IF 4.2 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

ICT Express

Pub Date : 2025-12-01 DOI: 10.1016/j.icte.2025.11.010

Hanseon Joo , Hayoung Choi , Ook Lee , Minjong Cheon

Kolmogorov–Arnold Networks (KANs) have garnered attention for replacing fixed activation functions with learnable univariate functions, but they exhibit practical limitations, including high computational costs and performance deficits in general classification tasks. In this paper, we propose the Modulation Joint KAN (MJKAN), a novel neural network layer designed to overcome these challenges. MJKAN integrates a FiLM (Feature-wise Linear Modulation)-like mechanism with Radial Basis Function (RBF) activations, creating a hybrid architecture that combines the non-linear expressive power of KANs with the efficiency of Multilayer Perceptrons (MLPs). We empirically validated MJKAN’s performance across a diverse set of benchmarks, including function regression, image classification, and natural language processing. The results demonstrate that MJKAN achieves superior approximation capabilities in function regression tasks, significantly outperforming MLPs, with performance improving as the number of basis functions increases. Conversely, in image and text classification, its performance was competitive with MLPs but revealed a critical dependency on the number of basis functions. We found that a smaller basis size was crucial for better generalization, highlighting that the model’s capacity must be carefully tuned to the complexity of the data to prevent overfitting. In conclusion, MJKAN offers a flexible architecture that inherits the theoretical advantages of KANs while improving computational efficiency and practical viability.

Kolmogorov-Arnold网络（KANs）因用可学习的单变量函数取代固定的激活函数而引起了人们的关注，但它们表现出实际的局限性，包括高计算成本和一般分类任务的性能缺陷。在本文中，我们提出了调制联合KAN (MJKAN)，一种新的神经网络层，旨在克服这些挑战。MJKAN将类似FiLM （Feature-wise Linear Modulation）的机制与径向基函数（RBF）激活集成在一起，创建了一个混合架构，将KANs的非线性表达能力与多层感知器（mlp）的效率相结合。我们通过各种基准测试验证了MJKAN的性能，包括函数回归、图像分类和自然语言处理。结果表明，MJKAN在函数回归任务中实现了优越的近似能力，显著优于mlp，并且随着基函数数量的增加，性能有所提高。相反，在图像和文本分类中，它的性能与mlp相当，但对基函数的数量有很大的依赖性。我们发现，较小的基大小对于更好的泛化至关重要，强调模型的容量必须仔细调整到数据的复杂性，以防止过拟合。综上所述，MJKAN提供了一种灵活的体系结构，它继承了KANs的理论优势，同时提高了计算效率和实际可行性。

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引用次数: 0

A comprehensive review of explainable AI in cybersecurity: Decoding the black box 网络安全中可解释人工智能的全面回顾：破解黑匣子

IF 4.2 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

ICT Express

Pub Date : 2025-12-01 DOI: 10.1016/j.icte.2025.10.004

Anshika Sharma , Shalli Rani , Mohammad Shabaz

Artificial Intelligence (AI) has been used extensively in all aspects of everyday life among people in recent times. Many techniques utilizing machine learning (ML) and deep learning (DL) models are being presented in this rapidly growing field of study. Most such models are generally regarded as “Black-Box” models since they are intrinsically complex and lack interpretable explanations for their decisions and conclusions. The lack of transparency increases the issue in the field of cybersecurity as implementing critical decisions in a system that cannot provide explanations for itself offers some evident risks. The lack of interpretability and transparency in existing AI techniques would make users distrust the models used to defend against cyberattacks, particularly given the increasingly complex and diverse nature of cyberattacks. Thus, Explainable Artificial Intelligence (XAI) must be utilized to construct cyber security models that are more understandable while keeping high accuracy and that enable users to understand, be reliable, and manage the future of cyber defence systems. This study provides a comprehensive survey of existing literature on using XAI to mitigate these challenges of cybersecurity black-box models. It emphasizes the significance of explainability in boosting faith and transparency in AI-driven systems and presents a thorough taxonomy of XAI techniques and technologies for cybersecurity applications. The study describes the evaluation criteria that are used to evaluate the effectiveness of XAI models, addresses different kinds of attacks like malware, phishing, and network intrusions, and shows how XAI techniques may mitigate these risks by providing a comprehensible understanding of model decisions. Along with the real-world case studies, it also explores the industrial applications of XAI in cybersecurity and examines the challenges in implementing XAI technology. The survey concludes with a review of the limitations of the existing XAI techniques and makes recommendations for future research, such as the requirement for more reliable XAI frameworks that can function in real-time and across a variety of cyber threat situations.

