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Balancing Quality and Budget Considerations in Mobile Crowdsourcing


Authors: C. Miao, H. Yu, Z. Shen, and C. Leung
Title: Balancing Quality and Budget Considerations in Mobile Crowdsourcing
Abstract: Mobile/spatial crowdsourcing is a class of crowdsourcing applications in which workers travel to specific locations in order to perform tasks. As workers may possess different levels of competence, a major research challenge for spatial crowdsourcing is to control the quality of the results obtained. Although existing mobile crowdsourcing systems are able to track a wide range of performance related data for the participating workers, there still lacks an automated mechanism to help requesters make key task allocation decisions including: 1) to whom should a task to allocated; 2) how much to pay for the result provided by each worker; and 3) when to stop looking for additional workers for a task. In this paper, we propose a budget-aware task allocation approach for spatial crowdsourcing (Budget-TASC ) to help requesters make these three decisions jointly. It considers the workers’ reputation and proximity to the task locations to maximize the expected quality of the results while staying within a limited budget. Furthermore, it supports payments to workers based on how their track records. Extensive experimental evaluations based on a large-scale real-world dataset demonstrate that Budget-TASC outperforms the state-of-the-art significantly in terms of reduction in the average error rate and savings on the budget.
Keywords: Budget allocation; Reputation; Trust; Mobile crowdsourcing; Crowdsensing
Journal Name: Decision Support Systems, vol. 90
Publisher: Elsevier
Year: 2016
Accepted PDF File: Balancing_Quality_and_Budget_Considerations_in_Mobile_Crowdsourcing_accepted.pdf
Permanent Link: http://dx.doi.org/10.1016/j.dss.2016.06.019
Reference: C. Miao, H. Yu, Z. Shen, and C. Leung, “Balancing quality and budget considerations in mobile crowdsourcing,” Decision Support Systems, vol. 90, pp. 56–64, October 2016.
bibtex: 
@article {LILY-j28,
   author 	= {Miao, Chunyan and Yu, Han and Shen, Zhiqi and Leung, Cyril},
   title 	= {Balancing Quality and Budget Considerations in Mobile Crowdsourcing},
   journal 	= {Decision Support Systems},
   year 	= {2016},
   month 	= {October},
   volume 	= {90},
   number 	= {},
   pages 	= {56-64},
   publisher 	= {Elsevier},
}