Trust Management Model for Secure Internet of Vehicles: Comparison
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Please note this is a comparison between Version 2 by Yuanfang Chen and Version 1 by Yuanfang Chen.

车联网（The Internet of Vehicles (IoV) enables vehicles to share data that help vehicles perceive the surrounding environment. However, vehicles can spread false information to other IoV）使车辆能够共享数据，帮助车辆感知周围环境。但是，车辆可以将虚假信息传播到其他车联网节点;这种不正确的信息会误导车辆并导致交通混乱，因此需要车辆信任模型来检查消息的可信度。 nodes; this incorrect information misleads vehicles and causes confusion in traffic, therefore, a vehicular trust model is needed to check the trustworthiness of the message. 

  • Internet of Vehicles
  • blockchain
  • trust management

随着5G和6G的普及[1]以及可编程V2X环境和基于区块链的V2X（车辆到一切）技术[2]的应用，车联网已经迎来了快速发展。在车联网中，车辆可以与其他节点共享感知信息，包括交通安全信息、天气信息、道路信息等，并从其他节点获取服务[3]，从而提高交通安全和效率。

Introduction

然而，车辆可能不可靠，我们需要解决如何在使用车联网之前，根据车辆的历史行为来评估车辆发送的消息的可靠性，并根据车辆的历史行为量化评估措施[4]（即信任值）的问题。例如，在车联网中，攻击者可能出于自私的原因控制车辆传播虚假信息，从而导致错误的环境感知和驾驶决策，从而危及驾驶员的安全并造成严重的交通事故[5]。
在车联网中，信任模型背后的基本原则是通过识别和取消恶意车辆及其产生的虚假消息来确保数据的可靠传输[6]。信任管理机制可以帮助车辆计算接收到的消息的可信度[7]，以提高车辆决策的准确性。综上所述，现有的信任管理机制一般可分为集中式信任管理和分布式信任管理[8]。集中式信任管理存在单点故障等问题，分布式信任管理存在信任值更新延迟等问题。
区块链作为比特币的核心技术，是一个分布式账本[9]。由于其分散和不可变的特性，区块链可以记录和更新车辆的信任值。有了区块链，即使少数RSU出现存储错误或被攻击者控制，整个网络的共识结果仍然可以得到保护。因此，一些研究者将区块链与信任管理机制相结合，解决上述集中式和分布式信任管理的问题。
然而，基于区块链或单层区块链的信任管理机制研究仍存在一些问题。首先，车辆每次验证交易时都需要存储一个完整的区块链账本或向相邻的全节点发送请求进行验证，这无疑会增加车辆的负担，浪费车辆的资源。其次，由于区块链节点数量非常大，覆盖面活跃广，也很难根据地理位置、通信流量、节点密度等客观因素进行分级管理。最后，由于车辆数据的重要性并不相同，如果系统不区分消息的重要性，车辆和RSU之间的数据存储和数据共享效率低下。因此，如何使系统能够存储和共享不同重要性级别的数据是一个问题。
考虑一个场景，车辆With the Apopularity 需要了解街道 A 的交通和业务情况，车辆 Aof 5G and 6G 向附近的[1] RSUand 发送请求（我们假设每辆车都配备了一个车载单元 （OBU），该单元使用专用短程通信 （DSRC） 或蜂窝 V2X （C-V2X）the application of programmable V2X environments and blockchain-based V2X (vehicle to everything) technologies [2], the IoV has embraced rapid development. In IoV, vehicles can share their perceived information with other nodes, including traffic safety information, weather information, road information, etc., and obtain services from other nodes [3], thus, improving traffic safety and efficiency.
However, vehicles can be unreliable, and we need to solve the problems of how to evaluate the reliability of the message sent by the vehicle and quantify an evaluation measure [4] (i.e., trust value) based on the historical behavior of the vehicle before utilizing IoV. For example, in IoV, vehicles may be controlled by attackers to spread false information for selfish reasons, thus, leading to false environmental perception and driving decision-making and thus, endangering the safety of drivers and causing serious traffic accidents [5].
In IoV, the basic principle behind the trust model is to ensure the reliable transmission of data by identifying and canceling malicious vehicles and the false news generated by them [6]. The trust management mechanism can help vehicles calculate the credibility of received messages [7] to improve the accuracy of vehicles in decision-making. In summary, the existing trust management mechanisms can be generally divided into centralized trust management and distributed trust management [8]. Centralized trust management has problems such as single points of failure, while distributed trust management has problems such as the delayed update of trust value.
Blockchain, as bitcoin’s core technology, is a distributed ledger [9]. Due to its decentralized and immutable characteristics, blockchain can record and update vehicles’ trust values. With blockchain, even if a small number 通信技术与of RSU 进行微波通信）。收到请求后，Rs have storage errors or are controlled by attackers, the consensus results of the entire network can still be protected. Therefore, some researchers combine blockchain with trust management mechanisms to solve the above problems of centralized and distributed trust management.
However, there are still some problems in the research of trust management mechanisms based on the blockchain or single-layer blockchain. First of all, the vehicles need to store a complete blockchain ledger or send a request to the adjacent full node for verification every time the transaction is verified, which will undoubtedly increase the burden of the vehicle and waste the vehicle’s resources. SUecondly, because the number of blockchain nodes is very large and the coverage is active and wide, it is also difficult to conduct hierarchical management according to objective factors such as geographical location, communication traffic, and node density. Finally, because the importance of vehicle data is not the same, the data storage and data 会查询车辆 A 的信任值，并允许车辆 A 在其信任值高于特定阈值时使用该服务。RSU查询RSU区块链中的相关数据，并通过安全传输通道将其返回给车辆A。然后，车辆A可以根据RSU返回的数据和自身车辆区块链中的数据，充分了解前方区域的情况。 但是，车辆和RSU都可能成为恶意节点并恶意行为，从而影响系统中的其他节点。例如，恶意sharing between vehicles and RSUs is inefficient if the system does not distinguish the importance of messages. Therefore, how to enable the system to store and share data of different levels of importance is RSUa 可能会篡改车辆信任值，恶意车辆可能会发送虚假消息。针对恶意节点问题，我们提出一种基于双层区块链的信任管理机制。该机制分为三个部分。其中，第一部分是双层区块链，第二部分是系统架构，最后一部分是共识机制。我们通过这三个部分介绍了所提出的双层区块链信任管理机制。problem.

