Incentivizing Multimedia Data Acquisition for Machine Learning System

  • Yiren Gu
  • Hang ShenEmail author
  • Guangwei Bai
  • Tianjing Wang
  • Hai Tong
  • Yujia Hu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11336)


To address restrictions on data collection, incentivizing multimedia data acquisition for machine learning system is proposed. This paper presents an effective QoI (Quality-of-Information)-aware incentive mechanism in multimedia crowdsensing, with the objective of promoting the growth of an initial training model. Firstly, an incentive model is constructed in the form of reverse auction to maximize the 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, a practical incentive mechanism to solve the auction problem is designed, which is shown to be truthful, individually rational and computationally efficient. Extensive simulation results demonstrate the proposed incentive mechanism produces close-to-optimal social welfare noticeably and high-QoI dataset is obtained. In particular, a significant performance improvement for machine learning model growth is achieved with lower complexity.


Multimedia crowdsensing Incentive mechanism Machine learning QoI Auction 



The authors gratefully acknowledge the support and financial assistance provided by 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 Natural Science Foundation of the Jiangsu Higher Education Institutions of China under Grant No. 15KJB520015, and Nangjing Municipal Science and Technology Plan Project under Grant No. 201608009.


  1. 1.
    Guo, B., Han, Q., Chen, H., et al.: The emergence of visual crowdsensing: challenges and opportunities. IEEE Commun. Surv. Tutor. PP(99), 1 (2017)Google Scholar
  2. 2.
    Li, Y., Jeong, Y.S., Shin, B.S., et al.: Crowdsensing multimedia data: security and privacy issues. IEEE Multimed. 24(4), 58–66 (2017)CrossRefGoogle Scholar
  3. 3.
  4. 4.
    Restuccia, F., Ghosh, N., Bhattacharjee, S., et al.: Quality of information in mobile crowdsensing: survey and research challenges. ACM Trans. Sens. Netw. 13(4), 34 (2017)CrossRefGoogle Scholar
  5. 5.
    Howard, A.G., Zhu, M., Chen, B., et al.: MobileNets: efficient convolutional neural networks for mobile vision applications, arXiv preprint arXiv:1704.04861 (2017)
  6. 6.
    Hsu, W.N., Glass, J.: Extracting domain invariant features by unsupervised learning for robust automatic speech recognition, arXiv preprint arXiv:1803.02551 (2018)
  7. 7.
    Leroux, S., Molchanov, P., Simoens, P., et al.: IamNN: iterative and adaptive mobile neural network for efficient image classification, arXiv preprint arXiv:1804.10123 (2018)
  8. 8.
    Hara, K., Sun, J., Moore, R., et al.: Tohme: detecting curb ramps in google street view using crowdsourcing, computer vision, and machine learning. In: Proceedings of the 27th Annual ACM Symposium on User Interface Software and Technology, pp. 189–204 (2014)Google Scholar
  9. 9.
    Anguelov, D., Dulong, C., Filip, D., et al.: Google street view: capturing the world at street level. Computer 43(6), 32–38 (2010)CrossRefGoogle Scholar
  10. 10.
    Sun, F., Huang, G.B., Wu, Q.M.J., et al.: Efficient and rapid machine learning algorithms for big data and dynamic varying systems. IEEE Trans. Syst. Man Cybern. Syst. 47(10), 2625–2626 (2017)CrossRefGoogle Scholar
  11. 11.
    Man, H.C., Hou, F., Huang, J.: Delay-sensitive mobile crowdsensing: algorithm design and economics. IEEE Trans. Mob. Comput. PP(99), 1 (2018)Google Scholar
  12. 12.
    Xu, Y., Zhou, Y., Mao, Y., et al.: Can early joining participants contribute more? - timeliness sensitive incentivization for crowdsensing (2017)Google Scholar
  13. 13.
    Myerson, R.B.: Optimal auction design. Math. Oper. Res. 6(1), 58–73 (1981)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Cheng, Y., Li, X., Li, Z., et al.: AirCloud: a cloud-based air-quality monitoring system for everyone (2014)Google Scholar
  15. 15.
  16. 16.
    Krontiris, I., Albers, A.: Monetary incentives in participatory sensing using multi-attributive auctions. Parallel Algorithms Appl. 27(4), 317–336 (2012)Google Scholar
  17. 17.
    Duan, L., Kubo, T., Sugiyama, K., et al.: Incentive mechanisms for smartphone collaboration in data acquisition and distributed computing. In: Proceedings of IEEE INFOCOM, pp. 1701–1709 (2012)Google Scholar
  18. 18.
    Faltings, B., Li, J.J., Jurca, R.: Incentive mechanisms for community sensing. IEEE Trans. Comput. 63(1), 115–128 (2014)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Yang, D., Xue, G., Fang, X., et al.: Incentive mechanisms for crowdsensing: crowdsourcing with smartphones. IEEE/ACM Trans. Netw. 24(3), 1732–1744 (2016)CrossRefGoogle Scholar
  20. 20.
    Clarke, E.H.: Multipart pricing of public goods. Public Choice 11(1), 17–33 (1971)CrossRefGoogle Scholar
  21. 21.
    Groves Jr., T.F.G., Groves, T.: Incentives in Teams[J]. Econometrica 41(4), 617–631 (1973)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Feng, Z., Zhu, Y., Zhang, Q., et al.: TRAC: truthful auction for location-aware collaborative sensing in mobile crowdsourcing. In: Proceedings of IEEE INFOCOM, pp. 1231–1239 (2014)Google Scholar
  23. 23.
    Cormen, T.T., Leiserson, C.E., Rivest, R.L.: Introduction to algorithms. Resonance 1(9), 14–24 (2009)zbMATHGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Yiren Gu
    • 1
  • Hang Shen
    • 1
    • 2
    Email author
  • Guangwei Bai
    • 1
  • Tianjing Wang
    • 1
  • Hai Tong
    • 1
  • Yujia Hu
    • 1
  1. 1.College of Computer Science and TechnologyNanjing Tech UniversityNanjingChina
  2. 2.Department of Electrical and Computer EngineeringUniversity of WaterlooWaterlooCanada

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