## 5 Results
### Discussions  
The experiments allowed comparing the energy consumption of the various SNs detailed in Table 1 with the SN used in this study. This comparison enables an assessment of related studies against the study conducted, as shown in Table 4.  
To date, scientific literature lacks a practical implementation of the \"FloatingBlue\" architecture and does not report on its performance results. Although the study [44] offers the possibility of simulating the \"FloatingBlue\" architecture, it did not conduct specific tests simulating the combined use of DTN and BLE. The \"FloatingBlue\" proposal achieved success due to the software services developed in the application layer of the DM and the embedded system developed in the e-node. The graph in Figure 8 emphasizes optimizing the PDU message size to save energy in advertisement message transmissions.  
\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 μW & 15 mW & 7 \\
[40] & 2018 & Zigbee & [ ✓] & 181 mW & 825 mW & 100 \\
[41] & 2007 & Zigbee & [ ✓] & 80 mW & NA & 1000 \\
[7] & 2020 & LoRa & [ ✓] & 72 μW & 648 μW & NA \\
[6] & 2020 & LoRa & [ ✓] & 231 mW & 660 mW & NA \\
[8] & 2018 & LoRa & [ ✓] & 24 mW & 27 mW & NA \\
[42] & 2022 & IEEE 802.11 & [ ] & 9 μJ & 41 μJ & 25 \\
[43] & 2020 & IEEE 802.11 & [ ] & 14 μW & 494 μJ & NA \\
[44] & 2011 & Various & [ ✓] & 633 nJ & 100 mW & 5 \\
[17] & 2018 & BLE & [ ✓] & NA & NA & \(\sim\)200 \\
[24] & 2017 & BLE & [ ✓] & 3.5 μJ & 120 μJ & \(\sim\)200 \\
[45] & 2016 & BLE & [ ] & NA & 57 mW & NA \\ FloatingBlue & 2024 & BLE & [ ✓] & 2.25 μJ & 11.8 μJ & \(\sim\)200 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparative analysis of beacon power consumption: FloatingBlue vs. related works.  
Figure 13: Relationship between communication range and TX power of the SN.  
One of the main challenges in WSNs is balancing energy consumption with communication range. Technologies that offer longer communication ranges tend to consume more energy, making applications impractical in scenarios where energy is a limited resource. In the case of \"FloatingBlue\", tests have shown that increasing the BLE transmission power results in a significant increase in communication range, as illustrated in Figure 13. However, this extended range comes with a proportional increase in energy consumption. The trade-off between energy consumption and communication range is critical in situations where frequent battery replacement is impractical, and the FloatingBlue architecture can be applied, such as in wildlife monitoring, forest monitoring, precision agriculture, and disaster scenarios.  
Therefore, it is essential to find a balance where the range is maximized without compromising the battery life of the sensor nodes. To achieve this balance, different levels of BLE transmission power and implementing intelligent energy management algorithms can be investigated to optimize the relationship between consumption and performance.  
According to Table 4, the proposed beacon strategy for integration into the architecture stands out for having one of the lowest transmission consumption levels, attributed to the optimization of advertising messages and the bare-metal embedded software. In idle mode, favorable energy consumption is observed in the SN, which, despite being higher than those of studies using WuR technology [44], presents a significant advantage in terms of range. Thus, \"FloatingBlue\" offers lower transmission consumption and idle mode consumption similar to WuR, but with superior range, representing the main advancements compared to existing similar architectures.