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Reinforced Reliable Worker Selection for Spatial Crowdsensing Networks

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Database Systems for Advanced Applications (DASFAA 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11446))

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Abstract

Spatial Crowdsensing Networks limit the sensing tasks in some special places where workers should sense data for them. Due to the lack of a priori information about quality of workers, guaranteeing the quality of the sensing tasks remains a key challenge. In this paper, we model the quality of workers through two factors, namely bias and variance, which describe the continuous value feature of sensing tasks. After calibrating the bias, we should iteratively estimate worker variances more and more accurately. Meanwhile, we should select more reliable workers with low variances to finish sensing tasks. This is a classic exploration and exploitation dilemma. Therefore, to overcome the dilemma, we design a novel Multi-Armed Bandit (MAB) algorithm which is based on Upper Confidence Bounds (UCB) scheme and combined with a weighted data aggregation scheme to calculate a better ground truth of a sensing task. Then, we prove the expected sensing error of sensing tasks can be bounded according to the regret bound of the MAB in our setting. In simulation experiments, we use a real world data set to validate the theoretical results of our algorithm and it outperforms two baselines significantly in different settings.

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Notes

  1. 1.

    https://www.dropbox.com/s/3643ygf0jvu11vs/Technical_Report.pdf?dl=0.

  2. 2.

    https://darksky.net/dev.

References

  1. Antos, A., Grover, V., Szepesvári, C.: Active learning in multi-armed bandits. In: Freund, Y., Györfi, L., Turán, G., Zeugmann, T. (eds.) ALT 2008. LNCS (LNAI), vol. 5254, pp. 287–302. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-87987-9_25

    Chapter  MATH  Google Scholar 

  2. Auer, P., Cesa-Bianchi, N., Fischer, P.: Finite-time analysis of the multiarmed bandit problem. Mach. Learn. 47(2–3), 235–256 (2002)

    Article  Google Scholar 

  3. Aydin, B.I., Yilmaz, Y.S., Li, Y., Li, Q., Gao, J., Demirbas, M.: Crowdsourcing for multiple-choice question answering. In: AAAI, pp. 2946–2953 (2014)

    Google Scholar 

  4. Boim, R., Greenshpan, O., Milo, T., Novgorodov, S., Polyzotis, N., Tan, W.C.: Asking the right questions in crowd data sourcing. In: ICDE, pp. 1261–1264 (2012)

    Google Scholar 

  5. Bubeck, S., Munos, R., Stoltz, G.: Pure exploration in multi-armed bandits problems. In: Gavaldà, R., Lugosi, G., Zeugmann, T., Zilles, S. (eds.) ALT 2009. LNCS (LNAI), vol. 5809, pp. 23–37. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-04414-4_7

    Chapter  Google Scholar 

  6. Cao, C.C., She, J., Tong, Y., Chen, L.: Whom to ask? Jury selection for decision making tasks on micro-blog services. PVLDB 5(11), 1495–1506 (2012)

    Google Scholar 

  7. DataTech: Datatech modeling contest public data sample, Zhejiang Division Co. China Mobile Co. (2017). http://datatech.zjdex.com

  8. Du, Y., Xu, H., Sun, Y.-E., Huang, L.: A general fine-grained truth discovery approach for crowdsourced data aggregation. In: Candan, S., Chen, L., Pedersen, T.B., Chang, L., Hua, W. (eds.) DASFAA 2017. LNCS, vol. 10177, pp. 3–18. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-55753-3_1

    Chapter  Google Scholar 

  9. Fan, J., Li, G., Ooi, B.C., Tan, K., Feng, J.: iCrowd: an adaptive crowdsourcing framework. In: SIGMOD, pp. 1015–1030 (2015)

    Google Scholar 

  10. Guha, S., Munagala, K.: Approximation algorithms for budgeted learning problems. In: STOC, pp. 104–113 (2007)

    Google Scholar 

  11. Ho, C., Jabbari, S., Vaughan, J.W.: Adaptive task assignment for crowdsourced classification. In: ICML, pp. 534–542 (2013)

    Google Scholar 

  12. Ho, C., Vaughan, J.W.: Online task assignment in crowdsourcing markets. In: AAAI (2012)

    Google Scholar 

  13. Jin, H., Su, L., Nahrstedt, K.: Theseus: incentivizing truth discovery in mobile crowd sensing systems. In: MobiHoc, pp. 1:1–1:10 (2017)

    Google Scholar 

  14. Lappas, T., Liu, K., Terzi, E.: Finding a team of experts in social networks. In: SIGKDD, pp. 467–476 (2009)

    Google Scholar 

  15. Li, Q., et al.: A confidence-aware approach for truth discovery on long-tail data. PVLDB 8(4), 425–436 (2014)

    Google Scholar 

  16. Li, Q., Li, Y., Gao, J., Zhao, B., Fan, W., Han, J.: Resolving conflicts in heterogeneous data by truth discovery and source reliability estimation. In: SIGMOD, pp. 1187–1198 (2014)

