CN107330754B - Mobile crowd sensing excitation method for cooperative task - Google Patents

Mobile crowd sensing excitation method for cooperative task Download PDF

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CN107330754B
CN107330754B CN201710412151.1A CN201710412151A CN107330754B CN 107330754 B CN107330754 B CN 107330754B CN 201710412151 A CN201710412151 A CN 201710412151A CN 107330754 B CN107330754 B CN 107330754B
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徐佳
饶正强
徐力杰
王磊
戴华
徐小龙
李涛
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Nanjing University of Posts and Telecommunications
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Abstract

The invention discloses a mobile crowd sensing incentive method facing cooperative tasks, which allows a user to submit unique quotations for a plurality of tasks and ensures that each cooperative task can be completed by a group of mutually compatible users. The invention considers that the perception activity is initiated in an online community, and a reverse auction process is embodied between the social network application platform and online community users. The user submits a bidding document containing a compatible user set to the social network application platform according to the social relationship in the community, and the social network application platform divides the user into a plurality of compatible user groups according to the compatible user set submitted by the user. Then selects the enrollees and calculates the reward for each user. The mobile crowd sensing excitation method provided by the invention meets the requirements of computational effectiveness, personal rationality and deception prevention.

Description

Mobile crowd sensing excitation method for cooperative task
Technical Field
The invention belongs to the crossing field of mobile internet and algorithmic game theory, and particularly relates to a cooperative task oriented mobile crowd sensing incentive method.
Background
With the development of technologies such as mobile internet, embedded sensors, etc., smart phones have become very popular. The method for sensing and collecting large-scale data by using ubiquitous smart phone users is a novel sensing mode. Mobile crowd-sourcing awareness is considered a novel data awareness and collection model with great potential due to its wide spatio-temporal coverage, low cost, excellent scalability, and ubiquitous application scenario. At present, some projects realize different applications in the fields of health care, intelligent transportation, social networks, environment monitoring and the like based on mobile crowd sensing.
However, these current applications assume that the participants are voluntarily and actively engaged in data perception, which is often impractical. Because the participants need to consume device energy, computing power, storage space, data traffic, etc. to accomplish the crowd sensing task, the participants need to get a certain amount of incentive to offset these losses. Successful implementation of crowd-sourcing aware applications depends on the number of participants and the quality of the data, both of which are not guaranteed without incentive. Therefore, the design of the incentive scheme is important in crowd sensing applications.
The establishment of a fingerprint database, the collection of geographic data and the like in a large range all require the mutual cooperation of mobile phone users to complete tasks. In the face of these cooperative tasks, users hope to finish the task together with the trusting person, thus can better go to distribute the task, improve the quality of the service and protect personal privacy, but the prior art does not allow users to choose trusting users to finish the task together, the compatibility among users is neglected.
The invention considers the publishing of a mobile crowd sensing activity with multi-cooperative tasks in an online community. Users in the online community are interested in participating in the perception task. Each collaborative task requires a certain number of compatible users to complete. The invention mines the compatibility between users from the social relationship between users in the online community. The user submits a bidding document containing a compatible user set to the social network application platform according to the social relationship in the community, and the social network application platform divides the user into a plurality of compatible user groups according to the compatible user set submitted by the user. Then selects the enrollees and calculates the reward for each user. The mobile crowd sensing excitation method provided by the invention meets the requirements of computational effectiveness, personal rationality and deception prevention.
Disclosure of Invention
The invention aims to solve the technical problem of providing a mobile crowd sensing excitation method facing cooperative tasks aiming at the defects in the background technology.
The technical solution of the invention is as follows:
consider a mobile crowd-sourcing awareness system that includes a social networking application platform and an online community in which a collection of users exist.
