Ad Hoc Networks最新文献_第3页

MUFO: Multi-UAV flight optimization for enhancing connectivity in remote driving services MUFO：多无人机飞行优化，增强远程驾驶服务的连通性

IF 4.8 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Ad Hoc Networks

Pub Date : 2025-12-30 DOI: 10.1016/j.adhoc.2025.104129

Van-Linh Nguyen , Lan-Huong Nguyen , Ren-Hung Hwang

Besides autonomous driving, remote driving is a typical example of leveraging communications to eliminate high-risk driving situations in which drivers are fatigued. However, remote driving remains challenging, particularly in urban areas where buildings’ absorption and reflection may significantly hamper control signals. This paper proposes MUFO, a deep-reinforcement-learning-based multi-UAV flight-optimization framework whose objectives are twofold: (i) path planning to determine optimal UAV trajectories that sustain stable links for remote-driving vehicles, and (ii) efficient deployment to minimize the number of UAVs and their energy consumption while guaranteeing service continuity with minimum data rate. First, the coverage and flight cost issues are defined in a multi-objective optimization problem with constraints on UAV energy and collision avoidance. Based on a built-in map of weak signal areas, a novel technique is proposed: a multi-agent deep deterministic policy gradient (MADDPG) scheme. The goal is to determine the best flying strategy for the UAVs to fly over weak signal areas, enhance signal strengths, and relay connectivity when the remote vehicles arrive there. The simulation results show that MADDPG in MUFO outperforms state-of-the-art deep learning methods and searches by up to 8% of deployment efficiency (energy savings, number of deployed UAVs), particularly when there is a high density of ground traffic jam areas and UAVs are required to hover at those areas for an unexpected additional time. MUFO’s strength is that it considerably improves the deployment efficiency of UAVs via cumulative learning from many trials or completed missions.

除了自动驾驶之外，远程驾驶是利用通信来消除驾驶员疲劳的高风险驾驶情况的典型例子。然而，远程驾驶仍然具有挑战性，特别是在城市地区，建筑物的吸收和反射可能会严重阻碍控制信号。本文提出了一种基于深度强化学习的多无人机飞行优化框架MUFO，其目标有两个：(i)路径规划，以确定最优无人机轨迹，为远程驾驶车辆维持稳定的链路；（ii）高效部署，以最小化无人机数量及其能耗，同时保证以最小数据速率服务连续性。首先，将覆盖和飞行成本问题定义为无人机能量和避碰约束的多目标优化问题；基于内置的弱信号区域映射，提出了一种新技术：多智能体深度确定性策略梯度（madpg）方案。目标是确定无人机在弱信号区域飞行的最佳飞行策略，增强信号强度，并在远程车辆到达时中继连接。仿真结果表明，在MUFO中，madpg比最先进的深度学习方法和搜索效率（节能，部署的无人机数量）高出8%，特别是当存在高密度的地面交通堵塞区域并且需要无人机在这些区域悬停意想不到的额外时间时。MUFO的优势在于，它通过从许多试验或完成的任务中累积学习，大大提高了无人机的部署效率。

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引用次数: 0

On minimizing the energy consumption in NB-Fi networks with restrictions on packet loss rate and duty cycle 基于丢包率和占空比限制的NB-Fi网络能耗最小化研究

IF 4.8 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Ad Hoc Networks

Pub Date : 2025-12-27 DOI: 10.1016/j.adhoc.2025.104132

Dmitry Bankov, Anastasiia Fedorishcheva, Polina Levchenko, Andrey Lyakhov, Evgeny Khorov

