基于Wi-Fi的烟雾实时检测方法

doi:10.11959/j.issn.1000-0801.2019150

Abstract

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%.

Key words: wireless channel, channel state information, smoke detection, machine learning

CLC Number:

TN929

WU Zhefu,TANG Wanli,CHEN Cheng,FANG Luping. Real-time detection method of smoke based on Wi-Fi[J]. Telecommunications Science, 2019, 35(6): 70-77.

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References 15

[1]	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.
[2]	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.
[3]	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.
[4]	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.
[5]	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.
[6]	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.
[7]	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.
[8]	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.
[9]	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.
[10]	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.
[11]	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.
[12]	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.
[13]	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.
[14]	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.
[15]	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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场景名称	面积	天线间距
视距环境1	2 m× 2 m	1.8 m
视距环境2	6 m× 6 m	0.5～3.5 m
视距环境3	8 m× 8 m	3m
非视距环境1	5 m× 2 m	1m
非视距环境2	8 m× 2 m	6m

分类器	KNN	NB	SVM	BP
分类精度	83.1%	84.46%	89.43%	93.51%
时间/s	0.29	0.20	1.5	72.40

Real-time detection method of smoke based on Wi-Fi

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