A vehicle cooperative task offloading method based on incentive mechanism

Abstract

The present application belongs to the technical field of vehicle cooperative task offloading, and particularly relates to a vehicle cooperative task offloading method based on an incentive mechanism, which comprises the following steps: step one, establishing a network model, a computable vehicle utility model and an offloading vehicle utility model; step two, proposing a vehicle private information estimation method based on an exploration and development algorithm, predicting the private information of the vehicle, classifying the types of vehicles with idle computing resources, and designing optimal contract projects; step three, satisfying the constraints of individual rationality, incentive compatibility and monotonicity, and establishing an optimization problem of maximizing the expected utility of the offloading vehicle; the present application designs to maximize the expected utility of the offloading vehicle under the conditions of satisfying the IR and IC constraints, and secondly, in order to solve the problem of unknown private information of the computable vehicle, a mobile vehicle private information estimation algorithm is proposed, so that the scheme of the present application can effectively reduce the cost and improve the income of the offloading vehicle.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of vehicle cooperative task offloading, and particularly relates to a vehicle cooperative task offloading method based on an incentive mechanism. BACKGROUND

[0002] In the context of information asymmetry, mobile vehicles usually do not actively provide their private information to vehicles that need resources, such as preferences for resource sharing and the total amount of available resources. Moreover, since computing tasks consume resources and energy, self-interested vehicles may not actively share their resources without any incentive. Contract theory is considered a powerful tool for microeconomics to solve information asymmetry problems. Therefore, in recent years, researchers have conducted in-depth research on incentive mechanisms for mobile vehicles in vehicular edge computing (VEC) networks.

[0003] By using centralized infrastructure in VEC networks to design contracts for mobile vehicles, the computing vehicles can be incentivized to participate in sharing resources. In this regard, some research results are as follows: (1) Computing resource allocation and task allocation optimization in vehicle fog computing, in order to incentivize vehicles to participate in sharing resources, an effective incentive mechanism based on contract theory is designed, which specifies the relationship between the amount of computing resources shared by vehicles and the rewards. The base station designs the contract project and maximizes the expected utility of the BS. Considering the particularity of the high-speed mobile environment and the possibility that vehicles may cheat the server. (2) Task division and offloading scheme in vehicular edge computing network, a channel model is designed to capture the dynamic characteristics of the channel, and contract theory is used to solve the cheating problem of vehicles, where the contract includes the division point of the task and the price of the roadside unit renting vehicles. (3) Vehicle threat sharing based on federated learning, an incentive mechanism based on contract theory is designed according to the multi-dimensional private characteristics of vehicles, and the case where a selfish vehicle uses private information related to cost to gain private benefits is considered. By using self-disclosed attributes to incentivize vehicles to provide resources.

[0004] Due to the mobility of vehicles, the network topology between vehicles at different time slots may change, and the private information of vehicles may also change. Therefore, in the context of dynamic changes of mobile vehicles, it is still a problem to be solved to design a reasonable and effective incentive mechanism. Existing work designs contracts for the characteristics of each type of vehicle, such as dividing vehicle types according to private characteristics such as maximum remaining computing resources, stay time, acceleration, or cost. A series of contract projects are designed for computing vehicles to choose from to solve the problem of information asymmetry.

[0005] But due to the uneven deployment of base stations, the communication range cannot cover a large area of geographical area, and the mobile vehicle under the non-communication coverage range is unwilling to share the idle resources, and the unloading vehicle cannot perform task calculation in time, which leads to the occurrence of road danger. And due to the uneven distribution of task flow, it is easy to cause infrastructure network congestion during peak period, and it is difficult to design an effective incentive mechanism to encourage mobile computing vehicles to participate in resource sharing. Most of the existing documents use centralized infrastructure to design incentives, and rarely consider the incentive design between vehicles in distributed VEC network. SUMMARY

[0006] To solve the above technical problems, the application provides a vehicle cooperative task offloading method based on an incentive mechanism, which comprises the following steps:

[0007] Step one, establish a network model, a computing vehicle utility model and an offloading vehicle utility model;

[0008] Step two, a vehicle private information estimation method based on exploration and development algorithm is proposed to predict the private information of the vehicle, classify the vehicles with idle computing resources, and design the optimal contract project;

[0009] Step three, meet the constraints of individual rationality, incentive compatibility and monotonicity, and establish an optimization problem of maximizing the expected utility of the offloading vehicle;

[0010] Step four, for the optimization problem, reduce the number of IR and IC constraints by exploring the relationship between adjacent vehicle types;

[0011] Step five, use the Lagrange multiplier method in convex optimization algorithm to solve the optimization problem.

