RF-AbVib: Environment-independent vibration monitoring using COTS RFID devices

Abstract

Endowing IoT devices with self-security monitoring capabilities without relying on external hardware marks a significant advancement in the field. RFID-equipped smart cabinets, while providing robust protection for sensitive items such as documents and electronic devices, remain vulnerable to violent break-ins or physical disturbances such as slapping and shaking, which produce characteristic vibration patterns. We demonstrated that the cabinet’s integral RFID system can inherently detect such vibrations, thus enhancing its self-security. However, overcoming environmental dependency remains a critical challenge: variations in the shape, size, material, and spatial arrangement of items inside the cabinet interfere with RFID signal propagation, resulting in complex multipath effects that compromise vibration-sensing accuracy and weaken security detection. To address this limitation and enable self-security monitoring, we proposed RF-AbVib, a novel solution that utilizes commercial off-the-shelf RFID readers in conjunction with a fixed reference tag mounted on the inner wall of the cabinet to achieve environment-independent vibration monitoring. We pre-trained and fine-tuned a meta-learning model to enable RF-AbVib to process variable-length data and adapt to diverse environmental conditions. Furthermore, we proposed a bilateral threshold filtering (BTF) algorithm combined with discrete wavelet transform (DWT) to remove outliers and hardware noise while preserving subtle vibration features in RFID signals. Evaluated across 31 distinct environments, RF-AbVib achieved 95.59 % accuracy in detecting three abnormal behaviors with only one sample, regardless of the reference tag’s position, orientation, or type. Relevant data has been uploaded to the RF-AbVib dataset.