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Effective truth discovery and fair reward distribution for mobile crowdsensing

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Effective truth discovery and fair reward distribution for mobile crowdsensing. / Shi, Fengrui; Qin, Zhijin; Wu, Di; McCann, Julie A.

In: Pervasive and Mobile Computing, Vol. 51, 12.2018, p. 88-103.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Shi, F, Qin, Z, Wu, D & McCann, JA 2018, 'Effective truth discovery and fair reward distribution for mobile crowdsensing', Pervasive and Mobile Computing, vol. 51, pp. 88-103. https://doi.org/10.1016/j.pmcj.2018.09.007

APA

Shi, F., Qin, Z., Wu, D., & McCann, J. A. (2018). Effective truth discovery and fair reward distribution for mobile crowdsensing. Pervasive and Mobile Computing, 51, 88-103. https://doi.org/10.1016/j.pmcj.2018.09.007

Vancouver

Author

Shi, Fengrui ; Qin, Zhijin ; Wu, Di ; McCann, Julie A. / Effective truth discovery and fair reward distribution for mobile crowdsensing. In: Pervasive and Mobile Computing. 2018 ; Vol. 51. pp. 88-103.

Bibtex

@article{8d358f6c144a41509fe828fc27ad89f3,
title = "Effective truth discovery and fair reward distribution for mobile crowdsensing",
abstract = "By leveraging the sensing capabilities of consumer mobile devices, mobile crowdsensing (MCS) systems enable a number of new applications for Internet of Things (IoT), such as traffic management, environmental monitoring, and localisation. However, the sensing data collected from the crowd workers are of various qualities, making it difficult to discover the ground truth and maintain the fairness of incentivisation schemes. In this paper, we propose a truth discovery algorithm based on a two-stage Maximum Likelihood Estimator (MLE), which explicitly characterises the heterogeneous sensing capabilities of the crowd and is able to estimate ground truth accurately using only a small amount of data from IoT infrastructures. Moreover, based on the truth discovery algorithm, two reward distribution schemes, LRDS and MRDS, are proposed to ensure fairness of rewarding the crowd according to their effort levels. We evaluate the estimation accuracy of the truth discovery algorithm and the fairness of the reward distribution schemes using both simulations and real-world MCS campaigns. The evaluation results indicate that the proposed methods achieve superior performance compared with state-of-the-art methods in terms of estimation accuracy and fairness of reward distribution. {\textcopyright} 2018 Elsevier B.V.",
keywords = "Incentivisation, Maximum Likelihood Estimator, Mobile crowdsensing, Truth discovery, Environmental management, Internet of things, Crowd sensing, Environmental Monitoring, Internet of Things (IOT), Maximum likelihood estimator, Maximum likelihood estimators (MLE), State-of-the-art methods, Maximum likelihood estimation",
author = "Fengrui Shi and Zhijin Qin and Di Wu and McCann, {Julie A.}",
year = "2018",
month = dec,
doi = "10.1016/j.pmcj.2018.09.007",
language = "English",
volume = "51",
pages = "88--103",
journal = "Pervasive and Mobile Computing",
issn = "1574-1192",
publisher = "Elsevier Science B.V.",

}

RIS

TY - JOUR

T1 - Effective truth discovery and fair reward distribution for mobile crowdsensing

AU - Shi, Fengrui

AU - Qin, Zhijin

AU - Wu, Di

AU - McCann, Julie A.

PY - 2018/12

Y1 - 2018/12

N2 - By leveraging the sensing capabilities of consumer mobile devices, mobile crowdsensing (MCS) systems enable a number of new applications for Internet of Things (IoT), such as traffic management, environmental monitoring, and localisation. However, the sensing data collected from the crowd workers are of various qualities, making it difficult to discover the ground truth and maintain the fairness of incentivisation schemes. In this paper, we propose a truth discovery algorithm based on a two-stage Maximum Likelihood Estimator (MLE), which explicitly characterises the heterogeneous sensing capabilities of the crowd and is able to estimate ground truth accurately using only a small amount of data from IoT infrastructures. Moreover, based on the truth discovery algorithm, two reward distribution schemes, LRDS and MRDS, are proposed to ensure fairness of rewarding the crowd according to their effort levels. We evaluate the estimation accuracy of the truth discovery algorithm and the fairness of the reward distribution schemes using both simulations and real-world MCS campaigns. The evaluation results indicate that the proposed methods achieve superior performance compared with state-of-the-art methods in terms of estimation accuracy and fairness of reward distribution. © 2018 Elsevier B.V.

AB - By leveraging the sensing capabilities of consumer mobile devices, mobile crowdsensing (MCS) systems enable a number of new applications for Internet of Things (IoT), such as traffic management, environmental monitoring, and localisation. However, the sensing data collected from the crowd workers are of various qualities, making it difficult to discover the ground truth and maintain the fairness of incentivisation schemes. In this paper, we propose a truth discovery algorithm based on a two-stage Maximum Likelihood Estimator (MLE), which explicitly characterises the heterogeneous sensing capabilities of the crowd and is able to estimate ground truth accurately using only a small amount of data from IoT infrastructures. Moreover, based on the truth discovery algorithm, two reward distribution schemes, LRDS and MRDS, are proposed to ensure fairness of rewarding the crowd according to their effort levels. We evaluate the estimation accuracy of the truth discovery algorithm and the fairness of the reward distribution schemes using both simulations and real-world MCS campaigns. The evaluation results indicate that the proposed methods achieve superior performance compared with state-of-the-art methods in terms of estimation accuracy and fairness of reward distribution. © 2018 Elsevier B.V.

KW - Incentivisation

KW - Maximum Likelihood Estimator

KW - Mobile crowdsensing

KW - Truth discovery

KW - Environmental management

KW - Internet of things

KW - Crowd sensing

KW - Environmental Monitoring

KW - Internet of Things (IOT)

KW - Maximum likelihood estimator

KW - Maximum likelihood estimators (MLE)

KW - State-of-the-art methods

KW - Maximum likelihood estimation

U2 - 10.1016/j.pmcj.2018.09.007

DO - 10.1016/j.pmcj.2018.09.007

M3 - Journal article

VL - 51

SP - 88

EP - 103

JO - Pervasive and Mobile Computing

JF - Pervasive and Mobile Computing

SN - 1574-1192

ER -