[0012] Preferably, the computing vehicle utility model comprises:

[0013] The computing vehicle utility model is expressed by a function:

[0014] O h =η h -E h

[0015] Wherein, O h represents the computing vehicle utility model function, η h represents the unit communication association time reward given by the offloading vehicle to the computing vehicle; E h represents the energy consumption price of the computing vehicle in unit communication association time;

[0016] E h represents:

[0017]

[0018] Among them, ε h Indicates the unit cost of energy consumption, s h Indicates the calculable vehicle capacitance coefficient, f h represents the computing resources that can be shared by computing vehicles, t h Indicates the communication association time between vehicles;

[0019] t h express:

[0020]

[0021] Where S represents the relative displacement between the calculable vehicle and the unloaded vehicle, v h represents the speed of the calculable vehicle, and v represents the speed of the unloaded vehicle;

[0022] Unloading vehicles distinguish computable vehicles with different associated time, energy consumption cost and capacitance coefficient by the type of computable vehicles, divide vehicles into multiple discrete types, and represent the vehicle type βl:

[0023]

[0024] Among them, t h represents the communication association time between vehicles, ε h Indicates the unit cost of energy consumption, s h represents the capacitance coefficient;

[0025] Therefore, the vehicle type is β l The utility function of the computable vehicle is updated as:

[0026]

[0027] Among them, O l represents the updated computable vehicle utility model function, η l Indicates that after the update, the vehicle type is changed to β. l The unit communication association time reward of the calculable vehicle, f l Indicates that the vehicle type after update is β l Computational resources that can be shared by computing vehicles, β l Indicates the vehicle type.

[0028] Preferably, establishing a vehicle unloading utility model includes:

[0029] The benefit of offloading a vehicle is related to the computational latency of the task. The computational time required to offload the task to the local execution is expressed as:

[0030]

[0031] where f0represents the computing capacity of the offloading vehicle, d m represents the size of the mthtask, k m represents the number of CPU cycles required to process a unit task;

[0032] In the case of a given task size, the computational delay of the task has a linear relationship with the number of CPU cycles required to process a unit task, and the offloading vehicle will update the utility model function of the offloading vehicle to the computational vehicles of type l as follows:

[0033]

[0034] where U l represents the updated utility model function of the offloading vehicle, η l represents the time price reward given to the computational vehicles of type β l , and a represents the unit benefit factor of the delay, f0represents the computing capacity of the offloading vehicle, and f l represents the computing resources that can be shared by the computational vehicles of type β l .

[0035] Preferably, the vehicle private information estimation method based on the exploration and development algorithm comprises:

[0036] The private information of the vehicle represents the unit cost of energy consumption. At the initial time, the offloading vehicle knows the maximum and minimum values of the unit cost of energy consumption, and divides them into L equal intervals, each interval corresponding to an initial energy consumption cost ε l of a vehicle type.

[0037] The offloading vehicle broadcasts the contract project (f l , η l ) to the computational vehicles, and after receiving the feedback, updates the energy consumption unit cost value ε l according to the average value ψ l,t of the energy consumption unit cost of all computational vehicles of vehicle type l at time slot t and the uncertainty measure A l，t of the vehicle type, which represents:

[0038]

[0039] where ψ l，t and A l，t represent:

[0040]

[0041]

[0042] where π l represents the computational vehicles of type βl the vehicle probability, b l denotes the vehicle type β l a balancing factor, |H(t)| denotes the total number of computable vehicles at time slot t, Q l，t denotes the contract acceptance function at time slot t, f l , η l denotes the average energy consumption of the set of computable vehicles of vehicle type β l at time slot t, ε l，h denotes the energy consumption unit cost of a computable vehicle h.

[0043] Preferably, the constraints of individual rationality, incentive compatibility and monotonicity are satisfied, establishing an optimization problem representation that maximizes the expected utility of offloaded vehicles:

[0044]

[0045]

[0046] C3: β1<...< β l <...< β L , 1≤l≤L

[0047] wherein C1 denotes the IR constraint, C2 denotes the IC constraint, C3 denotes the vehicle type ordering constraint in ascending order, L denotes the size of the set of vehicle types, l, k denote the indices of the set of vehicle types, β l , β k denote the vehicle type, f0 denotes the computational capacity of an offloaded vehicle, f l , f k denote the computational resources that a computable vehicle of vehicle type β l , β k can share, α denotes the unit benefit factor of latency, η l , η k denotes the unit communication association time reward that an offloaded vehicle gives to a computable vehicle of vehicle type β l , β k

[0048] Preferably, the number of IR, IC constraints is reduced by exploring the relationship between adjacent vehicle types, updating the optimization problem as:

[0049]

[0050]

[0051] C3: β1<...< β l <...< β L , 1≤l≤L

[0052] ​Wherein, C1 represents IR constraint, C2 represents IC constraint, C3 represents vehicle type sorting constraint in ascending order, β l represents vehicle type, f0 represents the computing power of the offloading vehicle, f l represents the vehicle type, and f l represents the computing resource that the computable vehicle of the vehicle type β l represents the unit communication association time reward given by the offloading vehicle to the computable vehicle of the vehicle type β l represents the unit communication association time reward given by the offloading vehicle to the computable vehicle of the vehicle type β

