Twinning action for spreading excellence in Artificial Intelligence of Things

As the year comes to a close, we look back at the 3rd AIoTwin Summer School, held in June in conjunction with the SpliTech 2025 Conference. The summer school brought together PhD students, researchers, and industry professionals to explore key challenges and advances in Artificial Intelligence of Things (AIoT), edge intelligence, and distributed AI systems. The programme featured a strong lineup of keynote lectures covering trustworthy decentralized infrastructures using blockchain technologies, human–robot interaction, and artificial intelligence–driven next-generation 6G networks. In addition, a series of in-depth tutorials addressed current research and practical challenges, including machine learning under data distribution shifts, neural network compression for resource-constrained devices, time-series forecasting methods, edge load-balancing strategies for multi-camera object tracking in urban traffic scenarios, and decentralized training of graph neural networks for vehicle speed prediction. Hands-on workshops showcased two open-source solutions developed within the AIoTwin ecosystem: the AIoTwin middleware for orchestrating hierarchical federated learning across large numbers of clients, and the SmartEdge toolchain for developing collaborative applications over dynamic, semantically described edge networks. The summer school also included a dedicated thematic session at the SpliTech 2025 Conference on Artificial Intelligence of Things and Edge Intelligence. Topics ranged from AIoT architectures and edge AI algorithms to real-time data processing, continual learning, security, privacy, energy efficiency, and communication protocols. Participants additionally had the opportunity to attend broader conference sessions, fostering networking and knowledge exchange across academia and industry. This video highlights the key moments, discussions, and collaborations that defined the 3rd AIoTwin Summer School and contributed to its success.

The main results and achievements of the AIoTwin project were presented on December 5, 2025 at the 9th International Workshop on Advanced Cooperative Systems (IWACS 2025) . The event was organized by the DATACROSS project and the Centre of Research Excellence for Data Science and Cooperative Systems at UNIZG-FER, providing a vibrant platform for engaging the national research community. Prof. Ivana Podnar Žarko, AIoTwin Project Coordinator, stressed that the project has successfully achieved its primary goal: to significantly strengthen the scientific excellence and innovation capacity of UNIZG-FER in the field of Artificial Intelligence of Things (AIoT) and edge AI. The following measurable achievements demonstrate the impact of the AIoTwin project in boosting research capacity and collaboration: 7 joint research papers co-authored with researchers from partner institutions 6 scientific trainings  of doctoral students (short-term staff exchanges) at partner institutions in Europe 5 software components released as open-source software in the field of AIoT 4 project proposals with AIoTwin partners submitted to calls under Horizon Europe 3 successful international summer schools organized (Šibenik 2023, Dubrovnik 2024, Split 2025) Special attention was given to present key results from the project research component : the Orchestration Middleware for Cloud-Edge-IoT continuum.  Prof. Podnar Žarko highlighted advancements in: Reactive Orchestration for Hierarchical Federated Learning : Methods for dynamically managing federated learning pipelines to ensure resilience and communication efficiency. QoS-Aware Load Balancing in the Computing Continuum : Techniques for maintaining predefined service quality metrics (QoS) across heterogeneous cloud and edge resources under variable workloads, nodes and network conditions.

The AIoTwin project was represented on December 3, 2025 at the national webinar organized by the Agency for Mobility and Programs of the European Union, dedicated to the Twinning instrument of the Horizon Europe program . The event, which targeted researchers and administrative staff, focused on providing key information for achieving a successful Twinning application during the final call in 2026 under Horizon Europe. Prof. Ivana Podnar Žarko , AIoTwin Project Coordinator, participated as a panelist in the featured discussion and shared first-hand experiences from coordinating the AIoTwin Twinning project.   The panel discussion offered comprehensive, practical advice covering the entire project lifecycle: Preparing a Winning Proposal : Strategies and key elements required for a successful application. Project Execution and Impact : Main challenges encountered during project execution and methods for maximizing research and institutional impact. Project Outcomes : Concrete examples of knowledge transfer and capacity building achieved through Twinning. AIoTwin is a prime example of how Twinning funding can be used to accelerate research capacities and build strong research networks in advanced fields such as AIoT, Edge AI, and the Cloud-Edge-IoT continuum.

A new AIoTwin blog post is now available, presenting recent research on running WebAssembly (WASM) on resource-constrained IoT devices. The article explores the practical limits, trade-offs, and opportunities of using WASM on microcontrollers with kilobytes of RAM, highlighting how lightweight runtimes can provide portability and sandboxed execution while balancing energy, memory, and performance constraints. Key topics covered in the blog post include: Benchmarking two lightweight WASM runtimes (wasm3 and WAMR) on three microcontrollers: Raspberry Pi Pico, ESP32-C6, and Nordic nRF5340 Measuring execution time, memory footprint, and energy consumption Comparing WASM execution with native C for typical IoT workloads (bubble sort and CRC-16) Trade-offs between performance, portability, and sandboxing in constrained devices Insights into runtime selection for modular, cross-platform IoT applications Authored by Mislav Has, the blogpost explains how WASM can simplify cross-platform deployment, enable secure execution of dynamic modules, and support unified toolchains, while highlighting scenarios where native code remains essential for performance-critical or ultra-low-power tasks. We are also pleased to announce that this work, titled “WebAssembly on Resource-Constrained IoT Devices: Performance, Efficiency, and Portability,” was presented at the ScaleSys workshop within the IoT 2025 conference . More details on the technical implementation, results, and evaluation scenarios can be found in the article here . Read the full blog post [ here ].

