CN112907340A - Bidirectional auction model-based incentive method and system in crowd-sourcing perception - Google Patents
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Abstract
The invention belongs to the technical field of crowd sensing and discloses an incentive method and an incentive system based on a two-way auction model in crowd sensing1，r2，r3，...rMW ═ W, a set of N data providers1，w2，w3，...wN}; in the crowd-sourcing perception system, assume a task requester rjOnly one task can be submitted to the platform and each data provider wiCan provide a set of interested tasks and corresponding bids, task requesters rjThe provided task composition set T＝{t1，t2，t3，...tM}; wherein, a bidirectional auction model is adopted in the crowd-sourcing perception system. The invention provides a bidirectional auction-based moduleThe type of incentive mechanism is centered on users, adopts a two-way auction model, and is more suitable for actual application scenes with a plurality of task requesters; and the relation between the task difficulty and the perception capability is considered, so that the task distribution 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-sourcing perception refers to a new perception network that combines the mobile perception and social computing features of intelligent devices with the idea of "crowdsourcing". One key issue in crowd sensing services is the design of incentive mechanisms to increase the service aggressiveness of data providers so that data providers can provide sensing services. Most of the existing incentive models assume that only one task requester exists, neglects the interaction behavior between the perception task requester and the data provider, and are difficult to meet the application scenario with a plurality of task requesters. Firstly, researching an incentive mechanism of crowd sensing and constructing a multi-requestor and multi-provider bidirectional auction model; secondly, the existing research only considers the influence of the task perception capability of the data provider on the task result, but does not consider the relation between the task difficulty and the perception capability, so that the task difficulty provided by the task requester in the two-way auction model and the task perception capability of the data provider are respectively modeled, the internal factors and the external factors influencing the data provider are comprehensively considered, the relation between the quality of data provided by the data provider and the task difficulty is considered by the internal factors, the measure of the external factors uses two indexes of the liveness and the distance similarity of the user, and the comprehensive score of the data provider is finally obtained; and finally, analyzing the effectiveness of the incentive method from four aspects of calculation effectiveness, personal rationality, budget balance and authenticity.
The most used are reward payment incentives based on game theory, which are mostly designed with a user or platform as the center. Most classically, two excitation models were designed: the platform-centric incentive model employs the Stainberg game and the user-centric incentive model employs the 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; the technical scheme of the reverse auction model is used, the user quality is mostly modeled into Gaussian distribution, and the evolution process of the mean value and the variance of the user quality variable distribution along with the increase of the turns is deduced; in the prior art, the relationship 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 turns is deduced.
Through the above analysis, the problems and defects of the prior art are as follows:
(1) the existing incentive models mostly assume that only one task requester exists, neglect to sense the interaction behavior between the task requester and the data provider, and are difficult to meet the application scenario with a plurality of task requesters.
(2) The existing research only considers the influence of the task sensing ability of a data provider on a task result, and does not consider the relation between task difficulty and sensing ability.
The difficulty in solving the above problems and defects is:
the existing work cannot integrate the relationship between task difficulty and perception capability of a data provider to build a model while ensuring four properties of calculation effectiveness, personal reasonability, budget balance and authenticity of the two-way auction.
The significance of solving the problems and the defects is as follows:
therefore, the invention provides an incentive method based on a two-way auction model, the two models are respectively established for the task perception capability and the task difficulty of the data provider, the relationship between the two models is fully considered, the satisfaction degree of the task requester and the data provider is improved, and the calculation effectiveness, personal reasonability, budget balance and authenticity of the two-way auction are ensured.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides an incentive method and an incentive system based on a two-way auction model in crowd sensing.
The invention is realized in such a way that an excitation method based on a two-way auction model in crowd-sourcing perception comprises the following steps: in the crowd sensing system, a set R ═ R composed of a platform and M task requesters1，r2，r3，...rMW ═ W, a set of N data providers1，w2，w3，...wN}; in the crowd-sourcing perception system, assume a task requester rjOnly one task can be submitted to the platform and each data provider wiCan provide a set of interested tasks and corresponding bids, task requesters rjThe provided task composition set T ═ T { [ T } { (T } is1，t2，t3，...tM}; wherein, a bidirectional auction model is adopted in the crowd-sourcing perception system.
Further, the incentive method and system based on the two-way auction model in the crowd-sourcing perception comprises the following steps:
step one, a task requester set R submits respective type information
Feeding the platform; wherein the type information includes a maximum task value
Etc.; the task requester submits the task information provided by the task requester to the platform so as to ensure the integrity of the task information and facilitate the selection of a data provider;
step two, the platform issues the received tasks issued by the task requester to the data provider; the platform is responsible for arranging the received task sets so as to ensure the comprehensiveness of task release;
step three, after receiving the tasks issued by the platform, the data providers select the interested tasks, submit the type information to the platform, and select the appropriate winning data provider; consider a collection of interest of data providers to facilitate long-term participation by the data providers;
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 task requester and the data provider, and then calculates 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 task fee; for ensuring that the utility of the task requester is greater than 0;
and step seven, the winning data provider receives the reward from the corresponding winning task requester to ensure that the utility of the data provider is greater than 0.
