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QoI-aware incentive for multimedia crowdsensing enabled learning system

  • Yiren Gu
  • Hang ShenEmail author
  • Guangwei Bai
  • Tianjing Wang
  • Xuejun Liu
Special Issue Paper
  • 9 Downloads

Abstract

While much research has been devoted to algorithm improvement of the machine learning model for multimedia applications, relatively little research has focused on the acquisition of massive multimedia datasets with strict data demands for model training. In this paper, we propose a Quality-of-Information (QoI) aware incentive mechanism in multimedia crowdsensing, with the objective of promoting the growth of an initial training model. We begin with a reverse auction incentive model to maximize social welfare while meeting the requirements in quality, timeliness, correlation, and coverage. Then, we discuss how to achieve the optimal social welfare in the presence of an NP-hard winner determination problem. Lastly, we design an effective incentive mechanism to solve the auction problem, which is shown to be truthful, individually rational and computationally efficient. Our evaluation study is carried out using a real multimedia dataset. Extensive simulation results demonstrate that the proposed incentive mechanism produces close-to-optimal social welfare noticeably, while accompanied by accelerating the growth of the machine learning model with a high-QoI dataset.

Keywords

Multimedia crowdsensing Incentive Machine learning Quality-of-information (QoI) Auction 

Notes

Acknowledgements

The authors gratefully acknowledge the support and financial assistance provided by the National Project Funding for Key R & D Programs under Grant no. 2018YFC0808500, the National Natural Science Foundation of China under Grant no. 61502230, 61501224 and 61073197, the Natural Science Foundation of Jiangsu Province under Grant no. BK20150960, the Jiangsu Key Laboratory of Big Data Security and Intelligent Processing, NJUPT, under Grant no. BDSIP1910, the Natural Science Foundation of the Jiangsu Higher Education Institutions of China under Grant no. 15KJB520015, and the Nanjing Municipal Science and Technology Plan Project under Grant no. 201608009. The authors thank the anonymous reviewers who provided constructive feedback on earlier pieces of this work, appearing at ICA3PP [39].

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.College of Computer Science and TechnologyNanjing Tech UniversityNanjingChina
  2. 2.Jiangsu Key Laboratory of Big Data Security and Intelligent Processing, Nanjing University of Posts and TelecommunicationsNanjingChina

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