## 2 Related Works
The use of layers, clusters, and inter-cluster routing based on the heuristic algorithm has improved the balance of energy consumption of MCR-UWSN and KACO algorithms; however, there is no detailed description of the performance change in algorithms under different network traffic conditions. Although PB-ACR provides differentiated routing for different priority services, it does not reduce the overall energy consumption of the network. Although ACOR can improve communication efficiency by sampling service data, the sampling technology is not suitable for applications having a large number of irregular and abrupt data transmission requirements, and ACOR does not address the problem of abnormal data. SDA is used for event detection and source determination. When the sensor's sensing data are transmitted upward along the routing tree, SDA comprehensively determines whether the underwater network generates abnormal data by comparing the sensing data of multiple adjacent sensor nodes with the normal sensing range. The neighboring sensor nodes only collect and forward their sensing data to the receiving node when these sensor nodes jointly detect possible events. This strategy avoids the unnecessary routing of perceptual data that is unfavorable to event detection, reduces energy consumption, and increases network capacity. However, this fusion can save energy for the network only when it is used to judge abnormal conditions. DBP is an event detection method based on linear prediction. The core idea of DBP is to use a simple model to capture the trend in data changes, and use flexible rules to calculate and deal with the existing interference. One of the limitations of DBP is its low prediction accuracy, which is suitable for short-term linear trend data. Although both SDA and DBP are based on realizing the communication of sensing data and can also be applied to underwater 3D networks, they focus on the detection of events, and do not address the construction of transmission routes and the methods for delivering data from sensor nodes to sink nodes in detail. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Network** & **Route Establishment Method** & **Application Content** & \begin{tabular}{c} **Communication** \\ **Efficiency** \\ \end{tabular} \\ \hline VBF [33], & 3D Network & \begin{tabular}{c} Location vector and \\ neighbor information \\ \end{tabular} & No distinction & Low \\ \hline RLOR [16], & 3D Network & \begin{tabular}{c} Delay, energy consumption and \\ reliability, establish routing based \\ on reinforcement \\ learning algorithm \\ \end{tabular} & Traffic & Middle \\ MCR-UWSN [14], & 3D Network & \begin{tabular}{c} Energy, depth; layer or cluster; \\ establish routing based on \\ heuristic algorithm \\ \end{tabular} & No distinction & Middle \\ \hline PB-ACR [39], & 3D Network & \begin{tabular}{c} Energy consumption; establish \\ routing based on ant \\ colony algorithm \\ \end{tabular} & Prioritization, data & Middle \\ \hline SDA [41], & 3D Network & \begin{tabular}{c} neighbor information \\ \end{tabular} & \begin{tabular}{c} Content fusion \\ and prediction \\ \end{tabular} & Middle \\ \hline TBDP [25] & \begin{tabular}{c} AUV auxiliary network \\ \end{tabular} & \begin{tabular}{c} Carry transmission data based \\ on AUV \\ \end{tabular} & Content prediction & Middle \\ \hline EP-ADTA & 3D Network & \
\begin{tabular}{c} Delay and energy consumption, \\ and establish routing based on \\ reinforcement learning \\ \end{tabular} &
\begin{tabular}{c} Content prediction \\ and fusion \\ \end{tabular} & High \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Several typical routing protocols for underwater wireless sensor networks. TBDP is a two-level bidirectional prediction model that is suitable for 3D underwater wireless sensor networks with an AUV, and realizes communication by relying on the data carried by the AUV.