CN112907340B - Excitation method and system based on two-way auction model in crowd sensing - Google Patents
Excitation method and system based on two-way auction model in crowd sensing Download PDFInfo
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Abstract
The invention belongs to the technical field of crowd sensing, and discloses an excitation method and system based on a bidirectional auction model in crowd sensing, wherein in the crowd sensing system, a set R= { R formed by a platform and M task requesters 1 ，r 2 ，r 3 ，...r M Set w= { W of N data providers 1 ，w 2 ，w 3 ，...w N -a }; in the crowd sensing system, a task requester r is assumed j Only one task can be submitted to the platform and each data provider w i Can provide a task set which is interested by itself and the corresponding bid, task requester r j The provided task constitutes the set t= { T 1 ，t 2 ，t 3 ，...t M -a }; wherein, the crowd sensing system adopts a bidirectional auction model. The excitation mechanism based on the bidirectional auction model provided by the invention uses a user as a center, adopts the bidirectional auction model, and is more suitable for the actual application scene with a plurality of task requesters; and the relation between task difficulty and perception capability is considered, so that the task allocation is more accurate.
Description
Technical Field
The invention belongs to the technical field of crowd sensing, and particularly relates to an excitation method and system based on a bidirectional auction model in crowd sensing.
Background
At present, crowd sensing refers to a new sensing network combining the concept of crowdsourcing by utilizing the characteristics of mobile sensing and social computing of intelligent equipment. One key problem in crowd sensing services is the design of incentive mechanisms that increase the service enthusiasm of data providers so that the data providers can provide a sensing service. The existing incentive model mostly assumes only one task requester, ignores the interaction behavior between the perceived task requester and the data provider, and is difficult to satisfy the application scenario in which a plurality of task requesters exist. Firstly, researching a crowd-sourced stimulus mechanism, and constructing a multi-requester multi-provider two-way auction model; secondly, the existing research only considers the influence of the task perception capability of the data provider on the task result, and does not consider the relation between the task difficulty and the perception capability, so that the task difficulty provided by the task requester and the task perception capability of the data provider in the two-way auction model are respectively modeled, the intrinsic factors and the extrinsic factors affecting the data provider are comprehensively considered, the relation between the quality of the data provided by the data provider and the task difficulty is considered by the intrinsic factors, the activity degree and the distance similarity of the user are used for measuring the extrinsic factors, and the comprehensive score of the data provider is finally obtained; finally, the effectiveness of the incentive method is analyzed from four aspects of calculation effectiveness, personal rationality, budget balance and authenticity.
Most of the most used are rewards pay incentives based on game theory, mostly designed with the user or platform as the center. The most classical is to design two excitation models: the platform-centric incentive model employs a Stebert game and the user-centric incentive model employs a reverse auction model. The technical scheme of the two-way auction model is used, and the relation between the user quality and the task quality requirement is considered; modeling the user quality into Gaussian distribution by using a reverse auction model technical scheme, and deducing the evolution process of the mean and variance of the user quality variable distribution along with the increase of rounds; in the prior art, the relation between the user quality and the task difficulty is considered, the user quality and the task difficulty are modeled into Gaussian distribution, and the evolution process of the user quality variable distribution and the task difficulty along with the increase of the turn is deduced.
Through the above analysis, the problems and defects existing in the prior art are as follows:
(1) Most of the existing incentive models assume that only one task requester is provided, so that the interaction behavior between the perceived task requester and the data provider is ignored, and the application scene with a plurality of task requesters is difficult to meet.
(2) The existing research only considers the influence of the task perception capability of the data provider on the task result, and does not consider the relation between the task difficulty and the perception capability.
The difficulty of solving the problems and the defects is as follows:
the existing work cannot integrate the relation between task difficulty and the perception capability of a data provider to build a model while guaranteeing the four properties of calculation effectiveness, personal rationality, budget balance and authenticity of the two-way auction.
The meaning of solving the problems and the defects is as follows:
therefore, the invention provides an incentive method based on a bidirectional auction model, which fully considers the relation between the task requester and the data provider by respectively establishing the model for the task perception capability and the task difficulty of the data provider, improves the satisfaction degree of the task requester and the data provider, and ensures the calculation effectiveness, the personal rationality, the budget balance and the authenticity of the bidirectional auction.
Disclosure of Invention
Aiming at the problems existing in the prior art, the invention provides an excitation method and an excitation system based on a bidirectional auction model in crowd sensing.
The invention is realized in such a way that the excitation method based on the bidirectional auction model in crowd sensing comprises the following steps: in a crowd sensing system, a set R= { R consisting of one platform and M task requesters 1 ，r 2 ，r 3 ，...r M Set w= { W of N data providers 1 ，w 2 ，w 3 ，...w N -a }; in the crowd sensing system, a task requester r is assumed j Only one task can be submitted to the platform and each data provider w i Can provide a task set which is interested by itself and the corresponding bid, task requester r j The provided task constitutes the set t= { T 1 ，t 2 ，t 3 ，...t M -a }; wherein, the crowd sensing system adopts a bidirectional auction model.
