Abstract An important data in intelligent ocean is the sea surface temperature (SST). Most of the previous works on SST prediction deal with independent spatial point, ignoring the spatial correlation of two‐dimensional intelligent ocean, which leads to instability of prediction accuracy. Therefore, in this paper, a deep learning model based on convolutional gated recurrent unit is proposed for the SST prediction of two‐dimensional intelligent ocean. Input and output of the proposed model are both spatiotemporal SST data, which means the model directly process spatiotemporal data. This method adds the spatial information of the intelligent ocean temperature data, thereby improving the accuracy. From the experiments, it turns out that the proposed model shows superior performance compared to another three prevailing deep learning models.

With the rapid development of Industrial Internet of Things (IIoT), the emergence of credible federated learning provides a more effective solution for it. In this article, we use the credible collaboration between large language models (LLMs) and reinforcement learning (RL) model to improve the autonomous decision-making efficiency of autonomous underwater vehicle (AUV), reduce resource and power consumption, and solve robust decision-making problem in open environments. First, considering the complex terrain and hydrodynamic environment in the ocean, we construct a 3-D ocean simulation environment with high accuracy and high reliability to simulate the behavioral constraints of AUV in the real ocean. Second, we integrate LLaMA model into the decision-making process of AUV, utilizing its powerful information processing capability for environmental analysis and action selection, so as to improve the decision-making generalization ability of AUV in dynamic ocean environments. Finally, we propose proximal policy advantage estimation (PPAE) method and achieve safe and efficient path planning for AUV based on LLMs decision output and dynamic field environment information. The experimental results show that our method achieves a good effect in improving the decision accuracy and robustness of the AUV, which proves the effectiveness of the LLMs in the application of underwater intelligent agent control decision.

With the advancement of communication technology from 5G to 6G, future communication networks will no longer be limited to land and air, and the ocean will also become the battlefield for 6G networks. The expansion of the network has expanded the scope of Intelligent Autonomous Transport Systems (IATS). As a new type of underwater transport system, Autonomous Underwater Vehicle (AUV) has gained popularity due to their advantages of autonomy, endurance, and concealment. In practical applications, it is necessary to fully consider the impact of uncertain marine environments on AUV’s motion, and also design stable control unit to achieve AUV formation. The core of the control unit is the AUV formation control algorithm, which should enable AUV to complete path planning and obstacle avoidance while ensuring formation control. In order to solve the above problems, an Intelligent Multi-agent path planning and formation control algorithm based on Value-decomposition networks (IMV) is proposed in this paper. Specifically, a three-dimensional high-resolution marine simulation environment located in the Mariana Trench is established, the state transition function and reward function are well designed under uncertain conditions for stable Multi-Agent Reinforcement Learning (MARL) mechanism, a Value-Decomposition Networks (VDN) based training framework is constructed to improve the convergence speed of the proposed method. The experimental results verify the excellent performance of the IMV method proposed in this paper, demonstrating that our method can outperform other methods in the aspect of stability, adaptability, intelligence, and timeliness.

By combining transportation information with physical elements, transportation cyber-physical systems (T-CPS) take advantage of the strengths of information technology and show great potential in terms of efficiency, safety, and control. T-CPS covers land, air, and underwater domains involving vehicles, drones, and autonomous underwater vehicles (AUVs), facilitating our lives and creating productivity. However, underwater T-CPS faces greater difficulties and challenges than the first two areas. On the one side, underwater equipment is generally expensive and thus requires a high level of safety. On the other side, the complexity of the marine environment causes uncertainty in the control. To address these challenges, this paper proposes an expert experience-enhanced control approach designed to enhance AUV reliability and safety. Firstly, we model AUV cluster control, including the complex underwater environment and cooperative control strategy, and refine this problem into a Markov decision problem (MDP) model based on the leader-follower strategy. Subsequently, a multi-agent reinforcement learning cluster control algorithm is developed on the framework of Centralized Training Distributed Execution (CTDE) to improve the learning and exploration capabilities of AUVs. Finally, we propose an expert experience-enhanced strategy that reduces the impact of non-smooth environments and also ameliorates the limitation of relying exclusively on rule-based experience. Experiments compare the linear and triangular AUV formation control tasks, and the proposed approach shows promising superiority and possesses sound stability in dynamically changing environments.

