A Multibranch U-Shaped Tunable Encoding Chipless RFID Strain Sensor for IoT Sensing System

计算机科学 无芯片射频识别 结构健康监测 射频识别 微控制器 无线传感器网络 钥匙(锁) 编码(内存) 物联网 嵌入式系统 计算机网络 电气工程 计算机安全 工程类 人工智能
作者
Lan Chen,Luyi Liu,Lei Kang,Zhichong Wan,Guochun Wan,Liyu Xie
出处
期刊:IEEE Internet of Things Journal [Institute of Electrical and Electronics Engineers]
卷期号:10 (6): 5304-5320 被引量:45
标识
DOI:10.1109/jiot.2022.3221938
摘要

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.
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