Further, in step one, each task requester will submit its own type information during the initial stage of the auction
For the platform, the platform is provided with a plurality of air holes,
respectively representing the submitted task and its value,
corresponding to the average difficulty and discrimination of the task, | tjI represents the number of unit tasks needing to be completed;
assuming that the difficulty of the known tasks obeys a Gaussian distribution, i.e. task tjIs modeled as a random variable psijObeying a Gaussian distribution
Wherein mujIn order to average the difficulty level,
dividing into a distinction degree; mu.sjThe larger the task is, the greater the task difficulty is, and the lower the accuracy is;
the larger the task, the more dispersed the task difficulty, the more obvious the overall difference distinction, and the utility of the service requester is:
further, in the third step, after receiving the tasks issued by the platform, the data providers select the interested tasks, submit the type information to the platform, and select the appropriate winning data provider, including:
after receiving the tasks released by the platform, the data provider selects the interested tasks, each wiE.g. W will be type information
Submitting to a platform; wherein (t)k，cik) Representing data providers wiE.g. W selects to complete task tkGiven completion cost value c for a unit taskik(ii) a The platform then uses the assessment model to pick the appropriate winning data provider, providing data quality, the data provider's utility being:
further, in 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 task requester and the data provider, and then calculates each data set, including:
the platform calculates an evaluation summary between the task requester and the data provider by using an evaluation model according to the information of the task requester and the data providerRate, and then using a picking algorithm, a matching and pricing algorithm to find a winning set R of task requesterswWinning set of data providers WwA winning task requester fee set Q and a winning data provider reward set P; after each winning data provider and winning task requester are matched, the utility of the platform is:
further, the evaluation model mainly comprises two parts, namely an internal factor and an external factor, and comprises the following steps:
(1) extrinsic factors: the method is characterized in that the influence of external conditions of a current data provider on the accuracy of task completion of the current data provider is measured by using two indexes of liveness and distance similarity of a user.
(2) Intrinsic factors: the quality of the task is the capability of the data provider, namely the quality of the task, and the quality of the task answered by the known user is assumed to be in accordance with Gaussian distribution, namely the user wiThe quality of the task completed by the epsilon W can be modeled as a random variable QiObeying a Gaussian distribution
While the difficulty of the task follows a Gaussian distribution, i.e.
Let Delta beij＝Qi-ψjRepresents user wiQuality of completing task by belonging to W and task tjThe difference between the difficulties, based on the two assumptions, is obtained
(3) Evaluating probabilities
Since the task is classified into two categories, the data provider still has
Is answered correctly with probability. Therefore, the probability of the user completing the task is modeled using a sigmoidal function, which is expressed as follows:
wherein, P (Delta)ij) Representing the probability of the data provider answering the question correctly.
Further, the extrinsic factors include:
activeness of user
The time of the user participating in the task last time can be obtained through the extracted historical data of the data provider, and the activity of the data provider can be obtained through calculating the distance between the task issuing time and the time of the data provider participating in the task last time. The formula for calculating the liveness of the data provider is as follows:
wherein, ActiIs the activity of the user, beta is the attenuation coefficient of the user activity, tnowIs the task release time, tpreIs the time of last participation in the task from the task distribution time data provider, and if the user is in a state of not participating in the task for a long time, the value of beta is set to 1.
Distance similarity
When the data provider participates in the perception task, 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 for selecting the data provider. By extracting the current data of the data provider, the distance similarity between the two can be calculated by using the cosine similarity, and the calculation formula is as follows:
wherein (x)i，yi，zi) Is thatLocation coordinates of data provider, (x)i，yi，zi) Is the location coordinate, dist, of the task requesteri，jThe larger the value, the closer the distance between the two is; disti，jThe closer the value is to 1, the closer the two locations are; disti，jThe closer the value is to-1, the farther away the two locations are illustrated.
Probability model
Assuming that the influence of the extrinsic condition of each data provider on the accuracy of task completion of each data provider is not influenced by other data providers, the influence probability of the extrinsic factor on the accuracy of task completion is selected and used as a logistic function, and the calculation formula is as follows:
wherein r isijIs the influence on the precision of the discrimination problem of a data provider under the influence of two indexes of liveness and distance similarity, rijThe larger the value of (c), the more accurate the task is completed.
Further, the selection algorithm includes:
inputting: a task requester set R and a data provider set W;
and (3) outputting: task requester candidate set RsData provider candidate set Ws。
Step 1: task requester candidate set RsData provider candidate set WsAll values are assigned as empty sets;
step 2: when the task requester set R is not an empty set, circulating;
and step 3: ending the circulation when the task requester set R is an empty set, and outputting the finally obtained task requester candidate set RsData provider candidate set Ws。
Further, in step 2, performing a loop when the task requester set R is not an empty set, including:
(1) iteratively choosing task requester in set R
R having the largest valuejAs candidate users;
(2) all are selected rjData provider w providing tasksiAnd it is
Is composed ofjIf is SjNull means no data provider selection rjThen r will bejRemoving from the set R; otherwise will rjAnd a candidate user set SjSeparately joining task requester candidate set RsData provider candidate set WsAnd r isjExcluded from the set R.
