Abstract
To address restrictions on data collection, incentivizing multimedia data acquisition for machine learning system is proposed. This paper presents an effective QoI (Quality-of-Information)-aware incentive mechanism in multimedia crowdsensing, with the objective of promoting the growth of an initial training model. Firstly, an incentive model is constructed in the form of reverse auction to maximize the social welfare while meeting the requirements in quality, timeliness, correlation and coverage. Then, we discuss how to achieve the optimal social welfare in the presence of an NP-hard winner determination problem. Lastly, a practical incentive mechanism to solve the auction problem is designed, which is shown to be truthful, individually rational and computationally efficient. Extensive simulation results demonstrate the proposed incentive mechanism produces close-to-optimal social welfare noticeably and high-QoI dataset is obtained. In particular, a significant performance improvement for machine learning model growth is achieved with lower complexity.
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Acknowledgements
The authors gratefully acknowledge the support and financial assistance provided by the National Natural Science Foundation of China under Grant No. 61502230, 61501224 and 61073197, the Natural Science Foundation of Jiangsu Province under Grant No. BK20150960, the Natural Science Foundation of the Jiangsu Higher Education Institutions of China under Grant No. 15KJB520015, and Nangjing Municipal Science and Technology Plan Project under Grant No. 201608009.
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Gu, Y., Shen, H., Bai, G., Wang, T., Tong, H., Hu, Y. (2018). Incentivizing Multimedia Data Acquisition for Machine Learning System. In: Vaidya, J., Li, J. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2018. Lecture Notes in Computer Science(), vol 11336. Springer, Cham. https://doi.org/10.1007/978-3-030-05057-3_11
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DOI: https://doi.org/10.1007/978-3-030-05057-3_11
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