Vehicular Communications最新文献_第3页

Decentralized multi-hop data processing in UAV networks using MARL 利用 MARL 在无人机网络中进行分散式多跳数据处理

IF 5.8 2区计算机科学 Q1 TELECOMMUNICATIONS

Vehicular Communications

Pub Date : 2024-11-15 DOI: 10.1016/j.vehcom.2024.100858

Indu Chandran, Kizheppatt Vipin

Unmanned Aerial Vehicles (UAVs) have become integral to numerous applications, prompting research towards enhancing their capabilities. For time-critical missions, minimizing latency is crucial; however, current studies often rely on sending data to ground station or cloud for processing due to their limited onboard capacities. To leverage the networking capabilities of UAVs, recent research focuses on enabling data processing and offloading within the UAV network for coordinated decision-making. This paper explores a multi-hop data offloading scheme designed to optimize the task processing and resource management of UAVs. The proposed distributed strategy uses multi-agent reinforcement learning, where UAVs, each with varying computational capacities and energy levels, process and offload tasks while managing energy consumption and latency. The agents, represented as actor-critic models, learn and adapt their actions based on current state and environment feedback. The study considers a consensus-based method to update learning weights, promoting cooperative behavior among the agents with minimum interaction. Through multiple training episodes, the agents improve their performance, with the overall system achieving faster convergence with high rewards, demonstrating the viability of decentralized data processing and offloading in UAV networks.

无人驾驶飞行器（UAV）已成为众多应用中不可或缺的一部分，促使人们研究如何增强其能力。对于时间紧迫的任务来说，最大限度地减少延迟至关重要；然而，由于无人机的机载能力有限，目前的研究通常依赖于将数据发送到地面站或云端进行处理。为了充分利用无人机的网络能力，最近的研究重点是在无人机网络内实现数据处理和卸载，以便协调决策。本文探讨了一种多跳数据卸载方案，旨在优化无人机的任务处理和资源管理。所提出的分布式策略采用多代理强化学习，每个无人机都具有不同的计算能力和能量水平，在处理和卸载任务的同时管理能量消耗和延迟。代理以行为批判模型为代表，根据当前状态和环境反馈来学习和调整自己的行动。研究考虑了一种基于共识的方法来更新学习权重，以最小的互动促进代理之间的合作行为。通过多次训练，代理提高了性能，整个系统实现了快速收敛和高回报，证明了无人机网络中分散式数据处理和卸载的可行性。

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引用次数: 0

Optimization of electric vehicle charging and scheduling based on VANETs 基于 VANET 的电动汽车充电和调度优化

IF 5.8 2区计算机科学 Q1 TELECOMMUNICATIONS

Vehicular Communications

Pub Date : 2024-11-15 DOI: 10.1016/j.vehcom.2024.100857

Tianyu Sun , Ben-Guo He , Junxin Chen , Haiyan Lu , Bo Fang , Yicong Zhou

Vehicular Ad-hoc Networks (VANETs) provide key support for the achievement of intelligent, safe, and efficient driverless transportation systems through real-time communication between vehicles and vehicles, and vehicles and road infrastructure. This paper investigates a joint optimization problem of electric vehicles (EVs) charging management and resource allocation based on VANETs. EV charging requires significantly more time than refueling conventional vehicles, a key factor behind people's reluctance to transition from internal combustion engine vehicles to EVs. Previous works have primarily concentrated on fully-charged vehicles and random matching, which does not solve the problems of vehicle charging delays and long customer waiting times. Considering these factors, we propose a distributed multi-level charging strategy and level-by-level matching method. Specifically, EVs and passengers are categorized into classes based on battery power and target mileage. Vehicles are then allocated to customers in the same or lower levels. Furthermore, the Attentive Temporal Convolutional Networks-Long Short Term Memory (ATCN-LSTM) model is leveraged to predict historical traffic data, supporting anticipatory decision-making. Subsequently, we develop a hierarchical charging and rebalancing joint optimization framework that incorporates charging facility planning. Experimental results obtained under various model parameters exhibit the method's commendable performance, as evidenced by metrics such as operating cost, system response time, and vehicle utilization.