近年来，人工智能（AI）已广泛应用于人们日常生活的各个方面。在这个快速发展的研究领域中，许多利用机器学习（ML）和深度学习（DL）模型的技术正在出现。大多数这样的模型通常被认为是“黑盒”模型，因为它们本质上是复杂的，并且缺乏对其决策和结论的可解释的解释。缺乏透明度增加了网络安全领域的问题，因为在一个无法为自己提供解释的系统中实施关键决策会带来一些明显的风险。现有人工智能技术缺乏可解释性和透明度，这将使用户不信任用于防御网络攻击的模型，特别是考虑到网络攻击日益复杂和多样化的性质。因此，必须利用可解释的人工智能（XAI）来构建更易于理解的网络安全模型，同时保持高准确性，并使用户能够理解、可靠和管理网络防御系统的未来。本研究对使用XAI缓解网络安全黑箱模型挑战的现有文献进行了全面调查。它强调了可解释性在提高人工智能驱动系统的信心和透明度方面的重要性，并提出了用于网络安全应用的XAI技术和技术的全面分类。该研究描述了用于评估XAI模型有效性的评估标准，解决了恶意软件、网络钓鱼和网络入侵等不同类型的攻击，并展示了XAI技术如何通过提供对模型决策的可理解理解来减轻这些风险。除了现实世界的案例研究，它还探讨了XAI在网络安全中的工业应用，并研究了实现XAI技术的挑战。该调查总结了现有XAI技术的局限性，并为未来的研究提出了建议，例如对更可靠的XAI框架的需求，这些框架可以在实时和各种网络威胁情况下发挥作用。

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引用次数: 0

Beamforming for beam-squint effect mitigation in LEO satellite communication systems 低轨卫星通信系统中波束形成的波束斜视效应缓解

IF 4.2 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

ICT Express

Pub Date : 2025-12-01 DOI: 10.1016/j.icte.2025.09.005

Jung Hoon Lee , Pansoo Kim , Jae-Young Lee , Kyungrak Son

This paper proposes an efficient beamforming technique to mitigate the beam squint effect in wideband low Earth orbit (LEO) satellite communication systems. Based on the dynamic-subarray with fixed-true-time-delays (DS-FTTD) architecture, the proposed method optimizes the beamforming structure for multi-user transmission using statistical channel state information (CSI). Unlike the original DS-FTTD design, which assumes point-to-point communication and relies on instantaneous CSI, the proposed scheme is tailored for the practical constraints of LEO systems. Simulation results demonstrate that the proposed approach significantly improves beamforming gain and system throughput compared to conventional methods.

本文提出了一种有效的波束形成技术，以减轻宽带近地轨道卫星通信系统中的波束斜视效应。该方法基于DS-FTTD结构，利用统计信道状态信息（CSI）优化多用户传输波束形成结构。与最初的DS-FTTD设计不同，它假设点对点通信并依赖于瞬时CSI，所提出的方案是针对LEO系统的实际约束而量身定制的。仿真结果表明，与传统方法相比，该方法显著提高了波束形成增益和系统吞吐量。

引用次数: 0

Early time-series classification with SPRT and normalizing flow 用SPRT和正态流进行早期时间序列分类

IF 4.2 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

ICT Express

Pub Date : 2025-12-01 DOI: 10.1016/j.icte.2025.09.015

Jun Hee Jo, Kae Won Choi

This paper proposes a novel approach for early time-series classification, addressing the trade-off between prediction accuracy and earliness, which is critical in real-time applications. The Sequential Probability Ratio Test (SPRT) provides an optimal solution but relies on prior knowledge of the data’s probability distribution, which is an assumption often impractical in real-world scenarios. Existing studies commonly assume a normal distribution, which limits classification performance in complex data. To overcome this limitation, we integrate normalizing flow into the SPRT framework, enabling the estimation of conditional probability distributions through a series of invertible transformations. This approach allows for precise probability estimation, improving the accuracy of early classification. Experimental results on a preprocessed dataset demonstrate that the proposed model significantly enhances classification performance, offering a promising direction for advancing early time-series classification.

