A vehicle networking person-vehicle separation trust management method based on a double-layer block chain

Abstract

The application belongs to the field of trust management in Internet of Vehicles security, and relates to a trust management method for separation of people and vehicles in Internet of Vehicles based on double-layer blockchains, which comprises the following steps: performing people-vehicle authentication on drivers and vehicles, and adding the authenticated vehicles into a trust management system of Internet of Vehicles; a data-requesting vehicle requests information, and a data-providing vehicle provides information; after the data-requesting vehicle receives the information, the data-providing vehicle is subjected to trust evaluation, and a new credit value is obtained; the vehicle credit value blockchain is updated based on an AS-PBFT algorithm according to the new credit value; a driver recent trust table is established according to the updated vehicle credit value blockchain; the active state of the driver is evaluated, and a new credit value of the driver is calculated according to the active state of the driver and the driver recent trust table, and the driver credit value blockchain is updated according to the new credit value of the driver; the trust information of people and vehicles is stored in double-layer blockchains, and the accuracy of the trust degree of the system is improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of trust management in Internet of Vehicles security, and particularly relates to a trust management method for separation of people and vehicles in Internet of Vehicles based on double-layer blockchains. BACKGROUND

[0002] In recent years, with the rapid development of economy and the accelerated urbanization process, China's transportation industry has developed rapidly, especially the rise of new energy vehicle industry, and the number of private cars has grown rapidly, which has brought great challenges to traffic safety. To cope with these challenges, researchers have begun to focus on using advanced communication technology to improve traffic management. Vehicle Ad Hoc Network (VANET) as an extension of Mobile Ad Hoc Network (MANET) has attracted much attention. Due to the highly dynamic topology of VANET, its security and privacy issues have attracted much attention, and many researchers have proposed trust management systems based on VANET to deal with the security threats posed by potential malicious users in the Internet of Vehicles.

[0003] Traditional trust management systems mainly include centralized, semi-centralized and distributed three types, among which the centralized system has problems of low reliability and single point failure, and the semi-centralized system can only partially solve these problems. Blockchain technology is considered a feasible technical solution in VANET due to its decentralization, consistency, tamper resistance and transparency. The Internet of Vehicles (IoV) model based on blockchain is composed of pre-authorized RSUs and lightweight vehicle nodes, which realizes data sharing and transaction verification, and improves the identity authentication and data security of the system. However, the blockchain in VANET currently has the problems of fast update frequency and multiple update nodes, which brings storage and computing pressure to the Internet of Vehicles nodes, so the consensus and block rate of traditional blockchain cannot meet the VANET system.

[0004] With the rapid development of new mode economy such as online car-hailing and shared cars, the relationship between vehicles and drivers has become complex, and the traditional IoV trust management mode that only considers the trust degree of vehicles has the problem of trust bias. In the vehicle social network (VSN), the behavior of the driver will affect its trust degree and importance in the social system, and this trust relationship is essentially a personal social trust between people. In the shared transportation mode, the vehicle and the driver are often in a non-1-to-1 state, and the historical behavior of the vehicle is not necessarily honest, which means that the current driver is trustworthy, and the historical reputation value of the driver is not necessarily high, which means that the current vehicle driven by the driver is trustworthy.

[0005] Most of the current trust management research ignores the consideration of the trust relationship between the driver and the vehicle, the existing trust evaluation mechanism cannot prevent malicious vehicle attacks caused by trust bias, and the blockchain architecture in the current trust management system of the Internet of Vehicles is not suitable for the Internet of Vehicles scene, resulting in problems such as large system storage pressure and slow node consensus efficiency. SUMMARY

[0006] To solve the above technical problems, the present application adopts a kind of based on double-layer blockchain's Internet of Vehicles separation of people and vehicles trust management method, and the Internet of Vehicles trust management system includes: vehicle, local auxiliary roadside unit, trusted leader roadside unit and double-layer blockchain;The double-layer blockchain includes: vehicle reputation value blockchain VBC and driver reputation value blockchain DBC;

[0007] The separation of people and vehicles trust management method includes:

[0008] S1, the driver and the vehicle are authenticated, and the authenticated vehicle is added to the Internet of Vehicles trust management system;

[0009] S2, the data request vehicle requests information in the Internet of Vehicles trust management system, and the data providing vehicle provides information to the data request vehicle in the Internet of Vehicles trust management system;

[0010] S3, after the data request vehicle receives the information of the data providing vehicle, the trust of the data providing vehicle is evaluated, and the new reputation value of the data providing vehicle is obtained;

[0011] S4, according to the new reputation value of the data providing vehicle, the vehicle reputation value blockchain VBC is updated based on the AS-PBFT algorithm;

[0012] S5, according to the updated vehicle reputation value blockchain VBC, the driver recent trust table is established;

[0013] S6, the active state of the driver is evaluated, the new reputation value of the driver is calculated according to the active state of the driver and the driver recent trust table, and the driver reputation value blockchain DBC is updated according to the new reputation value of the driver.

