Changes in ocean temperature over time have important implications for marine ecosystems and global climate change. Marine temperature changes with time and has the features of closeness, period, and trend. This paper analyzes the temporal dependence of marine temperature variation at multiple depths and proposes a new ocean-temperature time-series prediction method based on the temporal dependence parameter matrix fusion of historical observation data. The Temporal Dependence-Based Long Short-Term Memory (LSTM) Networks for Marine Temperature Prediction (TD-LSTM) proves better than other methods while predicting sea-surface temperature (SST) by using Argo data. The performances were good at various depths and different regions.

The emerging Internet of Underwater Things (IoUT) and deep learning technologies are combined to provide a novel, intelligent, and efficient data processing and analyzing schema, which facilitates the sensing and computing abilities for the smart ocean. The underwater acoustic (UWA) communication network is an essential part of IoUT. The thermocline, in which temperature and density change drastically, affects the connectivity and communication performance between IoUT nodes, as well as the network topologies. In this paper, we propose DeepOcean, a deep learning framework for spatio-temporal ocean sensing data prediction, which consists of a generative module and a prediction module. We implement the generative module with a multi-layer perceptron (MLP) to capture the spatial dependencies and construct high-resolution data based on sparse observations. The prediction module is implemented with our proposed Multivariate Convolutional LSTM (MVC-LSTM) neural network, which captures both the spatio-temporal dependencies and the interactions of different oceanographic features for prediction. We evaluate the effectiveness of DeepOcean with Argo data, where the proposed framework outperforms fifteen state-of-art baselines in terms of accuracy.

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.

Underwater wireless sensor networks (UWSNs) stand as promising technologies facilitating diverse underwater applications. However, the major design issues of the considered system are the severely limited energy supply and unexpected node malfunctions. This paper aims to provide fair, efficient, and reliable (FER) communication to the imperfect and energy-constrained UWSNs (IC-UWSNs). Therefore, we formulate a FER-communication optimization problem (FERCOP) and propose ICRL-JSA to solve the formulated problem. ICRL-JSA is a deep multi-agent reinforcement learning (MARL)-based optimizer for IC-UWSNs through joint link scheduling and power allocation, which automatically learns scheduling algorithms without human intervention. However, conventional RL methods are unable to address the challenges posed by underwater environments and IC-UWSNs. To construct ICRL-JSA, we integrate deep Q-network into IC-UWSNs and propose an advanced training mechanism to deal with complex acoustic channels, limited energy supplies, and unexpected node malfunctions. Simulation results demonstrate the superiority of the proposed ICRL-JSA scheme with an advanced training mechanism compared to various benchmark algorithms.

Providing qualified and fair communications for underwater wireless sensor networks (UWSNs) has garnered considerable interest in light of scarce resources and dynamic channel conditions. Fairness is critical in various situations, including emergency communications in resource-constrained underwater networks that balance load and energy amongst nodes to optimize network performance. Existing solutions for fair communications, on the other hand, frequently come at the expense of network capacity. This paper focuses on the power management strategy that jointly optimizes the reuse, fairness, and capacity of UWSNs while also proposing a new metric for fair spatial reuse in networks: the fair reuse index. We observe that the fair reuse index for UWSNs is strongly reliant on the network density, channel conditions, and application requirements. As a result, UWSNs commonly exhibit load imbalance and struggle to meet required network lifetimes. Towards this end, we propose DMPM, a Deep Multi-agent reinforcement learning-based Power Management strategy for increasing the fair reuse of UWSNs. DMPM strives to maximize the network’s fair reuse while allowing for gentle network capacity decrease. In two representative communication scenarios, numerical results demonstrate that DMPM achieves a significantly better trade-off between network capacity and fair reuse than baseline techniques. We also construct three reward functions for DMPM and discuss how different reward functions affect node behaviors. Delivery delays of different models are discussed. We hope that the work provided in this study will prove to be invaluable in the design and optimization of UWSNs.

This paper investigates the fair-effective communication and robustness in imperfect and energy-constrained underwater acoustic sensor networks (IC-UASNs). Specifically, we investigate the impact of unexpected node malfunctions on the network performance under the time-varying acoustic channels. Each node is expected to satisfy Quality of Service (QoS) requirements. However, achieving individual QoS requirements may interfere with other concurrent communications. Underwater nodes rely excessively on the rationality of other underwater nodes when guided by fully cooperative approaches, making it difficult to seek a trade-off between individual QoS and global fair-effective communications under imperfect conditions. Therefore, this paper presents a SE mi- CO operative P ower A llocation approach (SECOPA) that achieves fair-effective communication and robustness in IC-UASNs. The approach is distributed multi-agent reinforcement learning (MARL)-based, and the objectives are twofold. On the one hand, each intelligent node individually decides the transmission power to simultaneously optimize individual and global performance. On the other hand, advanced training algorithms are developed to provide imperfect environments for training robust models that can adapt to the time-varying acoustic channels and handle unexpected node failures in the network. Numerical results are presented to validate our proposed approach.