## 2 Related Studies
DTNs offered a robust solution for data transfer in extreme conditions such as environmental disaster scenarios, where the native network infrastructure of the location had been affected, and inhospitable environments, as discussed in [6; 7; 11; 13; 32; 33]. For example, [PERSON] et al. [13] proposed using DTN to assist firefighting teams in environmental disaster scenarios. Another implementation of DTN in challenging environments was discussed in [34], which stood out for creating a DTN-based WSN for ecological monitoring in the Antarctic continent. Additionally, [PERSON] et al. [33] explored the use of DTNs in a sub-aquatic context, proposing a network architecture for sensors operating under high-latency conditions typical of underwater communication. Furthermore, DTNs demonstrated outstanding agricultural sector and environmental monitoring potential. In [8], the authors proposed an architecture for WSNs employing DTNs for monitoring environmental parameters and providing geopositioning without relying on traditional network infrastructure. In agriculture, [PERSON] et al. [35] proposed using DTN nodes on agricultural tractors to collect data from static sensors dispersed across large properties. Opportunistic Networks (OppNets) are a type of DTN characterized by inheriting the exact mechanisms that ensure communication between nodes subject to disconnections (store-carry-forward). Still, they are not limited to the TCP/IP stack [36; 37]. In [17], the authors developed an opportunistic network using collars equipped with BLE for monitoring the physiological activities of wild animals in remote and extensive habitats. In this setup, the animals wearing the collars acted as data mules, facilitating the transport and transmission of information as they moved through the environment. This approach enabled efficient large-scale monitoring without the need for fixed infrastructure, leveraging the natural mobility of animals to optimize data collection. Additionally, in the context of OppNets, [PERSON] and [PERSON] [38] proposed a strategy to minimize energy consumption in opportunistic networks; in particular, they used BLE advertising messages running on the background of smartphones as data mules. This technique significantly reduced energy demand by maintaining communication at low consumption levels. Some studies in the literature presented architectures that used BLE-based WSNs for communication with data mules equipped with DTN. However, only some studies integrated all these elements. [PERSON] et al. [35] presented simulated models employing the BLE/DTN/data mule architecture, but they needed to conduct practical experiments that encompassed the proposal made in the current study. Table 1 summarizes the scientific literature studies similar to the proposed WSN architecture that adopted data mules in the WSN architecture, exploring communication consumption or range as a result. The table also presents some properties used in the research, such as the adoption of DTN, including OppNet, and the technology used for the end node of the WSN architecture. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Work** & **Year** & **End-Node Technology** & **DTN/OppNet** & **Sleep Mode** & **Tx Mode** & **Range (m)** \\ \hline
[39] & 2019 & Zigbee & [ ] & 50 mW & 15 mW & 7 \\
[40] & 2018 & Zigbee & [ ] & 181 mW & 825 mW & 100 \\
[41] & 2007 & Zigbee & [ ] & 80 mW & NA & 1000 \\
[7] & 2020 & LoRa & [ ] & 72 mW & 648 uW & NA \\
[6] & 2020 & LoRa & [ ] & 231 mW & 660 mW & NA \\
[8] & 2018 & LoRa & [ ] & 24 mW & 27 mW & NA \\
[42] & 2022 & IEEE 802.11 & [ ] & 9 ul & 41 ul & 25 \\
[43] & 2020 & IEEE 802.11 & [ ] & 14 ulW & 494 ul & NA \\
[44] & 2011 & Various & [ ] & 633 nJ & 100 mW & 5 \\
[17] & 2018 & BLE & [ ] & NA & NA & 200 \\
[24] & 2017 & BLE & [ ] & 3.5 ul & 120 ul & 200 \\
[45] & 2016 & BLE & [ ] & NA & 57 mW & NA \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of related works.