[0014] The parameters of the Internet of Vehicles trust management system include: an elliptic curve with a base point P and a one-way hash function H(·);The people and vehicle authentication process of the driver and the vehicle includes:

[0015] S11, the driver d i Personal information and vehicle key Input to the vehicle c j Login;The personal information includes identity information And biological characteristics

[0016] S12, vehicle c j select a random number N1 to calculate N1P, send N1P and current timestamp T1 to local auxiliary roadside unit L-RSU k , and send authentication request and signature information to trusted leading roadside unit ; wherein k is the index of local auxiliary roadside unit, k0 is the index of trusted leading roadside unit;

[0017] S13, query the data of vehicle reputation value block chain VBC to obtain registration information and send the registration information and current timestamp T2 to L-RSU k as confirmation information; wherein, is the dynamic identity password of man and vehicle, is the hash value key of driver's biological characteristics, is auxiliary public information;

[0018] S14, L-RSU k select a random number N2 to calculate N2P, and send N2P and confirmation information to vehicle c j together;

[0019] S15, vehicle c j restore the random key calculate the hash value key of driver's biological characteristics calculate the hash value key K generated based on random number r = H (N2·N1P||N2P||N1P), calculate the authentication key j of vehicle c send to L-RSU k ; L-RSU k calculate the hash value key K generated based on random number r = H (N2·N1P||N2P||N1P) and authentication key

[0020] S16, L-RSU k determine whether it is equal to if equal, reply to vehicle c j the message of verification success, and add vehicle c j to the trust management system of Internet of Vehicles; otherwise, prohibit vehicle c j to join the trust management system of Internet of Vehicles.

[0021] The trust evaluation on the data providing vehicle comprises:

[0022] S31, L-RSU k calculating an information quality evaluation value of the data providing vehicle;

[0023] S32, L-RSU k obtaining, from the DBC, a direct sharing interaction history between the data requesting vehicle and the data providing vehicle, and calculating a direct interaction trust value according to the direct sharing interaction history;

[0024] S33, L-RSU k obtaining, from the DBC, information of neighbor vehicles of the data requesting vehicle and the data providing vehicle, and calculating an indirect interaction trust value according to the information of the neighbor vehicles;

[0025] S34, L-RSU k calculating a new reputation value of the data providing vehicle according to the information quality evaluation value, the direct interaction trust value and the indirect interaction trust value.

[0026] The information quality evaluation value InfoQ (j′,j,t) is calculated according to the following formula:

[0027]

[0028] wherein rel represents a message relevance index, Dist represents a distance coefficient of the data requesting vehicle c j′ and the data providing vehicle c j , and TL represents a time freshness of information request and information reception.

[0029] The indirect interaction trust value is calculated according to the following formula:

[0030]

[0031] wherein M is the number of neighbor vehicles m, the neighbor vehicle is a vehicle that can directly communicate with the data requesting vehicle c j′ and the data providing vehicle c j when the two vehicles communicate, represents a recommendation rating of the neighbor vehicle m to the data providing vehicle at time t, InfoS (m,t) represents an evaluation value of the neighbor vehicle m at time t, TR (m,t) represents a weight of the reputation value of the driver of the neighbor vehicle m at time t.

[0032] updating the vehicle reputation value blockchain VBC according to the new reputation value of the data providing vehicle comprises:

[0033] S41, selecting a consensus committee in the vehicle reputation value blockchain VBC;

[0034] S42, performing AS-PBFT consensus on the new reputation value of the data providing vehicle according to the consensus committee, and if consensus is reached, updating the new reputation value of the data providing vehicle to the VBC.

[0035] The nodes of the vehicle reputation value blockchain VBC are vehicles, and each node includes: a reputation value of the vehicle; and selecting a consensus committee, including:

[0036] S411, setting a threshold value Cre thr Selecting nodes with reputation values greater than the threshold value Cre in the VBC to obtain a first node group; thr

[0037] S412, calculating the active level sleep of the nodes in the first node group, and selecting nodes with an active level sleep = 0 in the first node group to obtain a second node group;

[0038] S413, setting the size of the consensus committee σ, and if the number of nodes in the second node group exceeds σ, selecting nodes with reputation values ranked in the top σ to form the consensus committee;

[0039] S414, randomly generating a leader node in the consensus committee, and taking the remaining nodes of the consensus committee as auxiliary nodes.

[0040] Performing AS-PBFT consensus includes:

[0041] S421, the L-RSU k announcing that each node in the consensus committee participates in consensus;

[0042] S422, the L-RSU k transmits a request message to the leader node, and the leader node confirms the request message;

[0043] S423, after confirming the request message, the leader node sends a pre-preparation message to the auxiliary nodes, and the auxiliary nodes verify the pre-preparation message, if the verification is successful, the auxiliary nodes enter the preparation phase; otherwise, the auxiliary nodes fail to reach consensus;

[0044] S424, the auxiliary nodes in the preparation phase send preparation information to all the remaining nodes in the consensus committee, and all the remaining nodes verify the preparation information, if the verification is successful, the auxiliary nodes enter the confirmation phase; otherwise, the auxiliary nodes fail to reach consensus;

[0045] S425, the auxiliary nodes in the confirmation phase transmit a confirmation message to the leader node;

[0046] S426, if the leader node obtains more than 3f+1 confirmation messages, consensus is reached, and the remaining all nodes in the VBC and the L-RSU​k Reply with confirmation of the consensus reached; otherwise, the consensus fails; where f is the number of malicious nodes in the consensus committee.

