# An Energy-Efficient Evolutionary Clustering Technique for Disaster Management in IoT Networks
## 1 Introduction  
The Internet of Things (IoT) includes a large number of distributed nodes which are capable of sensing and interacting remotely [1; 2]. Wireless Sensor Networks (WSNs) are classified as the major players of IoT networks and provide a wireless connection between constrained devices. In IoT-based WSNs, nodes transmit collected data (such as environmental information) to a central destination known as a sink node through a wireless channel [3; 4]. Due to the recent advancements in hardware design, sensor devices are tiny and have a low cost, enabling their deployment in harsh environments such as nuclear power plants and forests [2]. However, sensor devices have very limited battery power, requiring the design of energy efficient algorithms [2; 5]. Limitations in energy resources have a direct impact on the application performance, as sensors with depleted batteries can cause network disconnections and consequently packet losses [6; 7; 8; 9]. In a multi-hop sensor network, it is crucial to devise algorithms and mechanisms in such a way to select the optimal route for data transfer to reduce energy consumption [2; 5; 10; 11]. There are several IoT applications that are classified based on their requirements and specifications. Safety-critical IoT applications have very strict requirements in terms of reliability and timeliness. This class of applications is defined as a set of behaviors and actions which are taken to prevent, control, and overcome disasters (e.g., fires and earthquakes) [2; 6; 9; 12]. We define disaster management as techniques that deal with devising algorithms and protocols that provide an enhanced quality ofservice in safety-critical applications. Forest fire detection is an example of a safety-critical application; an unexpected fire can cause a great deal of environmental and ecological damage while threatening human life [13; 14]. Regardless of the cause of the crisis, disasters such as earthquakes and fires create an emergency position and have irreparable effects on human society. Recently, due to the escalation of disasters, researchers have focused on the design of warning and remote control systems [15]. IoT protocols and algorithms provide more energy-efficient, portable, and scalable solutions to various disaster problems [2]. This technology provides an open opportunity for the development of disaster-resilient smart environments such as smart community solutions. In this paper, the proposed method uses IoT networks to remotely monitor forest fires. If a disaster occurs, an early warning system is supposed to perform disaster reporting, connecting to a central control unit that takes preventive actions. Due to the weather situation, forest fires may undergo sudden changes that make the monitoring process more complex as the location and spread of fire varies over time [13]. Furthermore, due to the non-uniform distribution of events and the uncontrolled behavior of fire, sensor nodes are unlikely to have a uniform energy consumption. There are some works in the literature focusing on clustering and routing approaches for forest fire management [13; 16; 17; 18; 19; 20]. Most of these works are focused on rapid fire detection and have neglected the energy efficiency of the network as one of the key elements [17; 19]. To the best of our knowledge, existing research works into WSNs in disaster management cover a uniform distribution of events [18; 19; 21]. These methods cannot be practically efficient in disaster management with a non-uniform distribution of events, which is more common in real experiments. Thus, disaster management using IoT-based WSNs requires an appropriate routing to be created from the cluster heads (CHs) to the sink in the disaster area. Clustering is one of the techniques which is widely used for hierarchical routing [22; 23; 24; 25; 26]. The purpose of clustering is to search for and find optimal CH nodes. Due to the complexity of searching for optimal nodes as CHs, clustering in WSNs is time-consuming.