Trust Management Mechanism of Double-Layer Blockchain

本文提出一种基于双层区块链的信任管理机制DLBTM，以解决车联网中针对车辆与RSU通信的恶意攻击。利用双层区块链，可以减轻车联网负担，实现车联网节点分级管理，保护车辆隐私，实现分级数据存储和共享，实现车联网节点的有效信任评估和管理。值得注意的是，与同类算法相比，我们的类型算法和消息评估器选择算法具有更低的时间复杂度，仿真实验表明，我们的信任管理机制能够有效地识别恶意节点。因此，我们的DLBTM在复杂的车联网环境中是有效和可行的。对于未来的研究，我们将在我们的模型中引入激励机制来促进合作行为。

Consider a scenario where vehicle A needs to know about the traffic and business situation on street A. Vehicle A sends a request to the nearby RSU (we assume that each vehicle is equipped with an Onboard Unit (OBU), which uses Dedicated Short Range Communication (DSRC) or Cellular-V2X (C-V2X) communication technology for micro-wave communication with the RSU). Upon receiving the request, the RSU queries the trust value of vehicle A and allows vehicle A to use the service if its trust value is above a certain threshold. RSU queries relevant data in the RSU blockchain and returns it to vehicle A through a secure transmission channel. Vehicle A can then fully understand the situation of the front area according to the data returned by RSU and the data in its own vehicle blockchain.

However, vehicles and RSUs can both become malicious nodes and act maliciously, affecting other nodes in the system. For example, malicious RSUs may tamper with vehicle trust values, and malicious vehicles may send false messages. To solve the problem of malicious nodes, we propose a trust management mechanism based on the double-layer blockchain. This mechanism is divided into three parts. Of which, the first part is the double-layer blockchain, the second part is the system architecture, and the last part is the consensus mechanism. We introduce the proposed double-layer blockchain trust management mechanism through these three parts.

In this paper, we propose a double-layer blockchain-based trust management mechanism DLBTM to solve the malicious attacks targeted on the communication among vehicles and RSUs in IoV. Using the double-layer blockchain, we can reduce the burden of vehicles in IoV, realize hierarchical management of nodes in IoV, protect vehicular privacy, implement hierarchical data storage and sharing, and realize effective trust evaluation and management of vehicle nodes. Remarkably, our message classification on type algorithm and message evaluator selection algorithm has lower time complexity compared with similar algorithms, and simulation experiments show that our trust management mechanism can effectively identify malicious nodes. Therefore, our DLBTM is effective and feasible in complex IoV environments. For future research, we will introduce an incentive mechanism into our model to promote cooperative behavior.

论文亮点：Highlights of the paper:

  1. 提出双层区块链结构，允许根据消息重要性选择性存储数据，降低存储压力。
  2. 使用逻辑回归算法计算车辆节点信任值，准确判断好节点和坏节点。
  3. 采用先进的共识机制，如Ouroboros，使系统安全可靠。
  4. 通过仿真测试，在各种条件下都能有效识别90%以上的恶意节点。
  5. 与现有算法相比，该方法具有较低的时间和空间复杂度。
  6. Proposes a double-layer blockchain structure that allows selective data storage based on message importance, reducing storage pressure.
  7. Uses logistic regression algorithms to compute vehicle node trust values, accurately judging good and bad nodes.
  8. Employs advanced consensus mechanisms like Ouroboros to make the system secure and reliable.
  9. Tested through simulations, can effectively identify over 90% of malicious nodes under various conditions.
  10. Compared to existing algorithms, this method has lower time and space complexity.

References

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