CN115174615A - Origin information-based distributed Internet of vehicles dynamic trust management method - Google Patents

Origin information-based distributed Internet of vehicles dynamic trust management method Download PDF

Info

Publication number
CN115174615A
CN115174615A CN202210741334.9A CN202210741334A CN115174615A CN 115174615 A CN115174615 A CN 115174615A CN 202210741334 A CN202210741334 A CN 202210741334A CN 115174615 A CN115174615 A CN 115174615A
Authority
CN
China
Prior art keywords
trust
message
vehicle
value
vehicles
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202210741334.9A
Other languages
Chinese (zh)
Other versions
CN115174615B (en
Inventor
曹越
王宇
吕臣臣
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Wuhan University WHU
Original Assignee
Wuhan University WHU
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Wuhan University WHU filed Critical Wuhan University WHU
Priority to CN202210741334.9A priority Critical patent/CN115174615B/en
Publication of CN115174615A publication Critical patent/CN115174615A/en
Application granted granted Critical
Publication of CN115174615B publication Critical patent/CN115174615B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L63/00—Network architectures or network communication protocols for network security
    • H04L63/20—Network architectures or network communication protocols for network security for managing network security; network security policies in general
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W12/00—Security arrangements; Authentication; Protecting privacy or anonymity
    • H04W12/30—Security of mobile devices; Security of mobile applications
    • H04W12/37—Managing security policies for mobile devices or for controlling mobile applications
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W12/00—Security arrangements; Authentication; Protecting privacy or anonymity
    • H04W12/60—Context-dependent security
    • H04W12/66—Trust-dependent, e.g. using trust scores or trust relationships
    • 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]

Landscapes

  • Engineering & Computer Science (AREA)
  • Computer Security & Cryptography (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Computing Systems (AREA)
  • Computer Hardware Design (AREA)
  • General Engineering & Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Medical Informatics (AREA)
  • Traffic Control Systems (AREA)

Abstract

The invention discloses a distributed internet of vehicles dynamic trust management method based on origin information. In the entity trust evaluation stage, the message receiver performs direct trust calculation on the sender by using a Bayesian method, and aggregates the trust value into a combined trust value by combining the suggestions of the neighbor nodes. And when the message receiving node refers to the suggestion, recommending a trust value according to the neighbor node calculated by the road side unit so as to evaluate the acceptability of the indirect trust. Upon determining that the sender is trusted, the message receiving node performs a data-centric trust value calculation for the message based on the origin information. The trust value of a received message is evaluated in two dimensions: the trustworthiness of the message originating node and the trustworthiness of the message forwarding path. In addition, the scheme fully considers the dynamics of trust, and provides a dynamic updating mechanism based on a global threshold value and a trust attenuation mechanism.