Further, the motivation method and system based on the bidirectional auction model in crowd sensing comprises the following steps:
step one, a task requester set R submits respective type informationFeeding a platform; wherein the type information includes maximum task value +.>Etc.; task information provided by a task requester is submitted to a platform by the task requester so as to ensure the integrity of the task information and facilitate the selection of a data provider;
step two, the platform distributes the task distributed by the received task requester to the data provider; the platform is responsible for sorting the received task set so as to ensure the comprehensiveness of task release;
step three, after the data provider receives the task issued by the platform, selecting the task of interest, submitting the type information to the platform, and selecting the proper winning data provider; considering the interesting set of data providers to facilitate the data providers' ability to participate for a long period of time;
Step four, the platform calculates the evaluation probability between the task requester and the data provider by using an evaluation model according to the information of the two parties, and then obtains each data set; according to the proposed algorithm, the data provider and the task requester can be better matched;
step five, the winning data provider uploads the data to the platform after completing the task; in the step, the platform feeds the submitted result back to the task requester so as to ensure that the task requester can participate for a long time;
step six, the winning task requester pays the cost of the task; for ensuring that the utility of the task requester is greater than 0;
step seven, the winning data provider receives consideration from the corresponding winning task requester for ensuring that the utility of the data provider is greater than 0.
Further, in step one, each task requester submits its own type information during the initial stage of the auctionFor the platform->Respectively represent the value of the submitted task and the task thereof, < ->Respectively corresponding to the average difficulty and the distinguishing degree of the task, |t j The I represents the number of unit tasks that need to be completed;
assuming that the difficulty of a known task follows a gaussian distribution, i.e. task t j Will be modeled as a random variable ψ j Obeying gaussian distributionWherein mu j For average difficulty->Is a differentiation index; mu (mu) j The larger the task difficulty is, the lower the accuracy is; />The larger the task difficulty is, the more dispersed the overall difference distinction is, and the utility of the task requester is as follows:
further, in the third step, after the data provider receives the task issued by the platform, the data provider selects the task of interest, submits the type information to the platform, and picks out the appropriate winning data provider, including:
after the data provider receives the tasks issued by the platform, the data provider selects the task of interest, each w i E W will type informationSubmitting the data to a platform; wherein (t) k ，c ik ) Representing a data provider w i E W selects completion task t k Given completion cost value c for unit task ik The method comprises the steps of carrying out a first treatment on the surface of the The platform then uses the assessment model to pick the appropriate winning data provider, providing the data quality, the utility of which is:
in the fourth step, the platform calculates an evaluation probability between the task requester and the data provider by using an evaluation model according to the information of both the task requester and the data provider, and then obtains each data set, including:
the platform calculates the evaluation probability between the task requester and the data provider by using an evaluation model according to the information of the two parties, and then calculates a winning set R of the task requester by using a selection algorithm, a matching and a pricing algorithm w Winning set of data provider W w A winning task requester fee set Q, a winning data provider reward set P; after matching each winning data provider to winning task requester is completed, the utility of the platform is:
further, the evaluation model mainly comprises two parts, namely an intrinsic factor and an extrinsic factor, and comprises the following steps:
(1) External factors: the method refers to the influence of the external conditions of the current data provider on the accuracy of the current data provider in completing tasks, and the two indexes of the liveness and the distance similarity of the user are used for measuring.
(2) Intrinsic factors: is the quality of the completion task, which is the ability of the data provider itself, given that the quality of the user's response to the task is known to follow a gaussian distribution, user w i The quality of the E W completion task is modeled as a random variable Q i Obeying gaussian distributionWhereas the difficulty of the task obeys a gaussian distribution, i.e +.>Let delta ij ＝Q i -ψ j Representing user w i Quality of E W completing task and task t j The difference between the difficulties is based on the two assumptions, and can be obtained
(3) Evaluating probability
Since the task is two-class, the data provider still hasIs correct. Therefore, the probability of the user completing the task is modeled using a sigmoidal function, the functional expression is as follows:
Wherein P (delta) ij ) Representing the probability that the data provider correctly answers the question.
Further, the extrinsic factors include:
(1) user liveness
The time of the last participation of the user in the task can be obtained through the extracted historical data of the data provider, and the liveness of the data provider can be obtained through calculating the distance between the task release time and the time of the last participation of the data provider in the task. The calculation formula of the liveness of the data provider is as follows:
wherein, act i Is the user activity, beta is the decay coefficient of the user activity, t now Is the task release time, t pre The time of the last participation of the task from the task release time data provider is set to be 1 if the user is in a state of not participating in the task for a long time.
(2) Distance similarity
The location of the data provider and the distance between the data acquisition sites in the perception task are also one of the performance metrics of the data provider when participating in the perception task. By extracting the current data of the data provider, the cosine similarity can be used to calculate the distance similarity between the two, and the calculation formula is as follows:
wherein, (x) i ，y i ，z i ) Is the location coordinates of the data provider, (x) i ，y i ，z i ) Is the position coordinates of the task requester, dist i，j The larger the value, the closer the distance between the two is; dist (dist) i，j The closer the value is to 1, the closer the two places are; dist (dist) i，j The closer the value is to-1, the farther the two sites are indicated.
(3) Probability model
Assuming that the influence of the extrinsic conditions of each data provider on the accuracy of the task completion is not influenced by other data providers, the probability of influence of using a logistic function as an extrinsic factor on the accuracy of the task completion is selected as follows:
wherein r is ij Is the influence of two indexes of liveness and distance similarity of a data provider on the accuracy of distinguishing problems, and r ij The larger the value of (c), the higher the accuracy of completing the task.
