# Mobile Charging Scheduling Approach for Wireless
## 5 Experimental Results
### Comparison Results of Charging Performance against the Baseline Approaches  
The comparison results on the charging performance of different approaches, based on different target lifetime values and the different number of sensors, with the proposed approach, are summarized in Table 5. We adopt (8) as the reward function or fitness function of all approaches to highlight the charging performance. The test includes three scales of the network and three target lifetimes, where three scales of the network are set as JSSTC 50, JSSTC100, and JSSTC200, and the target lifetime is set as 400 s, 600 s, and 800 s, respectively. The charging performance evaluation coefficients mainly include mean tour \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Approach** & **JSSTC10** & **JSSTC50** & **JSSTC100** & **JSSTC200** \\ \hline DRL-TCC & 0.9 & 0.7 & 0.5 & 0.4 \\ ACRL & 0.9 & 0.7 & 0.6 & 0.5 \\ TSCA & 0.9 & 0.8 & 0.7 & 0.6 \\ NJNP & 0.9 & 0.8 & 0.7 & 0.6 \\ GREEDY & 1 & 0.8 & 0.8 & 0.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Optimal fix charging upper threshold of different baseline approaches. \begin{table}
\begin{tabular}{c c} \hline \hline
**Approach** & **The Structure of the Neural Network** \\ \hline \multirow{4}{*}{\begin{tabular}{c} The proposed \\ approach \\ \end{tabular} } & \multicolumn{2}{c}{Embedding (input size = \(4\times n+10\), } \\  & hidden size1 = 128, hidden size2 = 128, output size = 64) & \\  & Q-network (Bi-GRU input size = 64, hidden size = 64, } \\  & action1 output size = \(n+1\), action2 output size = 10, q-value output size = 1) & \\ \hline \hline \multirow{4}{*}{DRL-TCC} & \multicolumn{2}{c}{Actor network} & \multicolumn{2}{c}{Critic network} \\ \cline{2-3}  & Encoder & Decoder & \\ \hline \multirow{4}{*}{\begin{tabular}{c} DRL-TCC \\ \end{tabular} } & 2-layer MLP * & \multirow{4}{*}{\begin{tabular}{c} Pointer network \\ (input size = 128, \\ hidden size2 = 128, \\ hidden size2 = 128, \\ output size = 128) \\ \end{tabular} } & 4-layer MLP * \\  & 2-layer MLP * & \multirow{4}{*}{\begin{tabular}{c} Pointer network \\ (input size = 128, \\ hidden size = 256, \\ output size = \(n+1\)) \\ \end{tabular} } & 4-layer MLP * \\  & 2-layer MLP * & \multirow{4}{*}{\
\begin{tabular}{c} Pointer network \\ (input size = 128, \\ hidden size = 128, \\ output size = \(n+1\)) \\ \end{tabular} } & 4-layer MLP * \\  & 2-layer MLP * & \multirow{4}{*}{
\begin{tabular}{c} 4-layer MLP * \\ (input size = 3\times n, \\ hidden size = 128, \\ hidden size = 128, \\ output size = \(n+1\)) \\ \end{tabular} } \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The structure of the neural network in different approaches. length (\(\overline{L_{max}}\)), the standard deviation (std) of tour length, and the average number of failed sensors \(\overline{N_{d}}\)). Specifically, the tour length represents the cumulative moving distance of MC during the charging task, which is used to evaluate the impact of adaptive charging decisions on the moving cost, obtained by \(L_{max}=\sum_{k=1}^{k}d_{k,k-1}\). As shown in Table 5, the proposed approach always has the lowest number of failed sensors in any scenario, but the average tour length is also the longest. Further, as the network scale increases, the advantages of reinforcement learning approaches over heuristic approaches gradually become prominent. The proposed approach always has the least number of failed sensors. This is because it utilizes Bi-GRU to predict the charging decision based on past and future state information and dynamically adjusts the charging upper threshold according to the charging demand, reducing the risk of sensor failure. The proposed approach moves longer than the other approaches because it can adjust the charging upper threshold adaptively and let MC leave the currently charged sensor in advance to charge the sensor with more urgent charging demands.