Further, the evaluation algorithm includes:
inputting: task requester candidate set RsData provider candidate set Ws；
And (3) outputting: a set of probabilities S is evaluated.
Step 1: assigning all the evaluation probability sets to be empty sets;
step 2: for task requester candidate set RsMiddle task requester rjCircularly iterating according to the sequence in the set;
and step 3: finally, the task requester candidate set R is traversedsAll task requesters r injThen the set R is endedsAnd (4) outputting the set S.
Further, in step 2, the task requester candidate set RsMiddle task requester rjAnd circularly iterating according to the sequence in the set, wherein the iteration comprises the following steps:
(1) first, r is obtainedjCorresponding set SjThen set SjW iniCircularly iterating according to the sequence;
(2) then dig out w from the user logiThe current user information is used for calculating the user activity
Distance similarity
Secondly, deriving the extrinsic factor pair data provider w according to the two informationiEffect of accuracy of completed task
(3) According to the data provider wiThe quality of the completed task is modeled as a random variable QiObeying a Gaussian distribution
The difficulty of the task obeys a Gaussian distribution
And (5) obtaining the final evaluation probability:
(4) will gather SjW iniIs estimated to be a probability P (Δ)ij) Adding the set S;
(5) set SjFinishing the set S when all the users in the set traversejThe cycle of (2).
Further, the matching and pricing algorithm includes:
inputting: task requester candidate set RsData provider candidate set WsEvaluating a probability set S;
and (3) outputting: task requester winning set RwWinning set of data providers WwA set of winning task requester fees Q, a set of winning data provider rewards P.
Step 1: task requester winning set RwWinning set of data providers WwThe winning task requester fee set Q and the winning data provider reward set P are all assigned as empty sets；
Step 2: solving a task requester candidate set RsIn
Minimum task requester rthAnd from the set RsRemoving the waste residues;
and step 3: task requester candidate set RsAccording to
Is arranged from large to small, i.e.
And 4, step 4: for the orderly arranged sets RsCircularly traversing according to the sequence;
and 5: end set RsLoop of outputting the winning set R of task requesterswWinning set of data providers WwA set of winning task requester fees Q, a set of winning data provider rewards P.
Further, in step 4, the pairs are arranged in sequence to form a set RsAnd circularly traversing according to the sequence, comprising:
(1) first, a set R is obtainedsMiddle task requester rjCost q ofj；
(2) Then will be
Corresponding set SjWill aggregate SjW iniAccording to
Iteration is circularly performed in a sequence from small to large, namely;
(3) in a sorted set SjTo find out the satisfying condition
Data provider w ofi；
(4) If k is ═ SjIf is then set SjW iniAll satisfy the condition, according to the sorted set SjCalculate w sequentiallyiIs paid
And will gather SjAll of wiP of (a)ijPut into set P, set SjAll of wiAll put into the set Ww、rjPut into the set Rw；
(5) If k is 0 < k < | SjIf is then set SjW iniPartially satisfying the condition, according to the sorted set SjCalculate w sequentiallyiIs paid
And will gather SjAll of w satisfying the conditioniP of (a)ijPut into set P, set SjAll of w satisfying the conditioniPut into the set Ww、rjPut into the set Rw；
(6) If k is 0, it represents the set SjW iniAll the task requesters do not meet the condition, and the next task requester r starts to be traversed according to the sequencing sequencej。
Another object of the present invention is to provide a system for stimulating a bi-directional auction model in crowd sensing using the method for stimulating a bi-directional auction model in crowd sensing, the system comprising:
a type information submitting module for submitting respective type information through the task requester set R
Feeding the platform; wherein the type information includes a maximum task value
The task issuing module is used for issuing the received tasks issued by the task requester to the data provider through the platform;
the winning data provider selecting module is used for selecting the interested tasks after the data providers receive the tasks issued by the platform, submitting the type information to the platform and selecting the proper winning data provider;
the data set solving module is used for calculating the evaluation probability between the task requester and the data provider by using an 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 the data to the platform after the tasks are completed by the winning data providers;
the task fee payment module is used for paying the fee of the task through the winning task requester;
and the reward receiving module is used for receiving the reward from the corresponding winning task requester through the winning data provider.
It is another object of the present invention to provide a computer program product stored on a computer readable medium, comprising a computer readable program for providing a user input interface to implement said bi-directional auction model based incentive method in crowd sensing when executed on an electronic device.
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 bi-directional auction model-based incentive method in crowd sensing.