Further, the selection algorithm includes:
input: a task requester set R, a data provider set W;
and (3) outputting: task requester candidate set R s Data provider candidate set W s 。
Step 1: task requester candidate set R s Data provider candidate set W s All values are assigned as empty sets;
step 2: cycling when the task requester set R is not an empty set;
step 3: ending the loop when the task requester set R is an empty set, and outputting a final task requester candidate set R s Data provider candidate set W s 。
Further, in step 2, the cycling when the task requester set R is not an empty set includes:
(1) Iterative selection of task requester set RR of maximum value j As candidate users;
(2) All choices r j Data provider w for providing tasks i And it isIs set S of components of (a) j If S is j Null, i.e. no data provider selection r j Will r j Removing from the set R; otherwise will r j And candidate user set S j Respectively joining task requester candidate sets R s Data provider candidate set W s And r is as follows j Is removed from the collection R.
Further, the evaluation algorithm includes:
input: task requester candidate set R s Data provider candidate set W s ；
And (3) outputting: the set of probabilities S is evaluated.
Step 1: all the evaluation probability sets are assigned as empty sets;
step 2: for task requester candidate set R s Task requester r in j Iterating according to the sequence loop in the set;
step 3: finally traversing task requester candidate set R s All task requesters r in (1) j Ending the set R s Is output, set S.
Further, in step 2, the pair of task requester candidate sets R s Task requester r in j Iterating in a sequential loop in the set, comprising:
(1) First, r is calculated j Corresponding set S j Then set S j W of (3) i Sequentially performing loop iteration;
(2) Then, w is mined according to the user log i Current user information, and the user activity level is obtainedDistance similarity->Second, according to the two information, external factors are obtained for the data provider w i Influence of accuracy of completing the task->
(3) Providing according to dataW is the same as i The quality of the completed task is modeled as a random variable Q i Obeying gaussian distributionThe difficulty of the task obeys the Gaussian distribution, namely +.>And (3) obtaining a final evaluation probability:
(4) Will be set S j W of (3) i Is (a) estimated probability P (Δ) ij ) Adding a set S;
(5) Set S j Finishing the set S after all users have traversed j Is a cyclic version of (a).
Further, the matching and pricing algorithm includes:
input: task requester candidate set R s Data provider candidate set W s Evaluating a probability set S;
and (3) outputting: task requester winning set R w Winning set of data provider W w A winning task requester fee set Q, a winning data provider reward set P.
Step 1: task requester winning set R w Winning set of data provider W w All of the winning task requester fee set Q, winning data provider reward set P are assigned as empty sets;
Step 2: solving for task requester candidate set R s In (a)Minimum task requester r th And from set R s Removing the materials;
step 3: grouping task requester candidates R s According toIs arranged from large to small, i.e
Step 4: for the set R arranged in sequence s Traversing in a sequential cycle;
step 5: end set R s Output of the cycle of task requester winning set R w Winning set of data provider W w A winning task requester fee set Q, a winning data provider reward set P.
Further, in step 4, the pair of sequentially arranged sets R s A sequential loop traversal comprising:
(1) First, find the set R s Task requester r in j Cost q of (2) j ；
(2) Then willCorresponding set S j Will be set S j W of (3) i According to->Sequentially performing loop iteration from small to large, namely;
(3) In the ordered set S j Find out the meeting conditionData provider w of (a) i ；
(4) If it is k= |s j I, then represent set S j W of (3) i All satisfy the conditions according to the ordered set S j Sequentially calculating w i Is paid by (a)And will aggregate S j All w of (3) i P of (2) ij Put into a set P, a set S j All w of (3) i Put all into a collection W w 、r j Put into collection R w ；
(5) If 0 < k < |S j I, then represent set S j W of (3) i Partially meets the condition, according to the ordered set S j Sequentially calculating w i Is paid by (a)And will aggregate S j W of all meeting the conditions i P of (2) ij Put into a set P, a set S j W of all meeting the conditions i Put into a collection W w 、r j Put into collection R w ；
(6) If k=0, then set S is represented j W of (3) i Neither satisfies the condition, and the next task requester r is traversed according to the ordering order j 。
Another object of the present invention is to provide a system for stimulating a crowd sensing based on a bidirectional auction model using the method for stimulating a crowd sensing based on a bidirectional auction model, the system for stimulating a crowd sensing based on a bidirectional auction model comprising:
a type information submitting module for submitting respective type information via the task requester set RFeeding a platform; wherein the type information includes maximum task value +.>
The task issuing module is used for issuing the task issued by the received task requester to the data provider through the platform;
the winning data provider selecting module is used for selecting an interested task, submitting type information to the platform and selecting a proper winning data provider after the winning data provider receives the task issued by the platform;
The data set solving module is used for calculating the evaluation probability between the task requester and the data provider by using the evaluation model according to the information of the task requester and the data provider through the platform, and then solving each data set;
the data uploading module is used for uploading data to the platform after completing the task through a winning data provider;
the task fee payment module is used for paying the fee of the task through a winning task requester;
and the reward receiving module is used for receiving rewards from corresponding winning task requesters through winning data providers.
It is a further object of the present invention to provide a computer program product stored on a computer readable medium, comprising a computer readable program, which when executed on an electronic device, provides a user input interface for implementing the method of incentive based on a bi-directional auction model in crowd sensing.
It is another object of the present invention to provide a computer readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the method of incentive based on a bi-directional auction model in crowd sensing.
