Chrome Extension
WeChat Mini Program
Use on ChatGLM

Fire Detection Using Commodity WiFi Devices

Global Communications Conference (GLOBECOM)(2021)CCF C

Univ New South Wales

Cited 7|Views19
Abstract
WiFi Sensing has received tremendous attention in Recent Literature, demonstrating the ability to leverage ubiq-uitous commercial WiFi devices to sense Human activities and environmental occupancy. We identify that in all environments fire-safety is vital, and this paper demonstrates the suitability for using WiFi to sense fire. Using commodity Raspberry Pi devices on the 5GHz WiFi band we demonstrate a temporal shift in WiFi Channel State Information (CSI) Amplitude, before, during, and after the ignition of a flame. We further emphasise the presence of fire by observing the spread of CSI Amplitudes, noting that CSI takes much more diverse values in the presence of fire. This result is exacerbated by the frequency selective behaviour of OFDM subcarriers, where some subcarriers displayed larger variation in CSI amplitude due to the fire. The WiFi Fire sensing model was evaluated in an ideal setup with a gas flame to remove material deformation as a variable, and subsequently in a real-world scenario with the ignition of building cladding.
More
Translated text
Key words
WiFi Sensing,Channel State Information,Flame Detection,Fire Alarm,Experimental Demonstration,Wireless Networks
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper

要点】:本文通过使用商用WiFi设备,展示了利用5GHz频段的Raspberry Pi设备监测火灾的可能性,并通过观察WiFi信道状态信息(CSI)的幅度变化来探测火灾的发生和蔓延,创新地提出了基于WiFi的火灾监测方法。

方法】:通过监测火灾前后及过程中WiFi CSI幅度的变化,强调火灾的存在。

实验】:在理想环境下,使用气体火焰作为数据集,排除了材料变形的影响;在真实世界中,通过建筑覆层点燃来验证WiFi火灾监测模型的有效性。