[0053] Advantages of the present application:

[0054] The present application provides a vehicle cooperative task offloading method based on an incentive mechanism, studies how to design a reasonable and effective incentive mechanism to encourage computable vehicles to participate and share resources, first, models the utility model of the offloading vehicle and the utility model of the computable vehicle, designs an incentive mechanism scheme based on contract by comprehensively considering the influence of the mobility and private information of the vehicle on the incentive, maximizes the expected utility of the offloading vehicle under the condition of meeting the IR and IC constraint conditions. Secondly, in order to solve the problem of unknown private information of the mobile vehicle, a mobile vehicle private information estimation algorithm is proposed. Finally, the feasibility of the scheme is verified by simulation, compared with other incentive schemes, the scheme can effectively reduce the cost and improve the income of the offloading vehicle. BRIEF DESCRIPTION OF DRAWINGS

[0055] Figure 1 is the principle block diagram of the present application;

[0056] Figure 2 is the network model diagram in the present application;

[0057] Figure 3 is the comparison diagram of the unit reward obtained by different computable vehicle types in the present application;

[0058] Figure 4 is the comparison diagram of the vehicle computing resource obtained by different computable vehicle types in the present application;

[0059] Figure 5 is the comparison diagram of the income of the computable vehicle in the present application

[0060] Figure 6 is the comparison diagram of the cost of the computable vehicle in the present application

[0061] Figure 7 is the comparison diagram of the income of the offloading vehicle in the present application. DETAILED DESCRIPTION

[0062] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, but not all the embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0063] Various embodiments of the present application can exist in a range of forms, and it should be understood that the description in a range of forms is merely for the convenience and brevity, and should not be understood as a rigid limitation on the scope of the present application; therefore, it should be considered that the described range has specifically disclosed all possible sub-ranges and single values in the range. For example, it should be considered that the range description from 1 to 6 has specifically disclosed sub-ranges, such as from 1 to 3, from 1 to 4, from 1 to 5, from 2 to 4, from 2 to 6, from 3 to 6, etc., and single numbers in the described range, such as 1, 2, 3, 4, 5 and 6, which is applicable regardless of the range. In addition, whenever a numerical range is indicated in the present application, it refers to any cited number (fraction or integer) in the indicated range. Unless otherwise specifically stated, various raw materials, reagents, instruments and equipment used in the present application can be purchased from the market or can be prepared by existing equipment.

[0064] In the present application, the orientation words such as "upper" and "lower" are specifically the directions of the drawing surface in the drawings, unless otherwise stated. In addition, in the present application, the terms "include", "contain" and the like mean "include but are not limited to". In the present application, the relationship terms such as "first" and "second" are merely used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between the entities or operations. In the present application, "and / or" describes the association relationship of the associated objects, which means that there can be three kinds of relationships, for example, A and / or B can represent the cases of A alone, A and B together, and B alone. Wherein A and B can be singular or plural. In the present application, "one or more" means one or more, and "multiple" means two or more. "At least one", "at least one of the following" or the like means any combination of the items, including any combination of single or multiple items. For example, "at least one of a, b or c", or "at least one of a, b and c" can represent a, b, c, a-b (a and b), a-c (b-c, or a-b-c, wherein a, b and c can be single or multiple.

[0065] As Figures 1-7As shown, the embodiment of the application provides a vehicle cooperative task offloading method based on incentive mechanism, comprising the following steps:

[0066] Step one, establish network model, computable vehicle utility model and offloading vehicle utility model;

[0067] For the scenario of information asymmetry, considering the mobility and private information of vehicles, establish network model, computable vehicle utility model and offloading vehicle utility model;

[0068] The network model is as shown in the figure Figure 2 As shown, a distributed VEC network containing one offloading vehicle and multiple computable vehicles is considered. The offloading vehicle can offload multiple sub-tasks to different computable vehicles. The computable vehicles with idle resources will not process the tasks sent by the offloading vehicle unconditionally, and will not actively provide their private information, so the contract-based incentive mechanism scheme is adopted to encourage selfish computable vehicles to perform task computation and provide their private information.

[0069] The offloading vehicle may generate multiple sub-tasks for task offloading computation at time slot T, and the offloaded tasks are represented as: Sub-task d m is represented by a two-tuple: d m = (d m , k m ), where d m represents the sub-task size, and k m represents the CPU cycles required to process a unit task. The offloading vehicle may have multiple co-moving computable vehicles within its communication range at time slot t, represented by the set: H(t) = {H h}, h∈H, H = {1,..., h,..., H}.

