Abstract
In mobile crowd sensing (MCS), appropriate rewards are always expected to compensate the participants for their consumptions of physical resources and involvements of manual efforts. Hence, the research about incentive mechanisms is essential and useful for MCS, and the existing works focus on optimizing one of the performance, such as the data quality, or the platforms profit, so, how to design an incentive mechanism which not only can guarantee the high data quality but also can maximize the profit of platforms is challenging issue. In this paper, we propose a quality-based online incentive mechanism with unknown users cost distributions for MCS. The scheme uses a Markov Decision Procession (MDP) to online adjust the incentive price such that it tends to the optimal price. Moreover, the data quality request is also considered in our scheme. Furthermore, we evaluate the performances of our proposed scheme with single task and multiple tasks, extensive simulation results show that can perform better in profit and regret than the existing incentive scheme.
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