Low-power wide-area networks (LPWANs) are a widely adopted solution for collecting data from remote sensors. One of the possible LPWAN solutions is NB-Fi (Narrow Band Fidelity), which has two options to ensure reliable data delivery. The first one uses acknowledgments. However, the regulation rules of the ISM (Industrial, Scientific, and Medical) bands used by NB-Fi define the minimal duty cycle, which limits the total intensity of packet transmissions by base stations and sensors. As the number of sensors in an NB-Fi network is typically high, the duty cycle restriction may prevent the base station from sending some acknowledgments. Another option to ensure the delivery of sensor data is to use unsolicited retries, which increases not only data reliability but also both the network load and the energy consumption of the sensors. This paper sheds light on how to combine and configure these two options in order to provide the required transmission reliability with minimal sensors’ energy consumption and comply with the duty cycle restrictions. For that, we develop a mathematical model of an NB-Fi network and propose an algorithm based on this model for choosing the ratio of sensors that use acknowledgments and the number of transmission attempts for sensors that use unsolicited retries. Numerical results confirm that the algorithm minimizes the sensors’ energy consumption while satisfying the restrictions on the duty cycle and the packet loss rate.

低功耗广域网（lpwan）是一种广泛采用的远程传感器数据采集解决方案。一种可能的LPWAN解决方案是NB-Fi（窄带保真度），它有两种选择来确保可靠的数据传输。第一个使用确认。然而，NB-Fi使用的ISM（工业、科学和医疗）频段的监管规则定义了最小占空比，这限制了基站和传感器传输数据包的总强度。由于NB-Fi网络中的传感器数量通常很高，占空比限制可能会阻止基站发送一些确认。确保传感器数据传输的另一个选择是使用未请求的重试，这不仅增加了数据可靠性，还增加了网络负载和传感器的能耗。本文阐述了如何结合和配置这两种选择，以提供所需的传输可靠性与最小的传感器的能量消耗，并符合占空比的限制。为此，我们开发了NB-Fi网络的数学模型，并提出了一种基于该模型的算法，用于选择使用确认的传感器比例和使用非请求重试的传感器的传输尝试次数。数值结果表明，该算法在满足占空比和丢包率限制的情况下，最大限度地降低了传感器的能耗。

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引用次数: 0

Optimizing task allocation in Mobile Crowdsensing with multiple opportunistic users and participatory UAVs 基于多机会用户和参与式无人机的移动众测任务分配优化

IF 4.8 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Ad Hoc Networks

Pub Date : 2025-12-26 DOI: 10.1016/j.adhoc.2025.104128

Shi Yu, Bing Shi, Xiao Su, Saisai Li, Shuai Li, Xing Tang

Mobile Crowdsensing (MCS) refers to the use of human users or unmanned aerial vehicles (UAVs) equipped with mobile devices to collect sensing data. However, existing MCS methods suffer from insufficient coverage due to sparse population distribution in some sensing areas and restrictions on UAV flights in densely populated urban regions. To address this issue, we propose a collaborative approach that combines opportunistic users, who are passively engaged and widely distributed across cities, with participatory UAVs, which are capable of covering sparsely populated regions. Specifically, we introduce a hybrid task allocation strategy called MUMUHTA (Multi-User Multi-UAV Hybrid Task Allocation) to optimize sensing coverage. The strategy assumes that grid heat is derived from historical user trajectory data, and UAVs are assigned only tasks without autonomous control. MUMUHTA uses user trajectory prediction and greedy recruitment for opportunistic users, along with per-slot UAV matching based on the Kuhn–Munkres algorithm, while a dynamic switching rule determines when UAVs take over tasks from users. Simulation experiments using the Rome user trajectory dataset and Shanghai Telecom task dataset show that MUMUHTA improves the task completion rate by an average of 29.70%, 8.11%, 9.69%, 5.05%, and 15.38% compared to benchmark strategies: MPU, MOU-Random, DLMV-MPU, DLMV(T)-MPU, and HR-DLVCS.

