CN112131524A - Online incentive mechanism method for crowd sensing system - Google Patents

Online incentive mechanism method for crowd sensing system Download PDF

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CN112131524A
CN112131524A CN202010957162.XA CN202010957162A CN112131524A CN 112131524 A CN112131524 A CN 112131524A CN 202010957162 A CN202010957162 A CN 202010957162A CN 112131524 A CN112131524 A CN 112131524A
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刘通
方璐
熊赟
童维勤
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Fudan University
University of Shanghai for Science and Technology
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Abstract

The invention belongs to the technical field of crowd sensing and discloses an online incentive mechanism method for a crowd sensing system, which comprises the following steps of modeling the crowd sensing system, a sensing task, the reliability of a user and social influence; secondly, selecting an active user to execute the perception task each time by adopting an iterative selection algorithm on the basis of the bidding document, the reliability and the social influence of the user and the value of the perception data collected after finishing each perception task; and step three, calculating the reward of the active user each time, and giving the reward to the platform when receiving all perception data needing to be submitted by the platform. The method and the system quantitatively depict the utility brought to the platform by the social influence of the intelligent device user in the crowd sensing system, and consider that the social influence of the intelligent device user can dynamically change according to the position requirement of the sensing task and the potential contribution of the social friends of the intelligent device user, thereby conforming to the actual situation.

Description

Online incentive mechanism method for crowd sensing system
Technical Field
The invention relates to the technical field of crowd sensing, in particular to an online incentive mechanism method for a crowd sensing system.
Background
Due to the increasing maturity of wireless communication technologies (such as 5G, Bluetooth, WiFi) and the popularity of mobile smart devices (such as mobile smart terminals, wearable devices, etc.) in daily life, a new data collection model, crowd sensing, is emerging. A typical crowd sensing system generally comprises a cloud platform and a plurality of mobile intelligent device users distributed in different areas, all the mobile intelligent device users participating in sensing collect sensing data according to the requirements of the platform and upload the sensing data to the cloud platform, and the cloud platform collects a large amount of sensing data and then further mines the value of the data to extract useful information.
Since the mobile intelligent device users participate in the sensing data collection to generate cost overhead, the crowd sensing system needs to design an incentive mechanism to improve the participation enthusiasm of the intelligent device users, so as to improve the quantity and quality of the sensing data. On one hand, intelligent device users of the crowd sensing system have different attributes, such as different quality of collected sensing data, different geographical range capable of sensing and different social influence on other users; on the other hand, users are rational and selfish, and may lie on their own real cost overheads to the cloud platform in order to maximize personal interests. Therefore, it is very difficult to design a reasonable online incentive mechanism to improve the user engagement, so as to minimize the user cost and maximize the quality and quantity of the perception data. The existing motivation mechanism of related research work design is mostly established on the assumption that the participating users are sufficient, however, the problem of insufficient participating intelligent device users still exists in the practical system.
Disclosure of Invention
The invention aims to provide an online incentive mechanism method for a crowd sensing system aiming at the defects of the prior art, designs a user online selection method and an instant user reward calculation method, and maximizes the quality and quantity of sensing data while minimizing the user cost on the premise of comprehensively considering that different intelligent device users have different sensing abilities and different social influences.
The invention can be realized by the following technical scheme:
an online incentive scheme method for crowd sensing system, comprising the steps of:
step one, modeling a crowd sensing system, a sensing task, the reliability of a user and social influence;
secondly, selecting an active user to execute the perception task each time by adopting an iterative selection algorithm on the basis of the bidding document, the reliability and the social influence of the user and the value of the perception data collected after finishing each perception task;
and step three, calculating the reward of the active user by using a payment method every time, and giving the reward to the platform when receiving all perception data needing to be submitted by the platform.
Further, the interest area covered by the crowd sensing system
Figure BDA0002679130310000021
The partition is carried out, and the partition is carried out,
Figure BDA0002679130310000022
working time required for sensing data collection
Figure BDA0002679130310000026
The equal-interval division is carried out,
Figure BDA0002679130310000023
using directed graph to conquer each user u in intelligence systemiSocial relationships between users, and further characterize user u with weighted centrality and weighted tight centralityiThe social influence of (a) is,the weighting centrality is used for measuring the u of the active useriThe weighted close centrality represents the propagation speed of the user message in the network measured by the shortest distance between the users in the social network, wherein,
Figure BDA0002679130310000024
Figure BDA0002679130310000025
representing a set of users owned by the crowd sensing system.