Description

Origin information-based distributed Internet of vehicles dynamic trust management method
Technical Field
The invention relates to the technical field of vehicle networking trust management, in particular to a distributed vehicle networking dynamic trust management method based on origin information.
Background
With the development of times, the internet of vehicles is used as a new field with high integration of informatization and industrialization, and plays a great role in guaranteeing traffic safety and improving travel efficiency. However, with the continuous development of the car networking, the security problem is more and more obvious, the privacy and even the life security of the user are threatened, and the car networking security becomes an unbearable problem. As a national key construction project, the China Communication Standardization Association (CCSA) has conducted related technical research in the aspects of intelligent transportation and internet of vehicles, including: the research on the mobile Internet plus the automobile safety problem and key technology and the research report on the information safety of the vehicle-mounted intelligent terminal. The comprehensive transportation standard committee under the transportation department promotes the research of multiple projects such as an intelligent internet driving information safety standard system framework, an intelligent internet driving vehicle-mounted intelligent terminal information safety technical requirement and a test method, and the like, and clear standards and specifications are provided from the system architecture, the related test method and the safety standard so as to promote the standard construction of the vehicle networking safety in coordination and promotion by multiple levels and multiple departments.
Due to the high mobility of vehicles and the frequency of network topology, neighboring vehicles often cannot fully trust each other. When a malicious vehicle is present in the network, an attacker may intentionally propagate false messages. For example, a malicious vehicle may broadcast a message that the road is clear when a traffic accident or traffic congestion actually occurs. These misbehaviors can greatly compromise traffic safety or the efficiency of transportation systems. Therefore, how to effectively evaluate the credibility of the vehicle is an important issue in the internet of vehicles.
The key step for solving the above security problem is to establish an effective trust mechanism, before any measures are taken for the received information, to judge whether the message sender is trusted and to check whether the information content is trusted, to propagate accurate, real and up-to-date trust content between network entities.
The inventor of the present application finds that the method of the prior art has at least the following technical problems in the process of implementing the present invention:
in the prior art, the internet of vehicles trust management is mainly divided into three models: an entity-centric trust model, a data-centric trust model, and a combined trust model. The entity-centric trust model aims at evaluating node trustworthiness through different indicators (e.g., forwarding behavior, evaluation behavior, node type, etc.). However, the evaluation of the trust value depends on the interaction between the vehicles, and if the interaction between the vehicles is less and the trust evidence is insufficient, the evaluation effect may be poor; the trust model with data as the center aims at evaluating the message credibility, and has the defects that only the credibility of the message is evaluated, and the stable trust relationship between nodes cannot be established; the combined trust model combines the characteristics of entity trust and data trust and simultaneously evaluates the credibility of the nodes and the messages. The method is characterized in that the evaluation of data trust is usually carried out on the basis of node trust, the trust evaluation process is more complex, and the system overhead is large.
Although three trust management models exist currently, the existing vehicle networking trust model generally only considers a single application scene and lacks a trust model considering multiple application scenes, most researches lack a trust calculation method supporting dynamic updating, so that the trust evaluation real-time performance is not strong, and meanwhile, the dynamic adaptive capacity aiming at a trust decision mechanism is insufficient, and the dynamic strategic behavior change of vehicle nodes is difficult to deal with.
Disclosure of Invention
The invention provides a distributed Internet of vehicles dynamic trust management method based on origin information, which is used for solving or at least partially solving the technical problem of low reliability of an evaluation result in the prior art.
In order to solve the technical problem, the invention provides a distributed internet of vehicles dynamic trust management method based on origin information, which comprises the following steps:
when the message receiving vehicle receives the message of the message sending vehicle, judging whether the message receiving vehicle has interactive history with the message sending vehicle, if not, the message receiving vehicle firstly initializes the trust evidence of the vehicle locally and then carries out entity trust evaluation on the message sending vehicle, otherwise, the message receiving vehicle carries out entity trust evaluation on the message sending vehicle;
the message receiving vehicle judges whether the trust value of the message sending vehicle is larger than a global trust threshold value or not according to the entity trust evaluation result, if not, the message sending vehicle is judged to be not trusted, if so, the message sent by the message sending vehicle is evaluated by taking data as a center based on origin information, wherein the global trust threshold value is obtained by carrying out weighted aggregation on trust evidence uploaded by the road side unit at regular time through the cloud control center, then the trust evidence is sent to the road side unit through the cloud control center, and then the trust threshold value is shared to the message receiving vehicle through the road side unit.
In one embodiment, the format of the trust evidence for the initialized vehicle is:
TE j ＝{ID j ，DT i，j ，RT j ，IT i，j ，CT i，j ，CS j ，α，β，t}
where i and j represent vehicles, i represents a message receiving vehicle, j represents a message sending vehicle, IDj represents an identity unique identifier of the message sending vehicle j, DT i，j 、IT i，j And CT i，j Respectively representing the direct trust value, the indirect trust value and the combined trust value of the message receiving vehicle i to the message sending vehicle j, RT j Represents the recommended trust value, CS, of the message sending vehicle j issued by the road side unit j And the contribution score of the message sending vehicle j is shown, alpha and beta respectively represent the historical good message forwarding behavior and the malicious message forwarding behavior of the message sending vehicle j, and the initial value is 1,t and is the time stamp of the latest trust evaluation.
In one embodiment, a message receiving vehicle performs an entity trust assessment on a message sending vehicle, comprising:
the message receiving vehicle carries out direct trust calculation on the message sending vehicle by using a Bayesian method to obtain a direct trust value;
the message receiving vehicle carries out indirect trust calculation on the message sending vehicle according to the suggestion of the neighbor vehicle to obtain an indirect trust value, wherein the suggestion of the neighbor vehicle comprises a recommended trust value of the neighbor vehicle to the message sending vehicle and a direct trust value of the neighbor vehicle to the message sending vehicle, the recommended trust value of the neighbor vehicle to the message sending vehicle is obtained by a road side unit after cosine similarity calculation according to trust evidence uploaded by passing vehicles and local trust evidence of the road side unit, and then the road side unit sends the result to the neighbor vehicle;
and the message receiving vehicle aggregates the direct trust value and the indirect trust value into a combined trust value which is used as an entity evaluation result of the message sending vehicle.
In one embodiment, the message receiving vehicle performs direct trust calculation for the message sending vehicle using a bayesian approach, using the following equation:
Figure BDA0003715435570000031
where α and β represent respectively historical good message forwarding behavior and malicious message forwarding behavior of the messaging vehicle j, x i，j And y i，j Good message forwarding behavior and malicious message forwarding behavior, DT, of the message sending vehicle j to the message receiving vehicle i in the current interaction i，j A direct trust value for message receiving vehicle i to message sending vehicle j.
In one embodiment, the message receiving vehicle performs indirect trust calculation on the message sending vehicle according to the suggestion of the neighbor vehicle to obtain an indirect trust value IT i，j The method is realized by adopting the following formula:
