Incentive Mechanism Based on Multi-constrained Worker Selection in Mobile Crowdsourcing

With the rapid development of mobile crowdsourcing, crowdsourcing programs in the market have sprung up.They distribute tasks and use the power of the crowd to perform the tasks for collecting data and an effective incentive mechanism in mobile crowdsourcing becomes very important.However, the existing incentive mechanisms nowadays partially consider the reputation value, location and execution time of workers, which makes it difficult for crowdsourcing platform to select high-quality workers and assign multiple tasks on limited budgets or other constraints.To solve the above problems, this paper proposes an incentive mechanism on the basis of the multi-constrained worker selection（MSIM）,which relies on two related algorithms.One is the algorithm of worker selection based on improved reverse auction model, which comprehensively considers many important limitations to select great workers to perform the tasks, such as worker reputation, geographical location, task completion degree and result quality.The other