By combining all the technical schemes, the invention has the advantages and positive effects that: the incentive method based on the two-way auction model in the crowd sensing provided by the invention uses the user as the center incentive model, and the two-way auction model is adopted because 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. The evaluation algorithm provided by the invention considers the relationship between task difficulty and perception capability; the proposed two-way auction model takes into account the preferences of the data provider, allows the data provider to select multiple tasks, and further proposes a picking algorithm, a matching and pricing algorithm. Meanwhile, the evaluation algorithm is applied to more models, and is not limited to the two-way auction model; the provided incentive 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 the task difficulty and the perception capability is considered, so that the task distribution is more accurate.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used 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 it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flow chart of an incentive method based on a bi-directional auction model in crowd sensing provided by an embodiment of the present invention.
Fig. 2 is a schematic diagram of an incentive method based on a bi-directional auction model in crowd sensing provided by the embodiment of the invention.
FIG. 3 is a block diagram of an incentive system based on a bi-directional auction model in crowd sensing according to an embodiment of the present invention;
in the figure: 1. a type information submission module; 2. a task issuing module; 3. a winning data provider selection 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 5 are analysis diagrams of simulation results of model budget balancing provided by the embodiment of the present invention.
Fig. 6 and 7 are graphs of simulation results analysis of the authenticity of the model provided by the example of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Aiming at the problems in the prior art, the invention provides an incentive method and an incentive system based on a two-way auction model in crowd-sourcing perception, and the invention is described in detail below with reference to the accompanying drawings.
As shown in fig. 1, the incentive method based on the bi-directional auction model in crowd sensing provided by the embodiment of the present invention includes the following steps:
s101, the task requester set R submits respective type information
Feeding the platform; wherein the type information includes a maximum task value
S102, the platform issues the received tasks issued by the task requester to a data provider;
s103, after receiving the tasks issued by the platform, the data providers select the interested tasks, submit the type information to the platform, and select the appropriate winning data providers;
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 task requester and the data provider, and then calculates each data set;
s105, the winning data provider uploads the data to the platform after completing the task;
s106, the winning task requester pays the task fee;
s107, the winning data provider receives a reward from the corresponding winning task requester.
A schematic diagram of an incentive method based on a bi-directional auction model in crowd sensing provided by the embodiment of the present invention is shown in fig. 2.
As shown in fig. 3, the incentive system based on the bi-directional auction model in crowd sensing provided by the embodiment of the present invention includes:
a type information submitting module 1 for submitting respective type information through the task requester set R
Feeding the platform; wherein the type information includes a maximum task value
The task issuing module 2 is used for issuing the received tasks issued by the task requester to the data provider through the platform;
the winning data provider selecting module 3 is used for selecting the interested tasks after the data providers receive the tasks issued by the platform, submitting the type information to the platform and selecting the proper winning data provider;
the data set solving module 4 is used for calculating the evaluation probability between the task requester and the data provider by using an 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 5 is used for uploading data to the platform after the tasks are completed by the winning data providers;
a task fee payment module 6 for paying the fee of the task through the winning task requester;
and the reward receiving module 7 is used for receiving the reward from the corresponding winning task requester through the winning data provider.
The technical solution of the present invention will be further described with reference to the following examples.
The invention mainly researches an incentive model taking a user as a center, and a 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, so that a bidirectional auction model is adopted.
First, system overview
In the crowd sensing system, there is onePlatform, set of M task requesters R ═ { R1，r2，r3，...rMW ═ W, a set of N data providers1，w2，w3，...wN}. In this system, assume a task requester rjOnly one task can be submitted to the platform and each data provider wiCan provide a set of interested tasks and corresponding bids, task requesters rjThe provided task composition set T ═ T { [ T } { (T } is1，t2，t3，...tM}. A two-way auction model is employed in the crowd sensing system of the present invention.
1. The task requester set R submits respective type information
For the platform, its type information includes the maximum task value
And the like. In the initial stage of auction, each task requester will submit its own type information
For the platform, the platform is provided with a plurality of air holes,
respectively representing the submitted task and its value,
corresponding to the average difficulty and discrimination of the task, | tjAnd | represents the number of unit tasks that need to be completed. The task types provided by each task requester are different, for example, a task of whether a parking lot has a position or not can be submitted, a task of whether a bus arrives at a station or not can also be submitted, the requirements of the two tasks are different, and different types of tasks can be distinguished through the submitted type information. It can be assumed that the difficulty of a known task obeys a Gaussian distribution, i.e., task tjCan be builtModulo a random variable psijObeying a Gaussian distribution
Wherein mujIn order to average the difficulty level,
is classified into a discrimination degree. Mu.sjThe larger the task is, the greater the task difficulty is, and the lower the accuracy is;
the larger the task, the more dispersed the task difficulty and the more obvious the overall difference distinction. Such as whether the parking lot has a position or not and whether the bus arrives at the station or not. The utility of the task requester can be derived as:
2. the platform issues the received tasks issued by the task requester to the data provider;
3. after receiving the tasks released by the platform, the data provider selects the tasks in which the data provider is interested, wherein each wiE.g. W will have its type information
Submitting to a platform, wherein (t)k，cik) Representing data providers wiE.g. W selects to complete task tkGiven completion cost value c for a unit taskik. The platform then uses the assessment model to pick the appropriate winning data provider, further providing data quality. The utility of the available data providers is:
the data provider can select an interested task set according to the existing conditions, can select whether a parking lot has a task of a position or not and can also select whether a bus arrives at the station or not;
4. the platform can be used according to the information of both the task requester and the data providerThe evaluation model calculates the evaluation probability between the two, and then uses the selection algorithm, the matching algorithm and the pricing algorithm to obtain the task requester winning set RwWinning set of data providers WwA set of winning task requester fees Q, a set of winning data provider rewards P. After each winning data provider and winning task requester are matched, the utility of the platform is:
the evaluation model mainly comprises an internal factor and an external factor, and is specifically introduced as follows:
(1) extrinsic factors: the method is characterized in that the influence of external conditions of a current data provider on the accuracy of task completion of the current data provider is achieved, and therefore two indexes of liveness and distance similarity of a user are used for measurement.
