CN109347852B - Lightweight Internet of vehicles trust evaluation method - Google Patents

Lightweight Internet of vehicles trust evaluation method Download PDF

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CN109347852B
CN109347852B CN201811315880.6A CN201811315880A CN109347852B CN 109347852 B CN109347852 B CN 109347852B CN 201811315880 A CN201811315880 A CN 201811315880A CN 109347852 B CN109347852 B CN 109347852B
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CN109347852A (en
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刘志全
翁健
马建峰
李盈
兰奕明
魏凯敏
冯丙文
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Jinan University
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Abstract

The invention discloses a lightweight Internet of vehicles trust evaluation method, which specifically comprises the following steps: s1, when the previous interaction of the nodes is finished, both interaction parties generate a trust certificate containing a digital signature of the interaction party for the other party according to the interaction experience and send the trust certificate to the other party; s2, after receiving the new trust certificate, the two interacting parties verify the signature information and update the local storage to store at most eta pieces of trust certificate which is most beneficial to the two interacting parties, wherein eta belongs to Z+Is a system parameter; s3, when the potential interaction starts, both the potential interaction parties send the locally stored trust certificate to the other party to prove that the potential interaction parties are trusted; and S4, the two potential interaction parties verify the authenticity of the trust certificate through the digital signature information, derive the trust value of the other party according to the authenticity and decide whether to agree to interact with the trust certificate, and interact if and only if the two parties agree. The invention does not depend on a trust center and a road side unit, and better conforms to the large-scale and distributed characteristics of the Internet of vehicles.

Description

Lightweight Internet of vehicles trust evaluation method
Technical Field
The invention relates to the technical field of Internet of vehicles safety, in particular to a lightweight Internet of vehicles trust evaluation method.
Background
At present, traffic accidents, road congestion and environmental pollution become key problems in cities around the world, and the internet of vehicles plays a vital role in urban road traffic as a main application of the internet of things in the automobile industry and a core component of an intelligent traffic system.
However, due to the characteristics of large scale, open, distributed, sparse and highly dynamic, the internet of vehicles is vulnerable to malicious behaviors and attacks, and the security and reliability have gradually become bottlenecks restricting the further development of the internet of vehicles and concern whether the internet of vehicles can be applied to real road environments. The existing scheme mostly adopts digital signature and cryptography technology, and the reliability of the node (namely, a vehicle) and the quality of the message cannot be evaluated.
Trust management plays a vital role in the internet of vehicles, enabling each node to pre-evaluate the trust values of other nodes and messages to avoid serious consequences from malicious nodes and unrealistic messages. Currently, trust management in the internet of vehicles is still in the infancy stage, and only a few trust evaluation schemes are proposed. Based on the architecture, existing solutions can be roughly divided into two categories, namely infrastructure-based solutions and ad hoc solutions.
Li et al [ X.Li, J.Liu, X.Li, and W.Sun, "RGTE: A publication-based global trust establishment In VANETs," In Proceedings of the 5th International Conference on Intelligent network and Collabolitive Systems,2013, pp.210-214 ] propose a global trust establishment scheme RGTE for the Internet of vehicles, wherein the reputation management center is responsible for collecting trust information of all legal nodes and calculating reputation scores thereof. The model assumes that the reputation management center is completely credible and online in real time, requires higher maintenance cost, and has inherent defects of single-point failure, large time delay and the like. Wei et al [ Z.Wei, F.R.Yu, and A.Boukerche, "Trust based security enhancements for Vehicular ad hoc Networks," In Proceedings of the 4th International Symposium on Development and Analysis of Intelligent Vehicular Networks and Applications,2014, pp.103-109 ] comprehensively consider direct Trust based on historical interactions and indirect Trust based on Trust recommendations, and propose a distributed Trust evaluation method for Vehicular networking where direct Trust is derived from Bayesian formulas and indirect Trust is derived from D-S evidence theory. This model requires the trusted people to gather trust recommendation information about the trusted people, often resulting in large time, bandwidth consumption.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provide a lightweight vehicle networking trust evaluation method, which does not depend on a trust center and a road side unit and better conforms to the large-scale and distributed characteristics of the vehicle networking.
