Journal ArticleDOI
RTRC: a reputation-based incentive game model for trustworthy crowdsourcing service
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TLDR
A game-based incentive mechanism, namely RTRC, is proposed to stimulate the crowds to contribute accurate information and to motivate the requesters to return accurate feedbacks and an evolutionary game is designed to model the dynamic of user-strategy selection.Abstract:
The ubiquity of mobile devices have promoted the prosperity of mobile crowd systems, which recruit crowds to contribute their resources for performing tasks. Yet, due to the various resource consumption, the crowds may be reluctant to join and contribute information. Thus, the low participation level of crowds will be a hurdle that prevents the adoption of crowdsourcing. A critical challenge for these systems is how to design a proper mechanism such that the crowds spontaneously act as suppliers to contribute accurate information. Most of existing mechanisms ignore either the honesty of crowds or requesters respectively. In this paper, considering the honesty of both, we propose a game-based incentive mechanism, namely RTRC, to stimulate the crowds to contribute accurate information and to motivate the requesters to return accurate feedbacks. In addition, an evolutionary game is designed to model the dynamic of user-strategy selection.read more
Citations
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Journal ArticleDOI
A survey on game theoretical methods in Human–Machine Networks
Xueqin Liang,Zheng Yan,Zheng Yan +2 more
TL;DR: This paper extensively review the literature about game theoretical methods in HMNs, in particular focusing on its typical systems such as crowdsourcing, an elemental HMN and Internet of Things (IoT), a hybrid HMN, as well as Bitcoin.
Journal ArticleDOI
An Incentive Mechanism in Mobile Crowdsourcing Based on Multi-Attribute Reverse Auctions.
TL;DR: It is proved that the proposed online incentive mechanism has the properties of computational efficiency, individual rationality, budget-balance, truthfulness and honesty.
Journal ArticleDOI
Improve Reputation Evaluation of Crowdsourcing Participants Using Multidimensional Index and Machine Learning Techniques
Yanrong Huang,Min Chen +1 more
TL;DR: A reputation evaluation model for crowdsourcing participants of Random Forest based on Linear Discriminant Analysis is proposed, showing that the LDA-RF model on accuracy, F1-measure, generalization ability and robustness are better than those of other models, and it has better performance and effectiveness.
Journal ArticleDOI
An Incentive Mechanism Based on a Bayesian Game for Spatial Crowdsourcing
TL;DR: This paper designs a precise incentive mechanism based on a Bayesian game for SC that successfully avoids the conditions limited by the Gibbard–Satterthwaite impossibility theorem and proposes a geometric primitive matching algorithm based on the Jaccard coefficient that can greatly improve data quality.
Proceedings ArticleDOI
Multidimensional Reputation Evaluation Model for Crowdsourcing Participants Based on Big Data
Yanrong Huang,Min Chen +1 more
TL;DR: Empirical analysis and results show that MREM model is more comprehensive, dynamic and accurate than the existing model of crowdsourcing platform, and has stronger practicability.
References
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Reputation-based framework for high integrity sensor networks
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Proceedings ArticleDOI
Financial incentives and the "performance of crowds"
Winter Mason,Duncan J. Watts +1 more
TL;DR: It is found that increased financial incentives increase the quantity, but not the quality, of work performed by participants, where the difference appears to be due to an "anchoring" effect.
Proceedings ArticleDOI
Knowledge sharing and yahoo answers: everyone knows something
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Journal ArticleDOI
A Privacy-Preserving and Copy-Deterrence Content-Based Image Retrieval Scheme in Cloud Computing
TL;DR: A unique watermark is directly embedded into the encrypted images by the cloud server before images are sent to the query user, and when image copy is found, the unlawful query user who distributed the image can be traced by the watermark extraction.
Proceedings ArticleDOI
GeoCrowd: enabling query answering with spatial crowdsourcing
Leyla Kazemi,Cyrus Shahabi +1 more
TL;DR: This paper introduces a taxonomy for spatial crowdsourcing, and focuses on one class of this taxonomy, in which workers send their locations to a centralized server and thereafter the server assigns to every worker his nearby tasks with the objective of maximizing the overall number of assigned tasks.