Vehicular Communications最新文献_第10页

A comprehensive survey of deep reinforcement learning in UAV-assisted IoT data collection 深度强化学习在无人机辅助物联网数据采集中的综合研究

IF 5.8 2区计算机科学 Q1 TELECOMMUNICATIONS

Vehicular Communications

Pub Date : 2025-06-25 DOI: 10.1016/j.vehcom.2025.100949

Oluwatosin Ahmed Amodu , Huda Althumali , Zurina Mohd Hanapi , Chedia Jarray , Raja Azlina Raja Mahmood , Mohammed Sani Adam , Umar Ali Bukar , Nor Fadzilah Abdullah , Nguyen Cong Luong

Unmanned Aerial Vehicles (UAVs) play a critical role in data collection for a wide range of Internet of Things (IoT) applications across remote, urban, and marine environments. In large-scale deployments, UAVs often face complex decision-making challenges, for which Deep Reinforcement Learning (DRL) has emerged as a promising solution. This paper presents a comprehensive review of research on UAV-assisted IoT utilizing DRL, covering key research questions relating to DRL algorithm variants, deployment objectives, architectural features, integrated technologies, UAV roles, optimization constraints, energy management strategies, and performance metrics. Findings indicate that value-based and actor-critic algorithms are the most commonly employed, targeting objectives such as path planning, transmit power control, scheduling, velocity and altitude control, and charging optimization. Other architectural considerations include clustering, security, obstacle avoidance, buffered sensors, and multi-UAV coordination. Beyond data collection, UAVs are also used for tasks such as device selection, data aggregation, and sensor charging, with energy management primarily achieved through charging and energy harvesting techniques. Performance is typically assessed using metrics like energy efficiency, throughput, latency, packet loss, and Age of Information (AoI). The paper concludes by outlining several promising research directions and open challenges critical to the successful deployment of UAVs as aerial communication platforms, especially in IoT data collection. By organizing existing work across key themes and outlining promising future directions, this review offers a valuable reference for researchers and technology professionals alike.

无人机（uav）在远程、城市和海洋环境中广泛的物联网（IoT）应用的数据收集中发挥着关键作用。在大规模部署中，无人机经常面临复杂的决策挑战，深度强化学习（DRL）已成为一种有前途的解决方案。本文全面回顾了利用DRL的无人机辅助物联网应用，涵盖了关键研究问题、DRL算法变体、部署目标、架构特征、集成技术、无人机角色、优化约束、能源管理策略和性能指标。研究结果表明，基于价值的算法和行为者批评算法是最常用的，针对的目标包括路径规划、传输功率控制、调度、速度和高度控制以及充电优化。架构考虑包括集群、安全性、避障、缓冲传感器和多无人机协调。除了数据收集，无人机还用于设备选择、数据聚合和传感器充电等任务，主要通过充电和能量收集技术实现能量管理。通常使用能效、吞吐量、延迟、数据包丢失和信息年龄（Age of Information, AoI）等指标来评估性能。最后，本文概述了几个有前途的研究方向和开放的挑战，这些挑战对无人机在物联网数据收集中的成功部署至关重要。

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引用次数: 0

HQA: Hybrid Q-learning and AODV multi-path routing algorithm for Flying Ad-hoc Networks 飞行自组织网络的混合q -学习和AODV多路径路由算法

