Mobile crowdsensing has become an efficient paradigm for performing large-scale sensing tasks. Many quality-aware incentive mechanisms for mobile crowdsensing have been proposed. However, most of them measure the data quality by one single metric from a specific perspective. Moreover, they usually use the real-time quality, which cannot provide sufficient incentive for the workers with long-term high quality. In this paper, we refine the generalized data quality into the fine-grained ability requirement. We present a mobile crowdsensing system to achieve the fine-grained quality control, and formulate the problem of maximizing the social cost such that the fine-grained ability requirement of all sensing tasks can be satisfied. To stimulate the workers with long-term high quality, we design two ability reputation systems to assess workers’ fine-grained abilities online. The incentive mechanism based on the reverse auction and fine-grained ability reputation system is proposed. We design a greedy algorithm to select the winners and determine the payment based on the bids and fine-grained ability reputation of workers. Through both rigorous theoretical analysis and extensive simulations, we demonstrate that the proposed mechanisms achieve computational efficiency, individual rationality, truthfulness, whitewashing proof, and guaranteed approximation. Moreover, the designed mechanisms show prominent advantage in terms of social cost and average ability achievement ratio.

移动人群感知已成为执行大规模感知任务的有效范例。已经提出了许多用于移动人群感知的质量感知激励机制。然而，他们中的大多数从特定的角度通过一个单一的指标来衡量数据质量。而且，他们通常使用实时质量，不能为长期高质量的工人提供足够的激励。在本文中，我们将广义数据质量细化为细粒度的能力要求。我们提出了一个移动的群体感知系统来实现细粒度的质量控制，并制定了最大化社会成本的问题，以满足所有传感任务的细粒度能力要求。激励长期高素质的劳动者，我们设计了两个能力声誉系统来在线评估工人的细粒度能力。提出了基于逆向拍卖和细粒度能力声誉系统的激励机制。我们设计了一个贪心算法来选择获胜者并根据工人的出价和细粒度的能力声誉来确定支付。通过严格的理论分析和广泛的模拟，我们证明了所提出的机制实现了计算效率、个体合理性、真实性、粉饰证明和保证近似。此外，所设计的机制在社会成本和平均能力成就率方面显示出突出的优势。我们设计了一个贪心算法来选择获胜者并根据工人的出价和细粒度的能力声誉来确定支付。通过严格的理论分析和广泛的模拟，我们证明了所提出的机制实现了计算效率、个体合理性、真实性、粉饰证明和保证近似。此外，所设计的机制在社会成本和平均能力成就率方面显示出突出的优势。我们设计了一个贪心算法来选择获胜者并根据工人的出价和细粒度的能力声誉来确定支付。通过严格的理论分析和广泛的模拟，我们证明了所提出的机制实现了计算效率、个体合理性、真实性、粉饰证明和保证近似。此外，所设计的机制在社会成本和平均能力成就率方面显示出突出的优势。