[0047] Establishing a driver's recent trust list includes:

[0048] S51, L-RSU k Send a request to DBC to obtain driver d i Recently driven vehicle c j Vehicle C j Cre's reputation value (j,t) Driver d i Driving vehicle c j driving distance And driver d i Distance traveled by all vehicles in the RDD all And based on the driving distance and RDD all Calculate driver d i With vehicle c j Matching degree

[0049] S52, L-RSU k According to driver d i Recently driven vehicle c j Vehicle C j Cre's reputation value (j,t) And driver d i With vehicle c j Matching degree Construct a driver's recent trust table; where t is the vehicle c j The timestamp used to calculate the reputation score.

[0050] Calculate the driver's new reputation score include:

[0051]

[0052] Where N is the number of rows in the driver's recent trust table, η is a parameter related to the active state, and j n This indicates the vehicle c corresponding to the nth row of the driver's recent trust table. j , t n For vehicle c in the nth row of the driver's recent trust table j The timestamp for calculating the reputation score, where n is the row index of the driver's recent trust table, Rep max λ is the maximum credit value preset for drivers in the system, and λ is an adjustment parameter used to adjust the magnitude of the change in the exponential function.

[0053] Beneficial effects:

[0054] 1. The application establishes a human-vehicle separation trust management architecture in the IoV scenario, adopts a double-layer blockchain to store the trust information of people and vehicles respectively, and improves the accuracy of the trust evaluation mechanism of the system; 2. The application designs a new human-vehicle authentication method through biological characteristics and key verification, realizes the trust conversion from the driver to the vehicle; 3. The application scores the vehicle behavior through direct interaction, indirect interaction and message quality evaluation, ensures the overall trust degree and safety of the system; 4. The application proposes an AS-PBFT consensus algorithm with adaptive adjustment of group size, selects nodes through activity and reputation value, improves the consensus rate and reduces the storage pressure. BRIEF DESCRIPTION OF DRAWINGS

[0055] Figure 1 A vehicle networking reputation management system model based on double-layer blockchain is provided for the embodiment of the application;

[0056] Figure 2 A flowchart of a human-vehicle separation trust management method based on double-layer blockchain for the embodiment of the application is provided;

[0057] Figure 3 A flowchart of a human-vehicle authentication process of a driver and a vehicle is provided for the embodiment of the application;

[0058] Figure 4 A consensus committee selection flowchart is provided for the embodiment of the application;

[0059] Figure 5 A schematic diagram of the overall process of the consensus algorithm is provided for the embodiment of the application. DETAILED DESCRIPTION

[0060] The technical solutions in the embodiments of the application will be described clearly and completely below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, not all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the application.

[0061] As shown in the figure, the vehicle networking trust management system of the application is composed of a trusted leader roadside unit (T-RSU), a local secondary roadside unit (L-RSU), a vehicle and a double-layer blockchain; Figure 1

[0062] ​Among them, the T-RSU belongs to a trusted entity, is difficult to be broken, has strong computing power, and also serves as a management center of vehicle registration information in the system, and is responsible for maintaining an upper-layer blockchain, and alternately generates and verifies blocks to ensure the security and integrity of data in the system; the L-RSU belongs to a semi-trusted entity, is responsible for collecting reputation value evaluation influence information, and calculates the current reputation value of a local vehicle node according to a predetermined algorithm; at the same time, the L-RSU exists in large quantities in a local vehicle network to provide communication services for local vehicles, including data transmission and information broadcasting, and the like, and reports important data to the T-RSU; the vehicle serves as a full node of the blockchain in the system, participates in the maintenance and consensus process of the blockchain to ensure the distributed storage and security of the blockchain data, and inherits the historical reputation value of the driver when the driver logs in, and moves in the region; the behavior of the vehicle will affect the reputation value of the driver, and the change of the reputation value is recorded on the blockchain to realize transparent reputation management.

[0063] The blockchain serves as a core technology for trust management and data sharing in the system, has the characteristics of decentralization, transparency and traceability, all transactions and changes in reputation values are recorded on the blockchain to prevent data tampering and fraudulent behavior; the blockchain of the application is composed of a driver reputation value blockchain (DBC) and a vehicle reputation value blockchain (VBC) to store long-term and short-term trust information.

