## 6 Conclusions  
Forest fire susceptibility zoning is one of the first and one of the unavoidable steps in forest fire risk reduction and mitigation. It makes it possible to obtain data on where forest fires are more likely to occur and to identify certain \"hot spots\" where their occurrence is most expected. Based on that, decisions can be made that will prevent possible consequences, save the lives of people and other living beings, and preserve tangible and intangible goods. Forest fire mitigation is especially important when it comes to protected natural areas, due to their vulnerability.  
Nature Park Golija is one of the most important protected natural areas in Serbia, considering its size, geodiversity, biodiversity, and the fact that its largest part is the UNESCO MaB Reserve Golija-Studenica. According to the Forest Fire Susceptibility Index method, 24.71% of the nature park territory is considered to be in the zones of very high and high forest fire susceptibility, and according to the fuzzy AHP method, that share is 23.68%. Those figures are very unfavorable; therefore, in areas that are considered to be of higher susceptibility, it is necessary to intensify the implementation of measures in order to reduce forest fire risk. When considering forest fire susceptibility in the MaB reserve, zones of very high and high forest fire susceptibility are recorded on 26.85% (Forest Fire Susceptibility Index) and 25.75% (fuzzy AHP).  
The design and deployment of the proposed IoT-based sensor network and the overall system that encompasses real-time measurements, forest fire risk prediction, and fire outbreak detection present a cost-effective, reliable, and flexible long-term solution for the collection of meteorological data and data on fire outbreak occurrence, and for the improved short-term and long-term forest fire susceptibility assessment, which will certainly enable a more advanced forest fire prediction, detection, and monitoring activities. Additionally, due to the constant collection of all-important data, this system could be continuously improved through the usage of more advanced inference techniques (i.e., machine learning and deep learning), without substantial investments.  
Obtained results can be applied for adequate forest fire risk management, improvement of the monitoring, and an early warning system in the Republic of Serbia. The forest fire susceptibility maps and spatial data obtained by research are of the utmost importance for decision makers for better preparedness, coordination, and intervention in the case of forest fire occurrence. The lessons learned from the study area will help scale up the forest fire susceptibility assessment on the territory of the whole country, and especially in protected natural areas.  
Conceptualization, LN. and G.B.M.; methodology, I.N., M.M., G.B.M., and D.L.; formal analysis, S.D. ([PERSON]) and I.S.; data curation, T.L. and M.T.; writing--original draft preparation, I.N., S.D. ([PERSON]), G.B.M., and D.L.; writing--review and editing, I.N., S.D. ([PERSON]), G.B.M., and D.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.