Activeness of user
The time of the user participating in the task last time can be obtained through the extracted historical data of the data provider, and the activity of the data provider can be obtained through calculating the distance between the task issuing time and the time of the data provider participating in the task last time. The formula for calculating the liveness of the data provider is as follows:
Distance similarity
When the data provider participates in the perception task, 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 for selecting the data provider. By extracting the current data of the data provider, the distance similarity between the two can be calculated by using the cosine similarity, and the calculation formula is as follows:
wherein (x)i，yi，zi) Is the location coordinate of the data provider, (x)i，yi，zi) Is the location coordinate, dist, of the task requesteri，jThe larger the value, the closer the distance between the two is. disti，jThe closer the value is to 1, the closer the two locations are; disti，jThe closer the value is to-1, the farther away the two locations are illustrated. Assuming that a task requester issues a task whether a parking lot at a specified location has a location, and the distance similarity is the distance between the location and the current location of the data provider;
probability model
Assuming that the influence of the extrinsic condition of each data provider on the accuracy of task completion of each data provider is not influenced by other data providers, we choose to use the logistic function as the influence probability of the extrinsic factor on the accuracy of task completion, and the calculation formula is as follows:
wherein r isijIs the influence on the precision of the discrimination problem of a data provider under the influence of two indexes of liveness and distance similarity, rijThe larger the value of (c), the more accurate the task is completed.
(2) Intrinsic factors: the data providers themselves have the capacity, namely the quality of completing the task, and the previous reference only considers the capacity of the data providers themselves and does not consider the relationship between the capacity of the data providers themselves and the task difficulty. It can be assumed that the quality of the known user response task follows a Gaussian distribution, i.e. user wiThe quality of the task completed by the epsilon W can be modeled as a random variable QiObeying a Gaussian distribution
While the difficulty of the task follows a Gaussian distribution, i.e.
Let Delta beij＝Qi-ψjRepresents user wiQuality of completing task by belonging to W and task tjThe difference between the difficulties, based on the above two assumptions, can be obtained
(3) Evaluating probabilities
For example, when a task of whether a parking lot has a position is completed, the data provider still has the task of whether the parking lot has a position because the task is classified into two categories
Is answered correctly. Therefore, we model the probability of the user completing the task using the sigmoidal function, which is expressed as follows:
wherein, P (Delta)ij) Representing the probability of the data provider answering the question correctly.
5. The winning data provider uploads the data to the platform after completing the task;
6. the winning task requester pays for the task;
7. the winning data provider receives a reward from the corresponding winning task requester.
Second, algorithm design
In the crowd sensing 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. In order 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
Inputting: a task requester set R and a data provider set W;
and (3) outputting: task requester candidate set RsData provider candidate set Ws。
Step 1: task requester candidate set RsData provider candidate set WsAll values are assigned as empty sets;
step 2: when the task requester set R is not an empty set, circulating;
(1) first, iteratively selecting task requester in set R
R having the largest valuejAs candidate users;
then all are selected rjData provider w providing tasksiAnd it is
Is composed ofjIf is SjNull means no data provider selection rjThen r will bejRemoving from the set R; otherwise will rjAnd a candidate user set SjSeparately joining task requester candidate set RsData provider candidate set WsAnd r isjExcluded from the set R.
And step 3: ending the circulation when the task requester set R is an empty set, and outputting the finally obtained task requester candidate set RsData provider candidate set Ws。
2. Evaluation algorithm
Inputting: task requester candidate set RsData provider candidate set Ws；
And (3) outputting: a set of probabilities S is evaluated.
Step 1: assigning all the evaluation probability sets to be empty sets;
step 2: for task requester candidate set RsMiddle task requester rjCircularly iterating according to the sequence in the set;
first, r is obtainedjCorresponding set SjThen set SjW iniCircularly iterating according to the sequence;
then dig out w from the user logiThe current user information is used for calculating the user activity
Distance similarity
Secondly, deriving the extrinsic factor pair data provider w according to the two informationiEffect of accuracy of completed task
According to the data provider wiThe quality of the completed task is modeled as a random variable QiObeying a Gaussian distribution
The difficulty of the task obeys a Gaussian distribution
And (5) obtaining the final evaluation probability:
will gather SjW iniIs estimated to be a probability P (Δ)ij) Adding the set S;
set SjFinishing the set S when all the users in the set traversejThe cycle of (2);
and step 3: finally, the task requester candidate set R is traversedsAll task requesters r injThen the set R is endedsAnd (4) outputting the set S.
