Our research group is based in Reims, Grand Est (France), within a French higher education and research institution. We specialise in cyber-physical systems (CPS), IoT-based sensing architectures, real-time control, and model-based diagnostics, with applications to distributed and embedded monitoring systems. Our current research focuses on the development of digital twin frameworks for fault detection, condition monitoring, and predictive diagnostics in interconnected physical systems. We design and validate intelligent supervision layers that bridge physical instrumentation with data-driven and model-based reasoning, leveraging IoT sensor networks, communication protocols, and machine learning techniques. Our experimental work involves deploying and testing CPS architectures on instrumented test benches, which gives us strong hands-on experience in system integration, signal acquisition, and automated fault classification.
Digital twins, DC microgrids, fault detection, real-time monitoring, , ICT infrastructure, smart sensing, anomaly detection, machine learning, distributed energy systems, distributed monitoring systems, precision agriculture, water infrastructure, decentralised systems, data-driven modelling.
We seek to join multidisciplinary consortia addressing the following PRIMA 2026 topics, where our expertise in CPS, IoT, control, and diagnostics can bring a clear and focused technical contribution:
Topic 1.1.1-2026 – Decentralised Brackish Water Desalination: We can contribute to the supervision and control layer of decentralised desalination units by designing IoT-based monitoring architectures and digital twin models for real-time fault detection and operational diagnostics. We can, also, contribute the design of energy management digital twins for decentralised desalination units powered by renewable microgrids.
Topic 2.1.1-2026 – Water Allocation and Non-Perennial Watercourses: We propose to contribute I oT sensor network architectures and real-time monitoring architectures for distributed sensor networks deployed along non-perennial watercourses. Drawing on our experience in data-driven fault detection and state estimation, we can support the development of smart water flow monitoring systems integrated with digital twin models to enhance decision support for water allocation and ecosystem restoration.
Topic 2.2.1-2026 – Remote and Non-Invasive ICT Monitoring Against Disease and Pest Infestation: Our competencies in embedded ICT systems, remote sensing data fusion, and machine learning classifiers align directly with the development of non-invasive monitoring platforms for early detection of crop disease and pest infestation. We can contribute to the design of low-power intelligent sensing nodes, anomaly detection algorithms, and digital twin-based simulation environments for precision agriculture applications.
We are targeting a role as scientific partner, contributing primarily to ICT architecture, real-time monitoring, CPS modelling, and diagnostic work packages.
Our group brings expertise in transposing CPS and IoT diagnostic methodologies across application domains, from energy systems to environmental and agricultural monitoring. We are particularly interested in connecting with Mediterranean partners to co-design solutions adapted to local deployment constraints. We are available to contribute to proposal writing from early stages, including technical work package definition and methodology design. Consortium coordinators are welcome to reach out to discuss complementarity and collaboration modalities
