CN111510883A - Internet of vehicles oriented layered trust model and trust value calculation method thereof - Google Patents
Internet of vehicles oriented layered trust model and trust value calculation method thereof Download PDFInfo
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- CN111510883A CN111510883A CN202010338993.9A CN202010338993A CN111510883A CN 111510883 A CN111510883 A CN 111510883A CN 202010338993 A CN202010338993 A CN 202010338993A CN 111510883 A CN111510883 A CN 111510883A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/30—Services specially adapted for particular environments, situations or purposes
- H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
- H04W4/44—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for communication between vehicles and infrastructures, e.g. vehicle-to-cloud [V2C] or vehicle-to-home [V2H]
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Abstract
The invention discloses a hierarchical trust model facing the Internet of vehicles and a trust value calculation method thereof, wherein the hierarchical trust model facing the Internet of vehicles comprises a first layer trust model: RSUs are not present and the information exchanged is delay sensitive; second tier trust model: RSUs are present and the exchanged information is partially delay tolerant; third tier trust model: RSUs are present and the information exchanged is delay tolerant. The method for calculating the trust value of the layered trust model comprises the steps that the method for calculating the trust value of the first layer of trust model comprises direct trust value calculation and inter-vehicle recommended trust value calculation, the method for calculating the trust value of the second layer of trust model comprises direct trust value calculation and RSU recommended trust value calculation, and the method for calculating the trust value of the third layer of trust model comprises authority recommended trust value calculation. According to the layered trust model for the Internet of vehicles and the trust value calculation method thereof, problems in different application scenes are processed according to different communication scenes, and wrong relay decisions are reduced.
Description
Technical Field
The invention relates to the technical field of Internet of vehicles safety, in particular to a layered trust model for Internet of vehicles and a trust value calculation method thereof.
Background
With the continuous increase of automobile holding capacity, traffic accidents are more frequent, and an Intelligent Transportation System (ITS) becomes an inevitable trend of urban traffic development as a key technology for improving the utilization rate of traffic infrastructure, relieving traffic pressure, reducing the traffic accident rate and other traffic problems. Internet of vehicles (IoV) has attracted attention from numerous researchers as the next technical highlight for ITS, aiming to improve the security and information service level of data transmission between vehicles.
The internet of vehicles realizes intelligent traffic management, decision and control through human-vehicle-road, and the trust management is a hot spot and difficult problem of the current research. Trust management is defined as the evaluation of interactions between vehicles, mainly to deal with internal attackers with valid certificates, avoiding the use of cryptographic-based security mechanisms, thus reducing computational overhead. Existing trust-based solutions are generally divided into three categories: entity-oriented Models (ETM), Data-oriented Models (DTM), and Hybrid Models (HTM). The difference is different revocation mechanisms including revocation of illegitimate entities and revocation of malicious messages. Generally, a trust value is given by direct trust and recommendation trust, the larger the recommendation set is, the more accurate the trust value is, the higher the service quality is, but the additional overhead is increased, and a certain security requirement cannot be met.
The vehicle networking is vulnerable to malicious nodes in the multi-hop communication process, the safety requirement and the service quality are difficult to balance, and the current trust model does not solve the problem of processing different safety levels in different application scenes according to different communication scenes, so that more attackers exist in the network and a lot of wrong relay decisions are generated.
Disclosure of Invention
The invention aims to provide a layered trust model facing the internet of vehicles and a trust value calculation method thereof aiming at the defects in the prior art, so that the problems of different safety levels in different application scenes are solved according to different communication scenes, and wrong relay decisions are reduced.
In order to achieve the purpose of the invention, the invention adopts the following technical scheme:
a car networking oriented hierarchical trust model, comprising:
first tier trust model: RSUs are not present and the information exchanged is delay sensitive;
second tier trust model: RSUs are present and the exchanged information is partially delay tolerant;
third tier trust model: RSUs are present and the information exchanged is delay tolerant.
In addition, the technical scheme also comprises the following subsidiary technical scheme:
the application scene of the Internet of vehicles comprises the following steps:
real-time security class application: the first layer trust model is obtained through direct trust and recommendation trust among vehicles;
convenient application: the second layer trust model is obtained by direct trust and RSU recommendation trust of different vehicles in jurisdiction;
background class application: and the third layer trust model is obtained by integrating RSU recommendation trust in different communication ranges through an authority.
