Incentivizing Truthful Data Quality for Quality-Aware Mobile Data Crowdsourcing
Author:
Affiliation:
1. Auburn University, Department of Electrical and Computer Engineering, Auburn, Alabama, USA
2. The Ohio State University, Department of Electrical and Computer Engineering & Department of Computer Science and Engineering, Columbus, Ohio, USA
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3209582.3209599
Reference27 articles.
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2. I. Koutsopoulos "Optimal incentive-driven design of participatory sensing systems " in IEEE International Conference on Computer Communications (INFOCOM) 2013. I. Koutsopoulos "Optimal incentive-driven design of participatory sensing systems " in IEEE International Conference on Computer Communications (INFOCOM) 2013.
3. D. Lee J. Kim H. Lee and K. Jung "Reliable multiple-choice iterative algorithm for crowdsourcing systems " in ACM International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS) 2015. 10.1145/2745844.2745871 D. Lee J. Kim H. Lee and K. Jung "Reliable multiple-choice iterative algorithm for crowdsourcing systems " in ACM International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS) 2015. 10.1145/2745844.2745871
4. N. B. Shah and D. Zhou "Double or nothing: Multiplicative incentive mechanisms for crowdsourcing " in Conference on Neural Information Processing Systems (NIPS) 2015. N. B. Shah and D. Zhou "Double or nothing: Multiplicative incentive mechanisms for crowdsourcing " in Conference on Neural Information Processing Systems (NIPS) 2015.
5. Y. Liu and M. Liu "An online learning approach to improving the quality of crowd-sourcing " in ACM International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS) 2015. 10.1145/2745844.2745874 Y. Liu and M. Liu "An online learning approach to improving the quality of crowd-sourcing " in ACM International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS) 2015. 10.1145/2745844.2745874
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