Further, at each time slice tτAnd step two, the platform iteratively selects active users with high utility brought to the platform by unit cost and social influence according to the iterative selection method in the step two to execute perception tasks, and then calculates reward for the selected active users by using a payment method until all the active users are traversed and the iteration is finished.
Further, the iterative selection algorithm in the second step includes the following steps: suppose that at each time slice tτAt the beginning, each active user
Figure BDA0002679130310000031
Submission of bidding documents to crowd sensing system
Figure BDA0002679130310000032
Wherein the content of the first and second substances,
Figure BDA0002679130310000033
represents the current set of active users and,
Figure BDA0002679130310000034
representing user uiThe time of the online of the time is long,
Figure BDA0002679130310000035
representing user uiThe perceived task that can be performed is,
Figure BDA0002679130310000036
representing user uiProvide for
Figure BDA0002679130310000037
Cost required for sensing data of all tasks in the set, user completion set
Figure BDA0002679130310000038
The cost of all tasks in
Figure BDA0002679130310000039
Figure BDA00026791303100000310
To represent tτUser u in time sliceiWith an indication vector
Figure BDA00026791303100000311
To represent active users
Figure BDA00026791303100000312
Whether it is selected to perform a task, wherein,
Figure BDA00026791303100000313
Figure BDA00026791303100000314
indicating reporting label
Figure BDA00026791303100000315
Active user uiAt time slice tτIs selected to perform the task(s),
step I, initializing parameters and indicating a vector IτElement (1) of
Figure BDA00026791303100000316
All are set to zero;
step II, the platform is used for each current active user
Figure BDA00026791303100000317
Calculating the utility delta of the perception data and the social influence of the selected data to the platformi(Iτ)＝Ui(Iτ)+Wi(Iτ) The calculation formula is as follows:
Figure BDA00026791303100000318
Figure BDA00026791303100000319
W(Iτ)＝log(1+ω(Iτ)+ν(Iτ)),
Figure BDA00026791303100000320
wherein the content of the first and second substances,
Figure BDA00026791303100000321
representing the value of the perceptual data that has been collected for each perceptual task, qiWhich is indicative of the degree of reliability of the user,
Figure BDA00026791303100000322
Figure BDA00026791303100000323
represents the center of the weighted degree,
Figure BDA00026791303100000324
Which represents the weighted tight centrality of the device,
Figure BDA00026791303100000325
representing perceptual tasks, vkIndicates that the corresponding different areas lkIs a perception task
Figure BDA00026791303100000326
Initial value, U (I), brought to crowd sensing system by collected sensing dataτ) Watch (A)When the user selects the case as IτUtime, utility, U, that the user's sensory data can bring to the platformi(Iτ) Representing platform selection user uiAnd no user u is selectediIs poor in data utility, W (I)τ) When the user selects the condition as IτThe social influence of the user can bring utility to the platform, Wi(Iτ) Representing platform selection user uiAnd no user u is selectediSocial influence of (a) is poor in utility;
step III, calculating to obtain the unit cost to realize the utility
Figure BDA0002679130310000041
Maximum active user
Figure BDA0002679130310000042
Namely, it is
Figure BDA0002679130310000043
The active user
Figure BDA0002679130310000044
From the current active user set
Figure BDA0002679130310000045
By deletion in
Figure BDA0002679130310000046
Determining utility of sensory data available to the active user to bring to the platform
Figure BDA0002679130310000047
Higher than the cost of reporting
Figure BDA0002679130310000048
If yes, selecting the active user to execute the perception task and updating
Figure BDA0002679130310000049
Then calculating a reward for the active user
Figure BDA00026791303100000410
And then continues according to the above.
And IV, if not, repeating the step III until the active user with the maximum utility can be realized by reselecting the unit cost until the unit cost is up to
Figure BDA00026791303100000411
I.e., all users are traversed, the iteration ends.
