In the near future, the Internet of Vehicles (IoV) is foreseen to become an inviolable part of smart cities. The integration of vehicular ad hoc networks (VANETs) into the IoV is being driven by the advent of the Internet of Things (IoT) and high-speed communication. However, both the technological and non-technical elements of IoV need to be standardized prior to deployment on the road. This study focuses on trust management (TM) in the IoV/VANETs/ITS (intelligent transport system). Trust has always been important in vehicular networks to ensure safety. A variety of techniques for TM and evaluation have been proposed over the years, yet few comprehensive studies that lay the foundation for the development of a “standard” for TM in IoV have been reported. The motivation behind this study is to examine all the TM models available for vehicular networks to bring together all the techniques from previous studies in this review. The study was carried out using a systematic method in which 31 papers out of 256 research publications were screened. An in-depth analysis of all the TM models was conducted and the strengths and weaknesses of each are highlighted. Considering that solutions based on AI are necessary to meet the requirements of a smart city, our second objective is to analyze the implications of incorporating an AI method based on “context awareness” in a vehicular network. It is evident from mobile ad hoc networks (MANETs) that there is potential for context awareness in ad hoc networks. The findings are expected to contribute significantly to the future formulation of IoVITS standards. In addition, gray areas and open questions for new research dimensions are highlighted.

中文翻译：

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最先进的IoV信任管理元综合系统文献综述（SLR）

在不久的将来，预计车辆互联网（IoV）将成为智慧城市不可侵犯的一部分。物联网（IoT）和高速通信的出现推动了车载自组织网络（VANET）到IoV的集成。但是，IoV的技术和非技术元素都需要在道路上部署之前进行标准化。这项研究的重点是IoV / VANETs / ITS（智能传输系统）中的信任管理（TM）。信任在车辆网络中一直很重要，以确保安全。多年来，已经提出了多种用于TM和评估的技术，但是，很少有全面的研究为IoV中TM的“标准”的发展奠定基础。这项研究的目的是研究可用于车辆网络的所有TM模型，以将本次回顾中先前研究中的所有技术结合在一起。使用系统的方法进行了研究，从256个研究出版物中筛选了31篇论文。对所有TM模型进行了深入分析，并突出了每种模型的优缺点。考虑到基于AI的解决方案对于满足智慧城市的需求是必要的，因此我们的第二个目标是分析在车辆网络中纳入基于“上下文感知”的AI方法的含义。从移动自组织网络（MANET）可以明显看出，在自组织网络中存在上下文感知的潜力。预期这些发现将为IoVITS标准的未来制定做出重大贡献。