The purpose of the invention is realized by the following technical scheme: a lightweight Internet of vehicles trust evaluation method specifically comprises the following steps:
s1, when the previous interaction of the nodes is finished, both interaction parties generate a trust certificate containing a digital signature of the interaction party for the other party according to the interaction experience and send the trust certificate to the other party;
s2, after receiving the new trust certificate, the two interacting parties verify the signature information and update the local storage to store at most eta pieces of trust certificate which is most beneficial to the two interacting parties, wherein eta belongs to Z+Is a system parameter;
s3, when the potential interaction starts, both the potential interaction parties send the locally stored trust certificate to the other party to prove that the potential interaction parties are trusted;
and S4, the two potential interaction parties verify the authenticity of the trust certificate through the digital signature information, derive the trust value of the other party according to the authenticity and decide whether to agree to interact with the trust certificate, and interact if and only if the two parties agree.
Preferably, in step S1, the specific format of the trust certificate is:
TC(B,A)＝(ID(B),ID(A),RT(B,A),WG(B),TS(B,A),DS(B,A))
wherein B and A represent nodes, B is a prover, and A is a recipient; id (B) and id (a) represent unique identifiers of prover B and recipient a, respectively; RT (B, a) represents an evaluation value vector, in the specific format:
RT(B,A)＝(RT(B,A,1),RT(B,A,2),…,RT(B,A,n))
where n represents the number of trust aspects, RT (B, A, i) (i ∈ [1, n ]) represents the evaluation value of the i-th trust aspect of the prover B to the recipient A; wg (b) represents a weight value vector in the following specific format:
WG(B)＝(WG(B,1),WG(B,2),…,WG(B,n))
wherein WG (B, i) (i ∈ [1, n ]) represents the preference level of interest of prover B for the ith trust aspect; TS (B, a) represents a timestamp when TC (B, a) is generated; DS (B, a) represents digital signature information.
Further, the RT (B, a, i) is expressed as linguistic variables, including "very good", "general", "bad", and "very bad"; the WGs (B, i) are expressed as linguistic variables, including "very high", "medium", "low", and "very low".
Preferably, in step S2, after receiving the trust certificate TC (B, a) generated by the certifier B for itself, the recipient a first verifies the signature information DS (B, a), and then determines the magnitude relationship between the number nm (a) of locally stored trust certificates and η: if NM (A) < η, the receiver A stores TC (B, A) directly; if nm (a) ═ η, the recipient a calculates a weighted evaluation value corresponding to each trust certificate, and selects and stores the η pieces of trust certificates most favorable to the recipient a according to the weighted evaluation value, and deletes other trust certificates, wherein the trust certificates considered by the recipient a comprise TC (B, a) and the η pieces of trust certificates stored locally.
Preferably, in step S2, the specific step of calculating the weighted evaluation value by TC (B, a) is:
s2.1, converting RT (B, A, i) in TC (B, A) into fuzzy evaluation RF (B, A, i) and clear evaluation RC (B, A, i), wherein the specific format of the RF (B, A, i) is as follows:
RF(B,A,i)＝(RF(B,A,i,1),RF(B,A,i,2),RF(B,A,i,3),RF(B,A,i,4))
wherein RF (B, A, i,1) is not less than 0 but not more than RF (B, A, i,2) is not less than RF (B, A, i,3) but not more than RF (B, A, i,4) is not less than 100; RC (B, a, i) is the symbol distance of RF (B, a, i), which can be derived from the following formula:
Figure GDA0001927186520000041
s2.2, converting WG (B, i) in TC (B, A) into fuzzy weight WF (B, i) and clear weight WC (B, i), wherein specific format of WF (B, i) is as follows:
WF(B,i)＝(WF(B,i,1),WF(B,i,2),WF(B,i,3),WF(B,i,4))
wherein WF (B, i,1) is not less than 0 but not more than 0 and not more than WF (B, i,2) is not less than WF (B, i,3) but not more than WF (B, i,4) is not less than 10; WC (B, i) can be derived from the following formula:
Figure GDA0001927186520000042
s2.3, the blur evaluation value SF (B, a) corresponding to TC (B, a) can be calculated as:
Figure GDA0001927186520000043
the clear evaluation value SC (B, a) corresponding to TC (B, a) can be calculated as:
Figure GDA0001927186520000044
s2.4, the recipient a only considers the time decay weight WT (B, a) when selecting the most favorable trust attestation, which is calculated as:
Figure GDA0001927186520000045
wherein TN represents the current timestamp; TS (B, A) is the time stamp contained in TC (B, A); ω represents the time window size; theta is a time attenuation factor, and controls the attenuation speed of WT (B, A) along with the time difference;
s2.5, the weighted evaluation value SW (B, a) corresponding to TC (B, a) can be calculated as:
SW(B,A)＝SC(B,A)*WT(B,A)。
preferably, in step S3, the specific format of the trust certificate set is as follows:
TCs(A)＝{TC(B1,A),TC(B2,A),…,TC(BNM(A),A)}
wherein NM (A) is less than or equal to eta.
