QoI-aware incentive for multimedia crowdsensing enabled learning system

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


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.


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



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].


  1. 1.
    Howard, A.G., Zhu, M., Chen, B., et al. MobileNets: efficient convolutional neural networks for mobile vision applications (2017). arXiv preprint arXiv:1704.04861
  2. 2.
    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
  3. 3.
    Hsu, W.N., Glass, J. Extracting domain invariant features by unsupervised learning for robust automatic speech recognition (2018). arXiv preprint arXiv:1803.02551
  4. 4.
    Leroux, S., Molchanov, P., Simoens, P., et al. IamNN: iterative and adaptive mobile neural network for efficient image classification (2018). arXiv preprint arXiv:1804.10123
  5. 5.
    Guo, B., Han, Q., Chen, H., et al.: The emergence of visual crowdsensing: challenges and opportunities. IEEE. Commun. Surv. Tutor. 19(4), 2526–2543 (2017)CrossRefGoogle Scholar
  6. 6.
    Li, Y., Jeong, Y.S., Shin, B.S., et al.: Crowdsensing multimedia data: security and privacy issues. IEEE. Multimedia. 24(4), 58–66 (2017)CrossRefGoogle Scholar
  7. 7.
    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
  8. 8.
    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
  9. 9.
    Ni, J., Zhang, K., Xia, Q., et al.: Enabling strong privacy preservation and accurate task allocation for mobile crowdsensing. IEEE Transactions on Mobile Computing (2018)Google Scholar
  10. 10.
    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
  11. 11.
    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
  12. 12.
    Faltings, B., Li, J.J., Jurca, R.: Incentive mechanisms for community sensing. IEEE. Trans. Comput. 63(1), 115–128 (2014)MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    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
  14. 14.
    Ye, Q., Zhuang, W.: Distributed and adaptive medium access control for internet-of-things-enabled mobile networks. IEEE. Internet. Things. J. 4(2), 446–460 (2017)CrossRefGoogle Scholar
  15. 15.
    Guo, B., Chen, H., Han, Q., et al.: Worker-contributed data utility measurement for visual crowdsensing systems. IEEE Trans. Mob. Comput. 16(8), 2379–2391 (2017)CrossRefGoogle Scholar
  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.
    Wen, Y., Shi, J., Zhang, Q., et al.: Quality-driven auction-based incentive mechanism for mobile crowd sensing. IEEE. Trans. Veh. Technol. 64(9), 4203–4214 (2015)CrossRefGoogle Scholar
  18. 18.
    Wang, Y., Jia, X., Jin, Q., et al.: QuaCentive: a quality-aware incentive mechanism in mobile crowdsourced sensing (MCS). J. Supercomput. 72(8), 2924–2941 (2016)CrossRefGoogle Scholar
  19. 19.
    Man, H.C., Hou, F., Huang, J:. Delay-sensitive mobile crowdsensing: algorithm design and economics. IEEE Trans. Mobile Comput., PP(99), p. 1, (2018)Google Scholar
  20. 20.
    Xu, Y., Zhou, Y., Mao, Y., et al.: Can early joining participants contribute more?—Timeliness sensitive incentivization for crowdsensing (2017). arXiv preprint arXiv:1710.01918
  21. 21.
    Aberer, K., Sathe, S., Chakraborty, D., et al.: OpenSense:open community driven sensing of environment. In: Proceedings of the 2010 ACM SIGSPATIAL International Workshop on GeoStreaming, pp. 39–42, (2010)Google Scholar
  22. 22.
    Hoh, B., Yan, T., Ganesan, D., et al.: TruCentive: a game-theoretic incentive platform for trustworthy mobile crowdsourcing parking services. In: IEEE International Conference on Intelligent Transportation Systems, pp. 160–166, (2012)Google Scholar
  23. 23.
    Yan, T., Hoh, B., Ganesan, D., et al.: Crowdpark: A crowdsourcing-based parking reservation system for mobile phones. In: University of Massachusetts at Amherst Tech. Report, (2011)Google Scholar
  24. 24.
    Mathur, S., Jin, T., Kasturirangan, N., et al.: ParkNet: drive-by sensing of road-side parking statistics. In: International Conference on Mobile Systems, Applications, and Services. pp. 123–136, (2010)Google Scholar
  25. 25.
    Matyas, S., Matyas, C., Schlieder, C., et al.: Designing location-based mobile games with a purpose: collecting geospatial data with CityExplorer. In: International Conference on Advances in Computer Entertainment Technology, pp. 244–247, (2008)Google Scholar
  26. 26.
    Wang, Y., Hu, W., Wu, Y., et al. SmartPhoto: a resource-aware crowdsourcing approach for image sensing with smartphones. In: ACM International Symposium on Mobile Ad Hoc Networking and Computing, pp. 113–122, (2014)Google Scholar
  27. 27.
    Arev, I., Park, H.S., Sheikh, Y., et al.: Automatic editing of footage from multiple social cameras. ACM. Trans. Graph. 33(4), 1–11 (2014)CrossRefGoogle Scholar
  28. 28.
    Hua, Y., He, W., Liu, X., et al.: SmartEye: real-time and efficient cloud image sharing for disaster environments. Computer Communications, in Proceedings of IEEE INFOCOM, pp. 1616–1624, (2015)Google Scholar
  29. 29.
    Guo, B., Chen, H., Yu, Z., et al.: FlierMeet: a mobile crowdsensing system for cross-space public information reposting, tagging, and sharing. IEEE Trans. Mob. Comput. 14(10), 2020–2033 (2015)CrossRefGoogle Scholar
  30. 30.
    Tuite, K., Snavely, N., Hsiao, D.Y., et al.: PhotoCity: training experts at large-scale image acquisition through a competitive game. In: Sigchi Conference on Human Factors in Computing Systems, pp. 1383–1392, (2011)Google Scholar
  31. 31.
    Wu, Y., Wang, Y., Hu, W., et al.: Resource-aware photo crowdsourcing through disruption tolerant networks. In: IEEE International Conference on distributed computing systems, pp. 374–383, (2016)Google Scholar
  32. 32.
    BelgiumTS Dataset, Accessed 13 Jan 2017
  33. 33.
  34. 34.
    Cheng, Y., Li, X., Li, Z., et al.: AirCloud: a cloud-based air-quality monitoring system for everyone. In: Proceedings of the 12th ACM Conference on Embedded Network Sensor Systems, pp. 251–265, (2014)Google Scholar
  35. 35.
    Clarke, E.H.: Multipart pricing of public goods. Public. Choice. 11(1), 17–33 (1971)CrossRefGoogle Scholar
  36. 36.
    Groves, T.: Incentives in Teams. Econometrica. 41(4), 617–631 (1973)MathSciNetCrossRefzbMATHGoogle Scholar
  37. 37.
    Myerson, R.B.: Optimal auction design. Math. Oper. Res. 6(1), 58–73 (1981)MathSciNetCrossRefzbMATHGoogle Scholar
  38. 38.
    Cormen, T.T., Leiserson, C.E., Rivest, R.L.: Introduction to algorithms. Resonance. 1(9), 14–24 (2009)zbMATHGoogle Scholar
  39. 39.
    Yiren, G., Hang, S., Guangwei, B., Tianjing, W., Hai, T., Yujia, H.: Incentivizing multimedia data acquisition for machine learning system. In Proceedings of 18th International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP), pp. 142–158, (2018)Google Scholar

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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