本文提出了一种新的早期时间序列分类方法，解决了预测精度和早期性之间的权衡，这在实时应用中至关重要。序列概率比检验（SPRT）提供了一个最优解决方案，但依赖于数据概率分布的先验知识，这在现实场景中通常是不切实际的假设。现有的研究通常假设正态分布，这限制了在复杂数据中的分类性能。为了克服这一限制，我们将归一化流集成到SPRT框架中，通过一系列可逆变换来估计条件概率分布。这种方法允许精确的概率估计，提高早期分类的准确性。在预处理数据集上的实验结果表明，该模型显著提高了分类性能，为推进早期时间序列分类提供了一个有希望的方向。

引用次数: 0

PROPER: A PROxy pair for uplink PERformance enhancement in wireless access networks PROPER：无线接入网络中用于上行链路性能增强的代理对

IF 4.2 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

ICT Express

Pub Date : 2025-12-01 DOI: 10.1016/j.icte.2025.10.009

Tacettin Ayar, Deniz Turgay Altilar

Mobile end-users cannot utilize high upload bandwidths available in wireless access networks since wireless link based packet losses dramatically degrade TCP performance. We propose a TCP performance enhancing proxy pair called PROPER that detects wireless-based packet losses early and prevents TCP performance degradation. PROPER is transparent to TCP and requires no modifications on either TCP sender or TCP receiver. PROPER works in harmony with various TCP variants such as Reno, CUBIC, Veno and BBR. Netem-based performance and fairness emulation tests show that PROPER not only prevents TCP performance degradation on wireless access networks but also can safely coexist with regular TCP traffic.

移动终端用户无法利用无线接入网络中可用的高上传带宽，因为基于无线链路的数据包丢失会显著降低TCP性能。我们提出了一种称为PROPER的TCP性能增强代理对，它可以早期检测基于无线的数据包丢失并防止TCP性能下降。PROPER对TCP是透明的，不需要对TCP发送方或接收方进行任何修改。适当的工作与各种TCP变体，如Reno， CUBIC， Veno和BBR和谐。基于网络的性能和公平性仿真测试表明，PROPER不仅可以防止TCP在无线接入网络上的性能下降，而且可以安全地与常规TCP流量共存。

引用次数: 0

Reasoning beyond limits: Advances and open problems for LLMs 超越极限的推理：法学硕士的进展和开放问题

IF 4.2 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

ICT Express

Pub Date : 2025-12-01 DOI: 10.1016/j.icte.2025.09.003

Mohamed Amine Ferrag , Norbert Tihanyi , Merouane Debbah

Recent breakthroughs in generative reasoning have fundamentally reshaped how large language models (LLMs) address complex tasks, enabling them to dynamically retrieve, refine, and organize information into coherent, multi-step reasoning chains. Techniques such as inference-time scaling, reinforcement learning, supervised fine-tuning, and distillation have been effectively applied to state-of-the-art models, including DeepSeek-R1, OpenAI’s o1 and o3, GPT-4o, Qwen-32B, and various Llama variants, significantly enhancing their reasoning capabilities. In this paper, we present a comprehensive review of the top 27 LLMs released between 2023 and 2025, such as Mistral AI Small 3 24B, DeepSeek-R1, Search-o1, QwQ-32B, and Phi-4, and analyze their core innovations and performance improvements.

We also provide a detailed overview of recent advancements in multilingual large language models (MLLMs), emphasizing methods that improve cross-lingual reasoning and address the limitations of English-centric training. In parallel, we present a comprehensive review of progress in State Space Model (SSM)-based architectures, including models like Mamba, which demonstrate improved efficiency for long-context processing compared to Transformer-based approaches. Our analysis covers training strategies such as general optimization techniques, mixture-of-experts (MoE) configurations, retrieval-augmented generation (RAG), chain-of-thought prompting, self-improvement methods, and test-time compute scaling and distillation frameworks.

Finally, we identify key challenges for future research, including enabling multi-step reasoning without human supervision, improving robustness in chained task execution, balancing structured prompting with generative flexibility, and enhancing the integration of long-context retrieval and external tools.

