TCAM: A Truthful Combinatorial Auction Mechanism for Crowdsourcing Systems

Crowdsourcing has shown its efficiency in obtaining information by harnessing the intelligence of a large crowd of human workers. It is essential to employ incentive mechanisms, typically auction, to motivate workers and collect sufficient data, since performing crowdsourcing tasks will always consume considerable resources, e.g., CPU or battery resource. To this end, we focus on the problem of heterogeneous task allocation with budget constraint in the crowdsourcing systems and propose a truthful auction mechanism which can maximize the profit of the task requester. In this paper, we first prove the NP-hardness of the studied problem and design a near-optimal task allocation mechanism with partial enumeration which can maximize the profit of the requester. Then, we judiciously design a bid-independent payment calculation mechanism to ensure the truthfulness of the participants. Finally, we prove that the proposed crowdsourcing task auction mechanism can achieve truthfulness and individual rationality. The extensive simulation results also corroborate with our theoretical analysis.