[0064] Specifically, the DBC serves as an upper-layer blockchain, records all historical registered driver information globally, is maintained by a T-RSU with good global performance, and is updated at a set update time node of the system; the VBC serves as a lower-layer blockchain, records information exchange records between local vehicles in a current region, traffic accidents and real-time traffic information occurring on the same day, so that the vehicle can obtain timely and time-sensitive information; the vehicle node cannot directly access the VBC information, and the vehicle node will initiate a request to the L-RSU when needed, and the L-RSU will read the related information and forward it to the vehicle node after confirming that the conditions are allowed; each region is maintained by a separate VBC, when a vehicle performs reputation interaction in a certain region, the vehicle enters another region, and the traffic information in the previous region does not affect its activities in the current region; the vehicle and the local vehicles in a certain region maintain the blockchain together, the VBC is small in size and fast in update frequency, and AS-PBFT is used for consensus.

[0065] The system employs two data sharing methods: regular service requests and traffic announcements. Regular service requests primarily involve information queries between vehicles, such as navigation requests, parking or gas station location searches, and entertainment information services. When a vehicle needs a service, it can request it from nearby vehicles. Traffic announcements are more common when traffic incidents occur (such as accidents or congestion), and witnessing vehicles broadcast this information to VBC and other vehicles.

[0066] The specific steps for regular service requests, which are used as a low-priority data sharing method, are as follows:

[0067] 1) When data is requested by vehicle c j′ When a certain service is needed, vehicle c j′ To the nearby L-RSU k After sending a service request and receiving and verifying its validity, the L-RSU... k Broadcast a service request message to all vehicle nodes in the VBC; where k is the index of the L-RSU and j′ is the index of the data requesting vehicle.

[0068] 2) Data provided by vehicle c j To L-RSU k Send shared data, L-RSU k Vehicle C needs to be inspected. j Current driver credit score Is it greater than a certain threshold Rep? thr To prevent malicious vehicles from sending large amounts of malicious messages and disrupting system operation, data that meets the requirements will be uploaded to VBC.

[0069] 3) Data request vehicle c j′ Other vehicles interested in the data can directly download the relevant data from VBC. The downloaded data can be used to meet the service requests of the vehicles; where j is the index of the vehicle requesting the data.

[0070] 4) Vehicles downloading the data will evaluate the quality of the information to ensure its accuracy and reliability. This evaluation information will be recorded on the blockchain for other vehicles to reference.

[0071] Traffic condition notices generally convey relatively urgent information, therefore they have a high priority, and the relevant steps are as follows:

[0072] 1) When traffic conditions are announced to vehicle c j When a traffic incident (such as an accident or congestion) is detected, it will broadcast the information to the L-RSU. k And other vehicles. Specifically, the data-providing vehicle broadcasts traffic event information to VBC nodes and nearby vehicles. This information may include the accident location, road closures, and traffic congestion levels.

[0073] 2) information receiver vehicle c j′ After obtaining the traffic information, appropriate measures can be taken to avoid traffic accidents or optimize the driving route, for example, changing the driving route to avoid congested areas or accident sites; the received information helps the driver make timely decisions to ensure driving safety and efficiency.

[0074] 3) information receiving vehicle c after a certain time j′ The vehicle c providing the information can be selected j for reputation evaluation. These evaluation information will be recorded on the blockchain, helping to maintain the reputation management mechanism of the entire vehicle networking trust management system.

[0075] Based on the above trust management system architecture, the present application provides a vehicle networking trust management method based on double-layer blockchain for people and vehicles, as shown in the flowchart Figure 2 , including:

[0076] S1, authenticate the driver and the vehicle, and add the vehicle that passes the authentication to the vehicle networking trust management system;

[0077] S2, the data request vehicle in the vehicle networking trust management system requests information, and the data providing vehicle in the vehicle networking trust management system provides information to the data request vehicle;

[0078] S3, after the data request vehicle receives the information from the data providing vehicle, the trust of the data providing vehicle is evaluated, and a new reputation value of the data providing vehicle is obtained;

[0079] S4, update the vehicle reputation value blockchain VBC based on the AS-PBFT algorithm according to the new reputation value of the data providing vehicle; the AS-PBFT is an adaptive Byzantine fault tolerance;

[0080] S5, establish a recent trust table of the driver according to the updated vehicle reputation value blockchain VBC;

[0081] S6, evaluate the active state of the driver, calculate a new reputation value of the driver according to the active state of the driver and the recent trust table of the driver, and update the driver reputation value blockchain DBC according to the new reputation value of the driver.

[0082] Furthermore, due to the special nature and importance of driver identity, this invention proposes a dual authentication scheme based on key and biometrics using a fuzzy extractor. It defines a cyclic group G of order q, an elliptic curve with base point P, and a secure and collision-resistant one-way hash function H(·). When a driver logs into a previously registered vehicle, the system performs identity authentication to ensure the accuracy of the identity information. Then, it verifies the driver's biometrics using fuzzy extraction technology and a random key, ultimately confirming the driver's identity and the vehicle's legitimacy.