The invention discloses a mobile crowd sensing incentive method facing cooperative tasks, which is characterized in that a reverse auction process is embodied between a social network application platform and a user, and the steps are as follows:
step 201: the social network application platform publishes a task set T ═ { T ═ T1,...,tmFor each task tjE T requires at least rjThe person goes to completion;
step 202: the set of users in the online community is U ═ 1, 2.., n }, and each user i ∈ U submits a label Bi＝(βi,bi,ξi) Wherein
Figure GDA0002657792230000021
Is kiSet of individual tasks, biPerforming a set of tasks beta for user iiMinimum reward desired, biAlso called quotes, ciPerforming a set of tasks beta for user iiTrue cost of xiiA set of a group of users with which user i can cooperate is called a compatible user set of user i;
step 203: the social network application platform performs a compatible user grouping process on all users, generating
Figure GDA0002657792230000022
D sets of compatible users in total
Figure GDA0002657792230000023
Is a set of d compatible user groups;
step 204: social network application platform in accordance with
Figure GDA0002657792230000025
Calculating an enrollee set S;
defining a utility u for an arbitrary user iiIs a rewardDifference from the real cost spent performing the task:
ui＝pi-ci (1)
the invention considers that the users are selfish and rational, and each user can maximize the utility of the user by submitting a dishonest set of compatible users or dishonest offers.
In step 204, the goal in computing the set of enrollees is to minimize the social cost so that each task in the set of tasks T can be completed by a group of compatible users. Where social cost refers to the sum of the costs of all the enrollment tasks. The present invention defines this problem as a socially optimal compatible user selection problem, which can be formalized as:
min∑i∈sci
Figure GDA0002657792230000024
step 205: calculating the reward p of each user i belonging to Ui；
Step 206: the social network application platform informs the selector, the selector executes the perception task and submits the perception data to the social network application platform;
step 207: the social network application platform pays a reward to the enrollee.
In step 203, the steps of the social network application platform performing a compatible user grouping process for all users are as follows:
step 301: initializing a set of users
Figure GDA0002657792230000031
Wherein is a constant, < n;
step 302: constructing a directed graph G about the used users by using the compatible user set: for any two users i, j belongs to U, i is not equal to j, if the user j is in a compatible user set of the user i, a directed edge from the user i to the user j is added;
step 303: order to
Figure GDA0002657792230000032
Randomly distributing each user in the directed graph G into x user subsets, and setting the x user subsets to be U respectively1,U2,...,UχLet A be { U ═ U1,U2,...,UχIs the set of χ user subsets;
step 304: randomly selecting from A
Figure GDA0002657792230000033
A subset of individual users, let AIs composed of
Figure GDA0002657792230000034
A set of subsets of individual users;
step 305: for each subset U of usersiE.g. A, if Ui∈AIf yes, go to step 306, otherwise go to step 307;
step 306: if it is not
Figure GDA0002657792230000035
Then U will beiAll users in (2) merge into a user set S(ii) a Otherwise, the slave UiIn which the starting point is selected to be non-UiThe front of the user with the most entries
Figure GDA0002657792230000036
A user who will do so
Figure GDA0002657792230000037
Individual users merge into a user set S；
Step 307: if it is not
Figure GDA0002657792230000038
Then U will beiAll users in (2) merge into a user set S(ii) a Otherwise, the slave UiIn which the starting point is selected to be non-UiThe front of the user with the most entries
Figure GDA0002657792230000039
A user who will do so
Figure GDA00026577922300000310
Individual users merge into a user set S；
Step 308: if SIf | <, then it is never at SRandomly selects-S from the users in (1)I users merge into S；
Step 309: deleting an out-of-S from the directed graph GAnd SAnd (3) solving all the strong connected components in the updated graph G by the edges related to the users, putting the users in each strong connected component into a compatible user group, and designing to generate in total
Figure GDA00026577922300000311
D sets of compatible users in total
Figure GDA00026577922300000312
Is a set of d compatible user groups;
step 310: return to
Figure GDA00026577922300000313
And (6) ending.
