CN111314883B - Internet of vehicles privacy perception data scheduling method based on incentive mechanism - Google Patents
Internet of vehicles privacy perception data scheduling method based on incentive mechanism Download PDFInfo
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Abstract
The invention discloses a vehicle networking privacy perception data scheduling method based on an incentive mechanism. Secondly, after the vehicle participates in the task through the incentive mechanism, in order to improve the efficiency of task completion, the key task is completed firstly. Then, in order to enable the completed mission-critical information to be safely transmitted to the target vehicle, the safety and the privacy of data transmission between the vehicle and the cloud server are achieved through a data interference mechanism. Finally, the vehicle is made to comply with the encryption scheme by the incentive mechanism. The invention solves the problem of vehicle privacy and enables the vehicle to participate in the execution of tasks, thereby maximizing the utility of the network, and meanwhile, the invention provides a data interference mechanism for protecting the completed key task information, so that the key information is safely transmitted to the target vehicle, thereby protecting the vehicle privacy information.
Description
Technical Field
The invention belongs to the technical field of data transmission of Internet of vehicles, and relates to a privacy perception scheduling method for a vehicle participating in a task, in particular to a method for identifying the privacy perception data transmission of the Internet of vehicles of a key task.
Background
With the rapid development of wireless sensor Networks and internet of things, vehicle networking (Vehicular networking) has become an emerging technology, and has wide industrial applications, such as intelligent driving, object tracking, intelligent transportation systems, and advertisement delivery management. Specifically, by identifying key tasks on the cloud server, the vehicle-mounted network can be applied to improve the work efficiency of urban residents. The Vehicle comprises two communication modes, namely Vehicle to Infrastructure communication (Vehicle to Infrastructure) and Vehicle to Vehicle communication (Vehicle to Vehicle). However, they are still susceptible to problems such as packet loss, communication delays, scheduling problems, and malicious network attacks. Especially, the stealing and tampering of the key information by the malicious node can cause huge loss to the vehicle-mounted network. Therefore, privacy aware data scheduling becomes very important.
In general, the participation rate of tasks is improved by utilizing an incentive mechanism, and the tasks are divided into key tasks or ordinary tasks. For example, in use cases such as distributing commercials over vehicles, a key task is how to accurately recommend valuable products to targeted consumers. However, existing mission critical recognition and motivation mechanisms are mainly used for mobile crowd sensing systems, where the movement of vehicles in a vehicle network is much faster than the mobility of objects in a mobile crowd sensing system. Therefore, new mission critical identification methods and incentive mechanisms need to be developed to efficiently identify and complete mission critical tasks.
Disclosure of Invention
The invention aims to transmit data in the Internet of vehicles reliably and safely, maximize the network utility and improve the efficiency and safety of completing tasks by vehicles.
The technical scheme adopted by the invention is as follows: a method for scheduling privacy perception data of the Internet of vehicles based on an incentive mechanism comprises a multidimensional incentive mechanism, a key task identification mechanism and a data interference mechanism; the method is characterized by comprising the following steps:
step 1: stimulating the vehicle to participate in the task in the Internet of vehicles according to the utility function, the welfare function and the score function; when the utility function is positive and the vehicle participates in the task, the welfare function and the score function are larger, so that the vehicle is more willing to participate in the task;
and 2, step: after the vehicle is stimulated to participate in the task, identifying a key task and obtaining more time to complete the key task; expectation number according to new verification key task
And expected number of unverified critical tasks Kt i The relationship between them is recognized in the shortest timeA critical task;
and step 3: according to interference condition 1:
and interference condition 2:
the completed key task information is safely transmitted to a target vehicle; wherein it is present>
Is the reliability level of the vehicle, S is the winner set, Γ i Is a set of tasks, pr (.) is a probability function, based on the results of the evaluation of the task>
Is a random number, is greater than or equal to>
Is a base variable, beta j Is a parameter selected by the system for the task, i denotes the ith shard file, τ j Task of j-th vehicle, w i Indicates the ith worker.
