Vehicular Communications最新文献

英文中文

Securing short-packet transmissions via partial NOMA: Performance analysis under keyhole fading 通过局部NOMA保护短包传输：锁孔衰落下的性能分析

IF 6.5 2区计算机科学 Q1 TELECOMMUNICATIONS

Vehicular Communications

Pub Date : 2026-04-01 Epub Date: 2026-01-10 DOI: 10.1016/j.vehcom.2026.100999

Sang-Quang Nguyen , Duy Tran Trung , Lam-Thanh Tu , Anh Le-Thi , Mui Van Nguyen

This paper proposes a novel secure downlink framework that integrates Partial Non-Orthogonal Multiple Access (PNOMA) with short-packet communications (SPC) under keyhole fading channels, tailored for ultra-reliable low-latency (URLLC) services. Unlike prior studies that addressed NOMA, SPC, or keyhole effects in isolation, our work is the first to jointly consider all three aspects in a unified design. Closed-form expressions for the average secure block error rate (SBLER) and block error rate (BLER) are derived under both partial and full transmission information (PTI/FTI) assumptions at the eavesdropper, together with asymptotic analysis capturing the impact of blocklength, power allocation, and keyhole severity. Numerical simulations confirm that the proposed PNOMA-SPC system consistently outperforms conventional NOMA scheme in terms of latency, reliability, and secrecy, even under strong eavesdropping conditions. These contributions provide new theoretical and practical insights into the secure design of multiple access schemes for next-generation 6G URLLC scenarios.

本文提出了一种新的安全下行链路框架，该框架将部分非正交多址（PNOMA）与锁孔衰落信道下的短包通信（SPC）相结合，为超可靠低延迟（URLLC）业务量身定制。与先前的研究不同，我们的工作是第一个在统一设计中共同考虑所有三个方面的研究，这些研究分别解决了NOMA、SPC或锁孔效应。在窃听者的部分和完全传输信息（PTI/FTI）假设下，推导了平均安全块错误率（SBLER）和块错误率（BLER）的封闭表达式，并对块长度、功率分配和锁孔严重性的影响进行了渐近分析。数值模拟证实，即使在强窃听条件下，所提出的PNOMA-SPC系统在延迟、可靠性和保密性方面始终优于传统的NOMA方案。这些贡献为下一代6G URLLC场景下多址方案的安全设计提供了新的理论和实践见解。

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引用次数: 0

Enhancing quantum-resistant data privacy in vehicular cloud networks using NIST-qualified FALCON algorithm 利用nist合格的FALCON算法增强车载云网络中抗量子数据隐私

IF 6.5 2区计算机科学 Q1 TELECOMMUNICATIONS

Vehicular Communications

Pub Date : 2026-04-01 Epub Date: 2025-12-30 DOI: 10.1016/j.vehcom.2025.100995

Mritunjay Shall Peelam , Brijesh Kumar Chaurasia , Man Mohan Shukla , Vinay Chamola

Road safety, congestion, pollution, and data security are critical challenges in the development of smart transportation systems. Vehicular Ad-hoc Networks (VANETs) form the backbone of such systems by enabling real-time communication, accident management, and traffic monitoring. However, the vast data generated in VANETs is increasingly vulnerable in the post-quantum era, where traditional cryptographic methods like RSA, ECC, and DSA fail to withstand quantum attacks. To address this, we propose the integration of the NIST-qualified Falcon algorithm, a lattice-based post-quantum cryptographic scheme, to ensure confidentiality, integrity, and resilience of vehicular communication. The proposed scheme is implemented and evaluated in a Vehicular Network Cloud (VNC) environment on different computational platforms, including Apple Silicon M1 Max and AMD Ryzen systems. Experimental results demonstrate that Falcon achieves practical signing and verification delays (22 ms and 17 ms on M1), while maintaining robust key generation performance even at higher bit lengths. Comparative analysis with RSA and ECC shows Falcon’s superiority in quantum resistance and a balanced trade-off between computational cost and communication efficiency. Although Falcon incurs relatively higher encryption and decryption delays, its security guarantees and scalability make it a strong candidate for deployment in VANETs. This research confirms that Falcon provides a feasible, quantum-resistant solution for securing smart transportation ecosystems while meeting the stringent real-time requirements of vehicular communications.