[0083] Specifically, such as Figure 3 As shown, the driver and vehicle authentication process includes:

[0084] S11, Driver d i Personal information and vehicle key Input to vehicle c j Log in; the personal information includes identity information. and biological characteristics

[0085] S12, Vehicle C j Select a random number N1, calculate N1P, and send N1P and the current timestamp T1 to L-RSU. k and to Send identity information Authentication Request And the signature information; where k is the index of the local auxiliary roadside unit and k0 is the index of the trusted leader roadside unit;

[0086] S13 Query the Vehicle Reputation Value (VBC) blockchain data to obtain registration information. and registration information Send the current timestamp T2 as confirmation information to L-RSU k ;in, A dynamic identity password for people and vehicles. The hash value key for the driver's biometrics. To supplement public information;

[0087] The registration information is obtained during the initial registration of the person and vehicle. The relevant verification information stored and the registration steps include:

[0088] Driver d i Personal information and vehicle key Input to vehicle c j Register; the personal information includes identity information. and biological characteristics Passing through vehicle cj The random key generation function Gen() of the fuzzy extractor on the input biometric feature generates a random key and auxiliary public information Calculate the encryption password Select a random number N0 to calculate the dynamic identity password of the driver and the vehicle Delete the vehicle key After that, the registration message is sent to for verification. If the verification is passed, the registration is successful.

[0089] S14, L-RSU k Select a random number N2 to calculate N2P, and deliver N2P and confirmation information to the vehicle c j ;

[0090] S15, vehicle c j Restore the random key using the recovery algorithm Rep() in the fuzzy extraction technology Calculate the hash value key of the driver's biometric feature Calculate the hash value key K generated based on the random number r = H (N2·N1P||N2P||N1P), calculate the authentication key of the vehicle c j Send to the L-RSU k ; L-RSU k Calculate the hash value key K generated based on the random number r = H (N2·N1P||N2P||N1P) and the authentication key

[0091] S16, L-RSU k Judge whether it is equal to If equal, reply to the vehicle c j the message of successful verification, and add the vehicle c j to the trust management system of the Internet of Vehicles; otherwise, prohibit the vehicle c j to join the trust management system of the Internet of Vehicles.

[0092] Request data from the data request vehicle c j′ Request data, and the data providing vehicle c j provides data. After the completion of this vehicle interaction process, the L-RSU k needs to calculate the vehicle reputation value of the vehicle c j behind the behavior;

[0093] ​In particular, the trust assessment of the data providing vehicle comprises:

[0094] S31, in order to consider the credibility of the message itself, filter out the vehicle message which is not real or misleading, in L-RSU k The information quality evaluation of the vehicle is carried out in L-RSU (j′,j,t) , and the calculation formula is:

[0095]

[0096] TL=η(t-t0) -ε

[0097] Wherein, rel represents the message correlation index, L-RSU k A subjective parameter in the evaluation of vehicle reputation, Dist represents the distance coefficient, TL represents the time freshness, t0 represents the information request time, t represents the information receiving time, d v represents the diameter of VBC area, d represents the distance between data request vehicle c j′ And data providing vehicle c j , ε and η represent the parameters introduced to adjust the change speed of time freshness.

[0098] S32, L-RSU k Get the direct sharing interaction history between data request vehicle and data providing vehicle from DBC, calculate the direct interaction trust value according to the direct sharing interaction history based on the beta distribution based reputation function, and the calculation formula is:

[0099]

[0100] Wherein, represents the direct interaction reputation value of vehicle c j′ To vehicle c j , pos and neg represent the positive and negative interaction times of vehicle c j′ And vehicle c j When data request vehicle and data providing vehicle carry out information interaction once, data request vehicle will carry out interaction evaluation once, which is positive or negative.

[0101] S33, in the Internet of vehicles environment, it is difficult to accurately evaluate the vehicle interaction by relying on single vehicle direct interaction evaluation, and it is necessary to introduce indirect interaction evaluation as a supplement to the overall behavior trust evaluation of vehicle, and obtain indirect interaction trust value;

[0102] The calculation process of indirect interaction trust value includes:

[0103] S331, L-RSUk In the DBC and VBC, the data request vehicle acquires information of neighbor vehicles of the data providing vehicle, the neighbor vehicles referring to all vehicles capable of directly communicating with the two vehicles when the data request vehicle communicates with the data providing vehicle; the information of the neighbor vehicles including a reputation value of the neighbor vehicles, a recommendation rating for the data providing vehicle, a driver's residence, a license plate location, a vehicle condition rating, a driving distance, and a vehicle distance from the data providing vehicle;

[0104] S332, constructing a neighbor vehicle evaluation table according to the driver's residence, the license plate location, the vehicle condition rating, the driving distance, and the vehicle distance from the data providing vehicle of the neighbor vehicles, and calculating a neighbor vehicle evaluation value according to the neighbor vehicle evaluation table;

[0105] As shown in Table 1, the neighbor vehicle evaluation table includes a plurality of records, each record including: an index, a category interval, and a score for the index (o a , o b ); wherein a and b are indices of the index;

[0106] Table 1 Neighbor vehicle evaluation table

[0107]

[0108] S333, calculating an indirect interaction trust value according to the reputation value, the recommendation rating, and the evaluation value of the neighbor vehicles;

[0109] Specifically, the indirect trust value calculation formula is:

[0110]

[0111] wherein M is the number of neighbor vehicles m, is the recommendation rating of the neighbor vehicle m at time t, indicating an evaluation of the neighbor vehicle for the data providing vehicle, 1 and 0 respectively indicating positive and negative evaluations; InfoS (m,t) is the evaluation value of the neighbor vehicle m at time t, TR (m,t) is a historical trust weight, is the reputation value of the driver of the neighbor vehicle m at time t, and I is an index of the neighbor vehicle.

