Search Results (71)

Industrial gateways serve as critical data aggregation points within the Industrial Internet of Things (IIoT), enabling seamless data interoperability that empowers enterprises to extract value from equipment data more efficiently. However, their role exposes a fundamental trade-off between computational efficiency and prediction accuracy—a contradiction yet to be fully resolved by existing approaches. The rapid proliferation of IoT devices has led to a corresponding surge in network traffic, posing significant challenges for traffic forecasting methods, while deep learning models like Transformers and GNNs demonstrate high accuracy in traffic prediction, their substantial computational and memory demands hinder effective deployment on resource-constrained industrial gateways, while simple linear models offer relative simplicity, they struggle to effectively capture the complex characteristics of IIoT traffic—which often exhibits high nonlinearity, significant burstiness, and a wide distribution of time scales. The inherent time-varying nature of traffic data further complicates achieving high prediction accuracy. To address these interrelated challenges, we propose the lightweight and theoretically grounded DOA-MSDI-CrossLinear framework, redefining traffic forecasting as a hierarchical decomposition–interaction problem. Unlike existing approaches that simply combine components, we recognize that industrial traffic inherently exhibits scale-dependent temporal correlations requiring explicit decomposition prior to interaction modeling. The Multi-Scale Decomposable Mixing (MDM) module implements this concept through adaptive sequence decomposition, while the Dual Dependency Interaction (DDI) module simultaneously captures dependencies across time and channels. Ultimately, decomposed patterns are fed into an enhanced CrossLinear model to predict flow values for specific future time periods. The Dream Optimization Algorithm (DOA) provides bio-inspired hyperparameter tuning that balances exploration and exploitation—particularly suited for the non-convex optimization scenarios typical in industrial forecasting tasks. Extensive experiments on real industrial IoT datasets thoroughly validate the effectiveness of this approach. Full article

Automatic Identification System (AIS) signals are critical for maritime traffic monitoring and collision avoidance. In satellite reception environments, signal collisions occur frequently due to large coverage areas and high ship density, severely degrading decoding performance. We propose a dual-branch deep learning architecture that combines precise boundary detection with segment-level classification to address this collision problem. The network employs a multi-scale convolutional backbone that feeds two specialized branches: one detects collision boundaries with sample-level precision, while the other provides semantic context through segment classification. We developed a satellite AIS dataset generation framework that simulates realistic collision scenarios including multiple ships, Doppler effects, and channel impairments. The trained model achieves 96% collision detection accuracy on simulated data. Validation on real satellite recordings demonstrates that our method retains 99.4% of valid position reports compared to direct decoding of the original signal. Controlled experiments show that intelligent collision removal outperforms random segment exclusion by 6.4 percentage points, confirming the effectiveness of our approach. Full article

Traffic violations and road accidents remain significant challenges in developing safe and efficient transportation systems. Despite technological advancements, improving vehicle detection accuracy and enabling real-time traffic management remain critical research priorities. This study proposes YOLO-LIO, an enhanced vehicle detection framework designed to address these challenges by improving small-object detection and optimizing real-time deployment. The system introduces multi-scale detection, virtual zone filtering, and efficient preprocessing techniques, including grayscale transformation, Laplacian variance calculation, and median filtering to reduce computational complexity while maintaining high performance. YOLO-LIO was rigorously evaluated on five datasets, GRAM Road-Traffic Monitoring (99.55% accuracy), MAVD-Traffic (99.02%), UA-DETRAC (65.14%), KITTI (94.21%), and an Author Dataset (99.45%), consistently demonstrating superior detection capabilities across diverse traffic scenarios. Additional system features include vehicle counting using a dual-line detection strategy within a virtual zone and speed detection based on frame displacement and camera calibration. These enhancements enable the system to monitor traffic flow and vehicle speeds with high accuracy. YOLO-LIO was successfully deployed on Jetson Nano, a compact, energy-efficient hardware platform, proving its suitability for real-time, low-power embedded applications. The proposed system offers an accurate, scalable, and computationally efficient solution, advancing intelligent transportation systems and improving traffic safety management. Full article