By combining all the technical schemes, the invention has the advantages and positive effects that: the excitation method based on the bidirectional auction model in crowd sensing provided by the invention uses the user as a central excitation model, and the reverse auction model considers the interaction between the sensing task provider and the platform, ignores the interaction between the sensing data provider and the sensing service requester, and stimulates the enthusiasm of the sensing data provider and the sensing service requester, so that the bidirectional auction model is adopted. The evaluation algorithm provided by the invention considers the relation between task difficulty and perceptibility; the proposed bi-directional auction model considers the preferences of the data provider, allowing the data provider to select multiple tasks, and in turn proposes a pick algorithm, a match and a pricing algorithm. Meanwhile, the assessment algorithm is used in more models, and is not limited to a two-way auction model; the proposed excitation mechanism based on the two-way auction model is more suitable for the actual application scene with a plurality of task requesters; and the relation between task difficulty and perception capability is considered, so that the task allocation is more accurate.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments of the present invention will be briefly described below, and it is obvious that the drawings described below are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of an incentive method based on a two-way auction model in crowd-sourced awareness, provided by an embodiment of the invention.
Fig. 2 is a schematic diagram of an incentive method based on a bidirectional auction model in crowd sensing according to an embodiment of the present invention.
FIG. 3 is a block diagram of a motivation system based on a two-way auction model in crowd sensing provided by an embodiment of the invention;
in the figure: 1. a type information submitting module; 2. a task issuing module; 3. a winning data provider pick module; 4. a data set solving module; 5. a data uploading module; 6. a task fee payment module; 7. and a reward receiving module.
Fig. 4 and fig. 5 are analysis diagrams of simulation results of model budget balancing provided by an embodiment of the present invention.
Fig. 6 and 7 are graphs of simulation results analysis of model authenticity provided by examples of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the following examples in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Aiming at the problems existing in the prior art, the invention provides an incentive method and system based on a bidirectional auction model in crowd sensing, and the invention is described in detail below with reference to the accompanying drawings.
As shown in fig. 1, the motivation method based on the bidirectional auction model in crowd sensing provided by the embodiment of the invention comprises the following steps:
s101, the task requester set R submits respective type informationFeeding a platform; wherein the type information includes maximum task value +.>
S102, the platform distributes the task distributed by the received task requester to the data provider;
s103, after the data provider receives the task issued by the platform, selecting the task of interest, submitting the type information to the platform, and selecting a proper winning data provider;
s104, the platform calculates the evaluation probability between the task requester and the data provider by using an evaluation model according to the information of the two parties, and then obtains each data set;
S105, uploading data to the platform by the winning data provider after completing the task;
s106, the winning task requester pays the cost of the task;
and S107, receiving rewards from the corresponding winning task requesters by the winning data providers.
The schematic diagram of the incentive method based on the bidirectional auction model in crowd sensing provided by the embodiment of the invention is shown in fig. 2.
As shown in fig. 3, the incentive system based on the bidirectional auction model in crowd sensing provided by the embodiment of the invention comprises:
a type information submitting module 1 for submitting respective type information via the task requester set RFeeding a platform; wherein the type information includes maximum task value +.>
The task issuing module 2 is used for issuing the task issued by the received task requester to the data provider through the platform;
the winning data provider selecting module 3 is used for selecting an interested task, submitting type information to the platform and selecting a proper winning data provider after the winning data provider receives the task issued by the platform;
the data set solving module 4 is used for calculating the evaluation probability between the task requester and the data provider by using the evaluation model according to the information of the two parties through the platform and then solving each data set;
A data uploading module 5, configured to upload data to the platform after completing the task by the winning data provider;
a task fee payment module 6 for paying the fee of the task by the winning task requester;
and the reward receiving module 7 is used for receiving rewards from corresponding winning task requesters through winning data providers.
The technical scheme of the invention is further described below by combining the embodiments.
The invention mainly researches an incentive model with a user as a center, and adopts a bidirectional auction model because the reverse auction model considers the interaction between a perception task provider and a platform, ignores the interaction between a perception data provider and a perception service requester and stimulates the enthusiasm of the perception data provider and the perception service requester.
1. Overview of the system
In a crowd sensing system, a set R= { R consisting of one platform and M task requesters 1 ，r 2 ，r 3 ，...r M Set w= { W of N data providers 1 ，w 2 ，w 3 ，...w N }. In this system, a task requester r is assumed j Only one task can be submitted to the platform and each data provider w i Can provide a task set which is interested by itself and the corresponding bid, task requester r j Task group providedSet t= { T 1 ，t 2 ，t 3 ，...t M }. A bi-directional auction model is employed in the crowd-sourced awareness system of the present invention.
