## 5 Simulation Results and Performance Analyses
### Performance Evaluation of \(CLCQ\)  
The V-value of the source node is employed to assess the depth of its comprehension of the network topology and the accuracy of its prediction of the optimal global path. When the V-value of the source node is observed to be stable or to exhibit a convergence trend, it can be inferred that the node has learned a relatively accurate optimal path selection strategy, resulting in a stable state of transmission efficiency and performance for the routing protocol within the network. Figure 8 illustrates the evolution of the V-value of the source node following the transmission of each packet in a network comprising 60 nodes. A comparison of the V-value change observed in this protocol with that observed in the QELAR protocol reveals several significant differences. With the continuous transmission of data packets, the V-value experiences a sharp decline and then gradually slows down, which suggests that the protocol progressively identifies the optimal route based on the current environmental conditions. In contrast, the V-value of QELAR slows down when the 20 th packet is sent, while the speed of this protocol begins to slow down when the 6 th packet is sent. This suggests that the protocol converges more quickly than QELAR. This rapid convergence is attributed to the optimization strategy employed by this protocol before data packet transmission. The forwarding candidate set is determined based on node depth, residual energy, and buffer state. By filtering the set of node neighbors, unnecessary exploration is reduced, thereby accelerating the convergence speed of the algorithm. This strategy not only enhances the efficacy of routing selection but also optimizes the performance of the entire network, thereby conferring upon the protocol enhanced adaptability and stability in a dynamic network environment. Figures 9 and 10 illustrate the impact of varying \(\varphi_{en1}\) and \(\varphi_{en2}\) on the total energy consumption and residual energy variance when 60 nodes are deployed in the network. \begin{table}
\begin{tabular}{c c} \hline \hline
**Simulation Parameters** & **Values** \\ \hline Transmission range & 1000 m \\ Frequency & 9.75 kHz \\ Receiving power & 0.6 W \\ Idle power & 1 mW \\ Transmission rate & 2 kb/s \\ Data packet size & 512 bit \\ Energy initialization of nodes & 1000 J \\ The number of nodes & [50,60,70,80,90,100] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The simulation parameters. Figure 8: V-value of the source node. Firstly, it can be observed from Figure 9 that, as the value of \(\varphi_{en2}\) increases, the total energy consumption displays an upward trend. Furthermore, it can be seen that, as the value of \(\varphi_{en1}\) increases, the energy consumption also increases, although the overall change is not significant. As the value of \(\varphi_{en2}\) increases, the network is more likely to select a node with a significant discrepancy between its residual energy and the average residual energy of its neighbors as the next hop node. This choice increases the transmission path length, thereby raising the overall energy consumption. This heightened energy consumption indicates that the network may be compromising some energy efficiency in pursuit of a balanced energy distribution. Figure 10 illustrates the trend of residual energy variance. As the values of \(\varphi_{en1}\) and \(\varphi_{en2}\) increase, the residual energy variance of the network gradually decreases. For instance, when \(\varphi_{en1}=1\) and \(\varphi_{en2}=0.9\), the residual energy variance is only 28.87% of that when \(\varphi_{en1}=0.1\) and \(\varphi_{en2}=0.1\). This is because the proportion of energy consumption in the reward function increases, rendering the residual energy of the node a crucial factor in routing selection. When \(\varphi_{en2}\) increases, the network will select a node with a significant discrepancy between the residual energy of its neighbors and its residual energy.