车载 Ad-hoc 网络（VANET）通过车辆与车辆、车辆与道路基础设施之间的实时通信，为实现智能、安全、高效的无人驾驶交通系统提供了关键支持。本文研究了基于 VANET 的电动汽车（EV）充电管理和资源分配的联合优化问题。电动汽车充电所需的时间远远多于传统汽车加油所需的时间，这也是人们不愿意从内燃机汽车过渡到电动汽车的一个关键因素。以往的工作主要集中在已充满电的车辆和随机匹配上，这并不能解决车辆充电延迟和客户等待时间过长的问题。考虑到这些因素，我们提出了分布式多级充电策略和逐级匹配方法。具体来说，根据电池电量和目标里程将电动汽车和乘客分为不同等级。然后将车辆分配给同一级别或更低级别的客户。此外，我们还利用注意力时空卷积网络-长短期记忆（ATCN-LSTM）模型来预测历史交通数据，从而支持预测性决策。随后，我们开发了一个包含充电设施规划的分层充电和再平衡联合优化框架。在各种模型参数下获得的实验结果表明，该方法的性能值得称赞，运营成本、系统响应时间和车辆利用率等指标都证明了这一点。

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引用次数: 0

Prediction-based data collection of UAV-assisted Maritime Internet of Things 基于预测的无人机辅助海上物联网数据收集

IF 5.8 2区计算机科学 Q1 TELECOMMUNICATIONS

Vehicular Communications

Pub Date : 2024-11-08 DOI: 10.1016/j.vehcom.2024.100854

Xiaoluoteng Song , Xiuwen Fu , Mingyuan Ren , Pasquale Pace , Gianluca Aloi , Giancarlo Fortino

In maritime data collection scenarios, due to the constraints of wireless communication and environmental factors such as wave motion, sea surface ducting effects, and sea surface curvature, floating sensor nodes are unable to establish direct data transmission links with the base station. The advent of unmanned aerial vehicle (UAV)-assisted Maritime Internet of Things (MIoT) provides a feasible solution to this challenge. However, in existing maritime environments, floating sensor nodes drift due to ocean currents, posing significant challenges for long-distance data transmission while maintaining a low age of information (AoI). Consequently, we introduce a prediction-based UAV-assisted data collection mechanism for MIoT. In this scheme, we first select convergence nodes responsible for gathering data from floating sensor nodes and forwarding it to passing UAVs. We then propose a dynamic clustering algorithm to allocate task areas to UAVs, with each area assigned to a single UAV for data collection from floating sensor nodes. To ensure stable data offloading by UAVs, we develop a UAV relay pairing algorithm to establish reliable air-to-air relay paths and provide two data offloading modes: distal UAV and proximate UAV. Owing to the drift of floating sensor nodes influenced by ocean currents, we employ a deep echo state network to predict the positions of floating sensor nodes and utilize a multi-agent deep deterministic policy gradient to solve the UAVs trajectory planning problem. Under this mechanism, the UAVs can adaptively adjust its flight path while exploring floating sensor nodes in dynamically changing ocean sensor node scenarios. Extensive experiments demonstrate that the proposed scheme can adapt to dynamic ocean environments, achieving low-AoI data collection from floating sensor nodes.

在海上数据采集场景中，由于无线通信和环境因素（如波浪运动、海面管道效应和海面曲率）的限制，浮动传感器节点无法与基站建立直接的数据传输链接。无人飞行器（UAV）辅助的海上物联网（MIoT）的出现为这一挑战提供了可行的解决方案。然而，在现有的海洋环境中，浮动传感器节点会因洋流而漂移，这给长距离数据传输同时保持低信息年龄（AoI）带来了巨大挑战。因此，我们为 MIoT 引入了一种基于预测的无人机辅助数据收集机制。在该方案中，我们首先选择汇聚节点，负责从浮动传感器节点收集数据，并将其转发给路过的无人机。然后，我们提出一种动态聚类算法，为无人机分配任务区域，每个区域分配给一架无人机，负责从浮动传感器节点收集数据。为确保无人机稳定地卸载数据，我们开发了一种无人机中继配对算法，以建立可靠的空对空中继路径，并提供两种数据卸载模式：远距离无人机和近距离无人机。由于浮动传感器节点受洋流影响而漂移，我们采用深度回波状态网络来预测浮动传感器节点的位置，并利用多代理深度确定性策略梯度来解决无人机轨迹规划问题。在这种机制下，无人机可以在动态变化的海洋传感器节点场景中，在探索浮动传感器节点的同时自适应地调整飞行路径。大量实验证明，所提出的方案能够适应动态海洋环境，实现从浮动传感器节点采集低影响范围数据。

