## 2 Related Studies  
In the context of WSN for applications in rural and remote environments, LPWAN technologies have demonstrated particular promise due to their capacity for long-range communication and the low energy consumption of their transceivers [5; 19]. When solely considering the aspect of sensor energy consumption, the BLE technology, particularly when used in the broadcasting communication mode, emerged as a promising alternative due to its low energy consumption. This was due to the minimal computational demand required for the transmission of broadcasting messages, which are known as advertisements [20]. Such devices were designated beacons. They were widely adopted in a variety of applications, including health monitoring for disease prevention [21; 22] and workplace safety monitoring through Personal Protective Equipment (PPE) equipped with beacons [16]. In comparison, in idle mode, BLE demonstrated energy efficiency similar to or superior to certain LPWAN technologies. The study by [PERSON] et al. [23], which utilized BLE beacons, reported results of 5.9 uA in sleep mode, while [PERSON] et al. [24] cited a consumption of 1.19 uA. However, it was observed that WSN solutions utilizing a secondary wake-up radio exhibited higher energy consumption, with levels reaching 270 nW, as discussed in reference to the literature [25]. This technology operated in conjunction with the primary transceiver, employing a low-power radio system designed to activate the device from its low-power radio system to transition the device from a low-power state to an operational mode. Despite the low consumption, implementation of a Wake-up Radio (WuR) introduced additional complexities to the device, requiring two radios and often two antennas. Furthermore, the range of WuR was typically limited to, between 5 and 30 m [26]. In contrast, BLE offered advantages such as simplicity of implementation and a more extended transmission range, making it preferable in various applications. Despite the highlighted advantages of BLE, studies such as those in [23; 24] were conducted in controlled environments, which may need to reflect real-world field conditions accurately. These limitations included the lack of testing in scenarios with varied environmental interference, which could impact BLE devices' energy performance and communication range. A strategy for device mobility scenarios is DTN, which enables data transmission regardless of an instantaneous end-to-end path between source and destination. These networks can operate effectively in situations with significant latencies and network interruptions between nodes [27; 28]. In addition, many studies suggested adopting DTNs using data mules based on UAVs [13; 14]. UAVs have rapidly expanded across various applications, from precision agriculture to environmental monitoring in sensitive ecosystems. In [29], the authors examined the effects and risks of digital technologies in pasture monitoring, using UAVs, sensors, and data communication networks to manage cattle and sheep. Similarly, [30] explored computer vision for detecting hares and roe deer through aerial drone images. These approaches demonstrated the potential of UAVs for monitoring different ecosystems and helping mitigate impacts on natural habitats, particularly in ecologically sensitive areas. For instance, in [31], the application of Deep Learning techniques to identify breeds and monitor crop health in rice fields using low-altitude UAVs showcased the efficiency of these technologies with minimal computational resources. Nevertheless, studies like [30; 31] also raised concerns about potential interference with wildlife and ecosystem degradation. Despite DTN being efficient in data mule application contexts, it was not beneficial to use DTN technologies in low-power end nodes in WSNs. This was because end nodes would have required hardware with significant processing power and high energy consumption to function as DTN nodes [6; 7; 8].