# Bio-Inspired Energy-Efficient Cluster-Based Routing Protocol for the IoT in Disaster Scenarios
## 1 Introduction  
The number of natural disasters has escalated due to global warming, environmental pollution, urbanisation, etc. According to statistics, an average of 60,000 people die from disasters each year [1]. Faster detection of disasters can save many lives. To achieve this, it is necessary to monitor environmental parameters (temperature, wind pressure, gas or chemical compositions, etc.) by implementing a wide range of sensors. The Internet of Things (IoT) represents an emerging paradigm that provides scalable solutions for a wide range of disaster-related problems [2]. The objects (e.g., mobile phones, radio-frequency identification (RFID) tags, sensors, etc.) in IoT networks can communicate with each other to perform complex tasks by collecting and processing data without human intervention [3]. The wireless sensor network (WSN), which acts as a virtual layer of the IoT network, covers a range of IoT applications for disaster management, including pre-disaster environmentmonitoring [4; 5], disaster preparedness [6], Early Warning Systems (EWSs) [7], and post-disaster response [8]. Figure 1 shows the WSN applications for disaster management. In WSN-based IoT applications, sensor nodes collect data from the operating environment and send these data to a sink or base station [9], where the data are then further processed to evaluate the situation. However, there are limitations to using WSN-based IoT networks in disaster situations: limited battery life, power outages, limited memory, etc. [10]. The fundamental paradigm of these networks--such as sensing the environment, data collection, and transmission--minimises the energy level of a node, which may result in the breakdown of the communication link between the sensing nodes and the sink, disrupting the whole process. Moreover, during a disaster, replacing sensors or batteries may not be possible. As a result, the entire network may collapse, causing delays in the response system. Hence, many researchers are working towards energy-efficient communication to increase the longevity of these networks. Cluster-based communication addresses the power-conserving issue by providing energy-efficient communication [11]. Clustering algorithms create a group of sensor nodes and decide which cluster head (CH) to associate with among various choices. In every cluster, there is a CH, and the CH is responsible for collecting data inside the cluster from nodes known as cluster members. The CH also directly manages communication with other CHs, an external gateway, or a sink. Transferring data to the base station (BS) or another CH requires high energy. Therefore, it is important to select or design an energy-efficient routing protocol to reduce the energy consumption of the CH nodes. Finding an optimal node as a CH is time-consuming and computationally expensive. Although it is an NP-hard problem, existing research shows that evolutionary algorithms can increase network efficiency [12; 13; 14; 15; 16; 17]. The authors of [18] proposed an energy-efficient clustering method using a Genetic Algorithm to optimise the search for cluster heads. It considers the residual energy, the number of CHs in the network, the distance between member nodes and the CH, the distance between the CH and the sink, and the energy required to transmit data from a cluster to a sink. A similar approach was reported in [19], using a similar parameter for the fitness function, except for the weight assignment for the CH's relative position within the cluster. In addition to the residual energy, the energy required to communicate with other nodes in the cluster and the sink was considered in [20]. However, the fitness function used for cluster formation in the above-mentioned research cannot directly apply to cluster formation in disaster scenarios. For instance, finding a cluster formation solution that supports higher scalability in a disaster scenario is necessary to re-establish communication in an affected area.