Preferably, the step S4 includes the following steps:
s4.1, when the potential interaction starts, after a node C serving as a trustee receives a trust certificate set TCs (A) of a node A serving as a receiver, the trust certificate in the trust certificate set TCs (A), namely TC (B), is extracted first1,A)、TC(B2,A)、…、TC(BNM(A)A), if NM (A) < eta, the trust value TV (C, A) of the person who gives the trust to the person who receives the trust A is set toA constant τ ∈ [0, 1); otherwise, the trustee C verifies the authenticity of the trustee C through the digital signature information in each trust certificate, then derives a weighted evaluation value corresponding to each trust certificate and calculates the TV (C, A); similarly, the trust value TV (A, C) of the applicant A to the receiver C can be obtained;
s4.2, if and only if TV (C, A) ≧ TH (C) and TV (A, C) ≧ TH (A), node A interacts with node C, wherein TH (C), TH (A) epsilon [0,1] are the trust threshold of node C, node A respectively.
Preferably, in step S4, the messenger C considers an interest preference similarity weight WS (C, B) in addition to the time attenuation weight WT (B, a), the weight being derived from a weighted euclidean distance DS (C, B) of the interest preference vector between the messenger C and the prover B, and the specific calculation formula is:
Figure GDA0001927186520000051
WS(C,B)＝1-DS(C,B)。
further, in step S4, the weighted evaluation value ST (C, B, a) corresponding to TC (B, a) may be calculated as:
ST(C,B,A)＝SC(B,A)*WT(B,A)*WS(C,B)
similarly, the messenger C can derive TC (B)1,A)、TC(B2,A)、…、TC(BηA) a corresponding weighted evaluation value, and calculating a trust value TV (C, a) for the recipient a, the specific calculation formula being:
Figure GDA0001927186520000061
compared with the prior art, the invention has the following advantages and beneficial effects:
1. the invention adopts a complete self-organizing mode, three roles of a letter applier, a letter acceptor, a certifier and the like are converted in different evaluation stages, and the method does not depend on a trust center and a road side unit, thereby being more suitable for a large-scale and distributed vehicle networking environment.
2. The invention stores and provides trust information by the receiver without the need of the collector, thereby greatly reducing time and bandwidth consumption and realizing rapid and lightweight trust evaluation.
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FIG. 1 is a schematic diagram of main steps of an embodiment of a lightweight vehicle networking trust evaluation method of the present invention.
FIG. 2 is a simplified example of a lightweight Internet of vehicles trust evaluation method embodiment of the present invention.
Detailed Description
For better understanding of the technical solutions of the present invention, the following detailed description is provided for examples of the present invention with reference to the accompanying drawings, but the embodiments of the present invention are not limited thereto.
Examples
As shown in fig. 1-2, the present embodiment does not include a trust center and a road side unit, but only includes a large number of common nodes (i.e., vehicles), and the communication is performed therebetween in a wireless ad hoc network manner. Each node has the role of either an issuer, a receiver, or a prover and is subject to transformation during different evaluation phases.
