CN110034958B - Vehicle networking pseudonym change incentive algorithm and change method based on SGUM theory - Google Patents
Vehicle networking pseudonym change incentive algorithm and change method based on SGUM theory Download PDFInfo
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- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
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- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
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- H—ELECTRICITY
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Abstract
The invention discloses a vehicle networking pseudonym change incentive algorithm and a change method based on an SGUM theory, and belongs to the technical field of vehicle networking. The excitation algorithm comprises the following steps: modeling a social group income maximization game model; (2) Establishing a pseudonymous change income function U of the Internet of vehicles n (a) (ii) a (3) constructing a potential function; (4) And designing a vehicle networking pseudonym change incentive mechanism based on a Markov chain, and solving the most significant solution. The changing method comprises the following steps: (1) the server triggers a pseudonym change incentive mechanism; (2) a system initialization stage; (3) Calculating social group profit S n (a n ,a ‑n ) (ii) a (4) Strategy updating behaviors are updated, and strategy updating is carried out on each vehicle in sequence; and (5) notifying the server of the vehicle's policy selection. The vehicles achieve a stable social group Nash balance, and the income obtained by each vehicle under the state is the optimal income which can be obtained by the vehicle under the current strategy combination.
Description
Technical Field
The invention relates to the technical field of vehicle networking, in particular to a vehicle networking pseudonym change incentive algorithm and a change method based on an SGUM theory.
Background
The vehicle networking (VANet, vehicles Ad-hoc Internet) is a huge interactive network formed by information such as vehicle position, speed and route. In the VANet, the vehicles firstly complete the collection of self environment and state information through devices such as a GPS, an RFID, a sensor, a camera image processing device and the like, then transmit and gather various self information to a central processing unit through an internet technology, and finally analyze and process the vehicle information through a computer technology, thereby calculating the optimal routes of different vehicles, reporting road conditions in time, arranging signal lamp periods and the like. In the internet of vehicles, users need to report their location information while acquiring location services, and the location information directly contains the privacy information of the users and implies other sensitive information. If such information is revealed to an untrusted third party authority, it may pose a serious threat to the personal privacy security of the user. Therefore, location privacy security has become a focus of attention, and more research is being conducted to protect the privacy security of users. In the position privacy protection of the Internet of vehicles, the most important mode is to change the pseudonyms of the vehicles, and the vehicles can effectively reduce the risks of being positioned and attacked by malicious attackers through reasonable and proper pseudonym change. For the vehicle to perform the behavior of changing the pseudonyms, the existing algorithm mainly comprises a K-anonymous scheme, a mixed area scheme, a group navigation scheme and the like.
Although the vehicle can obtain benefits when the vehicle is subjected to the kana change, the cost is generated due to factors such as battery energy consumption in the changing process and location-based service interruption, and how to effectively encourage the vehicle to participate in the kana change becomes a very important research hotspot. The game theory is an important theoretical approach to solving this problem. However, the existing methods based on the game theory only pay attention to two extreme cases. One is when designing the incentive algorithm, whose optimization goal is the overall Network performance (NUM) of the internet of vehicles. The game theory model for solving the problem assumes that all users in the internet of vehicles are privately and collaborating, and the optimization objectives are completely consistent, namely, the benefits of the whole internet of vehicles are optimized. The other is to design an incentive algorithm, and the optimization goal is to maximize the individual benefit (NCG) of a single user in the car networking. The game theory model addressing this problem assumes that all users in the internet of vehicles are selfish, rational, uncooperative, with their optimization goal being to maximize their own revenue. However, the advent of new networking applications such as car networking, mobile social networking, etc., makes these two assumptions no longer true. Because people in the novel network applications have rich social relationships and the intimacy degree of each relationship is different, users in the novel network applications are not completely privet and are not completely selfish and often show certain group sex, and the benefits of other users with different social relationships with the users are considered.