IF 5.8 2区计算机科学 Q1 TELECOMMUNICATIONS

Vehicular Communications

Pub Date : 2025-06-23 DOI: 10.1016/j.vehcom.2025.100947

Chen Sun, Liang Hou, Suqi Yu, Jian Shu

Reliable and efficient data transmission between Unmanned Aerial Vehicle (UAV) nodes is critical for the control of UAV swarms and relies heavily on effective routing protocols in Flying Ad-hoc Networks (FANETs). However, Q-learning-based FANET routing protocols, which are gaining widespread attention, face two significant challenges: 1) insufficient stability of Q-learning leads to unreliable route selection in certain scenarios and higher packet loss rates; 2) in void regions with frequent topology changes and vast path exploration spaces, the slow convergence of Q-learning fails to adapt quickly to dynamic environmental changes, thereby reducing the packet delivery rate (PDR). This paper proposes a hybrid Q-learning/AODV (HQA) multi-path routing algorithm that integrates Q-learning and the AODV protocols to address these challenges. HQA includes a Bayesian stability evaluator for adaptive Q-learning/AODV switching and a dual-update reward mechanism that integrates reliable AODV paths into Q-learning training, enabling rapid void recovery and latency-optimized routing. Experimental results demonstrate HQA's superiority over baseline protocols: Compared to AODV, HQA reduces average end-to-end delay by 13.6–23.9% and improves PDR by 5.4–9.1% in non-void and void states, respectively. It outperforms QMR by 2.2–6.3% in PDR while achieving 25.6% and 53.2% higher average PDR than QMR and AODV across network densities. The hybrid design accelerates convergence by 40% versus standalone Q-learning through AODV-assisted rewards, maintaining scalability under dynamic topology changes. These findings indicate that the HQA algorithm can more rapidly adapt to the rapid changes in FANETs and better handle void regions, offering a promising solution for enhancing the performance and reliability of FANETs.

无人机节点之间可靠、高效的数据传输对于无人机群的控制至关重要，并且在很大程度上依赖于飞行自组织网络（fanet）中有效的路由协议。然而，基于q -学习的FANET路由协议正受到广泛关注，面临着两大挑战：1)q -学习的稳定性不足，导致某些场景下路由选择不可靠，丢包率较高；2)在拓扑变化频繁、路径探索空间广阔的空洞区域，q -学习的收敛速度较慢，不能快速适应动态环境变化，从而降低了分组投递率（PDR）。本文提出了一种混合q -学习/AODV （HQA）多路径路由算法，该算法集成了q -学习和AODV协议来解决这些挑战。HQA包括一个用于自适应q -学习/AODV切换的贝叶斯稳定性评估器和一个双更新奖励机制，该机制将可靠的AODV路径集成到q -学习训练中，从而实现快速的空隙恢复和延迟优化路由。实验结果表明HQA优于基线协议：与AODV相比，HQA在非空和空状态下分别将端到端平均延迟降低13.6-23.9%，将PDR提高5.4-9.1%。在PDR方面，它比QMR高出2.2-6.3%，而在网络密度上，它的平均PDR比QMR和AODV分别高出25.6%和53.2%。与通过aodv辅助奖励的独立Q-learning相比，混合设计的收敛速度提高了40%，在动态拓扑变化下保持了可扩展性。这些结果表明，HQA算法能够更快地适应fanet的快速变化，更好地处理空洞区域，为提高fanet的性能和可靠性提供了一种有前途的解决方案。

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引用次数: 0

Securing the unforeseen: Enhancing VANET security with dynamic honeypots and attack rate analysis 确保不可预见的安全：用动态蜜罐和攻击率分析增强VANET安全性