[0112] S34, after the information quality evaluation, the direct interaction evaluation, and the indirect interaction evaluation are completed, the L-RSU k needs to combine the scores of multiple factors affecting the vehicle reputation to obtain a new reputation value, and the calculation formula of the new reputation value is:

[0113]

[0114] wherein α1, α2, and α3 are weights, and α1+α2+α3=1.

[0115] updating the vehicle reputation value blockchain VBC according to the data-provided vehicle new reputation value comprises:

[0116] S41, selecting a consensus committee in the vehicle reputation value blockchain VBC;

[0117] S42, when the VBC needs to generate a block, performing AS-PBFT consensus on the data-provided vehicle new reputation value according to the consensus committee, and if consensus is reached, updating the data-provided vehicle new reputation value to the VBC.

[0118] The nodes of the vehicle reputation value blockchain VBC are vehicles, and each node comprises: a reputation value of the vehicle, and a specific process of selecting a consensus committee is as shown in Figure 4 The specific process is as follows:

[0119] S411, setting a threshold Cre thr , selecting nodes in the VBC whose reputation values are greater than the threshold Cre thr , to obtain a first node group;

[0120] S412, constructing a candidate node group, calculating the active level sleep of the nodes in the first node group, selecting nodes with an active level sleep=0 in the first node group to obtain a second node group, and adding the remaining nodes in the first node group to the candidate node group;

[0121] The calculation of the active level sleep of the selected nodes comprises:

[0122] The active level sleep={0, 1, 2}, and the smaller the level number, the higher the activity, sleep can be evaluated according to the active time length and login interval of the vehicle driver, and specifically comprises:

[0123] obtaining the system time of the driver login and logout of the vehicle in the DBC, and calculating the active interval according to the system time; setting two active thresholds, if the active interval is greater than the first active threshold, it is zero level, sleep=0; if the active interval is less than the first active threshold and greater than the second active threshold, it is first level, sleep=1; if the active interval is less than the second active threshold, it is second level, sleep=2.

[0124] S413, setting the consensus committee size σ, if the number of nodes in the second node group exceeds σ, sorting the nodes in the second node group according to the reputation value, and selecting the node group with a reputation value ranking R in the front σ to form a consensus committee, and adding the remaining nodes to the candidate node group;

[0125] S413, randomly generating a leader node in the consensus committee, and taking the remaining nodes of the consensus committee as auxiliary nodes.

[0126] The specific process of AS-PBFT consensus is as follows: Figure 5 As shown, the details are as follows:

[0127] The size of the consensus committee σ must satisfy the malicious node tolerance criterion of PBFT (σ>3f+1); where f is the number of malicious nodes in the consensus committee, i.e. the number of nodes that are allowed to fail.

[0128] S421, L-RSU k The consensus committee members are notified to participate in the consensus process, and each member is waiting for the consensus process to begin.

[0129] S422, L-RSU k A request message is sent to the leader node, and the leader node confirms the request message.

[0130] S423. After confirming the request message, the leader node sends a pre-preparation message to the assistant node. The assistant node verifies the pre-preparation message. If the verification is successful, the assistant node enters the preparation phase; otherwise, the assistant node consensus fails.

[0131] S424. The auxiliary node that enters the preparation phase sends preparation information to all other nodes in the consensus committee. All other nodes verify the preparation information. If the verification is successful, the auxiliary node enters the confirmation phase; otherwise, the consensus of the auxiliary node fails.

[0132] S425. The auxiliary node that enters the confirmation phase sends a confirmation message to the leader node;

[0133] S426. Once the leader node receives more than 3f+1 confirmation messages, consensus is reached, and it notifies all other nodes in the VBC and the L-RSU. k Reply with confirmation; otherwise, consensus fails.

