## 6 Result and Discuss  
### Experimental Environment and Data  
The experiment was based on the application of underwater temperature monitoring. The average depth of the ocean is within 3000 m. At the same water depth, the seawater temperature in the range of 5000 m \(\times\) 5000 m is generally considered to be similar. Therefore, in the experiment, the range of underwater network was set to 5000 m \(\times\) 5000 m \(\times\) 2500 m. The transmission medium adopts an acoustic channel. The transmission speed of sound in water is affected by depth, temperature, and salinity. In the experiment, the sound velocity in seawater was set to 1500 m/s according to the common setting. The distance of sound propagation is related to its frequency. The communication distance of medium distance underwater acoustic communication is generally 1-10 km, and the bandwidth is in the order of 10 kHz. In the experiment, the frequency of sound was set to 10 kHz, and the communication distance and sensor distance were set to 1000 m, while the initial width of the virtual pipe was set to half of the communication distance. The adaptability of the algorithm should be verified by changing the density of different nodes. Therefore, in the experiment, 100, 200, or 300 nodes were selected to participate in the experiment. The initial energy of each node is 1000 J, the transmission power of the node is 10 W, the receiving power of the node is 3 W, the calculation energy power of the node is 48 mW, and the idle power of the node is 30 mW. The experimental data are derived from the temperature data observed 400 m underwater at the KEO station in the database of the National Oceanic and Atmospheric Administration (NOAA). In the data transmission experiment, the temperature data collected every 6 h are formed into data packets for transmission, the size of the original data packets is 50 bytes, and the transmission rate of the application is 1 kbps. In order to shorten the simulation process, the sending rate of the packet is 0.1 packet/s, and the simulation time is 2000 s. The temperature accuracy of 0.01, 0.1, and 0.2 \({}^{\circ}\)C was selected as the lowest accuracy allowed for application. All parameter settings of the experiment are displayed in Table 2. \begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Value** \\ \hline Underwater network & 5000 m \(\times\) 5000 m \(\times\) 2500 m \\ Transfer speed of sound & 1500 m/s \\ Frequency of sound & 10 kHz \\ Communication range of the node & 1000 m \\ Sensor range of the node & 1000 m \\ Initial width of the virtual pipe & 500 m \\ Number of nodes & 100, 200, 300 \\ Initial energy of the node & 1000 J \\ Transmission power of the node & 10 W \\ Receiving power of the node & 3 W \\ Calculation energy power of the node & 48 mW \\ Idle power of the node & 30 mW \\ Source of underwater temperature data & NOAA-KEO (\(-\)400 m) \\ Monitoring data transmission interval & 6 h \\ Origin data packet size & 50 bytes \\ Generation rate of the packet & 0.1 packet/s \\ Transmission rate of the application & 1 kbps \\ Simulation time & 2000 s \\ Lowest temperature accuracy & 0.01, 0.1, 0.2 \({}^{\circ}\)C \\ \(R_{0}\), \(\varphi_{c}\), \(\varphi_{t}\), \(\varphi_{l}\), \(\varphi_{1}\), \(\varphi_{2}\) & \(-\)1, 0.5, 0.5, 0.1, 0.1, 0.7, 0.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experiment settings. The simulation environment was built based on Python 3.9, and the EP-ARMA and RL-ADTA algorithms were also implemented based on Python 3.9. The simulation environment comprised a computer having an i7-9700k CPU with a dominant frequency of 3.6 GHz, 64 GB memory, and GT1080 GPU.