IF 5.8 2区计算机科学 Q1 TELECOMMUNICATIONS

Vehicular Communications

Pub Date : 2025-06-20 DOI: 10.1016/j.vehcom.2025.100946

Mohammed A. Abdelmaguid, Hossam S. Hassanein, Mohammad Zulkernine

Addressing known threats constitutes the foundational layer of cybersecurity defenses. However, the real challenge emerges in anticipating and mitigating unforeseen attacks. Current security methodologies work well against familiar threats but often struggle with new or unforeseen attacks. This paper examines the Trust Origin within Trust Management Systems (TMS) by linking it to the network attack rate, thereby refining trust assessments and predicting new attacks. Combining Machine Learning (ML) algorithms with honeypots, we offer a comprehensive defense for Vehicular Ad-hoc Networks (VANETs), adept at detecting anticipated and unexpected attacks through attack rate analysis. Our methodology evaluates the network's security status by examining its ability to identify known attacks, referred to as prepared-for attacks. Subsequently, this information serves as a foundation to predict future attacks that still need to be identified, termed unprepared-for attacks. Through extensive testing, we demonstrate the viability of a dual strategy that encompasses the detection of prepared-for attacks and the prediction of unprepared-for ones. Experimental results reveal a significant improvement in predicting unprepared-for attacks, evidenced by enhanced accuracy, precision, and recall. Additionally, we conduct experiments to determine the optimal deployment of honeypots for maximum efficiency.

解决已知威胁构成了网络安全防御的基础层。然而，真正的挑战出现在预测和减轻不可预见的攻击。当前的安全方法可以很好地应对熟悉的威胁，但往往难以应对新的或不可预见的攻击。本文研究了信任管理系统（TMS）中的信任起源，将其与网络攻击率联系起来，从而改进信任评估和预测新的攻击。我们将机器学习（ML）算法与蜜罐相结合，为车载自组织网络（vanet）提供全面的防御，擅长通过攻击率分析检测预期和意外攻击。我们的方法通过检查其识别已知攻击的能力来评估网络的安全状态，称为准备攻击。随后，这些信息将作为预测未来仍需要识别的攻击的基础，称为未准备的攻击。通过广泛的测试，我们证明了双重策略的可行性，该策略包括检测准备好的攻击和预测未准备的攻击。实验结果显示，在预测未准备的攻击方面有显著的改进，证明了提高的准确性、精确度和召回率。此外，我们进行实验，以确定蜜罐的最佳部署，以获得最大的效率。

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引用次数: 0

BFP-Net: A DL-based ISAC beamforming prediction method for extended vehicle bp - net：一种基于dl的扩展车辆ISAC波束形成预测方法

IF 5.8 2区计算机科学 Q1 TELECOMMUNICATIONS

Vehicular Communications

Pub Date : 2025-06-06 DOI: 10.1016/j.vehcom.2025.100945

Peng Chen , Ting Zhou , Zhimin Chen , Fan Meng , Jun Liu

To enable the next generation of connected autonomous vehicles, the millimeter wave (mmWave)-based integrated sensing and communication (ISAC) system will be a critical technology in future vehicle-to-everything (V2X) networks. However, the rapid mobility of vehicles and the narrow beamwidth of mmWave signals present significant challenges for beam alignment, and point-target modeling methods often lead to substantial overhead, high latency, and complications. To address these issues, in this paper, a hybrid analog-digital (HAD) multi-input multi-output (MIMO) ISAC framework is adopted for the mmWave-based V2X network to reduce hardware costs and power consumption. Then, considering the narrow beamwidth of the mmWave system, the vehicle is modeled as an extended surface target with multiple scattering points, and a new association technique for these points is developed to improve prediction accuracy. Hence, a deep learning (DL)-based beamforming prediction network, namely beamforming prediction network (BFP-Net), is designed according to the ISAC signal beam prediction protocol and enables roadside units (RSUs) to transmit ISAC signals effectively for both downlink communication and sensing operations. The BFP-Net leverages a convolutional neural network long-short-term memory (CNN-LSTM) architecture to capture spatial and temporal correlations, providing enhanced modeling capabilities for beam prediction. Moreover, for highly dynamic vehicles, the BFP-Net predicts optimal beams for future time slots by extracting features from the received echo signals and eliminates the repetitive beam training inherent in the traditional communication protocol. Simulation results demonstrate that the proposed method significantly outperforms extended Kalman filter (EKF)-based methods in the mmWave V2X scenario, achieving higher beam gains and better performance for high-speed vehicles, and substantially reduces the overhead associated with beam training compared to the conventional neural network relying on pilot signals.