1. Task requester set R submits respective type informationFor a platform, its type information includes maximum task value +.>Etc. In the initial stage of the auction, each task requester will submit its own type informationFor the platform->Respectively represent the value of the submitted task and the task thereof, < ->Respectively corresponding to the average difficulty and the distinguishing degree of the task, |t j I indicates the number of unit tasks that need to be completed. The task types provided by each task requester are different, for example, the task of whether a parking lot has a position or not can be submitted, the task of whether a bus arrives at a station or not can also be submitted, the requirements of the two tasks are different, and the different types of tasks can be distinguished through the submitted type information. It can be assumed that the difficulty of a known task follows a gaussian distribution, i.e. task t j Will be modeled as a random variable ψ j Obeying Gaussian distribution->Wherein mu j For average difficulty->To distinguish between the regions. Mu (mu) j The larger the task difficulty is, the lower the accuracy is;the larger the task difficulty, the more dispersed, and the more obvious the overall difference distinction. Such as whether the parking lot has a task difficulty of a position and whether the bus arrives at a station. The utility of the task requester can be derived as: / >
2. The platform distributes the task distributed by the received task requester to the data provider;
3. after the data provider receives the tasks issued by the platform, the data provider selects the tasks interested in the data provider, and each w i E W will send its type informationIs submitted to the platform, wherein (t k ，c ik ) Representing a data provider w i E W selects completion task t k Given completion cost value c for unit task ik . The platform then uses the assessment model to pick the appropriate winning data provider, further providing the data quality. The utility of the available data providers is:the data provider can select an interested task set according to the existing conditions of the data provider, can select whether a parking lot has a task of a position or not, and can also select whether a bus arrives at a station or not;
4. the platform calculates the evaluation probability between the task requester and the data provider by using an evaluation model according to the information of the two, and then calculates a winning set R of the task requester by using a selection algorithm, a matching and a pricing algorithm w Winning set of data provider W w A winning task requester fee set Q, a winning data provider reward set P. After matching each winning data provider to winning task requester is completed, the utility of the platform is: The evaluation model mainly comprises an intrinsic factor and an extrinsic factor, and is specifically described as follows:
(1) External factors: the method refers to the influence of the external conditions of the current data provider on the accuracy of the current data provider in completing tasks, so that two indexes of the liveness and the distance similarity of the user are used for measurement.
(1) User liveness
The time of the last participation of the user in the task can be obtained through the extracted historical data of the data provider, and the liveness of the data provider can be obtained through calculating the distance between the task release time and the time of the last participation of the data provider in the task. The calculation formula of the liveness of the data provider is as follows:
wherein, act i Is the user activity, beta is the decay coefficient of the user activity, t now Is the task release time, t pre The time of the last participation of the task from the task release time data provider is set to be 1 if the user is in a state of not participating in the task for a long time.
(2) Distance similarity
The location of the data provider and the distance between the data acquisition sites in the perception task are also one of the performance metrics of the data provider when participating in the perception task. By extracting the current data of the data provider, the cosine similarity can be used to calculate the distance similarity between the two, and the calculation formula is as follows:
Wherein, (x) i ，y i ，z i ) Is the location coordinates of the data provider, (x) i ，y i ，z i ) Is the position coordinates of the task requester, dist i，j The larger the value, the closer the distance between the two。dist i，j The closer the value is to 1, the closer the two places are; dist (dist) i，j The closer the value is to-1, the farther the two sites are indicated. Assuming that the task requester issues a task of whether the parking lot at a specified location has a location, and the distance similarity is a distance between the location and the current location of the data provider;
(3) probability model
Assuming that the influence of the extrinsic conditions of each data provider on the accuracy of its completion of a task is not influenced by other data providers, we choose the probability of influence of using a logistic function as an extrinsic factor on the accuracy of the completion of a task, the calculation formula is as follows:wherein r is ij Is the influence of two indexes of liveness and distance similarity of a data provider on the accuracy of distinguishing problems, and r ij The larger the value of (c), the higher the accuracy of completing the task.
(2) Intrinsic factors: the prior references only consider the capability of the data provider itself, namely the quality of completing the task, and do not consider the relationship between the capability of the data provider itself and the task difficulty. It can be assumed that the quality of the known user answer task obeys a gaussian distribution, i.e. user w i The quality of the E W completion task is modeled as a random variable Q i Obeying gaussian distributionWhile the difficulty of the task obeys a gaussian distribution, i.eLet delta ij ＝Q i -ψ j Representing user w i Quality of E W completing task and task t j The difference between the difficulties is based on the above two assumptions, that +.>
(3) Evaluating probability
For example, when a task of whether a parking lot has a position is completed, the data provider still has the following functions because the task is classified into two typesIs correct. Therefore, we model the probability of the user completing the task using a sigmoidal function, the functional expression is as follows:
wherein P (delta) ij ) Representing the probability that the data provider correctly answers the question.
5. The winning data provider uploads the data to the platform after completing the task;
6. the winning task requester pays the fee of the task;
7. the winning data provider receives consideration from the corresponding winning task requester.
2. Algorithm design
In a crowd-sourced system, the platform acts as an auctioneer that can select winners among data providers and task requesters and match each winning data provider with a winning task requester. To realize the whole process, the designed bidirectional auction mechanism mainly comprises three parts: evaluation algorithms, selection algorithms, matching and pricing algorithms.
1. Selection algorithm
Input: a task requester set R, a data provider set W;
and (3) outputting: task requester candidate set R s Data provider candidate set W s 。
Step 1: task requester candidate set R s Data provider candidate set W s All values are assigned as empty sets;
step 2: cycling when the task requester set R is not an empty set;
(1) FirstIterative selection of task requester set RR of maximum value j As candidate users;
then all choices r j Data provider w for providing tasks i And it isIs set S of components of (a) j If S is j Null, i.e. no data provider selection r j Will r j Removing from the set R; otherwise will r j And candidate user set S j Respectively joining task requester candidate sets R s Data provider candidate set W s And r is as follows j Is removed from the collection R.
Step 3: ending the loop when the task requester set R is an empty set, and outputting a final task requester candidate set R s Data provider candidate set W s 。
2. Evaluation algorithm
Input: task requester candidate set R s Data provider candidate set W s ；
And (3) outputting: the set of probabilities S is evaluated.