Figure BDA0003715435570000032
wherein
Figure BDA0003715435570000033
A set of neighbor vehicles for the message receiving vehicle i,
Figure BDA0003715435570000034
for message-receiving vehicles iNumber of neighboring vehicles, RT k And DT k，j Respectively, a recommended trust value of the neighbor vehicle k of the message receiving vehicle i and a direct trust value of the neighbor vehicle k to the message sending vehicle j.
In one embodiment, a trust value RT is recommended k The calculation method is as follows:
Figure BDA0003715435570000035
wherein the RSU is a road side unit,
Figure BDA0003715435570000036
set of vehicles with history of interaction with both vehicle k and roadside units, DT k，i Trust evidence uploaded for vehicles having history of interaction with both vehicle k and roadside units, DT RSU，i Is a local trust proof for the road side unit.
In one embodiment, the message receiving vehicle aggregates the direct trust value and the indirect trust value into a combined trust value CT i，j The method is realized by adopting the following formula:
Figure BDA0003715435570000037
wherein lambda is the interaction frequency of the message receiving vehicle i and the message sending vehicle j, theta is a preset fixed value and represents the minimum interaction frequency, DT, of the vehicle i which can accurately evaluate the trust value of the vehicle j i，j Indicating a direct trust value, IT, of the message-receiving vehicle i to the message-sending vehicle j i，j Indicating an indirect trust value of the message receiving vehicle i to the message sending vehicle j.
In one embodiment, the calculated combined confidence value decays over time, the combined confidence value CT i，j (t) decay with time is calculated as:
Figure BDA0003715435570000041
wherein CT0 is the combined trust value of the last evaluation, Δ t is the time difference between the current interaction and the last trust evaluation, ω is the minimum value of the trust attenuation, and m is the time required for the trust to attenuate from the initial value to the minimum value.
In one embodiment, a data-centric assessment of a message sent by a messaging vehicle based on origin information includes:
the destination vehicle of the message evaluates the first message confidence level ST according to the trust value of the origin node generated by the message M The specific calculation formula is as follows:
Figure BDA0003715435570000042
wherein d and s are the destination vehicle of the message and the origin node or vehicle of the message generation, IT, respectively d，s Is an indirect trust value;
the destination vehicle of the message evaluates the credibility PT of the second message according to the credibility value of the message forwarding path node M The specific calculation formula is as follows:
Figure BDA0003715435570000043
wherein
Figure BDA0003715435570000044
A set of pathway nodes for forwarding of message M,
Figure BDA0003715435570000045
number of path nodes for forwarding message M, IT d，i Is an indirect trust value;
message trust value aggregation is carried out on the first message credibility and the second message credibility to obtain the trust value FT of the final message M M The calculation formula is as follows:
FT M ＝Φ×ST M +(1-Φ)×PT M
where Φ is the weight parameter.
In one embodiment, the global trust threshold is obtained by:
the cloud control center takes the trust evidences uploaded by all the road side units as global trust evidences, and aggregates the combined trust values of the global trust evidences, wherein the calculation formula is as follows:
Figure BDA0003715435570000046
wherein
Figure BDA0003715435570000047
For the set of all vehicles in the network,
Figure BDA0003715435570000048
for the number of all vehicles in the network,
Figure BDA0003715435570000049
for the combined mean of trust of the local trust evidence of vehicle i,
according to the average value
Figure BDA00037154355700000410
Updating a global trust threshold, the global trust threshold
Figure BDA00037154355700000411
The calculation formula of (c) is:
Figure BDA00037154355700000412
wherein
Figure BDA00037154355700000413
Is a historical trust threshold.
Compared with the prior art, the invention has the advantages and beneficial technical effects as follows:
when a message receiving vehicle receives a message of a message sending vehicle, if the message receiving vehicle and the message sending vehicle have interactive history, entity trust evaluation is carried out on the message sending vehicle; when the credibility of the message sending vehicle is determined, the message sent by the message sending vehicle is evaluated by taking data as a center based on the origin information, namely, the credibility of the message is evaluated from the message source, so that more accurate credibility evaluation can be obtained, and the reliability of the evaluation is improved.
Furthermore, compared with the vehicle, the road side unit has higher reliability, so that bad mouth attack can be fundamentally resisted by utilizing the road side unit to carry out vehicle recommendation trust evaluation, and the evaluation reliability is further improved.
Furthermore, a dynamic trust threshold mechanism and a trust attenuation mechanism can be fully adapted to the characteristic of topology frequency change of the Internet of vehicles, so that the real-time performance and the dynamic adaptability are improved.
Drawings
In order to more clearly illustrate the embodiments or technical solutions of the present invention, the drawings used in the embodiments or technical solutions in the prior art are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a schematic diagram of a vehicle networking trust management framework in an embodiment of the present invention;
FIG. 2 is a flowchart of a vehicle networking trust management system in an embodiment of the present invention;
FIG. 3 is a schematic diagram of an entity trust calculation time series in an embodiment of the present invention.
Detailed Description
The invention aims to provide a vehicle networking trust management scheme which can identify malicious vehicles and false information at the same time and has strong instantaneity and high accuracy.
The main concept of the invention is as follows:
the nodes in the network mainly evaluate entity trust, and the nodes and the messages in the network are evaluated in an all-around multi-index manner with data trust as an auxiliary. In the entity trust evaluation stage, the message receiving node performs direct trust calculation on the sender by using a Bayesian method, and aggregates the trust value into a combined trust value by combining the suggestions of the neighbor nodes. When the message receiving node refers to the suggestion, the message receiving node recommends a trust value according to a neighbor node calculated by a road side unit (such as an electronic billboard or an intelligent bus light and other facilities) so as to evaluate the acceptability of the indirect trust. Upon determining that the sender is trusted, the message receiving node performs a data-centric trust value calculation for the message based on the origin information. The trust value of a received message is evaluated in two dimensions: the trustworthiness of the message originating node and the trustworthiness of the message forwarding path. In addition, the scheme fully considers the dynamics of trust, and a dynamic updating mechanism based on a global threshold value and a trust decay mechanism are provided.
The scheme designed by the invention has the following characteristics and advantages:
1. compared with the vehicle, the road side unit has higher reliability, so that the road side unit is used for vehicle recommendation trust evaluation to fundamentally resist bad mouth attack;
2. the key for evaluating the credibility of the message is to determine the credibility of the source of the message, so that the message can be more accurately evaluated for credibility based on the origin information;
3. the dynamic trust threshold mechanism and the trust attenuation mechanism can be fully adapted to the characteristic of topology frequency change of the Internet of vehicles.
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
The embodiment of the invention provides a distributed internet of vehicles dynamic trust management method based on origin information, which comprises the following steps:
when the message receiving vehicle receives the message of the message sending vehicle, judging whether the message receiving vehicle has interactive history with the message sending vehicle, if not, the message receiving vehicle firstly initializes the trust evidence of the vehicle locally and then carries out entity trust evaluation on the message sending vehicle, otherwise, the message receiving vehicle carries out entity trust evaluation on the message sending vehicle;
