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All Outputs (5)

Scalable and Reliable Data Framework for Sensor-enabled Virtual Power Plant Digital Twin (2025)
Journal Article
Singh, A., Demirbaga, U., Singh Aujla, G., Jindal, A., Jiang, J., & Sun, H. (2025). Scalable and Reliable Data Framework for Sensor-enabled Virtual Power Plant Digital Twin. IEEE Journal of Selected Areas in Sensors, 2, 108-120. https://doi.org/10.1109/JSAS.2025.3540956

Sensor-enabled distributed energy resources (DERs) provide various advantages, including a lower carbon footprint, yet effective management of millions of DERs is still an issue. Virtual power plants (VPP) can integrate several DERs into a unified op... Read More about Scalable and Reliable Data Framework for Sensor-enabled Virtual Power Plant Digital Twin.

COPS: Controller Placement in Next-Generation Software Defined Edge-Cloud Networks (2025)
Presentation / Conference Contribution
Singh Aujla, G., Jindal, A., Kaur, K., Garg, S., Chaudhary, R., Sun, H., & Kumar, N. (2025, June). COPS: Controller Placement in Next-Generation Software Defined Edge-Cloud Networks. Presented at 2025 IEEE International Conference on Communications (ICC), Montreal, Canada

To mitigate various challenges in the edge-cloud ecosystem, such as global monitoring, flow control, and policy modification of legacy networking paradigms, software-defined networks (SDN) have evolved as a major technology. However, the dependency o... Read More about COPS: Controller Placement in Next-Generation Software Defined Edge-Cloud Networks.

Green Reinforcement and Split Learning Framework for Edge-Fog-Cloud Continuum in 6G Networks (2025)
Presentation / Conference Contribution
Dua, A., Jindal, A., Singh Aujla, G., & Sun, H. (2025, June). Green Reinforcement and Split Learning Framework for Edge-Fog-Cloud Continuum in 6G Networks. Presented at 2025 IEEE International Conference on Communications (ICC), Montreal, Canada

6G applications rely on data-intensive AI models for network optimization. These demand a scalable and energy-efficient framework to handle massive device networks with stringent latency requirements which current solutions struggle to support. Even... Read More about Green Reinforcement and Split Learning Framework for Edge-Fog-Cloud Continuum in 6G Networks.

Green AutoML: Energy-Efficient AI Deployment Across the Edge-Fog-Cloud Continuum (2024)
Presentation / Conference Contribution
Dua, A., Singh Aujla, G., Jindal, A., & Sun, H. (2024, December). Green AutoML: Energy-Efficient AI Deployment Across the Edge-Fog-Cloud Continuum. Presented at IEEE Global Communications Conference - Workshop on Next-Gen Healthcare Fusion (NgHF): AI-driven Secure Integrated Networks for Healthcare IoT Systems, Cape Town, South Africa

The increasing demand for machine learning (ML) technologies has led to a significant rise in energy consumption and environmental impact, particularly within the context of distributed computing environments like the Edge-Fog-Cloud Continuum. This p... Read More about Green AutoML: Energy-Efficient AI Deployment Across the Edge-Fog-Cloud Continuum.