Step 1: all the evaluation probability sets are assigned as empty sets;
Step 2: for task requester candidate set R s Task requester r in j Iterating according to the sequence loop in the set;
first, r is calculated j Corresponding set S j Then set S j W of (3) i Sequentially performing loop iteration;
then, w is mined according to the user log i Current user information, and the user activity level is obtainedDistance similarity->Second, according to the two information, external factors are obtained for the data provider w i Influence of accuracy of completing the task->
According to the data provider w i The quality of the completed task is modeled as a random variable Q i Obeying gaussian distributionThe difficulty of the task obeys the Gaussian distribution, namely +.>And (3) obtaining a final evaluation probability:
will be set S j W of (3) i Is (a) estimated probability P (Δ) ij ) Adding a set S;
set S j Finishing the set S after all users have traversed j Is a cycle of (2);
step 3: finally traversing task requester candidate set R s All task requesters r in (1) j Ending the set R s Is output, set S.
3. Matching and pricing algorithm
Input: task requester candidate set R s Data provider candidate set W s Evaluating a probability set S;
and (3) outputting: task requester winning set R w Winning set of data provider W w A winning task requester fee set Q, a winning data provider reward set P.
Step 1: task requester winning set R w Winning set of data provider W w A winning task requester fee set Q, a winning data provider reward setP is all assigned as an empty set;
step 2: solving for task requester candidate set R s In (a)Minimum task requester r th And from set R s Removing the materials;
step 3: grouping task requester candidates R s According toThe sizes of (2) are arranged from big to small, namely +.>
Step 4: for the set R arranged in sequence s Traversing in a sequential cycle;
(1) First, find the set R s Task requester r in j Cost q of (2) j ；
(2) Then willCorresponding set S j Will be set S j W of (3) i According to->Sequentially performing loop iteration from small to large, namely;
(3) In the ordered set S j Find out the meeting conditionData provider w of (a) i ；
(4) If it is k= |s j I, then represent set S j W of (3) i All satisfy the conditions according to the ordered set S j Sequentially calculating w i Is paid by (a)And will aggregate S j All w of (3) i P of (2) ij Put into a set P, a set S j All w of (3) i Put all into a collection W w 、r j Put into collection R w ；
(5) If 0 < k < |S j I, then represent set S j W of (3) i Partially meets the condition, according to the ordered set S j Sequentially calculating w i Is paid by (a)And will aggregate S j W of all meeting the conditions i P of (2) ij Put into a set P, a set S j W of all meeting the conditions i Put into a collection W w 、r j Put into collection R w ；
(6) If k=0, then set S is represented j W of (3) i Neither satisfies the condition, and the next task requester r is traversed according to the ordering order j ；
Step 5: end set R s Output of the cycle of task requester winning set R w Winning set of data provider W w A winning task requester fee set Q, a winning data provider reward set P.
3. The key point and the point to be protected of the invention
(1) The proposed evaluation algorithm considers the relationship between task difficulty and perceptibility;
(2) The proposed bi-directional auction model considers the preferences of the data provider, allowing the data provider to select multiple tasks, and in turn proposes a pick algorithm, a match and a pricing algorithm.
4. Advantages of the invention
(1) The proposed incentive mechanism based on the two-way auction model is more suitable for the actual application scene with a plurality of task requesters.
(2) And the relation between task difficulty and perception capability is considered, so that task allocation is more accurate, and satisfaction is improved.
5. Alternative solution
The assessment algorithm is used in more models, not limited to the two-way auction model.
According to the invention, the evaluation algorithm can be used in different models, and the measurement indexes of the intrinsic factors and the extrinsic factors in the evaluation algorithm can be modified according to different use scenes, so that the satisfaction degree of the participants is improved.
The effects of the present invention are further illustrated by the following simulation experiments.
1. Simulation parameter setting
The invention adopts a mode of combining a real data set Gowalla and data simulation, wherein external factors of a data provider are mined from the real data set, and the rest parameters are set as follows: the number of task requesters is 50, the number of data providers is 100, the value range of the task value is [0,100], the value range of the bid value of the data providers is [0,10], the value ranges of the average difficulty and the distinction degree of the task are [0,4], and the user liveness coefficient is set to 0.5.
2. Simulation content and result analysis
From fig. 4, it can be seen that the utility of the data provider is non-negative, with dark colors representing the bid value of the data provider and light colors representing the reward of the data provider, with the simulation results being drawn randomly once.
It can be seen from fig. 5 that the utility of the task requester is non-negative, with the light color representing the task value of the task requester and the dark color representing the fee paid by the task requester. 7
The authenticity of the seller can be demonstrated from fig. 6.
The authenticity of the buyer can be demonstrated from fig. 7.
In the above embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When used in whole or in part, is implemented in the form of a computer program product comprising one or more computer instructions. When loaded or executed on a computer, produces a flow or function in accordance with embodiments of the present invention, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by a wired (e.g., coaxial cable, fiber optic, digital Subscriber Line (DSL), or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains an integration of one or more available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid State Disk (SSD)), etc.
The foregoing is merely illustrative of specific embodiments of the present invention, and the scope of the invention is not limited thereto, but any modifications, equivalents, improvements and alternatives falling within the spirit and principles of the present invention will be apparent to those skilled in the art within the scope of the present invention.