移动众测（Mobile Crowdsensing， MCS）是指利用人类用户或配备移动设备的无人机（uav）采集传感数据。然而，现有的MCS方法由于在一些传感区域人口分布稀疏，以及在人口密集的城市地区无人机飞行受到限制，存在覆盖不足的问题。为了解决这个问题，我们提出了一种协作方法，将被动参与并广泛分布在城市中的机会主义用户与能够覆盖人口稀少地区的参与式无人机相结合。具体来说，我们引入了一种称为MUMUHTA（多用户多无人机混合任务分配）的混合任务分配策略来优化传感覆盖。该策略假设网格热量来源于历史用户轨迹数据，并且无人机只分配任务而没有自主控制。MUMUHTA使用用户轨迹预测和贪婪招募机会用户，以及基于Kuhn-Munkres算法的每插槽无人机匹配，同时动态切换规则决定无人机何时接管用户的任务。基于罗马用户轨迹数据集和上海电信任务数据集的仿真实验表明，与MPU、mu - random、DLMV-MPU、DLMV(T)-MPU和HR-DLVCS等基准策略相比，MUMUHTA的任务完成率平均提高了29.70%、8.11%、9.69%、5.05%和15.38%。

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引用次数: 0

TSP-DRL: High-robustness service deployment for mobile virtual reality TSP-DRL：移动虚拟现实的高鲁棒性业务部署

IF 4.8 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Ad Hoc Networks

Pub Date : 2025-12-26 DOI: 10.1016/j.adhoc.2025.104126

Xuejian Chi , Xiaoya Jin , Dapeng Jiao

Mobile virtual reality (MVR), leveraging edge computing’s proximity to end devices, enables real-time responsiveness and improves users’ Quality of Experience (QoE), and has therefore attracted growing attention. However, the mobility and instability of edge environments render edge servers vulnerable to network attacks or hardware failures, undermining service continuity. Prior studies have devoted limited attention to the systemic impact of such failures. In this work, we study the problem of robust service deployment and scheduling, focusing on how to deploy service components to mitigate the degradation in user experience caused by server failures. This problem presents two challenges: (i) seamless takeover of users served by failed servers; and (ii) balancing robustness gains against the total system cost. To overcome these challenges, we design a two-stage service placement strategy based on deep reinforcement learning (TSP-DRL). In the first stage, an iterative search groups neighboring edge servers to identify those that can take over users from failed servers. In the second stage, a deep reinforcement learning agent models the complex relationship between robustness gains and total cost, enabling efficient service-component placement in dynamic environments. Finally, real trace-based data simulations indicate that, compared with state-of-the-art methods, TSP-DRL improves robustness gains by 12%–23% while reducing total system cost by 11%–14%.

移动虚拟现实（MVR）利用边缘计算与终端设备的接近性，实现了实时响应并提高了用户的体验质量（QoE），因此引起了越来越多的关注。但是，边缘环境的移动性和不稳定性使得边缘服务器容易受到网络攻击或硬件故障的影响，从而影响业务的连续性。先前的研究对此类失败的系统性影响关注有限。在这项工作中，我们研究了健壮的服务部署和调度问题，重点关注如何部署服务组件以减轻服务器故障导致的用户体验下降。这个问题提出了两个挑战：(i)无缝接管由故障服务器服务的用户；（ii）平衡鲁棒性收益与系统总成本。为了克服这些挑战，我们设计了一种基于深度强化学习（TSP-DRL）的两阶段服务放置策略。在第一阶段，迭代搜索对相邻的边缘服务器进行分组，以确定那些可以从故障服务器接管用户的服务器。在第二阶段，深度强化学习代理对鲁棒性增益和总成本之间的复杂关系进行建模，从而在动态环境中实现高效的服务组件放置。最后，基于真实轨迹的数据模拟表明，与最先进的方法相比，TSP-DRL的鲁棒性增益提高了12%-23%，同时将系统总成本降低了11%-14%。

