Wi-Fi meets ML: A survey on improving IEEE 802.11 performance with machine learning
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 …
position in providing Internet access thanks to their freedom of deployment and configuration …
Future smart cities: Requirements, emerging technologies, applications, challenges, and future aspects
Future smart cities are the key to fulfilling the ever-growing demands of citizens. Information
and communication advancements will empower better administration of accessible …
and communication advancements will empower better administration of accessible …
Federated reinforcement learning in iot: Applications, opportunities and open challenges
The internet of things (IoT) represents a disruptive concept that has been changing society in
several ways. There have been several successful applications of IoT in the industry. For …
several ways. There have been several successful applications of IoT in the industry. For …
Target localization using multi-agent deep reinforcement learning with proximal policy optimization
Target localization refers to identifying a target location based on sensory data readings
gathered by sensing agents (robots, UAVs), surveying a certain area of interest. Existing …
gathered by sensing agents (robots, UAVs), surveying a certain area of interest. Existing …
Blockchain-based trusted traffic offloading in space-air-ground integrated networks (sagin): A federated reinforcement learning approach
F Tang, C Wen, L Luo, M Zhao… - IEEE Journal on Selected …, 2022 - ieeexplore.ieee.org
In the future era of intelligent networks, communication technology and network architecture
need to be further developed to provide users with high-quality services. The Space-Air …
need to be further developed to provide users with high-quality services. The Space-Air …
[HTML][HTML] Cache in fog computing design, concepts, contributions, and security issues in machine learning prospective
The massive growth of diversified smart devices and continuous data generation poses a
challenge to communication architectures. To deal with this problem, communication …
challenge to communication architectures. To deal with this problem, communication …
Task offloading based on LSTM prediction and deep reinforcement learning for efficient edge computing in IoT
In IoT (Internet of Things) edge computing, task offloading can lead to additional
transmission delays and transmission energy consumption. To reduce the cost of resources …
transmission delays and transmission energy consumption. To reduce the cost of resources …
Fed-inforce-fusion: A federated reinforcement-based fusion model for security and privacy protection of IoMT networks against cyber-attacks
Abstract Internet of Medical Things (IoMT) has emerged as a combination of sensors,
healthcare devices, and Internet of Things (IoT) to deliver better and intelligent healthcare …
healthcare devices, and Internet of Things (IoT) to deliver better and intelligent healthcare …
Toward optimal MEC-based collision avoidance system for cooperative inland vessels: A federated deep learning approach
Cooperative collision avoidance between inland waterway ships is among the envisioned
services on the Internet of Ships. Such a service aims to support safe navigation while …
services on the Internet of Ships. Such a service aims to support safe navigation while …
Zero trust architecture empowered attack detection framework to secure 6G edge computing
H Sedjelmaci, N Ansari - IEEE Network, 2023 - ieeexplore.ieee.org
Recent advances in edge computing and its imminent adoption in Sixth Generation (6G)
wireless networks have led to a propitious paradigm defined as 6G edge computing …
wireless networks have led to a propitious paradigm defined as 6G edge computing …