AI at the network edge promises real-time perception and decision-making in safety-critical domains such as aerial robotics, autonomous vehicles, and 5G-enabled infrastructures. Yet, operating under resource constraints, dynamic, and adversarial conditions exposes edge AI systems to fragility, inefficiency, and security risks that threaten their safe operation. GuardAI, a Horizon Europe project, introduces a framework for resilient and trustworthy edge AI that unites three pillars: adversarial robustness, context-enhanced inference, and security-by-design. Initial project results include a diffusion-based adversarial purification framework optimized for real-time operation, lightweight deep unrolling architectures for LiDAR super-resolution with built-in outlier removal, and robust uncertainty quantification modules to improve confidence calibration. It further develops a context-enhanced inference engine that integrates visual, spatial, and operational context across multi-agent systems, and a risk-aware defense recommender that autonomously selects mitigation strategies based on evolving threat landscapes. Through representative Use Cases, covering monitoring with Unmanned Aerial Vehicle, decentralized 5G network analytics, and secure perception in connected autonomous vehicles, GuardAI demonstrates how robust and adaptive AI can be achieved within stringent edge constraints. Together, these technologies lay the groundwork for a new generation of secure, context-aware, and certifiable AI systems that can be trusted to operate autonomously in the physical world.

Pneumonia, a respiratory disease often caused by bacterial infection in the distal lung, requires rapid and accurate identification, especially in settings such as critical care. Initiating or de-escalating antimicrobials should ideally be guided by the quantification of pathogenic bacteria for effective treatment. Optical endomicroscopy is an emerging technology with the potential to expedite bacterial detection in the distal lung by enabling in vivo and in situ optical tissue characterisation. With advancements in detector technology, Optical endomicroscopy can utilize fluorescence lifetime imaging (FLIM) to help detect events that were previously challenging or impossible to identify using fluorescence intensity imaging. In this paper, we propose an iterative Bayesian approach for bacterial detection in FLIM. We model the FLIM image as a linear combination of background intensity, Gaussian noise, and additive outliers (labelled bacteria). While previous bacteria detection methods model anomalous pixels as bacteria, here the FLIM outliers are modelled as circularly symmetric Gaussian-shaped objects, based on their discrete shape observed through visual analysis and the physical nature of the imaging modality. A Hierarchical Bayesian model is used to solve the bacterial detection problem where prior distributions are assigned to unknown parameters. A Metropolis-Hastings within Gibbs sampler draws samples from the posterior distribution. The proposed method’s detection performance is initially measured using synthetic images, and shows significant improvement over existing approaches. Further analysis is conducted on real Optical endomicroscopy FLIM images annotated by trained personnel. The experiments show the proposed approach outperforms existing methods by a margin of +16.85% (F1) for detection accuracy.

Nowadays, with the developing technologies, IoT based solutions are increasing day by day. In the scope of this study, a test platform has been developed to test the IoT solutions developed by different companies according to oneM2M standards. The main objective of the study is to provide a national standardization for the control of IoT solutions. The developed InterOpT (InterOpability Tool) testing platform software tool is the automation system for controlling interoperability and compliance with standards on IoT network components. Based on the OneM2M IoT standard recognized by ETSI, this system performs the validity tests defined by the RFC and analyzes and automates the results. Provides interoperability and system audit reports on IoT structures. Improved testing platform, web and cloud-based service can offer. It can do traffic and stress tests especially for the system and it can also do log collection analysis automatically. The platform can support different embedded IoT protocols such as HTTP, CoAP, MQTT. As a result, with this project, we have developed a successful test automation platform that can perform interoperability and compliance tests by adhering to IoT protocols and OneM2M standards.