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Enhanced Basketball Shooting Performance Through Deep Pose Estimation and SAGIN-Based Feedback Systems 通过深度姿势估计和基于sagin的反馈系统增强篮球投篮表现
IF 2.5 4区 计算机科学 Q3 TELECOMMUNICATIONS Pub Date : 2025-12-22 DOI: 10.1002/ett.70320
Shuai Li, Sung-Pil Chung, Xiaoyan Ge, Arvind Dhaka

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.

提出了一种将深度姿态估计与空间-空地集成网络(SAGIN)反馈机制相结合的篮球投篮性能优化方法。核心思想是利用先进的3D人体姿态估计技术来捕捉拍摄过程中细粒度的身体运动学,将这些运动分解为可解释的运动阶段,并利用SAGIN提供超低延迟的纠正反馈。与现有的仅关注生物力学分析或基于网络的性能增强的方法相比,我们的框架建立了一个能够实时分析,校正和自适应学习的闭环系统。该方法由四个关键部分组成:(A)精确重建3D身体关节的深度姿态估计模块,(B)针对篮球投篮的相位运动分解机制,(C)基于sagin的反馈管道,确保低延迟信息传递,(D)统一的学习目标,同时优化姿态估计精度和投篮生物力学。实验结果表明,该系统显著优于现有方法,MPJPE达到25.4 mm(比基线方法降低15%-40%),射击精度达到90.4%(比现有系统提高12%-18%),反馈延迟达到38 ms(比地面系统降低63%),为智能运动训练提供了一个有希望的方向。
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引用次数: 0
Human Fall Detection in SAGIN Environment Using Ultrasonic Sensors and Hybrid Deep Learning 基于超声传感器和混合深度学习的SAGIN环境下人体跌倒检测
IF 2.5 4区 计算机科学 Q3 TELECOMMUNICATIONS Pub Date : 2025-12-22 DOI: 10.1002/ett.70321
Ankit D. Patel, Rutvij H. Jhaveri, Ashish D. Patel, Stella Bvuma

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.

跌倒检测系统(FDS)是许多环境辅助生活(AAL)系统中确保老年人安全的一个组成部分,特别是在服务不足、孤立和偏远地区,这些地区没有传统的通信系统。传统的FDS系统主要依赖于摄像头和可穿戴设备,这带来了隐私和可接受性等重大挑战。本文介绍了一种利用超声波传感器进行跌倒检测的非侵入性和非侵入性FDS,减轻了摄像系统和可穿戴设备带来的挑战,从而实现了保护隐私的人体跌倒检测。我们提出了一种融合循环神经网络(RNN)、长短期记忆(LSTM)和双向LSTM (BLSTM)的混合深度学习融合方法,该方法对时间序列数据的跌倒检测准确率达到98.14%。这项研究的主要动机是将我们的坠落探测系统与天空地一体化网络(SAGIN)框架相结合,以促进受不可靠通信系统影响的偏远和孤立地区的实时警报和应急响应。FDS与SAGIN框架的集成呈现了三层的多层处理,包括地面、空中和空间。在地面层,本地站点的边缘设备有助于以较低的延迟进行初始跌落检测。在空中层面,无人机等空中平台提供了更大的覆盖范围,便于数据中继。在空间层面,卫星促进了更长时间的全球互联互通、数据分析和管理。因此,SAGIN与FDS系统的集成确保了在偏远和孤立地区精确和实时的坠落检测,保证了通信网络的可用性。所提出的方法在边缘计算的帮助下减少了延迟,并展示了用于应急响应和健康监测的弹性和可扩展架构。
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引用次数: 0
Driving Digital Transformation in Quick Service Laboratory Supply Chains Through Statistical Anomaly Detection 通过统计异常检测推动快速服务实验室供应链的数字化转型
IF 2.5 4区 计算机科学 Q3 TELECOMMUNICATIONS Pub Date : 2025-12-18 DOI: 10.1002/ett.70322
Saeed Alzahrani, Surbhi B. Khan, Mohammed Alojail, Nidhi Bhatia

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.