In step 204, the social networking application platform operates according to
Figure GDA00026577922300000314
The step of calculating the set of candidates S is as follows:
step 401: initializing a set of enrollees
Figure GDA00026577922300000315
Step 402: for all compatible user groups
Figure GDA00026577922300000316
Executing step 403 to step 404;
step 403: order SkFor users who have been taken from a compatible group of users
Figure GDA00026577922300000317
In selectionThe set of the selected entrants is initialized Sk＝φ；
Step 404: for all tasks tjE is T, order
Figure GDA0002657792230000041
Figure GDA0002657792230000042
For compatible groups of users
Figure GDA0002657792230000043
Completion of task tjMinimal marginal cost of expense required;
step 405: for all tasks tje.T, performing step 406 to step 409;
step 406: for all compatible user groups
Figure GDA0002657792230000044
Order SkFor users who have been taken from a compatible group of users
Figure GDA0002657792230000045
Of the selected set of candidates, S'kFor groups of compliant subscribers
Figure GDA0002657792230000046
The set of the selected candidates in (1), initializing Sk＝φ，S'kPhi, Qk＝{i|i∈Sk,tj∈βiIn which QkIs SkTask t is the middlejA set of users for quotes;
step 407: if it is not
Figure GDA0002657792230000047
The submitted bidding document contains a task tjThe number of users is not less than rjAnd r isjMore than set QkThe number of users is
Figure GDA0002657792230000048
The selected price is lowest and the bidding document is submittedIncluding task tjFront r ofj-|Qk| users, will r thisj-|QkL users merge into set S'kAnd mixing S'kThe user's quotation adds up to get the value and gives
Figure GDA0002657792230000049
Step 408: for all compatible user groups
Figure GDA00026577922300000410
Find out
Figure GDA00026577922300000411
The compatible user group having the smallest value is set as
Figure GDA00026577922300000412
k′∈{1,2,...,d}；
Step 409: order Sk'＝Sk'∪S'k'，S＝S∪Sk'；
Step 410: and returning to the candidate set S, and ending.
In step 205, the social networking application platform calculates a reward p for each user i e UiThe steps are as follows:
step 501: for any user i belongs to U, the reward p is paidi＝0；
Step 502: performing steps 503 to 506 for all the candidates i e S;
step 503: for all tasks tjE T, performing step 504 to step 506;
step 504: from the set of users according to the method from step 401 to step 410
Figure GDA00026577922300000413
Task t is the middlejSelecting a set of candidates, wherein
Figure GDA00026577922300000414
Is composed of
Figure GDA00026577922300000415
A set of all users;
step 505: calculated according to the method described in steps 401 to 410
Figure GDA00026577922300000416
And
Figure GDA00026577922300000417
wherein
Figure GDA00026577922300000418
For performing task t without user ijThe minimum marginal cost that is required is,
Figure GDA00026577922300000419
for performing task t in the presence of user ijThe minimum marginal cost required;
step 506: if it is not
Figure GDA00026577922300000420
Then the reward of user i is made
Figure GDA0002657792230000051
Step 507: return reward vector p ═ for all users (p)1,p2,...,pn) And then, the process is ended.
Advantageous effects
Compared with the prior art, the invention adopting the technical scheme has the following technical effects:
1. compatibility among users is considered in the design of an incentive mechanism of the mobile crowd sensing system for the first time, so that the users can more efficiently complete cooperative tasks;
2. the computation time complexity is low, and the total time complexity of the excitation method is
Figure GDA0002657792230000052
Is a complete polynomial time method with calculation effectiveness;
3. the incentive method is personal, namely the amount of the reward paid to each candidate by the platform is greater than or equal to the real cost required by the user, so that the incentive method has positive effects on attracting a large number of users and improving the data quality;
suppose user i is selected to perform task tj. Because the user's reward calculation process steps 501-507 traverse all tasks and all compatible user groups in the same order as the user selection process steps 401-410, at task tjThe previous user selection results do not change. In the process of reward calculation, task tjIn step 505 there is
Figure GDA0002657792230000053
Thus is provided with
Figure GDA0002657792230000054
This is sufficient to prove
Figure GDA0002657792230000055
4. The incentive method is anti-cheating, when other users submit real quotations and compatible user sets of the users, even if the users adopt certain strategies to falsely report the quotations and the compatible user sets, the utility of the users cannot be increased, and therefore the users tend to submit the real quotations and the compatible user sets of the users. Anti-cheating is important to prevent market monopoly or collusion.