Further, the consideration of the active users in the step III
Figure BDA00026791303100000412
The calculation method comprises the following steps:
step I, initializing parameters, and initializing a virtual indication vector I' to IτWill activate the user
Figure BDA00026791303100000413
Is paid
Figure BDA00026791303100000414
Initialized to 0, and set virtual active users
Figure BDA00026791303100000415
Is initialized to
Figure BDA00026791303100000416
Step ii, if the active user
Figure BDA00026791303100000417
If selected, respectively calculating the active users
Figure BDA00026791303100000418
And all unselected active user sets
Figure BDA00026791303100000419
The perception data and social influence can bring about the platformEffect of (A)i(Iτ)、Δj(I') wherein,
Figure BDA00026791303100000420
step iii, calculating to obtain unit cost to realize utility
Figure BDA00026791303100000421
Maximum active user
Figure BDA00026791303100000422
Namely, it is
Figure BDA00026791303100000423
The active users
Figure BDA00026791303100000424
From the currently unselected active user set
Figure BDA00026791303100000425
By deletion in
Figure BDA00026791303100000426
Determining the active user
Figure BDA0002679130310000051
If the utility of the available sensing data brought to the platform is higher than the reported cost, updating
Figure BDA0002679130310000052
Then, judge
Figure BDA0002679130310000053
If yes, updating the active users
Figure BDA0002679130310000054
Is paid
Figure BDA0002679130310000055
Is calculated by the formula
Figure BDA0002679130310000056
Step iv, if not, repeatedly executing step iii until the active user
Figure BDA0002679130310000057
The required cost is higher than the self utility or all active users
Figure BDA0002679130310000058
And when the data is traversed, ending the iteration.
Further, if the iteration is over, the active user
Figure BDA0002679130310000059
The required cost is still lower than the self utility, and the active users are updated
Figure BDA00026791303100000510
Is paid
Figure BDA00026791303100000511
Is calculated by the formula
Figure BDA00026791303100000512
The beneficial technical effects of the invention are as follows:
(1) the method and the system quantitatively depict the utility brought to the platform by the social influence of the intelligent device user in the crowd sensing system, and consider that the social influence of the intelligent device user can dynamically change according to the position requirement of the sensing task and the potential contribution of the social friends of the intelligent device user, thereby conforming to the actual situation.
(2) The method selects an active user to execute the perception task by using the iterative selection algorithm, can adapt to the social influence dynamic characteristics of the user, and maximizes the quality and quantity of perception data while minimizing the cost consumption of the user, namely maximizing social welfare.
(3) The payment method of the reward of the active user for executing the perception task is matched with the user selection method of the invention, so that the intelligent device user can be ensured to report the real information of the intelligent device user, and the excess payment ratio of the platform is smaller than 0.9.
(4) The deduction proves that an incentive mechanism formed by the iterative selection algorithm and the payment method meets the requirements of calculation feasibility, individuality and authenticity, wherein after an intelligent device user in the individuality representation system participates in perception, the utility of the user is not negative, and the authenticity represents that the user reports real information of the user to the platform.
Drawings
FIG. 1 is a schematic diagram of the application of the online incentive mechanism based on the crowd sensing system after the social network is introduced;
FIG. 2 is a schematic flow diagram of the interaction of the platform of the present invention with smart device users in a crowd sensing system;
FIG. 3 is a schematic flow diagram of an iterative selection method of the present invention;
FIG. 4 is a flow chart illustrating a payment method of calculating a user's consideration of the present invention.
Detailed Description
The following detailed description of the preferred embodiments will be made with reference to the accompanying drawings.
As shown in fig. 1-4, the present invention provides an online incentive mechanism method for crowd sensing system, which can improve the participation of users in the crowd sensing system by motivating users to influence their social friends and attract more users to participate in sensing in consideration of the social relationship among users, and specifically comprises the following steps:
step one, modeling a crowd sensing system, a sensing task, the reliability of a user and social influence;
considering that a cloud platform in a crowd sensing system needs to collect real-time fine-grained sensing data, K interest areas POI needing to collect the sensing data are arranged,
Figure BDA0002679130310000061
working time required for sensing data collection
Figure BDA0002679130310000062
The equal-interval division is carried out,
Figure BDA0002679130310000063
each region lkAt each time slice tτAll have perception tasks
Figure BDA0002679130310000064
Initial value v brought to crowd sensing system by sensing data corresponding to different regional sensing taskskIs different.
