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Spatial Crowdsourcing Task Assignment Based on the Quality of Workers

Published: 28 July 2018 Publication History

Abstract

With the rapid development of mobile Internet, a variety of spatial crowdsourcing platforms have emerged and been widely applied. Task assignment is the core issue of spatial crowdsourcing. The existing methods of task assignment aim at assigning tasks to workers as much as possible, which lacks the guarantee of the quality of the tasks' answer. In this paper, two kinds of task assignment strategy based on the quality of workers are proposed to ensure the accuracy of the answer submitted by the workers as high as possible. The classical quality control algorithm, Incremental Quality Inference, is used to obtain the quality of workers. Capable worker strategy and maximum worker distance-quality strategy are proposed and compared with nearest work strategy to carry out task assignment based on the quality of workers computed by Incremental Quality Inference. Experimental results with discounted data in the offline shopping mall from crowdsourcing platform demonstrate the effectiveness of our approach.

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Cited By

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  • (2024)The multi-objective task assignment scheme for software crowdsourcing platforms involving new workersJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2024.10223736:10(102237)Online publication date: Dec-2024
  • (2023)A Framework of Quality-Aware Personalized Task Matching For Mobile Crowdsensing2023 IEEE 13th Symposium on Computer Applications & Industrial Electronics (ISCAIE)10.1109/ISCAIE57739.2023.10164864(309-314)Online publication date: 20-May-2023
  • (2021)A Cost-Efficient Framework for Crowdsourced Data Collection in Vehicular NetworksIEEE Internet of Things Journal10.1109/JIOT.2021.30657168:17(13567-13581)Online publication date: 1-Sep-2021
  • Show More Cited By

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cover image ACM Other conferences
ICCSE'18: Proceedings of the 3rd International Conference on Crowd Science and Engineering
July 2018
220 pages
ISBN:9781450365871
DOI:10.1145/3265689
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 July 2018

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Author Tags

  1. spatial crowdsourcing
  2. task assignment strategy
  3. worker quality

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ICCSE'18

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ICCSE'18 Paper Acceptance Rate 33 of 89 submissions, 37%;
Overall Acceptance Rate 92 of 247 submissions, 37%

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View all
  • (2024)The multi-objective task assignment scheme for software crowdsourcing platforms involving new workersJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2024.10223736:10(102237)Online publication date: Dec-2024
  • (2023)A Framework of Quality-Aware Personalized Task Matching For Mobile Crowdsensing2023 IEEE 13th Symposium on Computer Applications & Industrial Electronics (ISCAIE)10.1109/ISCAIE57739.2023.10164864(309-314)Online publication date: 20-May-2023
  • (2021)A Cost-Efficient Framework for Crowdsourced Data Collection in Vehicular NetworksIEEE Internet of Things Journal10.1109/JIOT.2021.30657168:17(13567-13581)Online publication date: 1-Sep-2021
  • (2021)Crowdsourcing usage, task assignment methods, and crowdsourcing platforms: A systematic literature reviewJournal of Software: Evolution and Process10.1002/smr.2368Online publication date: 30-Jun-2021
  • (2020)A Framework for Optimal Worker Selection in Spatial Crowdsourcing Using Bayesian NetworkIEEE Access10.1109/ACCESS.2020.30055438(120218-120233)Online publication date: 2020

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