[0070] Step two, a vehicle private information estimation method based on exploration and development algorithm is proposed to predict the private information of vehicles, classify vehicles with idle computing resources, and design optimal contract projects;

[0071] Considering that moving vehicles will not provide their private information, a vehicle private information estimation method based on exploration and development algorithm is proposed to predict the private information of vehicles, so that vehicles with idle computing resources can be classified to design optimal contract projects;

[0072] Step three, satisfy the constraints of individual rationality, incentive compatibility and monotonicity, and establish an optimization problem of maximizing the expected utility of the offloading vehicle;

[0073] In order to encourage the computable vehicles to participate and share resources and perform task computation, an optimization problem of maximizing the expected utility of offloading vehicles is established under the constraints of individual rationality, incentive compatibility and monotonicity;

[0074] Step four, for the optimization problem, the number of constraints of IR, IC is reduced by exploring the relationship between adjacent vehicle types;

[0075] Step five, the optimization problem is solved by using the Lagrange multiplier method in convex optimization algorithm.

[0076] The establishment of the computable vehicle utility model comprises:

[0077] When the computable vehicle performs task computation, the energy of the computable vehicle will be consumed, therefore, the utility function of the computable vehicle is defined as the reward obtained by the computable vehicle in unit communication association time minus the energy consumption price paid by the computable vehicle in performing task computation, and the computable vehicle utility model is represented by a function:

[0078] O h =η h -E h

[0079] Wherein, O h represents the computable vehicle utility model function, η h represents the reward of unit communication association time given by the offloading vehicle to the computable vehicle; E h represents the energy consumption price paid by the computable vehicle in unit communication association time;

[0080] E h represents:

[0081]

[0082] Wherein, ε h represents the unit cost of energy consumption, s h represents the capacitance coefficient of the computable vehicle, f h represents the computing resources that can be shared by the computable vehicle, t h represents the communication association time between vehicles;

[0083] When the relative speed of the computable vehicle and the offloading vehicle is positive, then the relative displacement is: Otherwise, the relative displacement is: Wherein, (x,y) is the position coordinate of the host vehicle, (x h ,y h ) is the position coordinate of the hth computable vehicle, t h represents:

[0084]

[0085] where S represents the relative displacement between the computable vehicle and the offloading vehicle, v h represents the speed of the computable vehicle, and v represents the speed of the offloading vehicle.

[0086] The longer the communication association time t between vehicles, the more stable the communication link between V2V, the greater the offloading willingness of the offloading vehicle to the computable vehicle, and the more willing to pay a higher reward.

[0087] Since the communication range of the offloading vehicle is limited, the number of computable vehicles belonging to the communication range is also limited, so the type set of the computable vehicle belongs to a discrete limited space. In the scenario of information asymmetry in the VEC network, all computable vehicles are divided into L types according to the communication association time, the capacitance coefficient and the unit cost of energy consumption of the computable vehicle. The unit cost of energy consumption is regarded as the private information of the computable vehicle, which is obtained by a private information estimation algorithm.

[0088] The offloading vehicle distinguishes the computable vehicle set with different communication association time, capacitance coefficient and unit cost of energy consumption by the type of the computable vehicle, divides the vehicles into multiple discrete types, and divides the vehicle type β l represents:

[0089]

[0090] where t h represents the communication association time between vehicles, ε h represents the unit cost of energy consumption, s h represents the capacitance coefficient.

[0091] where t h represents the communication association time between vehicles, ε h represents the unit cost of energy consumption, s h represents the capacitance coefficient.

[0092] The vehicle type set represents: All computable vehicle types are sorted in ascending order, that is, β1<...<β l <...<β L , 1≤l≤L.

[0093] Therefore, the utility function of the computable vehicle with vehicle type β l is updated as:

[0094]

[0095] where O l represents the updated computable vehicle utility model function, η l represents the updated offloading vehicle given to the vehicle type βl a unit communication association time reward of the computable vehicle, f l denotes the updated vehicle type, β l computable resources sharable by the computable vehicle, β l denotes the vehicle type.

[0096] The establishing of the offloading vehicle utility model comprises:

[0097] The benefit of the offloading vehicle is related to the computation delay of the task, and the computation time required by the offloading vehicle for offloading the task to the local execution is denoted as:

[0098]

[0099] wherein f0denotes the computation capability of the offloading vehicle, d m denotes the size of the mthtask, k m denotes the number of CPU cycles required for processing a unit task;

[0100] In the case of a given task size, the computation delay of the task is in linear relationship with The function of the offloading vehicle utility model for offloading the task to the computable vehicle of the vehicle type βl is updated as:

[0101]

[0102] wherein U l denotes the updated offloading vehicle utility model function, η l denotes the time price reward given to the computable vehicle of the vehicle type β l , α denotes the unit benefit factor of the delay, f0denotes the computation capability of the offloading vehicle, and f l denotes the computation resource sharable by the computable vehicle of the vehicle type β l .