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引用次数: 0

A survey of trust management mechanisms in the Internet of Vehicles 车联网信任管理机制研究

IF 4.8 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Ad Hoc Networks

Pub Date : 2025-12-24 DOI: 10.1016/j.adhoc.2025.104131

Zijiang Yang, Qi Tao

With the rapid growth of the automotive industry, the Internet of Vehicles (IoV) has become a vital platform for information exchange among vehicles and between vehicles and roadside infrastructure. However, due to complex network structures and high vehicle mobility, such exchanges are not always reliable. Trust management in IoV is therefore critical to ensuring safety and reliability. This paper reviews research on IoV trust management mechanisms. It first introduces the basic IoV models, and then identifies an evolutionary trajectory based on the existing literature: from early trust management models, to growing attention to privacy protection, and more recently to the adoption of emerging technologies such as blockchain for decentralized trust management. Based on this trajectory, the paper analyzes existing work from three perspectives: trust management models, privacy protection, and blockchain-IoV integration. Furthermore, this article systematically surveys experimental simulation platforms and evaluation indicators to clarify validation practices. Finally, by synthesizing the research landscape and highlighting key limitations and bottlenecks, the paper outlines future directions and priorities for IoV trust management in light of both technological advances and application needs.

随着汽车工业的快速发展，车联网（IoV）已成为车辆之间以及车辆与路边基础设施之间信息交换的重要平台。然而，由于复杂的网络结构和车辆的高移动性，这种交换并不总是可靠的。因此，在车联网中，信任管理对于确保安全性和可靠性至关重要。本文综述了车联网信任管理机制的研究进展。首先介绍了基本的车联网模型，然后根据现有文献确定了一个进化轨迹：从早期的信任管理模型，到对隐私保护的日益关注，再到最近采用区块链等新兴技术进行分散信任管理。基于这一轨迹，本文从信任管理模型、隐私保护和区块链-车联网集成三个角度分析了现有的工作。此外，本文还对实验仿真平台和评价指标进行了系统调查，以明确验证实践。最后，通过综合研究现状，突出关键限制和瓶颈，从技术进步和应用需求两方面概述了车联网信任管理的未来方向和优先事项。

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引用次数: 0

Collaborative DRL-driven task offloading for maritime edge computing 协同drl驱动的海上边缘计算任务卸载

IF 4.8 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Ad Hoc Networks

Pub Date : 2025-12-17 DOI: 10.1016/j.adhoc.2025.104124

Huogen Yang , Yiwen Hu , Zhongming Yang , Xiaohui Yang , Guangxue Yue

The introduction of Mobile edge computing enables resource-constrained maritime terminal users to access low-latency computing services; however, the dynamic nature of the marine environment and scarce resources render traditional computation offloading strategies inadequate for meeting actual demands, making task offloading a critical issue for achieving prompt and efficient service with optimal resource utilization. In particular, fine-tuning the offloading decision process is crucial for enhancing network stability and extending system endurance. To address these challenges, this paper proposes a deep reinforcement learning-based task offloading method for maritime edge computing. The method derives the optimal transmission power for task offloading and incorporates the power allocation problem into the offloading decision framework, ensuring that offloading decisions are efficiently executed within a specific power range. We model the task offloading problem as a Markov decision process, and based on this formulation, we design an improved Double Deep Q-Network (Double DQN) Energy-Delay Tradeoff Optimization algorithm (ID-EDTO), which enables the system to dynamically obtain state feedback from task requests and adapt its offloading strategies accordingly. Experimental results demonstrate that the proposed method outperforms both traditional baseline methods, such as random selection, Lyapunov optimization, and joint resource allocation, as well as DRL based algorithms including PPO, SAC, and A3C, in terms of reducing latency and energy consumption.

