Maximizing user type diversity for task assignment in crowdsourcing

Wang, Ana; Ren, Meirui; Ma, Hailong; Zhang, Lichen; Guo, Longjiang

doi:10.1007/s10878-020-00645-6

Cited by 5 publications

(1 citation statement)

References 37 publications

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“…The success of a crowdsourcing system largely depends on whether users are actively involved and their willingness to make efforts for sensing tasks [17,18]. Due to insufficient participants in perception tasks and data quality issues, crowdsourcing systems need to adopt appropriate incentive mechanisms to actively guide participants to participate in tasks and provide high-quality services [19,20]. Since the number of workers cannot maintain longterm participation, it is important to encourage workers to maintain higher enthusiasm under the condition that the number of workers does not decrease.…”

Section: Introductionmentioning

confidence: 99%

Multistrategy Repeated Game‐Based Mobile Crowdsourcing Incentive Mechanism for Mobile Edge Computing in Internet of Things

Chi

Wang

et al. 2021

Wireless Communications and Mobile Computing

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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.

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Section: Introductionmentioning

confidence: 99%