道路安全、拥堵、污染和数据安全是智能交通系统发展的关键挑战。车辆自组织网络（VANETs）通过实现实时通信、事故管理和交通监控，构成了此类系统的骨干。然而，VANETs中产生的大量数据在后量子时代变得越来越脆弱，传统的加密方法如RSA、ECC、DSA等无法抵御量子攻击。为了解决这个问题，我们提出了集成nist合格的Falcon算法，这是一种基于晶格的后量子加密方案，以确保车辆通信的保密性、完整性和弹性。该方案在不同计算平台（包括Apple Silicon M1 Max和AMD Ryzen系统）上的车辆网络云（VNC）环境中进行了实施和评估。实验结果表明，Falcon实现了实际的签名和验证延迟（在M1上分别为22 ms和17 ms），同时即使在较高的位长度下也能保持稳健的密钥生成性能。与RSA和ECC的比较分析表明，Falcon在量子抗性方面具有优势，并且在计算成本和通信效率之间进行了平衡权衡。尽管Falcon会导致相对较高的加密和解密延迟，但其安全性保证和可扩展性使其成为部署在vanet中的有力候选者。这项研究证实，Falcon为保护智能交通生态系统提供了一种可行的、抗量子的解决方案，同时满足了车辆通信的严格实时要求。

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引用次数: 0

The role of large language models (LLMs) in enhancing intelligent transportation systems: A survey 大语言模型（LLMs）在增强智能交通系统中的作用：一项调查

IF 6.5 2区计算机科学 Q1 TELECOMMUNICATIONS

Vehicular Communications

Pub Date : 2026-04-01 Epub Date: 2025-12-22 DOI: 10.1016/j.vehcom.2025.100996

Vikas Hassija , Tamonash Majumder , Debangshu Roy , Raja Piyush , Vinay Chamola

Large Language Models (LLMs) are transforming Intelligent Transportation Systems (ITS) by shifting operations from static, rule based systems toward adaptive, data-driven decision-making. This paper presents a comprehensive methodological and application-focused survey of LLMs in ITS, grounded in transformer-based architectures like GPT-4, BERT, and LlaMa. We analyze the technical challenge of integrating diverse multimodal data including sensor logs, visual inputs, and textual reports via cross-modal fusion strategies. The survey examines key applications such as traffic signal optimization, predictive maintenance, V2X communication, public transport scheduling, and route personalization. Furthermore, we detail core methodologies (e.g., fine- tuning, Chain-of-Thought prompting, federated learning, RLHF) used to enhance LLM performance under real-time conditions and assess explainability frameworks (SHAP, LIME) to foster trust. We also identify critical challenges, including model hallucination, privacy risks, resource demands, and latency constraints. By synthesizing insights from over 200 primary research contributions, this work offers a foundational reference for designing scalable, intelligent, and ethically aligned ITS architectures.

大型语言模型（llm）正在改变智能交通系统（ITS），将操作从静态的、基于规则的系统转变为自适应的、数据驱动的决策。本文以GPT-4、BERT和LlaMa等基于变压器的架构为基础，介绍了ITS中llm的综合方法和应用调查。我们分析了通过跨模态融合策略整合多种多模态数据的技术挑战，包括传感器日志、视觉输入和文本报告。该调查考察了交通信号优化、预测性维护、V2X通信、公共交通调度和路线个性化等关键应用。此外，我们详细介绍了核心方法（例如，微调，思维链提示，联邦学习，RLHF）用于提高LLM在实时条件下的性能，并评估可解释性框架（SHAP， LIME）以促进信任。我们还确定了关键的挑战，包括模型幻觉、隐私风险、资源需求和延迟限制。通过综合来自200多个主要研究贡献的见解，这项工作为设计可扩展，智能和道德一致的ITS架构提供了基础参考。

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引用次数: 0

Traffic aware adaptive neighbor discovery for vehicular networks 基于流量感知的车辆网络自适应邻居发现

