CSNet 2025 Keynotes

Keynote #1

Title: Large Perceptive Models for the future of Intelligent Connectivity

Abstract: The next evolution of the Internet of Things (IoT) is not about connecting more devices — it’s about making them understand us. In this talk, I introduce the emerging concept of Large Perceptive Models (LPMs): AI-driven systems that integrate large language models (LLMs) into the very fabric of IoT. LPMs act as both interpreters of multimodal IoT data and optimizers of user intent, translating raw sensor signals into meaningful narratives and converting natural language instructions into real-time control and optimization strategies This shift redefines the role of AI in IoT, from passive data processors to proactive collaborators. The result: a more human-centric, resilient, and explainable IoT, where users no longer configure devices, but simply converse with them. More on this can be found here: 2a91d671dd067c0258b1e40a9f77cdfd.pdf.

Bio: Mérouane Debbah is Professor at  Khalifa University of Science and Technology in Abu Dhabi and founding Director of the KU 6G Research Center. He is a frequent keynote speaker at international events in the field of telecommunication and AI. His research has been lying at the interface of fundamental mathematics, algorithms, statistics, information and communication sciences with a special focus on random matrix theory and learning algorithms. In the Communication field, he has been at the heart of the development of small cells (4G), Massive MIMO (5G) and Large Intelligent Surfaces (6G) technologies. In the AI field, he is known for his work on Large Language Models, distributed AI systems for networks and semantic communications. He received multiple prestigious distinctions, prizes and best paper awards (more than 50 IEEE best paper awards) for his contributions to both fields and according to research.com is ranked as the best scientist in France in the field of Electronics and Electrical Engineering. He is an IEEE Fellow, a WWRF Fellow, a Eurasip Fellow, an AAIA Fellow, an Institut Louis Bachelier Fellow, an AIIA Fellow  and a Membre émérite SEE. He is actually chair of  the IEEE Large Generative AI Models in Telecom (GenAINet) Emerging Technology Initiative and  a member of the Marconi Prize Selection Advisory Committee

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Keynote #2

Title: Securing the Skies: Advanced Technologies for Airspace Protection

Abstract: With drones becoming ubiquitous in civil airspace, their potential to transform industries is clear, but so is the growing threat of misuse. Malicious and unauthorized drone operations pose real risks to public safety and infrastructure. To stay ahead of these challenges, we need intelligent, integrated solutions for monitoring and control.

In this talk, I will introduce the latest developments from the Secure UTM Systems Lab, where we are advancing airspace security through a suite of cutting-edge technologies. Our work includes secure remote identification modules and services, computer vision-based drone matching and disambiguation, large language model-powered airspace monitoring, crowd sensing-driven surveillance, and intelligent contingency management systems.

Bio: Abdulhadi Shoufan received his PhD from Technische Universität Darmstadt, Germany, in 2007. He led the Security Hardware group at the Center for Advanced Security Research Darmstadt until 2010. Currently, he is an Associate Professor at Khalifa University. His research interests include zero-trust architecture, embedded security, cryptographic hardware, secure UAV operations, unmanned traffic management (UTM), learning technologies, and engineering education. He has collaborated on projects with several organizations, including Boeing, Lockheed Martin, PSEG, the Technology Innovation Institute, the UAE Ministry of Education, and Germany’s Federal Office for Information Security.