Search

The Internet of Things (IoT) serves as a crucial element in interconnecting diverse devices within the realm of smart technology. However, the energy consumption of IoT technology has become a notable challenge and an area of interest for researchers. With the aim of achieving an IoT with low power consumption, green IoT has been introduced. The use of unmanned aerial vehicles (UAVs) represents a highly innovative approach for creating a sustainable green IoT network. UAVs offer advantages in terms of flexibility, mobility, and cost. Moreover, device-to-device (D2D) communication is essential in emergency communications, due to its ability to support direct communication between devices. The intelligent reflecting surface (IRS) is also a hopeful technology which reconstructs the radio propagation environment and provides a possible solution to reduce co-channel interference resulting from spectrum sharing for D2D communications. The investigation in this paper hence focuses on energy-efficient UAV-IRS-assisted D2D communications for green IoT. In particular, a problem of optimization aimed at maximizing the system’s average energy efficiency (EE) is formulated, firstly, by simultaneously optimizing the power coefficients of all D2D transmitters, the UAV’s trajectory, and the base station (BS)’s active beamforming, along with the IRS’s phase shifts. Second, to address the problem, we develop a multi-agent twin delayed deep deterministic policy gradient (MATD3)-based scheme to find a near-optimal solution, where D2D transmitters, the BS, and the UAV cooperatively learn to improve EE and suppress the interference. To conclude, numerical simulations are conducted to assess the availability of the proposed scheme, and the simulation results demonstrate that the proposed scheme surpasses the baseline approaches in both convergence speed and EE performance.

In this study, we investigated the variations in production performance, health status, and gut microbiota of meat rabbits raised in the semi-confined barn during summer and winter. Compared to summer, rabbits reared in winter possessed significantly higher slaughter weight and carcass weight. Rabbits fed in the summer were more vulnerable to different stressors, which led to increased protein levels of HSP90, IL-1α, IL-1β, IL-2, and concentrations of MDA, but declined GSH and SOD activities. Additionally, significant differences in gut microbial communities were observed. Compared to the winter, rabbits fed in the summer had significantly lower and higher alpha and beta diversity. Both Firmicutes and Verrucomicrobiota were the dominant phyla, and they accounted for greater proportions in the winter than in the summer. At lower microbial taxa levels, several seasonal differentially enriched microbes were identified, such as Akkermansia muciniphila, the Oscillospiraceae NK4A214 group, the Christensenellaceae R-7 group, Alistipes, and Muribaculaceae. Functional capacities linked to microbial proliferation, nutrient metabolism, and environmental adaptive responses exhibited significantly different abundances between summer and winter. Moreover, strong interactions among different indicators were presented. Based on our findings, we not only proposed several potential strategies to ameliorate the undesirable effects of seasonal changes on the productivity and health of meat rabbits but also underscored the directions for future mechanistic studies of adaptation physiology.

This article presents two new chemical plume tracing (CPT) algorithms for using on autonomous underwater vehicles (AUVs) to locate hydrothermal vents. We aim to design effective CPT navigation algorithms that direct AUVs to trace emitted hydrothermal plumes to the hydrothermal vent. Traditional CPT algorithms can be grouped into two categories, including bio-inspired and engineering-based methods, but they are limited by either search inefficiency in turbulent flow environments or high computational costs. To approach this problem, we design a new CPT algorithm by fusing traditional CPT methods. Specifically, two deep reinforcement learning (RL) algorithms, including double deep Q-network (DDQN) and deep deterministic policy gradient (DDPG), are employed to train a customized deep neural network that dynamically combines two traditional CPT algorithms during the search process. Simulation experiments show that both DDQN- and DDPG-based CPT algorithms achieve a high success rate (>90%) in either laminar or turbulent flow environments. Moreover, compared to traditional moth-inspired method, the averaged search time is improved by 67% for the DDQN- and 44% for the DDPG-based CPT algorithms in turbulent flow environments.

Underwater target classification methods based on deep learning suffer from obvious model overfitting and low recognition accuracy in the case of small samples and complex underwater environments. This paper proposes a novel classification network (EfficientNet-S) based on EfficientNet-V2S. After optimization with model scaling, EfficientNet-S significantly improves the recognition accuracy of the test set. As deep learning models typically require very large datasets to train millions of model parameter, the number of underwater target echo samples is far more insufficient. We propose a deep convolutional generative adversarial network (SGAN) based on the idea of group padding and even-size convolution kernel for high-quality data augmentation. The results of anechoic pool experiments show that our algorithm effectively suppresses the overfitting phenomenon, achieves the best recognition accuracy of 92.5%, and accurately classifies underwater targets based on active echo datasets with small samples.

To meet the mission requirements of long-endurance and unmanned marine environment observation, the natural energy-driven unmanned surface vehicle (NSV) usually takes special sailing paths to increase energy capture to achieve the purpose of improving endurance. Aiming at the route planning problems of the “Wave Rider” NSV in the time-varying ocean wind field, this paper is organized as follows. Firstly, a visual modeling method of the real-time-varying ocean wind field for NSV is proposed. Then, through the wind energy capture experiment, the NSV system energy net output model is calculated, and a Dynamic Dijkstra algorithm considering wind energy capture (DW–Dijkstra) has been proposed in this paper based on a Dijkstra algorithm, of which weight function has been improved. Accordingly, the NSV long-endurance dynamic path planning method is designed. Finally, the DW–Dijkstra algorithm has been verified through a set of comparison simulations and a set of semi-physical comparison simulations. The results show that the DW–Dijkstra algorithm can plan a collision-free and high-efficiency energy capture path in the real-time-varying ocean wind field environment in the southern waters of China. Compared with the traditional A* algorithm and the Wind_A* algorithm, the proposed method can save energy by between 15.07% and 6.50%, respectively, which effectively increases the endurance of the NSV.

The three-dimensional (3D) path following problem of an underactuated autonomous underwater vehicle with ocean currents disturbances is addressed in this paper. Firstly, the motion equation under the ocean currents disturbance is established, and the dynamic model of 3D tracking error is constructed based on virtual guidance method. Then, a finite-time control scheme based on super-twisting observer and command filtered backstepping technology is proposed. We adopt super-twisting observer based on finite-time theory to observe the ocean currents disturbances for improving the system robust. A command filtered backstepping is proposed to replace the differential process in the conventional backstepping method for avoiding the differential expansion problem. The filter compensation loop is designed to ensure the accuracy of the filtered signal, and the anti-integration saturation link is designed considering the influence of integral saturation. Lyapunov stability theory is used to prove the stability of the underactuated AUV. Simulation studies are conducted to show the effectiveness and robustness of the controller.

The design, control, and implementation of an autonomous underwater vehicle (AUV) for collecting hydrological information from plateau rivers and lakes are presented in this paper. The hardware and software structures of the control system were previously described. A novel sliding mode controller (SMC) with combinational reaching law of vertical hovering motion is proposed to improve the robustness and stability. The S-plane control, a nonlinear controller with little parameters, is used in the horizontal motion. Besides, the navigation strategy based on the dead-reckoning algorithm, a path tracking based on the light-of-sight (LOS) algorithm, and a control allocation strategy considering saturation are present. Finally, experiments were performed in a tank and in a river in the Qinghai−Tibet Plateau to prove the feasibility and reliability of the AUV system.