Internet of vehicles digital twin trust evaluation method based on block chain

Abstract

The invention discloses an Internet of Vehicles digital twin trust evaluation method based on a block chain. The method comprises the following steps: firstly, calculating a node combination trust value and an authoritative combination trust value of each node in an Internet of Vehicles digital twin network through a base station; secondly, the base station generates a comprehensive trust value of each node according to the weight in combination with the node combination trust value and the authoritative combination trust value; and finally, the base station packages the comprehensive trust value together with the timestamp or verification information into a new block and adds the new block to the block chain, thereby completing digital twin trust evaluation of the Internet of Vehicles. According to the method, the accuracy and reliability of trust evaluation are ensured, the interference of malicious nodes is effectively eliminated, and the fairness and accuracy of the recommended trust value are ensured.

Description

Technical Field

[0001] The present invention relates to the field of Internet of Vehicles security, and in particular to a blockchain-based Internet of Vehicles digital twin trust assessment method. Background Art

[0002] The core of digital twin technology is to build a virtual space equivalent to the physical space, thereby improving the stability of the virtual environment. However, there are still many technical challenges in the process of achieving stable mapping between virtual and physical space. In particular, in the digital twin model, vehicles may have malicious behaviors, and such attacks may have a significant impact on the overall reliability of the mobile network.

[0003] In a self-organizing vehicle network, mutual trust between vehicles is the basis for achieving stable network operation. However, since vehicles are in a high-speed moving state and the communication time is extremely short, it is difficult for the system to evaluate the credibility of the interactive information in real time and accurately. Trust evaluation, as a key technology, is widely regarded as an effective means to solve the trust and privacy issues in self-organizing vehicle digital twin networks. An efficient trust evaluation system can evaluate the historical behavior of entities by assigning trust values, and then accurately judge their credibility.

[0004] Existing trust evaluation models are mainly divided into two categories: centralized trust models and decentralized trust models. Centralized trust models rely on central servers to manage the reputation of vehicles. However, as the coverage of intelligent transportation systems continues to expand, such models have exposed the following problems: high latency, performance bottlenecks in multi-request scenarios, and single point failure risks. In contrast, decentralized trust evaluation models have gradually become a hot research direction due to their distributed architecture.

[0005] In view of this, there is an urgent need to design a trust assessment method for vehicle network digital twins based on blockchain technology. This method aims to enable the authoritative agencies in the vehicle self-organizing digital twin network to identify and isolate potential malicious behaviors in real time through a distributed trust assessment mechanism, thereby maximizing the security and stability of the network.

[0006] At present, the existing technology, such as the patent with application publication number CN113380024A, proposes a reputation update method and trust calculation method based on the Internet of Vehicles. It classifies the vehicle set and uses a three-value subjective logic algorithm to calculate the reputation value, thereby realizing the trust evaluation between vehicles. However, this method mainly focuses on the interaction and trust evaluation between vehicles, and lacks consideration for the evaluation between base stations and vehicles. In addition, the existing technology does not fully combine the application scenarios of digital twin technology in the Internet of Vehicles, resulting in an incomplete trust evaluation system that is difficult to cover the multi-dimensional dynamic trust needs in the complex Internet of Vehicles environment. Summary of the invention

[0007] In view of the technical bottlenecks of the existing technology, which is that the solution is single and it is difficult to identify malicious nodes, the present invention proposes a new technical framework, which aims to efficiently solve the trust evaluation problem in the vehicle-mounted self-organizing digital twin network. This method provides a vehicle network digital twin trust evaluation method based on distributed trust evaluation by introducing blockchain technology. The present invention makes up for the above shortcomings. On the basis of the trust evaluation between vehicles, this method introduces the trust evaluation mechanism between base stations and vehicles, which comprehensively improves the accuracy and dynamic adaptability of trust evaluation. At the same time, blockchain technology is used to ensure the security and immutability of trust data, and the trust evaluation process is optimized by combining time decay, trust recommendation filtering and other technologies, which is suitable for complex and dynamic vehicle network digital twin system environments.

[0008] The technical solution adopted by the present invention is as follows:

[0009] S1. Calculate the node combination trust value of each node in the Internet of Vehicles digital twin network through the base station.