the message receiving vehicle judges whether the trust value of the message sending vehicle is larger than a global trust threshold value or not according to the entity trust evaluation result, if not, the message sending vehicle is judged to be not trusted, if so, the message sent by the message sending vehicle is evaluated by taking data as a center based on origin information, wherein the global trust threshold value is obtained by carrying out weighted aggregation on trust evidence uploaded by the road side unit at regular time through the cloud control center, then the trust evidence is sent to the road side unit through the cloud control center, and then the trust threshold value is shared to the message receiving vehicle through the road side unit.
Specifically, when the message receiving vehicle receives the message of the message sending vehicle, corresponding operation is performed according to whether the message sending vehicle has an interaction history. "Trust evidence" refers to trust-related parameters. The trust evidence of the local initialization vehicle means that the message receiving vehicle locally generates the trust evidence of a message sending vehicle. The entity trust evaluation result also refers to the final trust value calculated by the message receiving vehicle for the message sending vehicle.
If the trust value of the message sending vehicle is not greater than the trust threshold, the vehicle is considered to be not trusted, and the message forwarded by the vehicle is also not trusted, so that subsequent message evaluation is not performed. If the trust value of the messaging vehicle is greater than the global trust threshold, then the messaging vehicle is deemed authentic. But only the vehicle can be determined to be trustworthy and not the message forwarded by the vehicle, so that at this time, the message trust level needs to be further evaluated, specifically, the message sent by the message sending vehicle is evaluated in a data-centered manner based on the origin information.
In one embodiment, the format of the trust evidence for the initialized vehicle is:
TE j ＝{ID j ，DT i，j ，RT j ，IT i，j ，CT i，j ，CS j ，α，β，t}
where i and j represent vehicles or nodes, i represents a message receiving vehicle, j represents a message sending vehicle, ID j An identity unique identifier, DT, representing the messaging vehicle j i，j 、IT i，j And CT i，j Respectively representing the direct trust value, the indirect trust value and the combined trust value of the message receiving vehicle i to the message sending vehicle j, RT j Represents the recommended trust value, CS, of the message-sending vehicle j issued by the road side unit j And the contribution score of the message sending vehicle j is shown, alpha and beta respectively represent the historical good message forwarding behavior and the malicious message forwarding behavior of the message sending vehicle j, and the initial value is 1,t and is the time stamp of the latest trust evaluation.
In one embodiment, a message receiving vehicle performs an entity trust assessment on a message sending vehicle, comprising:
the message receiving vehicle carries out direct trust calculation on the message sending vehicle by using a Bayesian method to obtain a direct trust value;
the method comprises the steps that a message receiving vehicle carries out indirect trust calculation on a message sending vehicle according to suggestions of neighbor vehicles to obtain indirect trust values, wherein the suggestions of the neighbor vehicles comprise recommended trust values of the neighbor vehicles to the message sending vehicle and direct trust values of the neighbor vehicles to the message sending vehicle, the recommended trust values of the neighbor vehicles to the message sending vehicle are obtained after cosine similarity calculation is carried out on a roadside unit according to trust evidences uploaded by passing vehicles and local trust evidences of the roadside unit, and then the roadside unit sends the trust values to the neighbor vehicles;
and the message receiving vehicle aggregates the direct trust value and the indirect trust value into a combined trust value which is used as an entity evaluation result for the message sending vehicle.
Specifically, the neighbor vehicle refers to a neighbor vehicle of the message vehicle receiving vehicle. When the message receiving vehicles perform combined trust calculation, the weight distribution of direct trust and indirect trust can be performed according to the interaction times among the vehicles.
In one embodiment, the message receiving vehicle performs direct trust calculation for the message sending vehicle using a bayesian approach, using the following equation:
Figure BDA0003715435570000071
where α and β represent the historical good message forwarding behavior and malicious message forwarding behavior, respectively, of the messaging vehicle j, x i，j And y i，j Respectively good message forwarding behavior and malicious message forwarding behavior of the message sending vehicle j to the message receiving vehicle i in the current interaction, DT i，i A direct trust value for message receiving vehicle i to message sending vehicle j.
In the specific implementation process, the method further comprises the following steps of updating the values of alpha and beta after the direct trust calculation is completed:
Figure BDA0003715435570000072
where α 'and β' are updated values.
In one embodiment, the message receiving vehicle performs indirect trust calculation on the message sending vehicle according to the suggestion of the neighbor vehicle to obtain an indirect trust value IT i，j The method is realized by adopting the following formula:
Figure BDA0003715435570000073
wherein
Figure BDA0003715435570000081
A set of neighbor vehicles for the message receiving vehicle i,
Figure BDA0003715435570000084
number of neighbor vehicles, RT, for message receiving vehicle i k And DT k，j Recommendation messages for neighboring vehicles k, respectively message receiving vehicles iThe arbitrary value and the direct trust value of neighbor vehicle k to message sending vehicle j.
In one embodiment, a trust value RT is recommended k The calculation method is as follows:
Figure BDA0003715435570000082
wherein RSU is road side unit, v k，R Set of vehicles with history of interaction with both vehicle k and roadside units, DT k，i Trust evidence uploaded for vehicles having history of interaction with both vehicle k and roadside units, DT RSU，i Is a local trust proof for the road side unit.
Specifically, the trust evidence refers to information related to trust, and is generated by performing trust evaluation on a message sending vehicle when a message receiving vehicle receives a message of the message sending vehicle, wherein the format is as follows:
TE j ＝{ID j ，DT i，j ，RT j ，IT i，j ，CT i，j ，CS j ，α，β，t}
therein, trust evidence TE j Stored locally in the message-receiving vehicle, and generates a different TE when the message-receiving vehicle receives a message from a different vehicle X X These TEs X Collectively referred to as "trust evidence".
In the invention, all vehicles can be used as message sending vehicles and can also be used as message receiving vehicles (when the vehicles forward messages to other vehicles, the vehicles are message senders, and when the vehicles receive the messages forwarded by other vehicles, the vehicles are message receivers), so that theoretically all vehicles have trust evidences.
The roadside units have, in addition to their special functions, the most basic functions: receive messages and forward messages. When the vehicle serves as a message receiver, the trust evaluation is also carried out on the message sending vehicle X, and a trust evidence is generated: TE X . Thus, the local trust evidence for the RSU refers to the evaluation of the RSU as a message recipientAll of TE X A collection of (a).
In the present embodiment, i represents a message receiving vehicle, and i represents a message transmitting vehicle. The above X is for convenience of explanation only and represents an unknown vehicle.
In one embodiment, the message receiving vehicle aggregates the direct trust value and the indirect trust value into a combined trust value CT i，j The method is realized by adopting the following formula:
Figure BDA0003715435570000083
wherein lambda is the interaction frequency of the message receiving vehicle i and the message sending vehicle j, theta is a preset fixed value and represents the minimum interaction frequency, DT, of the vehicle i which can accurately evaluate the trust value of the vehicle j i，j Indicating a direct trust value, IT, of the message-receiving vehicle i to the message-sending vehicle j i，j Indicating an indirect trust value of the message receiving vehicle i to the message sending vehicle j.
Specifically, the weights of the direct trust and the indirect trust aggregate depend on the number of interactions between the message sending vehicle and the message receiving vehicle, and when the number of interactions between the message sending vehicle and the message receiving vehicle is enough, the direct trust of the receiving party can be considered to be extremely high, and the suggestion of a neighbor does not need to be referred.
In one embodiment, the calculated combined confidence value decays over time, the combined confidence value CT i，j (t) decay with time is calculated as:
Figure BDA0003715435570000091
wherein CT 0 For the combined trust value of the last evaluation, Δ t is the time difference between the current interaction and the last trust evaluation, ω is the minimum value of the trust decay, and m is the time required for the trust to decay from the initial value to the minimum value.