The social relationship in these new network applications is abstracted into a social relationship network, which is a network structure composed of many nodes and relationships between the nodes. Nodes generally refer to individuals or groups of organizations, and the social relationship network represents social relationships between individuals or organizations, and objects are concatenated according to the social relationships. Social relationship networks are formed by relying on various relationships, such as consanguinity, friendship, hobbies, working properties, value, ideality, conflict, and the like, thereby generating an intricate and complex network structure. The Social Group profit Maximization framework (SGUM) is a brand-new application framework of game theory research, and can be used for integrating Social relations into profit functions by introducing the Social relations among users and solving the Social Group profit Maximization so as to solve the problem of a continuous space between the two extreme problems of NUM and NCG.
Since the SGUM framework is proposed, the SGUM framework is widely applied to various application scenarios, including database-based spectrum channel selection, micro-cloud computing transfer, location privacy protection, mobile node caching policy, and fleet head selection during driving.
Although the vehicle can obtain the position privacy benefit by changing the pseudonym, the cost is generated by resources consumed by the device for changing the pseudonym and service loss caused by service interruption when the pseudonym is changed, and how to effectively stimulate the vehicle to participate in the pseudonym change is a very important research hotspot.
The kana-change incentive problem has been extensively studied in the internet of vehicles. The traditional location privacy protection technology mainly relates to three aspects, namely policy-based method, distortion-based method and encryption-based method. Policy based location privacy service privacy protection techniques constrain service providers by formulating some common privacy management rules and trusted privacy agreements. The main privacy policies are IETF and W3C, but since the policies themselves cannot perform privacy protection, they often depend on economic, social and regulatory pressures. Dewri et al propose a location service privacy protection technique based on a warping method, which mainly studies how to design an optimal privacy protection algorithm in consideration of attacker background knowledge and reasoning ability, thereby reducing the exposure risk of user privacy as much as possible. Bereford et al propose the concept of a blending area in which a plurality of vehicles simultaneously change their pseudonyms when the vehicles pass through the blending area without performing a behavior of changing the pseudonyms outside the blending area. Ghini ta et al propose a location service privacy protection technique based on an encryption method, which requires additional hardware and complex algorithm support, and has high calculation and communication overhead.
For social relations, chen et al propose an SGUM game model and use it in database-based spectrum channel selection. The SGUM game model considers the influence of a physical layer and the influence of social relations, and aims to maximize social group income. Tang et al further use the SGUM game model in the computation transfer of a micro-cloud device, and design a computation transfer incentive algorithm based on a Markov chain to decide whether the device uploads a computation task to the micro-cloud for processing. Gong et al use the SGUM model to solve pareto optima of social awareness for location privacy protection in mobile networks. Zhi et al apply the model to the selection of a cache strategy of a mobile node and the selection of a vehicle head during vehicle fleet driving respectively, and solve social consciousness nash equilibrium points in the model by designing a proper excitation mechanism, thereby prompting more selfish nodes to actively participate in an optimization process. Through literature search, research on an incentive mechanism of internet of vehicles pseudonym change and a distributed pseudonym change incentive algorithm of an SGUM game model is still blank at present.
Disclosure of Invention
Aiming at the technical problems in the prior art, the invention provides a technical scheme of a vehicle networking pseudonym change incentive algorithm and a vehicle networking pseudonym change method based on an SGUM theory based on the SGUM theory and an SGUM optimization framework, promotes vehicle users in a game to achieve social group Nash balance, and improves the privacy safety of vehicle networking positions.
The vehicle networking pseudonym change incentive algorithm based on the SGUM theory comprises the following specific steps:
the first step is as follows: modeling a social group income maximization game model, wherein the social group income comprises self strategy income;
the second step: establishing vehicle networking pseudonym change receivingBenefit function U n (a)；
The third step: constructing a potential function, wherein the potential function meets the requirement that the pseudonym change of the Internet of vehicles stimulates into a full potential game, and the potential game has social consciousness Nash balance of a pure strategy;
the fourth step: and designing a vehicle networking pseudonym change incentive mechanism based on a Markov chain, and solving an optimal solution.
Further, the social group profit in the first step includes a weighted sum of the policy profit of the user n and the policy profit of the user group having social relationship with the user n, and the social group profit function of the user n is as follows:
wherein N represents a set of users in the model, a represents a policy combination,
user m, w representing a social relationship with user n nm Representing social connection weights.
