Mobility-entropy–aware adaptive UWB sampling for energy-efficient real-time industrial digital twins

Industrial digital twins (DTs) must fuse data from operational technology (OT) and information technology (IT) platforms in real time. However, the high-frequency ultra-wideband (UWB) sampling needed for real-time fidelity can rapidly drain battery-powered tags, increasing battery-replacement and maintenance burden in large-scale deployments and jeopardizing service-level accuracy. To address this energy-accuracy trade-off, this paper defines mobility-entropy, a three-dimensional metric that quantifies the dynamic characteristics of a mobile entity. A lightweight on-device machine learning scheduler uses this metric to adjust the UWB sampling rate in real time across the end-to-end pipeline from sensor to DT renderer. Evaluated on a seven-anchor indoor testbed mirrored in real time on the MuJoCo DT platform, the proposed approach extends the average tag sleep time by 65.6% compared to a fixed-rate baseline while achieving a Digital Twin Projection Error (DTPE) as low as 3.15 cm across various mobility environments. The result is longer battery life and reduced telemetry data volume without sacrificing geometric accuracy, improving deployment practicality by lowering maintenance overhead and wireless traffic in industrial settings. We also explain how edge decisions are propagated through the integration layer to DT applications, positioning adaptive sensing within the operational technology (OT) to information technology (IT) to digital twin (DT) data flow. These results highlight the framework's potential for real-world industrial digital twin applications, including worker and asset tracking as well as safety monitoring, by enabling energy-efficient operation with reduced maintenance and communication overhead.