[0010] Each node in the digital twin network of the Internet of Vehicles reports the events received from other nodes to the authoritative base station in the current domain. The base station calculates the direct trust value of the node through the Bayesian reasoning method to quantify its trust level. The time decay function is introduced, and the target node calculates the recommended trust value based on the historical evaluation of the recommended node with which it interacts. The recommended trust filtering algorithm based on k-means is used to filter out malicious nodes, and the similarity between each trusted node and the source node is calculated in the filtered trusted nodes. The node recommended trust value is calculated based on the similarity and the latest comprehensive trust value of the recommended node on the blockchain. The node direct trust and node recommended trust are combined by weight to obtain the node combined trust value.

[0011] S2. Calculate the authoritative combined trust value of each node in the Internet of Vehicles digital twin network through the base station.

[0012] S21, determine the time t when the node joins the IoV digital twin network in , calculate the time interval tt from joining the network to the current time t in . Through the exponential decay function Assign a lower initial trust value to the newly added node, and gradually increase the trust value over time, and count the event reporting frequency R of node i i , and the average reporting frequency of all nodes in the network By calculation Evaluate the degree of deviation between node behavior and the overall expectations of the network, and finally comprehensively derive the authoritative direct trust value of the node based on the node joining time and reporting frequency deviation

[0013] S22: Maintain the historical base station information of each vehicle on the blockchain. The base station where the current node is located obtains the H base stations it has passed through the latest from the blockchain, and requests these base stations to obtain the trust value of the target node i when it has passed through the base station in the past. Where k represents the base station that the node has passed, H represents the first H base stations that the node has passed, and the time that has passed since the node left each base station is calculated. Using the time decay factor λ2, calculate the decay weight Calculate the authority recommendation trust at time t

[0014] S23, direct trust value of authority through preset weight ω And the authoritative recommendation trust value Perform weighted summation to calculate the authoritative combined trust value of the target node

[0015] S3. The base station generates a comprehensive trust value for each node by combining the node combination trust value and the authority combination trust value according to the weight.

[0016] S4. The base station packages the comprehensive trust value together with the timestamp or verification information into a new block and adds it to the blockchain to complete the trust assessment of the digital twin of the Internet of Vehicles.

[0017] Beneficial effects of the present invention:

[0018] (1) Trust value calculation based on interactive behavior: A direct trust value calculation method based on interactive behaviors such as message authenticity, data tampering, and packet loss is proposed to ensure the accuracy and reliability of trust evaluation.

[0019] (2) Introduction of recommendation trust filtering algorithm: A recommendation trust filtering mechanism based on the k-means clustering algorithm is adopted to effectively eliminate the interference of malicious nodes, thereby ensuring the fairness and accuracy of the recommended trust value.

[0020] (3) Introducing authoritative direct trust evaluation: The base station dynamically calculates the direct trust value based on the node behavior frequency and joining time, encouraging legitimate nodes to actively report real events and preventing hidden attacks by malicious nodes.

[0021] (4) Global evaluation of authoritative recommendation trust: Based on the collaborative trust mechanism among multiple base stations, the global historical behavior records of the target node are evaluated to improve the ability to identify malicious nodes.

[0022] (5) Comprehensiveness of the combined trust model: Through the dual combination of direct trust and recommended trust, node and authority combined trust values ​​are generated respectively, and a comprehensive trust evaluation system is constructed from the micro and macro levels to make up for the limitations of a single trust model and enhance the robustness and adaptability of the overall system.

[0023] The present invention will be explained in detail below with reference to the accompanying drawings and specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 is a flow chart of an embodiment of the present invention;

[0025] Figure 2 Schematic diagram of an embodiment of the present invention. DETAILED DESCRIPTION

[0026] To facilitate understanding of the present invention, the present invention will be described more comprehensively below with reference to the relevant drawings. Several embodiments of the present invention are given in the drawings, but the present invention can be implemented in different forms and is not limited to the embodiments described in the text. On the contrary, these embodiments are provided to make the content disclosed in the present invention more thorough and comprehensive.

