This paper introduces ROBUST-6G, a European research initiative aimed at designing a comprehensive security platform for future 6G networks. As 6G moves toward deeply integrated digital: physical systems, the project focuses on building security mechanisms that are data-driven, automated, trustworthy,… Read More »ROBUST-6G: Smart, Automated, and Reliable Security Service Platform for 6G
As wireless systems evolve toward 6G, AI becomes increasingly central to how networks operate, optimize themselves, and deliver advanced services. But with greater autonomy and complexity comes a critical requirement: the decisions made by AI models must be understandable, transparent,… Read More »Explainable AI for 6G Use Cases: Technical Aspects and Research Challenges
This paper introduces a new perspective on securing wireless status-update systems by combining physical-layer security with the concept of information freshness. Instead of relying on traditional secrecy metrics such as secrecy capacity or outage probability, the authors focus on Age… Read More »Secure Status Updates under Eavesdropping: Age of Information-based Secrecy Metrics
Federated Learning (FL) relies on many distributed clients training a shared global model without sending raw data to the server. However, one of the core challenges in FL is determining which clients should participate in each training round, especially when… Read More »Version Age-Based Client Scheduling Policy for Federated Learning
Monitoring large IoT networks is challenging, especially when devices operate in remote environments and faults cannot be easily observed. This paper introduces a semantics-aware active fault detection approach, where a monitor intelligently decides when to probe a remote sensor to… Read More »Semantics-Aware Active Fault Detection in Status Updating Systems
As large language models continue to grow, running them efficiently on small edge devices remains one of AI’s biggest challenges. This paper explores how LLM inference can be decentralized across a network of energy-harvesting devices each powered by intermittent renewable… Read More »Decentralized LLM Inference over Edge Networks with Energy Harvesting
This paper proposes a new method for estimating the reliability of machine learning predictions applied to Earth observation (EO) and health-related datasets, with a focus on forecasting mosquito abundance (MA). The authors develop a Variational Autoencoder (VAE)-based confidence metric that… Read More »A Latent Space Metric for Enhancing Prediction Confidence in Earth Observation Data
This paper proposes a lightweight wireless intrusion detection method that identifies 5G jamming attacks using spectrograms derived directly from received IQ samples. Instead of relying on higher-layer metrics like PER or SINR which can fluctuate naturally and often fail to… Read More »Detecting 5G Signal Jammers Using Spectrograms with Supervised and Unsupervised Learning
In this paper, the authors propose a physical-layer challenge–response authentication (CR-PLA) protocol designed specifically for drone communication. Instead of relying on cryptographic keys, the receiver drone (Bob) authenticates the sender by exploiting location-dependent wireless channel attenuation, influenced by path loss… Read More »Energy-Based Optimization of Physical-Layer Challenge-Response Authentication with Drones
In this paper, a challenge–response physical-layer authentication (CR-PLA) scheme is proposed in which the receiver uses an Intelligent Reflecting Surface (IRS) to validate the identity of a single-antenna transmitter. The method operates by randomly selecting IRS phase configurations and checking… Read More »Physical-Layer Challenge-Response Authentication with IRS and Single-Antenna Devices