Conflict-Aware Participant Recruitment for Mobile Crowdsensing

Zhang, Lichen; Yu, Deshui; Wang, Xiaoming; Guo, Longjiang

doi:10.1109/tcss.2019.2948957

Cited by 10 publications

(3 citation statements)

References 39 publications

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“…Assuming that the longterm utility of each stage is π k , the present value can be expressed as π k /ð1 − δÞ. According to Equation (19), the average return is obtained by…”

Section: Analysis Of Multistrategy Repeated Game Modelmentioning

confidence: 99%

“…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%

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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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“…Assuming that the longterm utility of each stage is π k , the present value can be expressed as π k /ð1 − δÞ. According to Equation (19), the average return is obtained by…”

Section: Analysis Of Multistrategy Repeated Game Modelmentioning

confidence: 99%

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

View full text Add to dashboard Cite

show abstract

“…[3] analysed urban noise pollution in real-time by encouraging numerous mobile sensing users to participate in tasks that enhance sensing data. Several studies have been conducted on data quality indicators [7][8][9]. For example, Ref.…”

Section: Introductionmentioning

confidence: 99%

TB-HQ: An Incentive Mechanism for High-Quality Cooperation in Crowdsensing

Zhao,

Zeng,

Wang

et al. 2024

Electronics

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Crowdsensing utilizes a range of sensing resources and participants, including mobile device sensors, to achieve collaborative sensing and information fusion. This enables it to handle complex social sensing tasks and provide more intelligent and real-time environment sensing services. Incentive mechanisms in crowdsensing are employed to address issues related to insufficient user participation and low-quality data submission. However, existing mechanisms fail to adequately consider reference points in user decision-making and uncertainty in the decision-making environment. This results in high incentive costs for the platform and limited effectiveness. On the one hand, the probabilities and utilities in the actual decision environment are defined based on user preferences, and uncertainty can lead to unpredictable impacts on users’ future gains or losses. On the other hand, users identify their choices based on certain known values, namely reference points. The factors influencing user decisions are not solely the absolute final result level but rather the relative changes or differences between the final result and the reference point. Therefore, to resolve this problem, we propose TB-HQ, an incentive mechanism for high-quality cooperation in crowdsensing, which simultaneously considers the reference points adopted by users in decision-making and the uncertainty caused by their preferences. This mechanism includes a task bonus-based incentive mechanism (TBIM) and a high quality-driven winner screening mechanism (HQWSM). TBIM motivates users to participate in tasks by offering task bonuses, which alter their reference points. HQWSM enhances data quality by reconstructing utility functions based on user preferences. Simulation results indicate that the proposed incentive mechanism is more effective in improving data quality and platform utility than the comparative incentive mechanisms, with a 32.7% increase in data quality and a 77.3% increase in platform utility.

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User Recruitment with Budget Redistribution in Edge-Aided Mobile Crowdsensing

Zhang

2020

Algorithms and Architectures for Parallel Processing

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Conflict-Aware Participant Recruitment for Mobile Crowdsensing

Cited by 10 publications

References 39 publications

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

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

TB-HQ: An Incentive Mechanism for High-Quality Cooperation in Crowdsensing

User Recruitment with Budget Redistribution in Edge-Aided Mobile Crowdsensing

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