为了实现下一代互联自动驾驶汽车，基于毫米波（mmWave）的集成传感和通信（ISAC）系统将成为未来车联网（V2X）网络的关键技术。然而，车辆的快速移动性和毫米波信号的窄波束宽度对波束对准提出了重大挑战，点目标建模方法通常会导致大量开销、高延迟和复杂性。为了解决这些问题，本文在基于毫米波的V2X网络中采用了混合模数（HAD）多输入多输出（MIMO） ISAC框架，以降低硬件成本和功耗。然后，考虑到毫米波系统的窄波束宽度，将车辆建模为具有多个散射点的扩展表面目标，并开发了一种新的散射点关联技术来提高预测精度。因此，根据ISAC信号波束预测协议设计了一种基于深度学习的波束形成预测网络，即波束形成预测网络（bbp - net），使路边单元（rsu）能够有效地传输ISAC信号进行下行通信和传感操作。bp - net利用卷积神经网络长短期记忆（CNN-LSTM）架构来捕获空间和时间相关性，为波束预测提供增强的建模能力。此外，对于高度动态的车辆，bp - net通过从接收到的回波信号中提取特征来预测未来时隙的最佳波束，并消除了传统通信协议中固有的重复波束训练。仿真结果表明，该方法在毫米波V2X场景中显著优于基于扩展卡尔曼滤波（EKF）的方法，在高速车辆中获得更高的波束增益和更好的性能，并且与依赖导频信号的传统神经网络相比，大大降低了波束训练相关的开销。

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引用次数: 0

A Multi-Head Attention mechanism assisted MADDPG algorithm for real-time data collection in Internet of Drones 基于多头注意机制的无人机互联网实时数据采集madpg算法

IF 5.8 2区计算机科学 Q1 TELECOMMUNICATIONS

Vehicular Communications

Pub Date : 2025-05-30 DOI: 10.1016/j.vehcom.2025.100944

A.K.M. Atiqur Rahman , Muntasir Chowdhury Mridul , Palash Roy , Md. Abdur Razzaque , Md. Rajin Saleh , Mohammad Mehedi Hassan , Md Zia Uddin

Flexible movement and rapid deployment capabilities of unmanned aerial vehicles (UAVs) or drones have enabled them to be ideal for fresh and real-time data collection in the Internet of Drones (IoD) network. With the rising demand for IoD applications, optimizing the Age of Information (AoI), and energy efficiency of drones has become a challenging problem. The existing literature works are either limited by considering single-drone data collection from 2D space or by not prioritizing data from diverse IoT devices. In this paper, we have developed an optimization framework for multi-drone-assisted data collection in 3D space, which brings a trade-off between minimizing drone energy consumption and AoI, exploiting the Mixed Integer Linear Programming (MILP) problem. However, due to the NP-hardness of the developed optimization framework for large networks, we have devised a Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm, supported and enhanced by a Multi-Head Attention (MHA) mechanism for multi-drone-assisted data collection to minimize drone energy consumption and AoI jointly, namely MECAO. The MHA in the MECAO system helps prioritize IoT data sources and ensures the timely collection of important data. This system enables the agents to coordinate effectively among themselves and provides innovative solutions to complex network issues. Our findings demonstrate substantial advancements in real-time data collection and drone performance, offering a practical and efficient solution for modern IoD applications. The developed MECAO system is implemented in the OpenAI Gym simulator platform, and the simulation trace file content depicts the improvement in AoI by up to 56% while the energy consumption is reduced by as high as 38.5%, respectively, compared to the state-of-the-art works.

