# Bio-Inspired Energy-Efficient Cluster-Based Routing Protocol for the IoT in Disaster Scenarios
## 2 Literature Review
The approach involves two phases: (1) an improved hybrid PSO-HSA for selecting cluster heads (CHs), optimising for energy efficiency, network closeness, and coverage using an adaptive weighted sum method, and (2) a PSO-based routing protocol with modified tree encoding to reduce invalid paths by selecting relay nodes. A new packet format includes a \"fire flag\" to indicate non-uniform events in disaster scenarios. A Genetic Algorithm (GA)-based clustering and routing algorithm was presented in ([PERSON] et al., 2018) to improve energy efficiency in WSNs. It showed sufficient improvements in energy consumption and network lifetime by using residual energy, distance, and routing phase in the fitness function. However, the network in a disaster scenario is dynamic, with rapid topology changes due to nodes joining and leaving the networks. The protocol is unable to manage such scenarios effectively. The dynamic nature of the network was considered in another GA-based algorithm ([PERSON] et al., 2018), and an energy-efficient dynamic routing adjustment was proposed using a virtual grid concept. The method requires fewer iterations to converge or find an optimal path. A hybrid PSO-GA improves energy efficiency and the packet delivery ratio compared to shortest-path, PSO, and GA routing approaches in wireless sensor networks ([PERSON] et al., 2018). The PSO-GA protocol addresses the limitations of traditional shortest-path and minimal energy depletion routing approaches, which can lead to unbalanced energy consumption and early network partitioning. The fitness function for a particle is computed based on the maximum lifetime of a cluster head. Another bio-inspired energy-efficient CH selection algorithm using Artificial Bee Colony (ABC) optimisation was proposed by the authors of ([PERSON] et al., 2018). This is a centralised approach where the BS performs all the calculations to compute the CH selection using fuzzy C-means clustering. It is considered that BS has infinite energy. The proposed method uses the node's remaining energy, density, and location to determine the cluster head. To improve intra-cluster communication, a polling control method based on busy/idle nodes was introduced, which can reduce energy consumption and improve overall network throughput. The result showed that the optimal number of clusters improves network performance, but only for the single hop to communicate with the CH to sink. Besides bio-inspired metaheuristic optimisation, an artificial intelligence-based clustering and routing mechanism has been studied in the literature to identify and select an optimal CH and route to the sink. A WSN can be presented as a graph, where sensors represent the nodes of the graph and their communication with other nodes forms the edges of the graph. The authors of ([PERSON] et al., 2018) provided a comprehensive review of graph-based deep learning models for various communication methods, including wired, wireless, and software-defined networks. The survey highlighted the use of graph convolutional networks (GCNs) and graph attention networks (GANs) for wireless link scheduling, resource allocation, and network flow optimisation. Furthermore, graph deep-embedding clustering and matrix decomposition can optimise sensor deployment in large complex networks ([PERSON] et al., 2018). A graph auto-encoder coupled with a graph attention network has been proposed to learn the embedded representation of sensor nodes, which can then be used to partition the large-scale graph into several subgraphs. Then, singular-value-QR decomposition is used to allocate nodes within each subgraph. Using a GCN, user-aware clustering with security resource allocation was proposed in ([PERSON] et al., 2018). The clustering is computed based on user coverage so that all sensors are covered by at least one CH, and the process runs iteratively until all nodes are covered. However, as it focuses on user security, the energy optimisation of a node is not considered.