Step S1, when the previous interaction of the node A, B is ended, B as a prover generates a trust certificate TC (B, a) for a as a recipient according to the interaction experience, and the specific format is:
TC(B,A)＝(ID(B),ID(A),RT(B,A),WG(B),TS(B,A),DS(B,A)) (1)
wherein ID (B) and ID (A) represent unique identifiers of prover B and recipient A, respectively; RT (B, a) represents an evaluation value vector, in the specific format:
RT(B,A)＝(RT(B,A,1),RT(B,A,2),…,RT(B,A,n)) (2)
where n represents the number of trust aspects, RT (B, A, i) (i e [1, n ]) represents the prover B's evaluation of the ith trust aspect of the recipient A, with the value represented as linguistic variables, such as "very good", "general", "bad", "very bad", etc.; wg (b) represents a weight value vector in the following specific format:
WG(B)＝(WG(B,1),WG(B,2),…,WG(B,n) (3)
where WG (B, i) (i e [1, n ]) represents the prover B interest preference level for the ith trust aspect, which value is also expressed as linguistic variables, such as "very high", "medium", "low", "very low", etc.; TS (B, a) represents a timestamp when TC (B, a) is generated; DS (B, a) represents digital signature information.
Similarly, B as the recipient generates TC (B, a) for a as the prover and sends it to the prover a. Similarly, prover a also generates and sends trust attestation TC (a, B) to recipient B.
Step S2, after receiving trust certificate TC (B, A) generated by certifier B for self, receiver A firstly verifies signature information DS (B, A), then judges the magnitude relation between quantity NM (A) of local stored trust certificate and eta, wherein eta belongs to Z+As system parameters: if NM (A) < η, the receiver A stores TC (B, A) directly; if nm (a) ═ η, the recipient a calculates a weighted evaluation value corresponding to each trust certificate, and selects and stores η pieces of trust certificates most favorable to the recipient a according to the weighted evaluation value, and deletes other trust certificates, wherein the trust certificates calculated by the recipient a include TC (B, a) and the η pieces of trust certificates stored locally.
Taking the calculation of the weighted evaluation value corresponding to TC (B, a) as an example: the RT (B, a, i) in TC (B, a) can be converted into fuzzy evaluation RF (B, a, i) and clear evaluation RC (B, a, i) by the existing fuzzy simple additive weighting system [ s.y.chou, y.h.chang, and c.y.shen ], "a fuzzy simple additive weighting system under group definition-mapping for failure location selection with object/sub-attribute," European Journal of Operational resource, 2008, vol.189, No.1, pp.132-145 ] ], wherein the specific calculation steps of RF (B, a, i) are:
RF(B,A,i)＝(RF(B,A,i,1),RF(B,A,i,2),RF(B,A,i,3),RF(B,A,i,4)) (4)
wherein RF (B, A, i,1) is not less than 0 and not more than RF (B, A, i,2) is not less than RF (B, A, i,3) and not more than RF (B, A, i,4) is not less than 100; RC (B, a, i) is the symbol distance of RF (B, a, i), which can be derived from the following formula:
Figure GDA0001927186520000081
similarly, WG (B, i) can be converted into fuzzy weight WF (B, i) and explicit weight WC (B, i), where WF (B, i) is in the specific format:
WF(B,i)＝(WF(B,i,1),WF(B,i,2),WF(B,i,3),WF(B,i,4)) (6)
wherein WF (B, i,1) is not less than 0 but not more than 0 and not more than WF (B, i,2) is not less than WF (B, i,3) but not more than WF (B, i,4) is not less than 10; WC (B, i) can be derived from the following formula:
Figure GDA0001927186520000082
where j, k are both temporary variables for summation.
Subsequently, the blur evaluation value SF (B, a) corresponding to TC (B, a) can be calculated as:
Figure GDA0001927186520000083
the clear evaluation value SC (B, a) corresponding to TC (B, a) can be calculated as:
Figure GDA0001927186520000084
the recipient a only considers the time decay weight WT (B, a) when selecting the most favorable trust attestation, which is calculated by the formula:
Figure GDA0001927186520000091
wherein TN represents the current timestamp; TS (B, A) is the time stamp contained in TC (B, A); ω represents the time window size; θ is a time decay factor, controlling the rate at which WT (B, A) decays with time difference.
Therefore, the weighted evaluation value SW (B, a) corresponding to TC (B, a) can be calculated as:
SW(B,A)＝SC(B,A)*WT(B,A) (11)
from equations (4) - (10), it can be inferred that the range of RC (B, A, i) is [0,100], and the ranges of WC (B, i), WT (B, A), SC (B, A), SW (B, A) are [0,1 ].