In order to achieve the above object, the present invention adopts another technical solution as follows:
a trust value calculation method of a layered trust model facing Internet of vehicles comprises direct trust value calculation and inter-vehicle recommended trust value calculation, a trust value calculation method of a second layer trust model comprises the direct trust value calculation and RSU recommended trust value calculation, and a trust value calculation method of a third layer trust model comprises authority recommended trust value calculation.
In addition, the technical scheme also comprises the following subsidiary technical scheme:
the direct trust value calculation method includes the steps of:
(1-1) defining parameters:
definition eij(x)∈[0，1]Is a node i pairThe trust value of the xth interaction of the node j, wherein 0 represents distrust and 1 represents complete trust;
definition HNDefining H for a historical interaction window of the direct trust value, wherein the historical interaction is continuous N times of interaction of a node i to a node j before the current interactionMA current interaction window of direct trust value, wherein the current interaction is continuous M times of interaction of a node i to a node j in the current interaction, and M < N;
(1-2) direct trust value calculation:
definition of DTij∈[0，1]A direct trust value of node i to node j, wherein 0 represents untrusted and 1 represents complete trust;
the direct trust value calculation is performed by the direct trust value history interactive window HNAnd direct trust value current interaction window HMTo derive said direct trust value DTijComprises the following steps:
DTij＝w*DTij(HN)+(1-w)*DTij(HM)，
wherein w is the equilibrium coefficient and w ∈ [0,0.5 ]]，DTij(HN) For direct trust value history interaction part, DTij(HM) The current interaction part is a direct trust value;
the direct trust value history interaction part DTij(HN) Comprises the following steps:
wherein, wN(x) Recording the history interactive window H at the direct trust value for the xth interactionNThe weight of (2);
the direct trust value current interaction part DTij(HM) Comprises the following steps:
wherein, wM(x) Recording the current interaction window H at the direct trust value for the xth interactionMThe weight of (2).
The method for calculating the recommended trust value between the vehicles comprises the following steps:
(2-1) obtaining a similarity coefficient:
defining a similarity matrix n x n as the trust rating of each node to other nodes, and then the trust rating of the node i to the node j is Rn*nRating the trust as Rn*nDefined as a similarity coefficient, then the similarity coefficient is:
(2-2) reliability calculation:
adding each row of the similarity coefficients to obtain the support degree (j) of each node to a certain node j, wherein the support degree (j) is as follows:
calculating the support degree support (j) regularization to obtain a credibility crd (j), wherein the credibility crd (j) represents the degree of support of one node by other nodes, and the credibility crd (j) is as follows:
(2-3) calculating a recommended trust value between vehicles:
defining VTijRecommending trust value between the vehicles of the node i to the node j, recommending trust value VT between the vehiclesijComprises the following steps:
the first tier trust model trust value calculation comprises the steps of:
(3-1) direct trust value reliability calculation:
definition of CTijIs the reliability of node i to node j, the reliability CTijFor describing the degree of reliability of the trust value;
definition CT (DT)ij) Directly trust value reliability for node i to node j, said direct trust value reliability CT (DT)ij) Comprises the following steps:
(3-2) calculating the reliability of the recommended trust value among the vehicles:
definition CT (VT)ij) Recommending trust value reliability between nodes i and j, wherein the trust value reliability CT (VT) is recommended between vehiclesij) Comprises the following steps:
(3-3) dynamic weight adjustment factor calculation:
α is defined as a dynamic weight adjustment factor, α is:
(3-4) first-layer trust model trust value calculation:
definition trust1ijA first level trust model trust value for node i to node j, the first level trust model trust value trust1ijComprises the following steps:
trust1ij＝α*DTij+(1-α)*VTij。
the RSU recommended trust value calculation comprises the following steps:
definition of RTRjRecommending trust value RT for RSU to node jRjComprises the following steps:
where n represents the number of times node i has a trust value with node j, trust1ijA first level trust model trust value for node i to node j.
