## 4 General Description of Adopted IoT-Based System for the Forest Fire Monitoring
### The Choice of Environmental Parameters for the Forest Fire Risk Prediction and Detection
* The narrowed set of parameters that include temperature and relative humidity is proposed for the forest fire risk prediction in combination with other sensors, such as vision sensors [73; 74] with fusion realized using Dempster-Shafer evidential theory, smoke and light intensity sensors [75; 76], or light intensity and carbon monoxide sensors [77] by using the fuzzy AHP method. The combined measurements of temperature, relative humidity, and carbon monoxide are employed for forest fire risk prediction with broad set of machine-learning algorithms [78], while the combined measurements of temperature, relative humidity, carbon monoxide, carbon dioxide, and smoke sensors are used as input in ANN [79; 80]. * A several simple solutions were proposed in which temperature measurements are combined with the measurements of smoke sensors or combustion sensors (i.e., combined carbon dioxide and nitric oxide measurements) [81; 82], or only temperature measurements are used [83], for the forest fire detection by using ANN. Additionally, a combination of temperature and carbon monoxide measurements is used in a simple forest fire detection system [84] with the predefined decision criteria. * Finally, a very complex solution in which soil parameters (soil temperature, soil moisture), air parameters (air temperature, air moisture, carbon dioxide concentration, wind direction, wind speed, precipitation, negative oxygen ion, PM 2.5), light parameters (global radiation, light intensity, sunshine hours threshold, photo synthetically active radiation), and plant parameters (needle-type stem moisture, non-destructive stem moisture) is used for forest fire prediction [85]. Considering that the basic meteorological parameters (T, H, W speed, and R) must be collected, and as a primary task of sensor network, we adopted the following set of meteorological parameters (temperature, relative humidity, wind speed, rainfall) for the real-time forest fire risk prediction, and in order to improve the performance of the real-time fire outbreak detection, a set of sensors for chemical gases concentration (oxygen, carbon monoxide, and carbon dioxide) is added. Additionally, by analyzing the presented wide range of proposed solutions for the forest fire prediction and detection, as well as the fact that there are no corresponding datasets available for the given area (that contain real-time measurements labeled with the information on the fire outbreak occurrence), which are needed for the training of ANN models or machine-learning algorithms, we here adopted the fuzzy AHP method. Therefore, the final proposal similar to the one presented in [57] is adopted for the real-time forest fire risk prediction and fire outbreak detection, which includes a set of aforementioned seven parameters, with the fuzzy AHP method being employed for the both tasks. More details are given in Section 2.4. Finally, the same set of meteorological parameters (temperature, relative humidity, wind speed, rainfall) is considered for the purpose of long-term and short-term forest fire susceptibility assessment. The Choice of Communication Technology for the IoT-Based Sensor Networks for Forest Fire Prediction and Detection  
The application of wireless sensor networks (WSN) that encompass a large number of Internet of Things (IoT) devices [86] in real-time forest fire prediction and detection has been proposed more than 15 years ago [87], with the argument that WSN can forecast forest fire in a more efficient manner than the traditional satellite-based approaches [88]. However, WSN deployment in a large-scale forest area, such as NP Golija, imposes certain challenges related to the power supply, maintenance, coverage, and infrastructure cost. Short-range wireless technologies such as Wi-Fi, Bluetooth, and ZigBee [60; 89] can use less complex sensor nodes for fire detection systems but close to the inhabited areas.