Compared with the prior art, the invention has the following advantages and beneficial effects:
(1) The existing incentive mechanism is only suitable for the mobile crowd perception environment, the incentive effect is poor, time is consumed, and the method reduces the incentive range and has better performance.
(2) Compared with the existing incentive mechanism, the method has the functions of key task identification and safety privacy protection, and can better protect the privacy of the user.
(3) The method and the system use the utility function, the welfare function and the score function to stimulate the vehicles to participate in the task, and achieve higher task participation rate. Meanwhile, the invention uses the key task identification method to identify the key task, thereby improving the efficiency of completing the key task.
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FIG. 1 is a flow chart of an embodiment of the present invention.
Detailed Description
In order to facilitate the understanding and implementation of the present invention for those of ordinary skill in the art, the present invention is further described in detail with reference to the accompanying drawings and examples, it is to be understood that the embodiments described herein are merely illustrative and explanatory of the present invention and are not restrictive thereof.
Referring to fig. 1, the method for scheduling privacy awareness data of the internet of vehicles based on an incentive mechanism, provided by the invention, comprises a multidimensional incentive mechanism, a mission critical identification mechanism and a data interference mechanism;
the method specifically comprises the following steps:
step 1: stimulating the vehicle to participate in the task in the Internet of vehicles according to the utility function, the welfare function and the score function; when the utility function is positive and the vehicle participates in the task, the welfare function and the score function are larger, so that the vehicle is more willing to participate in the task;
in this embodiment, the high quality video θ is aligned according to the receiver m m,t Yield of fragment quality and its previous fragment bit rate
Defining a utility function:
wherein X Q (r,θ m,t ) Is the receiver m to the high quality video theta m,t Gains in fragment quality; suppose X Q (r,θ m,t ) Representing the quality gain function of a single file fragment at time t with a bit rate r, where
Represents the loss of receiver m when the file is reduced from a higher bit rate transmission to a lower bit rate transmission at time t;
in a download operation of sender n to receiver m, the welfare function is defined as the difference between the utility of receiver m and the cost of sender n:
W nm,t (r)＝U m,t (r)-C n,t (r)；
wherein C is n,t (r) is the cost of sender n, assuming that the costs of the different tasks are independent of each other, hence cost C n,t (r) is expressed as:
define F (r, p) as a valid scoring function:
F(r,p)＝|MA|+p-C n,t (r)；
wherein, MA represents the matching degree of interest information between the source node and the destination node determined by the cloud scheduling system, and is represented as:
wherein S C Is the cost of the source node, D C Is the cost of the target node; p is the price that sender n pays when the system allocates all file fragments for calculating the node's score.
Step 2: after the vehicle is stimulated to participate in the task, identifying a key task and obtaining more time to complete the key task; expectation number according to new verification key task
And expected number of unverified critical tasks Kt i The relationship between the two identifies the critical task in the shortest time;
in the embodiment, the vehicle determines the final key task by calculating and analyzing the key task and the common task; all subtasks in round i have c i A time slot indicating that the execution time of the vehicle will consume c i ·t field Wherein c is i Also the frame size, t field Is the time for each time slot to acquire the key field;
detailed description of the invention the process is as follows: all subtasks in round i have c i A time slot indicating that the execution time of the vehicle will consume c i ·t field Wherein c is i Also the size of the frame, t field Is the time for each time slot to acquire the key field;
the vehicle expresses the consumed execution time as T i Expected number of verified tasks
And expected number of unverified tasks Kt i Execution time T consumed by vehicle i Determining>
Then, according to the expected number Kt of unverified key tasks in the current round and the previous round i Obtaining the minimum round times for identifying the key tasks, wherein the expected number of the unverified key tasks is assumed to be a positive real number; by solving for execution time and expected number of validated critical tasks
The minimum value of the ratio can determine the key task in the shortest time; after the task is completed, the task with higher value is a key task, the key task is judged according to the score function, and the task with the highest score is the key task.