3. Matching and pricing algorithms
Inputting: task requester candidate set RsData provider candidate set WsEvaluation summaryA rate set S;
and (3) outputting: task requester winning set RwWinning set of data providers WwA set of winning task requester fees Q, a set of winning data provider rewards P.
Step 1: task requester winning set RwWinning set of data providers WwThe winning task requester fee set Q and the winning data provider reward set P are all assigned as an empty set;
step 2: solving a task requester candidate set RsIn
Minimum task requester rthAnd from the set RsRemoving the waste residues;
and step 3: task requester candidate set RsAccording to
Is arranged from large to small, i.e.
And 4, step 4: for the orderly arranged sets RsCircularly traversing according to the sequence;
(1) first, a set R is obtainedsMiddle task requester rjCost q ofj；
(2) Then will be
Corresponding set SjWill aggregate SjW iniAccording to
Iteration is circularly performed in a sequence from small to large, namely;
(3) in a sorted set SjTo find out the satisfying condition
Data provider w ofi；
(4) If k is ═ SjIf is then set SjW iniAll satisfy the condition, according to the sorted set SjCalculate w sequentiallyiIs paid
And will gather SjAll of wiP of (a)ijPut into set P, set SjAll of wiAll put into the set Ww、rjPut into the set Rw；
(5) If k is 0 < k < | SjIf is then set SjW iniPartially satisfying the condition, according to the sorted set SjCalculate w sequentiallyiIs paid
And will gather SjAll of w satisfying the conditioniP of (a)ijPut into set P, set SjAll of w satisfying the conditioniPut into the set Ww、rjPut into the set Rw；
(6) If k is 0, it represents the set SjW iniAll the task requesters do not meet the condition, and the next task requester r starts to be traversed according to the sequencing sequencej；
And 5: end set RsLoop of outputting the winning set R of task requesterswWinning set of data providers WwA set of winning task requester fees Q, a set of winning data provider rewards P.
Thirdly, the key point and the point to be protected of the invention
(1) The proposed evaluation algorithm takes into account the relationship between task difficulty and perception capability;
(2) the proposed two-way auction model takes into account the preferences of the data provider, allows the data provider to select multiple tasks, and further proposes a picking algorithm, a matching and pricing algorithm.
Fourthly, the invention has the advantages of
(1) The proposed incentive mechanism based on the two-way auction model is more suitable for practical application scenarios with a plurality of task requesters.
(2) And the relation between the task difficulty and the perception capability is considered, so that the task allocation is more accurate, and the satisfaction is improved.
Fifth, alternative scheme
The evaluation algorithm is used in more models, not only in the two-way auction model.
The invention can use the evaluation algorithm in different models, and can modify the measurement indexes of the internal factors and the external factors in the evaluation algorithm according to different use scenes, thereby improving the satisfaction degree of participants.
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 other parameters are set as follows: the number of the task requesters is 50, the number of the data providers is 100, the value range of the task value is [0,100], the value range of the bid value of the data provider is [0,10], the value ranges of the average difficulty and the discrimination of the task are [0,4], and the user activity coefficient is 0.5.
2. Simulation content and result analysis
From the simulation results of random drawing once, it can be seen from fig. 4 that the utility of the data provider is non-negative, where the dark color represents the bid value of the data provider and the light color represents the reward of the data provider.
It can be seen from fig. 5 that the utility of the task requester is non-negative, where light colors represent the task value of the task requester and dark colors represent the fees paid by the task requester. 7
The authenticity of the seller can be demonstrated from fig. 6.
From fig. 7, the authenticity of the buyer can be demonstrated.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When used in whole or in part, can be implemented in a computer program product that includes one or more computer instructions. When loaded or executed on a computer, cause the flow or functions according to embodiments of the invention to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center via wire (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL), or wireless (e.g., infrared, wireless, microwave, etc.)). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that includes one or more of the 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)), among others.
The above description is only for the purpose of illustrating the present invention and the appended claims are not to be construed as limiting the scope of the invention, which is intended to cover all modifications, equivalents and improvements that are within the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. An excitation method based on a two-way auction model in crowd-sourcing perception is characterized by comprising the following steps: in the crowd sensing system, a set R ═ R composed of a platform and M task requesters1，r2，r3，...rMW ═ W, a set of N data providers1，w2，w3，...wN}; in the crowd-sourcing perception system, assume a task requester rjOnly one task can be submitted to the platform and each data provider wiCan provide a set of interested tasks and corresponding bids, task requesters rjThe provided task composition set T ═ T { [ T } { (T } is1，t2，t3，...tM}; wherein, a bidirectional auction model is adopted in the crowd-sourcing perception system.