Accurate multivariate traffic flow forecasting is critical for intelligent transportation systems yet remains challenging due to the complex interplay of temporal dynamics and spatial interactions. While Transformer-based models have shown promise in capturing long-range temporal dependencies, most existing approaches compress multidimensional observations into flattened sequences—thereby neglecting explicit modeling of cross-dimensional (i.e., spatial or inter-variable) relationships, which are essential for capturing traffic propagation, network-wide congestion, and node-specific behaviors. To address this limitation, we propose TSAformer, a novel Transformer architecture that explicitly preserves and jointly models time and dimension as dual structural axes. TSAformer begins with a multimodal input embedding layer that encodes raw traffic values alongside temporal context (time-of-day and day-of-week) and node-specific positional features, ensuring rich semantic representation. The core of TSAformer is the Two-Stage Attention (TSA) module, which first models intra-dimensional temporal evolution via time-axis self-attention then captures inter-dimensional spatial interactions through a lightweight routing mechanism—avoiding quadratic complexity while enabling all-to-all cross-node communication. Built upon TSA, a hierarchical encoder–decoder (HED) structure further enhances forecasting by modeling traffic patterns across multiple temporal scales, from fine-grained fluctuations to macroscopic trends, and fusing predictions via cross-scale attention. Extensive experiments on three real-world traffic datasets—including urban road networks and highway systems—demonstrate that TSAformer consistently outperforms state-of-the-art baselines across short-term and long-term forecasting horizons. Notably, it achieves top-ranked performance in 36 out of 58 critical evaluation scenarios, including peak-hour and event-driven congestion prediction. By explicitly modeling both temporal and dimensional dependencies without structural compromise, TSAformer provides a scalable, interpretable, and high-performance solution for spatiotemporal traffic forecasting. Full article

In the centralized scheduling mode of automated container terminals, Automated Guided Vehicles (AGVs) often experience decision-making delays caused by system information-processing bottlenecks, which significantly affect path-planning efficiency and are particularly evident in sudden-traffic scenarios. To address this issue, this paper incorporates the artificial potential field (APF) into the cost function of Model Predictive Control (MPC) and develops a dual-trigger mechanism for lane-change and lane-return MPC obstacle-avoidance framework (Event-Triggered Model Predictive Control, EMPC). This framework integrates an obstacle-triggered local optimization mechanism and a lane-change trigger, enabling AGV to perform autonomous and dynamically responsive local obstacle avoidance, thereby improving local path-planning efficiency. Furthermore, a Proximal Policy Optimization (PPO)-based strategy is introduced to adaptively adjust the obstacle-weighting parameters within the EMPC cost function, enhancing both obstacle-avoidance and lane-keeping performance. Under multi-lane overtaking conditions, a lane-change trigger—implemented as a dual-phase “lane-change–return” mechanism—is employed, in which lateral optimization is activated only during critical phases, reducing online computational load by at least 28% compared with conventional MPC strategies. The experimental results demonstrate that the proposed PPO–EMPC architecture exhibits high robustness, real-time performance, and scalability under dynamic and partially observable environments, providing a practical and generalizable decision-making paradigm for cooperative AGV operations in automated container terminals. Full article

With the proliferation of mobile devices, identifying previously unseen mobile applications has become a critical challenge in network security. Traditional application identification approaches rely heavily on fixed training categories and limited traffic features, making them ineffective in real-world environments. To address this problem, we propose KZGNN, a knowledge-enhanced zero-shot graph neural network for mobile application identification. KZGNN first constructs a unified mobile application knowledge graph that integrates high-level semantic metadata with fine-grained network behavior, enabling structured representation of application characteristics. Building on this, KZGNN introduces a relation-aware dual-channel propagation mechanism that separates semantic relations and behavioral interactions into dedicated GNN pathways and adaptively fuses them through attention. To support zero-shot recognition, KZGNN projects node embeddings and category semantics into a shared embedding space, where a structure-preserving constraint maintains global semantic geometry and improves generalization to unseen categories. Experiments on a dataset of 160 mobile applications show that KZGNN outperforms nine state-of-the-art traffic classification baselines and achieves a 5.2% improvement in identifying unseen application categories, demonstrating its effectiveness for mobile application identification in zero-shot scenarios. Full article