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引用次数: 0

Hybrid mutual authentication for vehicle-to-infrastructure communication without the coverage of roadside units 在没有路边装置覆盖的情况下，实现车辆与基础设施通信的混合相互认证

IF 5.8 2区计算机科学 Q1 TELECOMMUNICATIONS

Vehicular Communications

Pub Date : 2024-11-07 DOI: 10.1016/j.vehcom.2024.100850

Huizhi Tang, Abdul Rauf, Qin Lin, Guoqing Dou, Changshuai Qin

The security issues in Vehicle Ad Hoc Networks (VANETs) are prevalent within Intelligent Transportation Systems (ITS). To ensure the security of vehicle-to-infrastructure (V2I) communication, extensive research on V2I authentication has been conducted in recent years. However, these protocols often overlook the limitations of communication range, leading to failures in V2I communication. Consequently, addressing the challenge of secure V2I communication in areas not covered by distributed roadside units (RSUs) remains a significant task. To address these issues, the current study proposes an Anonymous Certificate-less Hybrid Mutual Authentication Protocol (ACHMAP) based on Vehicle-to-Vehicle-to-Infrastructure (V2V2I) communication. In the proposed protocol, a secure multi-hop link is established through vehicle-to-vehicle (V2V) mutual one-time token authentication. Subsequently, the out-of-coverage vehicle and relevant RSUs complete V2I mutual authentication using signcryption messages transmitted by vehicle nodes. In the security analysis, it is demonstrated that the entire V2V2I stage can resist various security attacks, such as replay attacks, impersonation attacks, and threats to user anonymity, while preserving confidentiality and integrity. We simulated the proposed protocol using Network Simulator 3 (NS-3) to confirm that the authentication mechanism has lower overhead and minimal authentication delay in V2V2I communication.

在智能交通系统（ITS）中，车辆无线网络（VANET）的安全问题非常普遍。为了确保车辆到基础设施（V2I）通信的安全性，近年来对 V2I 身份验证进行了大量研究。然而，这些协议往往忽略了通信范围的限制，导致 V2I 通信失败。因此，在分布式路边装置（RSU）未覆盖的区域解决 V2I 安全通信的挑战仍然是一项重要任务。为解决这些问题，本研究提出了一种基于车对车对基础设施（V2V2I）通信的匿名无证书混合相互验证协议（ACHMAP）。在提议的协议中，通过车对车（V2V）一次性相互令牌认证建立了安全的多跳链路。随后，覆盖范围外的车辆和相关 RSU 使用车辆节点传输的签名加密信息完成 V2I 相互认证。安全分析表明，整个 V2V2I 阶段可以抵御各种安全攻击，如重放攻击、冒充攻击和对用户匿名性的威胁，同时保持机密性和完整性。我们使用网络模拟器 3（NS-3）对所提出的协议进行了仿真，证实该认证机制在 V2V2I 通信中具有较低的开销和最小的认证延迟。

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引用次数: 0

Hierarchical federated deep reinforcement learning based joint communication and computation for UAV situation awareness 基于分层联合深度强化学习的无人机态势感知联合通信与计算

IF 5.8 2区计算机科学 Q1 TELECOMMUNICATIONS

Vehicular Communications

Pub Date : 2024-11-06 DOI: 10.1016/j.vehcom.2024.100853

Haitao Li, Jiawei Huang

The computation-intensive situational awareness (SA) task of unmanned aerial vehicle (UAV) is greatly affected by its limited power and computing capability. To solve this challenge, we consider the joint communication and computation (JCC) design for UAV network in this paper. Firstly, a multi-objective optimization (MOO) model, which can optimize UAV computation offloading, transmit power, and local computation resources simultaneously, is built to minimize energy consumption and task execution delay. Then, we develop Thompson sampling based double-DQN (TDDQN) learning algorithm which allows the agent to explore more deeply and effectively, and propose a joint optimization algorithm that combines TDDQN and sequential least squares quadratic programming (SLSQP) to handle the MOO problem. Finally, to enhance the training speed and quality, we incorporate federated learning (FL) into the presented joint optimization algorithm and propose hierarchical federated TDDQN with SLSQP (HF TDDQN-S) to implement the JCC design. Simulation results show that the introduced HF TDDQN-S can efficiently learn the best JCC strategy and minimize the average cost contrasted with the DDQN with SLSQP (DDQN-S) and TDDQN with SLSPQ (TDDQN-S) approach, and achieve the low average delay SA with power efficient.