快速服务实验室(QSL)提供必要的诊断服务,这些服务必须在有限的时间内完成,并依赖于整个供应链的协调解决方案才能成功运作。标准供应链管理方法的应用往往不能认识到QSL操作的可变和不可预测的性质,这极大地导致了缺货、延迟或库存过剩。本研究通过研究以发现异常行为模式为目的的机器学习算法应用于QSL供应链实践并产生价值的方法,探讨了传统供应链管理方法的不同方法。在检查和评估历史需求预测模式时,库存水平和操作性能度量将更容易识别为异常行为或不同的水平,例如需求峰值、未预期的库存不足水平和非典型的库存到达模式,从而对实验室操作产生干扰。机器学习模型可以通过监督或无监督来学习正常的操作行为,甚至可以通过模型训练实时检测异常。这些模式有助于采取主动干预措施,改善库存管理和分配计划,以及一般的服务提供。在构建检测模型的结果时,我们发现机器学习异常检测可以提供可操作的建议,提高供应链的弹性,减少缺货和过剩库存,同时保持更可控的服务水平。通过充分利用QSL供应链及其数字化转型故事中发现的简单线性和罕见事件的复杂性,我们对传统监测和预测方法的比较评估表明,我们的结果优于传统方法。
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引用次数: 0
Heuristic Path-Planning Techniques in Indoor Complex Three Dimensional Environment for Unmanned Aerial Vehicles 无人机室内复杂三维环境的启发式路径规划技术
IF 2.5 4区 计算机科学 Q3 TELECOMMUNICATIONS Pub Date : 2025-12-17 DOI: 10.1002/ett.70319
Pawan Kumar, Kunwar Pal, Prakash Kumar, Mahesh Chandra Govil, Dharmveer Singh Rajpoot, Ankit Vidyarthi

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.

无人驾驶飞行器(uav)在各种应用中无处不在,强调了对有效路径规划的需求,特别是在复杂的三维(3D)环境中。目前的启发式路径规划算法主要是为二维(2D)环境设计的,只有少数适合3D开放空间。然而,这些算法在3D室内或迷宫环境中的应用在很大程度上仍未被探索。为了解决这一差距,本研究在3D环境中实现了Dijkstra、Greedy BFS、A*、Beam Search A*、迭代深化A* (IDA*)、Theta*、加权A* (WA*)、动态加权A* (DWA*)、D*,并在3D室内和迷宫场景中进行了对比分析。我们根据路径长度、计算时间、达到目标所需的点数等参数评估它们的性能,尤其是内存消耗——由于机载内存有限,这是无人机的一个关键考虑因素。通过这一分析,我们为这些算法在复杂的3D环境中的行为提供了重要的见解,从而为未来无人机应用的最佳路径规划策略的选择和开发提供了信息。
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引用次数: 0
Dynamic Freight Order Allocation Optimization Under Carbon Tax Constraints in SAGIN: Multi-Objective Optimization and Sustainability Assessment 碳税约束下SAGIN货运订单动态分配优化:多目标优化与可持续性评价
IF 2.5 4区 计算机科学 Q3 TELECOMMUNICATIONS Pub Date : 2025-12-17 DOI: 10.1002/ett.70295
Binyan Liu, Peijun Xu, Song Sun, Changbing Jiang, Kaixiang Yang

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.

随着低碳经济的发展和天空地一体化网络(SAGIN)的出现,碳税政策对于推动在异构和动态网络环境下运营的货运行业的可持续优化至关重要。本文提出了一种适用于碳税约束下SAGIN情景的货运订单动态分配优化模型,同时兼顾客户满意度、运营成本、资源利用和碳排放控制等多个目标。首先,该模型结合碳税政策,根据SAGIN传输的实时数据动态调整排放成本和运输效率。其次,它通过SAGIN无处不在的连接性实现动态和随机设置,捕捉实际货运业务的需求波动特征。为了综合评价SAGIN环境下货运平台的可持续性,制定了包括碳排放、空载距离、总收益和运输效率在内的多个可持续性评估指标。实验结果证实,在碳税约束下,该模型显著提高了运行效率、降低了成本、最大限度地减少了排放,为实现低碳目标、保持经济发展和运行效益提供了强有力的决策支持。
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引用次数: 0
Mapping the Landscape of Abusive Content Detection in Social Networks: A Comprehensive and Scientometric Analysis 绘制社会网络中滥用内容检测的景观:一个全面的科学计量学分析
IF 2.5 4区 计算机科学 Q3 TELECOMMUNICATIONS Pub Date : 2025-12-16 DOI: 10.1002/ett.70310
Simrat Kaur, Ravneet Kaur, Sarbjeet Singh, Sakshi Kaushal

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.