And step 3: according to interference condition 1:
and interference condition 2:
the completed key task information is safely transmitted to a target vehicle; wherein +>
Is the reliability level of the vehicle, S is the winner set, Γ i Is a set of tasks, pr (.) is a probability function, based on the results of the evaluation of the task>
Is a random number, is greater than or equal to>
Is a base variable, beta j Is a parameter selected by the system for the task, i denotes the ith shard file, τ j Task of j-th vehicle, w i Represents the ith worker;
in the embodiment, a data interference mechanism is adopted, and firstly, a differential privacy technology is applied to protect the privacy of the vehicle; secondly, the utility and cost of the vehicle remain within reasonable ranges; and finally, according to the reliability level and the setting of random parameters, a data interference mechanism meets the conditions 1 and 2, so that complete key task information is protected. The server obtains a safe data result by satisfying condition 1 and condition 2 while ensuring that the vehicle participates in the task;
the specific implementation comprises the following substeps:
step 3.1: firstly, carrying out privacy protection by using a differential privacy technology;
the differential privacy is that M (χ { [ T ]) N×K →R K×1 As a mapping mechanism, any input data matrix that can be mapped to the interference result vector R, (χ { } { [ U ]) N×K Representing data of all workers, wherein x represents the data of the workers, {. T } represents the data of the sensor task, N represents a group of workers, and K represents a group of sensing tasks; then, if and only if for only one input and any
Any worker data of the two data matrices x and x' that are different above, mechanism M has η differential privacy, expressed as follows:
Pr[M(x)∈A]≤exp(η)Pr[M′(x)∈A]，
where η is a small positive number, commonly referred to as the privacy budget;
step 3.2: determining whether the information transmitted by the vehicle is key information;
in protecting the privacy of information, it is most important to protect the privacy of critical information, and therefore it is necessary to determine whether the information transmitted by this vehicle is critical information. Key information is transmitted by a privacy budget set by a cloud scheduling system to ensure trustPrivacy; since the reliability of the vehicle determines the mission quality of the vehicle, critical information transmitted by the vehicle is protected according to the reliability of the vehicle; the reliability of the vehicle for the classification task is: omega i,j ＝t j (1-α j ) In particular for tau j ∈T cat Worker w of the classification task in (1) i Reliability of omega i,j Defined as the reliability of the completed task, where T cat Representing discrete tasks that require a worker to complete; t is t j Is the time to complete the task, and alpha j Is the probability of an incomplete task; using ω = [ ω ] i,j ]∈[0,1] N×K A reliability matrix representing all staff; the reliability matrix omega is assumed to be prior information known by a cloud scheduling system; in fact, the system may maintain a history of ω, which may be obtained by the time a task was completed and the probability that the task was not completed. When a reliable vehicle transmits information, the information is protected; otherwise, the information will not be protected;
step 3.3: when transmitting critical information for reliable vehicle safety, it is desirable that the total cost of the worker be minimal;
each worker w i With only one bundle of tasks of interest
The cost of executing a task bundle, i.e. the perceived cost, is noted
Additionally, if η differential privacy is guaranteed, there is a cost of privacy leakage, namely a privacy cost, expressed as ≧ greater>
Thus, worker ω i Executing the task gamma, and when the privacy budget is eta, defining a cost function as follows:
wherein，
For a perceived cost in executing a task, be->
The privacy cost when the privacy budget for executing the task is eta, and gamma is the executed task, and ^ is greater than or equal to>
Is a task bundle; for people not belonging to the worker omega i Is taken over>
The tasks of (1), the inability to execute them or the execution of these tasks incurs significant costs, and therefore, the present embodiment assigns + ∞coststo these tasks;
step 3.4: it is desirable that the total cost of the worker be minimal and that worker w be desirable i The utility of (a) is maximal;
utility u of the worker i Is defined as:
wherein p is i Is the price that the system is paying,
is a perceived cost to the worker that is present>
Is the privacy cost of the worker when the privacy budget is epsilon, and S is the winner set; given a payment price p and a set of winners S, the total payment of the system is ≦>
Step 3.5: after minimizing the total cost of workers and maximizing their utility, to increase key creditsThe safety of information transmission enables a data interference mechanism to meet the conditions 1 and 2; namely, condition 1:
protecting private information, as well as constraints that ensure β for each classification task j Accuracy; for each task τ j ∈T cat The data interference mechanism satisfies condition 2:
Wherein->
Is a random variable, is greater than or equal to>
Is a fundamental fact, β j Is a parameter that the system chooses for this task.