无人机（uav）或无人机的灵活移动和快速部署能力使其成为无人机互联网（IoD）网络中新鲜和实时数据收集的理想选择。随着IoD应用需求的不断增长，优化无人机的信息时代（AoI）和能源效率已成为一个具有挑战性的问题。现有的文献作品要么受到单一无人机从二维空间收集数据的限制，要么没有优先考虑来自不同物联网设备的数据。在本文中，我们开发了一个用于多无人机辅助三维空间数据收集的优化框架，该框架利用混合整数线性规划（MILP）问题，在最小化无人机能耗和AoI之间进行权衡。然而，由于所开发的优化框架对于大型网络具有np -硬度，我们设计了一种多智能体深度确定性策略梯度（madpg）算法，该算法由多头注意（MHA）机制支持和增强，用于多无人机辅助数据收集，以最小化无人机能耗和AoI，即MECAO。MECAO系统中的MHA有助于对物联网数据源进行优先排序，确保重要数据的及时收集。该系统使agent能够有效地相互协调，为复杂的网络问题提供创新的解决方案。我们的研究结果表明，在实时数据收集和无人机性能方面取得了实质性进展，为现代IoD应用提供了实用高效的解决方案。所开发的MECAO系统在OpenAI Gym模拟器平台上实现，仿真跟踪文件内容显示，与最先进的作品相比，AoI提高了56%，能耗降低了38.5%。

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引用次数: 0

Cognitive UAV-assisted secure and reliable communications based on robust joint trajectory and power control optimization 基于鲁棒关节轨迹和功率控制优化的认知无人机辅助安全可靠通信

IF 5.8 2区计算机科学 Q1 TELECOMMUNICATIONS

Vehicular Communications

Pub Date : 2025-05-26 DOI: 10.1016/j.vehcom.2025.100941

Can Wang, Junhong Zhang, Helin Yang

The cognitive unmanned aerial vehicle (UAV) communication system has emerged as a pivotal technology in addressing the scarcity of spectral resources for UAV communications, but the jamming and eavesdropping attacks are severe due to the high-quality air-to-ground communication links. Consequently, this paper introduces a UAV-enabled cooperative jammer to disrupt the eavesdropping activities of active eavesdroppers by emitting artificial noise. Our objective is to jointly optimize the three-dimensional UAV trajectory and transmit power to maximize the secrecy communication rate under quality of service (QoS) requirement. To tackle the non-convex problem, the block coordinate descent (BCD) and successive convex approximation (SCA) methods are utilized to transform it into an approximate convex problem, and then we design an alternative optimization iterative algorithm to achieve suboptimal but efficient solution. Moreover, we extend the developed algorithm into an imperfect channel state information (CSI) scenario to maximize the worst-case secrecy rate by jointly optimizing the robust UAV's trajectory and transmit power, where the location uncertainties of ground primary, secondary, and eavesdropping devices are considered. Simulation results demonstrate that the proposed joint optimization algorithm significantly enhances system secrecy performance under different real-world settings compared to existing state-of-the-art algorithms.

认知型无人机通信系统已成为解决无人机通信频谱资源短缺的关键技术，但由于高质量的空对地通信链路，干扰和窃听攻击严重。因此，本文引入了一种无人机协同干扰机，通过发射人工噪声来干扰主动窃听者的窃听活动。我们的目标是在满足服务质量（QoS）要求的情况下，共同优化三维无人机的弹道和发射功率，使保密通信速率最大化。针对非凸问题，利用分块坐标下降法（BCD）和逐次凸逼近法（SCA）将其转化为近似凸问题，然后设计了一种替代优化迭代算法，以获得次优但有效的解。此外，我们将所开发的算法扩展到不完全信道状态信息（CSI）场景中，通过联合优化鲁棒无人机的轨迹和发射功率来最大化最坏情况下的保密率，其中考虑了地面主、次和窃听设备的位置不确定性。仿真结果表明，与现有的先进算法相比，所提出的联合优化算法显著提高了系统在不同现实环境下的保密性能。

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引用次数: 0

Millimeter-wave vehicular collaborative communication assisted by intelligent reflecting surface 智能反射面辅助毫米波车辆协同通信

