# Bio-Inspired Energy-Efficient Cluster-Based Routing Protocol for the IoT in Disaster Scenarios
## 2 Literature Review
The global agent then combines this information with data from the monitoring agent to handle different networks in the IoT system. The role of the dual-function agent is to produce optimised routing for overlapping areas. However, it is still not suitable for disaster scenarios, as the fitness function of the ACO algorithm does not consider the network's longevity. In [19], the authors proposed a method for choosing an optimal cluster head from a group of nodes using the BOA. The fitness function of the optimiser is based on the residual energy of the nodes, the distance between nodes, the distance to the sink, the node's degree of connectivity with others, and location. Upon selection of the CH, the route between the CH and the sink is optimised utilising ACO. The optimal path is selected based on distance, residual energy, and node degree. ACO struggles with scalability, especially in the context of large, intricate problems. As the size of the problem increases, the computational cost and the necessary iterations can grow significantly. Multi-hop communication between the sink and the CH was considered in [34] to increase network coverage. By integrating ABC with the Firefly Algorithm, an energy-efficient cluster-based routing protocol was proposed. A self-organising map neural network is used at the beginning of cluster formation. Afterwards, the Firefly Algorithm-based optimiser adjusts the cluster size dynamically, considering the distance between the CH and the sink and the CH's energy level. An optimised ACO algorithm is used to route the data to the base station, which can improve energy utilisation in inter-cluster communication. The authors of [14] introduced a combined ACO- and PSO-based clustering method to optimise energy dissipation and data transmission rates. They created a fitness function based on residual energy and inter-cluster distance for data aggregation. In cluster head (CH) formation, nodes with higher residual energy are more likely to become CHs. Once a CH is selected, it uses PSO to find the best routing path (i.e., the shortest path) to the sink node. However, this method leads to congestion on the optimal path, as it only considers the Euclidean distance when building the fitness function. Moreover, PSO is also used for cluster formation. [PERSON] et al. [35] proposed a PSO-based clustering and routing algorithm to optimise energy consumption in WSNs. The key parameters used in the PSO-based clustering algorithm are residual energy density, distance from the base station, and intra-cluster distance from the cluster head and other nodes, all aimed at extending the network's lifetime. The remaining energy of a node is an important parameter in forming a cluster; however, the distance to the sink and node connectivity with the neighbouring nodes can improve the performance of cluster-based communication [28]. Considering these parameters in the fitness function and using an efficient encoding scheme, the authors of [28] proposed a Harmony Search Algorithm (HSA) for clustering and routing in WSNs. It performs better than PSO- and LEACH-based routing protocols due to its mapping and effective fitness function. However, fault tolerance and delays are not considered when transmitting the information to the BS, which are critical in disaster scenarios. An integrated algorithm using both Particle Swarm Optimisation (PSO) and the Harmony Search Algorithm (HSA) was proposed in [29]. The algorithm aims to achieve global search with a rapid convergence rate by harnessing the high computational capabilities of the HSA and dynamic movements between regions to find an optimal solution with PSO. The results indicated improved residual energy and throughput using the standalone PSO algorithm and HSA. However, no performance evaluation was conducted for network lifetime, and delays were incurred in communication. [PERSON] et al. (2019) proposed a novel fitness function using energy efficiency, cluster closeness, and an improved PSO-HSA.