IF 6.5 2区计算机科学 Q1 TELECOMMUNICATIONS

Vehicular Communications

Pub Date : 2026-04-01 Epub Date: 2026-01-29 DOI: 10.1016/j.vehcom.2026.101008

Lei Ding, Yi Zhi, Lina Zhu, Lu Ren, Lei Liu, Changle Li

In vehicular networks, neighbor discovery is achieved via frequently broadcasting a certain kind of control message, which is called beacon message. Since there are both control messages and various types of service messages which coexist and share wireless channels with limited communication resources, it is essential to balance the resource allocation between control messages and service messages. Specifically, if too much communication resource is allocated to transmit the control message, the communication quality of services may be degraded. Conversely, it may lead to unstable network connections and further impact the communication quality of services in vehicular networks. Thus in this work, we focus on optimizing the broadcast rate of beacon messages in a vehicular network by jointly considering the vehicle mobility, the channel randomness and the multi-traffic network characteristics, for achieving the optimal tradeoff between the accuracy and overhead of neighbor discovery. We first establish a theoretical analysis model for deriving the closed-form relationship between the stable hitting probability and the broadcast rate of beacon messages. Based on that, we then propose an optimal neighbor discovery scheme called Mobility and Multi-Traffic based Adaptive Neighbor Discovery method (MMTAND), which can adjust the broadcast rate of beacon messages according to dynamical network environments and achieve the optimal tradeoff between the accuracy and overhead of neighbor discovery. Extensive simulation results show that the proposed method outperforms the existing methods in terms of performance, which is expected to be applied to neighbor discovery in practical vehicular networks.

在车载网络中，邻居发现是通过频繁广播某种控制消息来实现的，这种控制消息称为信标消息。由于控制消息和各种类型的服务消息共存，并且在有限的通信资源下共享无线信道，因此平衡控制消息和服务消息之间的资源分配至关重要。具体来说，如果分配过多的通信资源来传输控制消息，可能会降低业务的通信质量。反之，可能导致网络连接不稳定，进而影响车载网络的业务通信质量。因此，在本工作中，我们将重点考虑车辆移动性、信道随机性和多流量网络特性，优化信标消息在车载网络中的广播速率，以实现邻居发现精度和开销之间的最佳权衡。首先建立了理论分析模型，推导了信标报文稳定命中概率与广播率之间的封闭关系。在此基础上，提出了一种基于移动和多流量的自适应邻居发现方法（MMTAND），该方法可以根据动态网络环境调整信标消息的广播速率，在邻居发现的精度和开销之间实现最优权衡。大量的仿真结果表明，该方法在性能上优于现有方法，有望应用于实际车辆网络中的邻居发现。

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引用次数: 0

Task offloading based on lightweight identity authentication and genetic optimization for the internet of vehicles 基于轻量级身份认证和遗传优化的车联网任务卸载

IF 6.5 2区计算机科学 Q1 TELECOMMUNICATIONS

Vehicular Communications

Pub Date : 2026-04-01 Epub Date: 2026-02-04 DOI: 10.1016/j.vehcom.2026.101007

Mingfeng Huang , Peng Wang , Athanasios V. Vasilakos , Hai Zhong

Task offloading ensures low-latency responsiveness for computation-intensive Internet of Vehicles applications by dynamically distributing workloads across vehicle, edge, and cloud resources. However, due to the dynamicity of vehicle networking environment, open access characteristics, and complex interactions between vehicles and servers, existing offloading methods face dual challenges of security threats and insufficient optimization efficiency. To address this, a task offloading scheme based on lightweight identity authentication and genetic optimization is proposed in this paper. First, we design an anonymous authentication mechanism based on elliptic curves, combined with pseudo-identity generation, verifiable signatures, and timestamp technology. It ensures the privacy of vehicles while supporting malicious node tracking, thereby guaranteeing the trustworthiness of nodes participating in task offloading. After that, an improved genetic optimization model is proposed, integrating elite retention strategy, multi-point crossover-mutation operations, and resource allocation penalty functions to dynamically adapt to vehicle mobility and server resource states, achieving globally optimal offloading decisions. Finally, extensive experiments demonstrate that the proposed scheme significantly outperforms the baseline methods in terms of secure signature efficiency, authentication speed, and task processing performance. It reduces task latency by 6.29%-34.14%, and reduces energy consumption by 9.54%-35.36%.

任务卸载通过在车辆、边缘和云资源之间动态分配工作负载，确保了对计算密集型车联网应用程序的低延迟响应。然而，由于车联网环境的动态性、开放访问特性以及车辆与服务器交互的复杂性，现有的卸载方法面临着安全威胁和优化效率不足的双重挑战。针对这一问题，本文提出了一种基于轻量级身份认证和遗传优化的任务卸载方案。首先，我们设计了一种基于椭圆曲线的匿名认证机制，结合伪身份生成、可验证签名和时间戳技术。在保证车辆隐私的同时，支持恶意节点跟踪，从而保证参与任务卸载的节点的可信度。在此基础上，提出了一种改进的遗传优化模型，结合精英保留策略、多点交叉突变操作和资源分配惩罚函数，动态适应车辆移动性和服务器资源状态，实现全局最优卸载决策。最后，大量的实验表明，该方案在安全签名效率、认证速度和任务处理性能方面明显优于基准方法。任务延迟降低6.29%-34.14%，能耗降低9.54%-35.36%。