The crowd sensing system has N registered users
Figure BDA0002679130310000071
Reliability q per useri∈(0,1]Deriving from historical perceptual data provided by the user; the social relationship between users is characterized by a directed graph, nodes of the directed graph represent users, and each directed edge e is shown in FIG. 1ijWeight w ofijRepresentative user uiFor user ujThe strength of the influence of (2), the probability of each user appearing in a different area oi,kEstimating by historical user trajectories; data value collected by the probability of an inactive user appearing in each region and the task of the current time slice of each region
Figure BDA0002679130310000072
Calculating the potential contribution each inactive user can make to the system
Figure BDA0002679130310000073
Then, the social influence of the users is measured by the weighted centrality and the weighted tight centrality, wherein the weighted centrality is used for measuring the sum of the influences of the active users on the direct social neighbors of the active users, and the weighted tight centrality is obtained by the shortest distance d of the users in the social networki,jTo measure the propagation speed of user message in the network and simultaneously make the potential contribution of inactive user
Figure BDA0002679130310000074
And also as weights to both centrality calculations.
And step two, based on the bidding document, the reliability and the social influence of the user and the value of the collected perception data for completing each perception task, adopting an iterative selection algorithm and selecting one active user to execute the perception task each time. I.e. at each time slice tτAnd the platform iteratively selects active users with high utility brought to the platform by unit cost and social influence to execute the perception task according to an iterative selection method, and then calculates the reward for the selected active users by using a payment method until all the active users are traversed and the iteration is finished.
Modeling the interaction of the platform with the user, as shown in FIG. 2, assuming that at each time slice tτAt the beginning, each active user
Figure BDA0002679130310000075
Submission of bidding documents to crowd sensing system
Figure BDA0002679130310000076
Wherein the content of the first and second substances,
Figure BDA0002679130310000077
represents the current set of active users and,
Figure BDA0002679130310000078
representing user uiThe time of the online of the time is long,
Figure BDA0002679130310000079
representing user uiThe perceived task that can be performed is,
Figure BDA00026791303100000710
representing user uiProvide for
Figure BDA00026791303100000711
The cost required for sensing data of all tasks in the system is up to the userIn sets
Figure BDA00026791303100000712
The cost of all tasks in
Figure BDA00026791303100000713
Figure BDA00026791303100000714
To represent tτUser u in time sliceiWith an indication vector
Figure BDA00026791303100000715
To represent active users
Figure BDA00026791303100000716
Whether it is selected to perform a task, wherein,
Figure BDA0002679130310000081
Figure BDA0002679130310000082
indicating reporting label
Figure BDA0002679130310000083
Active user uiAt time slice tτIs selected to execute the task.
The crowd sensing system comprehensively considers the value of the sensing data collected by each sensing task
Figure BDA0002679130310000084
And user's bidding document
Figure BDA0002679130310000085
Degree of reliability qiWeighted centrality
Figure BDA0002679130310000086
Weighted tight centrality
Figure BDA0002679130310000087
The method comprises the steps of designing an iterative selection method to select proper active users to execute perception tasks, enabling the selected active users to drive more inactive users to be converted into active users to participate in perception in a mode of transmitting messages to social circles of the selected active users, then designing a payment method, calculating a reward for the selected active users by a platform to make up for cost consumption of the selected active users, and giving the reward when the platform receives all perception data needing to be submitted by the selected active users.
In summary, the problem of maximizing social benefits for crowd-sourcing perception systems can be formally expressed as
Figure BDA0002679130310000088
Wherein, U (I)τ) W (I) representing the utility that the perception data of alternative users can bring to the platformτ) Representing the utility that the social influence of alternative users can bring to the platform,
Figure BDA0002679130310000089
representing the cost of the selected users to perform the task.
