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 paper introduces a low-cost intelligent RFID monitoring system for future IoT applications. Through adding a multi-parameter 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 paper 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 paper 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.

Chipless radio frequency identification (RFID) technology has been widely used in the field of structural health monitoring (SHM), but most of the current research mainly focuses on the detection of mechanical properties and there are few studies on the multi-physical parameters (for example, temperature and humidity) in the climatic environment around the structure. Thus, it is necessary to design a small and compact sensor for multi-parameter detection. This paper proposes a multi-parameter chipless RFID sensor based on microstrip coupling, which supports 4-bit ID code and integrates two detection functions of temperature and humidity. Through linear normalization fitting, the sensitivity of the sensor is about 2.18 MHz/RH in the ambient relative humidity test and the sensitivity of the sensor is about 898.63 KHz/°C in the experimental test of water bath heating from 24.6 °C to 75 °C. In addition, this paper proposes an engineering application detection method, designs a lightweight dynamic spectrum detection and wireless transmission platform based on a lightweight vector network analyzer (VNA) and realizes the real-time extraction and transmission of RFID spectrum sensing data. The means are more flexible and economical than traditional experimental scenarios.

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.

In the field of structural health detection, since the sensor needs to be directly bonded to the measured object, it is difficult to stably transmit the RFID information and realize the passive wireless structure strain detection. To circumvent these difficulties, this paper innovatively proposes a dual-resonant circular microstrip patch antenna (CMPA) sensor with chipless RFID tag. The information of the RFID tag can be changed that allows the sensor to be deployed on a large scale. The separating sensor includes chipless RFID tag layer and dual-resonant CMPA layer, which can simultaneously reflect RFID tag information and strain information.The design of separated layers can minimize the resonant frequency shift of ID tags when stress is implemented on CMPA strain part. Current distribution analysis and equivalent circuit are used to study the strain size and direction. Experiments show that the offset of the resonant frequency of the sensor antenna has linear relationship with the lateral and longitudinal strains. The correlation coefficients are 0.9284 and 0.9235 respectively, and the sensitivity coefficient of the lateral and longitudinal strain is 528 MHz ϵ ⁻¹ and 384 MHz ϵ ⁻¹ . At the same time, the readability of the RFID tag information is maintained.

Over time, the structural connection gaps of a building can produce small displacements due to various vibrations during use, which are random in direction and affect a certain area, so sensors that can only detect a single direction and cannot be deployed for a long period of time are not conducive to assessing the health of the piece of area. Therefore, this paper proposes a displacement detection strategy for structural health monitoring (SHM), where the displacement sensor is a tag distributed on two substrates, which can be embedded in the building for real-time monitoring, the resonance frequency can be changed by the relative movement of the substrates and the tag structure can be simply altered to achieve the differentiation of the identity in the frequency domain, and wireless measurements can be achieved. The data reading part is carried by unmanned vehicle with microcontroller (MCU) and Nano vector network analyzer (VNA) as the core of the reading node, to achieve a certain area of multiple Chipless Radio frequency identification (RFID) sensors to read the data. The data relay part consists of MCU and several kinds of communication modules, which is responsible for relaying the data collected by the reader to realize wired/wireless long-distance communication. This three-part wireless sensor network (WSN) can achieve two displacement directions and length measurements to facilitate rapid assessment of the structural state, through the combination of reader nodes plus data relay nodes can be efficiently detected multiple targets, while the cost, large-scale deployment has a greater advantage.

The detection of chipless radio frequency identification (RFID) tags is mostly realized by vector network analyzers. In actual detection, this method is expensive and not flexible enough. A flexible new detection method implemented on the Universal Software Radio Peripheral (USRP) is proposed in this paper. The measurement object is a 6-bit chipless RFID coded tag sensor based on a spiral resonator trap circuit. The power detection function of the frequency band is designed and implemented on USRP’s open source platform GNU Radio. On this basis, for chipless RFID tags with spectrum coding, a decoding identification method based on maximum likelihood estimation is designed. The frequency domain curve of the scattered signal of the sample label is detected by the power detection method, and the decoding and identification test of the sample label is performed by the maximum likelihood estimation decoding identification method. It is shown that the maximum likelihood estimation decoding algorithm based on the USRP platform proposed can be effectively implemented in passive wireless networks through experimental results, which provides a new idea for the flexible and economical RFID tag detection method.