Wireless Sensor Networks (WSNs) are increasingly being used in mission-critical infrastructures. In such applications, they are evaluated on the risk of cyber intrusions that can target the already constrained resources. Traditionally, Intrusion Detection Systems (IDS) in WSNs have been based on machine learning techniques; however, these models fail to capture the nonlinear, temporal, and topological dependencies across the network nodes. As a result, they often suffer degradation in detection accuracy and exhibit poor adaptability against evolving threats. To overcome these limitations, this study introduces a hybrid deep learning-based IDS that integrates multi-scale convolutional feature extraction, dual-stage attention fusion, and graph convolutional reasoning. Moreover, bidirectional long short-term memory components are embedded into the unified framework. Through this combination, the proposed architecture effectively captures the hierarchical spatial–temporal correlations in the traffic patterns, thereby enabling precise discrimination between normal and attack behaviors across several intrusion classes. The model has been evaluated on a publicly available benchmarking dataset, and it has been found to attain higher classification capability in multiclass scenarios. Furthermore, the model outperforms conventional IDS-focused approaches. In addition, the proposed design aims to retain suitable computational efficiency, making it appropriate for edge and distributed deployments. Consequently, this makes it an effective solution for next-generation WSN cybersecurity. Overall, the findings emphasize that combining topology-aware learning with multi-branch attention mechanisms offers a balanced trade-off between interpretability, accuracy, and deployment efficiency for resource-constrained WSN environments. Full article

Autonomous vehicles represent a transformative technology in modern transportation, promising enhanced safety, efficiency, and accessibility in mobility systems. This paper presents a comprehensive autonomous vehicle system designed specifically for Vietnam’s traffic conditions, featuring a multi-layered approach to perception, decision-making, and control. The system utilizes dual 2D LiDARs, camera vision, and GPS sensing to navigate complex urban environments. A key contribution is the development of a specialized segmentation model that accurately identifies Vietnam-specific traffic signs, lane markings, road features, and pedestrians. The system implements a hierarchical decision-making architecture, combining long-term planning based on GPS and map data with short-term reactive planning derived from a bird’s-eye view transformation of segmentation and LiDAR data. The control system modulates the speed and steering angle through a validated model that ensures stable vehicle operation across various traffic scenarios. Experimental results demonstrate the system’s effectiveness in real-world conditions, achieving a high accuracy rate in terms of segmentation and detection and an exact response in navigation tasks. The proposed system shows robust performance in Vietnam’s unique traffic environment, addressing challenges such as mixed traffic flow and country-specific road infrastructure. Full article

In mixed traffic scenarios involving both motorized and non-motorized participants, accurately predicting future trajectories of surrounding vehicles remains a major challenge for autonomous driving. Predicting the motion of powered two-wheelers (PTWs) is particularly difficult due to their abrupt behavioral changes and stochastic interaction patterns. To address this issue, this paper proposes an enhanced Social-GAT model with a multi-module architecture for PTW trajectory prediction. The model consists of a dual-channel LSTM encoder that separately processes position and motion features; a temporal attention mechanism to weight key historical states; and a residual-connected two-layer GAT structure to model social relationships within the interaction range, capturing interactive features between PTWs and surrounding vehicles through dynamic adjacency matrices. Finally, an LSTM decoder integrates spatiotemporal features and outputs the predicted trajectory. Experimental results on the rounD dataset demonstrate that our model achieves an outstanding ADE of 0.28, surpassing Trajectron++ by 9.68% and Social-GAN by 69.2%. It also attains the lowest RMSE values across 0.4–2.0s prediction horizons, confirming its superior accuracy and stability for PTW trajectory prediction in mixed traffic environments. Full article