Further, the policy revenue function U n (a) The following were used:
wherein
Representing the total number of vehicles in the set of vehicles N at the current moment for which a change of pseudonym is decided, -d n Is a loss of privacy for the location of vehicle n, where d n E (0,1) and the specific value is carried out by the importance of the vehicle to the privacy of the current position; c. C n The standardization cost for vehicle n to make a kana change, c n ∈(0,1)；U n (a) Representing an individual strategy revenue function of the vehicle n under the strategy combination a;
for different vehicles n, stopping at the same timeLocation privacy benefits in a yard for a particular time interval
Are identical and are a constant, i.e. d n ′＝d m ' = d,1 ≦ N, m ≦ N, the new gain function is as follows:
further, the construction method of the potential function in the third step is as follows: assuming that the position privacy benefits of the vehicles in the area subjected to the pseudonymization are the same in a specific time interval and are a constant related to the number of vehicles in the area and the environment, d n ′＝d m ' = d; the social weighting between vehicles being symmetrical, i.e. w nm ＝w mn The potential function is represented as public (5):
wherein, I {E} Is a flag function, when event E holds {E} =1; otherwise, when the event E is not established I {E} ＝0。
Further, the social consciousness Nash balance of the full potential game with pure strategy is obtained by the following method:
from equations (2) and (4), the following can be derived:
from equation (5), the following can be derived:
wherein, w nm To indicate two functionsThe degree of affinity and sparseness between households,
indicating that vehicle user m is not in the social relationship diagram of vehicle user n, i.e. w nm =0; according to the formula (6) and the formula (7), the following relationship holds:
and if the game accords with the definition of the complete potential function, the game has the social consciousness Nash equilibrium of a pure strategy.
Further, the solving method of the optimal solution in the fourth step is as follows:
and (3) establishing a Markov approximate optimization solution aiming at the potential function of the formula (5):
where Ω represents the combined space of all policies, q a Representing the probability that the policy combination a is selected, theta represents the control parameter of the formula and passes
Ensuring the progressive optimality; when θ → ∞, the optimal solution that maximizes the potential function φ (a) will be chosen with a probability of 1; meanwhile, the optimal solution can be solved as follows:
the Internet of vehicles pseudonymization changing method comprises the following steps: a vehicle; an in-vehicle communication device supporting communication between the vehicle and the vehicle, the vehicle and the infrastructure; the roadside units are used as infrastructure construction in the Internet of vehicles system and distributed on two sides of a road to form a coverage network; the authentication center is used as a credit granting mechanism in the Internet of vehicles, and all equipment in the system needs to be registered and authenticated in the authentication center; the server is used for processing and storing data in the communication process; the method comprises the following specific steps:
(1) The server triggers a pseudonym change incentive mechanism;
(2) In the system initialization stage, after the vehicle is triggered, the cost c generated by the pseudonymous name change of the vehicle is calculated and analyzed n And the position privacy gain d obtained at the current moment, and setting a control parameter theta and a strategy updating frequency tau for the vehicle by the system n While each device randomly selects a policy a n ∈A n As an initial policy selection;
(3) Calculating social group income, selecting the number of vehicles for pseudonym change in the current area as A, and generating the social group income S of the user according to the strategy combination determined by the current equipment n (a n ,a -n )；
(4) Strategy behavior updating, entering a cyclic process, sequentially updating strategies of all vehicles, and reselecting a new strategy behavior a 'for the vehicle n' n ∈A n \a n As a policy to be updated, the social group profit S of the user at that time is calculated n (a′ n ,a -n ) The vehicle will perform policy updates with the following probabilities:
by probability
Remains in New policy a' n By probability
Update-back policy a n ；
(5) The server is notified of the vehicle's policy selection.
Further, the server in the step (1) triggers an excitation mechanism through existing infrastructure equipment, and notifies vehicles in the area to prepare for pseudonymous name change in a broadcasting mode; the capital construction equipment comprises a sensor at a traffic light and a pressure sensor on a parking space.
