## 5 Simulation Results  
### Simulation Setup  
This study, together with the proposed algorithms, was performed using the Matlab 2016a program, which runs on an Intel core i7 processor with a 3.40 GHz CPU and 8 GB of RAM. Experimental results were obtained according to many network and simulation parameters. The simulation results were carried out on the basis of network dimensions of 250 \(\times\) 250 m\({}^{2}\) and 500 \(\times\) 500 m\({}^{2}\) represent scenario 1 and scenario 2, respectively. The rest of the parameters that were used in the scenarios are same with each other. In order to measure the sensor node density, analyzes were performed using a randomly distributed 200 to 1000 sensor nodes in the detection area. Two network scenarios that were used with these two network dimensions are considered. In the network model, heterogeneous network structure is designed as normal, advanced, and super nodes. Advanced nodes were used for the selection of CH. The MS node is designed as a super node without energy restriction. One-fifth of the total number of nodes is distributed as advanced nodes with two times the energy from normal nodes. The transmission ranges are 50 m and 80 m for normal nodes and advanced nodes, respectively. In the proposed bacterial foraging-based CH selection algorithm, 50 to 100 bacterial cells were distributed in the initial population, but the proposed algorithm showed better solutions when using 72 bacterial cells. The proposed algorithm was performed 100 times for 0.5 h simulation time. The average of sample data that were obtained to assess network and energy performances was taken into account in the graphs. Tables 5 and 6 provide a list of the network and proposed algorithm parameters that were used in the simulations. All of the algorithms used in simulations were tested under the same comparison conditions. Figure 9: A flowchart of MS trajectory discovery. The possibility intensity function for a normal distribution with mean \(\mu\), standard deviation \(\sigma\), and variance \(\sigma^{2}\) is given in Equation (16). \(\propto\) is used for the trade-off between residual energy and node load. \(\beta\) is used for the trade-off between load distribution and energy consumption. As seen in Figure 10, after some simulation iterations, we set \(\propto\) and \(\beta\) at 0.6 and 0.4, respectively. We found these values in appropriate mean form, according to normal distribution. Additionally, we assume that \(\delta\) is the same value as that of \(\sigma\) is 0.05 in the simulations. \[f(x,\mu,\sigma)=\frac{1}{\sigma\sqrt{2\pi}}\exp{[-\frac{(x-\mu)^{2}}{2\sigma^ {2}}]} \tag{16}\] \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Parameters** & **Value** & **Parameters** & **Value** \\ \hline \(Problem_{size}\) & 2 & \(N_{ed}\) & 2 \\ \(sn_{num}\) & 50–100 & \(P_{ed}\) & 0.2 \\ \(Step_{size}\) & 0.1 & \(d_{attr}\) & 0.1 \\ \(N_{s}\) & 4 & \(w_{attr}\) & 0.2 \\ \(E_{max}\) & 2 J & \(h_{repel}\) & 0.1 \\ \(L_{max}\) & 5 & \(w_{repel}\) & 10 \\ \(D_{node}\) & 4 & \(R\) & 50–80 m \\ \(N_{c}\) & 50 & \(\propto\) & 0.6 \\ \(N_{re}\) & 4 & \(\beta\) & 0.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: List of the other parameters used in the proposed algorithms.