Skip to main content

Research Repository

Advanced Search

Outputs (14)

WIND: A Wireless Intelligent Network Digital Twin for Federated Learning and Multi-Layer Optimization (2025)
Journal Article
Singh, S. K., Comsa, I.-S., Trestian, R., Cakir, L. V., Singh, R., Kaushik, A., Canberk, B., Shah, P., Kumbhani, B., & Darshi, S. (online). WIND: A Wireless Intelligent Network Digital Twin for Federated Learning and Multi-Layer Optimization. IEEE Communications Standards Magazine, https://doi.org/10.1109/MCOMSTD.2025.3575511

The forthcoming wireless network is expected to support a wide range of applications, from supporting autonomous vehicles to massive Internet of Things (IoT) deployments. However, the coexistence of diverse applications under a unified framework pres... Read More about WIND: A Wireless Intelligent Network Digital Twin for Federated Learning and Multi-Layer Optimization.

Scenario Emulator for Intelligent Applications With IoT-DT Architecture (2025)
Presentation / Conference Contribution
Cakir, L. V., & Canberk, B. (2025, March). Scenario Emulator for Intelligent Applications With IoT-DT Architecture. Presented at IEEE Wireless Communications and Networking Conference (WCNC), Milan, Italy

Digital Twins (DTs) have become indispensable in 6G for intelligent applications with real-time monitoring, modelling, and optimization. However, validating them in real-world conditions using IoT integration remained a significant challenge. Due to... Read More about Scenario Emulator for Intelligent Applications With IoT-DT Architecture.

A Data Serialization-based Framework for Efficient IoT Management (2024)
Presentation / Conference Contribution
Huseynov, K., Cakir, L. V., Al-Shareeda, S., Özdem, M., & Canberk, B. (2024, November). A Data Serialization-based Framework for Efficient IoT Management. Presented at 2024 IEEE 10th World Forum on Internet of Things (WF-IoT), Ottawa, Canada

The Internet of Things (IoT) management relies on the efficient and timely transfer of data from sensors to applications. Processing required data transformations at the edge gateway introduces spatial complexity issues, particularly concerning resou... Read More about A Data Serialization-based Framework for Efficient IoT Management.

Generative AI-enabled Digital Twins for 6G-enhanced Smart Cities (2024)
Presentation / Conference Contribution
Duran, K., Cakir, L. V., Ozdem, M., Gursu, K., & Canberk, B. (2024, December). Generative AI-enabled Digital Twins for 6G-enhanced Smart Cities. Paper presented at IEEE Global Communications Conference (GLOBECOM) 2024, Cape Town, South Africa

6G networks are envisioned to enable a wide range of applications, such as autonomous vehicles and smart cities. However, this rapid expansion of network topologies makes the management of 6G wireless networks more complex and leads to performance de... Read More about Generative AI-enabled Digital Twins for 6G-enhanced Smart Cities.

Internet of Twins Approach: Digital-Twin-as-a-Platform Architecture (2024)
Journal Article
Cakir, L. V., Özdem, M., Ahmadi, H., Duong, T. Q., & Canberk, B. (2025). Internet of Twins Approach: Digital-Twin-as-a-Platform Architecture. IEEE Internet Computing, 29(1), 65-74. https://doi.org/10.1109/mic.2024.3491915

Digital twins (DTs) are becoming integral in sectors such as energy and manufacturing, catalyzing applications from monitoring and analysis to optimization and autonomous management. However, data in different formats, volumes, and qualities require... Read More about Internet of Twins Approach: Digital-Twin-as-a-Platform Architecture.

Digital Twin-empowered Green Mobility Management in Next-Gen Transportation Networks (2024)
Journal Article
Duran, K., Cakir, L. V., Fonzone, A., Duong, T. Q., & Canberk, B. (2024). Digital Twin-empowered Green Mobility Management in Next-Gen Transportation Networks. IEEE Open Journal of Vehicular Technology, 5, 1650-1662. https://doi.org/10.1109/ojvt.2024.3484956

Evolving transportation networks need seamless integration and effective infrastructure utilisation to form the next-generation transportation networks. Also, they should be capable of capturing the traffic flow data at the right time and promptly ap... Read More about Digital Twin-empowered Green Mobility Management in Next-Gen Transportation Networks.

Real-Time Digital Twin Platform: A Case Study on Core Network Selection in Aeronautical Ad-hoc Networks (2024)
Journal Article
Cakir, L. V., Kocak, M., Özdem, M., & Canberk, B. (2024). Real-Time Digital Twin Platform: A Case Study on Core Network Selection in Aeronautical Ad-hoc Networks. ITU Journal of Wireless Communications and Cybersecurity, 1(1), 41-46

The development of Digital Twins (DTs) is hindered by a lack of specialized, open-source solutions that can meet the demands of dynamic applications. This has caused state-of-the-art DT applications to be validated using offline data. However, this a... Read More about Real-Time Digital Twin Platform: A Case Study on Core Network Selection in Aeronautical Ad-hoc Networks.

Intelligent Digital Twin Communication Framework for Addressing Accuracy and Timeliness Tradeoff in Resource-Constrained Networks (2024)
Journal Article
Cakir, L. V., Thomson, C. J., Özdem, M., Canberk, B., Nguyen, V.-L., & Duong, T. Q. (2025). Intelligent Digital Twin Communication Framework for Addressing Accuracy and Timeliness Tradeoff in Resource-Constrained Networks. IEEE Transactions on Cognitive Communications and Networking, 11(3), 1954-1965. https://doi.org/10.1109/tccn.2024.3469234

The accuracy and timeliness tradeoff prevents Digital Twins (DTs) from realizing their full potential. High accuracy is crucial for decision-making, and timeliness is equally essential for responsiveness. Therefore, this tradeoff in DT communication... Read More about Intelligent Digital Twin Communication Framework for Addressing Accuracy and Timeliness Tradeoff in Resource-Constrained Networks.

AI in Energy Digital Twining: A Reinforcement Learning-Based Adaptive Digital Twin Model for Green Cities (2024)
Presentation / Conference Contribution
Cakir, V. L., Duran, K., Thomson, C., Broadbent, M., & Canberk, B. (2024, June). AI in Energy Digital Twining: A Reinforcement Learning-Based Adaptive Digital Twin Model for Green Cities. Presented at ICC 2024 - IEEE International Conference on Communications, Denver, CO, USA

Digital Twins (DT) have become crucial to achieve sustainable and effective smart urban solutions. However, current DT modelling techniques cannot support the dynamicity of these smart city environments. This is caused by the lack of right-time data... Read More about AI in Energy Digital Twining: A Reinforcement Learning-Based Adaptive Digital Twin Model for Green Cities.