The second tier trust model trust value calculation comprises the steps of:
definition trust2ijA second tier trust model trust value for node i to node j, the second tier trust model trust value trust2ijComprises the following steps:
the calculation of the authority recommendation trust value comprises the following steps:
definition of TTTjRecommending a trust value for an authority for a node j, the authority recommending a trust value TTTjComprises the following steps:
the third layer trust model trust value calculation comprises the following steps:
definition trust3ijA third tier trust model trust value for node j, the third tier trust model trust value trust3ijComprises the following steps:
trust3ij＝TTTj。
compared with the prior art, the invention has the advantages that:
the method comprises the steps that problems of different safety levels under different application scenes are processed according to different communication scenes, a three-layer trust model is specifically provided, and direct trust and vehicle recommendation trust are placed in a first-layer trust model and are mainly used for delay-sensitive real-time safety applications; placing the direct trust and the RSU recommended trust in a second layer trust model, wherein the second layer trust model is mainly used for convenient applications which are in the RSU communication range and partially delay tolerant; placing the authority recommended trust in a third-layer trust model, mainly used for background applications which are in an RSU communication range and delay tolerant, and establishing global trust among vehicles in the Internet of vehicles by using a third-party authority; the method can achieve a balance between the security requirement and the service quality, minimize the additional delay and reduce the end-to-end average delay; malicious nodes can be effectively detected, and the number of wrong decisions is reduced.
Drawings
FIG. 1 is a schematic diagram of a hierarchical trust model for Internet of vehicles according to the present invention.
FIG. 2 is a schematic diagram of trust value calculation of the Internet of vehicles oriented layered trust model in the invention.
Fig. 3 is a schematic diagram of the detection of bad mouth attacks using RSUs in the present invention.
Detailed Description
The present invention will be described in further non-limiting detail with reference to the following preferred embodiments and accompanying drawings.
As shown in fig. 1, the layered trust model for internet of vehicles according to a preferred embodiment of the present invention includes three layers of trust models, which are respectively:
first tier trust model: there is no roadside unit (hereinafter RSU) and the information exchanged is delay sensitive;
second tier trust model: RSUs are present and the exchanged information is partially delay tolerant;
third tier trust model: RSUs are present and the information exchanged is delay tolerant.
The application scenes of the Internet of vehicles are divided into three categories according to the priority of traffic, and the three categories comprise:
real-time security class application: the first layer trust model is obtained by direct trust and recommendation trust among vehicles;
convenient application: the second layer trust model is obtained by directly trusting and comprehensively managing the RSU recommendation trusts of different vehicles, and the recommendation set is relatively large;
background class application: and the third-layer trust model is obtained by integrating RSU recommendation trust in different communication ranges through an authority, and comprises entity recommendation in a global range.
Aiming at different scenes, different trust value calculation strategies are used, so that the extra delay is minimized, and the service quality is improved on the premise of ensuring the safe communication.
As shown in fig. 2, a trust value calculation method for a layered trust model for internet of vehicles includes a direct trust value calculation and an inter-vehicle recommended trust value calculation, a trust value calculation method for a second layer trust model includes a direct trust value calculation and an RSu recommended trust value calculation, and a trust value calculation method for a third layer trust model includes an authority recommended trust value calculation.
The direct trust value calculation method comprises the following steps:
(1-1) defining parameters:
definition eij(x)∈[0，1]A trust value of the x-th interaction of a node i (a source node, all nodes i below represent the source node) to a node j (a target node, all nodes j below represent the target node), wherein 0 represents untrusted and 1 represents complete trust;
definition HNDefining H for a historical interaction window of the direct trust value, wherein the historical interaction is continuous N times of interaction of a node i to a node j before the current interactionMThe current interaction is continuous M times of interaction of a node i to a node j in the current interaction, wherein M & lt N;
recording the current interaction as X, and then, recording a historical interaction value X as X-N +1, X-N +2, … X; the pre-interaction value X is X-M +1, X-M +2, … X, and M < N is satisfied, so
And in time sequence, HMAt HNThe head of (1).
(1-2) direct trust value calculation:
definition of DTij∈[0，1]A direct trust value of node i to node j, wherein 0 represents untrusted and 1 represents complete trust;
direct trust value calculation from direct trust value historical interaction window HNAnd direct trust value current interaction window HMDerived, direct trust value DTijComprises the following steps:
DTij＝w*DTij(HN)+(1-w)*DTij(HM)，
where w is a balance coefficient, w ∈ [0,0.5 ] because the direct trust relationship is decaying, the older interaction records have the least effect in the current trust evaluation, i.e., the direct trust value calculation gives higher weight to the latest interaction evidence.