The invention provides a vehicle networking privacy perception data scheduling method based on an incentive mechanism, which uses a multidimensional incentive mechanism to improve the task participation rate of vehicles. Meanwhile, the method comprises a key task identification and data interference mechanism, and more time is provided for safe and reliable data transmission.
The invention has the characteristics of high reliability, safety and the like. Compared with the traditional algorithm, the invention uses a multidimensional incentive mechanism, improves the task participation rate of the vehicle, and simultaneously provides a key task identification method and a data interference mechanism, so that the vehicle can complete the task more efficiently, and the privacy information of key data is protected.
It should be understood that parts of the specification not set forth in detail are well within the prior art.
It should be understood that the above description of the preferred embodiments is given for clarity and not for any purpose of limitation, and that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (3)
1. A method for scheduling privacy perception data of the Internet of vehicles based on an incentive mechanism comprises a multidimensional incentive mechanism, a key task identification mechanism and a data interference mechanism; the method is characterized by comprising the following steps:
step 1: stimulating the vehicle to participate in the task in the Internet of vehicles according to the utility function, the welfare function and the score function; when the utility function is positive and the vehicle participates in the task, the welfare function and the score function are larger, so that the vehicle is more willing to participate in the task;
step 2: after the vehicle is stimulated to participate in the task, identifying a key task and obtaining more time to complete the key task; expected number of critical tasks upon new verification
And expected number of unverified critical tasks Kt i The relationship between the two identifies the critical task in the shortest time;
and step 3: according to interference condition 1:
and interference condition 2:
The completed key task information is safely transmitted to a target vehicle; wherein it is present>
Is the reliability level of the vehicle, S is the winner set, Γ i Is a set of tasks, pr (.) is a probability function, based on the results of the evaluation of the task>
Is a random number, is greater than or equal to>
Is a base variable, beta j Is a parameter chosen by the system for the task, i denotes the ith fragmented file, τ j Task of j-th vehicle, w i Represents the ith worker;
a data interference mechanism is adopted, and firstly, a differential privacy technology is applied to protect the privacy of the vehicle; second, the utility and cost of the vehicle remain within reasonable limits; finally, according to the reliability level and the setting of random parameters, a data interference mechanism meets the conditions 1 and 2, so that complete key task information is protected; the server obtains a safe data result by satisfying condition 1 and condition 2 while ensuring that the vehicle participates in the task;
the specific implementation comprises the following substeps:
step 3.1: firstly, carrying out privacy protection by using a differential privacy technology;
the differential privacy is that M (χ { [ T ]) N×K →R K×1 As a mapping mechanism, any input data matrix that can be mapped to interference result vector R, (χ {. TXO }) N×K Representing data of all workers, wherein x represents the data of the workers, {. T } represents the data of the sensor task, N represents a group of workers, and K represents a group of sensing tasks; then, if and only if for only one input and any
Any worker data of the two data matrices x and x' that are different above, mechanism M has η differential privacy, expressed as follows:
Pr[M(x)∈A]≤exp(η)Pr[M′(x)∈A]，
where η is a small positive number called the privacy budget;
step 3.2: determining whether the information transmitted by the vehicle is key information;
key information is transmitted with a privacy budget to ensure information privacy; since the reliability of the vehicle determines the mission quality of the vehicle, critical information transmitted by the vehicle is protected according to the reliability of the vehicle; the reliability of the vehicle for the classification task is: omega i,j ＝t j (1-α j ) For use in τ j ∈Τ cat Worker w of the classification task in (1) i Reliability of omega i,j Defined as the reliability of a completed task, where T cat Representing discrete tasks that require a worker to complete; t is t j Is the time to complete the task, and alpha j Is the probability of an incomplete task; using ω = [ ω ] i,j ]∈[0,1] N×K A reliability matrix representing all staff; the reliability matrix ω is assumed to be known a priori information; when a reliable vehicle transmits information, the information is protected; otherwise, the information will not be protected;
