# Bio-Inspired Energy-Efficient Cluster-Based Routing Protocol for the IoT in Disaster Scenarios
## 1 Introduction
Therefore, the fitness function proposed in this work considers network coverage to achieve higher scalability and other energy-related parameters. An energy-efficient routing protocol identifies the best route to transfer data between the BS and the CH, which reduces energy consumption and enhances the lifetime of the network [21]. Several energy-efficient routing protocols have been studied to dynamically select the CH and jointly optimise the energy efficiency. However, these protocols cannot be directly applied to disaster scenarios due to sudden peak network traffic, power outages, and sensitivity to delays. Thus, this paper provides an evolutionary clustering and routing  
Figure 1: Different types of disaster applications for pre- and post-disaster scenarios. method capable of managing the energy consumption of nodes while considering the characteristics of a disaster area. Moreover, most existing works try to minimise energy consumption to increase network lifetime, whereas we consider energy efficiency to be the effective use of power for transmissions. This paper aims to prolong network lifetime by minimising the node's energy consumption during communication between the node and the sink. The Butterfly Optimisation Algorithm (BOA) with a modified fitness function is used for cluster formation due to its high stability and lower computational complexity. The objective of the modified fitness function is to prolong network lifetime and extend connectivity in a disaster scenario. This helps increase network lifetime. This paper proposes a bio-inspired energy-efficient clustering and routing (BICR) algorithm to enhance network lifetime in disaster scenarios. The network's lifetime is further increased by optimising the routing path generation using Particle Swarm Optimisation (PSO). The PSO algorithm is dynamic, prompts convergence, provides the highest throughput, and reduces energy consumption compared to other heuristic and mathematical approaches. Therefore, a hybrid BOA-PSO method should increase energy efficiency more than using these techniques separately. The main contributions of this paper are summarised as follows:  
1. Proposal of an improved BOA algorithm for optimal clustering by utilising search parameters critical to a disaster scenario, including residual energy, distance to the neighbours, distance to the BS, and network coverage. 2. Proposal of a shortest path multi-hop routing protocol using the PSO algorithm. Here, PSO is optimised using residual energy, the distance between the source and the destination, and the number of relay nodes involved in the path. 3. Improvements to the state-of-the-art method to enhance average energy consumption, network coverage, and network lifetime by dynamically adjusting the sink position in different locations. The rest of this paper is organised as follows. Section 2 highlights previous studies related to this research. Section 3 explains the network model considered in this research, while Section 4 describes the proposed CH formation with a fitness function tailored to disaster scenarios. Section 5 presents the proposed routing protocol for relaying emergency messages. The system simulation modelling and results are presented in Section 7. Finally, Section 8 concludes this paper. Table 1 lists the abbreviations used in this study. \begin{table}
\begin{tabular}{l l} \hline \hline
**Abbreviation** & **Definition** \\ \hline ABC & Artificial Bee Colony \\ \hline ACO & Ant Colony Optimisation \\ \hline BA & Bat Algorithm \\ \hline BBO & Biogeography-Based Optimisation \\ \hline BOA & Butterfly Optimisation Algorithm \\ \hline BOA-PSO & Butterfly Optimisation Algorithm-Particle Swarm Optimisation \\ \hline BICR & Bio-Inspired energy-efficient Cluster and Routing \\ \hline BS & Base Station \\ \hline CH & Cluster Head \\ \hline CNN & Convolutional Neural Network \\ \hline CSA & Cuckoo Search Algorithm \\ \hline DEEC & Distributed Energy-Efficient Clustering \\ \hline EWS & Early Warning System \\ \hline \hline \end{tabular}
\end{table}