[0027] Embodiment: A blockchain-based Internet of Vehicles digital twin trust assessment method, such as Figure 1 and Figure 2 As shown, the following steps are included:

[0028] S1. Calculate the node combination trust value of each node in the IoV digital twin network through the base station;

[0029] Specifically, in step S1, the process of calculating the node combination trust value of each node in the Internet of Vehicles digital twin network through the base station includes:

[0030] S11. Each node in the digital twin network of the Internet of Vehicles will report the events received from other nodes to the authoritative base station in the current domain. The base station calculates the direct trust value of the node through the Bayesian reasoning method to quantify its trust level. α and β represent the results of the direct interaction between the source node i and the target node j at time t. Considering the existence of the cold start problem where the two nodes have never interacted, the initial trust value is set to 0.5. Direct trust value NDT of node i to node i based on Beta distribution ij Calculated as:

[0031]

[0032] where α ij and β ijRepresents the accumulation of positive and negative interactions experienced by node i about node j, and uses message authenticity verification to determine whether the interaction is positive or negative: When node j observes the occurrence of event x somewhere, it broadcasts the message to nearby nodes including node i. When node i receives a message about event x from node j, it reports the message passed by j to the base station. The base station aggregates the messages about event x from all nodes at that moment based on Bayesian reasoning, determines the authenticity of each event, and thus determines whether the interaction between node i and node j is positive or negative.

[0033] Introduce the time decay function and maintain an interaction history table to store the results of the past M interactions, and calculate the α at time t ij and β ij value, thereby reducing the impact of past interaction results and enhancing the impact of existing interaction results:

[0034]

[0035]

[0036] in, and represents the accumulation of positive and negative interactions experienced by node i on node j at time t, λ 1 and λ 2 is the decay factor used to control the decay rate of e.

[0037] S12. The target node calculates the recommendation trust value based on the historical evaluation of the recommendation nodes that interact with it. The k-means-based recommendation trust filtering algorithm is used to filter out malicious nodes, and the similarity between each trusted node and the source node is calculated among the filtered trusted nodes. The similarity value indicates how similar the evaluation of node i and the recommendation node on the common target node is:

[0038]

[0039] Among them, Sim(i,r′ F ) represents i and r′ F Similarity, Set(i,r′ F ) represents i and r′ F The set of common target nodes, and Represent node i and node r′ respectively F The node direct trust value of node x, Num(Set(i,r′ F )) represents Set(i,r′ F ) is the number of nodes in .

[0040] Then, the node recommendation trust value is calculated based on the calculated similarity and the latest comprehensive trust value of the recommended node on the blockchain:

[0041]

[0042] in Represents the node recommendation trust value of node i to node j at time t. r′ F is the trust set R tc The recommended nodes in is the trust value of the recommended node recorded on the blockchain. Sim(i,r′ F ) is the node i and the node r′ F The similarity. is r′ F The node direct trust value with node j.

[0043] S13, according to the weight ω n Combining node direct trust with node recommendation trust to form node combination trust for the target node, improving the accuracy of trust value evaluation, and effectively eliminating the limitations of a single trust model, enhancing the comprehensiveness and reliability of the evaluation, n The value of is dynamically adjusted according to the actual road environment:

[0044]

[0045] in, is the combined trust of node i in node j at time t.

[0046] S2, calculate the authoritative combined trust value of each node in the IoV digital twin network through the base station;

[0047] Specifically, in step S2, the process of calculating the authoritative combined trust value of each node in the Internet of Vehicles digital twin network by the base station includes:

[0048] S21, determine the time t when the node joins the IoV digital twin network in , calculate the time interval tt from joining the network to the current time t in . Through the exponential decay function Assign a lower initial trust value to the newly added node, and gradually increase the trust value over time, and count the event reporting frequency R of node i i , and the average reporting frequency of all nodes in the network By calculation Evaluate the degree of deviation between node behavior and the overall expectations of the network, and finally comprehensively derive the authoritative direct trust value of the node based on the node joining time and reporting frequency deviation

[0049]

[0050] in Direct trust in authority, R i is the reporting frequency of node i, is the average node reporting frequency, ω ad is the weight value dynamically adjusted according to the road environment, and γ1 is the attenuation factor.

[0051] S22: Maintain the historical base station information of each vehicle on the blockchain. The base station where the current node is located obtains the H base stations it has passed through the latest from the blockchain, and requests these base stations to obtain the trust value of the target node i when it has passed through the base station in the past. Where k represents the base station that the node has passed, H represents the first H base stations that the node has passed, and the time that has passed since the node left each base station is calculated. Using the time decay factor γ2, calculate the decay weight To reduce the influence of earlier trust records, and thus emphasize more on the most recent trust evaluation, the authoritative recommendation trust at time t Calculated as:

[0052]

[0053] in Trust in authoritative recommendations. is the trust value of the node by the base stations that the node has passed through historically. is the time that has passed since node i left the base station.

