Gamification-based Incentive Mechanism for Participatory Sensing
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Gamification-based Incentive Mechanism for Participatory Sensing
Yoshitaka Ueyama, Morihiko Tamai, Yutaka Arakawa, Keiichi Yasumoto
Nara Institute of Sciense and Technology
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Abstract
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Incentive mechanism for participatory sensing
Proposed Incentive Gamification
Monetary Incentive
Define & solve a problem for selecting a set of users so that the cost (rewards) paid by the client is minimized
reducing the cost (rewards) for sensing PoI
Users(participate in sensing tasks)
Client(request sensing tasks)
reward
sensed info
Who is the best to ask?
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Outline
1. Background2. Related Work3. Basic Idea4. Proposed Method5. Experiment for Deriving Participation
Probability Model
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What is participatory sensing? Collect information from point of interest (PoI)
by asking mobile users to get and send back the information
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This road is
congested.
This café is not vacant.
Sunny around here.
Ex. ApplicationRoad congestion Vacancy in café/parkingWeather report
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Challenges in participatory sensing Need expensive reward for high-burden sensing
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Reward
Pains to input information
High reward
Client
Too far to PoI!
Embarrassed to take a photo here
Client want to minimize reward paid to users
e.g., moving, data input, mental load
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Outline
1. Background2. Related Work3. Basic Idea4. Proposed Method5. Experiment to Obtain Participation
Probability Model
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Related works
(2) Auction-based approach[5] Reduce rewards by allowing only a user who bids the
lowest price to make a sensing
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Goal is to reduce the cost paid by the client
(1) Game-theory based approach[3] Use game theory to predict each user’s action and derive the
minimum reward based on the prediction
UsersClientRewar
d
[3]L. Jaimes, IEEE INFOCOM 2012 [5] L.Duan, IEEE PerCom 2012
Want to reduce costs
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Problem of existing studies
Existing studies focused only on monetary incentive.
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Need other incentive to increase users’ motivation
Client wants to reduce the cost for sensing PoIBut
Cannot avoid increase of reward for high-burden sensingNeed high reward for increasing users’ motivation
Problem
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Basic Idea in our study
Employ gamification as incentive mechanism Motivate users by mental satisfaction
Try to reduce the cost by employing gamification04/09/2023 crwodsensing 9
proposal incentive
GamificationMonetary incentive
Monetary incentive Monetary
Incentive
gamificationcost reduction!
Satisfy users by two incentive
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Gamification
Incorporate gaming factor into non-gaming activities Users can enjoy participating activities like game
Gaming factor Level, title, mission, visualization of ranking among users, etc
Example of services with gamification foursquare ・・・ badges depending on the number of check-ins Nike+ ・・・ visualize & share of running-record
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Level, badges,mission, visualization
Gaming factor
Company activitiesEducation
SNS
Non-game activitiesincorporating
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Outline
1. Background2. Related Work3. Basic Idea4. Proposed Method5. Experiment to Obtain Participation
Probability Model
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Supposed participatory sensing
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Client Specifies PoI(Point of Interest) PoI: set of points Requests sensing of PoI to users nearby Tell reward points for the users
Users Determine participation in the requested sensing probabilistically Receive the specified reward points when completing the sensing
Red circles: PoI
reward
request
PoI: X
0.6
Ask users A and B for sensing X with
10 pts rewardProb.
Uses near X
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Incentive
Monetary incentive Give reward points to users who completes a sensing
(point can be exchanged to money)
Gamification-based incentive Level Scheme( airlines’ mileage service)≒
• Level is changed according to holding points• Users with higher level can get more point than lowers
Ranking Scheme• Visualize & share ranking among users by their holding points
Badge Scheme• Users who achieved specified condition get badges and points
e.g., complete sensing 5 times→beginner badge with 10 pts04/09/2023 crwodsensing 13
level1:×1.0level2:×1.2
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Reward Points Minimization Problem
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Select a set of users and determining reward points paid to each user Objective function: Minimize total reward points Constraint: probability of each PoI sensed by at
least one user > (a threshold, e.g., 0.95)
Client
Whom should I ask a sensing?
How much reward points?
Participate at prob 0.7 when reward is
20pts.
Participate at prob 0.5 when reward is
0.5
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Greedy algorithm
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CP =Reward Pts
Prob.
Greedily select a user with high participation probability and low reward points select users in descending order of CP
① Select users one by one in descending order of CP until constraint is satisfied
② If constraint is not satisfied, increase reward pts and repeat step 1
Algorithm flow
Client needs to predict participation prob of each user
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Outline
1. Background2. Related Work3. Basic Idea4. Proposed Method5. Experiment to Obtain Participation
Probability Model
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Experiment to obtain probability
In order to obtain participation probability, developed a participatory sensing system based on
foursquare and conducted user study• Obtain check-in information using FoursquareAPI• Send e-mail describing a sensing request to the user• Collect data of participation of users
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Check-in at restaurant Developed
System(NAIST Photo2)
Check-in info.
Request(e-mail)
Take a photo of menu of the dayReward: 20 pts.
earned points can be usedto purchase drinks, snacks, etc(100pts = 100JPY (1USD))
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Screenshots of the developed system
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Request(e-mail) Ranking Earned badges
Reward= base pts x level-dependent co-efficient
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Result: probability vs. users Experiment with/without gamification
18 users, one month, 480 requests
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A B C D E F G0
0.2
0.4
0.6
0.8
1
Gm有りGm無
Participation probability
Users who received 20 or more requests
Participation probability is increased by gamificationEnthusiasm for gamification is different among users
With GmWithout Gm
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10Pt 12Pt 15Pt 20Pt 30Pt 40Pt0.4
0.450.5
0.550.6
0.650.7
0.750.8
0.850.9
Gm有
Result: probability vs. rewards
Relationship between reward pts and probability
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Lv1(10Pt)
Lv2(12Pt)
Lv3(15Pt) Lv5(10Pt×3.0)
Lv4(10Pt×2.0)Q. Why increase?A. High level user have high prob.
Q. Why flat?A. Reward is too low.
Participation probability
With GmWithout Gm
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Result: probability vs. difficulty
Content of RequestProb.
with gm without gm
Take photo of landscape 0.93 0.67Take photo of parking usage 0.89 0.69Take photo of restaurant’s
limited menu 0.56 0.48
Take photo of congested level of facility
0.36 0.5
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Easy
Diffi-cult
Relationship between difficulty and probability
difficulty of request decrease participation probability
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Observation Evaluate the effectiveness of gamification
Calculate the number of users required to satisfy the constraint defined in the minimization problem
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Experimental Result
With Gm Without GmAverage of the
Probability0.71 0.54
The number of users requiredconstraint With Gm Without Gm
Success Prob.>0.90 2 3Success Prob.>0.95 3 4Success Prob.>0.98 4 6
Client can achieve a sensing even though a few users around PoI exist
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Summary
Propose gamification-based incentive mechanism for participatory sensing. Level Scheme, Ranking Scheme, Badge Scheme
Formulate rewards minimization problem To solve this problem, we conduced an experiment to
obtain the participation probability model• gamification increase the probability• reward do not effect the probability• difficulty of request decrease the probability
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Future work
Model an accurate participation probability based on the experimental result
Evaluate weather gamification can reduce the total reward points through the simulation study
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