移动边缘计算的引入使资源受限的海上终端用户能够访问低延迟的计算服务；然而，由于海洋环境的动态性和资源的稀缺性，传统的计算卸载策略已不能满足实际需求，任务卸载成为实现资源优化利用的快速高效服务的关键问题。特别是，微调卸载决策过程对于增强网络稳定性和延长系统耐久性至关重要。为了解决这些问题，本文提出了一种基于深度强化学习的海上边缘计算任务卸载方法。该方法推导出任务卸载的最优传输功率，并将功率分配问题纳入到卸载决策框架中，保证了任务卸载决策在特定功率范围内有效执行。将任务卸载问题建模为马尔可夫决策过程，并在此基础上设计了一种改进的双深度q -网络（Double Deep Q-Network， DQN）能量-延迟权衡优化算法（ID-EDTO），使系统能够从任务请求中动态获取状态反馈，并相应地调整卸载策略。实验结果表明，该方法在降低延迟和能耗方面优于随机选择、Lyapunov优化、联合资源分配等传统基线方法，也优于PPO、SAC、A3C等基于DRL的算法。

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引用次数: 0

Enhancing energy efficiency and QoS in Q-learning-based small-world WSNs 提高基于q学习的小世界无线传感器网络的能量效率和服务质量

IF 4.8 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Ad Hoc Networks

Pub Date : 2025-12-17 DOI: 10.1016/j.adhoc.2025.104123

Yan Zhao, Lintao Huang, Mengzhe Ren, Hanyang Shi

In a wireless sensor network (WSN), achieving efficient data transmission while extending network lifetime remains a critical issue. Sensor nodes may fail due to energy depletion during mass data transmission, which results in frequent changes in the network topology. The frequent topology changes not only increase the complexity of network management and maintenance, but also severely impair both energy efficiency and quality-of-service (QoS). To address these challenges, in this paper, we construct a small-world WSN (SW-WSN) and address its joint optimization problem of energy efficiency and QoS. In the constructed SW-WSN, long-range (LoRa) links are used to replace some of the conventional links between sensor nodes as well as between nodes and the gateway, and a small-world model is introduced to reduce the intermediate hop count for data transmission. A Q-learning-based adaptive link allocation algorithm is proposed to map all conventional and LoRa links to state space and action space respectively and learn to form state–action pairs through training, which determines an optimal link replacement strategy under different network states and ultimately constructs a self-optimizing network architecture SW-WSN. Then, considering the network topology, link capacity, delay tolerance, and node energy of SW-WSN, the joint optimization problem of energy efficiency and QoS is formulated as an instance of linear programming (LP) with the objective of maximizing energy efficiency while ensuring QoS, and a heuristic algorithm is further designed to obtain the optimal solution. As shown by the simulation results, the Q-learning-based SW-WSN exhibits excellent learning capability and convergence stability across different network scales, successfully achieving a relatively ideal balance between energy efficiency and QoS. The Q-learning-based SW-WSN demonstrates substantial improvements in both energy efficiency (alive or dead devices, and network residual energy) and QoS (average transmission delay, data throughput, and bandwidth utilization) compared with the reinforcement learning (RL)-based routing, low-energy adaptive clustering hierarchy (LEACH), conventional small-world characteristics (SWC), multihop data transmission, and direct data transmission methods.

在无线传感器网络（WSN）中，如何在延长网络寿命的同时实现高效的数据传输一直是一个关键问题。在大量数据传输过程中，传感器节点可能会因能量耗尽而失效，导致网络拓扑结构频繁变化。频繁的拓扑变化不仅增加了网络管理和维护的复杂性，而且严重影响了网络的能效和服务质量（QoS）。为了解决这些问题，本文构建了一个小世界WSN (SW-WSN)，并解决了其能效和QoS的联合优化问题。在构建的无线传感器网络中，采用远程链路（LoRa）代替部分传感器节点之间以及节点与网关之间的传统链路，并引入小世界模型来减少数据传输的中间跳数。提出了一种基于q学习的自适应链路分配算法，将所有常规链路和LoRa链路分别映射到状态空间和动作空间，并通过训练学习形成状态-动作对，确定不同网络状态下的最优链路替换策略，最终构建自优化网络架构SW-WSN。然后，考虑无线传感器网络的网络拓扑结构、链路容量、时延容限和节点能量等因素，将能效和QoS联合优化问题作为线性规划（LP）的实例，以保证QoS的同时实现能效最大化为目标，设计了启发式算法求解该优化问题。仿真结果表明，基于q -learning的SW-WSN在不同网络尺度上表现出优异的学习能力和收敛稳定性，成功地实现了能量效率和QoS之间相对理想的平衡。与基于强化学习（RL）的路由、低能量自适应聚类层次（LEACH）、传统的小世界特征（SWC）、多跳数据传输和直接数据传输方法相比，基于q学习的SW-WSN在能源效率（活设备或死设备，以及网络剩余能量）和QoS（平均传输延迟、数据吞吐量和带宽利用率）方面都有显著改善。