IF 5.8 2区计算机科学 Q1 TELECOMMUNICATIONS

Vehicular Communications

Pub Date : 2025-05-23 DOI: 10.1016/j.vehcom.2025.100940

Xiangrui Guan, Jianbin Xue, Han Zhang, Jialing Xu

The combination of the intelligent reflecting surface (IRS) with reconfigurable wireless propagation environment and the millimeter-wave (mmWave) with abundant bandwidth resources can play a great advantage over the rate and delay in vehicular communications. Considering the problem of non-line-of-sight (NLOS) communication between the requesting nodes (RNs) and the service nodes (SNs) in the mmWave vehicular system in this paper, we propose an IRS-assisted multi-hop vehicle-to-vehicle (V2V) cooperative communication method to realize low-delay vehicular communication. Aiming to minimize the communication delay of RNs, an optimization problem is formulated by optimizing the link selection and reflection coefficient matrix of IRS. To tackle the optimization problem, an alternate optimization algorithm is proposed to decompose the original optimization problem into two subproblems for iterative optimization. First, we establish a link selection mechanism based on link quality and vehicle distance and propose a link selection algorithm based on the evaluation function to select communication links for each RN. Then, in particular, we derive the closed-form expression based on successive convex approximation (SCA) techniques for updating the reflection coefficient matrix of IRS. The simulation results show that the IRS-assisted mmWave vehicular cooperative communication scheme proposed in this paper can effectively reduce the communication delay and improve the performance of the mmWave vehicular network.

具有可重构无线传播环境的智能反射面（IRS）与带宽资源丰富的毫米波（mmWave）相结合，可以在车载通信中发挥巨大的速率和时延优势。针对毫米波车载系统中请求节点（RNs）与服务节点（SNs）之间的非视距（NLOS）通信问题，提出了一种irs辅助的多跳车对车（V2V）协同通信方法，以实现车载低时延通信。以最小化RNs的通信延迟为目标，通过优化IRS的链路选择和反射系数矩阵，构造了一个优化问题。为了解决优化问题，提出了一种备选优化算法，将原优化问题分解为两个子问题进行迭代优化。首先，我们建立了基于链路质量和车辆距离的链路选择机制，并提出了基于评价函数的链路选择算法，为每个RN选择通信链路。然后，我们特别推导了基于连续凸逼近（SCA）技术的闭式表达式，用于更新IRS反射系数矩阵。仿真结果表明，本文提出的irs辅助毫米波车载协同通信方案能够有效降低通信时延，提高毫米波车载网络的性能。

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引用次数: 0

CLE-based authenticated key agreement with PUF-secured key for vehicle-to-infrastructure 基于cle的身份验证密钥协议与车辆到基础设施的puf安全密钥

IF 5.8 2区计算机科学 Q1 TELECOMMUNICATIONS

Vehicular Communications

Pub Date : 2025-05-22 DOI: 10.1016/j.vehcom.2025.100942

Suhui Liu , Liquan Chen , Liqun Chen , Yu Wang , Yaqing Zhu

Vehicle-to-infrastructure (V2I) communication is the basis for vehicles to obtain information about the road ahead. The confidentiality and reliability of V2I communication guarantee traffic safety and smooth flow. Authenticated key agreement (AKA) is the most commonly used technique to establish secure communication channels. Signature-based AKA inevitably exposes the identity information of vehicles, while Encryption-based AKA can bring deniability and high privacy, which means no adversary can know who sent the AKA message. Certificateless encryption (CLE) can simultaneously solve burdensome certificate management and key escrow. However, existing certificateless cryptography requires two loosely combined public keys to represent a device and does not consider the physical security of storing secret keys locally. This paper first designed an improved CLE scheme with one-device-one-public-key, and performance comparisons show that the proposed CLE has optimal storage and computation performance. Considering that rare work was put on encryption-based AKA, this paper proposed a deniable and privacy-preserving certificateless AKA for V2I communication by incorporating Physically Unclonable Function (PUF)-secured key management to prevent physical leakage of keys, named CLE-AKA-PUF. Feature comparison illustrates that CLE-AKA-PUF supports key escrow-free, dual authentication, physical security, deniability, and high privacy. Security proofs and performance analysis demonstrate the practicability and efficiency of CLE-AKA-PUF.