生成推理的最新突破从根本上重塑了大型语言模型（llm）处理复杂任务的方式，使它们能够动态地检索、提炼和组织信息到连贯的、多步骤的推理链中。推理时间缩放、强化学习、监督微调和蒸馏等技术已有效应用于最先进的模型，包括DeepSeek-R1、OpenAI的o1和o3、gpt - 40、Qwen-32B和各种Llama变体，显著提高了它们的推理能力。在本文中，我们全面回顾了2023年至2025年间发布的27个顶级llm，如Mistral AI Small 3 24B、DeepSeek-R1、search - 01、QwQ-32B和Phi-4，并分析了它们的核心创新和性能改进。我们还详细概述了多语言大型语言模型（mllm）的最新进展，强调了改进跨语言推理和解决以英语为中心的培训局限性的方法。同时，我们对基于状态空间模型（SSM）的体系结构的进展进行了全面的回顾，包括像Mamba这样的模型，与基于transformer的方法相比，它证明了长上下文处理的效率提高。我们的分析涵盖了训练策略，例如一般优化技术、专家组合（MoE）配置、检索增强生成（RAG）、思维链提示、自我改进方法以及测试时间计算缩放和蒸馏框架。最后，我们确定了未来研究的关键挑战，包括在没有人类监督的情况下实现多步骤推理，提高链式任务执行的鲁棒性，平衡结构化提示与生成灵活性，以及增强长上下文检索和外部工具的集成。

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引用次数: 0

Deep learning-based pilot-free channel estimation of UAV-FSO system using variational auto-encoder 基于深度学习的变分自编码器无人机- fso系统无导信道估计

IF 4.2 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

ICT Express

Pub Date : 2025-12-01 DOI: 10.1016/j.icte.2025.11.009

Yamuna Tumma, Mahesh Miriyala

Reliable channel estimation is critical for achieving high-speed and energy-efficient communication in Unmanned Aerial Vehicle-Free Space Optical (UAV-FSO) systems, particularly under dynamic impairments such as atmospheric turbulence (AT) and pointing errors (PEs). This paper proposes a pilot-free channel estimation framework based on a Variational Autoencoder (VAE). The system employs Intensity Modulation/Direct Detection (IM/DD) with

M

-ary one-hot encoded symbols (

M = 16

). The VAE encodes noisy received signals into a 128-dimensional latent space and reconstructs the transmitted data, effectively learning the joint effects of AT, PEs, and AWGN. Unlike prior works that primarily consider boresight or Gaussian-jitter PEs, this study explicitly incorporates a Nakagami-modeled PE distribution, capturing UAV-induced beam misalignment under mobility, vibration, and turbulence coupling. Simulation results show that the proposed VAE significantly outperforms conventional estimators (LS, MMSE, LMMSE) and deep learning baselines (AE, DNN, CNN) across various turbulence strengths. Under strong turbulence and PEs, the VAE attains nearly two-fold lower MSE compared to CNN and DNN. In addition, evaluation on real turbulence-impaired datasets further validates robustness and generalization. The proposed pilot-free scheme delivers accurate channel estimation, reduced BER, and improved spectral efficiency, making it suitable for real-time adaptive UAV-FSO communication.

可靠的信道估计对于实现无人机-无空间光学（UAV-FSO）系统的高速节能通信至关重要，特别是在大气湍流（AT）和指向误差（PEs）等动态损伤下。提出了一种基于变分自编码器（VAE）的无导频信道估计框架。系统采用强度调制/直接检测（IM/DD）， M=16个单热编码符号。VAE将接收到的带有噪声的信号编码到128维潜在空间中，并对传输数据进行重构，有效地学习了AT、pe和AWGN的联合效应。与先前主要考虑轴视或高斯抖动PE的工作不同，本研究明确地结合了nakagami模型的PE分布，捕获了无人机在移动、振动和湍流耦合下引起的波束错位。仿真结果表明，本文提出的VAE在不同湍流强度下显著优于传统估计器（LS、MMSE、LMMSE）和深度学习基线（AE、DNN、CNN）。在强湍流和pe条件下，VAE的MSE比CNN和DNN低近2倍。此外，对真实湍流受损数据集的评估进一步验证了鲁棒性和泛化性。提出的无导频方案提供了准确的信道估计，降低了误码率，提高了频谱效率，使其适合于实时自适应无人机- fso通信。

