基于Wi-Fi的烟雾实时检测方法

吴哲夫; 唐万利; 陈骋; 方路平

doi:10.11959/j.issn.1000-0801.2019150

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基于Wi-Fi的烟雾实时检测方法

研究与开发 | 更新时间：2024-06-05

- 基于Wi-Fi的烟雾实时检测方法
- Real-time detection method of smoke based on Wi-Fi
- 电信科学 2019年35卷第6期页码：70-77
- 作者机构：
  
  1. 浙江工业大学信息学院，浙江杭州 310023
  2. 华信咨询设计研究院有限公司，浙江杭州 310052
- 作者简介：
  
  [ "曹奇英（1960- ），男，博士，东华大学计算机科学与技术学院教授、博士生导师，主要研究方向为普适计算、智能信息处理。" ]
  [ "唐万利（1995- ），女，浙江工业大学信息学院硕士生，主要研究方向为无线网络烟雾检测技术。" ]
  [ "陈骋（1991- ），男，华信咨询设计研究院有限公司通信工程师，主要研究方向为无线传输网络研究和规划设计。" ]
  [ "方路平（1968- ），男，浙江工业大学信息学院教授、硕士生导师，CCF会员，主要研究方向为计算机网络、深度学习等。" ]
- 基金信息：
  
  浙江省自然科学基金资助项目;Zhejiang Provincial Natural Science Foundation of China(LY18F010025);浙江省自然科学基金资助项目;The Natural Science Foundation of Zhejiang Provincial of China(LY14F050004);浙江省自然科学基金资助项目;Zhejiang Provincial Natural Science Foundation of China(LY13F010011)
- DOI：10.11959/j.issn.1000-0801.2019150
  中图分类号： TN929
- 网络出版日期：2019-06，
  
  纸质出版日期：2019-06-20
- 稿件说明：
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吴哲夫, 唐万利, 陈骋, 等. 基于Wi-Fi的烟雾实时检测方法[J]. 电信科学, 2019,35(6):70-77.

Zhefu WU, Wanli TANG, Cheng CHEN, et al. Real-time detection method of smoke based on Wi-Fi[J]. Telecommunications science, 2019, 35(6): 70-77.
吴哲夫, 唐万利, 陈骋, 等. 基于Wi-Fi的烟雾实时检测方法[J]. 电信科学, 2019,35(6):70-77. DOI： 10.11959/j.issn.1000-0801.2019150.

Zhefu WU, Wanli TANG, Cheng CHEN, et al. Real-time detection method of smoke based on Wi-Fi[J]. Telecommunications science, 2019, 35(6): 70-77. DOI： 10.11959/j.issn.1000-0801.2019150.

摘要

与火灾相关的烟雾实时检测方法是保障公共安全的重要手段之一。针对现有火灾传感器系统的高成本和视频探测的拍摄盲区等问题，提出了一种基于 Wi-Fi 网络信道状态信息的火灾烟雾检测方法。该方法无需专用的火灾烟雾监测设备，通过研究火灾烟雾对无线传输信号的影响，提取信道状态信息中的烟雾特征参数并应用机器学习进行分类，从而实现环境火灾实时监测的目的。实验结果表明，基于 Wi-Fi 信道状态信息的环境烟雾检测准确率可达98.06%。

Abstract

Fire-related smoke real-time detection method is one of the important means to ensure public safety.A fire smoke detection method based on channel state information of Wi-Fi network was proposed to avoid the high cost of existing fire sensor system or the blind spot of video detection.The method need not require a dedicated fire smoke monitoring device.By studying the influence of fire smoke on wireless transmission signals

the smoke characteristic parameters in the channel state information were extracted and classified by machine learning

thereby realizing the real-time monitoring of environmental fire.The experimental results show that the accuracy of environmental smoke detection based on Wi-Fi channel status information can reach 98.06%.

关键词

Keywords

references

袁飞阁 . 基于烟雾浓度的火灾视频检测方法 [D ] . 武汉:华中科技大学 , 2013 .

YUAN F G . Smoke detection method based on smoke density with video [D ] . Wuhan:Huazhong University of Science and Technology , 2017 .

