Wang Yingxun, Mahmood Adnan, Sabri Mohamad Faizrizwan Mohd, Zen Hushairi, Kho Lee Chin
Faculty of Engineering, Universiti Malaysia Sarawak, Kota Samarahan 94300, Sarawak, Malaysia.
Faculty of Computer and Information Engineering, Qilu Institute of Technology, Jinan 250200, China.
Sensors (Basel). 2024 Jan 29;24(3):863. doi: 10.3390/s24030863.
The emerging yet promising paradigm of the Internet of Vehicles (IoV) has recently gained considerable attention from researchers from academia and industry. As an indispensable constituent of the futuristic smart cities, the underlying essence of the IoV is to facilitate vehicles to exchange safety-critical information with the other vehicles in their neighborhood, vulnerable pedestrians, supporting infrastructure, and the backbone network via vehicle-to-everything communication in a bid to enhance the road safety by mitigating the unwarranted road accidents via ensuring safer navigation together with guaranteeing the intelligent traffic flows. This requires that the safety-critical messages exchanged within an IoV network and the vehicles that disseminate the same are highly reliable (i.e., trustworthy); otherwise, the entire IoV network could be jeopardized. A state-of-the-art trust-based mechanism is, therefore, highly imperative for identifying and removing malicious vehicles from an IoV network. Accordingly, in this paper, a machine learning-based trust management mechanism, MESMERIC, has been proposed that takes into account the notions of direct trust (encompassing the trust attributes of interaction success rate, similarity, familiarity, and reward and punishment), indirect trust (involving confidence of a particular trustor on the neighboring nodes of a trustee, and the direct trust between the said neighboring nodes and the trustee), and context (comprising vehicle types and operating scenarios) in order to not only ascertain the trust of vehicles in an IoV network but to segregate the trustworthy vehicles from the untrustworthy ones by means of an optimal decision boundary. A comprehensive evaluation of the envisaged trust management mechanism has been carried out which demonstrates that it outperforms other state-of-the-art trust management mechanisms.
新兴且前景广阔的车联网（IoV）范式最近受到了学术界和工业界研究人员的广泛关注。作为未来智能城市不可或缺的组成部分，车联网的核心本质是通过车与万物通信，促进车辆与周边其他车辆、易受伤害的行人、支持性基础设施以及骨干网络交换安全关键信息，以期通过确保更安全的导航并保证智能交通流，减少不必要的道路事故，从而提高道路安全性。这就要求在车联网网络内交换的安全关键消息以及传播这些消息的车辆具有高度可靠性（即值得信赖）；否则，整个车联网网络可能会受到威胁。因此，一种基于最新技术的信任机制对于识别和清除车联网网络中的恶意车辆至关重要。相应地，本文提出了一种基于机器学习的信任管理机制MESMERIC，该机制考虑了直接信任（包括交互成功率、相似度、熟悉度以及奖惩等信任属性）、间接信任（涉及特定信任者对受托人相邻节点的信任度，以及上述相邻节点与受托人之间的直接信任）和上下文（包括车辆类型和运行场景），以便不仅确定车联网网络中车辆的信任度，还通过最优决策边界将值得信赖的车辆与不可信的车辆区分开来。对所设想的信任管理机制进行了全面评估，结果表明它优于其他现有先进的信任管理机制。
