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Quality-Assure and Budget-Aware Task Assignment for Spatial Crowdsourcing

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Collaborate Computing: Networking, Applications and Worksharing (CollaborateCom 2016)

Abstract

With the increasingly ubiquity of mobile devices and the rapid development of communication technologies, spatial crowdsourcing has become a hot topic research among academic and industry community. As participants may possess different capabilities and reliabilities, as well as the changeable locations and available time slots of both tasks and potential workers, a major challenge is how to assign spatial tasks to appropriate workers from lots of potential applicants, which should assure the result quality of the crowdsourcing task. Also, as different workers may receive variable rewards for the same task, the crowdsourcing budget renders task assignment more complicated. This paper focuses on the issue of quality assurance for task assignment in spatial crowdsourcing while considering budget limitation. The problem is first modeled as Quality-assure and Budget-aware Task Assignment (QBTA) problem. Then two two-phase greedy algorithms are proposed. Finally, experiments are conducted to show the effectiveness and efficiency of the algorithms.

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References

  1. Kazemi, L., Shahabi, C., Chen, L.: GeoTruCrowd: trustworthy query answering with spatial crowdsourcing. In: ACM SIGSPATIAL, Orlando, FL, USA, pp. 304–313 (2013)

    Google Scholar 

  2. Deng, D., Shahabi, C., Demiryurek, U.: Maximizing the number of worker’s self-selected tasks in spatial crowdsourcing. In: ACM SIGSPATIAL, Orlando, FL, USA, pp. 314–323 (2013)

    Google Scholar 

  3. Yu, H., Miao, C., Shen, Z., Leung, C.: Quality and budget aware task allocation for spatial crowdsourcing. In: AAMAS, pp. 1689–1690 (2015)

    Google Scholar 

  4. Kazemi, L., Shahabi, C.: GeoCrowd: enabling query answering with spatial crowdsourcing. In: ACM SIGSPATIAL, Redondo Beach, CA, USA, pp. 189–198 (2012)

    Google Scholar 

  5. Gao, D., Tong, Y., She, J., Song, T., Chen, L., Xu, K.: Top-k team recommendation in spatial crowdsourcing. In: WAIM, pp. 191–204 (2016)

    Google Scholar 

  6. Xie, X., Chen, H., Wu, H.: Bargain-based stimulation mechanism for selfish mobile nodes in participatory sensing network. In: SMAHCN, pp. 72–80 (2009)

    Google Scholar 

  7. Li, Y., Yiu, M.L., Xu, W.: Orient online route recommendation for spatial crowdsourcing task workers. In: SSTD, pp. 137–156 (2015)

    Google Scholar 

  8. Kittur, A., Nickerson, J.V., Bernstein, M., Gerber, E., Shaw, A., Zimmerman, J., Lease, M., Horton, J.: The future of crowd work. In: CSCW, pp. 1301–1318 (2013)

    Google Scholar 

  9. Sakurai, Y., Okimoto, T., Oka, M., Shinoda, M., Yokoo, M.: Ability grouping of crowd workers via reward discrimination. In: HCOMP, pp. 147–155 (2013)

    Google Scholar 

  10. Liu, Y., Zhang, J., Yu, H., Miao, C.: Reputation-aware continuous double auction. In: AAAI, pp. 3126–3127 (2014)

    Google Scholar 

  11. http://www.uber.com

  12. Khosravifar, B., Bentahar, J., Gomrokchi, M., Alam, R.: CRM: an efficient trust and reputation model for agent computing. In: KBS, pp. 1–16 (2012)

    Google Scholar 

  13. Yu, H., Shen, Z., Miao, C., An, B., Leung, C.: Filtering trust opinions through reinforcement learning. In: Decision Support Systems, pp. 102–113 (2014)

    Google Scholar 

  14. Fang, H., Guo, G., Zhang, J.: Multi-faceted trust and distrust prediction for recommender systems. In: Decision Support Systems, pp. 37–47 (2015)

    Google Scholar 

  15. Wahab, O.A., Bentahar, J., Otrok, H., Mourad, A: A survey on trust and reputation models for web services: single, composite, and communities. In: Decision Support Systems, pp. 121–134 (2015)

    Google Scholar 

  16. http://snap.stanford.edu/data/loc-gowalla.html

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Acknowledgments

This work is supported by National Natural Science Foundation of China under Grant No. 61572295; Innovation Method Fund of China No. 2015IM010200; Natural Science Foundation of Shandong Province under Grant No. ZR2014FM031; Science and Technology Development Plan Project of Shandong Province No. 2014GGX101047, No. 2015GGX101007, No. 2015GGX101015; Shandong Province Independent Innovation Major Special Project No. 2015ZDJQ01002, No. 2015ZDXX0201B03.

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Correspondence to Wei He .

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© 2017 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Wang, Q., He, W., Wang, X., Cui, L. (2017). Quality-Assure and Budget-Aware Task Assignment for Spatial Crowdsourcing. In: Wang, S., Zhou, A. (eds) Collaborate Computing: Networking, Applications and Worksharing. CollaborateCom 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 201. Springer, Cham. https://doi.org/10.1007/978-3-319-59288-6_6

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  • DOI: https://doi.org/10.1007/978-3-319-59288-6_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59287-9

  • Online ISBN: 978-3-319-59288-6

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