Transactions on Emerging Telecommunications Technologies最新文献

Future network technology provides a new stage for industrial production systems, especially smart manufacturing system (SMS), which is a fully integrated and collaborative factory platform that responds in real time to meet changing demands and conditions in the factory. In this paper, we propose a new spectrum allocation scheme for the macro/small cell overlaid SMS. To improve the communication performance, we develop a new solution concept, called the multi-criteria bargaining solution (MCBS), according to the combination of multi-criteria decision strategy and cooperative bargaining ideas. Through an interactive two-step manner, the main feature of MCBS is to harness the full synergy of heterogeneous cell coexisting infrastructure while maximizing mutual advantages in the two-tier cellular network. In an indoor smart factory environment, our hierarchical approach can effectively handle the multi-agent multi-criteria resource sharing problem. Finally, the extensive simulation results are presented to illustrate the potential advantages of our spectrum sharing policy. Especially, our proposed approach increases the network throughput, service payoff, and operator fairness by about 10%, 10%, and 20%, respectively, than the existing baseline protocols.

This paper introduces an AI-driven soccer training optimization method based on the space–air–ground integrated network, named SAGIN-Play, which integrates real-time multimodal data from ground sensors, aerial surveillance (drones), and cloud-based processing. The system is designed to optimize player performance, enhance tactical positioning, and provide real-time feedback during soccer training and matches. By leveraging wearable motion sensors, drone-based aerial surveillance, and cloud computing, the method enables precise tracking of player actions and interactions, facilitating personalized performance improvements. This paper evaluates the proposed method across various datasets, including SoccerNet, Kaggle Football, UCF101 Sports, and the player event system (PES), highlighting the effectiveness of SAGIN-Play in action recognition, tactical positioning, and real-time feedback precision. The method outperforms traditional techniques, such as object detection models, multi-object tracking, and reinforcement learning (RL), in key metrics, demonstrating its potential in dynamic soccer training environments. Additionally, an ablation study reveals the critical contributions of each SAGIN-Play component, particularly aerial surveillance and cloud-based processing, in optimizing player positioning and tactical execution. The study further demonstrates the system's ability to improve player performance through personalized feedback, showing significant progress in key skill areas like passing, shooting, and defense. Simulated live training sessions demonstrate substantial improvement in coordination, decision-making, and overall performance. The results underscore the importance of integrating multilayered data for real-time tactical adjustments in soccer training. Experimental results show that SAGIN-Play achieves 94.2% action recognition accuracy on SoccerNet and 92.8% tactical positioning accuracy on Kaggle Football, while reducing the average feedback latency from 650 ms to 240 ms compared with baseline models.

Industrial countries are moving toward digitizing the manufacturing processes in their factories by integrating the expected next-generation technologies such as software-defined networking (SDN), cloud computing, and industrial Internet-of-Things (IIoT). However, developing smart factories that combine these physical and cyber components faces critical challenges, particularly regarding the efficiency and security domains. For example, Distributed Denial of Service (DDoS) attacks in industrial environments could impact the progress of the automated processes and the availability of SDN-based networks. In this paper, we present a novel collaborative intrusion detection system (CIDS) approach for SDN-based industrial environments that integrates edge computing techniques to enhance security and operational efficiency. Our model optimizes resource utilization across dispersed industrial sites by uniquely combining three different IDSs: centralized-based IDS, edge-based Anomaly IDS (AIDS), and signature-based IDS (SIDS). The proposed approach establishes consistent, network-wide security policies to accommodate the varying processing capabilities. Moreover, the use of edge computing techniques minimizes the overhead introduced by the SDN controller located in the cloud layer and addresses scalability challenges in large-scale networks with heavy traffic loads. Evaluation is performed using the Mininet emulator, and the results reveal a detection accuracy of up to 98%. Furthermore, profiling outcomes of the centralized controller indicate a 50% reduction in traffic monitoring function calls, highlighting the efficiency and superiority of the proposed methodology, particularly for geographically dispersed industrial sites.

