TY - JOUR
T1 - Deep Reinforcement Learning for Internet of Things
T2 - A Comprehensive Survey
AU - Chen, Wuhui
AU - Qiu, Xiaoyu
AU - Cai, Ting
AU - Dai, Hong Ning
AU - Zheng, Zibin
AU - Zhang, Yan
N1 - Funding Information:
This work was supported in part by the National Key Research and Development Plan under Grant 2018YFB1003800; in part by the Macao Science and Technology Development Fund through Macao Funding Scheme for Key Research and Development Projects under Grant 0025/2019/AKP; in part by the National Natural Science Foundation of China under Grant 61802450; in part by the Natural Science Foundation of Guangdong under Grant 2018A030313005; in part by the Program for Guangdong Introducing Innovative and Entrepreneurial Teams under Grant 2017ZT07X355; and in part by the Pearl River Talent Recruitment Program under Grant 2019QN01X130. (Corresponding authors: Zibin Zheng; Hong-Ning Dai.)
Publisher Copyright:
© 2021 IEEE.
PY - 2021/7
Y1 - 2021/7
N2 - The incumbent Internet of Things suffers from poor scalability and elasticity exhibiting in communication, computing, caching and control (4Cs) problems. The recent advances in deep reinforcement learning (DRL) algorithms can potentially address the above problems of IoT systems. In this context, this paper provides a comprehensive survey that overviews DRL algorithms and discusses DRL-enabled IoT applications. In particular, we first briefly review the state-of-the-art DRL algorithms and present a comprehensive analysis on their advantages and challenges. We then discuss on applying DRL algorithms to a wide variety of IoT applications including smart grid, intelligent transportation systems, industrial IoT applications, mobile crowdsensing, and blockchain-empowered IoT. Meanwhile, the discussion of each IoT application domain is accompanied by an in-depth summary and comparison of DRL algorithms. Moreover, we highlight emerging challenges and outline future research directions in driving the further success of DRL in IoT applications.
AB - The incumbent Internet of Things suffers from poor scalability and elasticity exhibiting in communication, computing, caching and control (4Cs) problems. The recent advances in deep reinforcement learning (DRL) algorithms can potentially address the above problems of IoT systems. In this context, this paper provides a comprehensive survey that overviews DRL algorithms and discusses DRL-enabled IoT applications. In particular, we first briefly review the state-of-the-art DRL algorithms and present a comprehensive analysis on their advantages and challenges. We then discuss on applying DRL algorithms to a wide variety of IoT applications including smart grid, intelligent transportation systems, industrial IoT applications, mobile crowdsensing, and blockchain-empowered IoT. Meanwhile, the discussion of each IoT application domain is accompanied by an in-depth summary and comparison of DRL algorithms. Moreover, we highlight emerging challenges and outline future research directions in driving the further success of DRL in IoT applications.
KW - decision making
KW - Deep reinforcement learning
KW - Internet of Things
KW - resource allocation
UR - http://www.scopus.com/inward/record.url?scp=85104260250&partnerID=8YFLogxK
U2 - 10.1109/COMST.2021.3073036
DO - 10.1109/COMST.2021.3073036
M3 - Review article
AN - SCOPUS:85104260250
SN - 1553-877X
VL - 23
SP - 1659
EP - 1692
JO - IEEE Communications Surveys and Tutorials
JF - IEEE Communications Surveys and Tutorials
IS - 3
ER -