2. The incentive method based on two-way auction model in crowd sensing as claimed in claim 1, wherein said incentive method and system based on two-way auction model in crowd sensing comprises the following steps:
step one, a task requester set R submits respective type information
Feeding the platform; wherein the type information includes a maximum task value
Step two, the platform issues the received tasks issued by the task requester to the data provider;
step three, after receiving the tasks issued by the platform, the data providers select the interested tasks, submit the type information to the platform, and select the appropriate 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 task requester and the data provider, and then calculates 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 task fee;
and step seven, the winning data provider receives the compensation from the corresponding winning task requester.
3. The motivation method for bi-directional auction model in crowd-sourcing perception according to claim 2, characterized in that in step one, each task requester will submit its own type information in the initial stage of auction
For the platform, the platform is provided with a plurality of air holes,
respectively representing the submitted task and its value,
corresponding to the average difficulty and discrimination of the task, | tjI represents the number of unit tasks needing to be completed;
assuming that the difficulty of the known tasks obeys a Gaussian distribution, i.e. task tjIs modeled as a random variable psijObeying a Gaussian distribution
Wherein mujIn order to average the difficulty level,
dividing into a distinction degree; mu.sjThe larger the task is, the greater the task difficulty is, and the lower the accuracy is;
the larger the task, the more dispersed the task difficulty, the more obvious the overall difference distinction, and the utility of the service requester is:
4. the motivation method for bi-directional auction model in crowd sensing as claimed in claim 2, wherein in step three, after receiving the tasks issued by the platform, the data providers select the tasks of interest, submit the type information to the platform, and select the appropriate winning data provider, comprising:
after receiving the tasks released by the platform, the data provider selects the interested tasks, each wiE.g. W will be type information
Submitting to a platform; wherein (t)k，cik) Representing data providers wiE.g. W selects to complete task tkGiven completion cost value c for a unit taskik(ii) a The platform then uses the assessment model to pick the appropriate winning data provider, providing data quality, the data provider's utility being:
5. the incentive method based on bi-directional auction model in crowd sensing according to claim 2, wherein in step four, the platform calculates the evaluation probability between the task requester and the data provider according to the information of both parties, using the evaluation model, and then finds each data set, comprising:
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 task requester and the data provider, and then calculates a winning set R of the task requester by using a selection algorithm, a matching algorithm and a pricing algorithmwWinning set of data providers WwA winning task requester fee set Q and a winning data provider reward set P; after each winning data provider and winning task requester are matched, the utility of the platform is:
6. the incentive method for bi-directional auction based on crowd sensing according to claim 5, wherein said evaluation model mainly comprises two parts of intrinsic factor and extrinsic factor, comprising:
(1) extrinsic factors: the method refers to the influence of the external conditions of the current data provider on the accuracy of the task completion of the current data provider, and measures by using two indexes of liveness and distance similarity of a user, and comprises the following steps:
activeness of user
The time of the last task participation of the user can be obtained through the extracted historical data of the data provider, and the activity of the data provider can be obtained through calculating the distance between the task issuing time and the time of the latest task participation of the data provider; the calculation formula is as follows:
wherein, ActiIs the activity of the user, beta is the attenuation coefficient of the user activity, tnowIs the task release time, tpreThe time is the time which is the latest time from a task release time data provider to participate in the task, and if the user is in a state of not participating in the task for a long time, the beta value is set to be 1;
distance similarity
When the data provider participates in the perception task, the distance between the position of the data provider and a data acquisition place in the perception task is also one of the performance indexes for selecting the data provider; by extracting the current data of the data provider, the distance similarity between the two can be calculated by using the cosine similarity, and the calculation formula is as follows:
wherein (x)i，yi，zi) Is the location coordinate of the data provider, (x)i，yi，zi) Is the location coordinate, dist, of the task requesteri，jThe larger the value, the closer the distance between the two is; disti，jThe closer the value is to 1, the closer the two locations are; disti，jThe closer the value is to-1, the farther away the two locations are illustrated;
probability model
Assuming that the influence of the extrinsic condition of each data provider on the accuracy of task completion of each data provider is not influenced by other data providers, selecting the influence probability of the logistic function as an extrinsic factor on the accuracy of task completion, and calculating the formula as follows:
wherein r isijIs the influence on the precision of the discrimination problem of a data provider under the influence of two indexes of liveness and distance similarity, rijThe greater the value of (d), the higher the accuracy of completing the task;
(2) intrinsic factors: the quality of the task is the capability of the data provider, namely the quality of the task, and the quality of the task answered by the known user is assumed to be in accordance with Gaussian distribution, namely the user wiThe quality of the task completed by the epsilon W can be modeled as a random variable QiObeying a Gaussian distribution
While the difficulty of the task follows a Gaussian distribution, i.e.