[0103] The private information prediction algorithm of the vehicle comprises:

[0104] The private information of the vehicle denotes the unit cost of energy consumption, and in the initial time, the offloading vehicle knows the maximum value and the minimum value of the unit cost of energy consumption, and divides the same into L equal intervals, each interval corresponding to an initial energy consumption cost ε l of the vehicle type of the vehicle type l.

[0105] The offloading vehicle broadcasts the contract item (f l , η l ) to the computable vehicle, and after receiving the feedback, according to the average value ψ l，t of the unit cost of energy consumption of all the computable vehicles of the vehicle type l at time slot t, the uncertainty measure A l，t of the vehicle type., update the energy unit cost value ε l , represents:

[0106]

[0107] wherein ψ l，t and A l，t respectively represent:

[0108]

[0109]

[0110] wherein π l represents the probability of the vehicle type being β l , b l represents the balancing factor of the vehicle type β l , |H(t)| represents the total number of computable vehicles at time slot t, Q l , t represents the average energy consumption of the computable vehicle set of the vehicle type β l when accepting the contract (f l , η l ) at time slot t, and ε l，h represents the energy unit cost of the computable vehicle h.

[0111] In the scenario of information asymmetry, the offloading vehicle will pursue its own maximum benefit, and at the same time, the computable vehicle may deliberately provide false private information in order to obtain the highest reward. In order to solve this problem, the contract theory is used to design a suitable incentive mechanism to encourage the computable vehicle to participate in the sharing of resources and provide correct private information.

[0112] The offloading vehicle designs contract projects of different vehicle types, aiming to recruit computable vehicles willing to help perform the task calculation. The offloading vehicle designs the contract by maximizing its benefit, and each contract project represents a different type of computable vehicle.

[0113] When the computable vehicle chooses to accept a certain contract project, it is rational to choose the contract project that can guarantee its own interests and will not choose the contract project that has negative effect on it. Therefore, when designing the contract for the computable vehicle, the IR constraint needs to be met, that is, In the scenario of information asymmetry, the computable vehicle will not actively share its computing resources and private information with the offloading vehicle, but the computable vehicle will provide its private information in order to choose the most optimal contract project for itself. Based on this, the design of the contract also needs to meet the IC constraint, which can make the computable vehicle choose the most suitable contract project to obtain the maximum benefit, that is,

[0114] The above mentioned constraints of individual rationality, incentive compatibility and monotonicity are satisfied, and the optimization problem of maximizing the expected utility of unloading vehicles is formulated as:

[0115]

[0116]

[0117] C3: β1<...<β l <...<β L , 1≤l≤L

[0118] Among them, C1 represents the IR constraint, C2 represents the IC constraint, C3 represents the vehicle type sorting constraint in ascending order, L represents the size of the vehicle type set, l and k represent the index of the vehicle type set, β l , β k represents the vehicle type, f0 represents the computing power of the unloaded vehicle, and f l 、f k Indicates that the vehicle type is β l , β k The computing resources that can be shared by the computing vehicles, α represents the unit benefit factor of delay, η l ,η k Indicates that the unloading vehicle is given a vehicle type of β l , β k The unit communication association time reward of the vehicle can be calculated.

[0119] By exploring the relationship between adjacent vehicle types, the number of IR and IC constraints is reduced, and the optimization problem is updated to:

[0120]

[0121]

[0122] C3: β1<...<β l <...<β L , 1≤l≤L

[0123] Among them, C1 represents the IR constraint, C2 represents the IC constraint, C3 represents the vehicle type sorting constraint in ascending order, β l represents the vehicle type, f0 represents the computing power of the unloaded vehicle, and f l represents the computing resources that can be shared by the computable vehicles of vehicle type βl, α represents the unit benefit factor of delay, η l Indicates that the unloading vehicle is given a vehicle type of β l The unit communication association time reward of a vehicle can be calculated, and L represents the size of the vehicle type set.

[0124] Specifically: due to the complexity of the constraints of the above optimization problem, it is difficult to solve directly, and the IR and IC constraints need to be simplified. Therefore, the IR and IC constraints are simplified by the following lemma before solving the optimization problem.

[0125] Lemma 1: For any contract that meets the IC constraint, if and only if There is

[0126] Proof: According to the IC constraint, there are: And Two inequalities, by adding inequalities get (η l -η k )(β l -β t )≥0, thus get when β l ≥β k , η l ≥η k .

[0127] Lemma 2: For any contract (f l , η l ), if and only if There is When There is

[0128] Proof:

[0129] Sufficiency: According to the IC constraint condition, if there is:

[0130]

[0131] That is:

[0132]

[0133] Therefore, when η l >η k , f l >f k ; when η l =η k , f l =f k .

