Incentive Mechanism Design For Mobile Crowdsourcing

With the ubiquity of smart mobile terminal,the mobile crowdsourcing(MCS)which combines mobile technologies and crowdsourcing has a vigorous development.While participating in a crowdsourcing task,smartphone users not only consume their own resources,but also expose themselves to potential security and privacy threats.To stimulate individuals' participation and contribution,incentive mechanism is a key to the development of mobile crowdsourcing.The contributions of this thesis are threefold:(1)First,this thesis thoroughly compares the typical mobile crowdsourcing applications,summarizes the patterns of mobile crowdsourcing and presents the classification of various MCS aapplications.Specifically,we propose the general framework and typical workflow of mobile crowdsourcing system,puts forward four critical challenge: Task management,incentive,security and privacy,as well as quality control.Then,the existing solutions to those challenges are also discussed respectively.(2)Basically,the existing incentive mechanism only separately aimed at one of the four major challenges.In order to optimize the mobile crowdsourcing system more comprehensively,mobile crowdsourcing schemes should smoothly integrate various technologies to solve multiple challenges,and satisfy several/most desired features.This thesis proposes QuaCentive,a quality-aware incentive framework for mobile crowdsourcing system,which integrates three popular incentive methods: reverse auction,reputation and gamification,and can motivate crowd to provide high-quality sensed contents,stimulate crowdsourcers to give truthful feedback about quality of sensed contents,and make platform profitable.(3)The task of mobile crowdsourcing is becoming more and more complicated,requiring the task participants to have heterogeneous abilities to complete.The diverse demand for participants raises a challenge to recruit suitable participants.This paper proposes a teamwork based incentive mechanism.In detail,the task requesters publish the contents of the task,and meanwhile inform the platform the required ability of participants and the weight of different ability type.Then,crowdsourcing platform select the most cost-effective participants to form a team,to complete the task of high quality.Through the experiment,the advantage of the incentive mechanism based on team model is verified.