Participant selection is a fundamental challenge to perform sensing tasks with adequate data quality in various mobile crowdsensing (MCS) applications. In this paper, we explore participant selection mechanisms with sensing-gain constraints in MCS. First, we propose a novel quality-aware participant reputation model with active factors. Second, since user density differs in various applications, we propose two kinds of sensing-gain constrained participant selection mechanisms with both sufficient and insufficient user resources. Particularly, in the case of sufficient user resources, we formulate the sensing-gain objective on recruit cost and participant scale under constraints on data quality and task coverage, and propose a M ulti-S tage D ecision mechanism via G reedy strategy (MSD-G); in the case of insufficient user resources, we formulate the sensing-gain objective on data quality, abstract it as a 0-1 knapsack problem, and devise a S ensing-G ain C onstrained D ynamic P rogramming (SGC-DP) mechanism. Extensive simulations over a real-world dataset have verified that our participant reputation model with active factors can distinguish high-quality participants with different active levels, and our MSD-G and SGC-DP algorithms can effectively select suitable participants with ideal recruit budget and guaranteed data quality.

在各种移动人群感知（MCS）应用中，参与者的选择是执行具有足够数据质量的感知任务的一项基本挑战。在本文中，我们探索了具有感知增益约束的MCS参与者选择机制。首先，我们提出了一个具有主动因素的新颖的质量意识参与者声誉模型。其次，由于用户密度在各种应用中都不同，因此我们提出了两种具有足够的和不足的用户资源的感知增益受限的参与者选择机制。特别是，在足够的用户资源的情况下，我们制定了感应增益对下对数据质量和覆盖面的任务招收的约束成本和参与者的规模目标，并提出了一个中号ulti-小号踏歌d通过G芦苇策略（MSD-G）实现决策机制；在的用户的资源不足的情况下，我们配制传感增益客观上的数据的质量，抽象它作为一个0-1背包问题，并制定一个小号ensing- ģ AIN Ç onstrained d ynamic P在AGC（SGC-DP）的机制。通过对真实数据集的大量仿真，我们证明了具有活跃因素的参与者声誉模型可以区分具有不同活跃水平的高质量参与者，并且我们的MSD-G和SGC-DP算法可以有效地选择具有理想招聘预算并有保证的合适参与者数据质量。