In the era of big data, intelligent transportation systems are crucial for the development of smart cities, significantly impacting urban economic growth and planning. The integration of 6G networks and digital twin technology presents unprecedented opportunities to enhance urban traffic management through real-time data synchronization and high-fidelity simulations. Accurate traffic flow prediction is vital for congestion control, intelligent route planning, and effective urban traffic management. However, existing deep learning models often struggle to capture the complex spatio-temporal dependencies and dynamic spatial relationships inherent in urban traffic data, particularly in data-scarce environments. Given the spatial heterogeneity of urban data, where dense and sparse regions coexist, improving prediction accuracy in sparse areas is critical to ensuring overall forecasting performance. To address these challenges, we propose a novel framework called 6G-Enabled Digital Twin Collaborative Traffic Flow Prediction (DT-CTFP), which integrates advanced deep learning models within a 6G-supported digital twin environment. The framework leverages real-time data processing capabilities and ultra-low latency of 6G networks to capture complex traffic features and dynamic spatial dependencies. In data-rich regions, the Dynamic Graph Multi-Attention (DGMA) model is used to learn fine-grained spatio-temporal patterns, while for data-scarce regions, the Cross-Area Transfer Prediction (CATP) model utilizes meta-learning techniques to transfer knowledge from data-rich urban areas, improving prediction accuracy in areas with limited data. Experimental results demonstrate the superiority of the DT-CTFP framework, achieving up to 6% reductions in RMSE and 4% reductions in MAE across multiple datasets, highlighting its enhanced prediction accuracy and efficiency. These results emphasize the framework’s capacity to improve traffic management and vehicle-road cooperation within a digital twin smart city.

The fast anomaly detection and localization is essential for network management, however, it is also very challenging for the current networks due to the lack of flexible control and telemetry capabilities. Fortunately, the maturity of Deep Reinforcement Learning (DRL) and programmable networking technologies could shed a light on realizing fast and intelligent anomaly detection and localization. In the paper, we design a fast anomaly detection and localization system for programmable networks by leveraging the in-band network telemetry and flexible control capabilities of programmable networks. Based on the system, we propose a DRL-based abnormal link detection and localization algorithm. It can iteratively infer abnormal links based on the ingress-to-egress performance metrics of flows and the one-hop performance metrics of the flows on the already identified abnormal links. The simulation results show that our proposals can detect and localize link anomalies in a matter of seconds to tens of seconds with low network telemetry overhead.

Streaming time series retrieval (TSR) has been widely concerned in academia and industry. Considering the large volume, high dimensionality and continuous accumulation features of time series, there is limited capability to perform in-depth similarity searching directly on the raw time series data. Therefore, time series representation, which can provide the dimension reduction-based approximate results for the raw data, should be utilized in the first step for streaming TSR. However, the existing representation-based TSR methods mainly have two limitations: on the one hand, the representation efficiency of the current methods is too slow to adapt for real-time streaming time series representation; on the other hand, the retrieval efficiency of them is also not ideal, and thus fails to recognize the specific given sequence patterns on the streaming data effectively. In this paper, we present an efficient retrieval method on streaming time series. Concretely, our method can incrementally represent the features of streaming data to automatically prune the corresponding dissimilar sequences and retain the most similar candidates for efficient one-pass searching. Extensive experiments on real world datasets have been conducted to demonstrate the superiority of our method.

With the rapid development of information technology, we have already access to the era of big data. Time series is a sequence of data points associated with numerical values and successive timestamps. Time series not only has the traditional big data features, but also can be continuously generated in a high speed. Therefore, it is very time- and resource-consuming to directly apply the traditional time series similarity search methods on the raw time series data. In this paper, we propose a novel online segmenting algorithm for streaming time series, which has a relatively high performance on feature representation and similarity search. Extensive experimental results on different typical time series datasets have demonstrated the superiority of our method.

We present a novel integrated visualization system that enables interactive visual analysis of ensemble simulations of the sea surface height that is used in ocean forecasting. The position of eddies can be derived directly from the sea surface height and our visualization approach enables their interactive exploration and analysis.The behavior of eddies is important in different application settings of which we present two in this paper. First, we show an application for interactive planning of placement as well as operation of off-shore structures using real-world ensemble simulation data of the Gulf of Mexico. Off-shore structures, such as those used for oil exploration, are vulnerable to hazards caused by eddies, and the oil and gas industry relies on ocean forecasts for efficient operations. We enable analysis of the spatial domain, as well as the temporal evolution, for planning the placement and operation of structures.Eddies are also important for marine life. They transport water over large distances and with it also heat and other physical properties as well as biological organisms. In the second application we present the usefulness of our tool, which could be used for planning the paths of autonomous underwater vehicles, so called gliders, for marine scientists to study simulation data of the largely unexplored Red Sea.