面向时空特征评估的SDVN信任管理机制

doi:10.3778/j.issn.1002-8331.2111-0014

摘要/Abstract

摘要： 车载自组织网络（vehicular ad-hoc network，VANET）在提高交通效率和解决行车安全方面有着广阔的应用前景，但其发展的同时也带来了挑战。随着智能车辆的增加，现有架构不能满足车辆的快速增长需求，同时VANET的开放性也助长了车辆的恶意竞争。使用软件定义车载网络（software-defined vehicle network，SDVN）架构以提升车联网的网络性能，并基于此架构设计了一种信任评估与信任管理机制。信任评估方法考虑车辆的时空特征来限制和标准化车辆的行为，提高了车辆恶意行为检测的准确性。此外，该机制设计了一种基于联盟链Hyperledger Fabric的信任管理方法，借助区块链来实现车辆的数据安全，提高了信任值更新和查询的效率。实验分析表明，所提出的信任管理机制能够在SDVN网络中保证消息的一致性和安全性，缩短了共识时间，提高了信任管理的整体效率。

关键词: 信任评估, 信任管理, 软件定义车载网络（SDVN）, Hyperledger Fabric

Abstract: Vehicular ad-hoc network（VANET） has broad application prospects in improving traffic efficiency and solving traffic safety, but its development also brings challenges. With the increase of intelligent vehicles, the existing architecture cannot meet the rapid growth of vehicle demand, and the openness of VANET also encourages the vicious competition of vehicles. This paper uses software-defined vehicle network（SDVN） architecture to improve the network performance of the Internet of vehicles, and designs a trust evaluation and trust management mechanism based on this architecture. The trust evaluation method considers the temporal and spatial characteristics of vehicles to limit and standardize vehicle behaviors and improve the accuracy of vehicle malicious behavior detection. In addition, this mechanism designs a trust management method based on Hyperledger Fabric, which realizes the data security of vehicles with the help of blockchain, and improves the efficiency of updating and querying trust values. Experimental analysis shows that the proposed trust management mechanism can ensure message consistency and security in SDVN network, shorten the consensus time, and improve the overall efficiency of trust management.

Key words: trust assessment, trust management, software-defined vehicle network（SDVN）, Hyperledger Fabric

蔡沛婧, 罗威, 胡钰林, 吴静. 面向时空特征评估的SDVN信任管理机制[J]. 计算机工程与应用, 2023, 59(4): 261-268.

CAI Peijing, LUO Wei, HU Yulin, WU Jing. Trust Management Mechanism of SDVN for Spatio-Temporal Feature Evaluation[J]. Computer Engineering and Applications, 2023, 59(4): 261-268.

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