基于区块链的协作式车联网信任管理方案

张海波; 谭茂煌; 徐勇军; 李方伟; 王明月

doi:10.11999/JEIT240517

基于区块链的协作式车联网信任管理方案

doi: 10.11999/JEIT240517

1.
重庆邮电大学通信与信息工程学院重庆 400065
2.
公共大数据安全技术重庆市重点实验室重庆 401420

基金项目: 国家重点研发计划(SQ2023YFB250002402)，国家自然科学基金(62371082)，公共大数据安全技术重庆市重点实验室开放基金(CQKL-QJ202300002)，重庆市自然科学基金(CSTB2023NSCQ-BSX0007)

详细信息

作者简介:
张海波：男，副教授，硕士生导师，研究方向为车联网、区块链等

谭茂煌：男，硕士生，研究方向为车联网、信任管理等

徐勇军：男，教授，博士生导师，研究方向为车联网等

李方伟：男，教授，博士生导师，研究方向为移动通信安全等

王明月：女，讲师，研究方向为移动通信安全技术

通讯作者:
谭茂煌　563423917@qq.com

中图分类号: TN92
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出版历程
- 收稿日期: 2024-06-25
- 修回日期: 2025-02-14
- 网络出版日期: 2025-02-21

Trust Management Scheme for Collaborative Internet of Vehicles Based on Blockchain

1.
School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
Chongqing Key Laboratory of Public Big Data Security Technology, Chongqing 401420, China

Funds: The National Key Research and Development Program (SQ2023YFB250002402), The National Natural Science Foundation of China (62371082), Foundation of Chongqing Key Laboratory of Public Big Data Security Technology (CQKL-QJ202300002), The Natural Science Foundation of Chongqing (CSTB2023NSCQ-BSX0007)

