Deep learning in mobile and wireless networking: A survey

C Zhang, P Patras, H Haddadi - IEEE Communications surveys …, 2019 - ieeexplore.ieee.org
The rapid uptake of mobile devices and the rising popularity of mobile applications and
services pose unprecedented demands on mobile and wireless networking infrastructure …

Thirty years of machine learning: The road to Pareto-optimal wireless networks

J Wang, C Jiang, H Zhang, Y Ren… - … Surveys & Tutorials, 2020 - ieeexplore.ieee.org
Future wireless networks have a substantial potential in terms of supporting a broad range of
complex compelling applications both in military and civilian fields, where the users are able …

Q-learning algorithms: A comprehensive classification and applications

B Jang, M Kim, G Harerimana, JW Kim - IEEE access, 2019 - ieeexplore.ieee.org
Q-learning is arguably one of the most applied representative reinforcement learning
approaches and one of the off-policy strategies. Since the emergence of Q-learning, many …

Wi-Fi meets ML: A survey on improving IEEE 802.11 performance with machine learning

S Szott, K Kosek-Szott, P Gawłowicz… - … Surveys & Tutorials, 2022 - ieeexplore.ieee.org
Wireless local area networks (WLANs) empowered by IEEE 802.11 (Wi-Fi) hold a dominant
position in providing Internet access thanks to their freedom of deployment and configuration …

Reinforcement learning based routing in networks: Review and classification of approaches

Z Mammeri - Ieee Access, 2019 - ieeexplore.ieee.org
Reinforcement learning (RL), which is a class of machine learning, provides a framework by
which a system can learn from its previous interactions with its environment to efficiently …

Internet of things 2.0: Concepts, applications, and future directions

I Zhou, I Makhdoom, N Shariati, MA Raza… - IEEE …, 2021 - ieeexplore.ieee.org
Applications and technologies of the Internet of Things are in high demand with the increase
of network devices. With the development of technologies such as 5G, machine learning …

[HTML][HTML] A trusted routing scheme using blockchain and reinforcement learning for wireless sensor networks

J Yang, S He, Y Xu, L Chen, J Ren - Sensors, 2019 - mdpi.com
A trusted routing scheme is very important to ensure the routing security and efficiency of
wireless sensor networks (WSNs). There are a lot of studies on improving the …

Deep reinforcement learning for selecting demand forecast models to empower Industry 3.5 and an empirical study for a semiconductor component distributor

CF Chien, YS Lin, SK Lin - International Journal of Production …, 2020 - Taylor & Francis
A semiconductor distributor that plays a third-party role in the supply chain will buy diverse
components from different suppliers, warehouse and resell them to a number of electronics …

Including artificial intelligence in a routing protocol using software defined networks

S Sendra, A Rego, J Lloret, JM Jimenez… - 2017 IEEE …, 2017 - ieeexplore.ieee.org
Software defined network (SDN) is one of the most interesting research topic that is currently
being investigated. The inclusion of artificial intelligence (AI) can improve the performance of …

[HTML][HTML] Reinforcement learning-based routing protocols in flying ad hoc networks (FANET): A review

J Lansky, S Ali, AM Rahmani, MS Yousefpoor… - Mathematics, 2022 - mdpi.com
In recent years, flying ad hoc networks have attracted the attention of many researchers in
industry and universities due to easy deployment, proper operational costs, and diverse …