在线社交网络的兴起改变了人们互动、交换信息和相互联系的方式。但这种数字化演变也带来了一个重大挑战:滥用内容的泛滥。检测和减轻滥用内容对于促进安全和包容的在线环境非常重要。这项调查提供了在在线社交网络的滥用内容检测的最先进的方法的全面概述。本文首先定义了滥用内容及其在数字领域的各种表现形式,然后深入研究了在线社交网络的发展前景,强调了其动态和用户生成性质所带来的独特挑战。首先,对过去30年(1993-2023)的文献进行了科学计量分析,对主要关键词、文献、机构和国家进行了深入分析。此外,本调查还探讨了滥用内容检测的关键方法,包括机器学习方法、自然语言处理技术和深度学习模型。讨论了数据集管理和注释的重要性,它们在训练鲁棒和有效的模型中起着关键作用。该调查还强调了各种挑战、伦理影响和未来的研究方向,这些方向可以指导社交网络中更有效、更负责任的滥用内容检测系统的发展。
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引用次数: 0
Masi Entropy Based Multi-Level Thresholding Segmentation Using Reinforcement Learning Assisted Firefly Oriented Multiverse Optimizer 基于Masi熵的多级阈值分割,基于强化学习辅助的萤火虫导向多元宇宙优化器
IF 2.5 4区 计算机科学 Q3 TELECOMMUNICATIONS Pub Date : 2025-12-15 DOI: 10.1002/ett.70307
M. J. Garde, P. S. Patil

Segmentation is a crucial step to divide an image into background and foreground sections and uses different colors to identify the target portion. Different thresholding-based segmentations have been developed in the current research analysis; however, the problems are difficult to solve and take a long time. To overcome these existing limitations, a novel Masi entropy-based multi-level thresholding with an effective optimization algorithm is introduced for image segmentation. The input images are collected from the open-source dataset, namely the Berkeley Segmentation Dataset 500 (BSDS500) and the Cityscapes dataset. To remove noise and enhance image quality, use the Quantized Haar Wavelet Assisted Histogram Equalization (QuaWHe) technique in the pre-processing stage. After noise removal, image segmentation was performed by the 2D practical Masi entropy histogram function (2D-MentH) with the Reinforcement Learning-assisted fire-fly-oriented multiverse optimizer (RL-FF-MVO) algorithm. The RL-FF-MVO mechanism helps to select the optimal set of threshold values from the values obtained using the 2D-MentH mechanism. By integrating reinforcement learning, the optimization process's convergence speed and accuracy are greatly increased while computing overhead is decreased. The proposed model has obtained Peak Signal Noise Ratio (PSNR) and Structural Similarity Index (SSIM) values of 30.587 and 0.9995 at threshold level 10. At threshold level 10, the proposed model has obtained Mean Square Error (MSE) and Feature Similarity Index (FSIM) values of 0.8531 and 0.96 248. For the Probabilistic Rand Index (PRI), the proposed model obtains a value of 0.72003, and for Variation of Information (VOI), it achieves 4.0298 at threshold level 10. The proposed approach outperforms existing methods regarding segmentation quality and computational efficiency, making it suitable for applications requiring high-accuracy image analysis, such as autonomous systems and medical imaging.

