As one of the most promising networks, the spaceair-ground-aqua integrated network (SAGAIN) has the characteristics of wide coverage and large information capacity, which can meet various requests from users in different domains. With the rapid growth of data and information generated by the Internet of Things (IoT), SAGAIN has received much attention in recent years. However, the existing network architectures are not capable of providing personalized network services according to different task types in SAGAIN. Besides, they cannot deal with many problems in SAGAIN well, such as heterogeneous network disconnection, high network delay, intermittent interruption, unbalanced network load. In this paper, in order to solve the abovementioned problems, we propose a novel architecture for SAGAIN named task-oriented intelligent networking architecture (TOINA). First, we apply edge-cloud computing technology and network domain division in TOINA to realize intelligent networking and reduce the latency. Secondly, the task-oriented networking method is proposed to provide personalized network services and increase network intelligence. Thirdly, we intend to leverage the information center network (ICN) paradigm to build the SAGAIN and optimize the content naming rules. Furthermore, a pre-processing layer was added in the network protocol stack to perform the heterogeneous network convergence in SAGAIN. In addition, some security technologies related to network architecture are considered in SAGAIN. This paper presents the background, rationale, and benefits of the TOINA for SAGAIN. Besides, a specific case is studied to illustrate the network architecture work process further.

For an Autonomous Underwater Vehicle (AUV), navigation is a key functionality. Dead-Reckoning (DR) navigation is an important class among all the AUV navigation methods. In DR, the measurement errors of inertial sensors (such as gyroscopes and accelerometers) lead to accumulated errors with time, which affect navigation accuracy significantly. Especially, accumulated errors in fast-changing environments, such as waves near or on the surface, are tough to handle. In this paper, we propose a Neural-Network based AUV Navigation method for fast-changing environments, called NN-DR. NN-DR employs neural network to predict pitch angles accurately, which is our core contribution. In NN-DR, we smoothly integrate Kalman filter, neural network, and velocity compensation to reduce accumulated errors. Extensive simulation experiments are conducted to test the correctness and stability of NN-DR, and the results show that NN-DR is very effective in lowering accumulated errors. For instance, at time 300 seconds, NN-DR achieves superior performance on accuracy for navigation, about 160 times than the state-of-the-art DR methods, demonstrating great advantage on AUV navigation for fast-changing environments.

The coming 6G communication technology introduces the possibility of practice underwater Internet of Things (UIoT) applications with high-speed and reliable underwater communications. Among them, the cooperative coverage path planning (CPP) with autonomous underwater vehicles (AUVs) is a promising approach for enabling deep ocean exploration. The cooperative CPP in underwater is challenged by several marine factors, typically the harsh underwater communication environment, which brings difficulties in sharing the coverage progresses of AUVs and the environmental information such as the seafloor bathymetry, obstacles, etc. Accordingly, this paper proposes a novel CPP method based on 6G enabled cooperative AUVs, named the Pac-AUV. As the name suggests, the method is based on the mechanism of the Ms. Pac-Man game, where AUV-assisted subsea exploration is considered as cooperative Pac-Men sharing the Pac-Dots distributed on the seafloor. The Pac-AUV involves two steps in cooperative CPP. One is a dot-spreading-based mission assignment (DMA), which is discretely performed by each AUV and requires support from reliable underwater communication in sharing the Pac-Dots. The other step is virtual attraction-based coverage path planning (V-CPP), which adopts the virtual attraction force from the Pac-Dots, results in low computational complexities in generating the coverage paths and avoids obstacles. Simulations are performed to demonstrate the performance of the Pac-AUV, and the results prove the advantages of cooperative and balanced CPP executions.

Escorting a moving object in a subsea environment with cooperative autonomous underwater vehicles (AUVs) is a typical subject in Underwater Internet of Things (UIoT) applications. It involves two issues that should be studied. First, a mobile task assignment method is required to lead the AUVs to the escorting positions; then, a formation control scheme should be utilized to safely escort the moving object to the destination. Accordingly, in this paper, a comprehensive target escorting mechanism called the cooperative-control-based underwater target estimating mechanism (CUTE) is proposed, which includes a belief function method-based self-organizing map algorithm for task assignment and an artificial potential field-based formation control method. The task assignment method aims to establish smooth routes from the AUVs to the escort position while flexibly avoiding obstacles, and the formation control method aims to improve the monitoring coverage on the escort route by rotating the formation structure while following the moving object. Simulations show that the proposed CUTE method may be very practical in underwater target escorting scenarios.

Underwater acoustic sensor networks (UASNs) have been widely applied in marine monitoring, military reconnaissance, hydrology surveys, etc. Their location information is an important apriori knowledge when they are carried out underwater. However, the complex underwater environments impose great challenges on location acquisition, especially for autonomous underwater vehicles (AUVs), because of their mobility and finite power. In UASNs, existing location and navigation methods can offer AUVs position information, but they may either need a doppler velocity log (DVL), which is inefficient due to the complex underwater environments, or they may require additional localization infrastructure to deploy underwater, which suffers from large communication latency among AUVs, and costs enormous power. In this article, an efficient velocity estimation and location prediction method (VELP) in UASNs is proposed to avoid the above restrictions. It only utilizes collaborations based on communication among AUVs to achieve higher precision location with lower cost. Specifically, we apply an AUV-assisted velocity estimation algorithm with Doppler shift estimation in the physical layer of UASNs to improve the velocity estimation accuracy instead of the DVL. Meanwhile, we build a belief propagation-neural network-based location prediction model, which decreases the communication requirements and obviates introducing modeling errors. Extensive experimental results show VELP achieves superior performance on both accuracy and efficiency, demonstrating its great advantage in offering AUVs’ location information.

Providing qualified and sustainable communications is one of the key challenges for the Internet of Underwater Things (IoUT) facing constrained energy supplements, nonstationary environments, and severe communication interference. Owing to spatial separation, several nodes can (and are often required to) make transmissions simultaneously to maximize network capacity. However, existing transmission solutions often face the dilemma between maximizing local capacity and global concurrency. We break this dilemma via UDARMF, an underwater distributed and adaptive resource management framework, which maximizes network capacity by supporting an increased number of communications in the network. It is a distributed deep multiagent reinforcement learning framework that uses an observation encoder and a local utility network to coordinate the collaboration among underwater nodes by adaptively tuning its transmit parameters. We designed experiments to compare UDARMF with baselines in network capacity, concurrency, and energy efficiency. Extensive experiments were conducted to find the appropriate hyperparameters to achieve the optimal network performances. We also analyze the performance of UDARMF and baselines over diverse communication and lifetime requirements, communication environment, and energy storage. Simple closed-form approximations of UDARMF are given to reveal that an energy-constrained network’s capacity increases with available energy, following a linear trend on the logarithmic scale. Experimental results demonstrate that compared to other methods, UDARMF achieves a much better tradeoff between network capacity and concurrency, at which the lifetime requirements are satisfied. The proposed framework and the closed-form approximations are likely to become valuable tools in designing and analyzing IoUT.