FUSION OF COGNITIVE WIRELESS NETWORKS AND EDGE COMPUTING

被引：55

作者：

Gai, Keke ^{[1
]}

Xu, Kai ^{[2
]}

Lu, Zhihui ^{[3
,4
]}

Qiu, Meikang ^{[5
]}

Zhu, Liehuang ^{[1
]}

机构：

[1] Beijing Inst Technol, Sch Comp Sci & Technol, Beijing, Peoples R China

[2] Beijing Inst Technol, Sch Comp Sci & Technol, Comp Technol, Beijing, Peoples R China

[3] Fudan Univ, Sch Comp Sci, Shanghai, Peoples R China

[4] Engn Res Ctr Cyber Secur Auditing & Monitoring, Minist Educ, Shanghai, Peoples R China

[5] Columbia Univ, New York, NY USA

来源：

IEEE WIRELESS COMMUNICATIONS | 2019年 / 26卷 / 03期

基金：

中国国家自然科学基金;

关键词：

Data transfer - Internet of things - Reinforcement learning - Wireless networks - Learning algorithms;

D O I：

10.1109/MWC.2019.1800407

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

With the expeditious maturation of IoT, intelligent manufacturing is one of its derivatives as a beneficiary and consequence of the connected environment. No doubt this trend is changing our manners of production. However, on the other side, a large pool of connected devices also bring a new challenge in computing waste (e.g., energy waste) due to the increasing amount of connected devices in IoT and heavy data transfers. This article addresses this issue and discusses a novel method for achieving a cost efficiency goal. The model emphasizes the cognitive wireless communications in which edge computing techniques and reinforcement learning algorithms are combined. Experiment evaluations also assess and examine the model discussed in this article.

引用

页码：69 / 75

页数：7

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