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 …

Ap-side WLAN Analytics

P Nayak - 2019 - search.proquest.com
Monitoring the network performance experienced by the end user is crucial for managers of
wireless networks as it can enable them to remotely modify the network parameters to …

Optimization of Execution for Machine Learning Applications in the Computing Continuum

I Syrigos, N Angelopoulos… - 2022 IEEE Conference on …, 2022 - ieeexplore.ieee.org
Today, adoption of Machine Learning (ML) techniques is widespread and is encountered in
almost every aspect of our everyday lives. The plethora of IoT devices and the enormous …

Unlicensed spectrum forecasting: An interference umbrella based on channel analysis and machine learning

K Chounos, P Karamichailidis… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
In this work a novel framework for predicting future interference levels for IEEE 802.11
networks is developed and experimentally evaluated. At the heart of the framework lies a …

Experimental Evaluation of ML Models for Dynamic VNF Autoscaling

V Zalokostas-Diplas, N Makris… - … IEEE Conference on …, 2022 - ieeexplore.ieee.org
Network Functions Virtualization (NFV) is a key aspect deeply integrated in the latest 5G
networks, allowing for the provisioning of elastic resources that adapt in a flexible manner …

On the Implementation of a Cross-Layer SDN Architecture for 802.11 MANETs

I Syrigos, I Koukoulis, A Prassas… - ICC 2023-IEEE …, 2023 - ieeexplore.ieee.org
The adoption of the Software Defined Networking (SDN) paradigm is aggressively
expanding from traditional datacenter networks to 5G and IoT deployments due to its …

EELAS: Energy Efficient and Latency Aware Scheduling of Cloud-Native ML Workloads

I Syrigos, D Kefalas, N Makris… - 2023 15th International …, 2023 - ieeexplore.ieee.org
The widespread use of microservices and the use of cloud-native methodologies for the
deployment of services have increased the service providers' flexibility and management …

Federated-Learning-Assisted Failure-Cause Identification in Microwave Networks

T Tandel, O Ayoub, F Musumeci… - 2022 12th …, 2022 - ieeexplore.ieee.org
Machine Learning (ML) adoption for automated failure management is becoming pervasive
in today's communication networks. However, ML-based failure management typically …

Change Point Detection in WLANs with Random AP Forests

A Huet, J Krolikowski, JM Navarro, F Chen… - Companion of the 19th …, 2023 - dl.acm.org
Troubleshooting WiFi networks is knowingly difficult due to the variability of the wireless
medium. Complementary to existing works that focus on detecting short-term fluctuations of …

Classification of Home Network Problems with Transformers

J Dötterl, Z Hemmati Fard - Proceedings of the 39th ACM/SIGAPP …, 2024 - dl.acm.org
We propose a classifier that can identify ten common home network problems based on the
raw textual output of networking tools such as ping, dig, and ip. Our deep learning model …