无人飞行器（UAV）的计算密集型态势感知（SA）任务受到其有限功率和计算能力的极大影响。为解决这一难题，本文考虑了无人机网络的联合通信与计算（JCC）设计。首先，我们建立了一个多目标优化（MOO）模型，该模型可同时优化无人飞行器的计算卸载、发射功率和本地计算资源，使能耗和任务执行延迟最小化。然后，我们开发了基于汤普森采样的双DQN（TDDQN）学习算法，使代理能够更深入、更有效地探索，并提出了一种结合TDDQN和顺序最小二乘二次编程（SLSQP）的联合优化算法来处理MOO问题。最后，为了提高训练速度和质量，我们在联合优化算法中加入了联合学习（FL），并提出了分层联合 TDDQN 与 SLSQP（HF TDDQN-S）来实现 JCC 设计。仿真结果表明，与采用 SLSQP 的 DDQN（DDQN-S）和采用 SLSPQ 的 TDDQN（TDDQN-S）方法相比，引入的 HF TDDQN-S 可以高效地学习最佳 JCC 策略，并使平均成本最小化，同时实现了低平均延迟和高能效的 SA。

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引用次数: 0

Volunteer vehicle assisted dependent task offloading based on ant colony optimization algorithm in vehicular edge computing 车载边缘计算中基于蚁群优化算法的志愿车辆辅助依赖任务卸载

IF 5.8 2区计算机科学 Q1 TELECOMMUNICATIONS

Vehicular Communications

Pub Date : 2024-10-31 DOI: 10.1016/j.vehcom.2024.100849

Chen Cheng, Linbo Zhai, Yujuan Jia, Xiumin Zhu, Yumei Li

Vehicle Edge Computing improves the Quality of Service of vehicular applications by offloading tasks to the VEC server. However, with the continuous development of computation-intensive vehicular applications, the limited resources of the VEC server will not be enough to support these applications. Volunteer Computing-Based Vehicular Ad-hoc Networking (VCBV) proposes a concept of using vehicles as resources, which is considered to be a promising solution. In this paper, we study the multi-dependent task offloading problem in order to quickly and economically handle the overload task of the requesting vehicle in VCBV. Considering both task execution delay and execution cost, we formulate the problem of offloading the multi-dependent tasks of requesting vehicles to minimize total task completion time and execution cost. Since the offloading problem is NP-hard, an improved multi-objective Ant Colony Optimization algorithm is proposed. Firstly, we use a density-based clustering algorithm to form volunteer alliances that can contribute idle resources. Secondly, based on the volunteer alliances and RSUs, we use Analytic Hierarchy Process (AHP) to initialize pheromone concentration to make better decisions. Then, we design the update strategy of the pheromone concentration and heuristic information. Finally, we introduce Pareto optimal relationship to evaluate the results. A large number of simulation results verify that our algorithm has better performance than other alternatives.

车辆边缘计算（Vehicle Edge Computing）通过将任务卸载到 VEC 服务器来提高车辆应用的服务质量。然而，随着计算密集型车辆应用的不断发展，VEC 服务器的有限资源将不足以支持这些应用。基于志愿计算的车载 Ad-hoc 网络（VCBV）提出了将车辆作为资源的概念，被认为是一种很有前途的解决方案。本文研究了多依赖任务卸载问题，以便在 VCBV 中快速、经济地处理请求车辆的超载任务。考虑到任务执行延迟和执行成本，我们提出了请求车辆的多依赖任务卸载问题，以最小化总任务完成时间和执行成本。由于卸载问题是 NP 难问题，我们提出了一种改进的多目标蚁群优化算法。首先，我们使用基于密度的聚类算法来组建可以贡献闲置资源的志愿者联盟。其次，根据志愿者联盟和 RSU，我们使用层次分析法（AHP）初始化信息素浓度，以做出更好的决策。然后，我们设计信息素浓度和启发式信息的更新策略。最后，我们引入帕累托最优关系来评估结果。大量的模拟结果验证了我们的算法比其他算法具有更好的性能。

