# On-Demand Charging Management Model and Its Optimization for Wireless Renewable Sensor Networks
## 5 Results  
We present some numerical results in this section that show how ERMS-PSO performs in a real-world network. To evaluate the performance of the proposed ERMS-PSO, we used a network topology and parameter settings similar to those in [13; 14; 15; 16], except for the wireless network size, which we compared to different wireless network sizes. The  
Figure 3: PSO flow chart.  
simulations were run using Matlab software. The parameters used in this study are shown in Table 1.  
We run the results for the 50-node network. We compared our strategy with the traditional scheme with a constant cycle (TSCC) and an energy-efficient renewable scheme with a variable cycle (ERSVC). Table 2 shows the location and data rate of each node in a (100 \(\times\) 100) m\({}^{2}\) network. Figure 4 depicts the energy consumption results after the first step of ERMS-PSO optimization. These results compare the proposed energy renewable management system based on particle swarm optimization. As we can observe, for the five different network sizes used, ERMS-PSO consumed less energy than other schemes. Figure 5 shows the cycle number; each scheme runs from 100 to 500 m in network size.  
\begin{table}
\begin{tabular}{c c} \hline
**Simulation Parameters** & **Description of the Abbreviation** \\ \hline Nodes & 50 \\ Area length and width & 100, 200, 300, 400, 500 m \\ RS, BS & center \\ \(U\) & 5 W \\ \(V\) & 5 m/s \\ \(\lambda\) & 0.85 \\ Electricity quantity & 2.5 Ah \\ \(E_{max}\) & 10.8 KJ \\ \(E_{min}\) & 0.05 \(<E_{max}\) \\ Data rate \(R_{i}\) & [1, 10] kb/s \\ \(\beta_{1}\) & 50 nJ/b \\ \(\beta_{2}\) & 0.0013 pJ/b/m\({}^{4}\) \\ \(\alpha\) & 4 \\ \(\rho\) & 50 nJ/b \\ Inertia weight \(\omega\) & 2.1 \\ Cognitive coefficient \(c_{1}\) & 2.24 \\ Social coefficient \(c_{2}\) & 1.8 \\ Number of particles \(m\) & 20 \\ PSO iterations & 50 \\ \hline \end{tabular}
\end{table}
Table 1: Parameters used.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Node** & **Location** & **Data Rate** & **Node** & **Location** & **Data Rate** & **Node** & **Location** & **Data Rate** \\
**Index** & **(m)** & **(kb/s)** & **Index** & **(m)** & **(kb/s)** & **Index** & **(m)** & **(kb/s)** \\ \hline
1 & (42, 20) & 5 & 18 & (67, 26) & 3 & 35 & (40, 85) & 9  
As we can see from the above two figures, the fewer cycles that are run, the less energy consumed. Figure 6 shows the distance traveled by wireless mobile charging devices while charging nodes in a cycle. Our scheme outperforms others by using the shortest distance traveled by the WMCD, and this means that the distance is one of the most important issues to address because it contributes to the energy consumption. The more the WMCD travels, the more energy consumption and the less vacation time. Figure 7 shows the ratio of WMCD vacation time, and, as we can see, in our strategy, the vacation time decrease as the network area grows: for 500 m \(\times\) 500 m, we achieved 81.6% compared to other strategies, where ERSCV had a value of 79.3% and TCSS had a value of 75.6%.  
Figure 4: Energy consumption.  
Figure 5: ERMS-PSO running cycles.