## 4 Simulation Results  
The simulation experiment considers the network performance of WSNs data packets with different random deadlines for transmission scheduling. The objective is to minimize the number of lost packets (that is, the number of packets that are discarded when the remaining deadline is less than the remaining hop count during data transmission). In the simulation experiment, a long time slot is taken to analyze and compare the number of lost packets in this time slot for convenient comparison. Other parameters of the simulation are shown in Table 2.  
The real-time data transmission scheduling algorithm based on deep Q-learning is named the RS-DQL algorithm. There are two algorithms for comparison, including the classical EDF algorithm and an enhanced dynamic multi-priority data scheduling algorithm (EDP algorithm) [25]. The idea of the EDF algorithm is the earliest deadline first. The transmission queue of each time slot system is composed of multiple non-conflicting nodes with the shortest deadline. The idea of the EDP algorithm is to divide priority queues for data characteristics in the system, such as emergency data and periodic data. In the same queue, the priority of data transmission is determined according to the relationship between the remaining time of the current transmission state of different data and the remaining hop number. This chapter analyzes the network performance comparison between the RS-DQL algorithm and the other two algorithms under different conditions such as data deadline and the number of network nodes. In Table 3, the network topology of the simulation experiment is based on the communication interference diagram of WSNs sensor nodes in Figure 1. The data generation cycle of each sensor node is randomly set as 1.5 to 3.5 times the total hop number of sensor nodes. Therefore, small multiples represent short packet cutoff time, and large multiples represent long packet cutoff time. As shown in the table, the performance of the RS-DQL algorithm is significantly better than the other two algorithms, followed by the EDP algorithm. The scheduling performance of the EDF algorithm is nearly half of that of the RS-DQL algorithm, and it is the worst among the feasible algorithms. Figure 6 considers the changes in packet loss of the three algorithms as the size of the data generation cycle of the sensor node is an integer multiple of the total hop number of the node. In WSNs, the generation period of sensor node data is increased by 1.5 to 4.5 times the total hop count of the node. As the data generation period lengthens, the packet loss of the three algorithms decreases. The performance of the EDP algorithm is poor before 2.5 times, and is basically the same as the RS-DQL algorithm after 2.5 times. When the multiple is 4, the number of packets lost by EDP and RS-DQL is 0. However, the RS-DQL algorithm has the best performance and is more stable in the whole process. EDP algorithm has the largest variation with the increase in the data generation cycle. In contrast, the EDF algorithm has the lowest overall performance. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Learning Rate & \(\delta=0.01\) \\ Discount Factor & \(\gamma=0.9\) \\ Instant Reward \(r\) & \(k_{1}=k_{2}=0.5\) \\ \(\beta_{i}=1\) \\ Delay Reward \(R_{L}\) & \(\rho_{1}=0.5,\rho_{2}=0.3,\rho_{3}=0.2\) \\ \(\varepsilon_{\text{min}}=0.01\) \\ Correlation Coefficient of Development & \(\lambda=0.98\) \\ Strategy & \(T_{k}=1000\) \\ Episode & 400 \\ Number of Neurons in Each Layer of SAE & \([3M,26,26,13,M]\) \\ Nodes Count & 13 \\ Nodes Topology & Presented in Figure 1 \\ Matrix of Interference between Nodes & Presented in Figure 3 \\ Packet Cutoff Time & Randomly generated for each packet \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulation parameters.