This paper investigates the integration of the non-orthogonal multiple access (NOMA) technique and autonomous aerial vehicles (AAVs) in Internet of Vehicles (IoV), aiming to provide flexible access and improve communication coverage for vehicle platooning. The goal is to accurately analyze performance and reasonably optimize network design for AAV-assisted vehicle platooning in NOMA-enhanced IoV. To achieve this, an analytical solution is derived for the average achievable rate from the lead vehicle to follower vehicles over Rician fading channels. Leveraging this analytical solution, the Gauss-Chebyshev integration is employed to obtain the approximate solution. Then, we formulate a problem of maximizing the sum of secure rates by optimizing the trajectory and spectrum allocation. The formulated problem is constrained by the security requirement and imperfect channel state information. Addressing the NP-hard nature of this problem, an iterative optimization algorithm is developed, incorporating Q-learning and the graph theory to alternately adjust the trajectory and spectrum allocation. Finally, the simulation results show that the approximate solution matches well with the analytical solution, and the gap is less than 6%. Moreover, the proposed scheme has a significant performance improvement in the sum of secure rates compared with the state-of-the-art schemes.

Ensuring reliable and secure emergency communications in post-disaster scenarios is challenging, particularly when mobile communication infrastructures are damaged. In response to challenges in post-disaster emergency communications (PDEComs), this article proposes a cooperative relaying system (CRS) enhanced with Internet of Things (IoT) technology. The system features an airship equipped with buffers that serves as an aerial relay, designed to improve data transmission performance and communication security. To achieve this, it incorporates physical layer security techniques. Additionally, we introduce a hybrid mechanism that combines nonorthogonal multiple access (NOMA) and orthogonal multiple access (OMA), facilitating flexible resource allocation in IoT-enhanced CRSs. To fully leverage the advantages of the proposed airship-and-buffer aided CRS, we formulate a weighted secure sum rate (WSSR) maximization problem, jointly considering the power control, mode selection, and information security. Initially, we address the formulated WSSR maximization problem using Lyapunov optimization. Subsequently, the primal problem is divided into four cases, from which optimal power control and mode selection policies can be derived. This process is constrained by the stability of buffer queues and privacy transmission requirements. Finally, the simulation results show that the proposed scheme outperforms state-of-the-art schemes in terms of the WSSR. By adopting the airship and the hybrid NOMA/OMA mechanism, the WSSR can be increased by 29.9% and 96.4%, respectively. Moreover, we explore the impact of network parameters (e.g., the distance between the eavesdropper and airship, and decoding thresholds) on information security.

The accuracy of Fresh Tea Sprouts Detection (FTSD) is not high enough, which has become a big bottleneck in the field of vision-based automatic tea picking technology. In order to improve the detection performance, we rethink the process of FTSD. Meanwhile, motivated by the multispectral image processing, we find that more input information can lead to a better detection result. With this in mind, a novel Fresh Tea Sprouts Detection method via Image Enhancement and Fusion Single-Shot Detector (FTSD-IEFSSD) is proposed in this paper. Firstly, we obtain an enhanced image via RGB-channel-transform-based image enhancement algorithm, which uses the original fresh tea sprouts color image as the input. The enhanced image can provide more input information, where the contrast in the fresh tea sprouts area is increased and the background area is decreased. Then, the enhanced image and color image is used in the detection subnetwork with the backbone of ResNet50 separately. We also use the multilayer semantic fusion and scores fusion to further improve the detection accuracy. The strategy of tea sprouts shape-based default boxes is also included during the training. The experimental results show that the proposed method has a better performance on FTSD than the state-of-the-art methods.

This paper integrates intelligent reflecting surfaces (IRS) with unmanned aerial vehicles (UAV) to enhance the transmission performance of the Internet of Vehicles (IoV) through non-orthogonal multiple access (NOMA). It focuses on strengthening the signals from cell edge vehicles (CEVs) to the base station by optimizing the wireless propagation environment via an IRS-equipped UAV. The primary goal is to maximize the sum data rate of CEVs while satisfying the constraint of the successive interference cancellation (SIC) decoding threshold. The challenge lies in the non-convex nature of jointly considering the power control, subcarrier allocation, and phase shift design, making the problem difficult to optimally solve. To address this, the problem is decomposed into two independent subproblems, which are then solved iteratively. Specifically, the optimal phase shift design is achieved using the deep deterministic policy gradient (DDPG) algorithm. Furthermore, the graph theory is applied to determine the subcarrier allocation policy and derive a closed-form solution for optimal power control. Finally, the simulation results show that the proposed joint phase shift and resource management scheme significantly enhances the sum data rate compared to the state-of-the-art schemes, thereby demonstrating the benefits of integrating the IRS-equipped UAV into NOMA-enhanced IoV.

Air quality index (AQI) is a non-dimensional index for the description of air quality, and is widely used in air quality management schemes. A novel method for Air Quality Index Forecasting based on Deep Dictionary Learning (AQIF-DDL) and machine vision is proposed in this paper. A sky image is used as the input of the method, and the output is the forecasted AQI value. The deep dictionary learning is employed to automatically extract the sky image features and achieve the AQI forecasting. The idea of learning deeper dictionary levels stemmed from the deep learning is also included to increase the forecasting accuracy and stability. The proposed AQIF-DDL is compared with other deep learning based methods, such as deep belief network, stacked autoencoder and convolutional neural network. The experimental results indicate that the proposed method leads to good performance on AQI forecasting.

This paper investigates the application of low altitude platform station (LAPS) and device-to-device (D2D) techniques in maritime emergency communications. The hovering LAPS acts as an aerial base station to provide emergency telecommunications services, and the D2D technique is used to improve spectrum utilization. To make full use of the advantages of LAPS-and-D2D enabled networks, we formulate a sum rate maximization problem by jointly considering the deployment optimization and resource management (DORM). The formulated problem is constrained by the reliability and minimum capacity requirements. To solve this problem, an iterative optimization algorithm (IOA) is designed. First, by using the graphical method, the closed-form solution of optimal power allocation is derived. Next, we transform the spectrum reuse problem into a min-cost max-flow problem, and the Ford-Fulkerson algorithm is employed to solve it. Then, we present a meta-heuristic algorithm to optimize the LAPS deployment. Finally, the simulation results indicate that the proposed DORM scheme outperforms the state-of-the-art works in term of the sum rate. In addition, the convergence and performance gap of the designed IOA are demonstrated.