Let Delta beij＝Qi-ψjRepresents user wiQuality of completing task by belonging to W and task tjThe difference between the difficulties, based on the two assumptions, is obtained
(3) Evaluating probabilities
Since the task is classified into two categories, the data provider still has
Is answered correctly; thus, the probability of a user completing a task is modeled using a sigmoidal function, and the functional expression of the probability of a data provider answering a question correctly is as follows:
7. the incentive method in crowd-sourcing perception based on two-way auction model according to claim 5, wherein said pick algorithm comprises:
inputting: a task requester set R and a data provider set W;
and (3) outputting: task requester candidate set RsData provider candidate set Ws；
Step 1: task requester candidate set RsData provider candidate set WsAll values are assigned as empty sets;
step 2: and when the task requester set R is not an empty set, circulating, wherein the circulating comprises the following steps:
(1) iteratively choosing task requester in set R
R having the largest valuejAs candidate users;
(2) all are selected rjData provider w providing tasksiAnd it is
Is composed ofjIf is SjNull means no data provider selection rjThen r will bejRemoving from the set R; otherwise will rjAnd a candidate user set SjSeparately joining task requester candidate set RsData provider candidate set WsAnd r isjRemoving from the set R;
and step 3: ending the circulation when the task requester set R is an empty set, and outputting the finally obtained task requester candidate set RsData provider candidate set Ws。
8. The incentive method based on bi-directional auction model in crowd sensing according to claim 5, wherein said evaluation algorithm comprises:
inputting: task requester candidate set RsData provider candidate set Ws；
And (3) outputting: evaluating a probability set S;
step 1: assigning all the evaluation probability sets to be empty sets;
step 2: for task requester candidate set RsMiddle task requester rjAnd circularly iterating according to the sequence in the set, wherein the iteration comprises the following steps:
(1) calculating rjCorresponding set SjThen set SjW iniCircularly iterating according to the sequence;
(2) mining w from user logsiThe current user information is used for calculating the user activity
Distance similarity
Secondly, deriving the extrinsic factor pair data provider w according to the two informationiEffect of accuracy of completed task
(3) According to the data provider wiThe quality of the completed task is modeled as a random variable QiObeying a Gaussian distribution
The difficulty of the task obeys a Gaussian distribution
And (5) obtaining the final evaluation probability:
(4) will gather SjW iniIs estimated to be a probability P (Δ)ij) Adding the set S;
(5) set SjFinishing the set S when all the users in the set traversejThe cycle of (2);
and step 3: finally, the task requester candidate set R is traversedsAll task requesters r injThen the set R is endedsAnd (4) outputting the set S.
9. The incentive method in crowd-sourcing perception based on bi-directional auction model according to claim 5, wherein said matching and pricing algorithm comprises:
inputting: task requester candidate set RsData provider candidate set WsEvaluating a probability set S;
and (3) outputting: task requester winning set RwWinning set of data providers WwA winning task requester fee set Q and a winning data provider reward set P;
step 1: task requester winning set RwWinning set of data providers WwThe winning task requester fee set Q and the winning data provider reward set P are all assigned as an empty set;
step 2: solving a task requester candidate set RsIn
Minimum task requester rthAnd from the set RsRemoving the waste residues;
and step 3: task requester candidate set RsAccording to
Is arranged from large to small, i.e.
And 4, step 4: for the orderly arranged sets RsAnd circularly traversing according to the sequence, comprising:
(1) first, a set R is obtainedsMiddle task requester rjCost q ofj；
(2) Then will be
Corresponding set SjWill aggregate SjW iniAccording to
Iteration is carried out in a cycle from small to large;
(3) in a sorted set SjTo find out the satisfying condition
Data provider w ofi；
(4) If k is ═ SjIf is then set SjW iniAll satisfy the condition, according to the sorted set SjCalculate w sequentiallyiIs paid
And will gather SjAll of wiP of (a)ijPut into set P, set SjAll of wiAll put into the set Ww、rjPut into the set Rw；
(5) If k is 0 < k < | SjIf is then set SjW iniPartially satisfying the condition, according to the sorted set SjCalculate w sequentiallyiIs paid
And will gather SjAll of w satisfying the conditioniP of (a)ijPut into set P, set SjAll of w satisfying the conditioniPut into the set Ww、rjPut into the set Rw；
(6) If k is 0, it represents the set SjW iniAll the task requesters do not meet the condition, and the next task requester r starts to be traversed according to the sequencing sequencej；
And 5: end set RsLoop of outputting the winning set R of task requesterswWinning set of data providers WwA set of winning task requester fees Q, a set of winning data provider rewards P.
10. An incentive system based on the bi-directional auction model in crowd sensing for implementing the incentive method based on the bi-directional auction model in crowd sensing according to any one of claims 1 to 13, wherein the incentive system based on the bi-directional auction model in crowd sensing comprises:
a type information submitting module for submitting respective type information through the task requester set R
Feeding the platform; wherein the type information includes a maximum task value
The task issuing module is used for issuing the received tasks issued by the task requester to the data provider through the platform;
the winning data provider selecting module is used for selecting the interested tasks after the data providers receive the tasks issued by the platform, submitting the type information to the platform and selecting the proper winning data provider;
the data set solving module is used for calculating the evaluation probability between the task requester and the data provider by using an 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 the data to the platform after the tasks are completed by the winning data providers;
the task fee payment module is used for paying the fee of the task through the winning task requester;
and the reward receiving module is used for receiving the reward from the corresponding winning task requester through the winning data provider.
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