###### Abstract
However, recent advancements in sensor and measurement technology have introduced the adoption of fiber Bragg gratings [4] and fiber Bragg grating tiltmeters [5] to estimate fundamental characteristics of structural vibrations, such as primary natural frequencies. In the context of SHM systems, it is crucial to consider the optimal number of sensors required for effective monitoring [6]. Signal processing techniques are instrumental in analyzing vibration-based data to detect and assess structural abnormalities. By applying these techniques to vibration parameters, such as natural frequencies, mode shapes, and damping ratios, it becomes possible to extract valuable insights regarding the structural integrity [7]. In the case of non-stationary responses, the Hilbert-Huang Transform (HHT) and Empirical Mode Decomposition (EMD) have emerged as prominent techniques [8; 9]. However, the adoption of traditional wired vibration monitoring systems can be hindered by high costs associated with sensors, data acquisition, and peripherals. Over the past few decades, the Internet of Things (IoT) has emerged as a robust platform for wireless communications [10; 11]. The integration of sensors and IoT in SHM has revolutionized the field by enabling remote and continuous data acquisition, real-time analysis, and proactive maintenance strategies. Compact embedded devices and low energy requirements have paved the way for Micro-Electro-Mechanical Systems (MEMS)-based vibration monitoring systems [12; 13; 14; 15]. These systems have found applications in various research areas, including machine industries, civil infrastructures, earthquake engineering, and geotechnical engineering [16; 17; 18; 19]. However, accurate measurement of a wide range of acceleration amplitudes poses a significant challenge in structural vibration analysis [16; 20; 21; 22]. Despite this challenge, MEMS accelerometers have maintained their compact size, diverse range of models, and cost-effectiveness, making them highly suitable for structural monitoring applications [23; 24; 25; 26; 27]. On the other hand, ad hoc networks have garnered significant attention as they introduce a novel communication paradigm in recent decades [28]. These networks are characterized by decentralized wireless nodes that engage in collaborative interactions to achieve common objectives. This decentralized nature allows for enhanced flexibility and scalability in establishing and maintaining communication links. In the context of mobile ad hoc networks, which are highly dynamic and subject to frequent and unpredictable changes in network topology, several routing protocols have been proposed to address the associated challenges [29]. Furthermore, in the rapidly evolving landscape of heterogeneous IoT scenarios, proactive edge caching techniques have been rapidly developed to cater to the unique requirements of ad hoc networks [30]. However, one of the critical challenges in both IoT and ad hoc networks, including decentralized wireless networks, pertains to achieving accurate time synchronization among the network nodes [31; 32]. In IoT and ad hoc networks, achieving time synchronization for SHM is particularly challenging due to several factors. Traditional approaches use Global Positioning System (GPS) receivers for synchronization, but power dependency and signal obstruction pose challenges [33; 34; 35]. In the realm of precise clock synchronization, the Precise Time Protocol (PTP), also known as IEEE 1588, emerges as a notable option [36]. PTP leverages standard network lines to provide hardware-level time accuracy. This is achieved through hardware-assisted time stamping, which effectively reduces the physical distance between the reference clock's receive/transmit ports. However, it is important to consider that PTP requires additional hardware clocks and relies on packet exchange mechanisms [37]. An alternative approach is leveraging the Network Time Protocol (NTP) for synchronization [38; 39; 40; 41].