In one embodiment, a data-centric assessment of a message sent by a messaging vehicle based on origin information includes:
the destination vehicle of the message evaluates the first message confidence level ST according to the trust value of the origin node generated by the message M The specific calculation formula is as follows:
Figure BDA0003715435570000092
where d and s are the destination vehicle of the message and the origin node or vehicle of the message generation, IT, respectively d，s Is an indirect trust value;
the destination vehicle of the message evaluates the credibility PT of the second message according to the credibility value of the message forwarding path node M The specific calculation formula is as follows:
Figure BDA0003715435570000093
wherein
Figure BDA0003715435570000094
A set of pathway nodes for forwarding of message M,
Figure BDA0003715435570000095
number of path nodes for forwarding message M, IT d，i Is an indirect trust value;
message trust value aggregation is carried out on the first message credibility and the second message credibility to obtain the trust value FT of the final message M M The calculation formula is as follows:
FT M ＝Φ×ST M +(1-Φ)×PT M
where Φ is the weight parameter.
Specifically, the destination vehicle of the message is a message receiving vehicle, the first message reliability is the reliability of the message origin node, and the second message reliability is the reliability of the message forwarding path.
As an alternative, the message sending vehicle awards and penalizes the message receiving vehicle according to its trust evaluation behavior; for example, if the message receiving vehicle i does not perform a trust evaluation on the message sending vehicle j, the message sending vehicle j decreases the contribution score of the message receiving vehicle i, and if one party does not share the local trust evidence with the other party, the other party decreases the contribution score of the other party.
In one embodiment, the global trust threshold is obtained by:
the cloud control center takes the trust evidences uploaded by all the road side units as global trust evidences, and the combined trust values of the global trust evidences are aggregated, wherein the calculation formula is as follows:
Figure BDA0003715435570000101
wherein
Figure BDA0003715435570000102
For the set of all vehicles in the network,
Figure BDA0003715435570000103
for the number of all vehicles in the network,
Figure BDA0003715435570000104
for the combined mean of trust of the local trust evidence of vehicle i,
according to the mean value
Figure BDA0003715435570000105
Updating a global trust threshold, the global trust threshold
Figure BDA0003715435570000106
The calculation formula of (c) is:
Figure BDA0003715435570000107
wherein
Figure BDA0003715435570000108
Is a historical trust threshold.
In order to more clearly illustrate the technical solution provided by the present invention, the following detailed description is given by way of specific examples.
As shown in fig. 1, in order to implement the origin information driven distributed trust management scheme, three network entities are involved:
vehicle: each vehicle is equipped with an On-Board Unit (OBU) which is responsible for perception of traffic events and collection of trust evidence, and has certain data storage (which is responsible for storing local direct trust evidence and recommendation of neighbors) and computing power. Further, the vehicle can also communicate with the on-board units of other vehicles and nearby road side units via the on-board unit. Meanwhile, traffic event information and trust-related evidence information are exchanged between vehicles.
Road Side Unit (RSU): the road side unit is a fixed infrastructure deployed along a road and is responsible for forwarding messages and collecting and uploading trust evidence. In addition, the roadside unit needs to perform calculation of the recommended trust value, receive the global trust threshold updated by the central cloud control center, and share the global trust threshold to passing vehicles.
The central cloud control center: the central cloud control center is considered trusted, managing all road side units through wired connections. The system is responsible for collecting global trust data, dynamically updating a global trust threshold value, and distributing the updated global trust threshold value to the road side unit.
During vehicle communication, the vehicle can collect communication behaviors of neighboring vehicles. In particular, communication behavior refers to the number of good message forwarding behaviors (such as forwarding a message according to a predetermined message format) and the number of malicious message forwarding (such as missing part of a forwarded message field). The vehicle can perform a trust assessment on neighboring vehicles by processing this information. Whenever there is an interaction between vehicles, the message receiving vehicle makes a trust assessment of the message sending vehicle and exchanges a local trust table (consisting of trust evidence) as a recommendation. When a vehicle passes through the road side unit, a local trust table is uploaded to the road side unit, and the road side unit shares a trust threshold with the vehicle passing through the road side unit. After the road side unit collects the trust table uploaded by the vehicle, cosine similarity calculation is carried out to obtain a recommended trust value, and whether the vehicle is honest or not is judged. And each road side unit uploads the collected trust evidence to the cloud control center periodically, and the cloud control center performs weighted aggregation to realize the dynamic update of the global trust threshold.
As shown in fig. 2, the steps of implementing the origin information driven distributed dynamic trust management scheme in internet of vehicles are as follows:
s1, when a message receiving vehicle i receives a message of a message sending vehicle j, firstly, whether a trust evidence of the vehicle j exists is searched in a local database so as to judge whether an interaction history exists with the message sending vehicle j. If the vehicles i and j have no history of interaction, the vehicle i initializes the trust evidence of the vehicle j locally, and the format of the initialized trust evidence is as follows:
TE j ＝{ID j ，DT i，j ，RT j ，IT i，j ，CT i，j ，CS j ，α，β，t}
entity-centric trust value calculations are performed after initialization to assess the trustworthiness of the messaging vehicle j.
S2, as shown in the attached figure 3, the trust value calculation of the message receiving vehicle i by taking the entity as the center is mainly divided into three parts: direct trust evaluation, indirect trust evaluation, and combined trust evaluation.
S2.1, directly evaluating trust based on Bayesian Beta distribution. Let random variable a denote communication behavior, a = a denotes a good message forwarding number (e.g. forwarding a message according to a predetermined message format), a = β denotes a malicious message forwarding number (e.g. missing part field of forwarded message), a = b denotes i Be (. Alpha.,. Beta.). Suppose that the current message receiving vehicle i observes the message sending vehicle j forwarding multiple messages, of which there is x i，j Good message sum y i，j A malicious message, then the direct trust value DT i，j The calculation formula of (2) is as follows:
Figure BDA0003715435570000111
s2.2, the message receiving vehicle i requests suggestions from the neighbor vehicles, and the suggestions of the neighbor vehicles are weighted and aggregated into indirect trust, wherein the indirect trust value IT i，j The calculation formula of (2) is as follows:
Figure BDA0003715435570000112
wherein
Figure BDA0003715435570000113
A set of neighbor vehicles for the message receiving vehicle i,
Figure BDA0003715435570000114
number of neighbor vehicles, RT, for message receiving vehicle i k And DT k，j Respectively, a recommended trust value of the neighbor vehicle k of the message receiving vehicle i and a direct trust value of the neighbor vehicle k to the message sending vehicle j.
Wherein the recommended trust value RT of all vehicles k Calculated by the road side unit. Uploading the local trust evidence of the vehicle to the road side unit when the vehicle passes through the road side unit, calculating a recommended trust value by the road side unit according to the cosine similarity of the trust evidence uploaded by the passing vehicle and the local trust evidence of the road side unit, and recommending a trust value RT k The calculation formula of (c) is:
Figure BDA0003715435570000115
s2.3, the message receiving vehicle i aggregates the direct trust value and the indirect trust value into a combined trust value, and the combined trust value CT i，j The calculation formula of (2) is as follows:
Figure BDA0003715435570000116
wherein λ is the number of times of interaction between the message receiving vehicle i and the message sending vehicle j, and θ is a preset fixed value, which represents the minimum number of times of interaction that the vehicle i can accurately evaluate the trust value of the vehicle j. The weight of the direct trust and the indirect trust aggregation depends on the interaction times of the message sending vehicle and the message receiving vehicle, and when the interaction times are enough, the direct trust credibility of the receiving party can be considered to be extremely high, and the suggestion of a neighbor is not required to be referred.
