The structural damages of metallic structure components in many critical facilities and equipment may result in disasters that endanger human life. Existing Structural Health Monitoring (SHM) solutions commonly suffer from the limitations of bulky equipment, poor environmental adaptability, and high costs, which raise challenges for detection efficiency and large-scale multi-target monitoring. Radio Frequency Identification (RFID) sensing technology, featuring Non-Line-of-Sight (NLoS), flexible and pasteable, and easy deployment, show great promise for SHM. Recent studies have demonstrated the potential of RFID sensors for structural damage sensing including cracks, strain, and corrosion of metal structures, along with the analysis of parameters like crack width, structural deformation, and corrosion depth. This study provides a survey and in-depth analysis of recent technical progress in RFID sensor-based SHM. The main contributions include: (1) Classification of the novel sensing techniques and systems based on the functional model of RFID backscatter sensing; (2) Summarization of the common structural damage types and the feature extraction techniques of RFID sensing for SHM; (3) Survey of the recent progresses of the techniques, methods, and applications for RFID-based SHM; (4) Analysis of the challenges facing the state-of-the-art, including characterization and quantification of structure damage parameters and the impact of environmental factors, followed with an outlook of the future work. This study provides a timely reference for the innovation and practice of RFID sensing techniques in the field of SHM.

For the advantages of non-contact sensing, inventory identification, and cost-effective deployment, Radio Frequency Identification (RFID) localization has become a promising solution for some industrial Internet of Things (IoT) applications. However, its efficacy is often constrained by multi-antenna dependency, motion-induced phase distortions, and the inherent phase coupling between position and orientation, all of which hinder simultaneous detection of orientation and position in high accuracy. To overcome these issues, this investigation proposes a novel phase-decoupling model specifically designed for a single-antenna synthetic aperture radar (SAR) RFID system. The key contributions include: 1) Design and implementation of an adaptive dynamic phase compensation (ADPC) mechanism for decoupling motion-induced parameters from target backscatter signatures, effectively mitigating phase offsets caused by non-steady-state antenna trajectories; 2) Establishment of a novel phase-orientation model and a differential phase-adaptive peak detection (DPAPD) framework, which integrates differential measurements with threshold-optimized peak identification, achieving sub-degree angular resolution; 3) Development of a 3D SAR localization method incorporating phase decoupling and Particle Filter (PF) which achieves robust and consistent 3D localization with acceptable accuracy. This investigation provides a high-accuracy and cost-effective dynamic monitoring solution for RFID-based smart shelves, enabling advanced applications such as inventory tracking and tilt detection for fragile goods in automated warehouses.

Radio-Frequency Identification (RFID) positioning promises a prospective future for industrial automation and Industrial Internet of Things (IIoT) applications. However, the radio waves carry multiple parameters including position, orientation, and ambient environment factors, which raises challenges in simultaneous detection of position and orientation of product objects. This investigation proposes a simple RFID array-based position and orientation simultaneous detection technique for moving object in industrial chain. The main contributions of this investigation include: (1) Theoretical analysis and integrated model of position and orientation variation with the antenna parameters and interrogation variables in RF backscatter coupling-based sensing. (2) Development of an innovative simple RFID array-based phase separation technique with differential sensing, which determines the position-and orientation-induced phase without their mutual coupling impact. (3) Proposal of a simultaneous detection technique for moving objects’ position and orientation by integrating the Multiple Signal Classification (MUSIC) algorithm and hyperbolic positioning algorithm. In the experimental verification with a range from -75 cm to 75cm, the average error of position and orientation estimation is 4.29 cm and 4.89 degrees.

Without dependence on external anchors, the micro-electro-mechanical system inertial measurement unit (MIMU) allowing autonomous localization has promised great potential in wearable IoT applications, including kinematic analysis in sports, medical treatment, elderly care, and disaster rescue. However, the miniaturization of devices for unobtrusive sensing, algorithms minimizing the inherent accumulative errors of inertial devices, and the adaptivity of algorithms for various motion modalities are the key challenges. The removal of accumulative error in the continuous gait cycles with adaptive algorithms is a critical issue regarding localization accuracy, especially for low-cost devices. This investigation proposes an adaptive threshold-based zero-velocity update (ZUPT) algorithm to separate the timing of gait cycle phases and compensate for the residual velocity with a linear fitting approximation. The key contributions include: 1) a lightweight threshold-based zero-velocity detection algorithm to split the gait cycle phases of continuous walking; 2) a quaternion-based extended Kalman filter (EKF) algorithm to reduce the errors of the nonlinear operations for attitude prediction; 3) a linear fitting method for compensating the residual velocity in each gait cycle of continuous walking; and 4) the design of a miniature single-MIMU-based foot-mountable wearable device and the corresponding experimental studies to verify the proposed methods. Results show that the relative error is less than 3.0% for 2-D and 3-D trajectories, and the tests with different locomotion patterns demonstrate the adaptivity of the proposed algorithm compared to its peers. The results show that the presented techniques are capable of handling accumulative errors for low-cost MIMU-based systems with good adaptivity.