## 3 System Architecture  
### End-Edge-Cloud Architecture  
The fixed 3D underwater wireless sensor network is composed of shore-based/ship data centers, buoy sink nodes, underwater floating sensor/communication nodes, and bottomed sink sensor nodes [3; 45]. Both the bottom and floating sensor nodes have data acquisition capabilities. Based on the wireless communication mode, the data are sent to the underwater communication nodes, and then forwarded via each hop through the underwater communication nodes to send the monitoring data to the surface sink nodes, and then sent by the sink nodes to the data center via satellite. End-edge-cloud architectures include the cloud environment, edge network, and information end. In the Internet of Things, the information terminal is distributed in the monitoring environment and is responsible for data collection; the edge network provides data access channels for various information terminals and is responsible for data transmission; and the cloud environment gathers all kinds of data, is responsible for centralized data processing, and provides services for upper layer applications. Therefore, the sensor nodes responsible for data collection are defined as the information end; the underwater wireless communication network composed of the underwater sensor, communication, and sink nodes is defined as the edge network; and the broadband wireless communication network composed of shore- and ship-based data centers is uniformly defined as the cloud environment; this is shown in Figure 1.  
In the underwater monitoring application, the data obtained by the information terminal are transmitted via each hop through the edge network and continuously sent to the cloud environment. The change trend, acquisition accuracy, and outliers of monitoring data are very important for monitoring applications, but the huge quantity of transmitted data will pose large problems for the edge network. As the delay increases, the energy consumption increases, and the packet-sending success rate decreases. Therefore, before the data enters the edge network, the network throughput should be reduced and the data transmission performance improved by preprocessing to realize the transmission data compression. Although data preprocessing must allocate the computational energy consumption for underwater network nodes, the computational energy consumption is often far less than the communication energy consumption. According to [46], the energy consumed by executing 3000 instructions is equivalent to transferring 1 bit of data a distance of 100 m. With the rapid improvement of computing chip technology and computing modes [47], the gap between computing energy consumption and communication energy consumption will continue to widen. The data collected by underwater three-dimensional monitoring belongs to time series data, which have certain continuity in space and time, and it is possible to reduce the amount of data transmission through preprocessing. There are two methods to realize front-end monitoring data compression, i.e., using the adaptive sampling method or the time series data prediction algorithm. The frequency of adaptive sampling is closely related to the frequency of sampling data. If the sampling frequency is small, key data will be missed. If the sampling frequency is large, the effect of data compression will be lost. When the monitoring data changes dramatically in a short time, the adaptive sampling may not be able to capture the key data. Time series data prediction is based on historical data and data modeling to realize the learning and fitting of monitoring data, which can better reflect the change trend of the monitoring data. The parameters of the prediction model can be used to replace the actual monitoring data, and the data compression can be better realized. At the same time, selecting correction data of different threshold ranges can effectively adjust the accuracy of the monitoring application and avoid key data having drastic changes being missed.