Profit optimization in service-oriented data market: A Stackelberg game approach

doi:10.1016/j.future.2018.12.072

	Profit optimization in service-oriented data market: A Stackelberg game approach
	Shen, Bo 1; Shen, Yulong 1; Ji, Wen 2
	2019-06-01
发表期刊	FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
ISSN	0167-739X
卷号	95 页码:17-25
摘要	Deep learning has drawn a lot of attention recently. It is successful in a variety of applications from natural language processing to autonomous vehicle control. A main difference from traditional learning is that deep learning learns the representations of the data, i.e., the features, via greedy layer-wise pre-training. The characteristics of deep learning promotes it as a power tool to mine the data from Internet of Vehicles (boy). The paper focuses on the economic aspect of IoV and investigates deep learning enabled IoV market for data trading and processing. The economic model of IoV consists of three side: the data provider, the service provider, and the user. The data provider collects the data for the user. The user buys the raw data. The data is further processed by the service provider, who provides the learned features for the user to obtain some profit. To optimize the profit of three-sided participators, a Stackelberg game is proposed to model the interactions among them. We derive the equilibrium pricing mechanism of the providers and corresponding demands of the users. The existence and the uniqueness of the equilibrium strategies are proved. Our analysis reveals that the strategy of each participator is related to the utility of the user and the data/service provider's cost. To the best of our knowledge, this is the first time that the data provider and the service provider directly interact with the user in the data market. (C) 2019 Elsevier B.V. All rights reserved.
关键词	Internet of vehicles Big data Deep learning Economics Pricing
DOI	10.1016/j.future.2018.12.072
收录类别	SCI
语种	英语
资助项目	National Key R&D Program of China[2017YFB1400100] ; National Natural Science Foundation of China[61572466] ; Beijing Natural Science Foundation[4162059]
WOS研究方向	Computer Science
WOS类目	Computer Science, Theory & Methods
WOS记录号	WOS:000465509600002
出版者	ELSEVIER SCIENCE BV
引用统计	被引频次：14[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/4254
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Shen, Bo
作者单位	1.Xidian Univ, Sch Comp Sci & Technol, Xian 710071, Shaanxi, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Shen, Bo,Shen, Yulong,Ji, Wen. Profit optimization in service-oriented data market: A Stackelberg game approach[J]. FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE,2019,95:17-25.
APA	Shen, Bo,Shen, Yulong,&Ji, Wen.(2019).Profit optimization in service-oriented data market: A Stackelberg game approach.FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE,95,17-25.
MLA	Shen, Bo,et al."Profit optimization in service-oriented data market: A Stackelberg game approach".FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 95(2019):17-25.