1) The document proposes using gamification as an incentive mechanism for participatory sensing to motivate users and reduce costs for clients. 2) An experiment was conducted with 18 users over one month to obtain a participation probability model and found gamification increased probability while difficulty decreased it. 3) A reward minimization problem is formulated to select users and determine reward points to minimize total cost while ensuring sensing requirements are met.

1. Gamification-based Incentive
Mechanism for Participatory Sensing
Yoshitaka Ueyama, Morihiko Tamai,
Yutaka Arakawa, Keiichi Yasumoto
Nara Institute of Sciense and Technology

2. 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)
rewar
d
sensed
info
Who is the
best to ask?

3. Outline
1. Background
2. Related Work
3. Basic Idea
4. Proposed Method
5. Experiment for Deriving Participation Probability
Model
2014/3/25 crwodsensing 3

4. 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. Application
Road congestion
Vacancy in café/parking
Weather report

5. 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

6. Outline
1. Background
2. Related Work
3. Basic Idea
4. Proposed Method
5. Experiment to Obtain Participation Probability
Model
2014/3/25 crwodsensing 6

7. 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
UsersClient
Reward
[3]L. Jaimes, IEEE INFOCOM 2012 [5] L.Duan, IEEE PerCom 2012
Want to reduce
costs

8. 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 PoI
But
 Cannot avoid increase of reward for high-burden sensing
 Need high reward for increasing users’ motivation
Problem

9. Basic Idea in our study
 Employ gamification as incentive mechanism
 Motivate users by mental satisfaction
 Try to reduce the cost by employing gamification
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proposal
incentive
Gamification
Monetary
incentive
Monetary
incentive Monetary
Incentive
gamificationcost
reduction!
Satisfy users by two incentive

10. 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-in
 Nike+・・・visualize & share of running-record
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Level, badges,
mission, visualization
Gaming factor
Company activities
Education
SNS
Non-game activities
incorporating

11. Outline
1. Background
2. Related Work
3. Basic Idea
4. Proposed Method
5. Experiment to Obtain Participation Probability
Model
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12. 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 reward
Prob.
Uses near X

13. 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 pts
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level1：
×1.0
level2：
×1.2

14. 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 > 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

15. 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

16. Outline
1. Background
2. Related Work
3. Basic Idea
4. Proposed Method
5. Experiment to Obtain Participation Probability
Model
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17. 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 day
Reward：20 pts.
earned points can be used
to purchase drinks, snacks, etc
(100pts = 100JPY (1USD))

18. Screenshots of the developed system
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Request(e-mail) Ranking Earned badges
Reward= base pts x level-dependent co-efficient

19. Result: probability vs. users
 Experiment with/without gamification
 18 users, one month, 480 requests
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0
0.2
0.4
0.6
0.8
1
A B C D E F G
Gm有り
Gm無
Participationprobability
Users who received 20 or more requests
Participation probability is increased by gamificationEnthusiasm for gamification is different among users
With Gm
Without Gm

20. 0.4
0.5
0.6
0.7
0.8
0.9
10Pt 12Pt 15Pt 20Pt 30Pt 40Pt
Gm有
Gm無
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.
Participationprobability
With Gm
Without Gm

21. Result: probability vs. difficulty
Content of Request
Prob.
with gm without gm
Take photo of landscape 0.93 0.67
Take photo of parking usage 0.89 0.69
Take 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

22. 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 Gm
Average of the
Probability
0.71 0.54
The number of users required
constraint With Gm Without Gm
Success Prob.>0.90 2 3
Success Prob.>0.95 3 4
Success Prob.>0.98 4 6
Client can achieve a sensing even though a few users around PoI exist

23. 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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24. 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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