Vehicle i compares the combined trust value of j to the global trust threshold to determine if j is malicious. Because the trust threshold value is dynamic, the vehicle i requests the current trust threshold value from the road side unit, the road side unit requests the current trust threshold value from the cloud control center, and the vehicle i is shared with the current trust threshold value after the latest trust threshold value is obtained.
Furthermore, the locally stored historical trust values are not always as trusted as originally. Such as vehicle i, has performed a trust evaluation on vehicle j and stored the trust value locally. When vehicle i has not interacted with vehicle j for a significant period of time, vehicle i cannot determine whether vehicle j has the same degree of confidence as it did at first. Over time, vehicle i becomes increasingly unreferenced to the historical trust value of vehicle j. Which can be imagined as a "natural cooling process" by the business of buying:
at any time, all vehicles in MANETs have a current trust value.
If the vehicle performs a good routing behavior, the trust value of the vehicle is increased accordingly.
Over time, the confidence values for all vehicles gradually "cool" (i.e., decrease).
Thus, a function of the decay of the combined confidence value over time, CT, can be established according to Newton's law of cooling i，j (t) decay with time is calculated as:
Figure BDA0003715435570000121
wherein CT 0 For the combined trust value of the last evaluation, Δ t is the time difference between the current interaction and the last trust evaluation, ω is the minimum value of the trust decay, and m is the time required for the trust to decay from the initial value to the minimum value.
And S3, after the message receiving vehicle determines that the trust value of the sending vehicle is greater than the threshold value, calculating the trust value taking data as the center, and evaluating the trust value of the received message. An important issue in determining the trustworthiness of a message is determining the trustworthiness of the source of the message, assuming that the message M is generated by a source node s, the destination node is d, and the set of pathway nodes is P. M is forwarded by the forwarder to the destination node d, the trustworthiness of M then depends on s and the trustworthiness of each node forwarding the message. The invention judges the credibility of the message through two indexes: the credibility of the source node and the credibility of the message path node.
S3.1, d can know the source node and forwarding node of M from the header content of M. And d, after receiving the M, evaluating the credibility of the source node through indirect trust calculation. Then, the confidence level ST of the message is deduced according to the source node M The calculation formula of (c) is:
Figure BDA0003715435570000122
wherein IT d，s An indirect trust value for the destination vehicle d to the source node of the message.
S3.2, the credibility of the message is inversely proportional to the number of the path nodes and directly proportional to the trust values of the path nodes. Deducing the confidence level PT of the message according to the path node M The calculation formula of (c) is:
Figure BDA0003715435570000131
wherein
Figure BDA0003715435570000132
A set of pathway nodes for forwarding of the message M,
Figure BDA0003715435570000133
number of path nodes for forwarding message M, IT d，i Is an indirect trust value.
S3.3, carrying out message trust value aggregation, and finally obtaining the trust value FT of the message M M The calculation formula is as follows:
FT M ＝Φ×ST M +(1-Φ)×PT M
where Φ is the weight parameter. The message is apparently extremely trustworthy if the source node is not trustworthy. Therefore, the influence of the credibility of the source node on the trust value of the final message is larger, so that 0.5 < phi < 1.
And S4, the message sender rewards and punishes the message receiver according to the trust evaluation behavior of the message receiver, and the reward mechanism can stimulate nodes to carry out interaction and trust evaluation, promote the exchange of trust evidences and effectively relieve the influence of selfish nodes on the trust evaluation. The well-defined trust evaluation behavior of the present embodiment includes: (1) performing trust evaluation on the interactive nodes; and (2) sharing the local trust table with the interactive node. And if the two conditions are met simultaneously, the node is judged to have good trust evaluation behavior. We define pi to represent the privacy of the vehicle, and the value of pi is { -1,0,1}. The contribution score CS' of the vehicle is then:
CS′＝CS+π
where CS' is the updated contribution score and CS is the historical contribution score, where the initial value of CS is 0. Whenever a node fulfills a good trust evaluation behavior and the combined trust value of the node is above a threshold, pi =1, that is the node is rewarded. When a node performs a good trust evaluation behavior but the combined trust value of the node is below the threshold, then pi =0, i.e. the node is not rewarded. If a node does not perform good behavior, then π = -1, that is to say the node is penalized.
When the message receiving vehicle carries out entity evaluation on the message sending vehicle and the message receiving vehicle carries out indirect trust calculation on the message sending vehicle, the recommended trust value of the message sending vehicle needs to be used, and the recommended trust value passes through a road side listAnd (4) performing meta-computation. Specifically, the roadside unit collects trust evidences of all passing vehicles and calculates a recommended trust value of the vehicle by the similarity. Most studies today take into account the recommendations provided by neighboring vehicles, i.e. indirect trust. However, the reputation judgment of the neighbor vehicle is not perfect, that is, it cannot be accurately judged whether the suggestion provided by the neighbor vehicle is credible. There is therefore a need to establish an efficient mechanism to determine whether the suggestions of neighbors are honest. In the present invention, the recommended trust value RT of all vehicles is given by the limited computing power of the vehicle and the lower reliability than the roadside unit k Calculated by the road side unit. Uploading the local trust evidence of the vehicle to the road side unit when the vehicle passes through the road side unit, calculating a recommended trust value by the road side unit according to the cosine similarity of the trust evidence uploaded by the passing vehicle and the local trust evidence of the road side unit, and recommending a trust value RT k The calculation formula of (2) is as follows:
Figure BDA0003715435570000134
after the recommended trust value is calculated, the road side unit shares the new recommended trust value to all vehicles in the area.
Whether a node is malicious or not is generally determined by comparing the combined trust value of the node with a preset threshold (if the combined trust value is greater than the threshold, the node is a benign node, otherwise, the node is a malicious node). However, in an unknown internet of vehicles with a variable network topology, the size of the threshold cannot be accurately determined. Therefore, the invention proposes a global-based dynamic threshold updating mechanism. And the road side unit uploads all trust evidences to the cloud control center at regular time, and after acquiring the global trust threshold updated by the cloud control center, the road side unit shares the new threshold to all passing vehicles. That is, the global trust threshold is calculated and updated by the cloud control center.
The trust evidences uploaded by all road side units collected by the cloud control center are global trust evidences, the cloud control center firstly aggregates the combined trust values of the global trust evidences, and the calculation formula is as follows:
Figure BDA0003715435570000141
wherein
Figure BDA0003715435570000142
For the set of all vehicles in the network,
Figure BDA0003715435570000143
for the number of all vehicles in the network,
Figure BDA0003715435570000144
is a combined mean of trust for the local trust evidence of vehicle i, then based on the mean
Figure BDA0003715435570000145
Updating a global trust threshold, the global trust threshold
Figure BDA0003715435570000146
The calculation formula of (c) is:
Figure BDA0003715435570000147
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003715435570000148
is a historical trust threshold.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various modifications and variations can be made in the embodiments of the present invention without departing from the spirit or scope of the embodiments of the invention. Thus, if such modifications and variations of the embodiments of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to encompass such modifications and variations.

