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Multistrategy Repeated Game-Based Mobile Crowdsourcing Incentive Mechanism for Mobile Edge Computing in Internet of Things
Wireless Communications and Mobile Computing ( IF 2.146 ) Pub Date : 2021-01-27 , DOI: 10.1155/2021/6695696
Chuanxiu Chi 1, 2 , Yingjie Wang 1, 2 , Yingshu Li 1, 3 , Xiangrong Tong 1, 2
Affiliation  

With the advent of the Internet of Things (IoT) era, various application requirements have put forward higher requirements for data transmission bandwidth and real-time data processing. Mobile edge computing (MEC) can greatly alleviate the pressure on network bandwidth and improve the response speed by effectively using the device resources of mobile edge. Research on mobile crowdsourcing in edge computing has become a hot spot. Hence, we studied resource utilization issues between edge mobile devices, namely, crowdsourcing scenarios in mobile edge computing. We aimed to design an incentive mechanism to ensure the long-term participation of users and high quality of tasks. This paper designs a long-term incentive mechanism based on game theory. The long-term incentive mechanism is to encourage participants to provide long-term and continuous quality data for mobile crowdsourcing systems. The multistrategy repeated game-based incentive mechanism (MSRG incentive mechanism) is proposed to guide participants to provide long-term participation and high-quality data. The proposed mechanism regards the interaction between the worker and the requester as a repeated game and obtains a long-term incentive based on the historical information and discount factor. In addition, the evolutionary game theory and the Wright-Fisher model in biology are used to analyze the evolution of participants’ strategies. The optimal discount factor is found within the range of discount factors based on repeated games. Finally, simulation experiments verify the existing crowdsourcing dilemma and the effectiveness of the incentive mechanism. The results show that the proposed MSRG incentive mechanism has a long-term incentive effect for participants in mobile crowdsourcing systems.

中文翻译:

物联网中移动边缘计算的多策略重复基于游戏的移动众包激励机制

随着物联网时代的到来,各种应用需求对数据传输带宽和实时数据处理提出了更高的要求。通过有效利用移动边缘的设备资源,移动边缘计算(MEC)可以大大减轻网络带宽的压力并提高响应速度。边缘计算中的移动众包研究已经成为热点。因此,我们研究了边缘移动设备之间的资源利用问题,即移动边缘计算中的众包场景。我们旨在设计一种激励机制,以确保用户的长期参与和高质量的任务。本文设计了一种基于博弈论的长期激励机制。长期激励机制是鼓励参与者为移动众包系统提供长期和连续的质量数据。提出了基于多策略重复博弈的激励机制(MSRG激励机制),以指导参与者提供长期参与和高质量数据。所提出的机制将工人与请求者之间的交互视为重复游戏,并基于历史信息和折扣因素获得长期激励。此外,演化博弈论和生物学中的赖特-费舍尔模型被用来分析参与者策略的演化。在基于重复博弈的折扣因子的范围内找到最佳折扣因子。最后,仿真实验验证了现有的众包困境和激励机制的有效性。结果表明,提出的MSRG激励机制对移动众包系统的参与者具有长期的激励作用。
更新日期:2021-01-28
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