most citedLearning to Detect Anomalous Wireless Links in IoT Networks

23 citations · 51 across the 4 of their papers we have counts for

collaborators

5 papers

cs.LG2021★ 8 cited

Learning to Fairly Classify the Quality of Wireless Links

Gregor Cerar, Halil Yetgin, Mihael Mohorčič +1

Machine learning (ML) has been used to develop increasingly accurate link quality estimators for wireless networks. However, more in-depth questions regarding the most suitable cla…

cs.IT2020★ 1 cited

Minimum-Delay Routing for Integrated Aeronautical Ad Hoc Networks Relying on Passenger-Planes in the North-Atlantic Region

Jingjing Cui, Dong Liu, Jiankang Zhang +3

Relying on multi-hop communication techniques, aeronautical ad hoc networks (AANETs) seamlessly integrate ground base stations (BSs) and satellites into aircraft communications for…

cs.NI2020

Time-to-Provision Evaluation of IoT Devices Using Automated Zero-Touch Provisioning

Ivan Boškov, Halil Yetgin, Matevž Vučnik +2

The Internet of Things (IoT) is being widely adopted in today's society, interconnecting smart embedded devices that are being deployed for indoor and outdoor environments, such as…

cs.NI2020★ 23 cited

Learning to Detect Anomalous Wireless Links in IoT Networks

Gregor Cerar, Halil Yetgin, Blaž Bertalanič +1

After decades of research, the Internet of Things (IoT) is finally permeating real-life and helps improve the efficiency of infrastructures and processes as well as our health. As…

cs.NI2020★ 19 cited

On Designing a Machine Learning Based Wireless Link Quality Classifier

Gregor Cerar, Halil Yetgin, Mihael Mohorčič +1

Ensuring a reliable communication in wireless networks strictly depends on the effective estimation of the link quality, which is particularly challenging when propagation environm…