摘要

摘要: 针对车联网(IoV)中传统信任管理方案对恶意车辆的识别假阳率高、无法满足多样化服务且传统共识算法不适用于当前车联网环境的问题，该文提出了基于区块链的协作式车联网信任管理方案。构建了基于狄利克雷分布的信任管理模型，将车辆信任和协作服务划分为多个等级，针对不同服务调整信任等级阈值。设计了具有反馈机制的信任等级评价算法，考虑协作车辆当前状态、邻居推荐、历史信任信息、服务质量四方面因素，从协作前、后两阶段对协作车辆信任等级进行评价。改进了传统的工作量证明(PoW)共识算法，动态调整矿工节点出块难度。仿真结果表明，相比同类方案，所提方案在保证能够高效识别恶意节点的前提下，还能够进一步降低识别假阳率，提高协作成功率和共识效率。
- 车联网 /
- 信任管理 /
- 区块链 /
- 车辆协作 /
- 共识算法
Abstract: Objective The Internet of Vehicles (IoV) plays a pivotal role in the development of modern intelligent transportation systems. It enables seamless communication among vehicles, road infrastructure, and pedestrians, thereby improving traffic management, enhancing driving experiences, and optimizing resource utilization. However, existing IoV systems face a range of complex and urgent challenges. A major issue is the high false positive rate in identifying malicious vehicles. These vehicles, intending to disrupt network operations, may engage in harmful activities such as dropping packets or delaying transmissions. This not only compromises data transmission integrity but also poses a serious threat to the overall security and reliability of the IoV network. Furthermore, inaccurate identification may lead to the wrongful penalization of legitimate vehicles, disrupting their normal operations. Another challenge stems from the diverse and complex service requirements within IoV. These range from entertainment services that enhance user experience, to traffic efficiency services aimed at optimizing traffic flow, and highly sensitive services related to traffic safety and privacy. Unfortunately, existing solutions fail to adequately address these varied needs, leading to suboptimal service delivery and potential security risks. Traditional consensus algorithms also face significant limitations in the dynamic IoV environment. The high resource consumption and low efficiency of these algorithms not only waste valuable computational resources but also hinder timely and accurate information processing, affecting the overall performance of the IoV system. To address these issues, it is critical to develop an innovative solution to enhance the security, reliability, and adaptability of IoV systems. This paper proposes a collaborative trust management scheme based on blockchain technology, which aims to address these challenges and improve the overall performance of IoV. Methods To address the challenges outlined above, a comprehensive set of methods is designed. First, a trust management model based on the Dirichlet distribution is developed. This model classifies vehicle trust and collaborative services into multiple levels, each representing a different degree of trustworthiness and service quality. The trust level thresholds for different service types are finely tuned. For example, traffic safety and privacy-related services, which require high security and reliability, are assigned higher trust level thresholds, ensuring that only vehicles with a sufficient trust level can provide these critical services. Second, a trust level evaluation algorithm integrated with a feedback mechanism is developed. This algorithm considers four key factors: the current state of the collaborating vehicle, neighbor recommendations, historical trust data, and service quality. The evaluation process occurs in two distinct but complementary stages: before and after collaboration.Before collaboration, the vehicle's current state is thoroughly assessed, including its computing power, which determines its capacity to handle complex tasks; propagation delay, which indicates the timeliness of communication; and familiarity with the requesting vehicle, which can influence collaboration reliability. These factors, along with neighbor recommendations and historical trust data, contribute to an initial trustworthiness assessment. After collaboration, a feedback mechanism based on packet delivery ratio and time delay is applied. The packet delivery ratio measures the proportion of successfully delivered packets, while time delay reflects the responsiveness of the vehicle during communication. These metrics are used to adjust the vehicle's trust level, providing a more dynamic and accurate evaluation of its trustworthiness. Third, the traditional Proof of Work (PoW) consensus algorithm is enhanced by introducing a task priority index. This dynamic adjustment of block creation difficulty for miner nodes allows blocks containing critical trust information or high-priority service data to be added to the blockchain more quickly. This enhancement improves blockchain efficiency. Results and Discussions The simulation results provide compelling evidence for the effectiveness of the proposed scheme. In terms of malicious vehicle identification, as shown in (Fig. 3), although the initial identification rate of malicious vehicles is slightly lower than that of some binary-evaluation-based schemes, the proposed scheme demonstrates a significant reduction in the false positive rate. The comparison of false positive rates, presented in (Fig. 4), clearly illustrates that the proposed scheme outperforms existing methods. This improvement is attributed to the carefully designed trust level thresholds, which prevent ordinary vehicles with low-quality services from being misclassified as malicious when performing high-level services. Regarding the collaboration success rate, (Fig. 5) indicates that the proposed scheme performs better across various service scenarios and different proportions of malicious vehicles. Even when the proportion of malicious vehicles reaches 50%, the collaboration success rate for the three-level services remains above 80%, emphasizing the robustness and reliability of the proposed scheme. In terms of consensus efficiency, as shown in (Fig. 6), the improved algorithm outperforms the traditional PoW consensus algorithm. By dynamically adjusting to the actual conditions, the enhanced algorithm allows the Roadside Unit (RSU) responsible for the area to generate blocks more quickly when the task priority index is larger. This leads to faster processing of critical information and better alignment with the dynamic needs of the IoV collaborative scenario. Conclusions The collaborative trust management scheme based on blockchain proposed in this paper effectively addresses critical challenges in IoV systems, including malicious vehicle identification, service adaptability, and the applicability of consensus algorithms. By accurately classifying service types and vehicle trust levels, and by employing a comprehensive trust evaluation algorithm along with an enhanced consensus algorithm, this scheme significantly improves the security and trustworthiness of IoV systems. Furthermore, it provides a scalable solution for future IoV deployments, facilitating the broader adoption of IoV technology.
- Internet of vehicles /
- Trust management /
- Blockchain /
- Vehicle collaboration /
- Consensus algorithm

HTML全文

图 1 系统模型

下载: 全尺寸图片幻灯片

图 2 信任管理方案流程

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图 3 恶意车辆识别性能对比

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图 4 恶意车辆识别假阳率对比

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图 5 协作成功率对比

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图 6 区块生成时间对比

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表 1 仿真参数

参数	数值
总车辆数(辆)	1000
车辆密度(辆/km)	40
车辆速度(km/h)	40-60
车辆最大通信距离${D_{{\text{max}}}}$(m)	200
奖惩调节因子$ \lambda $	0.4,0.6,0.8
当前状态评估权重因子$ ({\alpha _1},{\alpha _2},{\alpha _3}) $	(1/3,1/3,1/3)
邻居推荐权重调整因子$r$	0.2
服务等级权重参数$ ({\omega _1},{\omega _2},{\omega _3}) $	(0.4,0.6,0.8)
信任阈值${\text{Thr}}{{\text{e}}_1}$	0.4
数据包投递率阈值${\text{Thr}}{{\text{e}}_2}$	0.8
哈希门限调控因子$\omega $	0.02
哈希门限调控参数$\theta $	3
难度调节参数${\text{Dap}}$	8

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