With the development of society and the economy, low-carbon and low-energy means of exploiting marine resources are receiving increasing attention. Autonomous path planning is a fundamental capability for IoT Autonomous Underwater Vehicle (AUV) to carry out ocean exploration tasks. Currently, the main issue lies in the numerous disturbances and uncertainties present in the marine environment during practical applications, which can significantly impact path planning, leading to high energy consumption and carbon emissions. To address this challenge, this paper presents a sustainable reinforcement learning algorithm for handling time-varying current disturbances to achieve low-carbon AUV path planning, which is delineated into three steps. Firstly, a three-dimensional time-varying current environment is established as the environmental framework for reinforcement learning, and the dynamic model of the AUV is formulated. Secondly, to enhance training efficiency and reduce AUV’s energy consumption, this paper puts forth the OCDRP (Ocean Current Disturbance Rejection PPO) algorithm, which incorporates tidal current information to enhance the AUV’s resilience to time-varying currents. Lastly, expectile regression methods are introduced to facilitate the algorithm’s convergence. Experimental results confirm the efficacy of the proposed algorithm and its adaptability to time-varying currents, making it an efficient, adaptable, and low-carbon sustainable path planning approach.

The Unmanned Aerial Vehicles (UAVs) are competent to perform a variety of applications, possessing great potential and promise. The Deep Neural Network (DNN) technology has enabled the UAV-assisted paradigm, accelerated the construction of smart cities, and propelled the development of the Internet of Things (IoT). UAVs play an increasingly important role in various applications, such as surveillance, environmental monitoring, emergency rescue, supplies delivery, for which a robust path planning technique is the foundation and prerequisite. However, existing methods lack comprehensive consideration of the complicated urban environment and do not provide an overall assessment of the robustness and generalization. Meanwhile, due to the resource constraints and hardware limitations of UAVs, the complexity of deploying the network needs to be reduced. This paper proposes a lightweight, reinforcement learning-based real-time path planning method for UAVs, Adaptive Soft Actor-Critic algorithm (ASAC), which optimizing training process, network architecture, and algorithmic models. First of all, we establish a framework of global training and local adaptation, where the structured environment model is constructed for interaction, and local dynamically varying information aids in improving generalization. Secondly, ASAC introduces a cross-layer connection approach that passes the original state information into the higher layers to avoid feature loss and improve learning efficiency. Finally, we propose an adaptive temperature coefficient, which flexibly adjusts the exploration probability of UAVs with the training phase and experience data accumulation. In addition, a series of comparison experiments have been conducted in conjunction with practical application requirements, and the results have fully proved the favorable superiority of ASAC.

Internet of Things (IoT) has extended its coverage to various spatial domains and has established interconnection to serve widespread applications of a larger spatial scale. Such IoT is called the space–air–ground–ocean-integrated network (SAGOI-Net), which consists of multiple battery-powered heterogeneous devices. Hence, energy efficiency is the key point of SAGOI-Net to be stably operated for a long time without manual maintenance. This article proposes a novel scheme of energy-efficient autonomous and decentralized SAGOI-Net establishment using an intelligent autonomous underwater glider (AUG) to serve marine applications. The proposed SAGOI-Net is energy efficient because the energy consumption is minimized by: 1) employing nonpropeller-driven AUG; 2) navigating AUG under water without acoustic sensor or extra energy-consuming vision sensors; and 3) equipping the self-navigation (SN) system based on lightweight neural network model to save the energy consumption of onboard computing resource. Moreover, assuming the AUG navigation problem as time-series regression, the proposed scheme designs SAGOI-Net to be autonomous and decentralized with the aid of lightweight long short-term memory (LSTM) network-based SN (SN-LSTM) system of AUG. The lightweight SN-LSTM model is trained end-to-end on dynamically modeled AUG motion information along with numerically modeled ocean environment data to quantitatively analyze the impact of the ocean environment on AUG. The simulation results demonstrate a superior performance of the AUG SN along with energy efficiency of the proposed SAGOI-Net.

Autonomous underwater vehicle (AUV) shows great potential in the Internet of Underwater Things (IoUT) system, in which the path planning algorithm plays a fundamental role. However, the complex underwater environment brings greater challenges to AUV path planning, especially the ocean current, which has a profound impact on time and energy consumption. This article focuses on the complex ocean current condition and proposes an underwater path planning method based on proximal policy optimization (UP4O). In this novel method, a deep reinforcement network is constructed to serve as a decision control to plan the moving direction of AUV. An information encoding module is developed to extract the features of the local obstacles. Furthermore, UP4O integrates the obstacle features with the current state information, including relative position, ocean current, and velocity, enabling the AUV to focus on the global direction and local obstacles at the same time. Additionally, to further adapt to the ocean current and shorten the time cost, UP4O expands the action space of AUV, realizing a fine and flexible action adjustment. The wide applicability of UP4O has been proved by numerous experiments. The proposed algorithm can always plan the time-saving and collision-free paths in complex underwater environments with various terrains and ocean current.