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18.05.2019 | Special Issue Paper

QoI-aware incentive for multimedia crowdsensing enabled learning system

verfasst von: Yiren Gu, Hang Shen, Guangwei Bai, Tianjing Wang, Xuejun Liu

Erschienen in: Multimedia Systems

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Abstract

While much research has been devoted to algorithm improvement of the machine learning model for multimedia applications, relatively little research has focused on the acquisition of massive multimedia datasets with strict data demands for model training. In this paper, we propose a Quality-of-Information (QoI) aware incentive mechanism in multimedia crowdsensing, with the objective of promoting the growth of an initial training model. We begin with a reverse auction incentive model to maximize 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, we design an effective incentive mechanism to solve the auction problem, which is shown to be truthful, individually rational and computationally efficient. Our evaluation study is carried out using a real multimedia dataset. Extensive simulation results demonstrate that the proposed incentive mechanism produces close-to-optimal social welfare noticeably, while accompanied by accelerating the growth of the machine learning model with a high-QoI dataset.

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Metadaten
Titel
QoI-aware incentive for multimedia crowdsensing enabled learning system
verfasst von
Yiren Gu
Hang Shen
Guangwei Bai
Tianjing Wang
Xuejun Liu
Publikationsdatum
18.05.2019
Verlag
Springer Berlin Heidelberg
Erschienen in
Multimedia Systems
Print ISSN: 0942-4962
Elektronische ISSN: 1432-1882
DOI
https://doi.org/10.1007/s00530-019-00616-w

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