aerOS Project to Showcase Cutting-Edge Edge–Cloud Innovation at IoT Tech Expo Europe 2025

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aerOS project is excited to announce its participation in the IoT Tech Expo Europe 2025, taking place on September 24–25 at RAI Amsterdam, Netherlands. As one of Europe’s flagship events for IoT and emerging technologies, the expo will gather over 7,000 industry professionals, 200+ expert speakers, and 200+ exhibitors, covering key topics such as IoT, AI, edge computing, cybersecurity, and more.

At this year’s event, aerOS will be featured among leading European innovators and EU-funded cluster projects, presenting its visionary work on a meta-operating system designed to enable secure, flexible, and vendor-neutral orchestration of computing resources across the edge–cloud continuum. The project’s ambition is to simplify distributed computing while fostering data autonomy and interoperability across diverse environments and sectors.

Visitors to the metaOS booth will experience live demonstrations, technical deep-dives, and interactive sessions that bring the project’s goals to life. Key use cases from the project’s pilot sites—including smart manufacturing, precision agriculture, renewable energy, smart ports, and intelligent buildings—will be showcased, offering attendees a firsthand look at how aerOS is driving digital transformation and enabling AI at the edge.

By participating in IoT Tech Expo Europe, aerOS strengthens its role in shaping the future of European digital infrastructure and contributing to a resilient, scalable, and open-edge ecosystem.

 Learn more and register here: https://www.iottechexpo.com/

“Dependency-Aware Microservice Deployment for Edge Computing: A Deep Reinforcement Learning Approach With Network Representation” Paper

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The popularity of microservices in industry has sparked much attention in the research community. Despite significant progress in microservice deployment for resource-intensive services and applications at the network edge, the intricate dependencies among microservices are often overlooked, and some studies underestimate the importance of system context extraction in deployment strategies. This paper addresses these issues by formulating the microservice deployment problem as a max-min problem, considering system cost and quality of service (QoS) jointly. We first study the attention-based microservice representation (AMR) method to achieve effective system context extraction. In this way, the contributions of different computing power providers (users, edge servers, or cloud servers) in the networks can be effectively paid attention to. Subsequently, we propose the attention-modified soft actor-critic (ASAC) algorithm to tackle the microservice deployment problem. ASAC leverages attention mechanisms to enhance decision-making and adapt to changing system dynamics. Our simulation results demonstrate ASAC’s effectiveness, prioritizing average system cost and reward compared to the other state-of-the-art algorithms.

Find more information here: https://ieeexplore.ieee.org/document/10663201 

“RMF: A Risk Measurement Framework for Machine Learning Models” Paper

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RMF: A Risk Measurement Framework for ML Models is a new tool designed to help teams assess the vulnerability of their machine learning models to real-world attacks. It evaluates how easy and costly it is to attack a model, helping teams prioritize the right defenses. Especially valuable for AI systems in security-critical sectors like finance and healthcare, RMF supports safer, more resilient deployment of machine learning

Find more here: https://dl.acm.org/doi/10.1145/3664476.3670867