gamification-based incentive mechanism for participatory sensing

Gamification-based Incentive Mechanism for Participatory Sensing

　 Yoshitaka Ueyama, Morihiko Tamai, Yutaka Arakawa, Keiichi Yasumoto

Nara Institute of Sciense and Technology

Abstract

04/09/2023 crwodsensing 2

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?

Outline

1. Background2. Related Work3. Basic Idea4. Proposed Method5. Experiment for Deriving Participation

Probability Model


What is participatory sensing? Collect information from point of interest (PoI)

by asking mobile users to get and send back the information


This road is

congested.

This café is not vacant.

Sunny around here.

Ex. ApplicationRoad congestion Vacancy in café/parkingWeather report

Challenges in participatory sensing Need expensive reward for high-burden sensing


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

Outline

1. Background2. Related Work3. Basic Idea4. Proposed Method5. Experiment to Obtain Participation

Probability Model


Related works

（２） Auction-based approach[5] Reduce rewards by allowing only a user who bids the

lowest price to make a sensing


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

Problem of existing studies

Existing studies focused only on monetary incentive.


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

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

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


Level, badges,mission, visualization

Gaming factor

Company activitiesEducation

SNS

Non-game activitiesincorporating

Outline


Probability Model


Supposed participatory sensing


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

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

Reward Points Minimization Problem


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

Greedy algorithm


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

Outline


Probability Model


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


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))

Screenshots of the developed system


Request(e-mail) Ranking Earned badges

Reward= base pts x level-dependent co-efficient

Result: probability vs. users Experiment with/without gamification

18 users, one month, 480 requests


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

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


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

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


Easy

Diffi-cult

Relationship between difficulty and probability

difficulty of request decrease participation probability

Observation Evaluate the effectiveness of gamification

Calculate the number of users required to satisfy the constraint defined in the minimization problem


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

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


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
