TCAM: A truthful combinatorial auction mechanism for crowdsourcing systems

Cui, Jingmei; Sun, Yu-E; Huang, He; Guo, Hansong; Du, Yang; Yang, Wenjian; Li, Meixuan

doi:10.1109/wcnc.2018.8377240

Cited by 8 publications

(3 citation statements)

References 13 publications

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“…In this paper, we choose TODA [12] and TCAM [13] for comparison. Among them, TODA is an optimal task assignment mechanism, but it is NP-hard.…”

Section: Methodsmentioning

confidence: 99%

Truthful incentive mechanism for multi-task assignment in crowdsouricng

Zhang¹,

Zhang²

2023

Sixth International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2023)

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Crowdsourcing is a very popular model on the Internet. It is a common task assignment method to assign multiple tasks according to workers' subjective preferences in crowdsourcing. However, workers may exchange for higher utility by providing false information. To this end, this paper proposes a truthful incentive mechanism for multi-task assignment in crowdsourcing. By introducing an incentive mechanism and designing the allocation algorithm and payment algorithm separately, workers will adopt the behavior that the platform expects to see when participating in task assignment. Experiments show that the task assignment mechanism is truthful, and satisfies individual rationality and incentive compatibility. Moreover, the mechanism can obtain results in linear time and has good computing performance.

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“…In this paper, we choose TODA [12] and TCAM [13] for comparison. Among them, TODA is an optimal task assignment mechanism, but it is NP-hard.…”

Section: Methodsmentioning

confidence: 99%

Truthful incentive mechanism for multi-task assignment in crowdsouricng

Zhang¹,

Zhang²

2023

Sixth International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2023)

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“…Constraint (16) ensures that for each acceptable pair (t i , w j ), either task t i is assigned to w k he prefers to worker w j , or worker w j is assigned t l he prefers to t i , or they are matched each other. k:h i,k >h i,j x i,k = 1 means that t i will be assigned to w k better than w j , otherwise k:h i,k >h i,j x i,k = 0. l:g l,j >g i,j…”

Section: B the Tass Mechanism Satisfies Tasks'mentioning

confidence: 99%

“…x l,j = 1 means that w j will be assigned to task t l better than t i , otherwise l:g l,j >g i,j x l,j = 0. Thus, if x i,j = 0 means task t i and worker w j do not match each other in this reverse auction, the condition that makes constraint (16) works is that k:h i,k >h i,j x i,k = 1 or l:g l,j >g i,j x l,j = 1. Then, there will be ''t i matches a worker better than w j or w j matches a task better than t i '', that is, (t i , w j ) does not constitute an obstacle stability pair, ensuring that the TASS mechanism satisfies task assignment with stable and satisfactory.…”

Section: B the Tass Mechanism Satisfies Tasks'mentioning

confidence: 99%

Multi-Attribute Crowdsourcing Task Assignment With Stability and Satisfactory

Xing

Wang

et al. 2019

IEEE Access

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Recently, crowdsourcing applications for smart cities have become more and more popular due to its higher work efficiency and lower work costs. However, the reasonable task assignment is still one of the important challenges for crowdsourcing. The existing researches on crowdsourcing task assignment focus on the tradeoff between maximizing the utility of platforms and minimizing the cost of requesters, but they lack of the considerations of stability and satisfactory. In this paper, we propose an intelligent multi attributes crowdsourcing task assignment with stability and satisfactory, called TASS. TASS can exploit the multi attributes to solve the stability of the transaction, and adopt the game theory to maximize the satisfaction of both sides during the task assignment. Next, we theoretically prove that the task assignment mechanism is truthfulness, individual rationality, stable and satisfactory assignment, and budget-balanced. Finally, we evaluate the performances of TASS with the state-of-the-art task assignment works. The experimental results show that TASS is better than the state-of-the-art task assignment works in terms of truthfulness, individually rationality, stable and satisfactory assignment, and balanced budget.INDEX TERMS Crowdsourcing, smart city, task assignment, game theory, stable matching.

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