# An Energy-Efficient Evolutionary Clustering Technique for Disaster Management in IoT Networks
## 2 Related Works  
In [22], the authors introduced a low-energy adaptive clustering hierarchy (LEACH), which is a popular distributed heuristic method. Clustering in this method is dynamic, random, probabilistic and periodic. LEACH determines a threshold for CH selection. It produces a random number between [0, 1] to select a CH in that period. One of the disadvantages of the LEACH protocol is the random selection of CHs, because all nodes can be eligible to be a CH. Another hierarchical heuristic method, named the threshold-sensitive energy efficient sensor network protocol (TEEN), was proposed to improve energy consumption using the two-layer structure for clustering [40]. In this method, two thresholds are applied: (i) the hard threshold (HT) and (ii) the soft threshold (ST). Each CH sends its threshold values to its members. Both thresholds are defined in order to reduce the number of transfers over the routing periods and thus to reduce energy consumption in the process. In [40], CH selection is based on probability, and the distribution of CHs will be non-uniform. The TEEN algorithm has improved clustering stability and energy efficiency compared to LEACH. Its main drawback is that nodes will never establish communication if the thresholds are not reached. To overcome the limitations of heuristic algorithms, such as inefficiently exploring the search space, meta-heuristic algorithms have been proposed for optimal CH selection [27; 29]. Access to every portion of the entire search space over each period is considered to be one of the most important criteria for increasing efficiency in these algorithms, which leads to optimal or near-optimal solutions. Meanwhile, evolutionary algorithms (EAs) such as PSO and HSA are some of the most famous population-based algorithms, and their fitness function determines the quality of the solutions [32]. The authors in [23] proposed an evolutionary PSO for energy-aware CH selection by combining criteria such as the Euclidean distance between the nodes and CHs and the remaining energy from particle energy to node energy, allowing an evaluation of the issues related to the degree of the nodes and the number of CH hops required to reach particle sinks. An analysis of the results proved that the inclusion of energy criteria in the fitness function plays a crucial role in energy efficiency. In [23], CH selection was based on probabilities, and the CH distribution was uniform. This method also focused more on the exploration stage than exploitation. In [41], the authors presented an evolutionary HSA inspired by music for CH selection. Musicians try to select better notes in order that their music can be heard better, which forms the concept of presenting the harmony fitness function. In other words, the best set of CHs is selected. The authors focused on the two criteria of energy (HE) and distance (HD) for harmony evaluation. The fitness function of the Hoang schema is as follows:  
\[\mathrm{f_{HS}=\alpha_{1}\times HD+\alpha_{2}\times HE} \tag{1}\]  
HD and HE are calculated according to Equations (2) and (3):  
\[\mathrm{HD=max_{k}\left\{\sum_{\forall\mathrm{m_{i}}\in CM_{k}}\frac{d\left( \mathrm{n_{i}},CH_{k}\right)}{\parallel CM\parallel}\right\} \tag{2}\]  
\[\mathrm{HE=\frac{\sum_{i=1}^{N}E\left(\mathrm{n_{i}}\right)}{\sum_{j=1}^{k}E \left(CH_{j}\right)}} \tag{3}\]  
It is noteworthy that \(\alpha\) is a weight factor, and its value lies between 0 and 1. The furthest Euclidean distance of each node involved in a cluster to its CH (\(CH_{k}\)) is evaluated according to HD, in which the number of the members in each cluster is presented by \(\parallel\) CM \(\parallel\). HE is also evaluated by dividing the sum of current energy in all network nodes \(E(n_{i})\) by the sum of the current stage of CH energy \(E(CH_{j})\). In [41], CH selection is random and CH distribution is non-uniform. Additionally, HSA is focused more on the exploitation stage than exploration. In [27], the authors introduced a two-tier PSO protocol for clustering and routing in WSNs entitled TPSO-CR.