SPECIAL SESSION #17

Gloria Cosoli

eCampus University, Italy

• gloria.cosoli@uniecampus.it

Marco Arnesano

eCampus University, Italy

• marco.arnesano@uniecampus.it

Antonio Luca Alfeo

eCampus University, Italy

• antonioluca.alfeo@uniecampus.it

Francesca Righetti

University of Pisa, Italy

• francesca.righetti@unipi.it

Nowadays metrology has to face unprecedented challenges as well as opportunities, driven by the integration of Artificial Intelligence (AI) and Information and Communication Technology (ICT). The rapid evolution of technology has enabled the development of advanced sensors for a wide range of application domains, from industry to healthcare, and from sport to fitness. The Internet of Things (IoT) fosters seamless interconnection among devices as well as real-time data sharing, enabling an immersive interaction between a certain environment and the related stakeholders by means of smart, autonomous monitoring systems.

Additionally, innovative AI algorithms are rapidly spreading, easing deep data analyses and providing meaningful parameters for a timely management of specific situations, including emergency contexts (e.g., accidents or extreme natural events like earthquakes) but also standard living scenarios (e.g., indoor environmental quality optimization in a building), thus supporting decision-making in an efficient and powerful way.

In this context, applications can exhibit different requirements in terms of measurement accuracy, while the availability of hardware and software resources may differ significantly. For these reasons, it is important to identify an appropriate trade-off between maximizing accuracy and the need of minimizing hardware, software, and computational costs related to advanced distributed data processing techniques. In fact, incorporating AI introduces new challenges in measurement, related to a rigorous measurement uncertainty analysis (made difficult by the “black-box” nature of many AI approaches) but also to the need of an efficiency/accuracy trade-off, linked to limited resources (e.g., edge or IoT devices) and computational costs.

This Special Session aims to promote the latest research contributions in this research field. Original papers are invited to be submitted pointing out how metrology needs to interact with information and communication technologies to achieve the best solutions in terms of metrological performance as well as computational efficiency, considering the specific application context and analysing the effective interaction between metrology and ICT.

Topics of interest include but are not restricted to:

• Metrological characterization of AI-based measurement systems;
• Measurement uncertainty analysis of AI-based measurement systems;
• Integration of AI into embedded measurement systems;
• Industrial data processing with AI technologies;
• Novel AI-based measurement strategies for harsh industrial contexts (e.g., high temperature, vibrations, corrosive agents);
• IoT systems, AI and ML strategies for predictive maintenance applications;
• AI-based Non-Destructing Testing (NDT) for quality control;
• Analysis of Explainable AI (XAI) technologies in metrology;
• Performance, cost, and energy–sustainability trade-off analysis for deploying AI models on edge computing architectures.

Gloria Cosoli received the B.S. degree in Biomedical Engineering (with honors) and the M.S. degree in Electronic Engineering (with honors) from Università Politecnica delle Marche (UNIVPM), Ancona, Italy, in 2011 and 2013, respectively. She received the Ph. D. degree in Mechanical Engineering from the same university in 2017 with a thesis titled “Study and Development of a Novel Radio Frequency Electromedical Device for the Treatment of Peri-Implantitis: Experimental Performance Analysis, Modelling of the Electromagnetic Interaction with Tissues and In Vitro and In Vivo Evaluation”. From 2023 to 2025, she was a research collaborator at DIISM; since May 2024, she has been an Associate Professor at the Department of Theoretical and Applied Sciences (DiSTA) of eCampus University, Novedrate, Italy. She is the author of 40 articles, 54 conference proceedings, and two national patents. Her research interests include non-invasive physiological measurements, numerical modeling, mechanical measurements, signal processing, and NDT. Prof. Cosoli received the IEEE MeMeA 2015 Best Poster Award and the IEEE MetroLivEnv 2023 Best Paper Award.

Marco Arnesano PhD in Mechanical and Thermal Measurements from Università Politecnica delle Marche, he is Full Professor and Director of the Department of Theoretical and Applied Sciences (DiSTA) at eCampus University, Italy. His research focuses on developing innovative measurement systems for living environments: indoor environmental monitoring and control through embedded and IoT sensors, wearable sensors, physiological measurements, signal processing, data analysis, and machine learning. Research activities are conducted within European and National projects as scientific responsible and principal investigator. He is the author of more than 80 peer-reviewed scientific publications for international journals and conference proceedings.

Antonio Luca Alfeo is an Associate Professor of the eCampus University. His research interest addresses the design of machine learning pipelines to analyze physiological, industrial and behavioral data using Deep Representation Learning and Explainable Artificial Intelligence. He is a visiting professor at the School of Computer Science and Electronic Engineering, University of Essex, UK. He is an associate Editor of the Expert Systems with Applications journal, Elsevier. He was involved in different EU and national research projects. He coordinated a research project in the field of Industry 4.0, specifically related to the optimization of real-world maintenance processes by using deep learning approaches. He graduated with an International Ph.D. Program in Smart Computing (University of Pisa, Florence, and Siena). He worked on different solutions based on Swarm Intelligence to analyze collective behaviors in Smart Cities with Professor Alex ‘Sandy’ Pentland as a visiting research fellow at the Massachusetts Institute of Technology.

Francesca Righetti received the bachelor’s and master’s degrees in computer engineering and the Ph.D. in information engineering from the University of Pisa, Pisa, Italy, in 2014, 2017, and 2021, respectively. She is an Assistant Professor at the Department of Information Engineering, University of Pisa. She has been involved in many national and international projects. Her research interests include Internet of Things, cloud/fog/edge computing, cybersecurity, and IoT applications for smart industry and smart healthcare. She has authored around 40 publications in international journals and peer-reviewed conference proceedings.
Dr. Righetti has served as the Guest Editor for the Special Issue on “Industrial IoT networks to Support Future Cloud-to-Things Applications,” in Ad Hoc Networks (Elsevier). She was also the TPC Co-Chair of the IEEE International Workshop in Smart Service Systems 2022, co-located with IEEE SMARTCOMP. She is currently involved in the organizing committee of IEEE PerCom 2026 and IEEE SMARTCOMP 2026. Finally, she served in the TPC of international conferences and workshops, including IEEE WoWMoM, IEEE SMARTCOMP, IEEE ISCC, IEEE CCNC, IEEE MSN, and IEEE MELECON.