# An Energy-Efficient Evolutionary Clustering Technique for Disaster Management in IoT Networks
## 2 Related Works
The three measures used in the fitness function of the clustering were network coverage, the remaining energy of nodes, and the link communication quality. Moreover, the two criteria utilized in the fitness function of the routing were energy-efficiency and link communication quality, which are described in the following. In this paper, the energy criteria are used in an attempt to reduce the number of the sender nodes; by doing so, the residual energy will be saved optimally. Furthermore, tree encoding is facilitated by applying PSO to construct optimal multi-hop routing and determine the Relay Nodes (RNs). Thus, Equation (4) is defined in a more energy-saving state (ER1) as follows:  
\[\mathrm{ER_{1}=\frac{||\mathrm{RNs}||}{||\mathrm{actCH}||}} \tag{4}\]  
ER1 divides the number of RNs by the number of active CHs in the corresponding period \(\parallel\) actCH \(\parallel\). In fact, Equation (4) reduces the number of active nodes. In addition, the RN with the highest level of energy should be offered as the sender node. ER2 adheres to that concept, as per Equation (5):  
\[\mathrm{ER_{2}=\frac{\sum_{i=1}^{N}E\left(node_{i}\right)}{\sum_{j=1}^{N}E \left(RN_{j}\right)}} \tag{5}\]  
Similarly, ER2 divides the sum of the energy of all nodes by the sum of the RN energy and moderates the energy consumption. Next, maximizing link quality (LiQ) between RNs in the multi-hop routing tree leads to the maximization of the packet delivery ratio (PDR). Hence, the worst communication link quality must be avoided in all sub-branches of the routing tree. Equation (6) is calculated according to this objective as follows [27]:  
\[\mathrm{LiQ=Max_{rn}\sum_{\forall\mathrm{m_{x}\in RN}}\frac{RSSI\left(\mathrm{ m_{x},m_{x+1}}\right)}{\mathrm{minRSSI}}} \tag{6}\]  
The received signal strength indicator (RSSI) is used in this paper to evaluate the link quality between the cluster nodes. In [27], the link quality is the ratio of the RSSI between the CMs and their CHs to minRSSI. In Equation (6), it can be observed that we prevent the selection of the worst link quality (minRSSI) as far as possible between two consecutive sender nodes RSSI (\(\mathrm{rn_{x},rn_{x+1}}\)). Hence,In order to maximize the quality of the clusters in terms of LiQ, we must minimize the worst quality of the clusters. So, the fitness function of routing is formulated as follows:  
\[\min\mathrm{F_{routing}}=\beta_{1}\times\mathrm{ER_{1}}+\beta_{2}\times\mathrm{ER _{2}}+\beta_{3}\times\mathrm{LiQ} \tag{7}\]  
where \(\beta_{j}\in[0,1]\). Furthermore, the authors in [29] produced a hybrid method based on PSO and HS algorithms for energy-efficient CH selection. The proposed meta-heuristic algorithm takes advantage of both algorithms, covering a global optimal in the search space (exploration) and going beyond the local optimal (exploitation). Furthermore, the authors were inspired by Equation (1) [41] as regards the fitness function, with the aim of energy-efficiently minimizing the average distance of the normal nodes to the CHs. In PSO-HSA [29], the particles are allowed to move from one region to another area of the search space based on the PSO algorithm. Since PSO encounters optimization limitations in high-dimensional scales, it is difficult to find any possible solution in the entire search space. Therefore, HSA helps PSO because of its high searching capacity. In [42], the authors used the cuckoo algorithm for clustering and employed an improved HSA for multi-hop routing in WSNs. They suggested that uniform energy consumption in the clustering and routing of large-scale wireless sensor networks is one of the main concerns in this area, because, due to traffic load, nodes near the sink suffer from high energy consumption. This paper also proposes criteria such as the energy, node degree, intra distance of clusters and the coverage ratio for the fitness function.