The establishment of the Underwater Internet of Things (UIoT) and the realization of interconnection between heterogeneous underwater intelligent devices are urgent global challenges. Underwater acoustic networking is the most suitable technology to achieve UIoT for medium to long ranges. This paper presents an underwater Wi-Fi network, called uw-WiFi, that utilizes a master–slave mode architecture. uw-WiFi is dedicated to solving the problem of underwater acoustic networking with limited coverage range and number of nodes. To ensure the reliability of different types of data in the network, a reliable segmentation transmission protocol based on data type is designed. Additionally, on-demand scheduling based on the reservation MAC protocol is developed to solve the channel resource sharing problem. The uw-WiFi system has undergone shallow sea tests, and the experimental results demonstrate that the uw-WiFi network is capable of achieving a network throughput of 500 bps or higher, indicating superior network performance.

Underwater sensor networks (UWSNs) play a crucial role in subsea operations like marine exploration and environmental monitoring. A major challenge for UWSNs is achieving effective and energy-efficient data collection, particularly in deep-sea mining, where energy limitations and long-term deployment are key concerns. This study introduces a Channel-Aware AUV-Aided Data Collection Scheme (CADC) that utilizes deep reinforcement learning (DRL) to improve data collection efficiency. It features an innovative underwater node traversal algorithm that accounts for unique underwater signal propagation characteristics, along with a DRL-based path planning approach to mitigate propagation losses and enhance data energy efficiency. CADC achieves a 71.2% increase in energy efficiency compared to existing clustering methods and shows a 0.08% improvement over the Deep Deterministic Policy Gradient (DDPG), with a 2.3% faster convergence than the Twin Delayed DDPG (TD3), and reduces energy cost to only 22.2% of that required by the TSP-based baseline. By combining a channel-aware traversal with adaptive DRL navigation, CADC effectively optimizes data collection and energy consumption in underwater environments.

The widespread use of Unmanned Underwater Vehicles (UUVs) in seafloor observatory networks highlights the need for docking stations to facilitate rapid recharging and effective data transfer. Floating docks are promising due to their flexibility, ease of deployment, and recoverability. To enhance understanding and optimize UUV docking with floating docks, we employ dynamic fluid body interaction (DFBI) to construct a seabed moored suspended dock (SMSD) model that features a guiding funnel, a suspended body, and a catenary of a mooring chain. This model simulates SMSD equilibrium stabilization in various ocean currents. Then, a UUV docking model with contact coupling is developed from the SMSD model to simulate the dynamic contact response during docking. The accuracy of the docking model was validated using previous experimental data. Through investigation of the UUV docking response results, sensitivity studies relating to volume, moment of inertia, mass, and catenary stiffness were conducted, thereby guiding SMSD optimization. Finally, sea tests demonstrated that the SMSD maintained stability before docking. During docking, the SMSD’s rotation facilitated smooth UUV entry. After the UUV docked, the SMSD was restored to its original azimuth, confirming its adaptability, stability, and reliability.

The most significant increase of current task is in the desire for operational flexibility and agility in large-scale underwater network application scenarios in recent years. In order to address the challenging problems in Underwater Wireless Sensor Networks (UWSNs), we propose a large-scale UWSN based on the cellular network architecture called Underwater Cellular (uw-Cellular) network. It is designed especially for application scenarios where a large number of both fixed and mobile network nodes exist in a wide area to monitor the underwater environment. We also design protocols in each network layer in order to ensure reasonable channel sharing, data forwarding path selection and data reliability. The purpose of the simulation study we implement is to evaluate the performance of the CLA routing strategy compared to the VBF and the MFLOOD protocols in the uw-Cellular network. We also deploy a twenty-node uw-Cellular network in the real-world environment as the field case study. The experimental results showed that the Data Rate between any nodes could reach above 500 bps, and the network Average Throughput was no less than 550 bps under various load situations.