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dc.contributor.authorZheng, Zijie
dc.contributor.authorSong, Lingyang
dc.contributor.authorHan, Zhu
dc.contributor.authorLi, Geoffrey Ye
dc.contributor.authorPoor, H. Vincent
dc.date.accessioned2020-05-11T16:14:22Z
dc.date.available2020-05-11T16:14:22Z
dc.date.issued2/28/2018
dc.identifier.citationCopyright 2018 IEEE Wireless Communications. This is a pre-print version of a published paper that is available at: https://ieeexplore.ieee.org/abstract/document/8304399. Recommended citation: Zheng, Zijie, Lingyang Song, Zhu Han, Geoffrey Ye Li, and H. Vincent Poor. "Game Theoretic Approaches to Massive Data Processing in Wireless Networks." IEEE Wireless Communications 25, no. 1 (2018): 98-104. DOI: 10.1109/MWC.2018.1700175. This item has been deposited in accordance with publisher copyright and licensing terms and with the author's permission.
dc.identifier.urihttps://hdl.handle.net/10657/6432
dc.description.abstractWireless communication networks are becoming highly virtualized with two-layer hierarchies, in which controllers at the upper layer with tasks to achieve can ask a large number of agents at the lower layer to help realize computation, storage, and transmission functions. Through offloading data processing to the agents, the controllers can accomplish otherwise prohibitive big data processing. Incentive mechanisms are needed for the agents to perform the controllers' tasks in order to satisfy the corresponding objectives of controllers and agents. In this article, a hierarchical game framework with fast convergence and scalability is proposed to meet the demand for real-time processing for such situations. Possible future research directions in this emerging area are also discussed.
dc.publisherIEEE Wireless Communications
dc.subjectGames
dc.subjectWireless networks
dc.subjectTask analysis
dc.subjectWireless sensor networks
dc.subjectBig Data
dc.titleGame Theoretic Approaches to Massive Data Processing in Wireless Networks
dc.typeArticle


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