[0134] Necessity: According to the IC constraint condition, if there is:

[0135]

[0136] That is:

[0137]

[0138] Thus, when f l f k , we have η l > η k ; when f l = f k , we have η l = η k .

[0139] Lemma 3: For any IR constraint of a contract, if the type 1 vehicle satisfies the IR constraint, then all types of vehicles satisfy the IR constraint, i.e.,

[0140] Proof: According to the IC constraint and the monotonicity of β, we have

[0141] Lemma 4: The IC constraint has monotonicity, and thus can be divided into a local downward incentive-compatible constraint:

[0142]

[0143] Similarly, it is simplified to a local upward incentive-compatible constraint:

[0144]

[0145] Proof of the local downward incentive-compatible constraint: According to the IC constraint, we can respectively obtain:

[0146]

[0147]

[0148] Since the IC has monotonicity, according to Lemma 1 and Lemma 2, we have:

[0149]

[0150] That is:

[0151]

[0152]

[0153] Therefore, we can conclude that:

[0154]

[0155] The local downward incentive-compatible constraint has been proven. Similarly, the local upward incentive-compatible constraint can be proven.

[0156] Through the above simplification of the IR and IC constraints, the optimization problem can be updated as:

[0157]

[0158]

[0159] C3: β1<...<β l <...<β L , 1≤l≤L

[0160] Among them, C1 represents the IR constraint condition, C2 represents the IC constraint condition, there are L IR constraints and L(L-1) IC constraints in total, and C3 represents the vehicle types sorted in ascending order;

[0161] L represents the size of the vehicle type set, β l represents the vehicle type, f0 represents the computing power of the unloaded vehicle, and represents the vehicle type as β l The computing resources that can be shared by the computing vehicles, α represents the unit benefit factor of delay, η l Indicates that the unloading vehicle is given a vehicle type of β l The unit communication association time reward of the vehicle can be calculated.

[0162] Example 1:

[0163] Problem solving:

[0164] Solution to the incentive mechanism problem:

[0165] The Lagrange multiplier method in the convex optimization algorithm is used to solve the above optimization problem. First, we get f l , l∈L, and then bring it into Get η l , l∈L is the optimal solution, and its algorithm pseudo code is shown in Algorithm 1.

[0166]

[0167]

[0168] Private information estimation algorithm:

[0169] The private information of the vehicle represents the unit cost of energy consumption. At the initial stage t = 1, the maximum unit cost of energy consumption known to the unloading vehicle is ε max and the minimum value ε min , and there are L types of vehicles. Unloading the vehicle will close the interval [ε min , ε max ] is divided into L equal intervals, and the minimum value of each interval is used as the initial value of the unit cost of energy consumption under different vehicle types, that is:

[0170]

[0171] The offloading vehicle designs the contract and sends it to the computable vehicles in the communication range through broadcasting. When the offloading vehicle receives the feedback of the computable vehicles, i.e., the offloading vehicle can obtain the private information of the computable vehicles in different vehicle types. At the end of t = 1, the unit cost value of energy consumption is updated as:

[0172]

[0173] where ψl,1represents the unit cost of energy consumption εhof the computable vehicle h in the vehicle type l at time slot t = 1 l，h The average value is calculated, which represents:

[0174]

[0175] where Q l，1 represents the set of computable vehicles of the vehicle type β l when accepting the contract (f l , η l ) at time slot t = 1, and |Q l，1 | represents the number of vehicles of the vehicle type β l ; π l represents the probability of the vehicle type being β l . |H(1)| represents the total number of the set of computable vehicles at time slot t = 1.

[0176] A l，1 is the uncertainty measurement factor of the vehicle type l at time slot t = 1, which represents:

[0177]

[0178] where b l represents the balance factor of the vehicle type l.

[0179] At time slot t > 1, the offloading vehicle updates the unit cost value of energy consumption at this moment according to the unit cost of energy consumption obtained at the previous time slot, i.e.,

[0180]

[0181] where ψ l，t and A l，t respectively represent:

[0182]

[0183]

[0184] where π l represents the probability of the vehicle type being βl The vehicle probability, b l Represents vehicle type β l The balance factor, |H(t)| represents the total number of vehicles that can be calculated at time slot t, Q l , t means accepting the contract at time slot t (f l , η l ), the vehicle type is β l The set of computable vehicles, ε l，h Indicates that the vehicle type is β l The unit cost of energy consumption of vehicle h can be calculated.

[0185] The private information ε of the vehicle is estimated by the private information estimation algorithm l To make an estimate, the vehicle types can be classified. The private information estimation algorithm is as shown in Algorithm 2:

[0186]

[0187]

[0188] Simulation results:

[0189] The optimal contract items (f l ,η l ), Figure 2 It shows that in different vehicle types, the unit reward η of the vehicle can be calculated l The change of , where the unit reward η of the vehicle can be calculated l Increases with the increase of vehicle types. Figure 3 According to Lemma 1, under the IR constraint, since the vehicle type is monotonically increasing, the unit reward η of the vehicle can be calculated l It is also monotonically increasing.

