Truthful Incentive Mechanism for Nondeterministic Crowdsensing with Vehicles

Gao, Guoju; Xiao, Mingjun; Wu, Jie; Huang, Liusheng; Chang, Hu

doi:10.1109/tmc.2018.2829506

Cited by 118 publications

(18 citation statements)

References 43 publications

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“…sensor quality, environment noise) and be recorded by the service provider. Similarly, in [27], Gao etc. propose a truthful incentive mechanism for vehicle-based MCSs that integrates the requirement of the Quality of sensing Data (QoD), where the QoD of each participant is determined by objective factors and profiled at the service provider.…”

Section: The Quality Of Crowdsensing Datamentioning

confidence: 88%

“…The quality of data reported by temporally recruited mobile agents is one of the most critical challenges to the promotion of practical MCSs [12]. Increasing results in [22], [23], [24], [25], [26], [27] consider crowdsensing data quality from different perspectives. In [22], Wen etc.…”

Section: The Quality Of Crowdsensing Datamentioning

confidence: 99%

“…(21)) and the payment rule (Eq. (27), Eq. (28)), the best strategy for ∀i ∈ M is to truthfully report their data quality indications (q * = q).…”

Section: Payment Rulementioning

confidence: 99%

“…According to Eq. (27) and (28), for each i ∈ E, the more accurateq i is (i.e. the smaller the difference betweenq i and q i is), the higher p i is.…”

Section: Self-estimation Of the Data Qualitymentioning

confidence: 99%

“…As an active research topic, the issue of data quality [12] has been attracting significant interests of researchers in the MCS field, where the understanding of the quality of crowdsensing data is multi-fold. The majority of existing research [22], [23], [24], [25], [26], [27] considers the quality of received data as an intrinsic and relatively stable property determined by objective factors such as the reporting participant's geological location, battery level, transmission bandwidth, etc. However, such a notion (similar to Quality of Service in traditional sensing systems) cannot reflect the impact of participant's subjective behaviors (particularly malicious behaviors [12], [28]) on the quality of the crowdsensed data.…”

Section: Introductionmentioning

confidence: 99%

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On the Data Quality in Privacy-Preserving Mobile Crowdsensing Systems with Untruthful Reporting

Zhao

Yang

McCann

2021

IEEE Trans. on Mobile Comput.

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The proliferation of mobile smart devices with ever improving sensing capacities means that human-centric Mobile Crowdsensing Systems (MCSs) can economically provide a large scale and flexible sensing solution. The use of personal mobile devices is a sensitive issue, therefore it is mandatory for practical MCSs to preserve private information (the user's true identity, precise location, etc.) while collecting the required sensing data. However, well intentioned privacy protection techniques also conceal autonomous, or even malicious, behaviors of device owners (termed as self-interested), where the objectivity and accuracy of crowdsensing data can therefore be severely threatened. The issue of data quality due to untruthful reporting in privacy-preserving MCSs has been yet to produce solutions. Bringing together game theory, algorithmic mechanism design, and truth discovery, we develop a mechanism to guarantee and enhance the quality of crowdsensing data without jeopardizing the privacy of MCS participants. Together with solid theoretical justifications, we evaluate the performance of our proposal with extensive real-world MCS trace-driven simulations. Experimental results demonstrate the effectiveness of our mechanism on both enhancing the quality of the crowdsensing data and eliminating the motivation of MCS participants, even when their privacy is well protected, to report untruthfully.

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Section: The Quality Of Crowdsensing Datamentioning

confidence: 88%

Section: The Quality Of Crowdsensing Datamentioning

confidence: 99%

“…(21)) and the payment rule (Eq. (27), Eq. (28)), the best strategy for ∀i ∈ M is to truthfully report their data quality indications (q * = q).…”

Section: Payment Rulementioning

confidence: 99%

“…According to Eq. (27) and (28), for each i ∈ E, the more accurateq i is (i.e. the smaller the difference betweenq i and q i is), the higher p i is.…”

Section: Self-estimation Of the Data Qualitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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On the Data Quality in Privacy-Preserving Mobile Crowdsensing Systems with Untruthful Reporting

Zhao

Yang

McCann

2021

IEEE Trans. on Mobile Comput.

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Blockchain‐based incentive mechanism for environmental, social, and governance disclosure: A principal‐agent perspective

Niu,

Fu,

Liu

et al. 2024

Corp Soc Responsibility Env

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Environmental, social, and governance (ESG) disclosure has drawn much attention from listed companies, investors, and regulators. In response to the increasing demand of investors and regulators for non‐financial information, listed companies have paid attention to publishing ESG reports consisting of environmental, social, and governance information. Listed companies are increasingly required to provide high‐quality information that is clear and comparable. However, the lack of incentive to listed companies makes it hard to improve the quality of ESG disclosure, and the cost of ESG disclosure leads to the uncontrollable quality of ESG reports and may even manipulation by opportunistic behaviors. In this paper, we illustrate the moral hazard problem in ESG disclosure from the perspective of investors and listed companies, in which the effort level for listed companies to provide high‐quality ESG report cannot be observed by investors. Then we propose a blockchain‐based incentive mechanism for ESG disclosure from a principal‐agent perspective to improve the information quality of ESG disclosure, where investors act as principal and listed companies act as agents. Token in blockchain technology is utilized as the rewards to improve the listed companies' reputation, thus increasing their chance of being promoted to investors for preferential investment opportunities in the blockchain platform. We then design the first‐best (FB) and second‐best (SB) optimal contracts based on classic principal‐agent model to overcome the moral hazard problem. Extensive simulations are conducted to demonstrate the effectiveness and feasibility of the incentive mechanism.

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Truthful Crowdsensed Data Trading Based on Reverse Auction and Blockchain

Xiao

Liu

et al. 2019

Lecture Notes in Computer Science

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Truthful Incentive Mechanism for Nondeterministic Crowdsensing with Vehicles

Cited by 118 publications

References 43 publications

On the Data Quality in Privacy-Preserving Mobile Crowdsensing Systems with Untruthful Reporting

On the Data Quality in Privacy-Preserving Mobile Crowdsensing Systems with Untruthful Reporting

Blockchain‐based incentive mechanism for environmental, social, and governance disclosure: A principal‐agent perspective

Truthful Crowdsensed Data Trading Based on Reverse Auction and Blockchain

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