Further, in the step (3), while the vehicle generates the social group profit of itself by combining the strategies determined by the current equipment, the average value is followed
The index distribution of (2) generates its own timer and starts the timer, and when the timer expires, strategy updating is performed on each vehicle.
Furthermore, when the vehicle decision in the system reaches social consciousness nash equilibrium, namely under the current strategy combination, no participant can improve the social group income through singly changing the own strategy action, the game process is ended by jumping out of the cycle, and meanwhile, each vehicle sends a message to inform the own strategy of a third-party server.
The invention has the beneficial effects that:
1. the algorithm integrates the social relationship into the change of the vehicle networking pseudonyms, and the attention point determined by the vehicle networking user strategy is not only the income of the user but also the maximization of the income of the vehicle networking user group with the social relationship;
2. the game model becomes a potential game by constructing a potential function, so that the designed vehicle networking pseudonym change incentive mechanism can reach the social group nash balance of a pure strategy theoretically proved;
3. the vehicle networking pseudonym change incentive algorithm based on the Markov chain is designed to promote the vehicles to reach a stable social group Nash balance, and the income obtained by each vehicle in the state is the optimal income which can be obtained by the vehicle under the current strategy combination.
Drawings
FIG. 1 is a diagram of a vehicle networking system architecture;
FIG. 2 is a schematic structural diagram of a physical relationship layer and a social relationship layer in an Internet of vehicles application;
figure 3 is a pseudocode description diagram of a Markov chain-based vehicle networking pseudonym change incentive algorithm.
Detailed Description
The technical scheme of the invention is further explained by combining the attached drawings of the specification.
A car networking vehicle application architecture that considers vehicle privacy and incentive mechanisms is shown in fig. 1. In an internet of vehicles environment, vehicles are considered mobile network nodes and Road infrastructure (RSU) is considered stationary network nodes. In the communication process, there is communication between the vehicle and the RSU as well as communication between the vehicle and the RSU. Generally, the following parts are included in the car networking system:
(1) A vehicle. The vehicle union is used as a user main body in the vehicle networking, and other facilities provide services for the vehicle. The vehicle is provided with an on-board communication device for supporting communication between the vehicle and the infrastructure.
(2) A roadside unit. Roadside units serve as infrastructure construction in the vehicle networking system and are distributed on two sides of a road in a geographical manner to form a coverage network.
(3) And (4) an authentication center. The certification center plays an important role in the car networking system, and is used as a credit granting mechanism in the car networking, and all devices in the system need to be registered and certified in the certification center.
(4) And (4) a server. The method has strong computing and storing capability, and is used for processing and storing data in the communication process.
When the vehicle runs to a key scene (such as a traffic light intersection, a parking lot and the like), the third-party server sends a broadcast notification to the vehicles in the region and simultaneously changes the pseudonyms. After receiving the broadcast, the vehicles in the area search their own pseudonym sets and select new pseudonyms, thereby changing the pseudonyms simultaneously with other vehicles in the vicinity. In this case, since a large number of vehicles in the area perform the pseudonym change operation at the same time, the vehicles after the change can confuse the tracking of the attacker, and the benefit of the position privacy protection of the vehicle is effectively improved.
In order to effectively stimulate the vehicle to participate in the pseudonym change, the SGUM game model is utilized to fully consider the states of the users in both physical network and social network, and a social group profit function is defined to prompt the vehicle users to participate in the pseudonym change. The physical relationship layer and the social relationship layer under the application of the internet of vehicles are as shown in fig. 2, and in the game process, a vehicle user not only pays attention to the income obtained by participating in the change of the pseudonyms, but also gives consideration to the income of other users with different social relationships with the vehicle user. Based on a potential game theory, the SGUM is applied to the design of a vehicle networking pseudonym change excitation mechanism, and a vehicle networking pseudonym change excitation algorithm based on a Markov chain is designed to promote user equipment to finally reach a stable social group Nash balance.
To better describe the practice of the present patent, the following definitions and symbolic illustrations are first given.
(1)U n (a) The method comprises the following steps Representing the benefit of user n in selecting policy combination a.