For any χ, it is true that the social networking application platform performs a compatible user grouping process for all users, i.e., steps 301-310. Because of the random grouping, the user's access set
Figure GDA0002657792230000056
The chance of (a) has no relation to what set of compatible users are submitted by itself. That is, no user can add an entry set by submitting a dishonest set of compatible users, regardless of what set of compatible users other users submit
Figure GDA0002657792230000057
An opportunity of (1).
It is demonstrated below that the user's selection process steps 401-410 are monotonic and that the reward p for each user iiIs a key value. Monotonicity is evident because a lower bid does not make user i's selection sequence further back.
The following demonstrates piIs a key value, i.e. user i offers more than piUser i will be caused to fail the auction. Note that for task tjIn the iteration of (2) is performed,
Figure GDA0002657792230000058
if user i quotes bi＞piThe user group containing user i will be replaced by another user group not containing user i, because
Figure GDA0002657792230000061
Means that
Figure GDA0002657792230000062
So user i does not win task t in the auctionj. In accordance with step 506 of the method,
Figure GDA0002657792230000063
since each task has selected a group of users without user i, user i will not win the auction.
Drawings
FIG. 1 is a reverse auction execution flow between a social networking application platform and a user in the present invention;
FIG. 2 is a flow of the social networking application platform performing compatible user grouping on all users in accordance with the present invention;
FIG. 3 is an execution flow of the social networking application platform selecting a set of enrollees in the present invention;
FIG. 4 is an execution flow of the social networking application platform in calculating reward for a winner of a social network.
Detailed Description
The technical scheme of the invention is further explained in detail by combining the attached drawings:
according to the compatible user set submitted by the user, the social network application platform divides the user into a plurality of compatible user groups. Then selecting the entrants and calculating a reward for each of the entrants.
Description of the nouns:
the mobile crowd sensing system: a large-scale data acquisition system utilizing a large amount of smart phone sensing data is characterized in that a mobile crowd sensing system is composed of a mobile crowd sensing platform located at the cloud end and a batch of smart phone users. The mobile crowd sensing platform at the cloud is a social network application platform; the smart phone user is in an online community.
The method comprises the following steps: the users selected by the incentive method provided by the invention are final participants of the mobile crowd sensing.
Utility of the user: the difference between the reward received by the user and the cost paid out. In the incentive method against fraud, the cost of the user is equal to the user's price quote.
The invention relates to a mobile crowd sensing incentive method facing cooperative tasks, which is characterized in that a reverse auction process is embodied between a social network application platform and a user, the execution flow is shown as a figure 1, and the steps are as follows:
step 201: the social network application platform publishes a task set T ═ { T ═ T1,...,tmFor each task tjE T requires at least rjThe person goes to completion;
step 202: the set of users in the online community is U ═ 1, 2.., n }, and each user i ∈ U submits a label Bi＝(βi,bi,ξi) Wherein
Figure GDA0002657792230000064
Is kiSet of individual tasks, biPerforming a set of tasks beta for user iiThe minimum reward that is desired to be obtained,ia set of a group of users with which user i can cooperate is called a compatible user set of user i;
step 203: the social network application platform performs a compatible user grouping process on all users, generating
Figure GDA0002657792230000071
D sets of compatible users in total
Figure GDA0002657792230000072
Is a set of d compatible user groups;
step 204: social network application platform in accordance with
Figure GDA0002657792230000073
Calculating an enrollee set S;
step 205: calculating the reward p of each user i belonging to Ui；
Step 206: the social network application platform informs the selector, the selector executes the perception task and submits the perception data to the social network application platform;
step 207: the social network application platform pays a reward to the enrollee.
