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A Framework for Learning the Pricing Model of Sensing Tasks in Crowdphotographing

  • Fei Hao
  • Huijuan Guo
  • Doo-Soon ParkEmail author
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 536)

Abstract

Crowdphotographing, an emerging self-service mode over the mobile Internet, is to recruit several users to take the pictures via incentive mechanism. Importantly, it can be used for business inspection and information collection for enterprises. This paper mainly studies the rationality and optimization of task pricing for crowdphotographing. In this paper, different multivariate linear regression models are established to analyze the task pricing, performance, membership information and so forth. The multivariate linear regression equation is eventually obtained, which efficiently solves the problem of task pricing.

Keywords

Crowdphotographing Pricing model Multivariate linear regression 

References

  1. 1.
    Zhang, X., Wu, Y., Huang, L., et al.: Expertise-aware truth analysis and task allocation in mobile crowdsourcing. In: IEEE International Conference on Distributed Computing Systems, pp. 922–932. IEEE (2017)Google Scholar
  2. 2.
    Wu, T., Dou, W., Ni, Q., Yu, S., Chen, G.: Mobile live video streaming optimization via crowdsourcing brokerage. IEEE Trans. Multimedia 19(10), 2267–2281 (2017)CrossRefGoogle Scholar
  3. 3.
    Tran-Thanh, L., Venanzi, M., Rogers, A., et al.: Efficient budget allocation with accuracy guarantees for crowdsourcing classification tasks. In: Twelfth International Conference on Autonomous Agents and Multi-Agent Systems, pp. 901–908 (2013)Google Scholar
  4. 4.
    Tang, H., Sun, Z.C., Chew, K.W.R., et al.: A 5.8 nW 9.1-ENOB 1-kS/s local asynchronous successive approximation register ADC for implantable medical device. IEEE Trans. Very Large Scale Integr. Syst. 22(10), 2221–2225 (2014)CrossRefGoogle Scholar
  5. 5.
    Dong, L., Zhou, J., Tang, Y.Y.: Effective and fast estimation for image sensor noise via constrained weighted least squares. IEEE Trans. Image Process. 27(6), 2715–2730 (2018)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Tong, Y., Chen, L., Zhou, Z., et al.: SLADE: a smart large-scale task decomposer in crowdsourcing. IEEE Trans. Knowl. Data Eng. 30(8), 1588–1601 (2018)CrossRefGoogle Scholar
  7. 7.
    Hao, F., Jiao, M., Min, G., et al.: Launching an efficient participatory sensing campaign: a smart mobile device-based approach. ACM Trans. Multimedia Comput. Commun. Appl. 12(1s), 18 (2015)CrossRefGoogle Scholar
  8. 8.
    Hao, F., Jiao, M., Min, G., et al.: A trajectory-based recruitment strategy of social sensors for participatory sensing. IEEE Commun. Mag. 52(12), 41–47 (2014)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.School of Computer ScienceShaanxi Normal UniversityXi’anChina
  2. 2.Department of Computer ScienceTaiyuan Normal UniversityTaiyuanChina
  3. 3.Department of Computer Software EngineeringSoonchunhyang UniversityAsanKorea

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