车对基础设施（V2I）通信是车辆获取前方道路信息的基础。V2I通信的保密性和可靠性保证了交通的安全和畅通。身份验证密钥协议（AKA）是建立安全通信通道最常用的技术。基于签名的AKA不可避免地暴露了车辆的身份信息，而基于加密的AKA可以带来可否认性和高隐私性，这意味着攻击者无法知道是谁发送了AKA消息。无证书加密可以同时解决繁琐的证书管理和密钥托管问题。然而，现有的无证书加密需要两个松散组合的公钥来表示设备，并且没有考虑在本地存储密钥的物理安全性。本文首先设计了一种改进的一设备一公钥CLE方案，性能比较表明该方案具有最优的存储性能和计算性能。考虑到基于加密的AKA很少投入工作，本文提出了一种可否认且保护隐私的V2I通信无证书AKA，该AKA结合了物理不可克隆功能（physical unclable Function， PUF）安全的密钥管理来防止密钥的物理泄漏，命名为CLE-AKA-PUF。特性对比表明，CLE-AKA-PUF支持免密钥托管、双重认证、物理安全、可否认性和高隐私性。安全性证明和性能分析证明了CLE-AKA-PUF的实用性和有效性。

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引用次数: 0

Perceptual data importance and freshness aware transmission in millimeter wave vehicular networks 毫米波车载网络中感知数据重要性和新鲜度感知传输

IF 5.8 2区计算机科学 Q1 TELECOMMUNICATIONS

Vehicular Communications

Pub Date : 2025-05-16 DOI: 10.1016/j.vehcom.2025.100939

Chenyuan He , Zhouyu Zhang , Yingfeng Cai , Hai Wang , Long Chen , Fenghua Huang

The extensive sharing of perceptual data between vehicles and between vehicles and roads has significantly enhanced the performance of intelligent transportation system (ITS). The current vehicular networks using sub-6 GHz struggle to meet the demands for high-rate, low-latency, and highly reliable communication. To address this issue, this paper proposes a perceptual data sharing strategy based on millimeter-wave (mmWave) communication technology. This strategy takes into account the characteristics of vehicular perceptual data, i.e., the importance and freshness of the data, and constructs a mixed-integer nonlinear sum-of-ratios optimization problem. To meet the stringent real-time decision-making requirements of vehicular networks, we leverage the transmission slot characteristics of the Time Division Multiple Access (TDMA) Medium Access Control (MAC) architecture to transform the nonlinear original problem into a series of approximate integer linear programming (ILP) problems. Then we employ maximum weight matching in graph theory to further reduce computational complexity, enabling the problem to be solved in polynomial time. Additionally, we have designed a brute-force algorithm to ensure the global optimum is achieved, thereby validating the performance of our proposed algorithm. Comparative simulation studies with the brute-force algorithm, the ILP solver, the edge coloring algorithm, our previously developed parameterization-based iterative algorithm (PIA), and the First-Come-First-Serve (FCFS) scheduling scheme verify the effectiveness of our proposed algorithm.

车辆之间以及车辆与道路之间的感知数据广泛共享，极大地提高了智能交通系统的性能。目前使用sub- 6ghz的车载网络难以满足高速率、低延迟和高可靠的通信需求。为了解决这一问题，本文提出了一种基于毫米波通信技术的感知数据共享策略。该策略考虑了车辆感知数据的重要性和新鲜度等特点，构建了一个混合整数非线性比例和优化问题。为了满足车载网络严格的实时决策要求，我们利用时分多址（TDMA）介质访问控制（MAC）架构的传输时隙特性，将非线性原始问题转化为一系列近似整数线性规划（ILP）问题。然后利用图论中的最大权值匹配进一步降低计算复杂度，使问题在多项式时间内得到解决。此外，我们还设计了一个蛮力算法来确保实现全局最优，从而验证了我们提出的算法的性能。通过与暴力破解算法、ILP求解器、边缘着色算法、我们之前开发的基于参数化的迭代算法（PIA）和先到先服务（FCFS）调度方案的对比仿真研究，验证了我们提出的算法的有效性。