相徐斌 . 基于视频的烟雾检测算法研究 [D ] . 杭州:浙江大学 , 2017 .

XIANG X B . The research of smoke detection algorithm on video [D ] . Hangzhou:Zhejiang University , 2017 .

程文彬 , 杜磊 , 刘依依 . 无线多传感器数据融合的家庭火灾检测系统设计 [J ] . 电信科学 , 2017 , 33 ( 9 ): 174 - 181 .

CHENG W B , DU L , LIU Y Y . Design of home fire detection system based on wireless multi-sensor data fusion [J ] . Telecommunications Science , 2017 , 33 ( 9 ): 174 - 181 .

KAABI R , FRIZZI S , BOUCHOUICHA M , et al . Video smoke detection review:state of the art of smoke detection in visible and IR range [C ] // International Conference on Smart,Monitored and Controlled Cities,Feb 17-19,2017,Kerkennah Islands,Tunisia . Piscataway:IEEE Press , 2017 : 81 - 86 .

BORGES P V , IZQUIERDO E . A probabilistic approach for vision-based fire detection in videos [J ] . IEEE Transactions on Circuits and Systems for Video Technology , 2010 , 20 ( 5 ): 721 - 731 .

FOGGIA P , SAGGESE A , VENTO M , et al . Real-time fire detection for video-surveillance applications using a combination of experts based on color,shape,and motion [J ] . IEEE Transactions on Circuits and Systems for Video Technology , 2015 , 25 ( 9 ): 1545 - 1556 .

WANG Y , WU K , NI L M , et al . WiFall:device-free fall detection by wireless networks [J ] . IEEE Transactions on Mobile Computing , 2017 , 16 ( 2 ): 581 - 594 .

WANG H , ZHANG D , WANG Y , et al . RT-fall:a real-time and contactless fall detection system with commodity Wi-Fi devices [J ] . IEEE Transactions on Mobile Computing , 2017 , 16 ( 2 ): 511 - 526 .

ALI K , LIU A X , WANG W , et al . Recognizing keystrokes using Wi-Fi devices [J ] . IEEE Journal on Selected Areas in Communications , 2017 , 35 ( 5 ): 1175 - 1190 .

WANG X , YANG C , MAO S , et al . Phasebeat:exploiting CSI phase data for vital sign monitoring with commodity Wi-Fidevices [C ] // The 37th IEEE International Conference on Distributed Computing Systems,June 5-8,2017,Atlanta,GA,USA . Piscataway:IEEE Press , 2017 : 1230 - 1239 .

LEE S , PARK Y D , SUH Y , et al . Design and implementation of monitoring system for breathing and heart rate pattern using Wi-Fi signals [C ] // 15th IEEE Annual Consumer Communications ＆ Networking Conference,Jan 12-15,2018,Sfax,Tunisia . Piscataway:IEEE Press , 2018 : 1 - 7 .

WU K , XIAO J , YI Y , et al . CSI-based indoor localization [J ] . IEEE Transactions on Parallel and Distributed Systems , 2013 , 24 ( 7 ): 1300 - 1309 .

孙君 , 谢利娟 , 许晖 , 等 . 15 GHz 频段下会议室内信道大尺度衰落特性测量与分析 [J ] . 电信科学 , 2016 , 32 ( 2 ): 13 - 19 .

SUN J , XIE L J , XU H , et al . Measurement and analysis on channel large scale fading characteristics of conference room at 15 GHz [J ] . Telecommunications Science , 2016 , 32 ( 2 ): 13 - 19 .

韩玲 , 张永明 , 方俊 , 等 . 室内火灾烟雾对微波衰减的影响研究 [J ] . 火灾科学 , 2016 , 25 ( 1 ): 47 - 52 .

HAN L , ZHANG Y M , FANG J , et al . Effect of indoor fire smokes on microwave attenuation [J ] . Fire Safety Science , 2016 , 25 ( 1 ): 47 - 52 .

HALPERIN D , HU W J , SHETH A , et al . Tool release:gathering 802.11n traces with channel state information [J ] . ACM SIGCOMM Computer Communication Review , 2011 , 41 ( 1 ):53.

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