(2) Full potential game: if game Γ has a function φ (a) so that for any user N ∈ N, when the policy of N is a n ,a n ′∈A n And the policy combination of the rest of users is a -n ∈Π i≠n A i Existence of equation U n (a n ′,a -n )-U n (a n ,a -n )＝φ(a n ′,a -n )-φ(a n ,a -n ). At this time, the game Γ is called a full potential game, the function Φ (a) is a potential function corresponding to the full potential game, and the full potential game has nash equilibrium with a pure policy.
In the definition, N represents a set of users in the model, a n A policy representing a selection of a user n, a -n Representing combinations of policies, U, of users in the set other than user n n (a n ,a -n ) Representing the functional benefit of user n under the current policy combination, A n All policy set spaces, φ (a), representing users n n ,a -n ) Representing the value of the potential function under the current policy combination a.
Game t = { N, { A) n },{S n } for SGUM game, where N represents the user set, A n To representPolicy composition space, S, for user n n Representing the social group benefit of the user n under the strategy, the following definitions are provided.
(3) Socially conscious nash equilibrium: a combination of policies in the model at that time is said to be a combination of policies in the model if and only if none of the participants are able to promote his social group profits by a single change in their own policy actions
For the social conscious nash balance in the SGUM game described above, it is expressed as follows:
wherein, a n A policy representing a selection of a user n, a -n Representing combinations of policies of users other than user n in the set, S n (a n ,a -n ) Representing the social group function gain of a of the user n under the current strategy combination, a * Represents the combination of strategies in the state of nash equilibrium,
representing the policy selection for user n in nash equilibrium.
Based on the above definition, the implementation steps of the patent are as follows:
the first step is as follows: and modeling a social group income maximization game model. In order to integrate social relationships among users into application scenarios, the revenue of a user n is defined to be mainly composed of two parts, one part is the own policy revenue U n (a) And part is the weighted sum of the strategic gains of the user group with social relationship with the user group. According to the social connection weight w between users in the social relationship graph nm The social group revenue function for user n may be defined as follows:
wherein N represents a modulusThe set of users in a profile, a represents a policy combination,
representing user m having a social relationship with user n. Policy revenue function U n (a) Corresponding expression modes are provided in different application scenes, so that the social group income function has good universality. But in every application, redefining the policy revenue function of the user is a problem that needs to be addressed with emphasis.
The second step is that: defining a vehicle networking pseudonym change revenue function U n (a) In that respect In an internet of vehicles application environment, if a vehicle n holds an original pseudonym at a certain time, such as when entering or leaving a parking lot, the probability that it is continuously tracked by an attacker is 1. At this time, the loss of privacy regarding the location of the vehicle n is not changed, and is still-d n Wherein d is n E (0,1) and the specific value is determined by the vehicle's own quantitative measure of privacy importance of the current location. Conversely, if vehicle n chooses to change its pseudonym and the policy of the other vehicle remains unchanged, the pseudonym set size in the current region is a. At this time, the probability that the vehicle n is continuously tracked decreases to
And its loss of location privacy is from-d n Is lowered to
Suppose that the vehicle n pays a standardized cost of c for a pseudonymous change n E (0,1), the cost mainly includes the resource consumed by the device to change the pseudonym and the service loss caused by interrupting the service when the pseudonym is changed. Thus, the gain function obtained after the vehicle n has undergone a pseudonym change is
Finally, the revenue function for vehicle n is expressed as follows:
wherein
Indicates the total number of vehicles, U, in the vehicle set N at the current time for which the pseudonym is determined to be changed n (a) Representing the individual revenue function of vehicle n under the strategic combination a. For the above equation, it is assumed that for different vehicles n, the position privacy benefits are obtained within a certain time interval in the same parking lot
Are identical and are a constant, i.e. d n ′＝d m ' = d,1 is less than or equal to N, and m is less than or equal to N. At this time, the above equation is modified to obtain a new revenue function as follows:
the third step: constructing a potential function: assuming that the position privacy benefits of the vehicles in the area subjected to the pseudonymization are the same in a specific time interval and are a constant related to the number of vehicles in the area and the environment, d n ′＝d m ' = d. At the same time, the social weighting between vehicles is symmetric, i.e. w nm ＝w mn . Next, a potential function needs to be constructed to make the SGUM game a potential game, so as to obtain social-awareness nash equilibrium of pure policy.
