Computer Engineer

Entreprise :

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Description du poste :

FUTURE - F ramework for U ncertainty-aware and T rustworthy U nified R easoning in E nabled CPS

Abstract

Cyber-Physical Systems (CPS) are increasingly deployed in safety-critical settings such as manufacturing, robotics, autonomous infrastructures, and intelligent buildings [1]. Smart Building systems, in particular, rely on dense networks of heterogeneous sensors to monitor energy consumption, indoor environmental quality, and occupant comfort. These systems must operate under heterogeneous data sources, incomplete information,sensor unreliability, and unpredictable disturbances [2]. Traditional AI-based decision systems provide deterministic outputs, often without expressing confidence levels or handling conflicting evidence [3].

FUTURE project aims to develop an uncertainty-aware and trust-oriented decision-making framework combining uncertainty modelling, AI-based predictions, and Dempster-Shafer theory. By integrating belief-function reasoning with multi-source evidence fusion, FUTURE enhances the resilience of CPS decision pipelines.

A realistic CPS scenario specifically, a Smart Building environment will serve as the experimental validation context, leveraging sensor data to evaluate uncertainty, reliability, and evidence-based decision performance.

Keywords :

Artificial Intelligence, Belief Functions Theory, Cyber-Physical Systems, Smart building, Data Fusion, Heterogenuous Sources.

Research work

Scientific Fields :

Artificial Intelligence, Trustworthy AI, Uncertainty Modelling, System Engineering, Information Fusion.

Work Program / Objectives :

The work program of FUTURE is structured around eight major tasks, each corresponding to a key component of the proposed uncertainty-aware decision-making framework. These tasks form a coherent pipeline, from data importation to experimental validation, and are described below.

Data Importation - As a first step, we identify a publicly accessible and representative dataset that reflects real-world smart-building sensor deployments. Such datasets must capture multiple sensing modalities relevant to CPS applications, enabling the evaluation of uncertainty-aware learning, reliability assessment, and evidence fusion in realistic operating conditions. For the FUTURE project, the selected test dataset is the CU-BEMS Smart Building Energy and IAQ Data 1 , which provides multi-sensor recordings of energy consumption, indoor air quality, and environmental variables across an operational smart- building environment.

Data Cleaning, Normalisation, and Harmonisation - Preprocess the raw data through noise filtering, unit conversion, timestamp alignment, and signal normalisation.

Uncertainty Modeling of Data - Estimate the data uncertainty of multi- heterogeneous sources.

Source-Aware Categorisation of Data - Organise the processed data into meaningful categories.

Development of an Uncertainty-Aware AI Model - Implement and train uncertain Neural Network or an evidential deep learning model capable to produce both predictions and associated uncertainty estimates.

Transformation of Model Outputs into Belief Masses - Convert the outputs of the AI model into to representative class-specific supports together with residual ignorance caused by uncertainty.

Evidence Fusion - Combine belief masses using different composition strategies depending the detected level of conflict.

Decision-Making under Uncertainty - Evaluate how uncertainty and conflicting evidence influence the robustness of the final decision.

1 https : / / www.kaggle.com / datasets / claytonmiller / cubems-smart-building-energy-and-iaq-data

Expected Scientific / Technical Output :

Scientific Contributions

• A unified uncertainty-aware decision framework for CPS.
• A novel integration of belief-function theory.
• Experimental analysis of DS-based fusion in real CPS environments, particularly in Smart Building systems.
• A reproducible methodology supporting trustworthy AI research.
• A scientific research report.

Technical Contributions

Impact

FUTURE strengthens resilience in CPS, reduces decision errors under uncertainty, and provides a reference framework for integrating uncertainty-aware AI in industrial settings.

Lab presentation :

CESI LINEACT (UR 7527), Laboratory for Digital Innovation for Businesses and Learning to Support the Competitiveness of Territories, anticipates and accompanies the technological mutations of sectors and services related to industry and construction. The historical proximity of CESI with companies is a determining element for our research activities. It has led us to focus our efforts on applied research close to companies and in partnership with them. A human-centered approach coupled with the use of technologies, as well as territorial networking and links with training, have enabled the construction of cross-cutting research; it puts humans, their needs and their uses, at the center of its issues and addresses the technological angle through these contributions. Its research is organized according to two interdisciplinary scientific teams and several application areas :

These two teams develop and cross their research in application areas such as :

Areas supported by research…