IoT applications are increasingly pushing the limits of cloud-only architectures. From autonomous driving to real-time monitoring, many services need ultra-low latency and can’t rely on distant cloud servers alone. In our latest AIoTwin blog post, Ivan Čilić breaks down how the Computing Continuum (CC) can meet these demands—and why adaptive, QoS-aware load balancing is essential for reliable performance. This work comes from his doctoral research at FER and ongoing collaboration with the  DSG Group at TU Wien as part of the AIoTwin project. Key highlights : How dynamic conditions in the CC break traditional load-balancing approaches Introducing QEdgeProxy — a decentralized, QoS-aware load balancer How QEdgeProxy uses local data to learn which service instances meet QoS requirements in real time How a Kubernetes-based implementation enables lightweight, scalable deployment Ivan’s work shows that decentralized QoS-aware load balancing is not only feasible but necessary to keep edge services responsive under real-world conditions. Read the full post here .

At the IEEE International Conference on Artificial Intelligence of Things ( IEEE AIoT 2025 ) , held in Osaka, Japan, from December 3 to 5, 2025 , the paper "Edge-Based Predictive Data Reduction for Smart Agriculture: A Lightweight Approach to Efficient IoT Communication"  was presented. The paper was authored by Dora Kreković, Mario Kušek, and Ivana Podnar Žarko from the Faculty of Electrical Engineering and Computing (FER), University of Zagreb , in collaboration with Danh Le Phuc from the Technical University of Berlin , reflecting a strong international research partnership between the institutions. Accepted following an international peer-review process, the paper was presented on-site  by Dora Kreković. The research addresses key challenges in smart agriculture by introducing a lightweight predictive edge AI approach that performs local inference and transmits data to the cloud only when meaningful deviations occur. Experimental results demonstrate communication reductions exceeding 92%, while maintaining high prediction accuracy. The study evaluates multiple deployment scenarios, including in-situ learning, cross-site model transfer, and satellite-to-ground inference , highlighting how satellite data can support deployments in locations without historical sensor measurements . Overall, the work underscores the potential of edge intelligence to enable scalable, efficient, and resilient smart agriculture systems , particularly in environments with limited connectivity.

The AIoTwin project is pleased to announce the successful completion of the tutorial, " Orchestrating Hierarchical Federated Learning Pipelines with the AIoTwin Middleware ," held in Vienna, Austria on November 18, 2025. The tutorial was part of the program at the 15th International Conference on the Internet of Things (IoT 2025) . The session, presented by project members Ivan Čilić, Ana  Petra Jukić, Katarina Vuknić, and Ivana Podnar Žarko, provided participants with hands-on experience in tackling the complexities of deploying Hierarchical Federated Learning (HFL) pipelines across the Cloud-Edge-IoT continuum. For more information, check the detailed news content. 

Cities today generate massive volumes of mobility data through sensors such as cameras, GPS, and roadside units. Traditional cloud-based systems struggle to process this data efficiently, particularly when real-time responsiveness is required. In a new AIoTwin blog post, Ivan Kralj explores how cloudlets , Edge AI , and adaptive learning are enabling scalable and accurate traffic prediction for smart cities. This work originates from Ivan’s research exchange in Sweden and ongoing collaboration with RISE Research Institutes of Sweden . Key Highlights: Traffic prediction is uniquely challenging due to the interconnected, dynamic nature of road networks. Fully centralized systems face limitations in scalability, latency, and reliability. Semi-decentralized training of Spatio-Temporal Graph Neural Networks (ST-GNNs) across distributed cloudlets improves resilience and efficiency. Sudden traffic events require specialized evaluation metrics — introducing Sudden Change in Speed Rate (SCSR) to detect congestion onset and recovery. Adaptive graph pruning reduces communication overhead while maintaining high prediction accuracy. Ivan’s research demonstrates that decentralized ST-GNN training is both practical and essential for future smart mobility systems, offering a more responsive and bandwidth-efficient approach to traffic prediction. Read the full post here .

We were delighted to participate in the FER Open Door Day on November 22, 2025. The event was organized under the following motto: VISIT - EXPLORE - STAY ! We showcased our work, projects, and research, including activities within the 𝗔𝗜𝗼𝗧𝘄𝗶𝗻 project. Through demonstrations and conversations with prospective students, we highlighted how our technologies contribute to the future of STEM and the broader community. Thank you to everyone who stopped by and engaged with us. We look forward to future opportunities to connect and inspire!

At the ScaleSys Workshop co-located with the IoT 2025 Conference , held from 18–21 November in Vienna, Austria , the paper “WebAssembly on Resource-Constrained IoT Devices: Performance, Efficiency, and Portability” was presented by Mislav Has . The paper is authored by Mislav Has, Tao Xiong, Fehmi Ben Abdesslem, and Mario Kušek . The work results from a collaboration with RISE , focusing on the experimental evaluation of WebAssembly technologies for deployment on highly resource-constrained IoT devices. The paper investigates the feasibility and practical implications of running WebAssembly (WASM) on such constrained platforms. It provides an in-depth experimental evaluation of lightweight WASM runtimes on microcontrollers with only kilobytes of RAM, focusing on trade-offs between performance, memory usage, energy efficiency, and portability. The study compares WASM execution with native C implementations for representative IoT workloads, offering insights into when WASM is a viable alternative and when native code remains necessary.