The potential function of the construction of this patent is shown as publication (5).
Wherein, I {E} Is a flag function, I when event E holds {E} =1; otherwise, when the event E is not established I {E} ＝0。
The existence of pure tactical socially conscious nash equilibrium is demonstrated. From equations (2) and (4), the following can be derived:
from equation (5), the following can be derived:
wherein, w nm Indicating the degree of affinity between the two users,
indicating that vehicle user m is not in the social relationship diagram of vehicle user n, i.e. w nm And =0. From equations (6) and (7), it is apparent that there is S n (1,a -n )-S n (0,a -n )＝φ(1,a -n )-φ(0,a -n ) This is true. Similarly, the following formula S can be obtained n (0,a -n )-S n (1,a -n )＝φ(0,a -n )-φ(1,a -n ) This is true. So far, the social group profit function S is passed n (a) And the demonstration of the potential function φ (a), the following relationship holds:
according to the definition of the full potential function, the SGUM model-based vehicle networking pseudonym change incentive mechanism can be determined to be a full potential game, the potential function of the full potential game is shown in a formula (5), and the game has the social awareness nash equilibrium of pure strategies.
The fourth step: designing a vehicle networking pseudonym change incentive mechanism based on a Markov chain, and solving an optimal solution: and (5) establishing a Markov approximate optimization solution aiming at the potential function of the formula (5).
Where Ω represents the combined space of all policies, q a Representing the probability that the policy combination a is selected, theta represents the control parameter of the formula and passes
Ensuring progressive optimality. When θ → ∞, the optimal solution that maximizes the potential function φ (a) will be chosen with a probability of 1. Meanwhile, the optimal solution can be solved as follows:
based on the theory, the method designs a vehicle networking pseudonym change incentive method based on the Markov chain, and determines whether a vehicle user participates in pseudonym change in a distributed mode. Because of the time-reversible nature of the Markov chain, it is always possible to achieve a unique stable profile independent of the initial system state and independent of the update sequence of states. Therefore, for any given initial strategy selection and updating sequence, the strategy selection of the equipment under the vehicle networking pseudonym change incentive mechanism can finally reach a stable distribution state, namely unique social awareness nash balance.
As shown in fig. 3, the social nash balance of the vehicle pseudonym change strategy is solved by setting a sufficiently large control parameter θ. Order timer n A timer is generated for each user by the system, the timer being exponentially distributed and having a value τ n . Timer for user n n Time of passage τ n Later due, it will then attempt to update the user n's own pseudonym change participation decision policy, and the updated policy will be different from the original policy. The flow of the algorithm is shown in fig. 3, and the scheme is designed to be 5 steps, which are described in detail as follows:
(1) The server triggers a pseudonym change incentive mechanism. The third party server triggers the incentive mechanism through existing infrastructure equipment (such as sensors at traffic lights, pressure sensors on parking spaces, etc.). When the signal lamp turns from green to red or vehicles enter or exit from the parking space, the server informs the vehicles in the area to prepare for changing the pseudonyms in a broadcasting mode.
(2) And (5) a system initialization stage. After the vehicle receives the broadcast sent by the third-party server, the cost c generated by the pseudonym change of the vehicle is calculated and analyzed n And the position privacy gain d obtained at the current moment, and substituting the factors into the distributed algorithm model. After entering the algorithm, the system firstly sets the control parameter theta and the strategy updating frequency tau for the vehicle n While each device randomly selects a policy a n ∈A n As its initial policy selection.
(3) And calculating social group income. Each vehicle n interacts with a third-party server to obtain the number A of vehicles selected to carry out the kana change in the current area, then the social group income of the vehicle is generated by combining strategies determined by current equipment according to a formula (1), and meanwhile, the mean value is followed as
Generates its own timer and starts the timer.
