All Outputs (6)

Q-learning driven routing for aeronautical Ad-Hoc networks (2022)
Journal Article
Bilen, T., & Canberk, B. (2022). Q-learning driven routing for aeronautical Ad-Hoc networks. Pervasive and Mobile Computing, 87, Article 101724. https://doi.org/10.1016/j.pmcj.2022.101724

The aeronautical ad-hoc network (AANET) is one of the critical methodologies to satisfy the Internet connectivity requirement of airplanes during their flights. However, the ultra-dynamic topology and unstable air-to-air link characteristics increase... Read More about Q-learning driven routing for aeronautical Ad-Hoc networks.

Digital Twin for 6G: Taxonomy, Research Challenges, and the Road Ahead (2022)
Journal Article
Masaracchia, A., Sharma, V., Canberk, B., Dobre, O. A., & Duong, T. Q. (2022). Digital Twin for 6G: Taxonomy, Research Challenges, and the Road Ahead. IEEE Open Journal of the Communications Society, 3, 2137-2150. https://doi.org/10.1109/OJCOMS.2022.32190

The concept of digital twin (DT) is constantly revealing as a key enabling technology for the deployment of mobile communication services envisaged for the sixth-generation (6G) Internet-of-Things (IoT). This paper aims at providing a comprehensive r... Read More about Digital Twin for 6G: Taxonomy, Research Challenges, and the Road Ahead.

A Proof Of Concept On Digital Twin-Controlled WiFi Core Network Selection For In-Flight Connectivity (2022)
Journal Article
Bilen, T., Ak, E., Bal, B., & Canberk, B. (2022). A Proof Of Concept On Digital Twin-Controlled WiFi Core Network Selection For In-Flight Connectivity. IEEE Communications Standards Magazine, 6(3), 60-68. https://doi.org/10.1109/MCOMSTD.0001.2100103

The in-flight connectivity (IFC) turns to a crucial need from luxury with technological advances. The WiFi-enabled IFC (W-IFC) meets most of this need by deploying access points within the aircraft. These access points can allow Internet connectivity... Read More about A Proof Of Concept On Digital Twin-Controlled WiFi Core Network Selection For In-Flight Connectivity.

Digital Twin-Enabled Intelligent DDoS Detection Mechanism For Autonomous Core Networks (2022)
Journal Article
Yigit, Y., Bal, B., Karameseoglu, A., Duong, T. Q., & Canberk, B. (2022). Digital Twin-Enabled Intelligent DDoS Detection Mechanism For Autonomous Core Networks. IEEE Communications Standards Magazine, 6(3), 38-44. https://doi.org/10.1109/MCOMSTD.0001.210

Existing distributed denial of service attack (DDoS) solutions cannot handle highly aggregated data rates; thus, they are unsuitable for Internet service provider (ISP) core networks. This article proposes a digital twin-enabled intelligent DDoS dete... Read More about Digital Twin-Enabled Intelligent DDoS Detection Mechanism For Autonomous Core Networks.

DTWN: Q-learning-based Transmit Power Control for Digital Twin WiFi Networks (2022)
Journal Article
Çakır, L. V., Huseynov, K., Ak, E., & Canberk, B. (2022). DTWN: Q-learning-based Transmit Power Control for Digital Twin WiFi Networks. EAI Endorsed Transactions on Industrial Networks and Intelligent Systems, 9(31), Article e5. https://doi.org/10.4108/

Interference has always been the main threat to the performance of traditional WiFi networks and next-generation moving forward. The problem can be solved with transmit power control(TPC). However, to accomplish this, an information-gathering process... Read More about DTWN: Q-learning-based Transmit Power Control for Digital Twin WiFi Networks.

Machine Learning-Based Digital Twin for Predictive Modeling in Wind Turbines (2022)
Journal Article
Fahim, M., Sharma, V., Cao, T., Canberk, B., & Duong, T. Q. (2022). Machine Learning-Based Digital Twin for Predictive Modeling in Wind Turbines. IEEE Access, 10, 14184-14194. https://doi.org/10.1109/access.2022.3147602

Wind turbines are one of the primary sources of renewable energy, which leads to a sustainable and efficient energy solution. It does not release any carbon emissions to pollute our planet. The wind farms monitoring and power generation prediction is... Read More about Machine Learning-Based Digital Twin for Predictive Modeling in Wind Turbines.