[0054] S23, using the preset weight ω to calculate the authoritative direct trust value of the target node at the current time t calculated in step S21 And the authoritative recommendation trust value of the target node at the current time t calculated in step S22 Perform weighted summation to calculate the authoritative combined trust value of the target node ω a The value of is dynamically adjusted according to the actual road environment:

[0055]

[0056] S3, comprehensive node combination trust value and authority combination trust value, fully integrate the node's own behavior performance and base station evaluation data, and generate the final comprehensive trust value of the node;

[0057] The node combination trust of the target node calculated in step S1 at the current time t is calculated by the preset weight ω And the authoritative combined trust value of the target node at the current time t calculated in step S2 Perform weighted summation to calculate the comprehensive trust value of the target node

[0058]

[0059] Among them, ω c is the weight.

[0060] S4, the base station packages the comprehensive trust value together with the timestamp into a new block and adds it to the blockchain;

[0061] Specifically, in step S4, the process in which the base station packages the comprehensive trust value together with the timestamp into a new block and adds it to the blockchain includes:

[0062] S41, the base station aggregates the comprehensive trust value of each node The corresponding timestamp is packaged into a new block, and the new block is added to the blockchain through a distributed consensus algorithm to ensure the immutability and security of the data:

[0063]

[0064] in, is the latest trust value of node h, is the trust value stored last time for this node on the blockchain, is the comprehensive trust value of node h, and p is the number of nodes that make trust evaluation on j at time l+1.

[0065] S42, the base station's latest trust value TV for each node in the new block l+1 A check is performed, and if the trust value of the node is lower than the preset trust value threshold, the node is marked as a malicious node to enhance network security and the effectiveness of trust assessment.

[0066] The above is an exemplary description of the present invention in conjunction with the accompanying drawings. It is obvious that the specific implementation of the present invention is not limited to the above-mentioned method. As long as such non-substantial improvements are made by adopting the method concept and technical solution of the present invention, or the concept and technical solution of the present invention are directly applied to other occasions without improvement, they are all within the protection scope of the present invention.

[0067] The present invention uses the Urban Traffic Simulator (SUMO) and the Network Simulator (NS2) to build a vehicle network digital twin network simulation environment containing 200 vehicle nodes and 20 base stations. In this environment, the vehicle node movement speed is set in the range of 10m / s-20m / s, the base station coverage range is 800m×800m, and the vehicle node communication range is 150m. At the same time, Hyperledger Fabric is used to build a blockchain simulation platform for storing and managing trust assessment data.

[0068] The method of the present invention is compared with three baseline methods: BTM, RBTM, and DTM. Among them, BTM calculates the trust value of surrounding vehicle nodes based on query space rationality, query frequency rationality, and location; RBTM uses a recommendation-based method to calculate node trust value by filtering malicious recommendations and combining confidence value, deviation value, and proximity center value; DTM has a classic weighted voting method, which is often used in trust management.

[0069] For each scenario, 40 rounds of experiments were conducted and the average value was taken. When the number of vehicle nodes is 200, in the attack mode where malicious nodes share false trust ratings with source nodes, the present invention is superior to the baseline method in terms of recall and accuracy. Even if the malicious nodes reach 50%, the performance of the present invention can still achieve more than 80% accuracy and recall, and when the number of malicious nodes is large, it can still provide accurate trust value judgment for normal nodes. In intermittent malicious attack modes, since malicious nodes are more difficult to identify, as the number of malicious nodes increases, the present invention can still achieve more than 80% accuracy and recall, and this performance is also higher than the recall and accuracy of the baseline method. In summary, it is fully proved that the method of the present invention is effective.

Claims

1. A blockchain-based Internet of Vehicles digital twin trust assessment method, characterized in that: The steps include: S1. Calculate the node combination trust value of each node in the IoV digital twin network through the base station; S2, calculate the authoritative combined trust value of each node in the IoV digital twin network through the base station; S3, the base station generates a comprehensive trust value for each node by combining the node combination trust value and the authority combination trust value according to the weight; S4. The base station packages the comprehensive trust value together with the timestamp or verification information into a new block and adds it to the blockchain to complete the trust assessment of the digital twin of the Internet of Vehicles.