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引用次数: 0

STAR-RIS-NOMA empowered vehicle-to-vehicle communications: Outage and ergodic capacity analysis STAR-RIS-NOMA 赋权车对车通信：中断和遍历容量分析

IF 5.8 2区计算机科学 Q1 TELECOMMUNICATIONS

Vehicular Communications

Pub Date : 2024-10-28 DOI: 10.1016/j.vehcom.2024.100852

Luxmi Kant Vishwakarma , Radhika Gour , Suneel Yadav , Adão Silva

This paper delves into the performance evaluation of a non-orthogonal multiple access (NOMA) enabled vehicle-to-vehicle (V2V) communication system empowered by simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS). Herein, we consider that a moving access point (AP) transmits superimposed signals to nearby and distant NOMA vehicles simultaneously via reflection and transmission through a STAR-RIS equipped vehicle with 2N reconfigurable elements, respectively. Specifically, by characterizing all V2V channels as double-Rayleigh fading distributed, we derive the outage probability (OP) and ergodic capacity (EC) expressions for each NOMA vehicle, by employing both perfect and imperfect successive interference cancellation (SIC) at nearby vehicle user. Furthermore, we present the asymptotic OP behavior at high signal-to-noise ratio (SNR) regime to gain deeper insights into the diversity order of NOMA vehicles. The findings reveal that the nearby vehicle under perfect SIC and far vehicle experience a diversity order of

\frac{N π^{4}}{256 - π^{2}}

, which is the function of number of reconfigurable elements (N) in the STAR-RIS. Whereas, a zero diversity order is obtained for nearby user under imperfect SIC case. Moreover, we analytically discuss the high SNR slopes of EC for both user vehicles. Furthermore, Monte-Carlo simulations are conducted to validate our analytical results under various channel and system parameter configurations. We also provide a comparison between the proposed scheme and STAR-RIS based orthogonal multiple access and cooperative relaying systems.

本文深入探讨了通过同时传输和反射可重构智能表面（STAR-RIS）实现的非正交多址（NOMA）车对车（V2V）通信系统的性能评估。在此，我们考虑由一个移动接入点（AP）通过配备有 2N 个可重构元件的 STAR-RIS 的车辆，分别通过反射和传输向附近和远处的 NOMA 车辆同时传输叠加信号。具体地说，通过将所有 V2V 信道描述为双瑞利衰落分布，我们得出了每个 NOMA 车辆的中断概率（OP）和遍历容量（EC）表达式，并在附近车辆用户处采用了完美和不完美的连续干扰消除（SIC）。此外，我们还提出了高信噪比（SNR）情况下的渐进 OP 行为，以深入了解 NOMA 车辆的分集顺序。研究结果表明，在完美 SIC 条件下，近车和远车的分集阶为 Nπ4256-π2，这是 STAR-RIS 中可重构元素数量（N）的函数。而在不完善 SIC 的情况下，附近用户的分集阶数为零。此外，我们还分析讨论了两个用户车辆的高信噪比 EC 斜坡。此外，我们还进行了蒙特卡洛模拟，以验证我们在各种信道和系统参数配置下的分析结果。我们还对所提出的方案与基于 STAR-RIS 的正交多址和合作中继系统进行了比较。

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引用次数: 0

Deep Reinforcement Learning based running-track path design for fixed-wing UAV assisted mobile relaying network 基于深度强化学习的固定翼无人机辅助移动中继网络运行轨迹路径设计