(4) And updating the strategy behavior. Entering a circulation process, and sequentially updating the strategies of all vehicles according to the expiration of the timer. If the timer of vehicle n expires, at this point, vehicle n reselects the new policy action a' n ∈A n \a n As a strategy to be updated by the user, the social group income S of the user at the moment is calculated n (a′ n ,a -n ). Each time the timer expires, there is one and only one vehicle user updating the policy behavior. Meanwhile, according to the new social group income, the vehicle carries out strategy updating with the following probability.
Analytically, the formula above, when the new strategic behavior of the vehicle provides better yield, i.e., S n (a′ n ,a -n )≥S n (a n ,a -n ) At this point the user will transition to the new policy a 'with probability 1' n The above. New strategy a 'selected according to the nature of potential game' n Social group income S of user n can be improved in potential game n (a) And simultaneously, the benefit of the potential function phi (a) can be improved. As the vehicle older strategy behavior provides better yield, S n (a′ n ,a -n )＜S n (a n ,a -n ) When this is the case, user n will be at probability
Stay in old policy a n By probability
Update to New policy a' n . Obviously, when the revenue generated by the new strategy selected by the user n is smaller than the revenue generated by the old strategy, the probability that the user n stays in the old strategy is as a function of the revenue of the old and new social groups n (a n ,a -n )-S n (a′ n ,a -n ) The increase in the difference increases.
(5) The server is notified of the vehicle's policy selection. When the vehicle decision in the system reaches the social consciousness Nash equilibrium, namely under the current strategy combination, no participant can promote the social group income of the participant by singly changing the strategy action, and the exit cycle ends the game process. At the same time, each vehicle sends a message informing the third party server of its own policy.
Claims (7)
1. A vehicle networking pseudonym changing method based on an SGUM theory is characterized in that a vehicle networking system comprises: a vehicle; an in-vehicle communication device supporting communication between the vehicle and the vehicle, the vehicle and the infrastructure; the roadside units serve as an infrastructure in the Internet of vehicles system and are distributed on two sides of a road to form a coverage network; the authentication center is used as a credit granting mechanism in the Internet of vehicles system, and all equipment in the Internet of vehicles system needs to be registered and authenticated in the authentication center; the server is used for processing and storing data in the communication process; the method comprises the following specific steps:
(1) The server triggers a pseudonym change incentive mechanism; the specific steps of constructing the change incentive mechanism are as follows:
the first step is as follows: modeling a social group income maximization game model, wherein the social group income comprises the weighted sum of the strategy income of the social group and the strategy income of a vehicle group having social relation with the social group, and the social group income function of the vehicle n is as follows:
wherein N represents a set of vehicles in the model, a represents a combination of strategies,
vehicles m, w representing social relationships with vehicle n nm Representing a social connection weight;
the second step is that: establishing a pseudonymous change income function U of the Internet of vehicles n (a) (ii) a Policy gain function U n (a) The following were used:
wherein
The total number of vehicles of the vehicle set N which need to change the pseudonym at the current moment in the strategy combination a is shown, wherein a n A value of 1 indicates that the vehicle n needs to change the pseudonym in the policy combination a, a n When the value is 0, it means that the vehicle n does not need to change the pseudonym in the policy combination a, and determines the total number of vehicles whose pseudonyms are to be changed, -d n Is a loss of privacy for the location of vehicle n, where d n Belongs to the field of China (0,1) and the specific numerical value is determined by quantitative measurement of the privacy importance of the vehicle to the current position; c. C n The standardization cost for the vehicle n to make a change of the pseudonym, c n ∈(0,1)；U n (a) Representing an individual strategy revenue function of the vehicle n under the strategy combination a;
position privacy benefits for different vehicles n within a specific time interval in the same parking lot
Are identical and are a constant, i.e. d n ′＝d m ' = d, 1. Ltoreq. N, m. Ltoreq. N, the new revenue function is as follows:
the third step: constructing a potential function, wherein the potential function meets the requirement that the pseudonym change of the Internet of vehicles stimulates into a full potential game, and the potential game has social consciousness Nash balance of a pure strategy;
the fourth step: designing a vehicle networking pseudonym change incentive mechanism based on a Markov chain, and solving an optimal solution;
