## 4 General Description of Adopted IoT-Based System for the Forest Fire Monitoring  
In this section, we give a general description and rationale for the adopted IoT-based system, which enables the continuous collection of the meteorological data for the purpose of improved forest fire susceptibility assessment in a long-term and short-term manner and the real-time prevention and detection of forest fire in the Nature Park Golija. This system must support all aforementioned functionalities, but also operate in the harsh environment of Nature Park Golija under the conditions of the lowest possible influence on the natural environment and wildlife in the given area. Additionally, the previously presented results of forest fire susceptibility analysis by using the fuzzy AHP method (in Section 3) are taken into the account regarding system architecture and the sensor node deployment strategy.  
In order to support intended functions, the proposed system must contain the large number of sensor nodes deployed in the given area. Due to the non-existent power grid in the area and the difficult maintenance conditions (i.e., battery change), the sensor nodes must operate on battery power supply, while these energy-constrained nodes could eventually be equipped with solar panels in order to prolong operating life at the cost of a higher sensor node unit and network price, [60]. Additionally, due to the lack of communication infrastructure, a natural choice to achieve sensor nodes' connectivity in the given large-scale sensor network and under the given environmental circumstances is the adoption of wireless communication technologies that can support long-range communication and low energy consumption. This leads to the choice of low-power wireless communication technologies characterized with the energy-aware protocols and sets the demand to adopt the lowest possible data rate between sensor nodes and central elements of the system that are needed to support the given application demands. Therefore, the obvious choice is to adopt the system architecture and operating principles in which all tasks that can be achieved by the local processing in the sensor nodes, without the frequent communication of measured data to the other network elements, should be realized in the edge processing manner (i.e., in sensor nodes). In this case, sensor nodes will only communicate the final results to the central elements of the system, thus minimizing the communication demand and data rate in the network and overall energy consumption. However, this should be  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Susceptibility Class** & **RC [\%]** & **Fuzzy AHP [\%]** \\ \hline Very high & 0.57 & 0.59 \\ High & 26.28 & 25.15 \\ Medium & 69.17 & 68.04 \\ Low & 3.97 & 6.21 \\ Very low & 0.00 & 0.003 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Share of forest fire susceptibility zones in total MaB reserve area.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Susceptibility** & \multicolumn{3}{c}{**RC [\%]**} & \multicolumn{3}{c}{**Fuzzy AHP[\%]**} \\
**Class** & **I** & **II** & **III** & **I** & **II** & **III** \\ \hline Very high & 0.11 & 0.70 & 0.47 & 0.11 & 0.69 & 0.48 \\ High & 32.44 & 34.17 & 23.49 & 32.04 & 34.11 & 22.38 \\ Medium & 66.22 & 63.01 & 71.05 & 65.59 & 61.80 & 69.56 \\ Low & 1.23 & 2.12 & 4.99 & 2.27 & 3.39 & 7.57 \\ Very low & 0.00 & 0.00 & 0.002 & 0.00 & 0.01 & 0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Share of forest fire susceptibility zones in total area of different regimes of protection.  
done in a manner that provides all necessary environmental sensory data needed in the central element of the system (i.e., in the cloud).  
In the following sub-section, the several design choices are presented with the appropriate discussion and explanation.