Cellular, wide-area, and non-terrestrial IoT: A survey on 5G advances and the road toward 6G
The next wave of wireless technologies is proliferating in connecting things among
themselves as well as to humans. In the era of the Internet of Things (IoT), billions of …
themselves as well as to humans. In the era of the Internet of Things (IoT), billions of …
Enabling massive IoT toward 6G: A comprehensive survey
Nowadays, many disruptive Internet-of-Things (IoT) applications emerge, such as
augmented/virtual reality online games, autonomous driving, and smart everything, which …
augmented/virtual reality online games, autonomous driving, and smart everything, which …
Machine learning in IoT security: Current solutions and future challenges
The future Internet of Things (IoT) will have a deep economical, commercial and social
impact on our lives. The participating nodes in IoT networks are usually resource …
impact on our lives. The participating nodes in IoT networks are usually resource …
A review of deep reinforcement learning for smart building energy management
Global buildings account for about 30% of the total energy consumption and carbon
emission, raising severe energy and environmental concerns. Therefore, it is significant and …
emission, raising severe energy and environmental concerns. Therefore, it is significant and …
Single and multi-agent deep reinforcement learning for AI-enabled wireless networks: A tutorial
Deep Reinforcement Learning (DRL) has recently witnessed significant advances that have
led to multiple successes in solving sequential decision-making problems in various …
led to multiple successes in solving sequential decision-making problems in various …
Deep reinforcement learning for Internet of Things: A comprehensive survey
The incumbent Internet of Things suffers from poor scalability and elasticity exhibiting in
communication, computing, caching and control (4Cs) problems. The recent advances in …
communication, computing, caching and control (4Cs) problems. The recent advances in …
Deep reinforcement learning in production systems: a systematic literature review
Shortening product development cycles and fully customisable products pose major
challenges for production systems. These not only have to cope with an increased product …
challenges for production systems. These not only have to cope with an increased product …
Federated reinforcement learning: Techniques, applications, and open challenges
This paper presents a comprehensive survey of Federated Reinforcement Learning (FRL),
an emerging and promising field in Reinforcement Learning (RL). Starting with a tutorial of …
an emerging and promising field in Reinforcement Learning (RL). Starting with a tutorial of …
Resource allocation based on deep reinforcement learning in IoT edge computing
By leveraging mobile edge computing (MEC), a huge amount of data generated by Internet
of Things (IoT) devices can be processed and analyzed at the network edge. However, the …
of Things (IoT) devices can be processed and analyzed at the network edge. However, the …
Adversarial Deep Learning approach detection and defense against DDoS attacks in SDN environments
MP Novaes, LF Carvalho, J Lloret… - Future Generation …, 2021 - Elsevier
Over the last few years, Software Defined Networking (SDN) paradigm has become an
emerging architecture to design future networks and to meet new application demands. SDN …
emerging architecture to design future networks and to meet new application demands. SDN …