## 1 Introduction  
Underwater wireless sensor networks are widely used in early disaster warning, pollutant monitoring, hydrological data monitoring and collection, marine resource exploration, auxiliary navigation, and marine military applications [1], and have become an important infrastructure component of the underwater Internet of Things [2]. Underwater wireless sensor networks consist of two parts, a surface wireless sensor network and an underwater wireless sensor network, and include three-dimensional (3D) mobile, fixed, or mixed networks. Data are collected by underwater sensor nodes, which can be transmitted from underwater to surface sink nodes in the form of hop-by-hop forwarding through communication nodes at different depths, or collected by AUV and then sent to the surface. The surface network part uses a radio to realize the information transmission between the underwater network and the shore-based data center. Although underwater data can be transmitted by radio, wireless light, sound, and other media, underwater long-distance data transmission still relies on the underwater acoustic mode. Compared with terrestrial wireless sensor networks based on RF communication, underwater wireless sensor networks are characterized by an unstable, uneven transmission environment; many interference factors; a small transmission channel capacity; a large delay; low reliability; limited energy of communication nodes; a limited energy supply; and a high degree of space-time dynamics [3; 4]; as a result of these characteristics, it is very difficult to realize underwater wireless network communication. At present, the underwater wireless channel is considered to be one of the most difficult-to-manage channels. In order to overcome the adverse effects of the underwater transmission environment, researchers have divided the protocol stack of underwater wireless sensor networks into a physical layer, data link layer, network layer, transmission layer, and application layer, and carried out cross-layer optimization design. They have adopted the methods of channel awareness [5; 6], energy consumption awareness [7; 8; 9; 10], and delay awareness [5; 11] to better select transmission channels. Layered [9; 12] or clustering [12; 13; 14; 15] methods have been adopted to divide the underwater network, balance the energy consumption, and extend the network life. Previous studies have endowed the transmission routing with intelligence, so that it can adapt to the changes in the transmission environment; optimize the transmission quality; bypass network holes [16; 17]; and realize efficient, reliable, and stable data transmission. However, due to the complex, dynamic, and extremely limited underwater transmission environment, when devising underwater monitoring applications having a stable and huge data transmission demand, it is not enough to only pay attention to the design and combination optimization of the physical layer, link layer, and network layer of the routing protocol. The uncontrolled demand for transmission of a large quantity of high-precision monitoring data will make the routing protocols ineffective due to congestion and energy depletion. The cross-layer design of routing protocols, which is not closely combined with the application layer, cannot maximize the use of transmission resources and achieve effective data transmission [18; 19]. It is necessary to design an adaptive data transmission method that can dynamically adjust the accuracy of data transmission according to the transmission conditions and application requirements while correctly selecting the relay node. End-edge-cloud architecture [20; 21], which is a hierarchical architecture based on terminal equipment, the edge network, and the cloud environment, has been gradually applied to the Internet of Things system.