Metamaterial absorbers, an emerging technology, are predominantly employed to reduce the radar cross section (RCS) of objects. This paper presents a novel unstressed chipless RFID sensor based on metamaterial absorbers, enabling passive wireless monitoring of crack widths on highly reflective metal surfaces without the requirement of background calibration. The proposed sensor integrates two elements based on a U-shaped resonator array, whose relative displacement is employed to characterize the crack width, effectively addressing the defect that the sensors based on monolithic patch are prone to fracture under force when monitoring large deformation. In wireless interrogation, the sensor utilizes the square loop consisting of two U-shaped resonators to absorb electromagnetic waves, encoding crack width information in the reflected signal's spectrum, thereby cleverly avoiding the interference of the reflected signal to the conventional chipless RFID backscattered signal. The equivalent circuit model of the sensor is established to analyze the absorption mechanism and sensing principle. Both simulation and experimental results demonstrate that the sensor’s resonant frequency exhibits an approximately linear increase corresponding to the widening of the crack. Furthermore, the experimental results also prove that increasing the number of sensor’s unit cells can enhances the signal absorption capacity, deepens the notch depth of reflected signals, and consequently extends the wireless reading distance. The proposed sensor based on metamaterial absorbers has the advantages of simple structure, low cost and stable wireless reading performance, which provides a competitive scheme for large-scale distributed sensor deployment in Internet of Things (IoT) and structural health monitoring (SHM).

In recent years, wearable devices for respiratory 2 volume measurement have demonstrated a trend towards 3 increased intelligence and miniaturization; they often need to 4 balance accuracy, comfort, and robustness to meet the needs of 5 long-term data collection. With the increasing application of 6 Internet of Things (IoT) devices in life, wearable devices also have 7 strong IoT attributes, they need to focus on interactivity with 8 external devices. Therefore, this paper proposes a noninvasive 9 flexible wearable device with respiratory detection, which is fixed 10 to the chest and abdomen during operation and can monitor tidal 11 volume (TV) and respiratory rate (RR) in near real-time, a design 12 that strikes a good balance. In the test, most users have less than 13 5% error in the measurement of RR and less than 15% error in 14 the measurement of TV in a stationary state. There will be a partial 15 decrease in accuracy in the exercise state, but it still has a reference 16 significance, which is suitable for providing data for long-term 17 monitoring. In addition, this paper proposes a lightweight 18 networking scheme, which helps the data interaction and 19 management of wearable devices or with other devices. The 20 content discussed in this paper is highly scalable and suitable for 21 different application scenarios of respiratory monitoring. This 22 kind of versatile and flexible solution is also a future trend. 23 24

A smart aggregate can provide the health state of a structural member for the next generation of intelligent infrastructures. This paper proposes an innovative passive aggregate with dual sensitivity enhancement for strain sensing, which has an augmented sensing unit and a compliant displacement amplification mechanism. The sensing unit is composed of a sensor based on a combined patch antenna with an air gap coupling feed to realize the sensitization enhancing. Compliant displacement amplification mechanism is used as the sensor sensitizing mechanism to further enhance the sensitivity. The packaging shell and displacement amplification mechanism of the smart aggregate are integrating 3D printed with VERO high toughness resin and embedded in concrete for experiment. Through sensor modeling and optimization, the effects of various parameters were analyzed to determine the optimal sensor configuration. Experimental results demonstrated the feasibility of the smart aggregate for monitoring internal concrete strain, achieving a maximum sensitivity of 110 MHz/le using quadratic curve fitting and 59 MHz/le with linear fitting. This innovative design provides an effective passive solution for structural health monitoring in complex concrete environments.

This article introduces a product traceability system based on the Internet of Things. The system uses computer vision to identify the characteristics of the Reed–Solomon (RS) code printed on the product packaging box to trace the origin of the product and then prevent fleeing goods. The traceability information combined with the RS code is marked by the outer packaging during the production process of the product. At the same time, product information will be registered on the back-end server in the cloud. When verifying product information, the user can obtain the features on the package through a portable device such as a mobile phone. Then, the computer vision algorithm in the cloud is connected to the network to automatically extract the information code and verify it. By comparing the extracted decoded information with the encoded information, which is registered, the production information and transportation information as well as anticounterfeiting information of the product can be obtained. Our well-designed tracking system can effectively combat the problems that may be encountered during information verification, such as malicious destruction of packaging information, packaging wear, and low quality of user photos. Finally, our experiments show that the system built by RS discrete matrix information code can trace and verify product information quickly and effectively with an anti-interference ability.

