## 1 Introduction  
Wireless sensor networks (WSNs), as a data-transfer source, have gained significant roles in a wide range of applications, primarily attributed to their cost effectiveness, functional capabilities, adaptability, and extensive coverage. Data collection becomes an essential task using sensor networks. However, the effectiveness of WSNs is compromised by obstructions that impair signal strength and introduce synchronization. Consequently, these limitations result in reduced operating distances and increased latency. Therefore, accurate data interpretation faces challenges, and synchronization is needed. Time synchronization is essential for accurate data interpretation in WSNs. It achieves reliable measurements, facilitates action coordination, and ensures seamless data exchange between devices. Numerous synchronization methods and protocols have been extensively studied and developed to address various tasks within the realm of data reading. These methodologies encompass diverse approaches, such as harnessing GPS modules, utilizing pulse-per-second (PPS) signals, and employing synchronization protocols that focus on estimating nondeterministic delays to achieve accurate time measurements [1, 2], the sensorimotor synchronization method [3], or the Sync-ESP-NOW protocol and other specialized protocols, such as NTP and PTP, for IoT devices [4, 5]. Furthermore, investigations into synchronization have extended to Bluetooth-based sensor networks [6] and time measurement in medical devices [7]. An extension of reference-broadcast infrastructure synchronization (RBIS) with wide coverage of multiple access points and a significantly improved timing accuracy superior to NTP was implemented in research presented in [8]. Special attention was given to developing systems that accurately mark events and interact with digital data on internet-of-things platforms [9]. Some studies include the development of hardware and architectures for IoT applications in the field of vibration measurement [10] and synchronized acquisition of ECG signals for emotion recognition [11]. The approach outlined in [12] proposed millisecond-precision synchronization with simple, budget-friendly, hardware, making it applicable in scenarios prioritizing cost and time constraints. Such a diverse spectrum of research reflects the general interest in time synchronization, its meaning in various contexts, and additional possibilities for application in modern information-collection and data-analysis scenarios. Data synchronization, regarding time, is a critical factor for successful implementations of data-fusion methods. Therefore, data synchronization in WSNs is a significant research problem. For example, in the research [13], time synchronization has been addressed through temperature-sensitive compensation in WSNs. Cluster-based maximum consensus time synchronization for industrial WSNs has also been explored in [14]. In the study [15], sensor data fusion has been applied to engine fault diagnosis through a multisensor approach. A similar approach based on multisensor system-on-chip platforms is presented in [16], where the authors implemented data fusion for sports activity monitoring in eHealth systems. The main advantage of implementing a multisensor approach via WSNs is that it allows for partially overcoming synchronization problems. Typically, data from all sensors are sent as a single data package, neglecting minor delays during the collection of data from sensors. The practical application of industrial big data also requires synchronization. In [17], data fusion has been used in the domain of cloud-control platforms to enhance their applicability. Sensor data fusion via wireless networks is a versatile and powerful tool with applications in mechanical fault diagnosis, indoor location services, medical diagnostics, wearable sensors, vehicle localization, aquaculture, robotics, industrial big data, healthcare, education, structural health monitoring, engine fault diagnosis, and sport activity monitoring.