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In order to avoid malicious competition and select high quality crowd workers to improve the utility of crowdsourcing system, this paper proposes an incentive mechanism based on the combination of reverse auction and multi-attribute auction in mobile crowdsourcing. The proposed online incentive mechanism includes two algorithms. One is the crowd worker selection algorithm based on multi-attribute reverse auction that adopts dynamic threshold to make an online decision for whether accept a crowd worker according to its attributes. Another is the payment determination algorithm which determines payment for a crowd worker based on its reputation and quality of sensing data, that is, a crowd worker can get payment equal to the bidding price before performing task only if his reputation reaches good reputation threshold, otherwise he will get payment based on his data sensing quality. We prove that our proposed online incentive mechanism has the properties of computational efficiency, individual rationality, budget-balance, truthfulness and honesty. Through simulations, the efficiency of our proposed online incentive mechanism is verified which can improve the efficiency, adaptability and trust degree of the mobile crowdsourcing system.

Taking the complex network environment as the research background, by use of cloud model theory, this paper proposed the trust evaluation model based on trust chain. This trust evaluation model could implement the conversion between qualitative and quantitative of trust. And it could identify malicious nodes and reduce unnecessary losses through the evaluation model. It separated the trust information according to the model properties and converted into cloud digital characteristics, which being transmitted and fused in the model. The model could solve the unreasonable problems result of a long trust chain in transmitting and fusing trust information . The simulation results confirm that the model can effectively control the authenticity of the final results. The simulating experiment of preventing malicious competition further confirms the feasibility and rationality of the trust evaluation model based on cloud model and trust chain. The proposed trust evaluation model may provide a new promising method for research of trust evaluation in complex networks environment. [ABSTRACT FROM AUTHOR]

In order to avoid malicious competition and select high quality crowd workers to improve the utility of crowdsourcing system, this paper proposes an incentive mechanism based on the combination of reverse auction and multi-attribute auction in mobile crowdsourcing. The proposed online incentive mechanism includes two algorithms. One is the crowd worker selection algorithm based on multi-attribute reverse auction that adopts dynamic threshold to make an online decision for whether accept a crowd worker according to its attributes. Another is the payment determination algorithm which determines payment for a crowd worker based on its reputation and quality of sensing data, that is, a crowd worker can get payment equal to the bidding price before performing task only if his reputation reaches good reputation threshold, otherwise he will get payment based on his data sensing quality. We prove that our proposed online incentive mechanism has the properties of computational efficiency, individual rationality, budget-balance, truthfulness and honesty. Through simulations, the efficiency of our proposed online incentive mechanism is verified which can improve the efficiency, adaptability and trust degree of the mobile crowdsourcing system. [ABSTRACT FROM AUTHOR]