In an online incentive mechanism based on the crowd sensing system, a platform in the crowd sensing system selects an appropriate intelligent device user to participate in a sensing task, so that the quality and the quantity of sensing data are maximized while the cost consumption of the user is minimized, namely the social welfare is maximized. The invention designs an iterative active user selection algorithm, in each iteration, the platform reasonably selects an active user to execute a perception task according to the utility and the cost of the platform brought by the perception data and the social influence energy of each active user, then calculates the reward of the selected user by using a designed payment method, and then selects the next active user. As shown in fig. 3, the iterative selection method is as follows:
step I, initializing parameters and indicating a vector IτElement (1) of
Figure BDA00026791303100000810
All are set to zero;
step II, the platform is used for each current active user
Figure BDA00026791303100000811
Calculating the utility delta of the perception data and the social influence of the selected data to the platformi(Iτ)＝Ui(Iτ)+Wi(Iτ) The calculation formula is as follows:
Figure BDA0002679130310000091
Figure BDA0002679130310000092
W(Iτ)＝log(1+ω(Iτ)+ν(Iτ)),
Figure BDA0002679130310000093
wherein the content of the first and second substances,
Figure BDA0002679130310000094
representing the value of the perceptual data that has been collected for each perceptual task, qiWhich is indicative of the degree of reliability of the user,
Figure BDA0002679130310000095
Figure BDA0002679130310000096
represents the center of the weighted degree,
Figure BDA0002679130310000097
Which represents the weighted tight centrality of the device,
Figure BDA0002679130310000098
representing perceptual tasks, vkIndicates that the corresponding different areas lkIs a perception task
Figure BDA0002679130310000099
Initial value, U (I), brought to crowd sensing system by collected sensing dataτ) When the user selects the condition as IτUtime, utility, U, that the user's sensory data can bring to the platformi(Iτ) Representing platform selection user uiAnd no user u is selectediIs poor in data utility, W (I)τ) When the user selects the condition as IτThe social influence of the user can bring utility to the platform, Wi(Iτ) Representing platform selection user uiAnd no user u is selectediSocial influence of (a) is poor in utility;
step III, calculating to obtain the unit cost to realize the utility
Figure BDA00026791303100000910
Maximum active user
Figure BDA00026791303100000911
Namely, it is
Figure BDA00026791303100000912
The active user
Figure BDA00026791303100000913
From the current active user set
Figure BDA00026791303100000914
By deletion in
Figure BDA00026791303100000915
Determining utility of sensory data available to the active user to bring to the platform
Figure BDA00026791303100000916
Higher than the cost of reporting
Figure BDA00026791303100000917
If yes, selecting the active user to execute the perception task and updating
Figure BDA00026791303100000918
Then calculating a reward for the active user
Figure BDA00026791303100000919
And then continues according to the above.
And IV, if not, repeating the step III until the active user with the maximum utility can be realized by reselecting the unit cost until the unit cost is up to
Figure BDA00026791303100000920
I.e., all users are traversed, the iteration ends.
And step three, calculating the reward of the active user by using a payment method every time, and giving the reward to the platform when receiving all perception data to be submitted by the platform.
If active user
Figure BDA0002679130310000101
If selected, the platform calculates its reward using a payment method
Figure BDA0002679130310000102
The payment method includes a virtual user selection process, similar to the second step, as shown in fig. 4, specifically including the following steps:
step I, initializing parameters, and initializing a virtual indication vector I' to IτWill activate the user
Figure BDA0002679130310000103
Is paid
Figure BDA0002679130310000104
Initialized to 0, and set virtual active users
Figure BDA0002679130310000105
Is initialized to
Figure BDA0002679130310000106
Ensuring that the virtual selection process does not affect the true active set of users selected
Figure BDA0002679130310000107
Step ii, if the active user
Figure BDA0002679130310000108
If selected, calculating the active users respectively
Figure BDA0002679130310000109
And all unselected active user sets
Figure BDA00026791303100001010
The perception data and social influence of (1) can bring utility delta to the platformi(Iτ)、Δk(I') wherein,
Figure BDA00026791303100001011
step iii, calculating to obtain unit cost to realize utility
Figure BDA00026791303100001012
Maximum active user
Figure BDA00026791303100001013
Namely, it is
Figure BDA00026791303100001014
The active user
Figure BDA00026791303100001015
From the currently unselected active user set
Figure BDA00026791303100001016
By deletion in
Figure BDA00026791303100001017
Determining the active user
Figure BDA00026791303100001018
If the utility of the available sensing data brought to the platform is higher than the reported cost, updating
Figure BDA00026791303100001019
Then, judge
Figure BDA00026791303100001020
If yes, updating the active users
Figure BDA00026791303100001021
Is paid
Figure BDA00026791303100001022
Is calculated by the formula
Figure BDA0002679130310000111
Step iv, if not, repeatedly executing step iii until the active user
Figure BDA0002679130310000112
The required cost is higher than the self utility or all active users
Figure BDA0002679130310000113
And when the data is traversed, ending the iteration.
