# Deep-Reinforcement-Learning-Based Joint Energy Replenishment and Data Collection Scheme for WRSN
## 5 Experimental Results
### Evaluation Metrics  
We introduce five key performance metrics to analyze and compare the optimization effects of D-JERDG on UAV-WRSN in terms of system charging efficiency, data collection efficiency, and UAV flight energy consumption.  
* Node death rate: The node death rate is determined by the proportion of dead nodes, where node \(i\) is considered dead when \(RE_{i}(t)=0\). A lower node death rate indicates a higher system charging efficiency. This metric determines which charging strategy can maintain the maximum number of surviving nodes in the experiment.
* Node data overflow rate: The node data overflow rate is determined by the proportion of nodes with data storage exceeding the maximum capacity, represented as \(D_{i}(t)=100\). This metric measures the effectiveness of UAV data collection and the clustering algorithm.
* UAV throughput: The UAV throughput is the ratio of the number of nodes accessed by the UAV to the total number of nodes within the maximum flight time. It measures the overall efficiency of the UAV-WRSN system.
* Average flight energy consumption: The average flight energy consumption is defined as the ratio of the total energy consumption during UAV flight to the maximum flight time. It is a key indicator for evaluating the energy consumption of the UAV-WRSN system.
* Time utilization rate: The time utilization rate represents the ratio of the hover time to the flight time of the UAV during a flight mission. A lower time utilization indicates a higher proportion of time occupied by UAV flights, serving as a metric for evaluating the data collection efficiency of the UAV. The time utilization rate \(R_{tu}\) is given as follows: \[R_{tu}=\frac{t_{hover}}{t_{fly}}(T=t_{hover}+t_{fly})\] (35)  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Values** \\ \hline Node transmit power \((P_{I})\) & \(1\times 10^{-3}\) W \\ Buffer capacity of nodes \((Q_{max})\) & 100 Mb \\ Battery capacity of nodes \((E_{max})\) & 800 J \\ The initial energy of nodes & \(rand(0\,J,800\) J) \\ The initial buffer length of nodes & \(rand(0\) Mb, 5 Mb) \\ Weighting coefficient \((\kappa)\) & 0.7 \\ Channel bandwidth\((B)\) & 1 MHz \\ Channel power gain \((\gamma_{0})\) & \(-30\) dB \\ Noise power \((\sigma^{2})\) & \(-90\) dBm \\ NLoS path loss coefficient \((\eta^{NLes})\) & 0.2 \\ Los probability coefficient \((\alpha,\beta)\) & \(10,0.6\) \\ Blade profile power \((P_{0})\) & 79.8563 W \\ Induced power \((P_{f})\) & 88.6279 W \\ Tip speed of rotor blade \((lt_{lip})\) & 120 m/s \\ Mean rotor induced velocity \((v_{0})\) & 4.03 m/s \\ Fuselage drag ratio \((d_{0})\) & 0.6 \\ Air density \((\rho_{air})\) & 1.225 km/m\({}^{3}\) \\ Rotor solidity \((S_{rotor})\) & 0.05 \\ Rotor disc area \((A_{rotor})\) & 0.503 m\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Main simulation parameters.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Values** \\ \hline Replay buffer \((P)\) & 8000 \\ Target network soft update rate \((\tau)\) & \(1\times 10^{-3}\) \\ Reward weights \((\eta_{1},\eta_{2},\eta_{3})\) & \(50,100,5\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._