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引用次数: 0

Adaptive scale-free topology optimization using deep reinforcement learning in UASNs 基于深度强化学习的自适应无标度拓扑优化

IF 4.8 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Ad Hoc Networks

Pub Date : 2025-12-16 DOI: 10.1016/j.adhoc.2025.104121

Haoran Cheng, Xiangyu Bai, Xiaoying Yang

In underwater acoustic sensor networks (UASNs), topology optimization strategies are effective ways to enhance network robustness, reduce transmission delay, and address issues such as high bit error rates and limited energy. However, current topology optimization strategies exhibit limitations in multifactorial consideration and dynamic adaptability, resulting in low energy efficiency and compromised robustness in UASNs. To address these issues, this paper proposes an adaptive scale-free topology optimization algorithm based on deep reinforcement learning (SFTG-DRL), aiming to ensure the network’s lifetime, reduce delay, and enhance the network’s fault tolerance. First, we optimize the transmission power of each sensor node to obtain its optimal power level, while proposing an improved preferential attachment model that incorporates node energy and depth information to achieve scale-free network characteristics. Then, deep reinforcement learning is applied to constrain minimum connections, further refining the topology for improved dynamic adaptability. Finally, extensive simulation experiments are conducted to validate the performance of the proposed algorithm, assessing aspects such as exploration strategy, node degree distribution, fault tolerance, network lifetime, and end-to-end delay.

在水声传感器网络（uasn）中，拓扑优化策略是提高网络鲁棒性、降低传输延迟、解决高误码率和能量有限等问题的有效途径。然而，目前的拓扑优化策略在多因素考虑和动态适应性方面存在局限性，导致usns的能量效率低，鲁棒性受损。针对这些问题，本文提出了一种基于深度强化学习的自适应无标度拓扑优化算法（SFTG-DRL），旨在保证网络的生存期，降低时延，增强网络的容错能力。首先，我们对每个传感器节点的传输功率进行优化，以获得其最优功率水平，同时提出了一种改进的优先依恋模型，该模型融合了节点能量和深度信息，以实现无标度网络特性。然后，应用深度强化学习来约束最小连接，进一步细化拓扑以提高动态适应性。最后，进行了大量的仿真实验来验证所提出算法的性能，评估了探索策略、节点度分布、容错性、网络生存期和端到端延迟等方面。

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引用次数: 0

Joint channel connectivity and interference management in DT-assisted cognitive vehicular networks dt辅助认知车辆网络的联合信道连接与干扰管理