Currently, the reader based on frequency modulated continuous wave (FMCW) radar used to dynamically interrogate the chipless RFID sensor needs to collect the envelope of the echo signal through the oscilloscope, which entails expensive acquisition equipment and occupies a considerable amount of space. This paper introduces a low-cost FMCW reader for chipless RFID sensor’s dynamic interrogation. The reader mixes the received signal with the local oscillator signal and down-converts it into a low-frequency signal, which can be directly sampled by a low-cost Analog to Digital Converter (ADC). Then the sensor’s resonant frequency can be extracted from the amplitude and phase variation of the reader’s sampled signal respectively within a single interrogation signal period. The working mechanism of the reader is analyzed theoretically, followed by verification through simulations performed in Advanced Design System (ADS). Hilbert-based algorithms are presented for restoring the amplitude and phase variations of sampled signal in time domain. Then the high-speed reader is employed to wirelessly query a crack sensor, and the measurement results acquired from the reader exhibit excellent concordance with those obtained from vector network analyzer (VNA), demonstrating the accuracy and reliability of the reader. Combined with other types of sensors, the reader can easily read information such as acceleration, dynamic temperature and humidity, providing massive dynamic data for structural health monitoring (SHM) and showing great potential in Internet of Things (IOT).

Structural health monitoring (SHM) is essential for modern large buildings and infrastructure. Radio frequency identification (RFID) widen a new paradigm for SHM in the Internet of Things (IoT) era. This article introduces a low-cost intelligent RFID monitoring system for future IoT applications. Through adding a multiparameter sensing strategy to the passive RFID tags that can be deployed on a large scale to form a sensor network, which expands the coverage of IoT in key positions health monitoring of buildings. In this work, a novel chipless RFID strain sensing tag is designed to characterize the magnitude and direction of metal surface strain, and a low-cost detection method suitable for large mechanical structures is proposed. The traditional strain antennas focus on identifying the strain characteristics without the function of encoding, and there are limitations, such as the rigid measurement methods and the expensive measurement instruments. The tag designed in this article integrates both strain sensing and encoding functions, and has the advantages of small size, high data capacity, and information reading is not easily affected by environmental noise. This article proposes an economical and flexible tag spectrum extraction method, which realizes the intelligent collection and transmission of tag data by connecting lightweight vector network analyzer (VNA) with microcontroller. Combined with the IoT, this method can be well applied to the security assessment and damage detection of large infrastructure structures, which provides a new approach to modern building health monitoring applications from integrated tag design to intelligent detection and risk assessment schemes.

Traditional acceleration sensors use cables to transport data and batteries to supply power, which may increase failure risk, deployment inconvenience, and maintenance difficulty. This article proposes a passive and wireless acceleration sensing system, namely, a newly designed patch-antenna-based sensing node interrogated by frequency-modulated continuous-wave (FMCW) radar. The acceleration is correlated with the location of a cantilever beam, whose oscillation alters the additional capacitance of the patch antenna and consequently the resonant frequencies. The testing range can be adjusted by changing the material and dimensions of the cantilever beam, which is designed up to ±110 m/s 2 with a sampling rate of 500 Hz to meet the requirements of different conditions. The working mechanism of the sensing system is analyzed theoretically and then verified by the simulation in COMSOL Multiphysics. Then, an experiment is carried out to evaluate the performance of the sensing system. Both the resonant frequency and remaining power can be utilized to extract the acceleration response, and results show a good fit with the actual response (i.e., an average error of 4.5%). Empowered by FMCW Radar, the passive accelerometer may have great potential in identifying structure strain mode, impedance-based detection of bolt loosening, automation in construction, etc.

A new structural health monitoring (SHM) program using spectrum sensing and radio-frequency identification technology is presented to measure structural strain. The proposed program involving universal software radio peripheral (USRP) provides an economical alternative to these type of measurements as compared to high-end equipment while this method can achieve more flexible data processing. The overall system for strain detection consists of three main parts: 1) a patch antenna as the sensor for strain; 2) an USRP as a measuring instrument; 3) a computer for data processing. The patch antenna can be used to measure structural strain by interrogating resonant frequency shift due to changes in antenna length. In this article we use the optimized energy detection algorithm applied on USRP to detect the spectrum of the patch antenna, the final measurement results show that the patch antenna sensor has a strain sensitivity 1.7678 kHz/ μ ε while the error between the measured value and the true value of the stress is 2.443% after corrected by machine learning.