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Title:Incentive mechanism design for mobile crowd sensing systems
Author(s):Jin, Haiming
Director of Research:Nahrstedt, Klara
Doctoral Committee Chair(s):Nahrstedt, Klara
Doctoral Committee Member(s):Srikant, Rayadurgam; Gunter, Carl A.; Mehta, Ruta; Li, Baochun
Department / Program:Computer Science
Discipline:Computer Science
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:Ph.D.
Genre:Dissertation
Subject(s):Incentive Mechanism
Mobile Crowd Sensing
Quality of Information
Privacy Preservation
Abstract:The recent proliferation of increasingly capable and affordable mobile devices with a plethora of on-board and portable sensors that pervade every corner of the world has given rise to the fast development and wide deployment of mobile crowd sensing (MCS) systems. Nowadays, applications of MCS systems have covered almost every aspect of people's everyday living and working, such as ambient environment monitoring, healthcare, floor plan reconstruction, smart transportation, indoor localization, and many others. Despite their tremendous benefits, MCS systems pose great new research challenges, of which, this thesis targets one important facet, that is, to effectively incentivize (crowd) workers to achieve maximum participation in MCS systems. Participating in crowd sensing tasks is usually a costly procedure for individual workers. On one hand, it consumes workers' resources, such as computing power, battery, and so forth. On the other hand, a considerable portion of sensing tasks require the submission of workers' sensitive and private information, which causes privacy leakage for participants. Clearly, the power of crowd sensing could not be fully unleashed, unless workers are properly incentivized to participate via satisfactory rewards that effectively compensate their participation costs. Targeting the above challenge, in this thesis, I present a series of novel incentive mechanisms, which can be utilized to effectively incentivize worker participation in MCS systems. The proposed mechanisms not only incorporate workers' quality of information in order to selectively recruit relatively more reliable workers for sensing, but also preserve workers' privacy so as to prevent workers from being disincentivized by excessive privacy leakage. I demonstrate through rigorous theoretical analyses and extensive simulations that the proposed incentive mechanisms bear many desirable properties theoretically, and have great potential to be practically applied.
Issue Date:2017-04-18
Type:Thesis
URI:http://hdl.handle.net/2142/97338
Rights Information:Copyright 2017 Haiming Jin
Date Available in IDEALS:2017-08-10
Date Deposited:2017-05


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