Claims (6)
1. The incentive method based on the bidirectional auction model in the crowd sensing is characterized by comprising the following steps of: in a crowd sensing system, a set R= { R consisting of one platform and M task requesters 1 ,r 2 ,r 3 ，…r M Set w= { W of N data providers 1 w 2 ，w 3 ，...w N -a }; in the crowd sensing system, a task requester r j Only one task can be submitted to the platform and each data provider w i Providing a set of tasks of interest to themselves and their corresponding bids, task requester r j The provided task constitutes the set t= { T 1 ，t 2 ，t 3 ，...t M -a }; wherein, the crowd sensing system adopts a bidirectional beatSelling a model;
the motivation method based on the bidirectional auction model in crowd sensing comprises the following steps:
step one, a task requester set R submits respective type information Feeding a platform; wherein the type information includes maximum task value +.>
Step two, the platform distributes the task distributed by the received task requester to the data provider;
step three, after the data provider receives the task issued by the platform, selecting the task of interest, submitting the type information to the platform, and selecting the proper winning data provider;
step four, the platform calculates the evaluation probability between the task requester and the data provider by using an evaluation model according to the information of the two parties, and then obtains each data set;
step five, the winning data provider uploads the data to the platform after completing the task;
step six, the winning task requester pays the cost of the task;
step seven, the winning data provider receives the rewards from the corresponding winning task requester;
in the fourth step, the platform calculates the evaluation probability between the task requester and the data provider by using an evaluation model according to the information of both the task requester and the data provider, and then obtains each data set, including:
the platform calculates the evaluation probability between the task requester and the data provider by using an evaluation model according to the information of the two parties, and then calculates a winning set R of the task requester by using a selection algorithm, a matching and a pricing algorithm w Winning set of data provider W w A winning task requester fee set Q, a winning data provider reward set P; matching each winning data provider with winning task requesterAfter completion, the utility of the platform is:
q j representing the set R s Task requester r in j Is a cost of (2);
p ij representing a data provider W i Completion task requester r j Rewards obtained by the provided tasks;
the evaluation model mainly comprises an intrinsic factor and an extrinsic factor, and comprises the following components:
(1) External factors: the method refers to the influence of the external conditions of the current data provider on the accuracy of completing tasks, and the method uses two indexes of the liveness and the distance similarity of the user to measure, and comprises the following steps:
(1) user liveness
Obtaining the time of the last time of the user to participate in the task through the extracted historical data of the data provider, and obtaining the liveness of the data provider through calculating the distance between the task release time and the last time of the data provider to participate in the task; the calculation formula is as follows:
wherein, act i Is the user activity, beta is the decay coefficient of the user activity, t now Is the task release time, t pre The time of the last participation of the task from the task release time data provider is set to be 1 if the user is in a state of not participating in the task for a long time;
(2) Distance similarity
The data provider is used for selecting one of the performance indexes of the data provider when participating in the perception task, wherein the distance between the position of the data provider and the data acquisition place in the perception task is also one of the performance indexes of the data provider; by extracting the current data of the data provider, the cosine similarity can be used to calculate the distance similarity between the two, and the calculation formula is as follows:
wherein, (x) i ，y i ，z i ) Is the location coordinates of the data provider, (x) j ，y j ，z j ) Is the position coordinates of the task requester, dist i，j The larger the value, the closer the distance between the two is; dist (dist) i，j The closer the value is to 1, the closer the two places are; dist (dist) i，j The closer the value is to-1, the farther the two sites are;
(3) probability model
Assuming that the external condition of each data provider affects the accuracy of completing the task, and is not affected by other data providers, the probability of affecting the accuracy of completing the task by using the logistic function as an external factor is selected, and the calculation formula is as follows:
wherein r is ij Is the influence of two indexes of liveness and distance similarity of a data provider on the accuracy of distinguishing problems, and r ij The larger the value of (2), the higher the accuracy of completing the task;
(2) Intrinsic factors: is the quality of the completion task, which is the ability of the data provider itself, given that the quality of the user's response to the task is known to follow a gaussian distribution, user w i The quality of the E W completion task is modeled as a random variable Q i Obeying gaussian distributionWhereas the difficulty of the task obeys a gaussian distribution, i.e +.>Let delta ij ＝Q i -ψ j Representing user w i Quality of E W completing taskBusiness t j The difference between the difficulties is based on the two assumptions, and can be obtained
(3) Evaluating probability
Since the task is two-class, the data provider still hasIs correct; thus, modeling the probability of a user completing a task using a sigmoidal function, the functional expression of the probability of a data provider correctly answering a question is as follows:
the evaluation model comprises:
input: task requester candidate set R s Data provider candidate set W s ；
And (3) outputting: evaluating a probability set S;
step 1: all the evaluation probability sets are assigned as empty sets;
step 2: for task requester candidate set R s Task requester r in j Iterating in a sequential loop in the set, comprising:
(1) Find r j Corresponding set S j Then set S j W of (3) i Sequentially performing loop iteration;
(2) Mining w from user logs i Current user information, and the user activity level is obtainedDistance similarity->Second, according to the two information, external factors are obtained for the data provider w i Influence of accuracy of completing the task->
(3) According to the data provider w i The quality of the completed task is modeled as a random variable Q i Obeying gaussian distributionThe difficulty of the task obeys the Gaussian distribution, namely +.>And (3) obtaining a final evaluation probability:
(4) Will be set S j W of (3) i Is (a) estimated probability P (Δ) ij ) Adding a set S;
(5) Set S j Finishing the set S after all users have traversed j Is a cycle of (2);
step 3: finally traversing task requester candidate set R s All task requesters r in (1) j Ending the set R s Is output, set S.