{"title":"Deep learning-based pilot-free channel estimation of UAV-FSO system using variational auto-encoder","authors":"Yamuna Tumma, Mahesh Miriyala","doi":"10.1016/j.icte.2025.11.009","DOIUrl":"10.1016/j.icte.2025.11.009","url":null,"abstract":"<div><div>Reliable channel estimation is critical for achieving high-speed and energy-efficient communication in Unmanned Aerial Vehicle-Free Space Optical (UAV-FSO) systems, particularly under dynamic impairments such as atmospheric turbulence (AT) and pointing errors (PEs). This paper proposes a pilot-free channel estimation framework based on a Variational Autoencoder (VAE). The system employs Intensity Modulation/Direct Detection (IM/DD) with <span><math><mi>M</mi></math></span>-ary one-hot encoded symbols (<span><math><mrow><mi>M</mi><mo>=</mo><mn>16</mn></mrow></math></span>). The VAE encodes noisy received signals into a 128-dimensional latent space and reconstructs the transmitted data, effectively learning the joint effects of AT, PEs, and AWGN. Unlike prior works that primarily consider boresight or Gaussian-jitter PEs, this study explicitly incorporates a Nakagami-modeled PE distribution, capturing UAV-induced beam misalignment under mobility, vibration, and turbulence coupling. Simulation results show that the proposed VAE significantly outperforms conventional estimators (LS, MMSE, LMMSE) and deep learning baselines (AE, DNN, CNN) across various turbulence strengths. Under strong turbulence and PEs, the VAE attains nearly two-fold lower MSE compared to CNN and DNN. In addition, evaluation on real turbulence-impaired datasets further validates robustness and generalization. The proposed pilot-free scheme delivers accurate channel estimation, reduced BER, and improved spectral efficiency, making it suitable for real-time adaptive UAV-FSO communication.</div></div>","PeriodicalId":48526,"journal":{"name":"ICT Express","volume":"11 6","pages":"Pages 1162-1166"},"PeriodicalIF":4.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145705608","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Placement optimization of multiple UAVs for energy-efficient maximal user coverage 多无人机布局优化，实现节能最大化用户覆盖

IF 4.2 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

ICT Express

Pub Date : 2025-12-01 DOI: 10.1016/j.icte.2025.10.003

Chen Zhang , Xiang Gui , Gourab Sen Gupta , Syed Faraz Hasan

This paper proposes a deterministic Global Optimization Algorithm (GOA) for UAV-assisted communications, developed as an enhancement to the benchmark Two-Stage Optimization Algorithm (TSOA). The algorithm simultaneously addresses the dual objectives of maximizing ground user (GU) coverage and minimizing total power consumption in multiple UAV systems. Unlike existing literature, which predominantly relies on heuristic approaches, GOA provides a more precise and systematic solution to achieve optimal performance. Comprehensive simulations demonstrate that GOA achieves a 3.68 % increase in coverage count versus SOA under clustered GU distributions while delivering energy savings approximately 2.47 % (uniform) and 2.6 % (clustered) relative to the TSOA benchmark. Crucially, these efficiency gains are realized while maintaining superior GU coverage maximization versus all benchmarked methods. Both numerical results and visual analyses conclusively validate the proposed algorithm's outperformance of existing benchmarks.

©2025 The Korean Institute of Communications and Information Sciences. Publishing Services by Elsevier B.V. This is an open access article under the CC BY-NCND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

本文提出了一种用于无人机辅助通信的确定性全局优化算法（GOA），作为对基准两阶段优化算法（TSOA）的改进。该算法同时解决了在多个无人机系统中最大化地面用户（GU）覆盖和最小化总功耗的双重目标。与现有文献主要依赖启发式方法不同，GOA提供了更精确和系统的解决方案来实现最佳性能。综合模拟表明，在集群GU分布下，与SOA相比，GOA实现了3.68%的覆盖率增加，同时相对于TSOA基准，GOA提供了大约2.47%（统一）和2.6%（集群）的能源节约。至关重要的是，与所有基准测试方法相比，这些效率增益是在保持优越的GU覆盖最大化的同时实现的。数值结果和可视化分析都最终验证了该算法优于现有基准测试的性能。©2025韩国通信与信息科学研究所。这是一篇基于CC by- ncnd许可（http://creativecommons.org/licenses/by-nc-nd/4.0/）的开放获取文章。

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引用次数: 0

Efficient beamforming training scheme using NOMA in mmWave WLANs 毫米波无线局域网中基于NOMA的高效波束形成训练方案

