# Formal Modeling of IoT and Drone-Based Forest Fire Detection and Counteraction System
## 1 Introduction  
IoT is anticipated to grow progressively due to the development of networking systems, availability of sensors, and computing networks [1]. Agriculture, industry, academia, and environment monitoring are major fields for the application of IoT technologies to constantly track and monitor [1]. Smart systems have been developed as a technical response to social and environmental challenges, and they have been applied worldwide to make the lives of people more productive and profitable. For these purposes, a wide number of applications are being created. Smart forests appear now as a feasible choice for long-term natural ecological sustainability, including environmental health analysis, especially in relation to climate change, wildlife tracking, surveillance systems, and early warning of fires and natural disasters [2]. The forest is a large region dominated by plants and animals, occupying almost four billion hectares, or about 30 percent of the world's land area [3]. Forests have a massive environmental impact that saves the earth from overheating and, to a larger degree, reduces human-caused pollution. Therefore, a fire in a forest induces a serious risk and may causean immense environmental disaster, increasing the loss of human lives and wildlife and causing harm to the environment and economy, such as biodiversity. These forest fires are rapidly spreading and unacknowledged, and every year, in many countries, many human beings and animals are injured and die [3]. Forest monitoring and regulation is therefore an extremely significant problem, and there is a need to find appropriate ways to minimize forest fires and preserve valuable resources. In recent years, researchers have been paying attention to the development of forest fire identification systems to address these problems. The majority of research has been conducted using most of these main strategies, such as human-based surveillance systems, satellite networks, image sensors, wireless sensor networks (WSN), and IoT [4]. A deep learning algorithm named convolutional neural network (CNN) has been utilized [5] with IoT and smart sensors to detect huge forest fire disasters. In [6], the authors presented a fire detection system as a convergence of WSN, UAV, and cloud computing. To classify a fire incident with greater precision, image processing techniques are also incorporated. In [7], information fusion method and WSN techniques have been used for detecting fires at the initial stages. The authors proposed a UAV-based system using an IR sensor for forest fire surveillance. In [8], to improve the reliability and accuracy of forest fire detection, this technique has taken advantage of both the light and motion properties of fire in IR images. For system development, formal models provide many advantages and are very beneficial because they collect the system requirements and specifications at an early stage to provide the accuracy and correctness of the model. The sensitive applications are required to be formalized before real-time testing to avoid any major loss. Therefore, without developing a formal model, it is not possible to make these applications error free. Furthermore, formal modeling-based development lead to the avoidance of major errors, such as run time errors, plan change at the time of errors, rapid changes in design, consumption of extra resources, and time. In this paper, IoT and drone-based forest fire detection and a counteracting system have been designed, and it is an extended version of our previous paper [9]. In this article, we have included one more major module of the system with the name Network, maintenance and failure detection with depth formal modeling, and system specifications. Furthermore, we have improved formal specifications of fire detection and counteraction systems to remove errors at the abstract level, which creates the main difference in contribution. IoT-based sensors are deployed on the trees, ground, and animals to collect information and send it to the control room to extinguish the fire.