This paper presents a comprehensive literature review on applications of deep
reinforcement learning (DRL) in communications and networking. Modern networks, eg …

The incumbent Internet of Things suffers from poor scalability and elasticity exhibiting in
communication, computing, caching and control (4Cs) problems. The recent advances in …

The rapid uptake of mobile devices and the rising popularity of mobile applications and
services pose unprecedented demands on mobile and wireless networking infrastructure …

With the prevalence of smart appliances, smart meters, and Internet of Things (IoT) devices
in smart grids, artificial intelligence (AI) built on the rich IoT big data enables various energy …

The scale of Internet-connected systems has increased considerably, and these systems are
being exposed to cyberattacks more than ever. The complexity and dynamics of …

Due to the recent progress in Deep Neural Networks, Reinforcement Learning (RL) has
become one of the most important and useful technology. It is a learning method where a …

With the rapid digitalization of various industries, mobile crowdsensing (MCS), an intelligent
data collection and processing paradigm of the industrial Internet of Things, has provided a …

The emergence of MCS technologies provides a cost-efficient solution to accommodate
large-scale sensing tasks. However, despite the potential benefits of MCS, there are several …

In the era of information, the green services of content-centric IoT are expected to offer users
the better satisfaction of Quality of Experience (QoE) than that in a conventional IoT …

Unmanned aerial vehicles (UAVs) have the potential to establish flexible and reliable
emergency networks in disaster sites when terrestrial communication infrastructures go …