step 3.3: when transmitting critical information for reliable vehicle safety, it is desirable that the total cost of the worker be minimal;
each worker w i With only one bundle of tasks of interest
The cost of executing a task bundle, i.e., the perceived cost, is recorded as @>
Additionally, if η differential privacy is guaranteed, there is a cost of privacy leakage, namely a privacy cost, expressed as ≧ greater>
Thus, worker ω i Executing the task gamma, and when the privacy budget is eta, defining a cost function as follows:
wherein,
for a perceived cost in executing a task, be->
The privacy cost when the privacy budget for executing the task is eta, and gamma is the executed task, and ^ is greater than or equal to>
Is a task bundle; for people not belonging to the worker omega i Is taken over>
The inability to execute them or the significant cost of executing them, these tasks being assigned a + ∞cost;
step 3.4: it is desirable that the total cost of the worker be minimal and that worker w be desirable i The utility of (a) is maximal;
utility u of the worker i Is defined as follows:
wherein p is i Is the price that the system is paying,
is a perceived cost to the worker that is present>
Is the privacy cost of the worker when the privacy budget is epsilon, and S is the winner set; given a payment price p and a set of winners S, the total payment of the system is ≥>
Step 3.5: after minimizing the total cost of workers and maximizing the utility thereof, in order to improve the security of key information transmission, the data interference mechanism is made to satisfy conditions 1 and 2; namely, condition 1:
protecting privacyInformation, as such, constraints may ensure β for each classification task j Accuracy; for each task τ j ∈Τ cat The data interference mechanism satisfies condition 2:
Wherein->
Is a random variable, is greater than or equal to>
Is a fundamental fact, β j Is a parameter that the system selects for this task.
2. The incentive mechanism-based vehicle networking privacy aware data scheduling method of claim 1, wherein: in step 1, the high quality video θ is aligned according to the receiver m m,t Yield of fragment quality and its previous fragment bit rate
Defining a utility function:
wherein XQ (r, theta) m,t ) Is the receiver m to the high quality video theta m,t Gains in fragment quality; suppose X Q (r,θ m,t ) Representing the quality gain function of a single file fragment at time t with a bit rate r, where
Represents the loss of receiver m when the file is reduced from a higher bit rate transmission to a lower bit rate transmission at time t;
in a download operation of sender n to receiver m, the welfare function is defined as the difference between the utility of receiver m and the cost of sender n:
W nm,t (r)＝U m,t (r)-C n,t (r)；
wherein C is n,t (r) is the cost of sender n, assuming that the costs of the different tasks are independent of each other, hence cost C n,t (r) is expressed as:
define F (r, p) as a valid scoring function:
F(r,p)＝|MA|+p-C n,t (r)；
wherein, the MA represents the matching degree of the interest information between the source node and the destination node, and is represented as:
wherein S C Is the cost of the source node, D C Is the cost of the target node; p is the price that sender n pays when the system allocates all file fragments to compute the node's score.
3. The incentive mechanism-based vehicle networking privacy aware data scheduling method of claim 2, wherein: in step 2, the vehicle determines the final key task by calculating and analyzing the key task and the common task; all subtasks in round i have c i A time slot indicating that the execution time of the vehicle will consume c i ·t field Wherein c is i Also the frame size, t field Is the time for each time slot to acquire the key field;
the specific implementation process of the step 2 is as follows: all subtasks in round i have c i A time slot indicating that the execution time of the vehicle will consume c i ·t field Wherein c is i Also the size of the frame, t field Is the time for each time slot to acquire the key field;
the vehicle expresses the consumed execution time as T i Expected number of verified tasks
And expected number of unverified tasks Kt i Execution time T consumed by vehicle i Is determined>
Then, according to the expected number Kt of unverified critical tasks in the current round and the previous round i Obtaining a minimum round number of times of identifying the key tasks, wherein the expected number of the unverified key tasks is assumed to be a positive real number; by solving for the expected number of execution times and validated critical tasks @>
The minimum value of the ratio can determine the key task in the shortest time; after the task is completed, the task with higher value is a key task, the key task is judged according to the score function, and the task with the highest score is the key task. />
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