Wavelength Division Multiplexing-based Passive Optical Networks (WDM-PONs) are among the most advanced optical networks without active elements, using a wide range of wavelengths to increase network reliability, scalability, and capacity. This ensures the provision of high quality, fast, and available services for end users. In this aim, traffic protection considerations have markedly enhanced their role. Traffic protection schemes can be divided into Point-To-MultiPoint (P2MP) and ring architectures. Traffic protection scenarios of access WDM-PONs in the P2MP architecture include Type B, dual-parented Type B, and Type C, while the ring architecture includes protected access and metropolitan-access WDM-PONs. Any potential traffic protection scheme can be represented by a corresponding reliability block diagram for the purpose of cost–benefit analysis. An important aspect of the WDM-PON design is presented by the Capital (CAPEXs) and Operational (OPEXs) Expenditures, which play a key role in network optimization. Managing them efficiently allows us to achieve an economically sustainable and efficient infrastructure of future passive optical networks involving traffic protection schemes. In this work, we focused on simulation model development for calculating the CAPEX and OPEX costs and the subsequent cost–benefit analysis of possible WDM-PON traffic protection schemes. Full article

With the advancement of battery technology and the promotion of the “dual carbon” policy, electric vehicles (EVs) have been widely used in industrial, commercial, and civil fields, and the charging infrastructure of highway service areas across the country has also shown a rapid development trend. However, the charging load of electric vehicles in highway scenarios exhibits strong randomness and uncertainty. It is affected by multiple factors such as traffic flow, state of charge (SOC), and user charging behavior, and it is difficult to accurately model it through traditional mathematical models. This paper proposes a hierarchical Markov chain Monte Carlo (HMMC) simulation method to construct a charging load prediction model with spatiotemporal coupling characteristics. The model hierarchically models features such as traffic flow, SOC, and charging behavior through a hierarchical structure to reduce interference between dimensions; by constructing a Markov chain that converges to the target distribution and an inter-layer transfer mechanism, the load change process is deduced layer by layer, thereby achieving a more accurate charging load prediction. Comparative experiments with mainstream methods such as ARIMA, BP neural networks, random forests, and LSTM show that the HMMC model has higher prediction accuracy in highway scenarios, significantly reduces prediction errors, and improves model stability and interpretability. Full article

The China Railway Express (China-Europe container railway freight transport) is pivotal to Eurasian freight, yet its transcontinental railway faces escalating cascading risks. We develop a coupled map lattice (CML) model representing the physical infrastructure layer and the operational traffic layer concurrently to quantify and mitigate cascading failures. Twenty critical stations are identified by integrating TOPSIS entropy weighting with grey relational analysis in dual-layer networks. The enhanced CML embeds node-degree, edge-betweenness, and freight-flow coupling coefficients, and introduces two adaptive cargo-redistribution rules—distance-based and load-based for real-time rerouting. Extensive simulations reveal that network resilience peaks when the coupling coefficient equals 0.4. Under targeted attacks, cascading failures propagate within three to four iterations and reduce network efficiency by more than 50%, indicating the vital function of higher importance nodes. Distance-based redistribution outperforms load-based redistribution after node failures, whereas the opposite occurs after edge failures. These findings attract our attention that redundant border corridors and intelligent monitoring should be deployed, while redistribution rules and multi-tier emergency response systems should be employed according to different scenarios. The proposed methodology provides a dual-layer analytical framework for addressing cascading risks of transcontinental networks, offering actionable guidance for intelligent transportation management of international intermodal freight networks. Full article