In step 203, the flow of the social network application platform performing compatible user grouping on all users is shown in fig. 2, and the steps are as follows:
step 301: initializing a set of users
Figure GDA0002657792230000074
Wherein is a constant, < n;
step 302: constructing a directed graph G about the used users by using the compatible user set: for any two users i, j belongs to U, i is not equal to j, if the user j is in a compatible user set of the user i, a directed edge from the user i to the user j is added;
step 303: order to
Figure GDA0002657792230000075
Randomly distributing each user in the directed graph G into x user subsets, and setting the x user subsets to be U respectively1,U2,...,UχLet A be { U ═ U1,U2,...,UχIs x usersA set of subsets;
step 304: randomly selecting from A
Figure GDA0002657792230000076
A subset of individual users, let AIs composed of
Figure GDA0002657792230000077
A set of subsets of individual users;
step 305: for each subset U of usersiE.g. A, if Ui∈AIf yes, go to step 306, otherwise go to step 307;
step 306: if it is not
Figure GDA0002657792230000078
Then U will beiAll users in (2) merge into a user set S(ii) a Otherwise, the slave UiIn which the starting point is selected to be non-UiThe front of the user with the most entries
Figure GDA0002657792230000079
A user who will do so
Figure GDA00026577922300000710
Individual users merge into a user set S；
Step 307: if it is not
Figure GDA00026577922300000711
Then U will beiAll users in (2) merge into a user set S(ii) a Otherwise, the slave UiIn which the starting point is selected to be non-UiThe front of the user with the most entries
Figure GDA00026577922300000712
A user who will do so
Figure GDA00026577922300000713
Individual users merge into a user set S；
Step 308: if SIf | <, then it is never at SRandomly selects-S from the users in (1)I users merge into S；
Step 309: deleting an out-of-S from the directed graph GAnd SAnd (3) solving all the strong connected components in the updated graph G by the edges related to the users, putting the users in each strong connected component into a compatible user group, and designing to generate in total
Figure GDA0002657792230000081
D sets of compatible users in total
Figure GDA0002657792230000082
Is a set of d compatible user groups;
step 310: return to
Figure GDA0002657792230000083
And (6) ending.
In step 204, the social networking application platform operates according to
Figure GDA0002657792230000084
The execution flow of calculating the candidate set S is shown in fig. 3, and the steps are as follows:
step 401: initializing a set of enrollees
Figure GDA0002657792230000085
Step 402: for all compatible user groups
Figure GDA0002657792230000086
Executing step 403 to step 404;
step 403: order SkFor users who have been taken from a compatible group of users
Figure GDA0002657792230000087
The selected candidate set is initialized Sk＝φ；
Step 404: for all tasks tjE is T, order
Figure GDA0002657792230000088
Figure GDA0002657792230000089
For compatible groups of users
Figure GDA00026577922300000810
Completion of task tjMinimal marginal cost of expense required;
step 405: for all tasks tje.T, performing step 406 to step 409;
step 406: for all compatible user groups
Figure GDA00026577922300000811
Order SkFor users who have been taken from a compatible group of users
Figure GDA00026577922300000812
Of the selected set of candidates, S'kFor groups of compliant subscribers
Figure GDA00026577922300000813
The set of the selected candidates in (1), initializing Sk＝φ，S'kPhi, Qk＝{i|i∈Sk,tj∈βiIn which QkIs SkTask t is the middlejA set of users for quotes;
step 407: if it is not
Figure GDA00026577922300000814
The submitted bidding document contains a task tjThe number of users is not less than rjAnd r isjMore than set QkThe number of users is
Figure GDA00026577922300000815
The selected price is lowest and the submitted bidding document contains the task tjFront r ofj-|Qk| users, will r thisj-|QkL users merge into set S'kAnd mixing S'kThe user's quotation adds up to get the value and gives
Figure GDA00026577922300000816
Step 408: for all compatible user groups
Figure GDA00026577922300000817
Find out
Figure GDA00026577922300000818
The compatible user group having the smallest value is set as
Figure GDA00026577922300000819
Step 409: order Sk'＝Sk'∪S'k'，S＝S∪Sk'；
Step 410: and returning to the candidate set S, and ending.