[0134] Establishing a driver's recent trust list includes:

[0135] S51, L-RSU k Send a request to DBC to obtain driver d i Recently driven vehicle c j Driver d i driving the most recently driven vehicle c j Cre's reputation value (j,t) Driver d i driving the most recently driven vehicle c j driving distance And driver d i Distance traveled by all vehicles in the RDD all And based on the driving distance and RDD allCalculate driver d i With vehicle c j Human-vehicle matching The driver-vehicle matching degree can measure a driver's performance. i Recently, with driving vehicle c j The degree of binding relationship;

[0136] S52, L-RSU k According to driver d i Recently driven vehicle c j Vehicle C j Cre's reputation value (j,t) And driver d i With vehicle c j Matching degree Construct a driver's recent trust table; where t is the vehicle c j The timestamp for calculating the reputation score; the driver's recent trust table is shown in Table 2:

[0137] Table 2 Driver Recent Trust Table

[0138]

[0139] Assessing the driver's activity level includes:

[0140] S61, L-RSU k Send a request to DBC to obtain driver d i System time T for logging into and out of the vehicle network trust management system log and T exit And according to system time T log and T exit Calculate the active interval TD di =T log -T exit ;

[0141] S62, Set the activity threshold TD thr According to the active interval TD di With active value threshold TD thr The driver's activity level is determined, and if the activity interval TD di Greater than the active value threshold TD thr If the result is positive, the member is considered active; otherwise, the member is considered inactive.

[0142] Calculate the driver's new reputation score Rep based on the driver's recent trust table and the driver's activity status. di The formula is as follows:

[0143]

[0144] wherein N is the number of rows of the driver recent trust table, η is a parameter related to the active state, j n represents the vehicle c corresponding to the n-th row of the driver recent trust table j , t n is the time stamp of the reputation value calculation of the vehicle c corresponding to the n-th row of the driver recent trust table j , n is the row index of the driver recent trust table, Rep max is the maximum reputation value of the driver preset by the system, and λ is an adjusting parameter used to adjust the change size of the exponential function.