[0190] Figure 4 The figure shows the changes in the computing resources shared by different vehicle types. As the number of vehicle types increases, the computing resources shared by different vehicles increases. l will also increase. Figure 4 And IC constraints, we can get the unit reward η of the computable vehicle l When the number of vehicles increases, the computing resources shared by the vehicles can be calculated. l will also increase, which is consistent with Lemma 2.

[0191] In order to verify that the proposed solution meets the two constraints of IR and IC under information asymmetry, Figure 5The benefits of the computable vehicles when the vehicle types are 3, 5, 7 and 9 are shown, in which the maximum benefit values of different types are indicated by black arrows. When the computable vehicles only have the option to select the contract items corresponding to their types, the benefits of the computable vehicles are the maximum, and the benefits are all greater than zero. For example, for the computable vehicles belonging to vehicle type 5, only the fifth contract item is selected, the benefit of the computable vehicle is the highest, and when other contract items are selected, the benefit of the computable vehicle is reduced. For the computable vehicles belonging to vehicle type 3, when it does not select the third contract item, the computable vehicle may have a negative benefit.

[0192] Figure 6 The changes of the costs of the three schemes in different vehicle types under information asymmetry are shown. The costs of the three schemes all increase with the increase of the vehicle types, because the shared computing resources of the computable vehicles also increase accordingly, resulting in the increase of the costs paid by the vehicles. Under information asymmetry, the cost of the scheme proposed in this chapter is the smallest, because the private information of the computable vehicles is predicted by algorithm 2, and different rewards are designed according to the different types of vehicles, which can effectively reduce the cost of the offloading vehicles. The Stackelberg-IA scheme cannot determine the types of vehicles, and needs to design rewards through the requested resource amount, and cannot design different reward strategies according to the types of vehicles. The reward of the LC-IA scheme is linearly related to the shared computing resources, and does not consider the influence of the private information of the computable vehicles on task offloading. When the shared computing resources increase, the cost paid by the vehicles also increases accordingly. Therefore, the costs of the two schemes are higher than the cost of the scheme proposed in this chapter. Figure 6

[0193] Figure 7 The changes of the benefits of the offloading parties of the three schemes under different vehicle types are shown. It can be seen that the benefits of the offloading parties of the three schemes all increase with the increase of the vehicle types. Figure 7 It can be seen that the benefits of the offloading parties of the three schemes all increase with the increase of the vehicle types. In the LC-LA scheme, the offloading party only performs linear pricing according to the shared computing resources without considering the influence of the private information of the vehicles on the benefits, so as to maximize the benefits. In the Stackelberg-IA scheme, the offloading party performs price strategy analysis according to the required resources without knowing the types of vehicles, so as to satisfy the positive utility. When the computable vehicles increase the price, the benefits of the offloading party are reduced. Therefore, in the information asymmetry scenario, the benefits of the offloading party in the scheme proposed in this chapter are greater than those in the Stackelberg-IA scheme and the LC-LA scheme.

[0194] ​The foregoing is considered as illustrative only of the principles of the application. Numerous modifications and changes will readily occur to those skilled in the art, and it is intended to embrace all such modifications and changes that fall within the scope of the application as defined by the appended claims. Accordingly, the application should not be limited to the above described embodiments, but should be given the broadest scope in accordance with the principles and novel features disclosed herein.

Claims

1. A method for vehicle cooperative task offloading based on incentive mechanism, characterized in that, The method comprises the following steps: Step one, for the asymmetric information scenario, considering the mobility of vehicles and private information factors, a network model, a computable vehicle utility model and an offloading vehicle utility model are established; The network model comprises a distributed VEC network of one offloading vehicle and multiple computable vehicles, the offloading vehicle can offload multiple sub-tasks to different computable vehicles, the computable vehicles with idle resources will not process the tasks sent by the offloading vehicle unconditionally, and will not actively provide their private information, and then a contract-based incentive mechanism scheme is adopted to encourage the selfish computable vehicles to perform task computation and provide their private information; The computable vehicle utility model function is represented as: O h = η h -E h wherein, O h represents a function for computing the vehicle utility model, η h represents a unit communication association time reward given by the offloading vehicle to the computing vehicle; E h represents the energy consumption price paid by the computing vehicle in a unit communication association time; E h Indicates: wherein ε h represents the unit cost of energy consumption, s h represents the computable vehicle's capacitance coefficient, f h represents the computable vehicle's shareable computing resources, t h represents the communication association time between vehicles; The establishment of the offloading vehicle utility model comprises: Task offloading to a vehicle type is β l a function of the offloading vehicle utility model of the computable vehicle: where U l denotes the updated offloading vehicle utility model function, η l denotes the time price reward given to the vehicle type β l computable vehicle, α denotes the unit benefit factor of the delay, f0denotes the computing power of the offloading vehicle, f l denotes the computing resource that the vehicle type β l computable vehicle can share; Step two, considering that the moving vehicles will not provide their private information, a vehicle private information estimation method based on an exploration and development algorithm is proposed to predict the private information of the vehicles, type classification is performed on the vehicles with idle computing resources, and an optimal contract project is designed; The vehicle private information estimation method based on the exploration and development algorithm comprises: The private information of the vehicle represents the unit cost of energy consumption. At the initial time, the offloading vehicle knows the maximum and minimum values of the unit cost of energy consumption, and divides them into L equal intervals, each of which corresponds to an initial energy consumption cost ε of a vehicle type l ; Step three, constraints of individual rationality, incentive compatibility and monotonicity are met, and an optimization problem of maximizing the expected utility of the offloading vehicle is established to encourage the computable vehicles to participate and share resources and perform task computation; Step four, for the optimization problem, the relationship between adjacent vehicle types is explored to reduce the number of constraints of individual rationality and incentive compatibility, and the optimization problem is updated; Step five, the Lagrange multiplier method in the convex optimization algorithm is used to solve the updated optimization problem, and an optimal vehicle cooperative task offloading scheme is obtained.

