基于 WiFi 的智能商品湿度传感 CSI 处理技术

Yirui Deng, Deepak Mishra, Shaghik Atakaramians, Aruna Seneviratne
{"title":"基于 WiFi 的智能商品湿度传感 CSI 处理技术","authors":"Yirui Deng, Deepak Mishra, Shaghik Atakaramians, Aruna Seneviratne","doi":"arxiv-2409.07857","DOIUrl":null,"url":null,"abstract":"Indoor humidity is a crucial factor affecting people's health and well-being.\nWireless humidity sensing techniques are scalable and low-cost, making them a\npromising solution for measuring humidity in indoor environments without\nrequiring additional devices. Such, machine learning (ML) assisted WiFi sensing\nis being envisioned as the key enabler for integrated sensing and communication\n(ISAC). However, the current WiFi-based sensing systems, such as WiHumidity,\nsuffer from low accuracy. We propose an enhanced WiFi-based humidity detection\nframework to address this issue that utilizes innovative filtering and data\nprocessing techniques to exploit humidity-specific channel state information\n(CSI) signatures during RF sensing. These signals are then fed into ML\nalgorithms for detecting different humidity levels. Specifically, our improved\nde-noising solution for the CSI captured by commodity hardware for WiFi\nsensing, combined with the k-th nearest neighbour ML algorithm and resolution\ntuning technique, helps improve humidity sensing accuracy. Our commercially\navailable hardware-based experiments provide insights into achievable sensing\nresolution. Our empirical investigation shows that our enhanced framework can\nimprove the accuracy of humidity sensing to 97%.","PeriodicalId":501034,"journal":{"name":"arXiv - EE - Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Smart CSI Processing for Accruate Commodity WiFi-based Humidity Sensing\",\"authors\":\"Yirui Deng, Deepak Mishra, Shaghik Atakaramians, Aruna Seneviratne\",\"doi\":\"arxiv-2409.07857\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Indoor humidity is a crucial factor affecting people's health and well-being.\\nWireless humidity sensing techniques are scalable and low-cost, making them a\\npromising solution for measuring humidity in indoor environments without\\nrequiring additional devices. Such, machine learning (ML) assisted WiFi sensing\\nis being envisioned as the key enabler for integrated sensing and communication\\n(ISAC). However, the current WiFi-based sensing systems, such as WiHumidity,\\nsuffer from low accuracy. We propose an enhanced WiFi-based humidity detection\\nframework to address this issue that utilizes innovative filtering and data\\nprocessing techniques to exploit humidity-specific channel state information\\n(CSI) signatures during RF sensing. These signals are then fed into ML\\nalgorithms for detecting different humidity levels. Specifically, our improved\\nde-noising solution for the CSI captured by commodity hardware for WiFi\\nsensing, combined with the k-th nearest neighbour ML algorithm and resolution\\ntuning technique, helps improve humidity sensing accuracy. Our commercially\\navailable hardware-based experiments provide insights into achievable sensing\\nresolution. Our empirical investigation shows that our enhanced framework can\\nimprove the accuracy of humidity sensing to 97%.\",\"PeriodicalId\":501034,\"journal\":{\"name\":\"arXiv - EE - Signal Processing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-09-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - EE - Signal Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2409.07857\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - EE - Signal Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.07857","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

无线湿度传感技术具有可扩展性和低成本的特点,是测量室内环境湿度而无需额外设备的理想解决方案。因此,机器学习(ML)辅助的 WiFi 传感被视为集成传感与通信(ISAC)的关键推动因素。然而,目前基于 WiFi 的传感系统(如 WiHumidity)精度较低。为解决这一问题,我们提出了一种基于 WiFi 的增强型湿度检测框架,该框架利用创新的滤波和数据处理技术,在射频传感过程中利用湿度特有的信道状态信息(CSI)特征。然后将这些信号输入多项式算法,以检测不同的湿度水平。具体来说,我们改进了用于 WiFisensing 的商品硬件捕获 CSI 的去噪解决方案,结合 kth 近邻 ML 算法和分辨率调整技术,有助于提高湿度感应的准确性。我们基于商用硬件的实验提供了对可实现的传感分辨率的深入了解。我们的实证调查表明,我们的增强型框架可将湿度传感精度提高到 97%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Smart CSI Processing for Accruate Commodity WiFi-based Humidity Sensing
Indoor humidity is a crucial factor affecting people's health and well-being. Wireless humidity sensing techniques are scalable and low-cost, making them a promising solution for measuring humidity in indoor environments without requiring additional devices. Such, machine learning (ML) assisted WiFi sensing is being envisioned as the key enabler for integrated sensing and communication (ISAC). However, the current WiFi-based sensing systems, such as WiHumidity, suffer from low accuracy. We propose an enhanced WiFi-based humidity detection framework to address this issue that utilizes innovative filtering and data processing techniques to exploit humidity-specific channel state information (CSI) signatures during RF sensing. These signals are then fed into ML algorithms for detecting different humidity levels. Specifically, our improved de-noising solution for the CSI captured by commodity hardware for WiFi sensing, combined with the k-th nearest neighbour ML algorithm and resolution tuning technique, helps improve humidity sensing accuracy. Our commercially available hardware-based experiments provide insights into achievable sensing resolution. Our empirical investigation shows that our enhanced framework can improve the accuracy of humidity sensing to 97%.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信