## 1 Introduction
A summary of research cases most relevant to synchronization in WSNs is provided in Table 1.  
Wireless sensor networks (WSNs) play a significant role in physical and environmental monitoring. Smart environmental monitoring (SEM) systems are being developed using internet-of-things (IoT) technologies and wireless sensor networks. This approach makes it possible to control air, water management, radiation emissions, etc., thoroughly. The reliability of these systems is based on proven machine-learning techniques and classifiers that ensure effective analysis and infusion of SEM [38]. In [39], the authors provide a comprehensive overview of the use of wireless sensor networks (WSNs), unmanned aerial vehicles (UAVs), and crowdsensing technologies. The authors identify cooperation between ground-based (WSN/crowdsensing) and aerial (UAVs) surveillance systems and the use of advanced signal-processing techniques as crucial components of future integrated large-scale environmental monitoring systems. Changes in the ecological environment have serious consequences for people, particularly air pollution. Using cloud storage and wireless sensor networks (WSNs), greater accuracy can be achieved in monitoring environmental factors as well as healthcare aspects. Such monitoring tasks are essential in smart cities, where air quality control is critical to maintaining population health and economic development [40]. There are three main challenges for environmental monitoring in buildings: optimizing the software for processing data from different types of sensors, data synchronization, and ensuring network stability when new sensors are connected or existing ones are removed. In [41], a platform and a software system for efficient data-acquisition collection, preprocessing, and packaging from various sensors were presented, simplifying the network's setup and ensuring its reliability. A gateway system developed for a wireless sensor network aimed at environmental monitoring is presented in [42]. This system allows the collection and storage of the data provided by the sensor network and ensures convenient remote access to this data through a website or console application. In [43], the authors presented the application of multiple consumer-grade internet-of-things (IoT) technologies to measure four common air pollutants. They identified the impact of indoor air quality on sleep quality and demonstrated that elevated levels of some air pollutants can negatively affect sleep quality while others can improve it. In mechanical fault diagnosis, multilevel and hierarchical information fusions are used in WSNs to address the challenge of real-time transmission of many vibration signals [44]. WSNs are also used for indoor location services, particularly for visually impaired individuals, by combining data from wireless networks with inertial sensors [15; 45; 46]. In medicine, sensor data fusion through wireless networks is particularly beneficial for improving diagnostics accuracy. Integration of eye-tracking data into magnetic angular rate gravity (MARG) sensor-based head-orientation estimation has been explored for applications in rehabilitation robotics and medical diagnostics [47]. Additionally, electronic medical records and the biomedical literature have been used to support the diagnosis of rare diseases through data fusion [48]. Sensor data fusion is also being applied in the context of wearable sensors for fall detection, where data-fusion techniques have been compared to single-sensor machine-learning techniques to improve fall detection accuracy [49]. In the area of vehicle localization, the adaptive diagnosis for fault-tolerant data fusion based on the \(\alpha\)-Renyi divergence strategy has been explored [50]. In the absence of syn  
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chronization between nodes, the localization problem in wireless networks is considered based on hybrid measurements of signal strength (RSS) and time of arrival (TOA).