If the iteration is finished, the active users
Figure BDA0002679130310000114
The required cost is still lower than the self utility, and the active users are updated
Figure BDA0002679130310000115
Is paid
Figure BDA0002679130310000116
Is calculated by the formula
Figure BDA0002679130310000117
It will be appreciated by those skilled in the art that these are merely examples and that many variations or modifications may be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is therefore defined by the appended claims.

Claims (6)

1. An online incentive scheme method for crowd sensing system, characterized by comprising the steps of:
step one, modeling a crowd sensing system, a sensing task, the reliability of a user and social influence;
secondly, selecting an active user to execute the perception task each time by adopting an iterative selection algorithm on the basis of the bidding document, the reliability and the social influence of the user and the value of the perception data collected after finishing each perception task;
and step three, calculating the reward of the active user by using a payment method every time, and giving the reward to the platform when receiving all perception data needing to be submitted by the platform.
2. The online incentive scheme method for crowd sensing system according to claim 1, wherein: region of interest to be covered by crowd sensing system
Figure FDA0002679130300000011
The partition is carried out, and the partition is carried out,
Figure FDA0002679130300000012
working time required for sensing data collection
Figure FDA0002679130300000018
The equal-interval division is carried out,
Figure FDA0002679130300000013
using directed graph to conquer each user u in intelligence systemiOf (c) betweenAnd (4) the intersection relation is further used for representing the user u by using weighted centrality and weighted tight centralityiThe weighted centrality is used to measure the active user uiThe weighted close centrality represents the propagation speed of the user message in the network measured by the shortest distance between the users in the social network, wherein,
Figure FDA0002679130300000014
Figure FDA0002679130300000015
representing a set of users owned by the crowd sensing system.
3. The online incentive scheme method for crowd sensing system according to claim 2, wherein: at each time slice tτAnd step two, the platform iteratively selects active users with high utility brought to the platform by unit cost and social influence according to the iterative selection method in the step two to execute perception tasks, and then calculates reward for the selected active users by using a payment method until all the active users are traversed and the iteration is finished.
4. The on-line stimulation mechanism method for the crowd sensing system according to claim 3, wherein the iterative selection algorithm in the second step comprises the following steps: suppose that at each time slice tτAt the beginning, each active user
Figure FDA0002679130300000016
Submission of bidding documents to crowd sensing system
Figure FDA0002679130300000017
Wherein the content of the first and second substances,
Figure FDA0002679130300000021
represents the current set of active users and,
Figure FDA0002679130300000022
representing user uiThe time of the online of the time is long,representing user uiThe perceived task that can be performed is,
Figure FDA0002679130300000024
representing user uiProvide for
Figure FDA0002679130300000025
Cost required for sensing data of all tasks in the set, user completion set
Figure FDA0002679130300000026
The cost of all tasks in
Figure FDA0002679130300000027
Figure FDA0002679130300000028
To represent tτUser u in time sliceiWith an indication vector
Figure FDA0002679130300000029
To represent active users
Figure FDA00026791303000000210
Whether it is selected to perform a task, wherein,
Figure FDA00026791303000000211
indicating reporting label
Figure FDA00026791303000000212
Active user uiAt time slice tτIs selected to perform the task(s),
step I, initializing parameters and indicating a vector IτElement (1) of
Figure FDA00026791303000000213
All are set to zero;
step II, the platform is used for each current active user
Figure FDA00026791303000000214
Calculating the utility delta of the perception data and the social influence of the selected data to the platformi(Iτ)＝Ui(Iτ)+Wi(Iτ) The calculation formula is as follows:
Figure FDA00026791303000000215
Figure FDA00026791303000000216
W(Iτ)＝log(1+ω(Iτ)+v(Iτ))，
Figure FDA00026791303000000217
wherein the content of the first and second substances,
Figure FDA00026791303000000218