DTij(HN) For direct trust value history interaction part, DTij(HM) The current interaction part is a direct trust value;
direct trust value history interaction part DTij(HN) Comprises the following steps:
wherein, wN(x) Recording the history interactive window H at the direct trust value for the xth interactionNThe weight of (2);
direct trust value current interaction part DTij(HM) Comprises the following steps:
wherein, wM(x) Recording the current interaction window H at the direct trust value for the xth interactionMWeight of, wN(x) And wM(x) Increasing with interaction time.
In vehicle recommendation trust, a recommendation node must satisfy two conditions: firstly, the recommendation node has at least one interaction with a target node (node j); and secondly, recommending the nodes as the nodes with higher reliability in the source node (node i).
The calculation method of the recommendation trust value between vehicles comprises the following steps:
(2-1) obtaining a similarity coefficient:
defining a similarity matrix n x n as the trust rating of each node to other nodes, and then the trust rating of the node i to the node j is Rn*nRating trust as Rn*nDefined as the similarity coefficient, the similarity coefficient is:
(2-2) reliability calculation:
adding each row of similar coefficients to obtain the support degree (j) of each node to a certain node j, wherein the support degree (j) is as follows:
calculating the support degree support (j) regularization to obtain credibility crd (j), wherein the credibility crd (j) represents the degree of support of a node j by other nodes, the higher the degree of support of a node by other nodes is, the higher the credibility of the node is, and the credibility crd (j) is as follows:
(2-3) calculating a recommended trust value between vehicles:
according to the trust recommendation values of different recommendation nodes, comprehensively obtaining a global vehicle recommendation trust value, defining VTijRecommending trust value between vehicles of the node i to the node j, recommending trust value VT between vehiclesijComprises the following steps:
wherein VTkjTaking out the trust rating of the node k to the node j from a trust rating matrix stored by the node k, namely VTkjValue of (d) corresponds to rkjIndicates that node k has direct interaction with node j.
The first layer trust model trust value calculation comprises the following steps:
(3-1) direct trust value reliability calculation:
definition of CTijReliability of node i to node j, reliability CTijThe method is used for describing the reliability of the trust value and solving the problem of coordination and deception of malicious nodes;
definition CT (DT)ij) The reliability of the direct trust value of the node i to the node j is given, if the discrete value of the direct trust value of the node i to the node j is lower, the interaction of the node is stableDefinite, i.e. higher, reliability and vice versa, directly trust value reliability CT (DT)ij) Comprises the following steps:
(3-2) calculating the reliability of the recommended trust value among the vehicles:
definition CT (VT)ij) Recommending trust value reliability between node i and node j, if the discrete value of the trust value of the recommended node to a certain target node is lower, indicating that the target node has higher reliability, and recommending trust value reliability CT (VT) between vehiclesij) Comprises the following steps:
(3-3) dynamic weight adjustment factor calculation:
generally speaking, direct trust takes more weight than recommended trust between vehicles because people prefer to trust their own judgment, but in real environment, there may be a situation that direct interaction is too little to cause the direct trust value to be unreliable, and it is difficult to confirm the weight value, so α is defined as a dynamic weight adjustment factor, α is defined as:
(3-4) first-layer trust model trust value calculation:
definition trust1ijA first level trust model trust value for node i to node j, the first level trust model trust value trust1ijComprises the following steps:
trust1ij＝α*DTij+(1-α)*VTij。
each time the vehicle interacts with the RSU, the vehicle sends a neighbor trust evaluation table, so that the RSU has global evaluation on different vehicle behaviors in different jurisdictions, and therefore a recommendation set of RSU recommendation trust is larger than a recommendation set of vehicle-to-vehicle recommendation trust, and the RSU recommendation trust value calculation comprises the following steps:
definition of RTRjRecommending trust value RT for RSU to node jRjComprises the following steps:
where n represents the number of times node i has a trust value with node j, trust1ijA first level trust model trust value for node i to node j.
In addition to being able to calculate RSU recommended trust values, the RSU may also be used to detect attackers within the network for received messages. As shown in fig. 3, the detection of bad mouth attacks using messages received by the RSU is shown. The letters in the figure represent vehicle nodes and the numbers represent trust values generated by the vehicle nodes during the interaction. The bad mouth attack spreads the negative evaluation of good nodes for the attacker and tries to damage the reputation of the good nodes in the network, so when a certain vehicle node is an attacker, the trust value given by the good node is negative, and particularly, the evaluation value of the rest nodes for the vehicle node is lower. When a bad-mouth attack occurs, the attacker considers all vehicles to be untrustworthy, and then in fig. 3, vehicle c and vehicle D are the attackers.
