Sensor networks are critical enablers of digital transformation across Industry 4.0 & 5.0, smart cities, environmental monitoring, and energy systems. As sensing systems evolve from individual instruments to complex, interconnected and intelligent networks, ensuring measurement traceability, uncertainty evaluation, and data trustworthiness becomes increasingly challenging. This special session addresses the emerging field of sensor network metrology, focusing on the development of metrological frameworks for distributed and heterogeneous sensing systems. It aims to bridge gaps between traditional metrology, digital technologies, and AI-driven data processing. Key challenges include the propagation of uncertainty in spatio-temporal measurement data, calibration methodologies at network level, and validation of machine-learning-based sensing systems.

The session builds on recent international initiatives, including FORUM MD activities on complex sensor networks, and aims to establish harmonized approaches for measurement traceability, calibration, and data quality assurance in digital sensing environments. The session will bring together experts from metrology institutes, academia, and industry to advance methodologies enabling robust, interoperable, and trustworthy sensor networks, supporting future digital infrastructure, digital twins, and AI-enabled systems.

Sensor networks; digital metrology, uncertainty propagation, calibration, data quality, IoT, AI in metrology, digital transformation