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引用次数: 0

Securing Automotive Data Flow: A Survey of Telematics Security Across Intra-Vehicle, V2X, and Cloud Layers 保护汽车数据流：跨车内、V2X和云层的远程信息处理安全调查

IF 6.7 2区计算机科学 Q1 TELECOMMUNICATIONS

Vehicular Communications

Pub Date : 2026-03-23 DOI: 10.1016/j.vehcom.2026.101024

Junjie Wu, Benjamin C.M. Fung, Natalia Stakhanova, Faiyaz Khan, Hanbo Yu

引用次数: 0

Maximizing Secrecy Performance for UAV-Based Two-Way Relay Systems Utilizing Friendly Jamming 利用友好干扰最大化无人机双向中继系统的保密性能

IF 6.7 2区计算机科学 Q1 TELECOMMUNICATIONS

Vehicular Communications

Pub Date : 2026-03-21 DOI: 10.1016/j.vehcom.2026.101020

Thanh Trung Nguyen, Manh Hoang Tran, Thanh-Lanh Le, Le The Dung

引用次数: 0

Intelligent Fluid Computing Framework for XAI-Enhanced Task Offloading and Resource Management in Vehicular Networks 基于xai的车辆网络任务卸载和资源管理的智能流体计算框架

IF 6.7 2区计算机科学 Q1 TELECOMMUNICATIONS

Vehicular Communications

Pub Date : 2026-03-19 DOI: 10.1016/j.vehcom.2026.101019

Naserali Noorani, Seyed Amin Hosseini Seno

引用次数: 0

UAVSyDM: UAV-Assisted Sybil Attack Detection Mechanism in Vehicular Ad Hoc Networks UAVSyDM：车载自组织网络中无人机辅助的Sybil攻击检测机制

IF 6.7 2区计算机科学 Q1 TELECOMMUNICATIONS

Vehicular Communications

Pub Date : 2026-03-16 DOI: 10.1016/j.vehcom.2026.101021

Santosh Kumar, Amol Vasudeva, Manu Sood

Vehicular Ad Hoc Networks (VANETs) are a core component of Intelligent Transportation Systems (ITS), enabling safety-critical communication between vehicles. However, VANETs are vulnerable to Sybil attacks, whereby an attacker will create several fake identities to disrupt network functionality through sending spurious traffic or safety information. The countermeasures that are currently in place mainly depend on fixed roadside infrastructure, which reduces their effectiveness in areas that have little roadside infrastructure or where roadside infrastructure is unstable. In an effort to address this weakness, this paper presents UAVSyDM, a UAV-based Sybil detector that is independent of roadside infrastructure. UAVSyDM uses unmanned aerial vehicles with an array of antennas that estimate the Direction of Arrival (DoA) using the Multiple Signal Classification (MUSIC) algorithm and combine spatial measurement with Received Signal Strength Indicator (RSSI) measurement and machine-learning classification to identify malicious identities. This framework was tested and run in the NS-3.45 simulator with IEEE 802.11p communication, a vehicle density of 100 to 500 nodes, and a Sybil attack ratio of up to 30%. The data set contains 6.02 million observations with 80 multidimensional attributes based on 650 different vehicle identifiers. Experimental findings using approximately 0.80 million testing samples demonstrate that UAVSyDM achieves a classification accuracy in the range of 81.9%–83.7% and a weighted F1-score of 0.8816. The system achieves a Receiver Operating Characteristic Area Under the Curve (ROC-AUC) of 0.7707 and maintains a low False Positive Rate (FPR) of approximately 2.29%, indicating reliable discrimination between legitimate and Sybil vehicle identities. Attack-specific analysis shows that UAVSyDM achieves its highest detection performance under indirect attack scenarios, with an F1-score of approximately 0.90, while maintaining stable detection performance under direct and power-control attacks. Scalability evaluation confirms consistent detection performance across different network densities, with F1-scores improving from 0.434 at 100 nodes to 0.612 at 500 nodes. Runtime analysis indicates an average inference latency of approximately 0.224 ms, demonstrating suitability for real-time vehicular network security applications. Feature ablation experiments further confirm the importance of UAV-based spatial features, with the removal of DoA features reducing the F1-score from 0.4855 to 0.4486, corresponding to a performance decrease of approximately 7.6%. These results collectively demonstrate that UAV-assisted observation provides an effective, scalable, and infrastructure-independent solution for Sybil attack detection in vehicular networks.

