# Bio-Inspired Energy-Efficient Cluster-Based Routing Protocol for the IoT in Disaster Scenarios
## 2 Literature Review  
It is important to note that numerous researchers have made significant contributions to the field of WSNs by proposing algorithms that enhance performance in terms of energy consumption, network coverage, and network availability. As energy is a critical factor in measuring WSN performance, extensive research has been conducted on clustering and routing algorithms to improve energy efficiency. Swarm-based optimisation methods are widely studied in the literature as one of the search strategies to find the global minimum. Finding an IoT node from a group of competitive nodes and identifying the best routing path from the source to the destination can be described as a search problem, where the objective is to minimise energy consumption and maximise network lifetime. Many researchers have focused on swarm-based methods to solve global optimisation problems, using single metaheuristic algorithms or hybrid models. This section delves into some notable previous research works, providing a comprehensive overview of existing knowledge. A plethora of optimisation algorithms have been developed that are fundamentally inspired by nature, such as the intelligent behaviours of birds, bees, fireflies, pigeons, etc. Some of the existing nature-inspired optimisation algorithms include Ant Colony Optimisation (ACO) [22], the Firefly Optimisation Algorithm (FOA) [23], Artificial Bee Colony (ABC) [24], Roach Infestation Optimisation (RIO) [25], the Cuckoo Search Algorithm (CSA) [26], Honey Bee Mating Optimisation (HBMO) [27], Particle Swarm Optimisation (PSO) [28], Pigeon-Inspired Optimisation (PIO) [29], the Bat Algorithm (BA) [30], the Lion Pride Optimiser (LPO) [31], the Butterfly Optimisation Algorithm (BOA) [32], the Lion Optimisation Algorithm (LOA) [33], and many more. ACO and PSO are extensively used for clustering and routing in WSNs. The authors of [12; 13] proposed ACO-based clustering and routing algorithms for IoT networks. The author of [12] proposed a routing algorithm using ACO to obtain the best routing benefits by categorising the IoT environment based on network types and applying \begin{table}
\begin{tabular}{l l} \hline \hline
**Abbreviation** & **Definition** \\ \hline FOA & Firefly Optimisation Algorithm \\ \hline GA & Genetic Algorithm \\ \hline GAN & Graph Attention Network \\ \hline GCN & Graph Convolutional Network \\ \hline HBMO & Honey Bee Mating Optimisation \\ \hline HSA & Harmony Search Algorithm \\ \hline IoT & Internet of Things \\ \hline LEACH & Low-Energy Adaptive Clustering Hierarchy \\ \hline LOA & Lion Optimisation Algorithm \\ \hline LPO & Lion Pride Optimiser \\ \hline PCA & Principal Component Analysis \\ \hline PIO & Pigeon-Inspired Optimisation \\ \hline PSO & Particle Swarm Optimisation \\ \hline PSO-GA & Particle Swarm Optimisation-Genetic Algorithm \\ \hline PSO-HSA & Particle Swarm Optimisation-Harmony Search Algorithm \\ \hline RFID & Radio-Frequency IDentification \\ \hline RIO & Roach Infestation Optimisation \\ \hline TDMA & Time-Division Multiple Access \\ \hline WSN & Wireless Sensor Network \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._the most suitable ACO to each network. Multiple network types in an IoT environment result in overlapping areas between completely different networks. However, the objective of the aforementioned algorithm is to find the optimal path between different networks, which does not consider the node's energy level. This is critical, as it may create a network partition problem due to overuse of the path. Moreover, the performance of the algorithm significantly suffers in areas where different networks intersect each other and create an overlapping region. To optimise routing for overlapping areas, the authors of [13] proposed a multi-agent-based ACO algorithm to avoid overlapping intersections. Four agents are considered: global, local, dual-function, and monitoring agents. The local agent is responsible for generating the routing for each IoT network.