## 4 General Description of Adopted IoT-Based System for the Forest Fire Monitoring
### Overall System Description
That is, further processing might be performed by the central server by using the corresponding data together with already stored information about vegetation type, slope index, and sensor location, in order to calculate wind direction and predict the fire spread direction by using the method defined in [57]. In both scenarios, values of the meteorological variables and chemical gases concentrations related with fire prevention or detection procedures will be stored in the central database for future purposes. One possible variant in the areas with the higher sensor node density is to organize sensor node in clusters so that each sensor node has a corresponding cluster head, i.e., sensor node equipped with a LoRa interface (LoRa node), which is deployed in the particular zone. The cluster members perform the same tasks as in the previous case (when all nodes are LoRa nodes) and send their messages through the wireless communication with the cluster head (i.e., LoRa node) achieved by using short-range communication technologies, e.g., ZigBee technology. The LoRa node in this case performs data aggregation and delivers all data for its cluster to the central server. Each sensor node sends a daily report with all the sensory data in standard format for the meteorological parameters (a similar format can be used for data on gases), in order to update the corresponding long-term database on a daily basis. These data are used for the long-term and short-term forest fire susceptibility assessment. In case of the long-term assessment (i.e., monthly or yearly), collected meteorological parameter data (historical data) are averaged and included in the fuzzy AHP method described in Sections 2.1-2.3 in its final sub-layer, similarly as in [17; 18; 19; 20]. Thus, the overall long-term forest fire susceptibility assessment will take into account local meteorological conditions. This will also ensure proper assessment and fire prevention activities in the long term, based on the local and more appropriate meteorological data. For the improved short-term (daily) forest fire susceptibility assessment, we here propose that the daily reported meteorological data are used to calculate the fire weather index (FWI) solely by four meteorological variables as input ones (i.e., temperature, wind speed, relative humidity at noon time, and accumulated precipitation during previous 24 h period) [17; 65; 67; 71; 99]. The FWI system produces six indices based on the referenced fuel type, that is: (1) fine fuel moisture code (FFMC) that is calculated by using a function of temperature, wind speed, relative humidity, and precipitation; (2) duff moisture code (DMC) that is calculated by using a function of temperature, relative humidity, and precipitation; (3) drought code (DC) that is calculated as a function of temperature and precipitation; (4) initial spread index (ISI) that is calculated as a function of FFMC and wind speed; (5) buildup index (BUI) that is calculated as a function of the DMC and DC; (6) FWI that is calculated as a function of ISI and BUI. Alternatively, FWI can be calculated by using the fuzzy inference method defined in [69]. So, calculated FWI values can be included in the fuzzy AHP method described in Sections 2.1-2.3 in its final sub-layer, i.e., similarly as in [17], in order to achieve short-term forest fire susceptibility assessment. For the long-term and short-term forest fire susceptibility assessment, the calculation for each elementary area is performed by using meteorological data averaged over the closest sensor nodes. The long-term deployment of a proposed IoT-based system would enable the collection of historical, meteorological, and other data. Such a database enables further research, especially if all fire occurrence in the area is properly documented. This will enable the application of more sophisticated inference methods (i.e., machine learning or deep learning) that could further improve the forest fire prevention performance of the overall system.