Abstract
Real-time prediction of spatial information such as road-traffic-related information has attracted much attention. Mobile crowdsensing (MCS), in which mobile user devices such as smartphones equipped with sensors work as distributed mobile sensors, is an effective way of collecting sensor data for real-time prediction of spatial information. Since user devices contributing to MCS incur various costs including energy cost and privacy risk, using incentive mechanisms is one approach to compensate for these costs. However, since, in general, the budget for incentive rewarding is limited, rewards should be effectively allocated with considering the contribution of sensor data to the accuracy in real-time prediction of spatial information, which has not been considered in any prior work. This paper presents a scheme to maximize the accuracy of real-time prediction when allocating incentive rewards to user devices. The proposed scheme estimates the contribution of each user device collecting and sending sensor data to the prediction accuracy. Then, the incentive reward received by a user device collecting and sending data increases in proportion to the contribution the data makes to prediction accuracy. Feature selection is used to extract the contribution of each input data point from a prediction model created by machine learning. Evaluation using a real road-traffic-related dataset demonstrated that the proposed scheme works better in terms of prediction accuracy for various cost conditions than a benchmark scheme.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Lv, Y., Duan, Y., Kang, W., Li, Z., Wang, F.Y.: Traffic flow prediction with big data: a deep learning approach. IEEE Trans. Intell. Transp. Syst. 16(2), 865–873 (2015)
Narain, A.: The global GIS and Spatial Analytics market to touch US\$88.3 Billion by 2020. https://www.geospatialworld.net/blogs/gis-and-spatial-analytics-market. Accessed 27 Oct 2019
Wang, X., Wu, W., Qi, D.: Mobility-aware participant recruitment for vehicle-based mobile crowdsensing. IEEE Trans. Veh. Technol. 67(5), 4415–4426 (2018)
Ganti, R.K., Ye, F., Lei, H.: Mobile crowdsensing: current state and future challenges. IEEE Commun. Mag. 49(11), 32–39 (2011)
Jin, H., Su, L., Chen, D., Nahrstedt, K., Xu, J.: Quality of information aware incentive mechanisms for mobile crowd sensing systems. In: Proceedings of the 16th ACM International Symposium on Mobile Ad Hoc Networking and Computing, pp. 167–176. ACM (2015)
Wang, J., Tang, J., Yang, D., Wang, E., Xue, G.: Quality-aware and fine-grained incentive mechanisms for mobile crowdsensing. In: Proceedings of the IEEE 36th International Conference on Distributed Computing Systems (ICDCS), pp. 354–363. IEEE (2016)
Guo, B., Chen, H., Han, Q., Yu, Z., Zhang, D., Wang, Y.: Worker-contributed data utility measurement for visual crowdsensing systems. IEEE Trans. Mob. Comput. 16(8), 2379–2391 (2017)
Peng, D., Wu, F., Chen, G.: Data quality guided incentive mechanism design for crowdsensing. IEEE Trans. Mob. Comput. 17(2), 307–319 (2018)
Chandrashekar, G., Sahin, F.: A survey on feature selection methods. Comput. Electr. Eng. 40(1), 16–28 (2014)
Gevrey, M., Dimopoulos, I., Lek, S.: Review and comparison of methods to study the contribution of variables in artificial neural network models. Ecol. Model. 160(3), 249–264 (2003)
Shinkuma, R., Nishio, T.: Data assessment and prioritization in mobile networks for real-time prediction of spatial information with machine learning. In: Proceedings of the IEEE ICDCS 2019 Workshops - NMIC 2019, July 2019
Inagaki, Y., Shinkuma, R., Sato, T., Oki, E.: Prioritization of mobile IoT data transmission based on data importance extracted from machine learning model. IEEE Access 7, 93611–93620 (2019)
Louppe, G.: Understanding random forests: from theory to practice. arXiv:1407.7502, July 2014
Piorkowski, M., Sarafijanovic-Djukic, N., Grossglauser, M.: CRAWDAD dataset epfl/mobility (v. 2009-02-24), February 2009. https://doi.org/10.15783/C7J010
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12(10), 2825–2830 (2011)
Fechner, G.T., Adler, H.E., Howes, D.H., Boring, E.G.: Elements of Psychophysics. Henry Holt Editions in Psychology. Holt, Rinehart and Winston, New York (1966)
Zeng, M., Yu, T., Wang, X., Su, V., Nguyen, L.T., Mengshoel, O.J.: Improving demand prediction in bike sharing system by learning global features. In: Machine Learning for Large Scale Transportation Systems (LSTS), KDD 2016 (2016)
Acknowledgements
This work was supported by JST PRESTO Grant no. JPMJPR1854 and JSPS KAKENHI Grant no. JP17H01732. Fatos Xhafa’s work is partially supported by Spanish Ministry of Science, Innovation and Universities, Programme “Estancias de profesores e investigadores sénior en centros extranjeros, incluido el Programa Salvador de Madariaga 2019”, PRX19/00155. On leave, University of Surrey, UK.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Shinkuma, R., Takagi, R., Inagaki, Y., Oki, E., Xhafa, F. (2020). Incentive Mechanism for Mobile Crowdsensing in Spatial Information Prediction Using Machine Learning. In: Barolli, L., Amato, F., Moscato, F., Enokido, T., Takizawa, M. (eds) Advanced Information Networking and Applications. AINA 2020. Advances in Intelligent Systems and Computing, vol 1151. Springer, Cham. https://doi.org/10.1007/978-3-030-44041-1_70
Download citation
DOI: https://doi.org/10.1007/978-3-030-44041-1_70
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-44040-4
Online ISBN: 978-3-030-44041-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)