[0145] The above examples further illustrate the objects, technical solutions and advantages of the present application. It should be understood that the above examples are merely preferred embodiments of the present application and are not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. made to the present application within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1.A method for managing trust between people and vehicles in a vehicle-to-everything network based on a double-layer blockchain, characterized in that, The vehicle networking trust management system comprises a vehicle, a local auxiliary roadside unit, a trusted leader roadside unit and a double-layer blockchain; the double-layer blockchain comprises a vehicle credit value blockchain VBC and a driver credit value blockchain DBC; and the human-vehicle separation trust management method comprises: S1, human-vehicle authentication is performed on a driver and a vehicle, and the vehicle that passes the authentication is added to the vehicle networking trust management system; S2, a data request vehicle requests information from a data providing vehicle in the vehicle networking trust management system, and the data providing vehicle provides information to the data request vehicle; S3, after the data request vehicle receives the information from the data providing vehicle, trust evaluation is performed on the data providing vehicle to obtain a new credit value of the data providing vehicle; S4, the vehicle credit value blockchain VBC is updated based on the AS-PBFT algorithm according to the new credit value of the data providing vehicle; S5, a driver recent trust table is established according to the updated vehicle credit value blockchain VBC; S6, the active state of the driver is evaluated, a new credit value of the driver is calculated according to the active state of the driver and the driver recent trust table, and the driver credit value blockchain DBC is updated according to the new credit value of the driver. 2.The vehicle-to-everything trust management method based on a double-layer block chain according to claim 1, characterized in that, The vehicle networking trust management system parameters comprise an elliptic curve with a base point P and a one-way hash function H(·); the human-vehicle authentication process of the driver and the vehicle comprises: S11, driver d i personal information and vehicle key input to vehicle c j performing login; the personal information includes identity information and biometric S12, vehicle c j select a random number N1 to calculate N1P, send N1P and the current timestamp T1 to the local auxiliary roadside unit L-RSU k , and send the authentication request and signature information to the trusted leading roadside unit ; wherein k is the index of the local auxiliary roadside unit, and k0 is the index of the trusted leading roadside unit. S13、 Query the data of the vehicle reputation value blockchain VBC to obtain registration information And send the registration information And the current timestamp T2 to the L-RSU as confirmation information k ; wherein, It is a human-vehicle dynamic identity password, It is a hash value key of the driver's biological characteristics, It is auxiliary public information; S14, L-RSU k selecting a random number N2, calculating N2P, and passing N2P and confirmation information together to the vehicle c j ; S15, vehicle c j Recover the random key using the recovery algorithm Rep(·) in the fuzzy extraction technique Compute the hash value key of the driver's biometric Compute the hash value key K based on the random number generation r = H(N2·N1P||N2P||N1P), compute the authentication key of the vehicle c j Send to L-RSU k ; L-RSU k Compute the hash value key K based on the random number generation r = H(N2·N1P||N2P||N1P) and the authentication key ​​ S16, L-RSU k determining whether equal if equal, reply to the vehicle c j a message of verification success, and add the vehicle c j into the trust management system of the Internet of Vehicles; otherwise, prohibit the vehicle c j into the trust management system of the Internet of Vehicles. 3.The vehicle-to-everything trust management method based on a double-layer blockchain according to claim 1, characterized in that, The trust evaluation on the data providing vehicle comprises: S31, L-RSU k The computing data provides the information quality evaluation value of the vehicle. S32, L-RSU k The data request vehicle obtains the direct sharing interaction history between the data request vehicle and the data providing vehicle from the DBC, and calculates a direct interaction trust value according to the direct sharing interaction history. S33, L-RSU k Obtain the information of neighbor vehicles of the data request vehicle and the data providing vehicle from the DBC, and calculate the indirect interaction trust value according to the information of the neighbor vehicles. S34, L-RSU k A new reputation value of the data providing vehicle is calculated according to the information quality evaluation value, the direct interaction trust value and the indirect interaction trust value. 4.The vehicle-to-everything trust management method based on a double-layer blockchain according to claim 3, characterized in that, Information quality evaluation value InfoQ (j′,j,t) The calculation formula is: wherein rel denotes a message relevance index, Dist denotes a distance coefficient of the data providing vehicle c j′ to the data requesting vehicle c j , and TL denotes a time freshness of the information request and the information reception. 5.The vehicle-to-everything trust management method based on double-layer blockchains according to claim 3, characterized in that, indirect interaction trust value The calculation formula is: where M is the number of neighbor vehicles m, which are data requesting vehicles c j′ with data providing vehicles c j communicating with the two vehicles, denotes the recommendation rating of a neighbor vehicle m at time t for a data providing vehicle, InfoS (m,t) denotes the evaluation value of a neighbor vehicle m at time t, TR (m,t) denotes the weight of the reputation value of the driver of a neighbor vehicle m at time t. 6.The vehicle-to-everything trust management method based on a double-layer blockchain according to claim 1, wherein, The updating of the vehicle credit value blockchain VBC according to the new credit value of the data providing vehicle comprises: S41, a consensus committee is selected in the vehicle credit value blockchain VBC; S42, AS-PBFT consensus is performed on the new credit value of the data providing vehicle according to the consensus committee, and if consensus is reached, the new credit value of the data providing vehicle is updated to the VBC. 7.The vehicle-to-everything trust management method based on a double-layer blockchain according to claim 6, wherein, The nodes of the vehicle credit value blockchain VBC are vehicles, and each node comprises a credit value of the vehicle; the selection of the consensus committee comprises: S411, set a threshold value Cre thr In the VBC, select nodes with a reputation value greater than the threshold value Cre thr to obtain a first node group; S412, the active level sleep of the nodes in a first node group is calculated, nodes with the active level sleep=0 are selected in the first node group to obtain a second node group; S413, the size σ of the consensus committee is set, and if the number of nodes in the second node group exceeds σ, nodes with the credit values ranked in the front σ are selected to form the consensus committee; S414, a leader node is randomly generated in the consensus committee, and the remaining nodes in the consensus committee are taken as auxiliary nodes. 8.The vehicle-to-everything trust management method based on a double-layer blockchain according to claim 6, wherein, The AS-PBFT consensus comprises: S421, L-RSU k Announce consensus committee nodes participating in consensus; S422, L-RSU k communicate the request message to a leader node, the leader node acknowledging the request message; S423, after the request message is confirmed, the leader node sends a pre-preparation message to the auxiliary nodes, the auxiliary nodes verify the pre-preparation message, and if the verification is successful, the auxiliary nodes enter a preparation phase; otherwise, the auxiliary nodes fail to reach consensus; S424, the auxiliary nodes in the preparation phase send preparation information to all the remaining nodes in the consensus committee, and all the remaining nodes verify the preparation information, and if the verification is successful, the auxiliary nodes enter a confirmation phase; otherwise, the auxiliary nodes fail to reach consensus; S425, the auxiliary nodes in the confirmation phase transmit confirmation messages to the leader node; S426, if the leader node obtains more than 3f+1 confirmation messages, consensus is reached, and all the remaining nodes in the VBC and the L-RSU k reply the confirmation result of consensus reaching; otherwise, consensus fails; wherein f is the number of malicious nodes in the consensus committee. 9.The vehicle-to-everything trust management method based on a double-layer blockchain according to claim 1, wherein, The establishment of the driver recent trust table comprises: S51, L-RSU k Send a request to DBC to obtain driver d i Recently driven vehicle c j Vehicle C j Cre's reputation value (j,t) Driver d i Driving vehicle c j driving distance And driver d i Distance traveled by all vehicles RDD all And based on the driving distance and RDD all Calculate driver d i With vehicle c j Matching degree S52, L-RSU k According to the driver d i Recently driven vehicles c j , vehicle c j The reputation value Cre (j,t) And the matching degree of driver d i With vehicle c j Build a driver recent trust table; wherein t is the timestamp of the reputation value calculation of vehicle c j .​ 10.The vehicle-to-everything trust management method based on a double-layer blockchain according to claim 9, wherein, calculating a new reputation value Rep for the driver di comprising: where N is the number of rows of the driver's recent trust table, η is a parameter related to the active state, j n represents the vehicle c corresponding to the n-th row of the driver's recent trust table j , t n is the reputation value calculation time stamp of the vehicle c corresponding to the n-th row of the driver's recent trust table j , n is the row index of the driver's recent trust table, Rep max is the maximum reputation value of the driver preset by the system, and λ is an adjustment parameter used to adjust the change size of the exponential function.

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