2. The method of claim 1, wherein, The establishment of the computable vehicle utility model comprises: The unloading vehicle distinguishes the computable vehicles with different associated time, energy consumption cost and capacitance coefficient by calculating the type of the vehicle, divides the vehicle into multiple discrete types, and divides the vehicle type β l represents: Thus, the vehicle type is updated as β l The utility function of the computable vehicle is updated as: where O l represents the updated computable vehicle utility model function, η l represents the updated unit communication association time reward given to a computable vehicle of type β l by the offloading vehicle, f l represents the updated computable vehicle of type β l shareable computing resources.

3. The method of claim 1, wherein, The establishment of the offloading vehicle utility model comprises: The income of the offloading vehicle is related to the computation time delay of the task, and the computation time t0 required by the offloading vehicle for offloading the task to the local for execution is represented as: where f0represents the computing power of the offloading vehicle, d m represents the size of the mth task, k m represents the number of CPU cycles required to process a unit task; The computing delay of a task has a linear relationship with the size of the task under a given task size.

4. The method of claim 1, wherein, The vehicle private information estimation method based on the exploration and development algorithm comprises: Offloading vehicles broadcast the contract project (f l ,η l ) to the computable vehicles, after receiving the feedback, according to the average value ψ l,t of the energy consumption unit cost of all computable vehicles in the vehicle type l at time t, the uncertainty metric A l,t of the vehicle type, update the energy consumption unit cost value ε l , which is represented as: wherein ψ l,t and A l,t respectively represent: where π l represents the probability of a vehicle being of type β l , b l represents the balancing factor for vehicles of type β l , |H(t)| represents the total number of computable vehicles at time slot t, Q l,t represents the average energy consumption of the set of computable vehicles of type β l when accepting contract (f l , η l ) at time slot t, and ε l,h represents the energy consumption unit cost of a computable vehicle h.

5. The incentive-based vehicle cooperative task offloading method of claim 1, wherein, The optimization problem of maximizing the expected utility of the offloading vehicle is represented by meeting the constraints of individual rationality, incentive compatibility and monotonicity: Subject to.C1: C2： C3: β1<... < β l <... < β L , 1≤l≤L wherein U denotes the offloading vehicle, C1 denotes the IR constraint, C2 denotes the IC constraint, C3 denotes the vehicle type ordering constraint in ascending order, L denotes the size of the vehicle type set, l, k denote the index of the vehicle type set, f0 denotes the computing capacity of the offloading vehicle, f l , f k denotes the computing resource that the computable vehicle of vehicle type β l , β k can share, α denotes the unit benefit factor of the delay, η l , η k denotes the unit communication association time reward that the offloading vehicle gives to the computable vehicle of vehicle type β l , β k .

6. The incentive-based vehicle cooperative task offloading method of claim 1, wherein, The optimization problem is updated by exploring the relationship between adjacent vehicle types to reduce the number of constraints of IR and IC, comprising: Subject to.C1: C2： C3: β1<... < β l <... < β L , 1≤l≤L Among them, U represents the unloading vehicle, C1 represents the IR constraint, C2 represents the IC constraint, C3 represents the vehicle type sorting constraint in ascending order, β l represents the vehicle type, f0 represents the computing power of the unloaded vehicle, and f l Indicates that the vehicle type is β l The computing resources that can be shared by the computing vehicles, α represents the unit benefit factor of delay, η l Indicates that the unloading vehicle is given a vehicle type of β l The unit communication association time reward of a vehicle can be calculated, and L represents the size of the vehicle type set.

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