Recent advancements in Intelligent Transportation Systems suggest that the roads will gradually be filled with autonomous vehicles that are able to drive themselves while communicating with each other and the infrastructure. As a representative driving pattern of autonomous vehicles, the platooning technology has great potential for reducing transport costs by lowering fuel consumption and increasing traffic efficiency. In this paper, to improve the driving efficiency of autonomous vehicular platoon in terms of fuel consumption, a path planning scheme is envisioned using deep reinforcement learning on the network edge node. At first, the system model of autonomous vehicles platooning is given on the common highway. Next, a joint optimization problem is developed considering the task deadline and fuel consumption of each vehicle in the platoon. After that, a path determination strategy employing deep reinforcement learning is designed for the platoon. To make the readers readily follow, a case study is also presented with instantiated parameters. Numerical results shows that our proposed model could significantly reduce the fuel consumption of vehicle platoons while ensuring their task deadlines.

Accurate and timely flood forecasting, facilitated by remote sensing technology, is crucial to mitigate the damage and loss of life caused by floods. However, despite years of research, accurate flood prediction still faces numerous challenges, including complex spatiotemporal features and varied flood patterns influenced by multiple variables. Moreover, long-term flood forecasting is always tricky due to the constantly changing conditions of the surrounding environment. In this study, we propose a heterogeneous dynamic temporal graph convolutional network (HD-TGCN) for flood forecasting. Specifically, we designed a dynamic temporal graph convolution module (D-TGCM) to generate a dynamic adjacency matrix by incorporating a multihead self-attention mechanism, enabling our model to capture the dynamic spatiotemporal features of flood data by utilizing temporal graph convolution operations on the dynamic matrix. Furthermore, to reflect the impact of multiple meteorological and hydrological features on the heterogeneity of flood data, we propose a novel approach that utilizes multiple parallel D-TGCMs for processing heterogeneous graph data and implements a fusion mechanism to capture varied flood patterns influenced by multiple variables. Experiments conducted on a real dataset in Wuyuan County, Jiangxi Province, demonstrate that the HD-TGCN outperforms the state-of-the-art flood prediction models in mean absolute error, Nash–Sutcliffe efficiency, and root-mean-square error, with improvements of 80.32%, 0.15%, and 73.99%, respectively, providing a more accurate flood forecasting method that will play a critical role in future flood disaster prevention and control.

Time series forecasting, particularly within the Internet of Things (IoT) and hydrological domains, plays a critical role in predicting future events based on historical data, which is essential for strategic decision making. Effective flood forecasting is pivotal for optimal water resource management and for mitigating the adverse impacts of flood events. While deep learning methods have demonstrated exceptional performance in time series prediction through advanced feature extraction and pattern recognition, they encounter significant limitations when applied to scenarios with sparse data, especially in flood forecasting. The scarcity of historical data can severely hinder the generalization capabilities of traditional deep learning models, presenting a notable challenge in practical flood prediction applications. To address this issue, we introduce MetaTrans-FSTSF, a pioneering meta-learning framework that redefines few-shot time series forecasting. By innovatively integrating MAML and Transformer architectures, our framework provides a specialized solution tailored for the unique challenges of flood prediction, including data scarcity and complex temporal patterns. This framework goes beyond standard implementations, delivering significant improvements in predictive accuracy and adaptability. Our approach leverages Model-Agnostic Meta-Learning (MAML) to enable rapid adaptation to new forecasting tasks with minimal historical data. Our inner architecture is a Transformer-based meta-predictor capable of capturing intricate temporal dependencies inherent in flood time series data. Our framework was evaluated using diverse datasets, including a real-world hydrological dataset from a small catchment area in Wuyuan, China, and other benchmark time series datasets. These datasets were preprocessed to align with the meta-learning approach, ensuring their suitability for tasks with limited data availability. Through extensive evaluation, we demonstrate that MetaTrans-FSTSF substantially improves predictive accuracy, achieving a reduction of up to 16%, 19%, and 8% in MAE compared to state-of-the-art methods. This study highlights the efficacy of meta-learning techniques in overcoming the limitations posed by data scarcity and enhancing flood forecasting accuracy where historical data are limited.

As the railway industry undergoes digital transformation driven by AI, big data, and 5G/6G, secure data transmission for IoT-oriented railway operations has become a critical challenge in high-speed railway (HSR) wireless communications. Covert communication, which conceals wireless transmissions from eavesdroppers, together with reconfigurable intelligent surfaces (RISs), MIMO, and millimeter-wave technologies, offers a promising solution for enhancing security in such dynamic environments. This paper presents a comprehensive review of RIS-assisted covert communication for mmWave MIMO channels in HSR IoT networks. We examine security threats and the limitations of existing schemes, introduce the system architecture and channel modeling approaches, and summarize Doppler shift compensation techniques for high-mobility scenarios. In particular, this review introduces a service-priority-based covert constraint design framework as an original analytical perspective, rather than a fully validated engineering solution, to improve adaptability in diverse HSR operational contexts. We further provide a critical comparative review of optimization algorithms, including convex relaxation, heuristic, and deep reinforcement learning approaches, with a focus on their applicability and complexity trade-offs, and outline future research directions. Beyond summarizing the existing literature, this review integrates new analytical perspectives and simulation-based illustrations derived from our recent publications, offering both a systematic reference and forward-looking insights for researchers in this emerging field.