In step 205, the social networking application platform calculates a reward p for each user i e UiThe execution flow of (2) is shown in fig. 4, and the steps are as follows:
step 501: for any user i belongs to U, the reward p is paidi＝0；
Step 502: performing steps 503 to 506 for all the candidates i e S;
step 503: for all tasks tjE T, performing step 504 to step 506;
step 504: the method of claim 3, from step 401 to step 410, from a set of users
Figure GDA0002657792230000091
Task t is the middlejSelecting a set of candidates, wherein
Figure GDA0002657792230000092
Is composed of
Figure GDA0002657792230000093
A set of all users;
step 505: the method of claim 3, steps 401 through 410, calculating
Figure GDA0002657792230000094
And
Figure GDA0002657792230000095
wherein
Figure GDA0002657792230000096
For performing task t without user ijThe minimum marginal cost that is required is,
Figure GDA0002657792230000097
for performing task t in the presence of user ijThe minimum marginal cost required;
step 506: if it is not
Figure GDA0002657792230000098
Then the reward of user i is made
Figure GDA0002657792230000099
Step 507: return reward vector p ═ for all users (p)1,p2,...,pn) And then, the process is ended.
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The above-mentioned embodiments, objects, technical solutions and advantages of the present invention are further described in detail, it should be understood that the above-mentioned embodiments are only illustrative of the present invention and are not intended to limit the present invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (4)

1. A mobile crowd-sourcing perception incentive method facing cooperative tasks is characterized in that: the method is characterized in that a reverse auction process is implemented between the social network application platform and a user, and comprises the following steps:
step 201: the social network application platform publishes a task set T ═ { T ═ T1,...,tmFor each task tjE T requires at least rjThe person goes to completion;
step 202: the set of users in the online community is U ═ 1, 2.., n }, and each user i ∈ U submits a label Bi＝(βi,bi,ξi) Wherein
Figure FDA0002657792220000011
Is kiSet of individual tasks, biPerforming a set of tasks beta for user iiMinimum reward desired, ξiA set of a group of users collaborating for user i, called user i's compatible set of users;
step 203: the social network application platform performs a compatible user grouping process on all users, generating
Figure FDA0002657792220000012
D sets of compatible users in total
Figure FDA0002657792220000013
Is a set of d compatible user groups;
step 204: social network application platform in accordance with
Figure FDA0002657792220000014
Calculating an enrollee set S;
step 205: calculating the reward p of each user i belonging to Ui；
Step 206: the social network application platform informs the selector, the selector executes the perception task and submits the perception data to the social network application platform;
step 207: the social network application platform pays a reward to the enrollee.
2. A collaborative task oriented mobile crowd-sourcing aware incentive method according to claim 1, wherein in step 203, the steps of the social networking application platform performing a compatible user grouping procedure for all users are as follows:
step 301: initializing a set of users
Figure FDA0002657792220000015
Wherein is a constant, < n;
step 302: constructing a directed graph G about the used users by using the compatible user set: for any two users i, j belongs to U, i is not equal to j, if the user j is in a compatible user set of the user i, a directed edge from the user i to the user j is added;
step 303: order to
Figure FDA0002657792220000016
Randomly distributing each user in the directed graph G into x user subsets, and setting the x user subsets to be U respectively1,U2,...,UχLet A be { U ═ U1,U2,...,UχIs the set of χ user subsets;
step 304: randomly selecting from A
Figure FDA0002657792220000017
A subset of individual users, let AIs composed of
Figure FDA0002657792220000018
A set of subsets of individual users;
step 305: for each subset U of usersiE.g. A, if Ui∈AIf yes, go to step 306, otherwise go to step 307;
step 306: if it is not
Figure FDA0002657792220000019
Then U will beiAll users in (2) merge into a user set S(ii) a Otherwise, the slave UiIn which the starting point is selected to be non-UiThe front of the user with the most entries
Figure FDA0002657792220000021
A user who will do so
Figure FDA0002657792220000022
Individual users merge into a user set S；
Step 307: if it is not
Figure FDA0002657792220000023
Then U will beiAll users in (2) merge into a user set S(ii) a Otherwise, the slave UiIn which the starting point is selected to be non-UiThe front of the user with the most entries
Figure FDA0002657792220000024
A user who will do so
Figure FDA0002657792220000025
Individual users merge into a user set S；
Step 308: if SIf | <, then it is never at SRandomly selects-S from the users in (1)I users merge into S；
Step 309: deleting an out-of-S from the directed graph GAnd SAnd (3) solving all the strong connected components in the updated graph G by the edges related to the users, putting the users in each strong connected component into a compatible user group, and designing to generate in total
Figure FDA0002657792220000026
D sets of compatible users in total
Figure FDA0002657792220000027
Is a set of d compatible user groups;
step 310: return to
Figure FDA0002657792220000028
And (6) ending.