IF 4.2 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

ICT Express

Pub Date : 2025-12-01 DOI: 10.1016/j.icte.2025.11.003

Seonjoo Choi , Hoki Baek , Jaesung Lim

Beamforming training (BFT) is a critical process for establishing directional links in millimeter-wave (mmWave) wireless local area networks. However, its performance significantly degrades due to frequent slot collisions. This paper proposes a non-orthogonal multiple access (NOMA)-based exhaustive search (NES) scheme, which allows multiple stations (STAs) to perform BFT concurrently by independently selecting predefined transmit power levels. We integrated the NOMA-based technique into a time-based beam collision avoidance (TBCA) scheme, named NOMA-TBCA (NTBCA). The proposed NES and NTBCA schemes are analytically modeled and evaluated through simulations. The results confirm that the combined approach effectively enhances the association probability and the throughput.

波束形成训练（BFT）是毫米波无线局域网中建立定向链路的关键过程。然而，由于频繁的槽碰撞，其性能显著下降。本文提出了一种基于非正交多址（NOMA）的穷举搜索（NES）方案，该方案允许多个站点（sta）通过独立选择预定义的发射功率电平同时执行BFT。我们将基于noma的技术集成到基于时间的波束碰撞避免（TBCA）方案中，命名为NOMA-TBCA （NTBCA）。本文对提出的NES和NTBCA方案进行了分析建模，并通过仿真对其进行了评价。结果表明，该组合方法有效地提高了关联概率和吞吐量。

引用次数: 0

Blockchain-based trust management systems in the Internet of Vehicles: A comprehensive survey 基于区块链的车联网信任管理系统综述

IF 4.2 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

ICT Express

Pub Date : 2025-12-01 DOI: 10.1016/j.icte.2025.09.012

Mahalinoro Razafimanjato, Malik Muhammad Saad, Dongkyun Kim

The Internet of Vehicles (IoV), a critical component of Intelligent Transportation Systems (ITS), enhances driving safety and traffic efficiency through real-time data exchange. However, the dynamic and heterogeneous nature of IoV introduces significant security and trust challenges. To address these, trust management systems have emerged as vital mechanisms to ensure the reliability and integrity of data exchanged between vehicles. Blockchain technology offers a robust framework for addressing security and trust issues in IoV environments. The decentralized, tamper-resistant, and transparent nature of the blockchain makes it suitable for complex vehicular environments. This survey provides an overview of state-of-the-art blockchain-based trust management systems in IoV. Following a systematic literature review that filtered 8,280 publications to 63 core studies from 2019 to 2024, we present a thematic classification of existing solutions, focusing on those employing public and private blockchains. Unlike previous surveys, our work focuses specifically on the intersection of blockchain and trust management systems in IoV by analyzing approaches across four dimensions: trust computation methods, such as game theory and AI-driven models; blockchain scaling solutions, including sharding, sidechains, and optimized consensus mechanisms; integration with emerging technologies such as 5G/6G, Digital Twins, and Federated Learning; and security and privacy mechanisms. Finally, this survey identifies current challenges and provides future research directions, highlighting the need for more scalable, adaptive, secure, and privacy-preserving trust management systems in IoV.

车联网（IoV）是智能交通系统（ITS）的重要组成部分，通过实时数据交换提高驾驶安全性和交通效率。然而，车联网的动态和异构特性带来了重大的安全和信任挑战。为了解决这些问题，信托管理系统已成为确保车辆之间交换数据的可靠性和完整性的重要机制。区块链技术为解决车联网环境中的安全和信任问题提供了一个强大的框架。区块链的分散性、防篡改性和透明性使其适用于复杂的车辆环境。本调查概述了车联网中最先进的基于区块链的信任管理系统。经过系统的文献综述，从2019年到2024年筛选了8280份出版物和63项核心研究，我们对现有解决方案进行了主题分类，重点关注那些使用公共和私有区块链的解决方案。与之前的调查不同，我们的工作主要集中在区块链和信任管理系统在车联网中的交叉，通过分析四个维度的方法：信任计算方法，如博弈论和人工智能驱动模型；区块链扩展解决方案，包括分片、侧链和优化的共识机制；与5G/6G、数字孪生、联邦学习等新兴技术的融合；安全和隐私机制。最后，本调查确定了当前的挑战，并提供了未来的研究方向，强调了在车联网中对更具可扩展性、适应性、安全性和隐私保护的信任管理系统的需求。

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引用次数: 0

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