Dr Gagangeet Aujla's Outputs (8)

Energy-based Predictive Root Cause Analysis for Real-Time Anomaly Detection in Big Data Systems (2025)
Presentation / Conference Contribution
Demirbaga, U., Singh Aujla, G., & Sun, H. (2025, June). Energy-based Predictive Root Cause Analysis for Real-Time Anomaly Detection in Big Data Systems. Presented at 2025 IEEE International Conference on Communications (ICC), Montreal, Canada

As the scale of data continues to grow exponentially, managing resource allocation and energy consumption in big data systems becomes increasingly complex and critical. Moreover, with big data systems, energy efficiency is more important daily. In cl... Read More about Energy-based Predictive Root Cause Analysis for Real-Time Anomaly Detection in Big Data Systems.

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.

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 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.

Two-fold Strategy towards Sustainable Renewable Energy Networks when Uncertainty is Certain (2024)
Journal Article
Garg, A., Singh, A., Singh Aujla, G., & Sun, H. (online). Two-fold Strategy towards Sustainable Renewable Energy Networks when Uncertainty is Certain. IEEE Transactions on Consumer Electronics, https://doi.org/10.1109/TCE.2024.3475581

With renewable energy sources (RESs) integrated into modern power systems, energy consumers participate in the energy market making the entire network more complex and uncertain. Therefore, adaptive strategies are needed to address the various uncert... Read More about Two-fold Strategy towards Sustainable Renewable Energy Networks when Uncertainty is Certain.

SecureFlow: Knowledge and data-driven ensemble for intrusion detection and dynamic rule configuration in software-defined IoT environment (2024)
Journal Article
Singh, A., Chouhan, P. K., & Aujla, G. S. (2024). SecureFlow: Knowledge and data-driven ensemble for intrusion detection and dynamic rule configuration in software-defined IoT environment. Ad Hoc Networks, 156, Article 103404. https://doi.org/10.1016/j.adhoc.2024.103404

There is a massive growth in the rate of heterogeneous devices configured in the Internet of Things (IoT) environment for efficient communication. The IoT devices are limited in resources, and there are no defined protocols in terms of security durin... Read More about SecureFlow: Knowledge and data-driven ensemble for intrusion detection and dynamic rule configuration in software-defined IoT environment.

Techno-Economic-Environmental Analysis for Net-Zero Sustainable Residential Buildings (2023)
Presentation / Conference Contribution
Garg, A., Aujla, G., & Sun, H. (2023, October). Techno-Economic-Environmental Analysis for Net-Zero Sustainable Residential Buildings. Presented at IEEE PES ISGT Europe 2023, Grenoble, France

Carbon emissions are becoming a global concern responsible for climate change. The renewable energy sources (RESs) such as wind, solar, biomass are gaining importance to reduce emissions in the energy sector. However, these sources depend highly on v... Read More about Techno-Economic-Environmental Analysis for Net-Zero Sustainable Residential Buildings.

Analyzing Impact of Data Uncertainty in Distributed Energy Resources using Bayesian Networks (2023)
Presentation / Conference Contribution
Garg;, A., Aujla, G., & Sun, H. (2023, October). Analyzing Impact of Data Uncertainty in Distributed Energy Resources using Bayesian Networks. Presented at 2023 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), Glasgow, UK

With the high penetration of distributed energy resources (DERs), distribution networks have become more prone to uncertainties associated with renewable energy sources (RESs). If not handled judiciously, these uncertainties may lead to interruption... Read More about Analyzing Impact of Data Uncertainty in Distributed Energy Resources using Bayesian Networks.