(2) In the initialization stage of the Internet of vehicles system, after the vehicle is triggered, the cost c generated by the pseudonymous change of the vehicle is calculated and analyzed n And the position privacy gain d obtained at the current moment is set for the vehicle by the vehicle networking system, and the control parameter theta and the strategy updating frequency tau are set for the vehicle by the vehicle networking system n Simultaneously, each vehicle randomly selects a strategy combination a belonging to B as an initial strategy combination selection, wherein B represents the set space of all strategy combinations of the vehicle n;
(3) Calculating social group income, selecting the number of vehicles for pseudonym change in the current area as A, and combining strategies determined by the current vehicles to generate the social group income S of the user n (a n ,a -n )，a -n When the number is 1, the vehicle is shown as a vehicle other than the vehicle N in the vehicle set N, and the pseudonym needs to be changed when the strategy combination a is used, a -n When the value is 0, the vehicle is other than the vehicle N in the vehicle set N, and the pseudonym does not need to be changed when the strategy combination a is combined;
(4) Updating the strategy combination, entering a circulation process, and sequentially carrying out strategy on each vehicleUpdating, the vehicle n reselects a new strategy combination a' belonged to B \ a as a strategy to be updated, and calculates the social group income S of the vehicle at the moment n (a′ n ,a -n ) The vehicle carries out strategy updating on the specific probability; the (4) updating strategy combination vehicle will carry out strategy updating with the following probability:
by probability
Stay in the new policy combination a', with probability
Updating the old strategy combination a;
(5) The server is notified of the vehicle's policy selection.
2. The SGUM theory-based vehicle networking pseudonymization method according to claim 1, wherein the potential function in the third step is constructed by: assuming that the position privacy benefits of the vehicles in the area subjected to the pseudonymization are the same in a specific time interval and are a constant related to the number of vehicles in the area and the environment, d n ′＝d m ' = d; the social connection weight between vehicles is symmetric, i.e. w nm ＝w mn The potential function is represented as public (5):
wherein, I {E} Is a flag function, I when event E holds {E} =1; otherwise, when the event E is not established I {E} ＝0。
3. The SGUM theory-based vehicle networking pseudonym changing method according to claim 2, wherein the social consciousness Nash balance of the perfect situation game existence pure strategy is obtained by the following method:
from equations (2) and (4), the following can be derived:
from equation (5), the following can be derived:
wherein, w nm A social connection weight is represented that is,
indicating that vehicle m is not in the social relationship diagram for vehicle n, i.e. w nm =0; according to the formula (6) and the formula (7), the following relationship holds:
and if the game accords with the definition of the complete potential function, the game has the social consciousness Nash equilibrium of a pure strategy.
4. The SGUM theory-based vehicle networking pseudonym changing method according to claim 3, wherein the solving method of the optimal solution in the fourth step is as follows:
and (3) establishing a Markov approximate optimization solution aiming at the potential function of the formula (5):
where Ω represents the combined space of all policies, q a Representing the probability that the policy combination a is selected, theta represents the control parameter and passes
Ensuring the progressive optimality; when θ → ∞, the optimal solution that maximizes the potential function φ (a) will be chosen with a probability of 1; meanwhile, the optimal solution can be solved as follows:
5. the vehicle networking pseudonymization method according to claim 1, wherein in the step (1), the server triggers the incentive mechanism through a sensor at an existing traffic light and a pressure sensor on a parking space, and the server informs vehicles in the area to prepare for pseudonymization in a broadcasting manner.
6. The method of claim 1, wherein in step (3), the vehicle follows the mean value of the social group profit while generating the social group profit of itself according to the policy combination determined by the current equipment
The index distribution of (2) generates its own timer and starts the timer, and when the timer expires, strategy updating is performed on each vehicle.
7. The vehicle networking pseudonym changing method according to claim 1, wherein when the vehicle decisions in the vehicle networking system reach a socially conscious nash balance, that is, under the current policy combination, none of the participants can improve the social group profits of the participants by singly changing their own policy actions, the exit cycle is ended, and simultaneously, each vehicle sends a message to inform the server as a third party of its own policy.
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