TY - JOUR
T1 - Fusion of Cognitive Wireless Networks and Edge Computing
AU - Gai, Keke
AU - Xu, Kai
AU - Lu, Zhihui
AU - Qiu, Meikang
AU - Zhu, Liehuang
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/6
Y1 - 2019/6
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85068610544&partnerID=8YFLogxK
U2 - 10.1109/MWC.2019.1800407
DO - 10.1109/MWC.2019.1800407
M3 - Article
AN - SCOPUS:85068610544
SN - 1536-1284
VL - 26
SP - 69
EP - 75
JO - IEEE Wireless Communications
JF - IEEE Wireless Communications
IS - 3
M1 - 8752525
ER -