representing the value of the perceptual data that has been collected for each perceptual task, qiWhich is indicative of the degree of reliability of the user,
Figure FDA00026791303000000219
Figure FDA00026791303000000220
the centrality of the degree of weighting is represented,
Figure FDA00026791303000000221
which represents the weighted tight centrality of the device,
Figure FDA00026791303000000222
representing perceptual tasks, vkIndicates that the corresponding different areas lkIs a perception task
Figure FDA00026791303000000223
Initial value, U (I), brought to crowd sensing system by collected sensing dataτ) When the user selects the condition as IτUtime, utility, U, that the user's sensory data can bring to the platformi(Iτ) Representing platform selection user uiAnd no user u is selectediIs poor in data utility, W (I)τ) When the user selects the condition as IτThe social influence of the user can bring utility to the platform, Wi(Iτ) Representing platform selection user uiAnd no user u is selectediSocial influence of (a) is poor in utility;
step III, calculating to obtain the unit cost to realize the utility
Figure FDA0002679130300000031
Maximum active user
Figure FDA00026791303000000319
Namely, it is
Figure FDA0002679130300000032
The active user
Figure FDA00026791303000000320
From the current active user set
Figure FDA0002679130300000033
By deletion in
Figure FDA0002679130300000034
Determining utility of sensory data available to the active user to bring to the platform
Figure FDA00026791303000000321
Higher than the cost of reporting
Figure FDA0002679130300000035
If yes, selecting the active user to execute the perception task and updating
Figure FDA0002679130300000036
Then calculating a reward for the active user
Figure FDA0002679130300000037
Continuing next in accordance with the above;
step IV, if not, repeating the step III until the active user with the maximum utility can be realized by reselecting the unit cost until the unit cost is up to
Figure FDA0002679130300000038
I.e., all users are traversed, the iteration ends.
5. The method according to claim 4, wherein the active user is compensated in step III
Figure FDA00026791303000000318
The calculation method comprises the following steps:
step I, initializing parameters, and initializing a virtual indication vector I' into IτWill activate the user
Figure FDA00026791303000000322
Is paid
Figure FDA0002679130300000039
Initialized to 0, and set virtual active users
Figure FDA00026791303000000310
Is initialized to
Figure FDA00026791303000000317
Step ii, if the active user
Figure FDA00026791303000000323
If selected, respectively calculating the active users
Figure FDA00026791303000000324
And all unselected active user sets
Figure FDA00026791303000000311
The perception data and social influence of (a) can bring utility delta to the platformi(Iτ)、Δj(I') wherein,
Figure FDA00026791303000000312
I′＝Iτ；
step iii, calculating to obtain the unit cost which can realize the utility
Figure FDA00026791303000000313
Maximum active user
Figure FDA00026791303000000325
Namely, it is
Figure FDA00026791303000000314
The active users
Figure FDA00026791303000000326
From the currently unselected active user set
Figure FDA00026791303000000315
By deletion in
Figure FDA00026791303000000316
Determining the active user
Figure FDA00026791303000000327
If the utility of the available sensing data brought to the platform is higher than the reported cost, updating
Figure FDA0002679130300000041
Then, judge
Figure FDA0002679130300000042
If yes, updating the active users
Figure FDA0002679130300000048
Is paid
Figure FDA0002679130300000043
Is calculated by the formula
Figure FDA0002679130300000044
Step iv, if not, repeatedly executing step iii until the active user
Figure FDA0002679130300000049
The required cost is higher than the self utility or all active users
Figure FDA0002679130300000045
And when the data is traversed, ending the iteration.
6. The online incentive scheme method for crowd sensing system according to claim 5, wherein: if the iteration is finished, the active user
Figure FDA00026791303000000410
The required cost is still lower than the self utility, and the active users are updated
Figure FDA00026791303000000411
Is paid
Figure FDA0002679130300000046
Is calculated by the formula
Figure FDA0002679130300000047
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* Cited by examiner, † Cited by third party
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116502862A (en) * 2023-06-09 2023-07-28 暨南大学 Method for task allocation based on social benefit in mobile crowd sensing

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