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基于RFID的无源感知机制研究综述

Survey on RFID-based Battery-less Sensing

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【作者】 王楚豫谢磊赵彦超张大庆叶保留陆桑璐

【Author】 WANG Chu-Yu;XIE Lei;ZHAO Yan-Chao;ZHANG Da-Qing;YE Bao-Liu;LU Sang-Lu;State Key Laboratory for Novel Software Technology (Nanjing University);College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics;Key Lab of High Confidence Software Technologies (Peking University), Ministry of Education;School of Computer Science, Peking University;

【通讯作者】 谢磊;

【机构】 计算机软件新技术国家重点实验室(南京大学)南京航空航天大学计算机科学与技术学院高可信软件技术教育部重点实验室(北京大学)北京大学计算机学院

【摘要】 随着物联网技术的飞速发展与广泛部署,物联网领域的应用需求逐步从"万物互联"转变成"人-机-物"的感知融合.在众多感知技术之中,射频识别技术(radio frequency identification, RFID)作为物联网领域的核心技术之一,由于标签的轻量级、可标记、易部署等特征,成为"无源感知"的重要媒介.为深入剖析无源感知的研究方法,了解当前无源感知的研究进展,以基于RFID的无源感知研究为主要切入点,根据感知研究的一般流程,从感知渠道、感知方法、感知范畴以及感知应用这4个层面对近年来基于RFID的无源感知研究工作进行阐述和分析.我们着重在各个层面上分析相关技术的研究进展,比较不同技术在感知应用中的优势和劣势,总结当前阶段无源感知的主要研究趋势,并对未来发展方向进行展望.

【Abstract】 With the rapid development and deployments of the Internet of Things(IoT) technology, the demands of IoT applications have changed from the connections of the ubiquitous passive objects to the fusion among “human-computer-objects”. As one of the key technologies in IoT, radio frequency identification(RFID) becomes one significant intermediary of battery-less sensing, due to the lightweight, labelling, and easy deployment of the RFID tags. In order to understand the research progress and methods, this study focuses on the battery-less sensing research based on RFID technology. Particularly, this paper describes and analyzes the research work on four aspects: signal sources, sensing modes, sensing targets, and application scenarios, according to the working flow of sensing research. This paper introduces the research progress in RFID-based sensing from these four aspects, and also discusses the advantages and disadvantages of different technologies among the four aspects. Finally, the existing research is summarized and promising directions are presented for future research.

【基金】 国家自然科学基金(61902175,61872174,61832008,61832005);江苏自然科学基金(BK20190293,BK20200067);北大百度基金(2019BD005)
  • 【文献出处】 软件学报 ,Journal of Software , 编辑部邮箱 ,2022年01期
  • 【分类号】TP391.44
  • 【被引频次】7
  • 【下载频次】1036
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