## 3 Dynamic CH Selection and Energy-Efficient Routing Design
### Proposed Methodology
#### 3.4.1 CH Selection Using ICOA  
Figure 3 represents the flow chart of selection of CH using ICOA method. The steps are as follows. Sensor nodes parameter and random initialization: Initially, the node's global population, including Np packs, each with Ns nodes, is initialized. At a certain time, \(t\), the \(p\)th pack population is an integer vector, and it is displayed as:  
\[S_{i}^{p,t}=(X_{1},X_{2},\ldots X_{d}) \tag{4}\]  
where the optimized problem dimension is represented as \(d\), and the \(i\)th threshold is represented as \(x_{i}\). \[S_{i,j}^{p,t}=\mathrm{lb}_{j}+\mathrm{R}_{j}\big{(}\mathrm{ub}_{j}-\mathrm{lb} _{j}\big{)},\ j=1,2,\ldots,d \tag{5}\]  
where lower and upper bounds are represented as \(\mathrm{ub}_{j}\) and \(\mathrm{lb}_{j}\); \(\mathrm{R}_{j}\) is a random value. ICOA Fitness function: From the sensors group in the network, the optimal CH is selected by the ICOA fitness function. The residual energy is considered in the fitness function to evade a dead node as a CH. Higher centrality is used to reduce the distance of transmission among the members. The mathematical forms of fitness functions and their definitions are discussed below:  
1. CH Residual energy  
CH collects data from ordinary sensor nodes and transmits it to BS in a network. Because the CH takes more energy to perform the preceding activities, the node with the most residual energy is the strongest choice to be a CH. The following Equation (6) describes residual energy (\(\mathrm{f}_{1}\)) \[\mathrm{f}_{1}=\sum_{i=1}^{m}\frac{1}{\mathrm{E}_{\mathrm{CHi}}}\] (6)  
Figure 2: Schematic Block Diagram of Proposed Methodology. where the ith CH's residual energy is \(\mathrm{E_{CHi}}\) (b) Distance among sensor nodes: It specifies the range among usual sensor nodes as well as its CH. The dissipation of energy for the node mostly depends on the transmission path distance. If the chosen node has a minimum transmitting distance near BS, then the node's energy consumption is small. Sensors to CH (f2) the distance is displayed in Equation (7). \[\mathrm{f_{2}}=\sum_{j=1}^{m}\sum_{i=1}^{\mathrm{I_{j}}}D\big{(}\mathrm{s_{i}}, \mathrm{CH_{j}}/\mathrm{I_{j}}\big{)}\] (7) where, \(D\big{(}\mathrm{s_{i}},\mathrm{CH_{j}}/\mathrm{I_{j}}\big{)}\) denoted the sensor i and \(\mathrm{CH_{j}}\) distance and \(\mathrm{I_{j}}\) is the sensor node's quantity belongs to CH. (c) CH and BS Distance: The energy consumption of the node is evaluated based on the distance via the transmitting track. When BS is situated away from CH, for example, data transmission will require a lot of energy. As a result, the abrupt drop in CH could be related to increased energy use. Therefore, throughout information transfer, the node that is closest to BS is selected. The optimal solution of distance among the BS (f3) and the CH is represented by the following Equation (8). \[\mathrm{f_{3}}=\sum_{i=1}^{m}D\big{(}\mathrm{CH_{j}},\;\mathrm{BS}\big{)}\] (8) where \(D\big{(}\mathrm{CH_{j}},\;\mathrm{BS}\big{)}\) is the distance between BS and \(\mathrm{CH_{j}}\). (d) Node degree It specifies how many sensor nodes each CH has. Because CHs with more cluster members lose their energy for a shorter period, the CHs with fewer sensors are chosen. Equation (9) expresses the degree of node (f4). \[\mathrm{f_{4}}=\sum_{i=1}^{m}\mathrm{I_{i}}\] (9) where \(\mathrm{I_{i}}\) is the number of CHi sensor nodes. (e) Node centrality Node centrality (f5) is an expression that expresses how far a node is from its neighbors, represented in Equation (9). \[\mathrm{f_{5}}=\sum_{i=1}^{m}\frac{\sqrt{\frac{\big{(}\sum_{i=1}^{m}\mathrm{D^{ 2}}(\mathrm{ij})\big{)}}{n(i)}}}{\mathrm{L}}\] (10) where \(n(i)\) is the number of CHi's neighboring nodes and \(\mathrm{L}\) is the network dimension. Every objective value is assigned a weight value. Several objectives are combined into a single function in this situation. \(\mathrm{S1},\mathrm{S2},\mathrm{S3},\mathrm{S4},\mathrm{S4},\mathrm{S5}\) are the weighted values. Equation (10)depicts the single objective function.