## 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  
In the case of the real-time early forest fire prediction and detection system, the main demand is that it should be able to notify firefighters as quickly as possible in order to minimize damage to wildlife, environment, infrastructure, and people caused by the fire. In that sense, the most important requirements of the forest fire detection systems include [61]: robust continuous monitoring, fast detection of fire outbreak, determination of the exact location of fire, fast notification, and minimization of probability of false alarms occurrence. The comprehensive state-of-the-art analysis of previously proposed forest fire prediction and detection solutions shows that this system can be classified in several categories [61; 62]:  
* Traditional human-based observation systems;
* Satellite-based systems;
* Optical and thermal sensors (cameras)-based systems;
* IoT-based sensor networks and wireless sensor networks (WSN)-based systems;
* Unmanned aerial vehicles (UAV)-based systems. As defined in the Introduction, the main task of the sensor network observed in this paper is the continuous measurement of meteorological parameters that can be collected and used for the forest fire susceptibility assessment, while the real-time forest fire prediction and detection is considered as a secondary (additional) task. In that sense, the main meteorological parameters that should be gathered and delivered for the forest fire susceptibility assessment are [17; 18; 19; 20]: temperature, relative humidity, wind speed, and rainfall. Of all aforementioned systems, only the WSN and IoT-based sensor networks-based systems inherently contain sensor nodes equipped with the set of sensors that measure temperature, relative humidity, wind speed, and rainfall. The operating principles of the other systems demand the use of other sensor types, i.e., video cameras sensitive to the visible spectrum of smoke during the day and a fire during the night, infrared (IR) thermal imaging cameras, IR spectrometers able to identify smoke, or light detection and ranging systems (LIDAR) in optical and thermal sensor-based systems and UAV-based systems. Thus, all systems, except WSN- and IoT-based sensor networks-based systems are considered to be out of scope of this paper, although some of them may have higher performance for the forest fire detection purposes under the given circumstances. Of course, here, the adopted system can be complemented with different forest fire detection components, but it would cause additional costs. The literature review of IoT-based sensor networks and WSN-based systems intended for the real-time forest fire risk prediction and fire outbreak detection showed the broad set of possible meteorological and other parameters used in these systems. The following is observed:  
* The most commonly used set of parameters consists of: temperature (T), relative humidity (H), wind speed (W\({}_{\text{speed}}\)), and rainfall (R), [17; 18; 57; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72]. For this set of parameters, the fuzzy AHP method is used [57; 63; 64; 69] for the forest fire risk prediction, sometimes with additional parameters for human behavior and environment [64], while these same parameters were also combined with the concentrations of chemical gases (oxygen, carbon monoxide, and carbon dioxide) in order to detect forest fire outbreaks [57]. Additionally, the same set of parameters is used as an input for artificial neural networks (ANN) [66; 68] or linear regression model [70], or to calculate the fire weather index (FWI) [65; 67; 71; 72] by using a pre-defined procedure, for the purpose of forest fire risk prediction.