Next-Generation Measurement Systems: AI-Driven Sensor Fusion, Measurement Uncertainty Quantification and Data Mining
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".
Deadline for manuscript submissions: 30 April 2026 | Viewed by 20
Special Issue Editor
Special Issue Information
Dear Colleagues,
This Special Issue will focus on the integration of artificial intelligence (AI), advanced sensor fusion techniques, measurement uncertainty quantification, and data mining to enhance the performance, trustworthiness, and intelligence of modern sensing solutions. With the emergence of new-generation measurement systems, we are witnessing a paradigm shift in how measurement data is acquired, processed, and interpreted across engineering and scientific fields.
Traditional measurement frameworks are increasingly being challenged by feature-rich, heterogeneous, and low-veracity data streams. To address these limitations, AI-driven approaches—e.g., machine learning (ML) and deep learning (DL)—enable real-time data fusion, anomaly detection, and feature extraction across distributed sensor networks. The application of data mining facilitates the discovery of hidden patterns, supports predictive diagnostics, and empowers adaptive measurement strategies.
Nevertheless, classical techniques based on measurement uncertainty quantification for system and data stream validation provide a solid methodological foundation to check the outcomes produced by these new approaches. It is therefore essential to consider and adapt them, identifying effective strategies to transfer such methods within these challenging contexts, to ensure robustness, traceability, and confidence in data-driven decisions, especially in safety-critical applications.
This Special Issue invites original research contributions, case studies, and review articles that focus on the design, implementation, and validation of intelligent measurement systems. Topics include but are not limited to the following: sensor-level AI; measurement uncertainty modeling; hybrid physics–ML models; edge computing for sensor networks; and novel applications in manufacturing, aerospace, energy, robotics, and healthcare. Together, these innovations pave the way for resilient, adaptive, and autonomous measurement infrastructures of the future, ensuring more accessible and actionable information for effective decision-making.
Dr. Antonella Gaspari
Guest Editor
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Keywords
- measurement systems
- artificial intelligence (AI)
- sensor fusion
- data mining
- measurement uncertainty quantification
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