## 4 Simulation Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **EDF** & **EDP** & **RS-DQL** \\ \hline Number of lost packets & 1569 & 1227 & 935 \\ Number of successfully sent packets & 1665 & 2007 & 2299 \\ Packet loss rate & 48.52\% & 37.94\% & 28.91\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Average number of lost packets for different algorithms. Figure 7 considers the changes of packet loss of the three algorithms as the number of sensor nodes increases from 5 to 25 in the case that the data generation cycle in WSNs is 3 times the total hop number between sensor nodes and destination nodes. As the number of nodes increases, the performance of the EDP algorithm is similar to the RS-DQL algorithm. Although the EDP algorithm has a good advantage when the data generation cycle of sensor nodes is a large integer multiple of the total hop number of data transmission, such as 3 times or more, the RS-DQL algorithm is still superior to EDP algorithm in overall network performance. The number of loss packets of the EDF algorithm is always high. When the data generation cycle is an integer multiple of the total hop number, the disadvantage of the EDF algorithm will be magnified. Therefore, the EDP and RS-DQL algorithms are better than the EDF algorithm. Figure 6: The number of lost packets as the data generation period increases. Figure 7: The number of lost packets as the sensor node increases. Figure 8 considers the changes of packet loss of the three algorithms in WSNs as the number of nodes gradually increases from 5 to 25. At this point, the generation period of sensor node data is a random value between 1.5 times and 3.5 times the total node hops. As the number of nodes increases, the number of lost packets of the EDF algorithm changes almost linearly. However, the EDF algorithm is not stable. With the increase of sensor nodes to 20, the increase of packet loss of this algorithm is significantly more than the other two algorithms. This indicates that the algorithm has inferior performance in WSNs with a large number of nodes and random deadlines. The RS-DQL algorithm is stable, and the number of lost packets is always the smallest, and the increase rate of lost packets is relatively stable. It is worth noting that although the simulation results in this paper are based on the topology in Figure 1, the strategies proposed in this paper are also applicable to other structures. When the topology structure of the network changes, the conflict interference matrix of Figure 3 needs to be calculated according to the topology structure. Then, the network is retrained according to the process in Section 3.4, and the algorithm proposed in this paper can be used in the new topology after the training is completed. The RS-DQL algorithm proposed in this paper obtains the most urgent data through the state-behavior description relationship of neural network in its Q-learning part. According to the network topology and data urgency, the concurrent node set based on the most urgent data is determined, so as to obtain the optimal action selection strategy. In the reward function formulation part, the goal is to transmit as much data as possible in WSNs to the destination node within its deadline. Consider the influence of factors such as communication constraints between nodes, data remaining deadline and remaining hops, and give reward and punishment feedback after the agent performs the action. In the deep learning part, a DQN network model is built. The SAE network model is used to establish the mapping relationship between state and behavior. The data transmission slot scheduling strategy of sensor nodes is obtained through DQN network model training.