{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/97338"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/97338","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Incentive mechanism design for mobile crowd sensing systems","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.","abstract_html":"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.","abstract_has_math":false,"creators":["Jin, Haiming"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Nahrstedt, Klara","Srikant, Rayadurgam","Gunter, Carl A.","Mehta, Ruta","Li, Baochun"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017-08-10T19:14:55Z","date_published":"2017-08-10T19:14:55Z","updated_at":"2026-07-22T22:24:32Z","subjects":["Incentive mechanism","Mobile crowd sensing","Quality of information","Privacy preservation"],"languages":["en"],"rights":["Copyright 2017 Haiming Jin"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/97338","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Nahrstedt, Klara","Srikant, Rayadurgam","Gunter, Carl A.","Mehta, Ruta","Li, Baochun"]},{"key":"dc:creator","label":"Author","values":["Jin, Haiming"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2017-08-10T19:14:55Z","2017-04-18","2017-05"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Incentive mechanism","Mobile crowd sensing","Quality of information","Privacy preservation"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2017 Haiming Jin"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/97338"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["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.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2017-08-10 without embargo terms","The student, Haiming Jin, accepted the attached license on 2017-04-12 at 09:26.","The student, Haiming Jin, submitted this Dissertation for approval on 2017-04-12 at 09:38.","This Dissertation was approved for publication on 2017-04-18 at 17:54.","DSpace SAF Submission Ingestion Package generated from Vireo submission #10714 on 2017-08-10 at 13:39:26","Made available in DSpace on 2017-08-10T19:14:55Z (GMT). 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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.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2017-08-10 without embargo terms","The student, Haiming Jin, accepted the attached license on 2017-04-12 at 09:26.","The student, Haiming Jin, submitted this Dissertation for approval on 2017-04-12 at 09:38.","This Dissertation was approved for publication on 2017-04-18 at 17:54.","DSpace SAF Submission Ingestion Package generated from Vireo submission #10714 on 2017-08-10 at 13:39:26","Made available in DSpace on 2017-08-10T19:14:55Z (GMT). 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