## 6 Result and Discuss
### Transmission Efficiency Analysis
#### 6.3.4 Packet-Sending Success Rate Analysis  
RL-ADTA (0.1, 0.2, 0.01) and RCAR are used for the comparison of the packet-sending success rate. During the simulation, the RL-ADTA and RCAR sent the same batch of data, and the actual amount of data sent by the four algorithms was required to be the same. The packet-sending success rate is shown in Figure 15.  
Figure 15 shows that the trend in the change in the packet-sending success rate of the four algorithms is that, with the increase in the number of nodes, the packet-sending success rate also increases synchronously. As the node density increases, more nodes participate in the data transmission, which improves the packet-sending success rate. In the experiment, the RL-ADTA has a larger data margin to retransmit data because the actual transmission data are compressed. Under the same conditions, the packet-sending success  
Figure 15: Comparison of packet-sending success rate of RCAR and RL-ADTA (0.1, 0.2, 0.01). Packet -sending success rate refers to the ratio of the data received and recovered by the sink node to the data sent and collected by the sensor node.  
rate of RL-ADTA is greatly improved compared with that of RCAR. When the accuracy\({}_{low}\) is set to 0.2, the packet-sending success rate of RL-ADTA is close to 100%. Compared with RCAR, when the number of nodes is 100, RL-ADTA (0.1) improves the packet-sending success rate by 32.2%; when the number of nodes is 200, RL-ADTA (0.1) improves the packet-sending success rate by 43.4%; and when the number of nodes is 300, RL-ADTA (0.1) improves packet-sending success rate by 20.7%.  
The amount of data actually transferred in underwater wireless sensor networks directly affects the efficiency of data transmission. Too much data transmission will increase the delay and energy consumption, and reduce the packet-sending success rate. However, too little data transmission will affect the accuracy of underwater monitoring applications. The location and residual energy of the relay nodes and the delay of the transmission channels also affect data transmission efficiency. Good routes will enable the packets to reach the target node with less forwarding nodes and less delay, balancing the energy consumption and extending the underwater network lifecycle. RL-ADTA can autonomously adjust the data transmission accuracy and the selection of routes to adapt to the changes in the underwater network. Through the comparative experiments, it is not difficult to find that the overall performance of RL-ADTA is significantly improved compared with RCAR. The better routing selection and more flexible control of data transmission accuracy makes RL-ADTA produce more computing delay and energy consumption, but the total delay and energy consumption are still less than those of RCAR, and the packet-sending success rate is higher. RL-ADTA obtains greater benefits at a low cost. The exchange of computing resources and communication resources is generally worthwhile. Moreover, with the improvement in of the prediction algorithm and computing power, the advantages will become increasingly obvious. Therefore, the data transmission efficiency of the RL-ADTA will be greater in future applications.