分割是将图像划分为背景和前景部分,并使用不同的颜色来识别目标部分的关键步骤。在目前的研究分析中,已经发展出了不同的基于阈值的分割方法;然而,这些问题很难解决,而且需要很长时间。为了克服这些局限性,提出了一种新的基于Masi熵的多级阈值分割算法,并提出了一种有效的图像分割优化算法。输入图像收集自开源数据集,即伯克利分割数据集500 (BSDS500)和城市景观数据集。为了去除噪声,提高图像质量,在预处理阶段采用量化Haar小波辅助直方图均衡化(QuaWHe)技术。去除噪声后,利用二维实用Masi熵直方图函数(2D- menth)和强化学习辅助的萤火虫导向多元宇宙优化器(RL-FF-MVO)算法对图像进行分割。RL-FF-MVO机制有助于从2D-MentH机制获得的值中选择最优阈值集。通过集成强化学习,大大提高了优化过程的收敛速度和精度,同时降低了计算开销。该模型在阈值水平为10时,峰值信噪比(PSNR)和结构相似指数(SSIM)分别为30.587和0.9995。在阈值水平为10时,该模型的均方误差(MSE)和特征相似度指数(FSIM)分别为0.8531和0.96 248。对于概率兰德指数(PRI),该模型的值为0.72003,对于信息变异(VOI),该模型在阈值水平10下达到4.0298。该方法在分割质量和计算效率方面优于现有方法,适用于需要高精度图像分析的应用,如自主系统和医学成像。
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引用次数: 0
Control Strategy for VANET Autonomous Driving Vehicles in Emergency Situations Based on Deep Learning 基于深度学习的紧急情况下VANET自动驾驶车辆控制策略
IF 2.5 4区 计算机科学 Q3 TELECOMMUNICATIONS Pub Date : 2025-12-11 DOI: 10.1002/ett.70302
Chu Kai, Shuai Liang

VANETs are essential for communication and coordination between autonomous vehicles, particularly in emergency scenarios where quick decisions are necessary. The proposed Deep Learning-based Control Approach for Autonomous Vehicles (DL-CA-AV) introduces a hybrid DL control framework that integrates Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) with an attention-driven decision-fusion mechanism for real-time maneuver control in VANET-enabled environments. The CNN module extracts spatial features from sensor and VANET communication data, while the LSTM network models temporal dependencies to predict dynamic vehicular states across time. These learned spatiotemporal representations are then passed to a reinforcement learning (RL) layer, where the actor–critic mechanism evaluates potential maneuvers and selects optimal control actions based on collision probability and situational awareness. The proposed approach encourages vehicles to autonomously choose the best emergency response (lane change, deceleration, or cooperative braking) while preserving stability and minimizing secondary risks. This study demonstrates the capability of DL-based VANET architectures to enable real-time autonomous driving control in hazardous environments, facilitating safer and more dependable intelligent mobility. The proposed framework achieves a collision probability of 29%, a latency below 225 ms, a detection accuracy above 85%, and a packet delivery ratio above 88%.

vanet对于自动驾驶汽车之间的通信和协调至关重要,特别是在需要快速决策的紧急情况下。提出的基于深度学习的自动驾驶汽车控制方法(DL- ca - av)引入了一种混合深度学习控制框架,该框架将卷积神经网络(cnn)和长短期记忆(LSTM)与注意驱动的决策融合机制集成在一起,用于在vanet支持的环境中进行实时机动控制。CNN模块从传感器和VANET通信数据中提取空间特征,而LSTM网络对时间依赖性进行建模,以预测车辆的动态状态。这些学习到的时空表征随后被传递到强化学习(RL)层,其中参与者-批评机制评估潜在的机动,并根据碰撞概率和态势感知选择最优控制动作。该方法鼓励车辆自主选择最佳应急响应(变道、减速或协同制动),同时保持稳定性并将次要风险降至最低。该研究展示了基于dl的VANET架构在危险环境中实现实时自动驾驶控制的能力,促进了更安全、更可靠的智能移动。该框架实现了29%的碰撞概率、低于225 ms的延迟、85%以上的检测准确率和88%以上的数据包投递率。
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引用次数: 0
Edge-Driven Federated Learning Approach for Distributed Assault Monitoring in Vehicular Networks 基于边缘驱动的联邦学习方法的车辆网络分布式攻击监控
IF 2.5 4区 计算机科学 Q3 TELECOMMUNICATIONS Pub Date : 2025-12-10 DOI: 10.1002/ett.70311
Hussain Alshahrani, Fadwa Alrowais, Mohammed H. Alghamdi, Mohammed Alqahtani, Somia A. Asklany, Mofadal Alymani, Ali Abdulaziz Alzubaidi, Radwa Marzouk

