基于區(qū)塊鏈的協(xié)作式車聯(lián)網信任管理方案

張海波; 譚茂煌; 徐勇軍; 李方偉; 王明月

doi:10.11999/JEIT240517

基于區(qū)塊鏈的協(xié)作式車聯(lián)網信任管理方案

doi: 10.11999/JEIT240517 cstr: 32379.14.JEIT240517

1.
重慶郵電大學通信與信息工程學院重慶 400065
2.
公共大數據安全技術重慶市重點實驗室重慶 401420

基金項目: 國家重點研發(fā)計劃(SQ2023YFB250002402)，國家自然科學基金(62371082)，公共大數據安全技術重慶市重點實驗室開放基金(CQKL-QJ202300002)，重慶市自然科學基金(CSTB2023NSCQ-BSX0007)

詳細信息

作者簡介:
張海波：男，副教授，碩士生導師，研究方向為車聯(lián)網、區(qū)塊鏈等

譚茂煌：男，碩士生，研究方向為車聯(lián)網、信任管理等

徐勇軍：男，教授，博士生導師，研究方向為車聯(lián)網等

李方偉：男，教授，博士生導師，研究方向為移動通信安全等

王明月：女，講師，研究方向為移動通信安全技術

通訊作者:
譚茂煌　563423917@qq.com

中圖分類號: TN92
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- 文章訪問數: 197
- HTML全文瀏覽量: 87
- PDF下載量: 38
- 被引次數: 0
出版歷程
- 收稿日期: 2024-06-25
- 修回日期: 2025-02-14
- 網絡出版日期: 2025-02-21
- 刊出日期: 2025-03-01

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: 針對車聯(lián)網(IoV)中傳統(tǒng)信任管理方案對惡意車輛的識別假陽率高、無法滿足多樣化服務且傳統(tǒng)共識算法不適用于當前車聯(lián)網環(huán)境的問題，該文提出了基于區(qū)塊鏈的協(xié)作式車聯(lián)網信任管理方案。構建了基于狄利克雷分布的信任管理模型，將車輛信任和協(xié)作服務劃分為多個等級，針對不同服務調整信任等級閾值。設計了具有反饋機制的信任等級評價算法，考慮協(xié)作車輛當前狀態(tài)、鄰居推薦、歷史信任信息、服務質量4方面因素，從協(xié)作前、后兩階段對協(xié)作車輛信任等級進行評價。改進了傳統(tǒng)的工作量證明(PoW)共識算法，動態(tài)調整礦工節(jié)點出塊難度。仿真結果表明，相比同類方案，所提方案在保證能夠高效識別惡意節(jié)點的前提下，還能夠進一步降低識別假陽率，提高協(xié)作成功率和共識效率。
- 車聯(lián)網 /
- 信任管理 /
- 區(qū)塊鏈 /
- 車輛協(xié)作 /
- 共識算法
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 RoaSide 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

圖 1 系統(tǒng)模型

下載: 全尺寸圖片幻燈片

圖 2 信任管理方案流程

下載: 全尺寸圖片幻燈片

圖 3 惡意車輛識別性能對比

下載: 全尺寸圖片幻燈片

圖 4 惡意車輛識別假陽率對比

下載: 全尺寸圖片幻燈片

圖 5 協(xié)作成功率對比

下載: 全尺寸圖片幻燈片

圖 6 區(qū)塊生成時間對比

下載: 全尺寸圖片幻燈片

表 1 仿真參數

參數	數值
總車輛數(輛)	1000
車輛密度(輛/km)	40
車輛速度(km/h)	40～60
車輛最大通信距離${D_{{\text{max}}}}$(m)	200
獎懲調節(jié)因子$ \lambda $	0.4,0.6,0.8
當前狀態(tài)評估權重因子$ ({\alpha _1},{\alpha _2},{\alpha _3}) $	(1/3,1/3,1/3)
鄰居推薦權重調整因子$r$	0.2
服務等級權重參數$ ({\omega _1},{\omega _2},{\omega _3}) $	(0.4,0.6,0.8)
信任閾值${\text{Thr}}{{\text{e}}_1}$	0.4
數據包投遞率閾值${\text{Thr}}{{\text{e}}_2}$	0.8
哈希門限調控因子$\omega $	0.02
哈希門限調控參數$\theta $	3
難度調節(jié)參數${\text{Dap}}$	8

下載: 導出CSV

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