Similarly, the receivers A calculate separatelyWeighted evaluation value SW (B) corresponding to η pieces of trust certification stored locally1,A)、SW(B2,A)、…、SW(BηA), then from SW (B, A), SW (B)1,A)、…、SW(BηAnd A) selecting eta larger values, storing corresponding trust certificates, and deleting other trust certificates.
Step S3, when the potential interaction starts, the recipient a wants to interact with the node C as the issuer, and the recipient a first takes out the locally stored trust certificate, whose set is denoted as tcs (a), that is:
TCs(A)＝{TC(B1,A),TC(B2,A),…,TC(BNM(A),A)} (12)
wherein NM (A) is less than or equal to eta. The recipient a then sends tcs (a) to the originator C to prove itself trustworthy.
Similarly, node C, the recipient, sends tcs (C) to node a, the issuer, to prove itself trustworthy.
Step S4, after the trust certificate set TCs (A) of the receiver A is received by the sender C, the authenticity of the trust certificate is firstly verified through the digital signature information, and the trust certificate, namely TC (B), is extracted from the trust certificate1,A)、TC(B2,A)、…、TC(BNM(A)A), if NM (A) < η, the trust value TV (C, A) of the sender C to the receiver A is set to a smaller constant τ ∈ [0, 1); otherwise, the applicant C verifies the authenticity of each trust certificate by the digital signature information in the certificate, then derives a weighted evaluation value corresponding to each trust certificate and calculates TV (C, a).
Taking the weighted evaluation value ST (C, B, a) corresponding to TC (B, a) derived by the carrier C as an example: the applicant C considers the interest preference similarity weight WS (C, B) derived from the weighted euclidean distance DS (C, B) of the interest preference vector of the applicant C and the prover B, in addition to the time decay weight WT (B, a) (i.e., equation (10)), and the specific calculation formula is:
Figure GDA0001927186520000101
WS(C,B)＝1-DS(C,B) (14)
therefore, the weighted evaluation value ST (C, B, a) corresponding to TC (B, a) can be calculated as:
ST(C,B,A)＝SC(B,A)*WT(B,A)*WS(C,B) (15)
according to the methods described in equations (13) - (15), the messenger C can derive TC (B)1,A)、TC(B2,A)、…、TC(BηA) a corresponding weighted evaluation value, and calculating a trust value TV (C, a) for the recipient a, with the specific formula:
Figure GDA0001927186520000102
from equations (13) - (16), it can be deduced that the ranges of WS (C, B), ST (C, B, A), TV (C, A) are all [0,1 ].
If TV (C, A) ≧ TH (C) (where TH (C) epsilon [0,1] is the trust threshold for node C), then the donor C agrees to interact with the recipient A and vice versa.
Similarly, the node a as the trusted party can derive the trust value TV (a, C) for the trusted party C according to the trust certificate set tcs (C) provided by the node C as the trusted party, and decide whether to agree to interact with the trusted party C according to the magnitude relation with the self-trust threshold th (a).
Node A interacts with node C if and only if TV (C, A) ≧ TH (C) and TV (A, C) ≧ TH (A).
The above embodiments are preferred embodiments of the present invention, but the present invention is not limited to the above embodiments, and any other changes, modifications, substitutions, combinations, and simplifications which do not depart from the spirit and principle of the present invention should be construed as equivalents thereof, and all such changes, modifications, substitutions, combinations, and simplifications are intended to be included in the scope of the present invention.

