mmWave-Based Crowd Sensing for Metaverse Applications

The growing adoption of the Metaverse raises critical challenges in real-time crowd sensing, where traditional vision-based systems struggle to balance high-resolution monitoring with user privacy. Cameras and other optical sensors, while effective in tracking movement and interactions, inherently capture personally identifiable information, creating ethical and legal concerns. This paper explores the use of millimetre-wave (mmWave) radar technology as a privacy-preserving, high-resolution solution for real-time crowd sensing in Metaverse applications. Recognising the limitations of traditional monitoring methods, such as visual surveillance and mobile-based tracking, this study presents a simulation-based framework for evaluating mmWave-enabled visitor tracking within a museum-style environment. A MATLAB-based simulator models realistic human mobility and sensor data, incorporating error models obtained from an experimental precision analysis of mmWave sensors. A combination of DBSCAN and K-Means clustering is then applied to estimate crowd formations, density, and mobility flows. Results demonstrate the effectiveness of mmWave in identifying dynamic crowd behavior while preserving user anonymity, highlighting its potential for immersive digital twins, XR experiences, and intelligent environment management in Cyber-Physical-Social Systems that underpin the Metaverse.