    Google Scholar 

  17. Meng, C., et al.: Truth discovery on crowd sensing of correlated entities. In: SenSys, pp. 169–182 (2015)

    Google Scholar 

  18. Mordacchini, M., et al.: Crowdsourcing through cognitive opportunistic networks. TAAS 10(2), 13:1–13:29 (2015)

    Article  Google Scholar 

  19. Rangi, A., Franceschetti, M.: Multi-armed bandit algorithms for crowdsourcing systems with online estimation of workers’ ability. In: AAMAS, pp. 1345–1352 (2018)

    Google Scholar 

  20. Raykar, V.C., et al.: Learning from crowds. J. Mach. Learn. Res. 11, 1297–1322 (2010)

    MathSciNet  Google Scholar 

  21. Robbins, H.: Some aspects of the sequential design of experiments. Bull. Am. Math. Soc. 55, 527–535 (1952)

    Article  MathSciNet  Google Scholar 

  22. Tran-Thanh, L., Chapman, A., FloresLuna, J.E.M.D., Rogers, A., Jennings, N.R.: Epsilon-first policies for budget-limited multi-armed bandits. In: NCAI, pp. 1211–1216 (2010)

    Google Scholar 

  23. Tran-Thanh, L., Chapman, A.C., Rogers, A., Jennings, N.R.: Knapsack based optimal policies for budget-limited multi-armed bandits. In: AAAI, pp. 1134–1140 (2012)

    Google Scholar 

  24. Yan, R., Song, Y., Li, C., Zhang, M., Hu, X.: Opportunities or risks to reduce labor in crowdsourcing translation? Characterizing cost versus quality via a pagerank-HITS hybrid model. In: IJCAI, pp. 1025–1032 (2015)

    Google Scholar 

  25. Yao, S., et al.: Recursive ground truth estimator for social data streams. In: IPSN, pp. 14:1–14:12 (2016)

    Google Scholar 

  26. Yin, X., Han, J., Yu, P.S.: Truth discovery with multiple conflicting information providers on the web. TKDE 20(6), 796–808 (2008)

    Google Scholar 

  27. Zhao, Z., Wei, F., Zhou, M., Chen, W., Ng, W.: Crowd-selection query processing in crowdsourcing databases: a task-driven approach. In: EDBT, pp. 397–408 (2015)

    Google Scholar 

  28. Zheng, Y., Cheng, R., Maniu, S., Mo, L.: On optimality of jury selection in crowdsourcing. In: EDBT, pp. 193–204 (2015)

    Google Scholar 

  29. Zheng, Y., Wang, J., Li, G., Cheng, R., Feng, J.: QASCA: a quality-aware task assignment system for crowdsourcing applications. In: SIGMOD, pp. 1031–1046 (2015)

    Google Scholar 

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Acknowledgment

This work was supported by the National Key R&D Program of China (2018YFB1004703), the National Natural Science Foundation of China (61872238, 61672353), the Shanghai Science and Technology Fund (17510740200), the Huawei Innovation Research Program (HO2018085286), and the State Key Laboratory of Air Traffic Management System and Technology (SKLATM20180X).

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Authors and Affiliations

  1. Shanghai Key Laboratory of Scalable Computing and Systems, Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China

    Yang Wang, Junwei Lu, Jingxiao Chen, Xiaofeng Gao & Guihai Chen

Authors
  1. Yang Wang
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  2. Junwei Lu
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  3. Jingxiao Chen
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  4. Xiaofeng Gao
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  5. Guihai Chen
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Corresponding author

Correspondence to Xiaofeng Gao .

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Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Guoliang Li

  2. Duke University, Durham, NC, USA

    Jun Yang

  3. University of Porto, Porto, Portugal

    Joao Gama

  4. Chiang Mai University, Chiang Mai, Thailand

    Juggapong Natwichai

  5. Beihang University, Beijing, China

    Yongxin Tong

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Wang, Y., Lu, J., Chen, J., Gao, X., Chen, G. (2019). Reinforced Reliable Worker Selection for Spatial Crowdsensing Networks. In: Li, G., Yang, J., Gama, J., Natwichai, J., Tong, Y. (eds) Database Systems for Advanced Applications. DASFAA 2019. Lecture Notes in Computer Science(), vol 11446. Springer, Cham. https://doi.org/10.1007/978-3-030-18576-3_15

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  • DOI: https://doi.org/10.1007/978-3-030-18576-3_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-18575-6

  • Online ISBN: 978-3-030-18576-3

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