Claims (8)

1. A lightweight Internet of vehicles trust evaluation method is characterized by comprising the following steps:
s1, when the previous interaction of the nodes is finished, both interaction parties generate a trust certificate containing a digital signature of the interaction party for the other party according to the interaction experience and send the trust certificate to the other party;
s2, after both interaction parties receive the new trust certification, the verification is carried outIt signs the information and updates the local store to hold at most η pieces of trust proof that are most favorable to itself, where η ∈ Z+Is a system parameter; in step S2, after receiving the trust certificate TC (B, a) generated by the certifier B for itself, the receiver a first verifies the signature information DS (B, a), and then determines the relationship between the number nm (a) of locally stored trust certificates and η: if NM (A) < η, the receiver A stores TC (B, A) directly; if NM (A) ═ eta, the receiver A calculates the weighted evaluation value corresponding to each trust certificate, selects the eta trust certificates most favorable to the receiver A for storage according to the weighted evaluation value, and deletes other trust certificates at the same time, wherein the trust certificates considered by the receiver A comprise TC (B, A) and the eta trust certificates stored locally;
s3, when the potential interaction starts, both the potential interaction parties send the locally stored trust certificate to the other party to prove that the potential interaction parties are trusted;
and S4, the two potential interaction parties verify the authenticity of the trust certificate through the digital signature information, derive the trust value of the other party according to the authenticity and decide whether to agree to interact with the trust certificate, and interact if and only if the two parties agree.
2. The lightweight internet of vehicles trust evaluation method of claim 1, wherein in step S1, the trust certificate has a specific format:
TC(B，A)＝(ID(B)，ID(A)，RT(B，A)，WG(B)，TS(B，A)，DS(B，A))
wherein B and A represent nodes, B is a prover, and A is a recipient; id (B) and id (a) represent unique identifiers of prover B and recipient a, respectively; RT (B, a) represents an evaluation value vector, in the specific format:
RT(B，A)＝(RT(B，A，1)，RT(B，A，2)，...，RT(B，A，n))
where n represents the number of trust aspects, RT (B, A, i) (i ∈ [1, n ]) represents the evaluation value of the i-th trust aspect of the prover B to the recipient A; wg (b) represents a weight value vector in the following specific format:
WG(B)＝(WG(B，1)，WG(B，2)，...，WG(B，n))
wherein WG (B, i) (i ∈ [1, n ]) represents the preference level of interest of prover B for the ith trust aspect; TS (B, a) represents a timestamp when TC (B, a) is generated; DS (B, a) represents digital signature information.
3. A lightweight internet of vehicles trust evaluation method according to claim 2, wherein the RT (B, a, i) is expressed as linguistic variables, including "very good", "general", "bad", and "very bad"; the WGs (B, i) are expressed as linguistic variables, including "very high", "medium", "low", and "very low".
4. The lightweight internet of vehicles trust evaluation method of claim 1, wherein in step S2, the specific step of calculating the weighted evaluation value by TC (B, a) is:
s2.1, converting RT (B, A, i) in TC (B, A) into fuzzy evaluation RF (B, A, i) and clear evaluation RC (B, A, i), wherein the specific format of the RF (B, A, i) is as follows:
RF(B，A，i)＝(RF(B，A，i，1)，RF(B，A，i，2)，RF(B，A，i，3)，RF(B，A，i，4))
wherein RF (B, A, i,1) is not less than 0 but not more than RF (B, A, i,2) is not less than RF (B, A, i,3) but not more than RF (B, A, i,4) is not less than 100; RC (B, a, i) is the symbol distance of RF (B, a, i), which can be derived from the following formula:
Figure FDA0002723367500000021
s2.2, converting WG (B, i) in TC (B, A) into fuzzy weight WF (B, i) and clear weight WC (B, i), wherein specific format of WF (B, i) is as follows:
WF(B，i)＝(WF(B，i，1)，WF(B，i，2)，WF(B，i，3)，WF(B，i，4))
wherein WF (B, i,1) is not less than 0 but not more than 0 and not more than WF (B, i,2) is not less than WF (B, i,3) but not more than WF (B, i,4) is not less than 10; WC (B, i) can be derived from the following formula:
Figure FDA0002723367500000022
s2.3, the blur evaluation value SF (B, a) corresponding to TC (B, a) can be calculated as:
Figure FDA0002723367500000023
the clear evaluation value SC (B, a) corresponding to TC (B, a) can be calculated as:
Figure FDA0002723367500000024
s2.4, the recipient a only considers the time decay weight WT (B, a) when selecting the most favorable trust attestation, which is calculated as:
Figure FDA0002723367500000031
wherein TN represents the current timestamp; TS (B, A) is the time stamp contained in TC (B, A); ω represents the time window size; theta is a time attenuation factor, and controls the attenuation speed of WT (B, A) along with the time difference;
s2.5, the weighted evaluation value SW (B, a) corresponding to TC (B, a) can be calculated as:
SW(B，A)＝SC(B，A)*WT(B，A)
TC (B, A) represents that B of the prover generates a trust certificate for A as the receiver according to the interaction experience, and RT (B, A, i) represents the evaluation value of the ith trust aspect of the prover B to the receiver A; WG (B, i) represents the preference level of interest of prover B in the ith trust aspect.
