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分阶段激励与惩罚机制在移动众包感知中的应用。

Staged Incentive and Punishment Mechanism for Mobile Crowd Sensing.

机构信息

School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China.

Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093, China.

出版信息

Sensors (Basel). 2018 Jul 23;18(7):2391. doi: 10.3390/s18072391.

Abstract

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.

摘要

激励机制对于招募移动用户参与感知任务并确保参与者提供高质量的感知数据至关重要。在本文中,我们研究了一种移动众包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。在招募阶段,我们引入支付激励系数,并设计了基于 Stackelberg 的博弈方法。参与者可以通过博弈互动进行招募。在感知阶段,我们提出了一种在互动中感知数据效用的算法。在感知任务完成后,可以使用受时空相关性影响的数据效用来过滤出获胜者。特别地,可以根据数据效用来积累参与者的声誉,并提出一种惩罚机制来减少恶意参与者造成的支付成本浪费。最后,我们基于真实数据对我们的解决方案进行了广泛的研究。广泛的实验表明,与现有的正向拍卖激励机制(PAIM)和逆向拍卖激励机制(RAIM)相比,我们提出的分阶段激励机制(SIM)可以有效地将激励行为从招募阶段扩展到感知阶段。它不仅在招募和感知阶段实现了实时激励,而且提高了感知数据的效用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/551e/6068587/7ec8559f1500/sensors-18-02391-g001.jpg

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