Claims (10)

1. A distributed internet of vehicles dynamic trust management method based on origin information is characterized by comprising the following steps:
when the message receiving vehicle receives the message of the message sending vehicle, judging whether the message receiving vehicle has interactive history with the message sending vehicle, if not, the message receiving vehicle firstly initializes the trust evidence of the vehicle locally and then carries out entity trust evaluation on the message sending vehicle, otherwise, the message receiving vehicle carries out entity trust evaluation on the message sending vehicle;
the message receiving vehicle judges whether the trust value of the message sending vehicle is larger than a global trust threshold value or not according to the entity trust evaluation result, if not, the message sending vehicle is judged to be not trusted, if so, the message sent by the message sending vehicle is evaluated by taking data as a center based on origin information, wherein the global trust threshold value is obtained by carrying out weighted aggregation on trust evidence uploaded by the road side unit at regular time through the cloud control center, then the trust evidence is sent to the road side unit through the cloud control center, and then the trust threshold value is shared to the message receiving vehicle through the road side unit.
2. The origin information-based distributed internet of vehicles dynamic trust management method of claim 1, wherein the format of the trust evidence of the initialized vehicle is:
TE j ＝{ID j ,DT i,j ,RT j ,IT i,j ,CT i,j ,CS j ,α,β,t}
where i and j represent vehicles, i represents a message receiving vehicle, j represents a message sending vehicle, ID j An identity unique identifier, DT, representing the messaging vehicle j i,j 、IT i,j And CT i,j Respectively representing the direct trust value, the indirect trust value and the combined trust value of the message receiving vehicle i to the message sending vehicle j, RT j Represents the recommended trust value, CS, of the message-sending vehicle j issued by the road side unit j Indicating message deliveryAnd the contribution score of the vehicle j, alpha and beta respectively represent the historical good message forwarding behavior and the malicious message forwarding behavior of the message sending vehicle j, and the initial value is 1,t and is the timestamp of the latest trust evaluation.
3. The method of claim 1, wherein the message receiving vehicle performs entity trust evaluation on the message sending vehicle, comprising:
the message receiving vehicle carries out direct trust calculation on the message sending vehicle by using a Bayesian method to obtain a direct trust value;
the message receiving vehicle carries out indirect trust calculation on the message sending vehicle according to the suggestion of the neighbor vehicle to obtain an indirect trust value, wherein the suggestion of the neighbor vehicle comprises a recommended trust value of the neighbor vehicle to the message sending vehicle and a direct trust value of the neighbor vehicle to the message sending vehicle, the recommended trust value of the neighbor vehicle to the message sending vehicle is obtained by a road side unit after cosine similarity calculation according to trust evidence uploaded by passing vehicles and local trust evidence of the road side unit, and then the road side unit sends the result to the neighbor vehicle;
and the message receiving vehicle aggregates the direct trust value and the indirect trust value into a combined trust value which is used as an entity evaluation result of the message sending vehicle.
4. The distributed internet of vehicles dynamic trust management method based on origin information of claim 3, wherein the message receiving vehicle uses Bayesian method to perform direct trust calculation for the message sending vehicle, and the method is implemented by using the following formula:
Figure FDA0003715435560000021
where α and β represent the historical good message forwarding behavior and malicious message forwarding behavior, respectively, of the messaging vehicle j, x i,j And y i,j Respectively for j cancellation of message sending vehicle in current interactionGood message forwarding behavior and malicious message forwarding behavior, DT, of the information receiving vehicle i i,j A direct trust value for message receiving vehicle i to message sending vehicle j.
5. The distributed Internet of vehicles dynamic trust management method based on origin information of claim 3, wherein the message receiving vehicle performs indirect trust calculation on the message sending vehicle according to the suggestion of the neighbor vehicle to obtain an indirect trust value IT i,j The method is realized by adopting the following formula:
Figure FDA0003715435560000022
wherein
Figure FDA0003715435560000023
A set of neighboring vehicles for the message receiving vehicle i,
Figure FDA0003715435560000024
number of neighbor vehicles, RT, for message receiving vehicle i k And DT k,j Respectively, a recommended trust value of the neighbor vehicle k of the message receiving vehicle i and a direct trust value of the neighbor vehicle k to the message sending vehicle j.
6. The method of claim 5, wherein the trust value RT is recommended based on the origin information of the distributed Internet of vehicles k The calculation method of (A) is as follows:
Figure FDA0003715435560000025
wherein the RSU is a road side unit,
Figure FDA0003715435560000026
set of vehicles with history of interaction with both vehicle k and roadside units, DT k,i To K-sum with the vehicleVehicle uploaded trust evidence with interactive history, DT, on side units RSU,i Is a local trust proof for the road side unit.
7. The origin information-based distributed dynamic trust management method for internet of vehicles according to claim 3, wherein the message receiving vehicle aggregates the direct trust value and the indirect trust value into a combined trust value CT i, The method is realized by adopting the following formula:
Figure FDA0003715435560000027
wherein lambda is the interaction frequency of the message receiving vehicle i and the message sending vehicle j, theta is a preset fixed value and represents the minimum interaction frequency, DT, of the vehicle i which can accurately evaluate the trust value of the vehicle j i,j Indicating a direct trust value, IT, of the message-receiving vehicle i to the message-sending vehicle j i,j Indicating an indirect trust value of the message receiving vehicle i to the message sending vehicle j.
8. The origin information-based distributed dynamic trust management method for internet of vehicles according to claim 3, wherein the calculated combined trust value decays with time, and the combined trust value CT i,j (t) decay with time is calculated as:
Figure FDA0003715435560000028
wherein CT 0 For the combined trust value of the last evaluation, Δ t is the time difference between the current interaction and the last trust evaluation, ω is the minimum value of the trust decay, and m is the time required for the trust to decay from the initial value to the minimum value.
9. The method of claim 1, wherein the data-centric evaluation of messages sent by the messaging vehicles based on the origin information comprises:
the destination vehicle of the message evaluates the first message confidence level ST according to the trust value of the origin node generated by the message M The specific calculation formula is as follows:
Figure FDA0003715435560000031
wherein d and s are the destination vehicle of the message and the origin node or vehicle of the message generation, IT, respectively d,s Is an indirect trust value;
the destination vehicle of the message evaluates the credibility PT of the second message according to the credibility value of the message forwarding path node M The specific calculation formula is as follows:
Figure FDA0003715435560000032
wherein
Figure FDA0003715435560000033
A set of pathway nodes for forwarding of message M,
Figure FDA0003715435560000034
number of path nodes for forwarding message M, IT d,i Is an indirect trust value;
message trust value aggregation is carried out on the first message credibility and the second message credibility to obtain the trust value FT of the final message M M The calculation formula is as follows:
FT M ＝Φ×ST M +(1-Φ)×PT M
where Φ is the weight parameter.
10. The distributed internet of vehicles dynamic trust management method based on origin information of claim 1, wherein the global trust threshold is obtained by the following steps:
the cloud control center takes the trust evidences uploaded by all the road side units as global trust evidences, and aggregates the combined trust values of the global trust evidences, wherein the calculation formula is as follows:
Figure FDA0003715435560000035
wherein
Figure FDA0003715435560000036
For the set of all vehicles in the network,
Figure FDA0003715435560000037
for the number of all vehicles in the network,
Figure FDA0003715435560000038
is the combined trust average of the local trust evidence for vehicle i,
according to the mean value
Figure FDA0003715435560000039
Updating a global trust threshold, the global trust threshold
Figure FDA00037154355600000310
The calculation formula of (2) is as follows:
Figure FDA00037154355600000311
wherein
Figure FDA00037154355600000312
Is a historical trust threshold.
CN202210741334.9A 2022-06-27 2022-06-27 Distributed Internet of vehicles dynamic trust management method based on origin information Active CN115174615B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202210741334.9A CN115174615B (en) 2022-06-27 2022-06-27 Distributed Internet of vehicles dynamic trust management method based on origin information