Vehicular networks are essential to the advancement of intelligent transportation systems by enabling continuous data exchange between vehicles and infrastructure to support safe mobility. However, the distributed and dynamic nature of these networks creates opportunities for adversarial threats. Existing attack-detection models primarily rely on centralized architectures, which often suffer from high latency, privacy risks, and limited robustness against advanced attacks. To address these challenges, this study proposes the LIFT-SFD model. This lightweight federated learning framework integrates Smooth Federated Dropout (SFD) with trust-weighted mask-aware aggregation for secure and resource-aware training in vehicular ad hoc networks (VANET). Each vehicle trains a masked submodel, while smooth dropout regularization ensures stable convergence and reduced communication overhead. Then, an integrated assault monitoring module detects and reduces malicious behavior by assigning anomaly scores at the vehicle level, adjusting trust weights during RSU-level aggregation, and gradually filtering out malicious participants. The simulation of the models is performed using the VeReMi dataset, demonstrating high detection accuracy of 99.98% at round 5. It acts as a stable and trustworthy global model for future vehicular networks.

通过实现车辆和基础设施之间的持续数据交换,车辆网络对智能交通系统的发展至关重要,从而支持安全移动。然而,这些网络的分布式和动态性为对抗性威胁创造了机会。现有的攻击检测模型主要依赖于集中式架构,这种架构通常存在高延迟、隐私风险以及对高级攻击的鲁棒性有限的问题。为了应对这些挑战,本研究提出了LIFT-SFD模型。这种轻量级的联邦学习框架将平滑联邦辍学(SFD)与信任加权掩码感知聚合集成在一起,用于车辆自组织网络(VANET)中的安全和资源感知训练。每个车辆训练一个掩模子模型,而平滑dropout正则化确保稳定收敛和减少通信开销。然后,集成攻击监控模块通过在车辆级别分配异常分数,在rsu级别聚合时调整信任权重,逐步过滤掉恶意参与者,从而检测和减少恶意行为。使用VeReMi数据集对模型进行了模拟,在第5轮时显示出99.98%的高检测精度。它为未来的汽车网络提供了一个稳定可靠的全球模型。
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引用次数: 0
A Hybrid Framework for Enhancing VANET Network Security in Smart Cities Using Transfer Learning-Based Threat Detection and Mitigation 使用基于迁移学习的威胁检测和缓解增强智慧城市VANET网络安全的混合框架
IF 2.5 4区 计算机科学 Q3 TELECOMMUNICATIONS Pub Date : 2025-12-10 DOI: 10.1002/ett.70315
P. Soubhagyalakshmi, P. Saravanan, N. Jeenath Shafana, D. Shofia Priyadharshini, G. K. Sandhia

Instantaneous interaction and autonomous modes of transport are facilitated by Vehicular Ad Hoc Networks (VANETs), which play a crucial role in the development of smart cities. However, the flexible and autonomous architecture of VANETs poses significant security risks, including susceptibility to cyberattacks, privacy breaches, and information manipulation. These challenges are exacerbated by high node mobility and the large volume of heterogeneous data exchanged in modern urban environments. This study proposes a novel security framework that leverages transfer learning-based threat detection and mitigation techniques to address these issues. By utilizing pre-trained machine learning models fine-tuned with VANET-specific datasets, the approach reduces training time and computational costs while enabling efficient detection of anomalies and attacks. The objectives are to enhance network security, safeguard data integrity, and minimize latency in threat detection processes. Research findings indicate that the proposed framework outperforms traditional machine learning models in terms of scalability, resilience, and the ability to detect malicious activities. By providing a secure communication infrastructure for VANETs, this research contributes to the development of reliable and efficient smart city systems.

车辆自组织网络(VANETs)促进了即时交互和自主运输模式,这在智慧城市的发展中起着至关重要的作用。然而,VANETs灵活和自主的架构带来了重大的安全风险,包括容易受到网络攻击、隐私泄露和信息操纵。高节点移动性和现代城市环境中交换的大量异构数据加剧了这些挑战。本研究提出了一种新的安全框架,利用基于迁移学习的威胁检测和缓解技术来解决这些问题。通过使用预先训练的机器学习模型,并根据vanet特定的数据集进行微调,该方法减少了训练时间和计算成本,同时能够有效地检测异常和攻击。其目标是增强网络安全,保护数据完整性,并最大限度地减少威胁检测过程中的延迟。研究结果表明,所提出的框架在可扩展性、弹性和检测恶意活动的能力方面优于传统的机器学习模型。通过为vanet提供安全的通信基础设施,本研究有助于开发可靠、高效的智慧城市系统。
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引用次数: 0
期刊
Transactions on Emerging Telecommunications Technologies
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