# Formal Modeling of IoT and Drone-Based Forest Fire Detection and Counteraction System
## 2 Related Work  
A variety of detecting and monitoring systems have been proposed by the researchers; in the following section, a brief overview of these studies has been discussed. The multiple SC approaches were added to the MNP dataset to evaluate an efficient indicator that theoretically provides more reliable results for forest fires [16]. Authors have presented a generalization of the classical inference in the Fuzzy Rule-Based Structure by overlapping functions and overlapping indices in [17]. A novel smoke detection system using CS Adaboost has been suggested in [18] to efficiently and effectively identify early forest fire smoke, which is motivated by the findings of the research in the forest fire video. The static and complex structure analysis of fire in the detection of forest fires is proposed in [19]. The intense classifier of machine learning is used to identify the agent flame area as actual or non-flammable, depending on the derived texture characteristics. A systematic structure has been suggested in [20] by using WSN to identify and control forest fires. The purpose of the system is to identify the fire danger quickly. A new technique to identify flames from a stream of video has been introduced [21]. It took complete benefit of the flame's motion mechanism and information related to color. An Unmanned Aircraft System (UAS) for wildfire control has been proposed [22], which consists of multiple unmanned aircrafts and a base station. The study states how this data can be acquired remotely through an UAS that has infrared or visual on-board cameras. A robust video analyzing methodology for quick flame identification in monitoring footage through temporal smoothing and regression analysis is presented in [23]. In [24], authors introduce a hybrid technique of forest fire detection utilizing both color and movement functionality for the analysis of pictures taken from a camera attached  
Figure 1: System architecture. to a UAV that travels throughout the operation time. Add Net has been implemented [25] to the issue of detecting smoke in forest fires, where an event computation time's reliability and reduced wrong alert rate is important. An effective method based on CNN has been suggested [26] for detecting fires from recorded videos in uncertain monitoring contexts. This paper [27] analyzed an energy-friendly and computer-efficient CNN framework that can detect fires and localize the fire scenario influenced by the construction of squeeze networks for semantic understanding. The aim of the study [28] is to detect wildfires at the earliest possible stage by incorporating a monitoring framework mainly composed of drones. The closest authorities will immediately be informed of the fireplace. A solution for a smart system of networked nodes for fire detection is proposed [29] in small areas, such as small isolated homes and small villages, at a low cost. The solution includes the use of FIR sensors, which can detect, as well as locate, heat and perform some real-time protection mechanisms. Forest fire detection has been introduced in terms of early warnings by using many attached sensors for transmission and a satellite in the system for receiving signals and sending them to the ground station for further processing [30]. A neural network has been trained in this research [31], to conduct forest fire predictions. To predict fire hotspots, this system uses readily available remotely sensed information in the type of satellite pictures. It considers a fleet of homogeneous small drones that could only reach locations and then use payloads individually but can conduct searching, surveillance, tracking, monitoring, aid, reporting, and transfer duties together. Two algorithms for detecting forest fires based on knowledge fusion approaches have been suggested in [32]. The first algorithm utilizes a threshold approach and nodes that are fitted with sensors for humidity, temperature, and light.