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引用次数: 0

A privacy-preserving access control protocol for 6G supported intelligent UAV networks 一种支持6G的智能无人机网络隐私保护访问控制协议

IF 5.8 2区计算机科学 Q1 TELECOMMUNICATIONS

Vehicular Communications

Pub Date : 2025-05-14 DOI: 10.1016/j.vehcom.2025.100937

Khalid Mahmood , Salman Shamshad , Mohammad Hossein Anisi , Alessandro Brighente , Muhammad Asad Saleem , Ashok Kumar Das

Due to their autonomous operation, high mobility, and real-time communication capabilities, 6G-supported Unmanned Aerial Vehicles (6G-UAVs) (i.e., drones) are increasingly being utilized to enhance data collection and management in Intelligent Transportation Systems (ITSs). Despite their manifold benefits, 6G-supported UAV-based ITS (6G-U-ITS) faces unique security challenges beyond conventional cyber and physical threats. These include real-time authentication, impersonation attacks, physical tampering or cloning and protection against identity spoofing in highly dynamic environments. For instance, an attacker may steal a drone and use its identity to send authenticated malicious messages to the ITS, causing road accidents. Therefore, a secure authentication scheme must ensure resilience against UAV identity theft and unauthorized access while maintaining low-latency and computational efficiency to support the stringent real-time security requirements of 6G-U-ITS. Existing authentication schemes are not specifically designed to address these challenges, making it imperative to develop a lightweight and robust authentication mechanism tailored for 6G-U-ITS. Moreover, most of the existing protocols are vulnerable to physical tampering and impersonation attacks and also require high computation overhead. In this paper, to mitigate these limitations and satisfy the aforementioned requirements, we propose a secure access control protocol for 6G-U-ITS. To the best of our knowledge, this is the first security solution in the literature that can achieve security against UAVs physical attacks. Furthermore, we justify the robustness of the designed protocol against potential attacks through detailed formal and informal security assessment. Via testbed experiments, we show that our protocol achieves 20.66% and 22.82% higher efficiency on communication and computation overhead, respectively, compared to other contemporary competing protocols.

由于其自主操作、高机动性和实时通信能力，6g支持的无人机（即无人机）越来越多地用于增强智能交通系统（ITSs）的数据收集和管理。尽管具有多方面的优势，但6g支持的基于无人机的ITS （6G-U-ITS）面临着传统网络和物理威胁之外的独特安全挑战。这包括实时身份验证、模拟攻击、物理篡改或克隆，以及在高度动态环境中防止身份欺骗。例如，攻击者可能会窃取无人机，并使用其身份向its发送经过身份验证的恶意信息，从而导致交通事故。因此，一个安全的认证方案必须确保对无人机身份盗窃和未经授权访问的弹性，同时保持低延迟和计算效率，以支持6G-U-ITS严格的实时安全要求。现有的身份验证方案并不是专门为解决这些挑战而设计的，因此必须为6G-U-ITS开发一种轻量级且健壮的身份验证机制。此外，大多数现有协议容易受到物理篡改和模拟攻击，并且需要很高的计算开销。在本文中，为了减轻这些限制并满足上述要求，我们提出了一种6G-U-ITS的安全访问控制协议。据我们所知，这是文献中第一个可以实现针对无人机物理攻击的安全解决方案。此外，我们通过详细的正式和非正式的安全评估来证明所设计的协议对潜在攻击的鲁棒性。通过测试实验，我们的协议在通信和计算开销方面分别比其他当代竞争协议提高了20.66%和22.82%。

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