## 5 Discussion
The significance of this research is that it is not only applicable to protected natural assets, but can also be applied to other spatial units. The research should benefit state and local authorities, emergency management services, but also the local population. Another important goal of research presented in this paper, as stated in the Introduction, was to propose adequate system architecture and an underlying sensor network in order to enable the continuous collection of local meteorological data in order to amend and improve the forest fire susceptibility assessment performance achieved with the aforementioned fuzzy AHP method (which does not use meteorological data). The main design requirements for such a system were to enable gathering of local meteorological data, i.e., data collected by the sensors densely deployed in area of interest, in order to further improve the quality of the forest fire susceptibility assessment in a long-term manner (i.e., monthly or yearly by using averaged data) or in a short-term manner (i.e., daily by using current data). Additional goals included system ability to perform real-time forest fire risk assessment and fire outbreak detection, and to gather all necessary data for the future research and improvements of the overall forest monitoring system, and the future use of more advanced inference methods such as machine-learning and deep-learning models. The specific characteristics of the chosen area (NP Golija) set additional demands regarding power supply and efficiency, maintenance, use of long-range low-data-rate wireless tech nology. Additionally, the proposed system had to be cost effective (as it was intended for the large-scale usage and should be appropriate for the countries with the lower possible budget for these purposes). The above requirements, as elaborated in Section 3, set a specific set of demands in the sensor network and the overall system design. Therefore, the comprehensive state-of-the-art analysis of possible system architecture and sensor network operating and design principles was conducted, with the main results of that analysis presented in Section 3 through the elaborated discussion regarding the choice of environmental parameters, which should be monitored, and the choice of communication technology and architecture that is most suitable for the given application requirements. The main choice was to include a set of weather and environmental parameters (temperature, humidity, rainfall, wind speed, as well as carbon monoxide, carbon dioxide, and oxygen levels) for the long-term and short-term forest fire risk assessment (as an additional part of the fuzzy AHP method based on the other variables) and the real-time forest fire risk estimation and fire outbreak occurrence detection. We proposed modern and advanced system architecture, in the form of an IoT-based sensor network with LoRaWAN technology used in the access part (sensor network part) of the system in order to achieve reliable, low-rate, low-power, low-cost, and long-range bidirectional communication of sensor nodes. The supported security, reliability, and traffic demands in this network absolutely satisfies all current and future demands and also enables the design of low-cost and long-life sensor nodes for a large-scale, cost-effective, and low-maintenance system. It can be argued that there are other possible solutions for the real-time forest fire risk prediction and outbreak detection, such as those based on optical and thermal sensors or UAV. This kind of system might offer better real-time forest monitoring performance than the system adopted in this study. However, these systems do not include the ability for the long-term collection of meteorological and environmental data, which was one of the tasks, and can be viewed only as a possible additional segment, which would significantly increase system cost and complexity. Thus, the proposed system architecture and sensor network part fulfill all application requirements with the lower overall cost.