The second layer trust model trust value calculation comprises the following steps:
definition trust2ijA second tier trust model trust value for node i to node j, the second tier trust model trust value trust2ijComprises the following steps:
the authority and the RSU are connected by a wire. Because the authority needs to integrate all RSU recommendation trust evaluations, the calculation of the authority recommendation trust includes the first two layers of trust evaluations, and therefore the authority can have more comprehensive global evaluation on the node j (target node), so that the authority recommendation trust can only be used for delay-insensitive applications, and the calculation of the authority recommendation trust value comprises the following steps:
definition of TTTjRecommending trust value TT for the authority to the node jTjComprises the following steps:
the third layer trust model trust value calculation comprises the following steps:
definition trust3ijFor node j, a third tier trust model trust value trust3ijComprises the following steps:
trust3ij＝TTTj。
specifically, the trust value of the car networking oriented hierarchical trust model can be expressed as:
selecting trust models of different layers according to different application scenarios, calculating trust values of the trust models of different layers, and when an intermediate node receives a message, whether to forward or discard the message depends on the trust value evaluation, i.e. whether to forward or discard the message
Wherein, threshord is a designated threshold value used for evaluating the trust value, if the trust value is higher than the threshold value, the message is agreed to be forwarded, otherwise, the message is discarded.
According to the layered trust model facing the Internet of vehicles and the trust value calculation method thereof, the problems of different security levels under different application scenes are solved according to different communication scenes, and particularly, a three-layer trust model is provided, and direct trust and vehicle recommendation trust are placed in a first layer trust model and are mainly used for delay-sensitive real-time security applications; placing the direct trust and the RSU recommended trust in a second layer trust model, wherein the second layer trust model is mainly used for convenient applications which are in the RSU communication range and partially delay tolerant; placing the authority recommended trust in a third-layer trust model, mainly used for background applications which are in an RSU communication range and delay tolerant, and establishing global trust among vehicles in the Internet of vehicles by using a third-party authority; the method can achieve a balance between the security requirement and the service quality, minimize the additional delay and reduce the end-to-end average delay; malicious nodes can be effectively detected, and the number of wrong decisions is reduced.
It should be noted that the above-mentioned preferred embodiments are merely illustrative of the technical concepts and features of the present invention, and are intended to enable those skilled in the art to understand the contents of the present invention and implement the present invention, and not to limit the scope of the present invention. All equivalent changes and modifications made according to the spirit of the present invention should be covered within the protection scope of the present invention.
Claims (10)
1. A hierarchical trust model for internet of vehicles, comprising:
first tier trust model: RSUs are not present and the information exchanged is delay sensitive;
second tier trust model: RSUs are present and the exchanged information is partially delay tolerant;
third tier trust model: RSUs are present and the information exchanged is delay tolerant.
2. The internet of vehicles oriented hierarchical trust model according to claim 1, wherein: the application scene of the Internet of vehicles comprises the following steps:
real-time security class application: the first layer trust model is obtained through direct trust and recommendation trust among vehicles;
convenient application: the second layer trust model is obtained by direct trust and RSU recommendation trust of different vehicles in jurisdiction;
background class application: and the third layer trust model is obtained by integrating RSU recommendation trust in different communication ranges through an authority.
3. The internet-of-vehicles oriented layered trust model trust value calculation method as claimed in claim 1, characterized in that: the trust value calculation method of the first layer trust model comprises direct trust value calculation and inter-vehicle recommended trust value calculation, the trust value calculation method of the second layer trust model comprises direct trust value calculation and RSU recommended trust value calculation, and the trust value calculation method of the third layer trust model comprises authority recommended trust value calculation.
