CRITICAL INFRASTRUCTURE AWARD 2024
Sabtain Ahmad (Technische Universität Wien) erhält den Critical Infrastructure Award 2024 für sein Dissertationsprojekt Optimizing Edge Intelligence for Smart Environmental Monitoring: A Framework for Energy and Communication Efficiency.
Abstract der Dissertation
The growing reliance on critical infrastructures, such as water management, energy networks, and transportation systems, demands real-time monitoring and intelligent decision-making to ensure their safety and efficiency. However, these infrastructures generate vast amounts of data at the network edge, often in remote or resource-constrained environments, creating challenges in energy consumption, communication efficiency, and data privacy. Traditional cloud-based analytics struggle to process this data efficiently due to latency, bandwidth limitations, and privacy concerns.
Sabtain Ahmad's research addresses these challenges by developing AI-driven Edge Intelligence frameworks that enable energy-efficient, real-time decision-making at the network edge. By leveraging federated learning and edge computing, his work optimizes how AI models are trained and deployed on resource-limited edge devices, reducing the need for continuous data transmission while ensuring low-latency, privacy-preserving operations.
This research has direct applications in critical infrastructure resilience and is currently being adapted in multiple projects:
- HoloWaterAI – An AI-supported holographic water monitoring system in collaboration with Wiener Wasser, ensuring safe and sustainable water management.
- Data Center Monitoring for Energy Efficiency – A project that leverages AI-driven energy optimization techniques to enhance data center efficiency.
- Real-Time Livestock Monitoring in Alpine Regions – Using AI and edge computing to track livestock health and movement in resource-constrained mountainous areas, improving farm management and ensuring sustainable agriculture.
By advancing Edge AI for safety-critical infrastructures, his work enhances resilience, reduces operational costs, and provides scalable, real-world solutions for mission-critical applications across multiple domains.
Der Preisträger
Sabtain Ahmad is a PhD researcher at TU Wien, specializing in Edge AI and distributed machine learning for safety-critical infrastructures. He holds an MSc in Data Science (TU Berlin) and a BSc in Computer Science (FAST-NU), graduating Magna Cum Laude. His research excellence has earned him prestigious awards, including the Critical Infrastructure Award, NetIdee PhD Dissertation Award, and ICT R&D Award. He actively contributes as a reviewer for top AI and systems conferences, a program committee member, and a co-teacher at TU Wien. Engaged in international collaborations, his work bridges academia and industry to develop energy-efficient, AI-driven solutions for resilient and sustainable critical infrastructures.