Object identification with Pay-As-You-Go crowdsourcing

Ting Wu, Chen Jason Zhang, Lei Chen, Pan Hui, Siyuan Liu

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

1 Scopus citations


The conventional crowdsourcing paradigm requires an explicit task description and payment scheme. Requesters can then easily determine whether the crowdsourced results are satisfactory, and workers will have a fairly clear expectation of the monetary reward once the task is accomplished. However, such a paradigm becomes problematic when it is applied to Object Identification (OI) tasks. First, for OI tasks, it is difficult for requesters to evaluate whether sufficient numbers of objects have been found by an individual worker, warranting payment. Second, the same objects can be detected by many workers and ending up being unnecessary workload and inefficient performance. In this paper, we design a new crowdsourcing paradigm for OI tasks. Designing such a paradigm is challenging. Firstly, an easily-detected object can be found by multiple workers, which leads to an unfair situation that the requester has to make extra payments for the duplication. Secondly, there is usually a time limit to finish the overall crowdsourcing process, which demands efficient assignment strategy. To address these challenges, we propose solutions to achieve fairness by a Pay-As-You-Go (PAYG) mechanism and efficiency by a new worker-assignment scheme, Adaptive Worker Assignment (AWA). Extensive experiments are conducted to demonstrate the advantages of this new paradigm.

Original languageEnglish (US)
Title of host publicationProceedings - 2016 IEEE International Conference on Big Data, Big Data 2016
EditorsRonay Ak, George Karypis, Yinglong Xia, Xiaohua Tony Hu, Philip S. Yu, James Joshi, Lyle Ungar, Ling Liu, Aki-Hiro Sato, Toyotaro Suzumura, Sudarsan Rachuri, Rama Govindaraju, Weijia Xu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages8
ISBN (Electronic)9781467390040
StatePublished - 2016
Event4th IEEE International Conference on Big Data, Big Data 2016 - Washington, United States
Duration: Dec 5 2016Dec 8 2016

Publication series

NameProceedings - 2016 IEEE International Conference on Big Data, Big Data 2016


Other4th IEEE International Conference on Big Data, Big Data 2016
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Information Systems
  • Hardware and Architecture


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