# Research on High-Efficiency Routing Protocols for HWSNs Based on Deep Reinforcement Learning
## 5 Algorithm Simulation and Result Analysis
### Simulation Experiment on the Node Survival Rate in HWSNs  
After setting up the clustering, six algorithms are run with the same initial parameters to compare the node survival rate in HWSNs. As shown in Figure 7, the HDQN algorithm is compared with EEHCHR, 2L-HMGEAR, NCOGA, DEEC, and SEP. The aim is to observe how the node survival rate varies with the number of operational rounds, with the maximum number of rounds set to 1800 and a total of 100 nodes in the network. The NCOGA algorithm is the first to deplete the energy of an initial heterogeneous node at round 111. In contrast, the HDQN algorithm deplete its first heterogeneous node's energy at round 179, and the SEP algorithm experiences the depletion of its first node's energy at round 198. This indicates that SEP performs the best in terms of node survival rate during the early stages of operation, due to its specialized optimization design that accounts for the differences in initial energy levels among ordinary, advanced, and super nodes in HWSNs. This design effectively prolongs node survival, especially in the initial phases.  
However, as the number of operational rounds increases, from the depletion of the 25 th node to the depletion of all nodes, the HDQN algorithm consistently ranks first among all six algorithms. This superior performance is attributed to the HDQN's ability to allocate high-energy roles, such as Agents, cluster heads, and communication relays, to nodes with more initial energy. Additionally, the HDQN uses DNNs to intelligently manage and optimize information transmission paths. Thus, the HDQN algorithm demonstrates a significant advantage in terms of node survival rate in heterogeneous networks, as the number of surviving nodes gradually decreases over time.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Simulation Parameter** & **Values** \\ \hline Super energy-heterogeneous node initial & 900 \\ energy (mJ) & \\ Unit transmission energy consumption \(E_{elec}\) & \(5\times 10^{-5}\) \\ (mJ/bit) & \(1.3\times 10^{-12}\) \\ Multipath fading loss coefficient \(E_{amp}\) (mJ/bit) & \(1\times 10^{-10}\) \\ Free-space loss coefficient \(E_{f_{8}}\) (mJ/bit) & \(1\times 10^{-10}\) \\ Distance threshold \(d_{th}\) (m) & 87.7 \\ Energy per bit for information fusion \(E_{EDA}\) & \(5\times 10^{-6}\) \\ (mJ/bit) & \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 7: Comparison of simulation experiments on the number of surviving nodes in HWSNs.  
Table 2 provides specific data from the simulation experiment comparing the node survival rates in HWSNs. As the number of operational rounds increases, a higher number of surviving heterogeneous nodes indicates the better optimization of information transmission efficiency in the HWSNs. A sufficient number of surviving heterogeneous nodes ensures the coverage of the monitoring area and the completion of their assigned tasks. According to Table 2, the NCOGA algorithm was the first to deplete the energy of a node, while the 2L-HMGEAR and SEP algorithms saw the depletion of the first heterogeneous node's energy in the 195 th and 198 th rounds, respectively. As the number of rounds continues to increase, the HDQN algorithm leads in the rounds at which the energy of the 25 th, 50 th, 75 th, and 100 th nodes is depleted compared to the second-place SEP algorithm. Specifically, these rounds were 44, 79, 128, and 131, with improvements in node survival time proportions of 8.58%, 10.81%, 13.78%, and 7.65%, respectively. This indicates that the HDQN algorithm consistently extends the survival time of heterogeneous sensor nodes, thereby enhancing the efficiency and reliability of HWSNs.