# Wildfire Monitoring Based on Energy Efficient Clustering Approach for FANETS
## 1 Introduction
For example, to determine node fitness, they exchange their location and other characteristics [5; 6]. They run calculations to choose CHs based on these factors. Communication and calculations to establish clusters and elect CHs to consume network resources like bandwidth, processing power, and battery capacity are considered energy waste. IoT is a massive network of disseminated nodes that can detect and communicate with each other across great distances [5]. As they enable a wireless link between constrained devices, WSNs are regarded as the essential players in the IoT networks [4; 5; 6]. IoT-based WSNs use a wireless channel to send collected data (environmental data) to a central terminus known as a sink node [7]. Sensor node devices are becoming smaller and more economical due to recent developments in hardware design, allowing them to be used in adverse settings such as nuclear power plants and forests [7; 8]. On the other hand, sensor nodes have limited battery capacity, necessitating the creation of energy-efficient algorithms. As sensors with low batteries might create network disconnections and, as a result, packet losses, energy resource limits directly impact the application's performance [6; 7; 8]. Reduced energy use in multi-hop sensor networks necessitates the development of methodologies and procedures that select the most energy-efficient way for data transfer [8]. There are several types of Internet of Things applications, each with specifications and needs. Safety-critical IoT applications must fulfil high criteria for dependability and punctuality. Apps for disaster management are defined as behaviors and actions that help people prevent, control, and recover from disasters (fires and earthquakes). A disaster management approach is characterized as one that focuses on developing algorithmic and protocol solutions to improve service quality in safety-critical applications [9]. Forest fire detection is an example of a safety-critical application, since an unplanned fire can cause considerable environmental and ecological devastation and jeopardize human lives [10]. Calamities such as quakes and forest fires, regardless of their cause, put people in a situation of urgency and have lasting implications for human society [8; 9; 10; 11]. Academics have recently focused on developing disaster warning and remote-control systems due to the rising frequency and severity of disasters [11; 12]. It is possible to design more energy-efficient, portable, and scalable solutions to a wide range of catastrophic situations by utilizing IoT protocols and algorithms. This technology opens the door to disaster-resilient intelligent environments, such as intelligent community solutions, where calamities may be minimized. This study leverages the Internet of Things networks to propose a potential technique for remotely monitoring forest fires. As soon as a disaster occurs, an early warning system is anticipated to send a signal to a central control unit, taking the appropriate precautions. Forest fires may exhibit sudden behavioral changes in reaction to changes in climatic circumstances, making monitoring more challenging as the fire's position and spread fluctuate over time. Furthermore, SNs are unlikely to consume energy consistently because of the non-uniform distribution of events and the unpredictable behavior of fire. Forest fire management approaches that employ clustering and routing techniques have been the subject of several papers in the literature. These efforts have concentrated on quick-fire detection, with network energy efficiency being one of the most critical factors [5], as seen in Figure 2.  
As a result, to successfully manage catastrophes using IoT-based WSNs, a suitable routing channel from the cluster heads (CHs) to the sink in the disaster zone must be established. Clustering is one of the most often used hierarchical routing algorithms, and it is detailed in-depth below. Clustering aims to feasibly find and identify the most optimal CH nodes [13].