车辆自组织网络（VANETs）是智能交通系统（ITS）的核心组成部分，可实现车辆之间的安全关键通信。然而，vanet很容易受到Sybil攻击，攻击者会创建多个假身份，通过发送虚假流量或安全信息来破坏网络功能。目前的对策主要依赖于固定的路边基础设施，这在路边基础设施很少或路边基础设施不稳定的地区降低了对策的有效性。为了解决这一弱点，本文提出了UAVSyDM，一种独立于路边基础设施的基于无人机的Sybil探测器。UAVSyDM使用带有天线阵列的无人机，使用多信号分类（MUSIC）算法估计到达方向（DoA），并将空间测量与接收信号强度指标（RSSI）测量和机器学习分类相结合，以识别恶意身份。该框架在NS-3.45模拟器上进行了测试和运行，采用IEEE 802.11p通信，车辆密度为100 ~ 500个节点，Sybil攻击率高达30%。该数据集包含602万个观测数据，基于650种不同的车辆标识符，具有80个多维属性。使用约80万个测试样本的实验结果表明，UAVSyDM的分类准确率在81.9% ~ 83.7%之间，加权f1得分为0.8816。该系统实现了0.7707的接收机工作特征曲线下面积（ROC-AUC），并保持了约2.29%的低误报率（FPR），表明了合法和Sybil车辆身份的可靠区分。针对攻击的分析表明，UAVSyDM在间接攻击场景下检测性能最高，f1得分约为0.90，在直接攻击和功率控制攻击下检测性能保持稳定。可扩展性评估证实了在不同网络密度下的一致检测性能，f1分数从100个节点时的0.434提高到500个节点时的0.612。运行时分析表明，平均推理延迟约为0.224毫秒，证明了实时车载网络安全应用的适用性。特征消融实验进一步证实了基于无人机的空间特征的重要性，DoA特征的去除使f1得分从0.4855降至0.4486，性能下降约7.6%。这些结果共同表明，无人机辅助观察为车载网络中的Sybil攻击检测提供了有效、可扩展且与基础设施无关的解决方案。

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GSAAS: A group signature-based anonymous authentication scheme for VANETs GSAAS：基于组签名的vanet匿名认证方案

IF 6.5 2区计算机科学 Q1 TELECOMMUNICATIONS

Vehicular Communications

Pub Date : 2026-02-01 Epub Date: 2025-11-11 DOI: 10.1016/j.vehcom.2025.100988

Xinyang Deng , Xiaohong Wu , Qinggele Qi , Cong Zhao

In vehicular ad-hoc networks (VANETs), ensuring robust security for vehicle identities and messages while maintaining essential service functionalities presents a significant challenge. This paper proposes a group signature-based anonymous authentication scheme for VANETs (GSAAS). GSAAS supports anonymous vehicle authentication within a certificateless framework, effectively mitigating the complexities associated with certificate management and distribution. To alleviate the high computation overhead on the Trust Authority (TA) and minimize the communication delay associated with pseudonym requests, the base station (BS) is employed as the group manager, enabling efficient group maintenance and pseudonym management, facilitating seamless vehicle authentication while ensuring secure data transmission. Security analysis demonstrates that GSAAS is robust against various attacks. Furthermore, performance analysis highlights the superior efficiency of GSAAS compared to existing schemes, with significant improvements in both computation and communication overheads in VANETs.

在车辆自组织网络（vanet）中，在保持基本服务功能的同时确保车辆身份和信息的强大安全性是一个重大挑战。提出了一种基于组签名的vanet匿名认证方案（GSAAS）。GSAAS在无证书框架中支持匿名车辆身份验证，有效地降低了与证书管理和分发相关的复杂性。为了减轻TA （Trust Authority）高昂的计算开销，减少假名请求带来的通信延迟，采用基站BS （base station）作为组管理器，实现高效的组维护和假名管理，在保证数据安全传输的同时实现车辆无缝认证。安全性分析表明，GSAAS对各种攻击具有鲁棒性。此外，性能分析强调了与现有方案相比，GSAAS的效率更高，在VANETs的计算和通信开销方面都有显着改善。

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