Staged Incentive and Punishment Mechanism for Mobile Crowd Sensing.
Staged Incentive and Punishment Mechanism for Mobile Crowd Sensing.
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分阶段的激励和惩罚机制用于移动人群感测。
DOI：
10.3390/s18072391
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发表时间：
2018-07-23
期刊：
影响因子：
--
通讯作者：
Luo H
中科院分区：
文献类型：
--
作者：
Tao D;Zhong S;Luo H
具有激励机制对于招募移动的用户参与感测任务并确保参与者提供高质量的感测数据至关重要。本文研究了一种适用于移动的人群感知的分阶段激励和惩罚机制。我们首先将激励过程分为两个阶段：招募阶段和感知阶段。在招聘阶段，引入薪酬激励系数，设计了一种基于Stackelberg的博弈方法。参与者可以通过游戏互动来招募。在感知阶段，提出了一种交互中感知数据的效用算法。感知任务完成后，利用受时空相关性影响的数据效用筛选出优胜者。特别地，可以基于数据效用进行参与者的信誉积累，并给出了惩罚机制，以减少恶意参与者造成的支付成本浪费。最后，我们根据实际数据对我们的解决方案进行了广泛的研究。大量实验表明，与现有的正拍卖激励机制（PAIM）和反向拍卖激励机制（RAIM）相比，本文提出的分阶段激励机制（SIM）能够有效地将激励行为从招募阶段扩展到感知阶段.它不仅实现了在招募和感知阶段的实时激励，而且提高了感知数据的效用。
Having an incentive mechanism is crucial for the recruitment of mobile users to participate in a sensing task and to ensure that participants provide high-quality sensing data. In this paper, we investigate a staged incentive and punishment mechanism for mobile crowd sensing. We first divide the incentive process into two stages: the recruiting stage and the sensing stage. In the recruiting stage, we introduce the payment incentive coefficient and design a Stackelberg-based game method. The participants can be recruited via game interaction. In the sensing stage, we propose a sensing data utility algorithm in the interaction. After the sensing task, the winners can be filtered out using data utility, which is affected by time–space correlation. In particular, the participants’ reputation accumulation can be carried out based on data utility, and a punishment mechanism is presented to reduce the waste of payment costs caused by malicious participants. Finally, we conduct an extensive study of our solution based on realistic data. Extensive experiments show that compared to the existing positive auction incentive mechanism (PAIM) and reverse auction incentive mechanism (RAIM), our proposed staged incentive mechanism (SIM) can effectively extend the incentive behavior from the recruiting stage to the sensing stage. It not only achieves being a real-time incentive in both the recruiting and sensing stages but also improves the utility of sensing data.
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