An Incentive Mechanism Based on Behavioural Economics in Location-based Crowdsensing Considering an Uneven Distribution of Participants

Liu, Jiaqi; Yang, Yuying; Deng, Li; Deng, Xiaoheng; Huang, Shiyue; Liu, Hui

doi:10.1109/tmc.2020.3002586

Cited by 20 publications

(6 citation statements)

References 49 publications

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“…Although much effort has been devoted to investigating incentive mechanisms for crowdsensing task allocation in the literature [23][24][25], they usually focus on how to improve the social utility of crowdsensing task allocation. A common drawback shared by existing work is that an optimal solution with the objective of optimizing the social utility may result in the problem of unbalanced allocation, which may damage the social fairness.…”

Section: Related Workmentioning

confidence: 99%

Incentivizing Proportional Fairness for Multi-Task Allocation in Crowdsensing

Lu,

Liu,

Jia

et al. 2024

IEEE Trans. Serv. Comput.

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Section: Related Workmentioning

confidence: 99%

Incentivizing Proportional Fairness for Multi-Task Allocation in Crowdsensing

Lu,

Liu,

Jia

et al. 2024

IEEE Trans. Serv. Comput.

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“…Monetary incentives typically use profits or bonuses to compensate for sensing costs and apply to various sensing scenarios. Often, monetary incentives are better at motivating participants to participate [43].…”

Section: Mobile Crowdsensing Incentive Mechanismmentioning

confidence: 99%

Decision Analysis under Behavioral Economics—Incentive Mechanism for Improving Data Quality in Crowdsensing

et al. 2023

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Due to the profitability and selfishness of crowdfunding system users, under fixed budget conditions, there are problems, such as low task completion rate due to insufficient participants and low data quality. However, the existing incentive mechanisms are mainly based on traditional economics, which believes that whether users participate in tasks depends on whether the benefits of the task outweigh the costs. Behavioral economics shows that people judge the value of gains and losses according to a reference point. The weight given to losses is more important than the weight given to the same gains. Therefore, this article considers the impact of reference dependency and loss aversion on user decision-making and proposes a participant selection mechanism based on reference dependency (PSM-RD) and a quality assurance mechanism based on loss aversion (QAM-LA). PSM-RD uses reference points to influence user pricing and selects more participants based on relative value. QAM-LA pays additional rewards based on the data quality of participants and motivates them to improve data quality by reconstructing utility functions. The simulation results show that compared with the ABSee mechanism, data quality has improved by 17%, and the value of completed tasks has increased by at least 40%.

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“…In literature [93], Liu et al proposed an incentive method based on behavioral economics. The mechanism consists of two components, namely, participant selection and payment decision.…”

Section: 22mentioning

confidence: 99%

A Survey of Application and Key Techniques for Mobile Crowdsensing

Wang

2022

Wireless Communications and Mobile Computing

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Mobile crowdsensing (MCS) uses ubiquitous smart devices and network access technology to migrate sensing tasks from a centralized platform to a distributed computing terminal across time and space and provides new approaches for solving large-scale, diversified, and complex sensing problems. MCS research is of great importance and has attracted wide attention from researchers. Accordingly, this article summarizes the current research status of MCS. First, the typical applications of MCS are summarized. Then, the research status of key technologies, such as task allocation, incentivizing, data transmission, quality assurance, and evaluation methods, is considered. Finally, future research directions of MCS are given to provide a reference for in-depth MCS research.

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An Incentive Mechanism Based on Behavioural Economics in Location-based Crowdsensing Considering an Uneven Distribution of Participants

Cited by 20 publications

References 49 publications

Incentivizing Proportional Fairness for Multi-Task Allocation in Crowdsensing

Incentivizing Proportional Fairness for Multi-Task Allocation in Crowdsensing

Decision Analysis under Behavioral Economics—Incentive Mechanism for Improving Data Quality in Crowdsensing

A Survey of Application and Key Techniques for Mobile Crowdsensing

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