To address the challenges of low detection accuracy and weak generalization in UAV aerial imagery caused by complex ground environments, significant scale variations among targets, dense small objects, and background interference, this paper proposes an improved lightweight multi-scale small-object detection model, MBD-YOLO (MBFF module, BiMS-FPN, and Dual-Stream Head). Specifically, to enhance multi-scale feature extraction capabilities, we introduce the Multi-Branch Feature Fusion (MBFF) module, which dynamically adjusts receptive fields through parallel branches and adaptive depthwise convolutions, expanding the receptive field while preserving detail perception. We further design a lightweight Bidirectional Multi-Scale Feature Aggregation Pyramid Network (BiMS-FPN), integrating bidirectional propagation paths and a Multi-Scale Feature Aggregation (MSFA) module to mitigate feature spatial misalignment and improve small-target detection. Additionally, the Dual-Stream Head with NMS-free architecture leverages a task-aligned architecture and dynamic matching strategies to boost inference speed without compromising accuracy. Experiments on the VisDrone2019 dataset demonstrate that MBD-YOLO-n surpasses YOLOv8n by 6.3% in mAP50 and 8.2% in mAP50–95, with accuracy gains of 17.96–55.56% for several small-target categories, while increasing parameters by merely 3.1%. Moreover, MBD-YOLO-s achieves superior detection accuracy, efficiency, and generalization with only 12.1 million parameters, outperforming state-of-the-art models and proving suitable for resource-constrained embedded deployment scenarios. The superior performance of MBD-YOLO, which harmonizes high precision with low computational demand, fulfills the critical requirements for real-time deployment on resource-limited UAVs, showing great promise for applications in traffic monitoring, urban security, and agricultural surveying. Full article

Reliable three-dimensional (3D) object detection is critical for intelligent vehicles to ensure safety in complex traffic environments, and recent progress in multi-modal sensor fusion, particularly between LiDAR and camera, has advanced environment perception in urban driving. However, existing approaches remain vulnerable to occlusions and dense traffic, where depth estimation errors, calibration deviations, and cross-modal misalignment are often exacerbated. To overcome these limitations, we propose BEVAlign, a local–global feature alignment framework designed to generate unified BEV representations from heterogeneous sensor modalities. The framework incorporates a Local Alignment (LA) module that enhances camera-to-BEV view transformation through graph-based neighbor modeling and dual-depth encoding, mitigating local misalignment from depth estimation errors. To further address global misalignment in BEV representations, we present the Global Alignment (GA) module comprising a bidirectional deformable cross-attention (BDCA) mechanism and CBR blocks. BDCA employs dual queries from LiDAR and camera to jointly predict spatial sampling offsets and aggregate features, enabling bidirectional alignment within the BEV domain. The stacked CBR blocks then refine and integrate the aligned features into unified BEV representations. Experiment on the nuScenes benchmark highlights the effectiveness of BEVAlign, which achieves 71.7% mAP, outperforming BEVFusion by 1.5%. Notably, it achieves strong performance on small and occluded objects, particularly in dense traffic scenarios. These findings provide a basis for advancing cooperative environment perception in next-generation intelligent vehicle systems. Full article

To meet the dual demands of 6G cellular networks for high spectral efficiency and environmental sensing, this paper proposes a full-duplex (FD) integrated sensing and communication (ISAC) dynamic resource allocation framework. At the heart of the framework lies a dynamic frame structure that can self-adapt the time-domain resource ratio between sensing and communication, designed to flexibly handle complex traffic demands. In FD mode, however, the trade-off between communication and sensing performance, exacerbated by severe self-interference (SI), morphs into a non-convex, NP-hard multi-objective optimization problem (MOP). To tackle this, we propose an Adaptive Hybrid Memetic Multi-Objective Grey Wolf Optimizer (AM-MOGWO). Finally, simulations were conducted on a high-fidelity platform that integrates 3GPP-standardized channels, which was further extended to a challenging multi-cell interference scenario to validate the algorithm’s robustness. AM-MOGWO was systematically benchmarked against standard Grey Wolf Optimizer (GWO), random search (RS), and the genetic algorithm (GA). Simulation results demonstrate that in both the single-cell and the more complex multi-cell environments, the proposed algorithm excels in locating the Pareto-optimal solution set, where its solution set significantly outperforms the baseline methods. Its hypervolume (HV) metric surpasses the second-best approach by more than 93%. This result quantitatively demonstrates the algorithm’s superiority in finding a high-quality set of trade-off solutions, confirming the framework’s high efficiency in complex interference environments. Full article