IF 4.8 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Ad Hoc Networks

Pub Date : 2025-12-15 DOI: 10.1016/j.adhoc.2025.104094

Xuan Li , Wen Jiang , Wanting Wang , Tianqing Zhou , Kan Wang , Shuai Liu

In cognitive vehicular networks (CVNs), cognitive vehicles are permitted to opportunistically utilize idle spectrum bands. However, the reclaiming of channels by licensed users may result in significant interference or even network disconnection, failing to meet the reliable data transmission requirements in CVNs. Vehicles need to frequently exchange a large amount of information to cope with unpredictable topology changes and channel reuse scenarios, resulting in significant communication and computational overhead, which conflicts with the low-latency requirements of vehicular networks. To address this, we introduce digital twin (DT) technology into CVNs, enabling cognitive vehicles to effectively avoid transmission interruption caused by primary user channel occupancy. First, we propose a DT-assisted connectivity algorithm (DT-CA) that maps real-world vehicular networks to their digital replicas, enabling interaction in the virtual world. DT-CA assists vehicles in forming specific clusters to ensure channel connectivity. Subsequently, we propose a vehicle-to-vehicle (V2V) connectivity algorithm that quantifies vehicle mobility using communication probabilities and dynamically optimizes cluster structures. Finally, we conduct extensive simulation studies in different traffic scenarios, such as T-junctions and crossroads, which demonstrate that the DT-assisted algorithms have significant advantages in enhancing the connectivity and cluster stability of CVNs, while also exhibiting dynamic adaptability and low complexity.

在认知车辆网络（CVNs）中，认知车辆被允许投机地利用空闲频段。但是，有许可证的用户对信道的回收可能会造成明显的干扰，甚至导致网络中断，无法满足cvn中数据可靠传输的要求。车辆需要频繁地交换大量信息，以应对不可预测的拓扑变化和通道重用场景，这导致了巨大的通信和计算开销，这与车载网络的低延迟要求相冲突。为了解决这个问题，我们在cvn中引入了数字孪生（DT）技术，使认知车辆能够有效地避免因主用户信道占用而导致的传输中断。首先，我们提出了一种dt辅助连接算法（DT-CA），该算法将现实世界的车辆网络映射到它们的数字副本，从而实现虚拟世界中的交互。DT-CA帮助车辆形成特定的集群，以确保通道连接。随后，我们提出了一种车对车（V2V）连接算法，该算法使用通信概率量化车辆的移动性并动态优化集群结构。最后，我们在不同的交通场景下进行了大量的仿真研究，如t型路口和十字路口，结果表明dt辅助算法在增强CVNs的连通性和簇稳定性方面具有显著优势，同时还具有动态适应性和低复杂度。

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引用次数: 0

PriV2I: Privacy-preserving V2I authentication protocol with fine-grained access control PriV2I：具有细粒度访问控制的保护隐私的V2I身份验证协议

IF 4.8 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Ad Hoc Networks

Pub Date : 2025-12-13 DOI: 10.1016/j.adhoc.2025.104122

Zhengze Liu , Nianmin Yao , Shengyuan Bai , Tengyi Mai

As vehicular ad hoc networks (VANETs) increase in size and complexity, ensuring secure, flexible, and privacy-preserving vehicle-to-infrastructure (V2I) authentication remains a major challenge. Existing protocols often focus solely on identity verification, overlooking the need for access control based on vehicle attributes. Furthermore, vehicles must obtain authentication credentials from various trusted entities, including automakers, regulators, and government agencies. However, the absence of a unified credential issuance mechanism introduces fragmentation and inconsistencies during the registration process. To address these issues, we propose a V2I authentication protocol, called PriV2I, that integrates distributed credential issuance, attribute-based access control, and strong anonymity guarantees. During vehicle registration, our approach uses Shamir’s Secret Sharing with a threshold

t

of

n

across multiple certification authorities (CAs) to consolidate credentials. A vehicle credential can only be issued by a predefined threshold number of CAs, enhancing security and flexibility. Within the authentication protocol, Pointcheval-Sanders (PS) signatures enable fine-grained access control based on vehicle attributes such as type and role. Meanwhile, noninteractive zero-knowledge proofs protect identity privacy by allowing vehicles to prove credential possession and policy compliance without revealing sensitive information. The proposed scheme also supports batch authentication at Roadside Units (RSUs) to efficiently handle high-density environments and includes a comprehensive revocation mechanism to trace and revoke malicious vehicles promptly and securely. In our implementation, the computation cost during the authentication phase is 75.58 ms. The communication overhead per authentication exchange is 992 bytes across two messages. Overall, the protocol provides a secure, scalable, and privacy-preserving solution tailored to modern VANET environments.

