TY - JOUR
T1 - Movement-Based Incentive for Crowdsourcing
AU - Tian, Feng
AU - Liu, Bo
AU - Sun, Xiao
AU - Zhang, Xiaomei
AU - Cao, Guohong
AU - Gui, Lin
N1 - Funding Information:
Manuscript received August 13, 2016; revised November 8, 2016; accepted January 6, 2017. Date of publication January 17, 2017; date of current version August 11, 2017. This work was supported in part by the National Science Foundation under Grant CNS-1421578, by the National Natural Science Foundation of China under Grant 61471236, Grant 61420106008, and Grant 61671295, by the 111 Project (B07022), by the Shanghai Key Laboratory of Digital Media Processing, and by Shanghai Pujiang Program under Grant 16PJD029. The review of this paper was coordinated by Dr. F. Gunnarsson.
Publisher Copyright:
© 1967-2012 IEEE.
PY - 2017/8
Y1 - 2017/8
N2 - Most of the research on the incentive mechanism design in crowdsourcing has focused on how to allocate sensing tasks to participants to maximize the social welfare. However, none of them consider the coverage holes created by the uneven distribution of participants. As a result, most of the participants in some popular areas compete for tasks, while many tasks in unpopular areas cannot be completed due to the lack of participants. In this paper, we design a movement-based incentive mechanism for crowdsourcing, where participants are stimulated to move to the unpopular areas and complete the sensing tasks in these areas, which benefits both participants and the platform. We formulate a task allocation problem considering controlled mobility. Since the task allocation problem is NP-hard, we propose a greedy algorithm to solve it and design a critical payment policy to guarantee that participants declare their cost truthfully. Theoretical analysis shows that our proposed incentive mechanism satisfies the desired properties of truthfulness, individual rationality, platform profitability, and computational efficiency. Evaluation results show that the proposed movement-based incentive mechanism outperforms the existing solution under various conditions.
AB - Most of the research on the incentive mechanism design in crowdsourcing has focused on how to allocate sensing tasks to participants to maximize the social welfare. However, none of them consider the coverage holes created by the uneven distribution of participants. As a result, most of the participants in some popular areas compete for tasks, while many tasks in unpopular areas cannot be completed due to the lack of participants. In this paper, we design a movement-based incentive mechanism for crowdsourcing, where participants are stimulated to move to the unpopular areas and complete the sensing tasks in these areas, which benefits both participants and the platform. We formulate a task allocation problem considering controlled mobility. Since the task allocation problem is NP-hard, we propose a greedy algorithm to solve it and design a critical payment policy to guarantee that participants declare their cost truthfully. Theoretical analysis shows that our proposed incentive mechanism satisfies the desired properties of truthfulness, individual rationality, platform profitability, and computational efficiency. Evaluation results show that the proposed movement-based incentive mechanism outperforms the existing solution under various conditions.
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U2 - 10.1109/TVT.2017.2654355
DO - 10.1109/TVT.2017.2654355
M3 - Article
AN - SCOPUS:85027287303
SN - 0018-9545
VL - 66
SP - 7223
EP - 7233
JO - IEEE Transactions on Vehicular Technology
JF - IEEE Transactions on Vehicular Technology
IS - 8
M1 - 7820227
ER -