2. The method of claim 1, wherein in step one, each task requester submits own type information during an initial period of the auctionFor the platform->Respectively representing submitted tasks and their maximum task value,/->Respectively corresponding to the average difficulty and the distinguishing degree of the task, |t j The I represents the number of unit tasks that need to be completed;
assuming that the difficulty of a known task follows a gaussian distribution, i.e. task t j Will be modeled as a random variable ψ j Obeying gaussian distributionWherein mu j For average difficulty->Is a differentiation index; mu (mu) j The larger the task difficulty is, the lower the accuracy is; / >The larger the task difficulty is, the more dispersed the overall difference distinction is, and the utility of the task requester is as follows:
3. the method of incentive based on a two-way auction model in crowd-sourcing perception of claim 2, wherein in step three, the data provider, upon receiving the tasks issued by the platform, selects the task of interest, submits the type information to the platform, and picks out the appropriate winning data provider, comprising:
after the data provider receives the tasks issued by the platform, the data provider selects the task of interest, each w i E W will type informationSubmitting the data to a platform; wherein (t) k ，c ik ) Representing a data provider w i E W selects completion task t k Given completion cost value c for unit task ik The method comprises the steps of carrying out a first treatment on the surface of the The platform then uses the assessment model to pick the appropriate winning data provider, providing the data quality, the utility of which is:
4. the method of two-way auction model-based incentive in crowd-sourced perception of claim 3 wherein the selection algorithm comprises:
input: a task requester set R, a data provider set W;
and (3) outputting: task requester candidate set R s Data provider candidate set W s ；
Step 1: task requester candidate set R s Data provider candidate set W s All values are assigned as empty sets;
step 2: cycling when the set of task requesters R is not an empty set, including:
(1) Iterative selection of task requester set RR of maximum value j As candidate users;
(2) All choices r j Data provider w for providing tasks i And it isIs set S of components of (a) j If S is j Null, i.e. no data provider selection r j Will r j Removing from the set R; otherwise will r j And candidate user set S j Respectively joining task requester candidate sets R s Data provider candidate set W s And r is as follows j Removing from the set R;
step 3: ending the loop when the task requester set R is an empty set, and outputting a final task requester candidate set R s Data provider candidate set W s 。
5. The method of incentive based on a bi-directional auction model in crowd-sourcing of claim 4 wherein the matching and pricing algorithm comprises:
input: task requester candidate set R s Data provider candidate set W s Evaluating a probability set S;
and (3) outputting: task requester winning set R w Winning set of data provider W w A winning task requester fee set Q, a winning data provider reward set P;
Step 1: task requester winning set R w Winning set of data provider W w All of the winning task requester fee set Q, winning data provider reward set P are assigned as empty sets;
step 2: solving for task requester candidate set R s In (a)Minimum task requester r th And from set R s Removing the materials;
step 3: grouping task requester candidates R s According toIs arranged from large to small, i.e
Step 4: for the set R arranged in sequence s A sequential loop traversal comprising:
(1) First, find the set R s Task requester r in j Cost q of (2) j ；
(2) Then willCorresponding set S j Will be set S j W of (3) i According to->Sequentially looping iteration from small to large, execute toThe following steps (3) - (6);
(3) In the ordered set S j Find out the meeting conditionData provider w of (a) i ；
(4) If it is k= |s j I, then represent set S j W of (3) i All satisfy the conditions according to the ordered set S j Sequentially calculating w i Is paid by (a)And will aggregate S j All w of (3) i P of (2) ij Put into a set P, a set S j All w of (3) i Put all into a collection W w 、r j Put into collection R w ；
(5) If 0 < k < |S j I, then represent set S j W of (3) i Partially meets the condition, according to the ordered set S j Sequentially calculating w i Is paid by (a)And will aggregate S j W of all meeting the conditions i P of (2) ij Put into a set P, a set S j W of all meeting the conditions i Put into a collection W w 、r j Put into collection R w ；
(6) If k=0, then set S is represented j W of (3) i Neither satisfies the condition, and the next task requester r is traversed according to the ordering order j ；
Step 5: end set R s Output of the cycle of task requester winning set R w Winning set of data provider W w A winning task requester fee set Q, a winning data provider reward set P.
6. A two-way auction model-based incentive system in crowd-sourced awareness for implementing the two-way auction model-based incentive method in crowd-sourced awareness according to any one of claims 1 to 5, the two-way auction model-based incentive system in crowd-sourced awareness comprising:
a type information submitting module for submitting respective type information via the task requester set RFeeding a platform; wherein the type information includes maximum task value +.>
The task issuing module is used for issuing the task issued by the received task requester to the data provider through the platform;
the winning data provider selecting module is used for selecting an interested task, submitting type information to the platform and selecting a proper winning data provider after the winning data provider receives the task issued by the platform;
The data set solving module is used for calculating the evaluation probability between the task requester and the data provider by using the evaluation model according to the information of the task requester and the data provider through the platform, and then solving each data set;
the data uploading module is used for uploading data to the platform after completing the task through a winning data provider;
the task fee payment module is used for paying the fee of the task through a winning task requester;
and the reward receiving module is used for receiving rewards from corresponding winning task requesters through winning data providers.
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