随着车辆自组织网络（vanet）的规模和复杂性的增加，确保安全、灵活和保护隐私的车辆到基础设施（V2I）身份验证仍然是一个主要挑战。现有协议通常只关注身份验证，忽略了基于车辆属性的访问控制需求。此外，车辆必须从各种可信实体（包括汽车制造商、监管机构和政府机构）获得身份验证凭证。但是，由于缺乏统一的证书颁发机制，在注册过程中会出现碎片化和不一致性。为了解决这些问题，我们提出了一个V2I身份验证协议，称为PriV2I，它集成了分布式凭据发布、基于属性的访问控制和强匿名保证。在车辆注册期间，我们的方法在多个证书颁发机构（ca）之间使用阈值t为n的Shamir秘密共享来合并凭证。车辆凭证只能由预定义的ca阈值数量颁发，从而增强了安全性和灵活性。在身份验证协议中，Pointcheval-Sanders （PS）签名支持基于车辆属性（如类型和角色）的细粒度访问控制。同时，非交互式零知识证明通过允许车辆在不泄露敏感信息的情况下证明凭证的所有权和策略的合规性，从而保护身份隐私。建议计划亦支持路边单位的批量认证，以有效处理高密度环境，并包括一个全面的撤销机制，以迅速和安全地追踪和撤销恶意车辆。在我们的实现中，身份验证阶段的计算成本为75.58 ms。两个消息之间每个身份验证交换的通信开销为992字节。总的来说，该协议为现代VANET环境提供了一个安全、可扩展和隐私保护的解决方案。

{"title":"PriV2I: Privacy-preserving V2I authentication protocol with fine-grained access control","authors":"Zhengze Liu , Nianmin Yao , Shengyuan Bai , Tengyi Mai","doi":"10.1016/j.adhoc.2025.104122","DOIUrl":"10.1016/j.adhoc.2025.104122","url":null,"abstract":"<div><div>As vehicular ad hoc networks (VANETs) increase in size and complexity, ensuring secure, flexible, and privacy-preserving vehicle-to-infrastructure (V2I) authentication remains a major challenge. Existing protocols often focus solely on identity verification, overlooking the need for access control based on vehicle attributes. Furthermore, vehicles must obtain authentication credentials from various trusted entities, including automakers, regulators, and government agencies. However, the absence of a unified credential issuance mechanism introduces fragmentation and inconsistencies during the registration process. To address these issues, we propose a V2I authentication protocol, called PriV2I, that integrates distributed credential issuance, attribute-based access control, and strong anonymity guarantees. During vehicle registration, our approach uses Shamir’s Secret Sharing with a threshold <span><math><mi>t</mi></math></span> of <span><math><mi>n</mi></math></span> across multiple certification authorities (CAs) to consolidate credentials. A vehicle credential can only be issued by a predefined threshold number of CAs, enhancing security and flexibility. Within the authentication protocol, Pointcheval-Sanders (PS) signatures enable fine-grained access control based on vehicle attributes such as type and role. Meanwhile, noninteractive zero-knowledge proofs protect identity privacy by allowing vehicles to prove credential possession and policy compliance without revealing sensitive information. The proposed scheme also supports batch authentication at Roadside Units (RSUs) to efficiently handle high-density environments and includes a comprehensive revocation mechanism to trace and revoke malicious vehicles promptly and securely. In our implementation, the computation cost during the authentication phase is 75.58 ms. The communication overhead per authentication exchange is 992 bytes across two messages. Overall, the protocol provides a secure, scalable, and privacy-preserving solution tailored to modern VANET environments.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"183 ","pages":"Article 104122"},"PeriodicalIF":4.8,"publicationDate":"2025-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790980","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}

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