Using Greedy Random Adaptive Procedure to Solve the User Selection Problem in Mobile Crowdsourcing

With the rapid development of mobile networks and smart terminals, mobile crowdsourcing has aroused the interest of relevant scholars and industries. In this paper, we propose a new solution to the problem of user selection in mobile crowdsourcing system. The existing user selection schemes mainly i...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2019-07, Vol.19 (14), p.3158
Main Authors: Yang, Jian, Ban, Xiaojuan, Xing, Chunxiao
Format: Article
Language:English
Subjects:
Crowdsourcing
GRASP-AR
Knapsack problem
marginalism principle
mobile crowdsourcing
Optimization
Quality
Reputation management
Smartphones
user selection
Wireless networks
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Summary:With the rapid development of mobile networks and smart terminals, mobile crowdsourcing has aroused the interest of relevant scholars and industries. In this paper, we propose a new solution to the problem of user selection in mobile crowdsourcing system. The existing user selection schemes mainly include: (1) find a subset of users to maximize crowdsourcing quality under a given budget constraint; (2) find a subset of users to minimize cost while meeting minimum crowdsourcing quality requirement. However, these solutions have deficiencies in selecting users to maximize the quality of service of the task and minimize costs. Inspired by the marginalism principle in economics, we wish to select a new user only when the marginal gain of the newly joined user is higher than the cost of payment and the marginal cost associated with integration. We modeled the scheme as a marginalism problem of mobile crowdsourcing user selection (MCUS-marginalism). We rigorously prove the MCUS-marginalism problem to be NP-hard, and propose a greedy random adaptive procedure with annealing randomness (GRASP-AR) to achieve maximize the gain and minimize the cost of the task. The effectiveness and efficiency of our proposed approaches are clearly verified by a large scale of experimental evaluations on both real-world and synthetic data sets.
ISSN:1424-8220
1424-8220
DOI:10.3390/s19143158