Vehicular ad hoc networks (VANETs) enable the exchange of safety-critical messages, but their open and dynamic nature makes them vulnerable to message falsification and denial-of-service attacks. Federated learning (FL) offers a distributed defense mechanism, yet conventional approaches such as FedAvg degrade severely under non-IID client data and are highly sensitive to adversarial updates. To address these limitations, we propose FedCross-VAN, a novel FL framework that incorporates cross-domain behavioral priors with a similarity-weighted aggregation scheme. On the client side, FedCross-VAN employs a dual-objective loss that balances anomaly classification with alignment to external priors, improving generalization under heterogeneous data. On the server side, updates are aggregated proportionally to their embedding similarity with the priors, suppressing the influence of poisoned or noisy clients. Experiments on two benchmark datasets show that FedCross-VAN achieves up to approximately 2%–4% higher accuracy on HAR and 13% on KWS, converges in fewer rounds, and exhibits stronger robustness than FedAvg and No-Transfer FL. These findings establish FedCross-VAN as a practical and resilient framework for anomaly detection in next-generation intelligent transportation systems.

This paper presents a novel basketball shooting performance optimization method that integrates deep pose estimation with a Space-Air-Ground Integrated Network (SAGIN)-enabled feedback mechanism. The core idea is to leverage advanced 3D human pose estimation techniques to capture the fine-grained body kinematics during shooting, decompose these movements into interpretable motion phases, and utilize SAGIN to provide ultra-low-latency corrective feedback. Compared to existing methods that either focus solely on biomechanical analysis or network-based performance enhancement, our framework establishes a closed-loop system capable of real-time analysis, correction, and adaptive learning. The proposed method is composed of four key components: (A) a deep pose estimation module that accurately reconstructs 3D body joints, (B) a phase-wise motion decomposition mechanism tailored to basketball shooting, (C) a SAGIN-based feedback pipeline that ensures low-latency information delivery, and (D) a unified learning objective that simultaneously optimizes pose estimation accuracy and shooting biomechanics. Experimental results demonstrate that the proposed system significantly outperforms existing methods, achieving 25.4 mm MPJPE (15%–40% reduction compared to baseline methods), 90.4% shooting accuracy (12%–18% improvement over existing systems), and 38 ms feedback latency (63% reduction compared to ground-based systems), offering a promising direction for intelligent sports training.

Fall Detection Systems (FDS) are an integral part in many Ambient Assisted Living (AAL) systems for ensuring the safety of senior citizens, especially in the underserved, isolated, and remote areas where there is unavailability of conventional communication systems. The conventional FDS systems mainly rely on cameras and wearable devices that impose significant challenges like privacy and acceptability. This paper presents a non-invasive and non-intrusive FDS leveraging ultrasonic sensors for fall detection, mitigating the challenges posed by camera systems and wearable devices, resulting into privacy preserving human fall detection. We propose a hybrid deep learning fusion approach that fuses Recurrent Neural Networks (RNN), Long Short Term Memory (LSTM), and Bi-directional LSTM (BLSTM), which achieves an accuracy of 98.14% for fall detection from time-series data. The main motivation of this study is to integrate our Fall detection system with the Space-Air-Ground-Integrated Network (SAGIN) framework to facilitate real-time alerts and emergency responses in the remote and isolated areas affected by unreliable communication systems. The integration of the FDS with the SAGIN framework presents a multi-tier processing at three levels, including Ground, Air, and Space. At the Ground level, the edge devices at the local site facilitate initial fall detection with lower latency. At the Air level, the aerial platforms like drones present an extended coverage range and facilitate data relay. And at the Space level, the satellites facilitate global connectivity, data analysis, and management for a longer course of time. Thus, the SAGIN integration with FDS systems ensures precise and real-time fall detection in remote and isolated areas, guaranteeing the availability of the communication networks. The proposed approach reduces the latency with the help of edge computing and showcases a resilient and scalable architecture for emergency response and health monitoring.

