# Mobile Charging Scheduling Approach for Wireless
## 6 Discussion
### Impact of Sensor Energy Capacity on Charging Performance  
As shown in Figure 7a,b, as the sensor energy capacity gradually increases, the average tour length and the average number of failed sensors decrease gradually. This is because, with the increase of the sensor energy capacity, the initial remaining energy of the sensor will also increase, and the number of sensors with urgent charging demand will decrease accordingly. Thus, within the preset target lifetime 600 s, MC needs to spend more time  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Environment**} & \multirow{2}{*}{**Approach**} & \multicolumn{2}{c}{\(\overline{T_{target}\)+400}} & \multicolumn{3}{c}{\(\overline{T_{target}\)+600}} & \multicolumn{3}{c}{\(\overline{T_{target}\)+800}} \\  & & \(\overline{L_{max}}\) & **Std** & \(\overline{N_{d}}\) & \(\overline{L_{max}}\) & **Std** & \(\overline{N_{d}}\) & \(\overline{L_{max}}\) & **Std** & \(\overline{N_{d}}\) \\ \hline \multirow{8}{*}{JSSTC50} & MMDRL-JSSTC & 11.16 & 0.956 & 0 & 18.58 & 1.532 & 5 & 23.11 & 2.571 & 9 \\  & DRL-TCC & 8.19 & 0.863 & 0 & 16.73 & 1.486 & 6 & 20.61 & 2.887 & 14 \\  & ACRL & 7.56 & 0.751 & 0 & 15.98 & 1.317 & 7 & 19.18 & 2.735 & 16 \\  & GREEDY & 5.16 & 1.032 & 0 & 10.66 & 1.458 & 8 & 14.03 & 2.933 & 20 \\  & TSCA & 6.37 & 0.764 & 0 & 11.62 & 1.237 & 8 & 15.87 & 2.717 & 17 \\  & NJNP & 4.79 & 0.813 & 0 & 9.51 & 1.519 & 9 & 12.88 & 2.997 & 19 \\ \hline \multirow{8}{*}{JSSTC100} & MMDRL-JSSTC & 11.16 & 1.131 & 0 & 23.07 & 1.508 & 7 & 25.76 & 2.813 & 19 \\  & DRL-TCC & 8.19 & 0.961 & 0 & 18.33 & 1.913 & 9 & 22.62 & 3.217 & 23 \\ \cline{1-1}  & ACRL & 7.56 & 0.836 & 0 & 17.26 & 1.703 & 10 & 20.18 & 3.008 & 25 \\ \cline{1-1}  & GREEDY & 5.16 & 0.968 & 0 & 11.87 & 1.522 & 15 & 15.56 & 3.115 & 32 \\ \cline{1-1}  & TSCA & 6.37 & 0.825 & 0 & 13.91 & 1.415 & 12 & 17.01 & 2.916 & 27 \\ \cline{1-1}  & NJNP & 4.79 & 0.912 & 0 & 10.78 & 1.768 & 14 & 14.63 & 3.101 & 30 \\ \hline \multirow{8}{*}{JSSTC200} & MMDRL-JSSTC & 11.16 & 1.212 & 0 & 24.68 & 2.353 & 22 & 27.13 & 2.987 & 40 \\ \cline{1-1}  & DRL-TCC & 8.19 & 1.056 & 0 & 19.03 & 2.513 & 28 & 24.56 & 3.356 & 47 \\ \cline{1-1}  & ACRL & 7.56 & 0.993 & 0 & 18.17 & 2.257 & 31 & 22.06 & 3.115 & 53 \\ \cline{1-1}  & GREEDY & 5.16 & 1.186 & 0 & 12.12 & 2.391 & 43 & 16.18 & 2.834 & 88 \\ \cline{1-1}  & TSCA & 6.37 & 0.866 & 0 & 14.62 & 2.213 & 39 & 18.22 & 2.971 & 69 \\ \cline{1-1}  & NJNP & 4.79 & 0.783 & 0 & 11.97 & 2.465 & 36 & 15.79 & 3.213 & 72 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The comparison results of different approaches over test set.  
charging the sensor to its charging upper threshold. Thus, the charging time is prolonged, and the time for MC to move will also be reduced accordingly. Therefore, the average tour length and the average number of failed sensors decrease with the increase in sensor energy capacity.  
In addition, it can be found from Table 4 that the fixed charging upper threshold of the approaches based on the reinforcement learning framework is smaller than that of other heuristic approaches. Hence, the moving distance of the DRL-TCC and ACRL is slightly longer than that of the heuristic approaches, while they have fewer failed sensors. The proposed approach always has the least number of failed sensors during the test, thanks to the fact that MC can adaptively adjust the charging upper threshold according to the real-time charging demand and leave the current sensor in advance to charge the sensor with more urgent charging demand. However, the average tour length of the proposed approach is much longer than other approaches, which not only involves moving between sensors but also requires the MC to return to the charging service station more frequently to change the battery.