5. The lightweight internet of vehicles trust evaluation method of claim 1, wherein in step S3, the specific format of the trust certificate set is:
TCs(A)＝{TC(B1，A)，TC(B2，A)，...，TC(BNM(A)，A)}
where NM (A) ≦ η, NM (A) represents the number of trust attestations that the recipient A has locally stored.
6. The lightweight internet of vehicles trust evaluation method of claim 1, wherein the step S4 specifically comprises:
s4.1, when the potential interaction starts, after a node C serving as a trustee receives a trust certificate set TCs (A) of a node A serving as a receiver, the trust certificate in the trust certificate set TCs (A), namely TC (B), is extracted first1，A)、TC(B2，A)、...、TC(BNM(A)A), if NM (A) < η, the trust value TV (C, A) of the applicant C to the receiver A is set to a constant tau epsilon [0, 1); otherwise, the trustee C verifies the authenticity of the trustee C through the digital signature information in each trust certificate, then derives a weighted evaluation value corresponding to each trust certificate and calculates the TV (C, A); similarly, the trust value TV (A, C) of the applicant A to the receiver C can be obtained;
s4.2, if and only if TV (C, A) ≧ TH (C) and TV (A, C) ≧ TH (A), node A interacts with node C, wherein TH (C), TH (A) epsilon [0,1] are the trust threshold of node C, node A respectively.
7. The lightweight internet of vehicles trust evaluation method of claim 1, wherein in step S4, the messenger C considers an interest preference similarity weight WS (C, B) in addition to a time decay weight WT (B, a), the weight is derived from a weighted euclidean distance DS (C, B) of the interest preference vector between the messenger C and the prover B, and the specific calculation formula is:
Figure FDA0002723367500000041
WS(C，B)＝1-DS(C，B)
WC (B, i) is a clear weight corresponding to WG (B, i) which represents the interest preference level of prover B in the ith trust.
8. The lightweight internet of vehicles trust evaluation method of claim 7, wherein in step S4, the weighted evaluation value ST (C, B, a) corresponding to TC (B, a) can be calculated as:
ST(C，B，A)＝SC(B，A)*WT(B，A)*WS(C，B)
similarly, the messenger C can derive TC (B)1，A)、TC(B2，A)、...、TC(BηA) a corresponding weighted evaluation value, and calculating a trust value TV (C, a) for the recipient a, the specific calculation formula being:
Figure FDA0002723367500000042
TC (B, A) represents that B of the prover generates a trust certificate for A as the receiver according to the interactive experience;
SC (B, a) is a clear evaluation value corresponding to TC (B, a).
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Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104392373A (en) * 2014-12-02 2015-03-04 西安邮电大学 Interactive decision-making method based on key historical behaviors in cloud computing environment
CN106330910A (en) * 2016-08-25 2017-01-11 重庆邮电大学 Strong privacy protection dual authentication method based on node identities and reputations in Internet of vehicles

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101404592A (en) * 2008-11-14 2009-04-08 西安交通大学 Total trust degree decision method based on history evidence window
CN102222305A (en) * 2011-06-13 2011-10-19 北京理工大学 Creditworthiness evaluation method under distributed environment
US10083291B2 (en) * 2015-02-25 2018-09-25 Verisign, Inc. Automating internet of things security provisioning
CN106412912B (en) * 2016-06-22 2017-11-10 长安大学 A kind of method for trust evaluation of nodes towards car networking
CN107665405B (en) * 2017-09-26 2021-04-20 北京邮电大学 Vehicle credit management method and device

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104392373A (en) * 2014-12-02 2015-03-04 西安邮电大学 Interactive decision-making method based on key historical behaviors in cloud computing environment
CN106330910A (en) * 2016-08-25 2017-01-11 重庆邮电大学 Strong privacy protection dual authentication method based on node identities and reputations in Internet of vehicles

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