Expertise-Aware Truth Analysis and Task Allocation in Mobile Crowdsourcing

Xiaomei Zhang, Yibo Wu, Lifu Huang, Heng Ji, Guohong Cao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

20 Scopus citations

Abstract

Mobile crowdsourcing has received considerable attention as it enables people to collect and share large volume of data through their mobile devices. Since the accuracy of the collected data is usually hard to ensure, researchers have proposed techniques to identify truth from noisy data by inferring and utilizing the reliability of users, and allocate tasks to users with higher reliability. However, they neglect the fact that a user may only have expertise on some problems (in some domains), but not others. Neglecting this expertise diversity may cause two problems: low estimation accuracy in truth analysis and ineffective task allocation. To address these problems, we propose an Expertise-aware Truth Analysis and Task Allocation (ETA2) approach, which can effectively infer user expertise and then allocate tasks and estimate truth based on the inferred expertise. ETA2 relies on a novel semantic analysis method to identify the expertise domains of the tasks and user expertise, an expertise-aware truth analysis solution to estimate truth and learn user expertise, and an expertise-aware task allocation method to maximize the probability that tasks are allocated to users with the right expertise while ensuring the work load does not exceed the processing capability at each user. Experimental results based on two real-world datasets demonstrate that ETA2 significantly outperforms existing solutions.

Original languageEnglish (US)
Title of host publicationProceedings - IEEE 37th International Conference on Distributed Computing Systems, ICDCS 2017
EditorsKisung Lee, Ling Liu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages922-932
Number of pages11
ISBN (Electronic)9781538617915
DOIs
StatePublished - Jul 13 2017
Event37th IEEE International Conference on Distributed Computing Systems, ICDCS 2017 - Atlanta, United States
Duration: Jun 5 2017Jun 8 2017

Publication series

NameProceedings - International Conference on Distributed Computing Systems

Other

Other37th IEEE International Conference on Distributed Computing Systems, ICDCS 2017
CountryUnited States
CityAtlanta
Period6/5/176/8/17

All Science Journal Classification (ASJC) codes

  • Software
  • Hardware and Architecture
  • Computer Networks and Communications

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