2. The blockchain-based Internet of Vehicles digital twin trust assessment method according to claim 1 is characterized in that: The specific implementation process of step S1 is as follows: S11. Each node in the digital twin network of the Internet of Vehicles reports the events received from other nodes to the base station in the current domain. The base station calculates the direct trust value of the node through the Bayesian reasoning method. The direct trust value NDT of node i to node j based on Beta distribution ij Calculated as: where α ij and β ij represents the accumulation of positive and negative interactions experienced by node i with respect to node j; Introduce the time decay function and maintain an interaction history table to store the results of the past M interactions, and calculate the α at time t ij and β ij value: in, and represents the accumulation of positive and negative interactions experienced by node i on node j at time t, λ1 and λ2 are decay factors that control the decay rate of e; S12. Use the k-means-based recommendation trust filtering algorithm to filter out malicious nodes, and calculate the similarity between each trusted node and the source node among the filtered trusted nodes: Among them, Sim(i,r′ F ) represents i and r′ F Similarity, Set(i,r′ F ) represents i and r′ F The set of common target nodes of and Represent node i and node r′ respectively F The node direct trust value of node x, Num(Set(i,r′ F )) represents Set(i,r′ F ) The number of nodes in Then, the node recommendation trust value is calculated based on the calculated similarity and the latest comprehensive trust value of the recommended node on the blockchain: in represents the node recommendation trust value of node i to node j at time t; r′ F is the trust set R tc The recommended nodes in is the trust value of the recommended node recorded on the blockchain; Sim(i,r′ F ) is the node i and the node r′ F The similarity of is r′ F The direct trust value of nodes with node j; S13, according to the weight ω n Combine the node direct trust with the node recommended trust to form a node combined trust value for the target node: in, is the combined trust of node i in node j at time t.

3. The blockchain-based Internet of Vehicles digital twin trust assessment method according to claim 2 is characterized in that: The specific implementation process of step S2 is as follows: S21, determine the time t when the node joins the IoV digital twin network in , calculate the time interval tt from joining the network to the current time t in ; through an exponential decay function Assign an initial trust value to the newly added node, and gradually increase the trust value over time, and count the event reporting frequency R of node i i , and the average reporting frequency of all nodes in the network By calculation Evaluate the degree of deviation between node behavior and the overall expectations of the network, and finally comprehensively derive the authoritative direct trust value of the node based on the node joining time and reporting frequency deviation in Direct trust in authority, R i is the reporting frequency of node i, is the average node reporting frequency, ω ad is the weight value dynamically adjusted according to the road environment, γ1 is the attenuation factor; S22: Maintain the historical base station information of each vehicle on the blockchain. The base station where the current node is located obtains the H base stations it has passed through the latest from the blockchain, and requests these base stations to obtain the trust value of the target node i when it has passed through the base station in the past. Where k represents the base station that the node has passed through, and the time that has passed since the node left each base station is Using the time decay factor γ2, calculate the decay weight Authoritative recommendation trust at time t Calculated as: in is the trust value of the node by the base stations that the node has passed through historically. is the time that has passed since node i left the base station; S23, the authoritative direct trust value of the target node at the current time t by presetting the weight ω And the authoritative recommendation trust value of the target node at the current time t Perform weighted summation to calculate the authoritative combined trust value of the target node ω a The value of is dynamically adjusted according to the actual road environment:

4. The blockchain-based Internet of Vehicles digital twin trust assessment method according to claim 3 is characterized in that: The specific implementation process of step S3 is as follows: Trust in the target node's node combination at the current time t And the authoritative combined trust value of the target node at the current time t Perform weighted summation to calculate the comprehensive trust value of the target node Among them, ω c is the weight.

5. The blockchain-based Internet of Vehicles digital twin trust assessment method according to claim 4 is characterized in that: The specific implementation process of step S4 is as follows: S41, the base station aggregates the comprehensive trust value of each node Packed with the corresponding timestamp to form a new block, the new block is added to the blockchain through the distributed consensus algorithm: Among them TV j l+1 is the latest trust value of node j, TV j last is the last trust value of node j on the blockchain, is the comprehensive trust value of node j, and p is the number of nodes that make trust evaluation on j at time l+1; S42, the base station's latest trust value TV for each node in the new block l+1 A check is performed, and if the trust value of the node is lower than the preset trust value threshold, the node is marked as a malicious node.

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