Quick Service Laboratories (QSL) provide the necessary diagnostic services that have to be performed within limited time frames and rely on coordinated solutions across its supply chain to operate successfully. The application of standard supply chain management approaches often fails to recognize the variable and unpredictable nature of QSL operations, which significantly contributes to stockouts, delays, or surplus inventory. This study looks into a different approach to the traditional methodologies of supply chain management by investigating the means when machine learning algorithms with the purpose of discovering anomalous behavior patterns are applied to QSL supply chain practices and generate value. In examining and evaluating the historical demand forecasting patterns, inventory levels, and operational performance metrics will be more easily identifiable as anomalous behaviors or dissenting levels such as demand spikes, unanticipated inventory shortfall levels, and atypical arrival patterns of inventory to generate disruption to laboratory operations. Machine learning models can be supervised or unsupervised to learn normal operation behaviors, and even detect anomalies in real time through model training. These models facilitate proactive interventions that would improve inventory management and distribution planning, as well as service delivery in general. When building on the results of our detection modeling, we found that machine learning anomaly detection could provide actionable suggestions and improved supply chain resiliency, and reduce stockouts and excess inventory, all while maintaining more controlled service levels. Our comparative evaluation of conventional monitoring and forecasting methods demonstrates superior capabilities over traditional methods in our results, by resorting to fully utilizing the complexity of simple linear and rare events found in QSL supply chains and their digital transformation story.

Unmanned Aerial Vehicles (UAVs) are ubiquitous in diverse applications, underscoring the need for efficient path-planning, particularly in complex three-dimensional (3D) environments. Current heuristic path-planning algorithms are largely designed for two-dimensional (2D) contexts, with a select few adapted for 3D open spaces. The application of these algorithms in 3D indoor or maze environments, however, remains largely unexplored. To address this gap, this study implements Dijkstra's, Greedy BFS, A*, Beam Search A*, Iterative Deepening A* (IDA*), Theta*, Weighted A* (WA*), Dynamic Weighted A* (DWA*), D* in 3D-environment and presents a comparative analysis within 3D indoor and maze scenarios. We assess their performance based on parameters such as path length, computational time, the number of points needed to reach the goal, and notably, memory consumption-a key consideration in UAVs due to their limited onboard memory. Through this analysis, we provide crucial insights into the behavior of these algorithms in complex 3D environments, thus informing the selection and development of optimal path-planning strategies for future UAV applications.

With the advancement of low-carbon economy and the emergence of Space-Air-Ground Integrated Network (SAGIN), carbon tax policies have become essential to driving sustainable optimization in freight industries operating within heterogeneous and dynamic network environments. This paper proposes a dynamic freight order allocation optimization model suitable for SAGIN scenarios under carbon tax constraints, simultaneously addressing multiple objectives including customer satisfaction, operational costs, resource utilization, and carbon emissions control. First, the model incorporates carbon tax policies to dynamically adjust emission costs and transportation efficiency in response to real-time data transmitted via SAGIN. Second, it captures the demand fluctuations characteristic of actual freight operations through dynamic and random settings enabled by SAGIN's ubiquitous connectivity. To comprehensively evaluate freight platforms' sustainability in SAGIN contexts, multiple sustainability assessment indicators are developed, including carbon emissions, empty running distance, overall revenue, and transportation efficiency. Experimental results confirm that the model significantly improves operational efficiency, reduces costs, and minimizes emissions under carbon tax constraints, providing robust decision-making support for achieving low-carbon targets while maintaining economic development and operational effectiveness.

Rise of online social networks has transformed how people interact, exchange information and connect with one another. But this digital evolution has also brought forth a significant challenge: the proliferation of abusive content. Detecting as well as mitigating abusive content is important for fostering a safe and inclusive online environment. This survey provides a comprehensive overview of the state-of-the-art methods for abusive content detection in online social networks. The paper begins by defining abusive content and its various manifestations in the digital realm and then delves into the evolving landscape of online social networks, highlighting the unique challenges posed by their dynamic and user-generated nature. Firstly, a scientometric analysis of the literature pertaining to the last 30 years (1993–2023) has been performed through which a deep analysis of prominent keywords, documents, institutions, and countries have been conducted. Further, this survey explores the key approaches to abusive content detection, including machine learning methods, natural language processing techniques, and deep learning models. The importance of dataset curation and annotation, which play a pivotal role in training robust and effective models, has been discussed. The survey also highlights various challenges, ethical implications, and future research directions that can guide the development of more effective and responsible abusive content detection systems in social networks.