Deep Reinforcement Learning for Internet of Things: A Comprehensive Survey

【Author】 Chen, Wuhui; Qiu, Xiaoyu; Cai, Ting; Dai, Hong-Ning; Zheng, Zibin; Zhang, Yan

【Source】IEEE COMMUNICATIONS SURVEYS AND TUTORIALS

【影响因子】33.840

【Abstract】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.

【Keywords】Internet of Things; Cloud computing; Servers; Security; Reinforcement learning; Smart grids; Real-time systems; Deep reinforcement learning; Internet of Things; decision making; resource allocation

【发表时间】2021

【收录时间】2022-01-02

【文献类型】综述

【主题类别】

区块链技术-协同技术-物联网