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210741334.9A CN115174615B (en) 2022-06-27 2022-06-27 Distributed Internet of vehicles dynamic trust management method based on origin information

Publications (2)

Publication Number Publication Date
CN115174615A true CN115174615A (en) 2022-10-11
CN115174615B CN115174615B (en) 2024-04-19

Family

ID=83488171

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202210741334.9A Active CN115174615B (en) 2022-06-27 2022-06-27 Distributed Internet of vehicles dynamic trust management method based on origin information

Country Status (1)

Country Link
CN (1) CN115174615B (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115941332A (en) * 2022-12-08 2023-04-07 南京航空航天大学 Vehicle credibility measuring method based on block chain and recommendation trust

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2016188116A1 (en) * 2015-05-25 2016-12-01 华南理工大学 Credibility detection-based security routing protocol in vehicular ad hoc network
CN109195162A (en) * 2018-10-12 2019-01-11 暨南大学 It polymerize the message reliability appraisal procedure of two kinds of trust evaluations in a kind of car networking
CN111064800A (en) * 2019-12-26 2020-04-24 杭州云象网络技术有限公司 Block chain technology-based safe vehicle contact social network construction method
CN111565188A (en) * 2020-04-30 2020-08-21 长安大学 VANET trust model working method based on combination of message type and trust value confidence
CN112929845A (en) * 2021-01-26 2021-06-08 兰州理工大学 Vehicle networking node trust evaluation method and system based on block chain
CN114007217A (en) * 2020-07-27 2022-02-01 中移(苏州)软件技术有限公司 Data processing method, vehicle-mounted system and storage medium

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2016188116A1 (en) * 2015-05-25 2016-12-01 华南理工大学 Credibility detection-based security routing protocol in vehicular ad hoc network
CN109195162A (en) * 2018-10-12 2019-01-11 暨南大学 It polymerize the message reliability appraisal procedure of two kinds of trust evaluations in a kind of car networking
CN111064800A (en) * 2019-12-26 2020-04-24 杭州云象网络技术有限公司 Block chain technology-based safe vehicle contact social network construction method
CN111565188A (en) * 2020-04-30 2020-08-21 长安大学 VANET trust model working method based on combination of message type and trust value confidence
CN114007217A (en) * 2020-07-27 2022-02-01 中移(苏州)软件技术有限公司 Data processing method, vehicle-mounted system and storage medium
CN112929845A (en) * 2021-01-26 2021-06-08 兰州理工大学 Vehicle networking node trust evaluation method and system based on block chain

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
成坚;冯仁剑;许小丰;万江文;: "基于D-S证据理论的无线传感器网络信任评估模型", 传感技术学报, no. 12, 20 December 2009 (2009-12-20) *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115941332A (en) * 2022-12-08 2023-04-07 南京航空航天大学 Vehicle credibility measuring method based on block chain and recommendation trust

Also Published As

Publication number Publication date
CN115174615B (en) 2024-04-19

Similar Documents

Publication Publication Date Title
Yao et al. Using trust model to ensure reliable data acquisition in VANETs
Gazdar et al. A distributed advanced analytical trust model for VANETs
WO2020000924A1 (en) Message reliability evaluation method for aggregating two trust evaluations in internet of vehicles
CN112929845B (en) Vehicle networking node trust evaluation method and system based on block chain
CN110445788B (en) Content-oriented trust evaluation system and method under vehicle-mounted ad hoc network environment
Oluoch A distributed reputation scheme for situation awareness in vehicular ad hoc networks (VANETs)
CN114745127A (en) Node credibility authentication method in Internet of vehicles environment based on block chain
CN115174615B (en) Distributed Internet of vehicles dynamic trust management method based on origin information
Kerrache et al. Hierarchical adaptive trust establishment solution for vehicular networks
Gazdar et al. DTCF: A distributed trust computing framework for vehicular ad hoc networks
Gazdar et al. A trust-based architecture for managing certificates in vehicular ad hoc networks
CN110139278B (en) Method of safety type collusion attack defense system under Internet of vehicles
Liu et al. HDRS: A hybrid reputation system with dynamic update interval for detecting malicious vehicles in VANETs
CN113380024B (en) Reputation updating method and trust calculation method based on Internet of vehicles
Souissi et al. Towards a Self-adaptive Trust Management Model for VANETs.
Wan et al. A trust scheme based on vehicles reports of events in VANETs
Wang et al. BIBRM: A Bayesian inference based road message trust model in vehicular ad hoc networks
Costantino et al. CARS: Context aware reputation systems to evaluate vehicles' behaviour
Dahiya et al. A Feedback-Driven Lightweight Reputation Scheme for IoV
Fernandes et al. RS4VANETs-a decentralized reputation system for assessing the trustworthiness of nodes in vehicular networks
Al Falasi et al. Similarity-based trust management system: Data validation scheme
Li et al. A Trust Evaluation Method Based on Environmental Assessment in the Perception Layer of Internet of Vehicles
CN115051983B (en) Internet of vehicles trust management system and method based on blockchain
Alharthi et al. A formal method of trust computation in VANET: A spatial, temporal and behavioral approach
Meamari et al. TrCoin: A Blockchain-based Robust Trust Management System for VANET

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant