\)g). Despite these performance gains, the Xnode platform exhibited limited flexibility due to its proprietary hardware-software architecture,which restricts integration with third-party modules and complicates system scalability and maintenance over extended lifecycles. [PERSON] et al. (2022) [21] introduced the Low-cost Adaptable Reliable Accelerometer (LARA), a cost-effective wireless acceleration sensing system designed for SHM applications. Built on open-source platforms such as Arduino and Raspberry PI, LARA achieved a sampling frequency of 333 Hz and a noise density of 51 \(\upmu\)g/\(\surd\)Hz, while employing Network Time Protocol (NTP) to ensure accurate inter-sensor time synchronization. Data were locally stored on SD cards and transmitted wirelessly via a 4G USB dongle, enabling autonomous operation. Validation experiments, both in laboratory settings and on a short-span pedestrian bridge in Barcelona, revealed eigenfrequency estimation errors of less than 1.28% compared to commercial-grade sensors (HI-INC). These findings underscore the system's potential as a practical and scalable solution for economically viable wireless SHM. Nonetheless, additional studies are needed to evaluate long-term operational stability, environmental resilience, and the system's full-scale applicability. [PERSON] et al. (2023) [22] proposed an innovative approach to overcome the spatial constraints of fixed sensor deployments by integrating wireless sensor nodes with mobile robotic platforms. Their system leveraged optimization algorithms, such as Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO), to dynamically determine sensor trajectories and maximize spatial coverage. Unlike prior simulation-only studies, the proposed method was experimentally validated using a scaled bridge model, effectively linking theoretical design with empirical implementation. However, practical applications on long-span bridges remain untested, and key issues such as environmental durability (e.g., resistance to wind, humidity, and vibrations), energy consumption of mobile units, and real-time data communication stability require further exploration. [PERSON] et al. (2024) [23] developed a low-cost wireless data acquisition platform optimized for Operational Modal Analysis (OMA) of bridges. The system utilizes the ADXL355 MEMS accelerometer from Analog Devices in conjunction with the ESP32 microcontroller from Espressif Systems, offering a balanced trade-off between low power consumption and computational performance. Designed for extended operations exceeding five years, the system supports in-node data preprocessing, precise time synchronization, and a user-friendly configuration through a web-based interface.