IF 5.8 2区计算机科学 Q1 TELECOMMUNICATIONS

Vehicular Communications

Pub Date : 2024-10-28 DOI: 10.1016/j.vehcom.2024.100851

Tao Wang , Xiaodong Ji , Xuan Zhu , Cheng He , Jian-Feng Gu

This paper studies a fixed-wing unmanned aerial vehicle (UAV) assisted mobile relaying network (FUAVMRN), where a fixed-wing UAV employs an out-band full-duplex relaying fashion to serve a ground source-destination pair. It is confirmed that for a FUAVMRN, straight path is not suitable for the case that a huge amount of data need to be delivered, while circular path may lead to low throughput if the distance of ground source-destination pair is large. Thus, a running-track path (RTP) design problem is investigated for the FUAVMRN with the goal of energy minimization. By dividing an RTP into two straight and two semicircular paths, the total energy consumption of the UAV and the total amount of data transferred from the ground source to the ground destination via the UAV relay are calculated. According to the framework of Deep Reinforcement Learning and taking the UAV's roll-angle limit into consideration, the RTP design problem is formulated as a Markov Decision Process problem, giving the state and action spaces in addition to the policy and reward functions. In order for the UAV relay to obtain the control policy, Deep Deterministic Policy Gradient (DDPG) is used to solve the path design problem, leading to a DDPG based algorithm for the RTP design. Computer simulations are performed and the results show that the DDPG based algorithm always converges when the number of training iterations is around 500, and compared with the circular and straight paths, the proposed RTP design can save at least 12.13 % of energy and 65.93 % of flight time when the ground source and the ground destination are located 2000 m apart and need to transfer

5000 bit / Hz

of data. Moreover, it is more practical and efficient in terms of energy saving compared with the Deep Q Network based design.

本文研究了一种固定翼无人飞行器辅助移动中继网络（FUAVMRN），其中固定翼无人飞行器采用带外全双工中继方式为一对地面信源-信宿提供服务。研究证实，对于 FUAVMRN 来说，直线路径不适合需要传输大量数据的情况，而如果地面源-目的对的距离较远，圆形路径可能会导致吞吐量较低。因此，以能量最小化为目标，研究了 FUAVMRN 的运行轨迹路径（RTP）设计问题。通过将 RTP 划分为两条直线路径和两条半圆路径，计算出无人机的总能耗以及通过无人机中继从地面源传输到地面目的地的总数据量。根据深度强化学习的框架，并考虑到无人机的滚动角度限制，将 RTP 设计问题表述为马尔可夫决策过程问题，除了给出策略和奖励函数外，还给出了状态和行动空间。为了让无人机中继获得控制策略，使用了深度确定性策略梯度（DDPG）来解决路径设计问题，从而产生了一种基于 DDPG 的 RTP 设计算法。计算机仿真结果表明，当训练迭代次数为 500 次左右时，基于 DDPG 的算法总是收敛的；与圆形路径和直线路径相比，当地面信源和地面目的地相距 2000 m 且需要传输 5000bit/Hz 的数据时，所提出的 RTP 设计至少能节省 12.13% 的能量和 65.93% 的飞行时间。此外，与基于 Deep Q 网络的设计相比，该设计在节能方面更加实用和高效。

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引用次数: 0

EPAKA: An efficient and privacy-preserving authenticated key agreement scheme based on physical security for VANET EPAKA：基于物理安全的高效且保护隐私的 VANET 验证密钥协议方案

IF 5.8 2区计算机科学 Q1 TELECOMMUNICATIONS

Vehicular Communications

Pub Date : 2024-10-24 DOI: 10.1016/j.vehcom.2024.100847

Chunhua Jin , Penghui Zhou , Zhiwei Chen , Wenyu Qin , Guanhua Chen , Hao Zhang , Jian Weng

Vehicular ad hoc network (VANET) has been a promising technology in smart transportation system, which can enable information exchange between vehicles and roadside units (RSUs). However, the privacy of vehicles and RSUs is a critical challenge in VANET, as they may expose sensitive information to malicious attackers or unauthorized parties. Many existing authenticated key agreement (AKA) schemes aim to protect the privacy of vehicles and RSUs, but they often neglect the physical security of the devices involved in the communication. Therefore, we propose an efficient and privacy-preserving AKA scheme in VANET, which embeds physical unclonable function (PUF) and fuzzy extraction (FE) technology. PUF is a physical device that generates random strings based on their intrinsic characteristics and external inputs, which can protect the secrets in the devices from being stolen by attackers. FE can compensate for the drawbacks of PUF affected by environmental factors. Our scheme preserves the identity privacy of legitimate RSUs and vehicles, as well as intercepts and traces the identity of malicious attackers. In addition, we eliminate the involvement of the third party (TP) in the AKA phase to better meet the high-speed driving of vehicles. Finally, we conduct formal and informal security analyses in random oracle model (ROM), which prove that our scheme can resist various attacks. We also show in the performance analysis that our scheme has the lowest computational cost, communication overhead, and total energy consumption.