3. A collaborative task oriented mobile crowd-sourcing aware incentive method according to claim 1, wherein in step 204, the social networking application platform is based on
Figure FDA0002657792220000029
The step of calculating the set of candidates S is as follows:
step 401: initializing a set of enrollees
Figure FDA00026577922200000210
Step 402: for all compatible user groups
Figure FDA00026577922200000211
Executing step 403 to step 404;
step 403: order SkFor users who have been taken from a compatible group of users
Figure FDA00026577922200000212
The selected candidate set is initialized Sk＝φ；
Step 404: for all tasks tjE is T, order
Figure FDA00026577922200000213
Figure FDA00026577922200000214
For compatible groups of users
Figure FDA00026577922200000215
Completion of task tjMinimal marginal cost of expense required;
step 405: for all tasks tje.T, performing step 406 to step 409;
step 406: for all compatible user groups
Figure FDA00026577922200000216
Order SkFor users who have been taken from a compatible group of users
Figure FDA00026577922200000217
Of the selected set of candidates, S'kFor groups of compliant subscribers
Figure FDA00026577922200000218
The set of the selected candidates in (1), initializing Sk＝φ，S'kIs equal to phi, order
Figure FDA00026577922200000219
Wherein QkIs SkTask t is the middlejA set of users for quotes;
step 407: if it is not
Figure FDA00026577922200000220
The submitted bidding document contains a task tjThe number of users is not less than rjAnd r isjMore than set QkThe number of users is
Figure FDA00026577922200000221
The selected price is lowest and the submitted bidding document contains the task tjFront r ofj-|Qk| users, will r thisj-|QkL users merge into set S'kAnd mixing S'kThe user's quotation adds up to get the value and gives
Figure FDA00026577922200000222
Step 408: for all compatible user groups
Figure FDA00026577922200000223
Find out
Figure FDA00026577922200000224
The compatible user group having the smallest value is set as
Figure FDA00026577922200000225
Step 409: order Sk'＝Sk'∪S'k'，S＝S∪Sk'；
Step 410: and returning to the candidate set S, and ending.
4. A collaborative task oriented mobile crowd-sourcing aware incentive method according to claim 1, wherein in step 205, the social networking application platform calculates a reward p for each user i e UiThe steps are as follows:
step 501: for any user i belongs to U, the reward p is paidi＝0；
Step 502: performing steps 503 to 506 for all the candidates i e S;
step 503: for all tasks tjE T, performing step 504 to step 506;
step 504: from the set of users, steps 401 through 410
Figure FDA0002657792220000031
Task t is the middlejSelecting a set of candidates, wherein
Figure FDA0002657792220000032
Is composed of
Figure FDA0002657792220000033
A set of all users;
step 505: calculated according to the method described in steps 401 to 410
Figure FDA0002657792220000034
And
Figure FDA0002657792220000035
wherein
Figure FDA0002657792220000036
For performing task t without user ijThe minimum marginal cost that is required is,
Figure FDA0002657792220000037
for performing task t in the presence of user ijThe minimum marginal cost required;
step 506: if it is not
Figure FDA0002657792220000038
Then the reward of user i is made
Figure FDA0002657792220000039
Step 507: return reward vector p ═ for all users (p)1,p2,...,pn) And then, the process is ended.
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