Abstract
Crowdsensing is a novel concept that divides tasks between participants in order to get an accumulated result. To make the crowdsensing system work well and get better quality, it is indispensable to set up incentive mechanisms to get more workers involved. As far as the bidding of heterogeneous combinations is concerned, combinatorial auctions are the natural choice for workers to bid. Truthfulness and efficiency can be guaranteed based on the properties of VCG mechanism which will result in the higher bid price and high overpayment. To overcome this potential shortcoming of the VCG mechanism, we propose the core-selecting mechanism for the heterogeneous task auction under the crowdsensing market. Two payment rules are applied to the core-selecting auction based on linear programming and quadratic programming techniques to minimize the bidders’ incentives which deviate from their truthful-telling. After extensive simulation experiments, it is proved that our model can decrease the cost significantly.
Q. He and Y. Qiao—Contribute equally to this work.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Gunti, R.K., Yi, F., Len, H.: Mobile crowdsensing: current state and future challenges. IEEE Commun. Mag. 49, 32–39 (2011)
Goal, G., Nekzad, A., Singea, A.: Allocating tasks to workers with matching constraints: truthful mechanisms for crowdsensing markets. In: Proceedings of WWW, 2014, pp. 279–280 (2014)
Holmtrom, B.: Groves scheme on restricted domains. The Econometric Society, pp. 1137–1144, September 1979
Yuan, M.-C., Kang, I., Laung, K.-S.: A survey of crowdsensing systems. In: Proceedings of IEEE 3rd International Conference on Social Computing, Boston, MA, USA, pp. 766–773, October 2011
Yung, X., Wung, T., Ran, X., Yu, W.: Copula-based multidimensional crowd428 sourced data synthesis and release with local privacy. In: 2017 IEEE Global Communications Conference, GLOBECOM 2017, Singapore, pp. 1–6, December 2017
Zhe, X., An, J., Yung, M., Xiang, L., Yung, Q., Gui, X.: A fair incentive mechanism for crowdsensing in crowd sensing. IEEE Internet Things J 3, 1364–1372 (2016)
Nise, N., Ronen, A.: Computationally feasible VCG mechanisms. In: Proceedings of the 2nd ACM Conference on Electronic Commerce ACM, New York (2000)
Karlen, A.U., Kempe, D., Beyond, V.C.G.: Frugality of truthful mechanisms. In: IEEE Symposium on Foundations of Computer Science, pp. 615–624 (2005)
Duan, X., Liu, H., Tang, H., Cai, Q., Zhang, F., Han, X.: A novel hybrid auction algorithm for multi-UAVs dynamic task assignment. IEEE Access 8, 86207–86222 (2020). https://doi.org/10.1109/ACCESS.2019.2959327
Zhe, X., An, J., Yung, M., Xiang, L., Yang, Q., Gui, X.: A fair incentive mechanism for crowdsensing in crowd sensing. IEEE Internet Things J. 3, 1364–1372 (2016)
Qiao, Y., Song, Y., Wang, N., Wu, J., Zhang, L., Wang, C.: A false-name-proof protocol for multicast routing auctions. In: IEEE International Conference 2018, pp. 72–79 (2018)
Day, R., Cramton, J.: The quadratic core-selecting payment rule for combinatorial auctions. Oper. Res. 60(3), 588–603 (2012)
Acknowledgements
This paper is supported by the National Key Research and Development Program of China (Grant No. 2018YFB1403400), the National Natural Science Foundation of China (Grant No. 61876080), the Key Research and Development Program of Jiangsu (Grant No. BE2019105), the Collaborative Innovation Center of Novel Software Technology and Industrialization at Nanjing University.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
He, Q., Qiao, Y., Yang, S., Wang, C. (2021). Robust and Efficient Mechanism Design for Heterogeneous Task Crowdsensing. In: Liu, Z., Wu, F., Das, S.K. (eds) Wireless Algorithms, Systems, and Applications. WASA 2021. Lecture Notes in Computer Science(), vol 12939. Springer, Cham. https://doi.org/10.1007/978-3-030-86137-7_11
Download citation
DOI: https://doi.org/10.1007/978-3-030-86137-7_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-86136-0
Online ISBN: 978-3-030-86137-7
eBook Packages: Computer ScienceComputer Science (R0)