4. The internet of vehicles oriented hierarchical trust model trust value calculation method according to claim 3, wherein the direct trust value calculation method comprises the steps of:
(1-1) defining parameters:
definition eij(x)∈[0,1]A trust value for the xth interaction of node i to node j, wherein 0 represents untrusted and 1 represents complete trust;
definition HNDefining H for a historical interaction window of the direct trust value, wherein the historical interaction is continuous N times of interaction of a node i to a node j before the current interactionMA current interaction window of direct trust value, wherein the current interaction is continuous M times of interaction of a node i to a node j in the current interaction, and M < N;
(1-2) direct trust value calculation:
definition of DTij∈[0,1]A direct trust value of node i to node j, wherein 0 represents untrusted and 1 represents complete trust;
the direct trust value calculation is performed by the direct trust value history interactive window HNAnd direct trust value current interaction window HMTo derive said direct trust value DTijComprises the following steps:
DTij＝w*DTij(HN)+(1-w)*DTij(HM)，
wherein w is the equilibrium coefficient and w ∈ [0,0.5 ]]，DTij(HN) For direct trust value history interaction part, DTij(HM) The current interaction part is a direct trust value;
the direct trust value history interaction part DTij(HN) Comprises the following steps:
wherein, wN(x) Recording the history interactive window H at the direct trust value for the xth interactionNThe weight of (2);
the direct trust value current interaction part DTij(HM) Comprises the following steps:
wherein, wM(x) Recording the current interaction window H at the direct trust value for the xth interactionMThe weight of (2).
5. The internet of vehicles oriented layered trust model trust value calculation method according to claim 4, characterized in that the inter-vehicle recommended trust value calculation method comprises the following steps:
(2-1) obtaining a similarity coefficient:
defining a similarity matrix n x n as the trust rating of each node to other nodes, and then the trust rating of the node i to the node j is Rn*nRating the trust as Rn*nDefined as a similarity coefficient, then the similarity coefficient is:
(2-2) reliability calculation:
adding each row of the similarity coefficients to obtain the support degree (j) of each node to a certain node j, wherein the support degree (j) is as follows:
calculating the support degree support (j) regularization to obtain a credibility crd (j), wherein the credibility crd (j) represents the degree of support of one node by other nodes, and the credibility crd (j) is as follows:
(2-3) calculating a recommended trust value between vehicles:
defining VTijRecommending trust value between the vehicles of the node i to the node j, recommending trust value VT between the vehiclesijComprises the following steps:
6. the internet of vehicles oriented hierarchical trust model trust value calculation method according to claim 5, wherein the first layer trust model trust value calculation comprises the steps of:
(3-1) direct trust value reliability calculation:
definition of CTijIs the reliability of node i to node j, the reliability CTijFor describing the degree of reliability of the trust value;
definition CT (DT)ij) Directly trust value reliability for node i to node j, said direct trust value reliability CT (DT)ij) Comprises the following steps:
(3-2) calculating the reliability of the recommended trust value among the vehicles:
definition CT (VT)ij) Recommending trust value reliability between nodes i and j, wherein the trust value reliability CT (VT) is recommended between vehiclesij) Comprises the following steps:
(3-3) dynamic weight adjustment factor calculation:
α is defined as a dynamic weight adjustment factor, α is:
(3-4) first-layer trust model trust value calculation:
definition trust1ijA first level trust model trust value for node i to node j, the first level trust model trust value trust1ijComprises the following steps:
trust1ij＝α*DTij+(1-α)*VTij。
7. the internet of vehicles oriented hierarchical trust model trust value calculation method according to claim 6, wherein the RSU recommended trust value calculation comprises the steps of:
definition of RTRjRecommending trust value RT for RSU to node jRjComprises the following steps:
where n represents the number of times node i has a trust value with node j, trust1ijA first level trust model trust value for node i to node j.
8. The internet of vehicles oriented layered trust model trust value calculation method according to claim 7, wherein the second layer trust model trust value calculation comprises the steps of:
definition trust2ijA second tier trust model trust value for node i to node j, the second tier trust model trust value trust2ijComprises the following steps:
9. the internet of vehicles oriented layered trust model trust value calculation method according to claim 7, wherein the authority recommended trust value calculation comprises the following steps:
definition of TTTjRecommending a trust value for an authority for a node j, the authority recommending a trust value TTTjComprises the following steps:
10. the internet of vehicles oriented layered trust model trust value calculation method according to claim 7, wherein the third layer trust model trust value calculation comprises the following steps:
definition trust3ijA third tier trust model trust value for node j, the third tier trust model trust value trust3ijComprises the following steps:
trust3ij＝TTTj。
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