## 3 Energy Efficient Data Transmission in WSN
### Super Cluster Head Selection  
After the CH selection process, a super grouping head is elected since the selected CHs can maximize the network lifetime and energy efficiency. All CHs and SCHs are used to collect information from and forwarded to BS. In this paper, FIS is used for the SCH selection process. To control the consumption of energy and raise the lifetime of an SN for data transmission, the optimal CH is calculated. In this process, initially, the nodes in the WSN are tested by fuzzy blocks to determine the best nodes in each region, as a cluster leadership candidate. Each node in the WSN calculates the power of the node, the concentration of the node, and the center of the node. These values are given as inputs to the Fuzzy Logic System (FLS). FLS has four major mechanisms, namely fuzzy inference, fuzzy rules, fuzzy machine, and diffuser. Blurry changes are input into a fuzzy set of related entries. The fuzzy rule database encloses different fuzzy inference comments that perform in the IF-THEN arrangement. The fuzzy inference machine first calculates the fuzzy degrees of each fuzzy rule. Then, it creates vague conclusions from every ambiguous rule. The defuzzifier produces the output which changes the fuzzy value above into a smooth value using the gravity mode or centroid mode. MF is a curve in which every point in the information universe is translated to a member value between 0 and 1. Here, the degree of ambiguity in membership is determined using member functions (MFs), such as trapezoidal and triangular. The \"min\" function is utilized for the \"and\" operator in this manner, the \"max\" function is used for the \"or\" operator, and the centroid failure is used for the process [26]. The fuzzy structure is given in Figure 2.  
For fuzzy rule generation, every node of WSN computes descriptors such as the energy of the hub and local reserve of the node with the centralization of the node. The energy, concentration, and centrality are given as the inputs of the fuzzy system. Concerning the energy of the node, the acceptable energy level of the cluster node is elected for the grouping head. In this process, all the nodes know their used energy and the remaining energy. Here, each node that transmits \(K\) bit data to a distance is used in the same way as S energy, which is achieved from the equation. \[E_{s}(K,d)=E_{elec}\times K\times\varepsilon_{amp}\times K\times d^{2} \tag{20}\]  
The energy for receiving the k bit is calculated using the equation below. \[E_{R}(K)=E_{elec}\times K \tag{21}\]  
In the above equation, \(d\) is a constant that is associated with the distance between two hubs. \(E_{elec}\) and \(\varepsilon_{amp}\) are constant values which are given in the equation below. \[\varepsilon_{amp}=100p/bit/m^{2} \tag{22}\]  
\[E_{elec}=50 nI/bit \tag{23}\]  
Centralization of the node (\(NC\)):  
If the value of centrality is low, it means a lower amount of energy is required for transmitting data [27]. \(NC\) is calculated using the equation below. \[NC=\frac{\sqrt{U}}{NZ} \tag{24}\]  
where, \(U=\sum_{j\in M(i)}\frac{d^{2}(i,j)}{|M_{i}|}\), \(d\) denotes the distance between the grouping head and the hub, \(|M_{i}|\) represents the number of neighbors of node \(I\) and \(NZ\) is the size of the sensing area. The local distance of the node:  
The distance connecting the center hub with its neighbor is calculated. Figure 3 demonstrates the center node (C) and its neighbors within the r radius. The local distance of each node is calculated with the help of the equation below. \[LD=\sum_{i=1}^{n}D_{i} \tag{25}\]  
Figure 2: Fuzzy logic system. After the input of the fuzzy system calculation, the inputs are transferred to FLS. The fuzzy system's step-by-step procedure is described below. Step 1: Discretization:  
The acceptable energy level of the cluster node is selected for the cluster head. In this process, all the nodes know their used energy and the remaining energy.