车载特设网络（VANET）是智能交通系统中一项前景广阔的技术，它可以实现车辆与路边装置（RSU）之间的信息交换。然而，车辆和 RSU 的隐私保护是 VANET 面临的一个严峻挑战，因为它们可能会将敏感信息暴露给恶意攻击者或未经授权的各方。现有的许多认证密钥协议（AKA）方案都旨在保护车辆和 RSU 的隐私，但它们往往忽视了参与通信的设备的物理安全性。因此，我们在 VANET 中提出了一种高效且能保护隐私的 AKA 方案，其中嵌入了物理不可克隆函数（PUF）和模糊提取（FE）技术。PUF 是一种物理设备，可根据其内在特征和外部输入生成随机字符串，从而保护设备中的秘密不被攻击者窃取。FE 可以弥补 PUF 受环境因素影响的缺点。我们的方案既能保护合法 RSU 和车辆的身份隐私，又能拦截和追踪恶意攻击者的身份。此外，我们消除了第三方（TP）在 AKA 阶段的参与，以更好地满足车辆高速行驶的要求。最后，我们在随机甲骨文模型（ROM）中进行了正式和非正式的安全分析，证明我们的方案可以抵御各种攻击。我们还在性能分析中表明，我们的方案具有最低的计算成本、通信开销和总能耗。

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引用次数: 0

Blockchain-based fast handover authentication protocol for Internet of Vehicles in small industrial parks 基于区块链的小型工业园区车联网快速移交认证协议

IF 5.8 2区计算机科学 Q1 TELECOMMUNICATIONS

Vehicular Communications

Pub Date : 2024-10-24 DOI: 10.1016/j.vehcom.2024.100848

Junfeng Tian , Yue Shen , Yiting Wang

Autonomous vehicles safeguard the security and efficiency of Internet of Vehicles systems in small industrial parks by authenticating and exchanging real-time information with transportation infrastructure. Deploying a multi-server framework reduces the risk of message blocking and privacy information leakage from centralized services. However, in traditional handover authentication protocols, there are still potential security risks such as high-overhead performance issues and single point of failure attacks. Therefore, it is considered challenging to realize efficient authentication while protecting the privacy of vehicles. In this paper, we propose a secure and efficient handover authentication protocol for autonomous vehicles in a small industrial park to address the challenges. The protocol is based on blockchain and Pedersen verifiable secret sharing scheme, which not only ensures lightweight real-time interactions between autonomous vehicles and edge servers in multi-server environments, but also strictly protects the security and privacy of both vehicles and edge servers. We prove the semantic security of the protocol under the Real-Or-Random model and perform a informal analysis of its security attributes to show that it can withstand a wide range of malicious attacks. Performance evaluation shows that the proposed protocol satisfies more security requirements and has better computational efficiency and communication cost than other related protocols.

自动驾驶汽车通过与交通基础设施进行身份验证和实时信息交换，保障了小型工业园区车联网系统的安全性和效率。部署多服务器框架可降低信息阻塞和集中式服务泄露隐私信息的风险。然而，在传统的交接认证协议中，仍存在潜在的安全风险，如高开销性能问题和单点故障攻击。因此，如何在保护车辆隐私的同时实现高效的身份验证被认为是一项挑战。本文针对上述挑战，提出了一种安全高效的小型工业园区自动驾驶车辆交接认证协议。该协议基于区块链和 Pedersen 可验证的秘密共享方案，不仅保证了多服务器环境下自动驾驶车辆与边缘服务器之间的轻量级实时交互，还严格保护了车辆和边缘服务器的安全和隐私。我们证明了该协议在真实或随机模型下的语义安全性，并对其安全属性进行了非正式分析，证明它可以抵御各种恶意攻击。性能评估表明，与其他相关协议相比，所提出的协议能满足更多的安全要求，并具有更好的计算效率和通信成本。

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