Spectral analysis of forest fire noise for early detection using wireless sensor networks

Alexander A. Khamukhin, Silvano Bertoldo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Citations (Scopus)

Abstract

Crown fires are extremely dangerous, very difficult to fight and often have a rate of spread over 100 times more than a surface fire. Therefore, it is important to determine the type of forest fire in the early detection based on wireless sensor networks (WSNs) to adopt the proper strategy to fight the fire. It is shown that this could be done analyzing the noise power spectrum of forest fires: surface fires noise spectrum can be modeled as the red noise (gradual increase of trend line amplitude toward lower frequencies), while for crown fires noise spectrum trend line has an almost bell-shaped (Gaussian) type. The noise frequency range is relatively narrow for crown fires and ranged from 250 to 450 Hz. The intermediate type of fires (strong surface fire and incipient crown fire) has a transient noise spectrum from broadband red to narrowband Gaussian. The article presents the spectrums of 9 different forest fires. The different trend line of the forest fire noise power spectrum is the parameter that can be used to determine the type of forest fire in WSNs.

Original languageEnglish
Title of host publication2016 International Siberian Conference on Control and Communications, SIBCON 2016 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781467383837
DOIs
Publication statusPublished - 14 Jun 2016
Event2016 International Siberian Conference on Control and Communications, SIBCON 2016 - Moscow, Russian Federation
Duration: 12 May 201614 May 2016

Other

Other2016 International Siberian Conference on Control and Communications, SIBCON 2016
CountryRussian Federation
CityMoscow
Period12.5.1614.5.16

Fingerprint

Forest Fire
Spectral Analysis
Spectrum analysis
Wireless Sensor Networks
Wireless sensor networks
Fires
Power Spectrum
Line
Power spectrum
Broadband
Low Frequency

Keywords

  • acoustic emission
  • crown fire
  • early detection of forest fire
  • spectral analysis
  • surface fire
  • wireless sensor network

ASJC Scopus subject areas

  • Signal Processing
  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Modelling and Simulation
  • Computer Networks and Communications

Cite this

Khamukhin, A. A., & Bertoldo, S. (2016). Spectral analysis of forest fire noise for early detection using wireless sensor networks. In 2016 International Siberian Conference on Control and Communications, SIBCON 2016 - Proceedings [7491654] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SIBCON.2016.7491654

Spectral analysis of forest fire noise for early detection using wireless sensor networks. / Khamukhin, Alexander A.; Bertoldo, Silvano.

2016 International Siberian Conference on Control and Communications, SIBCON 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2016. 7491654.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Khamukhin, AA & Bertoldo, S 2016, Spectral analysis of forest fire noise for early detection using wireless sensor networks. in 2016 International Siberian Conference on Control and Communications, SIBCON 2016 - Proceedings., 7491654, Institute of Electrical and Electronics Engineers Inc., 2016 International Siberian Conference on Control and Communications, SIBCON 2016, Moscow, Russian Federation, 12.5.16. https://doi.org/10.1109/SIBCON.2016.7491654
Khamukhin AA, Bertoldo S. Spectral analysis of forest fire noise for early detection using wireless sensor networks. In 2016 International Siberian Conference on Control and Communications, SIBCON 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2016. 7491654 https://doi.org/10.1109/SIBCON.2016.7491654
Khamukhin, Alexander A. ; Bertoldo, Silvano. / Spectral analysis of forest fire noise for early detection using wireless sensor networks. 2016 International Siberian Conference on Control and Communications, SIBCON 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2016.
@inproceedings{953b8c9cac994a999fd980c35c890cb5,
title = "Spectral analysis of forest fire noise for early detection using wireless sensor networks",
abstract = "Crown fires are extremely dangerous, very difficult to fight and often have a rate of spread over 100 times more than a surface fire. Therefore, it is important to determine the type of forest fire in the early detection based on wireless sensor networks (WSNs) to adopt the proper strategy to fight the fire. It is shown that this could be done analyzing the noise power spectrum of forest fires: surface fires noise spectrum can be modeled as the red noise (gradual increase of trend line amplitude toward lower frequencies), while for crown fires noise spectrum trend line has an almost bell-shaped (Gaussian) type. The noise frequency range is relatively narrow for crown fires and ranged from 250 to 450 Hz. The intermediate type of fires (strong surface fire and incipient crown fire) has a transient noise spectrum from broadband red to narrowband Gaussian. The article presents the spectrums of 9 different forest fires. The different trend line of the forest fire noise power spectrum is the parameter that can be used to determine the type of forest fire in WSNs.",
keywords = "acoustic emission, crown fire, early detection of forest fire, spectral analysis, surface fire, wireless sensor network",
author = "Khamukhin, {Alexander A.} and Silvano Bertoldo",
year = "2016",
month = "6",
day = "14",
doi = "10.1109/SIBCON.2016.7491654",
language = "English",
booktitle = "2016 International Siberian Conference on Control and Communications, SIBCON 2016 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - GEN

T1 - Spectral analysis of forest fire noise for early detection using wireless sensor networks

AU - Khamukhin, Alexander A.

AU - Bertoldo, Silvano

PY - 2016/6/14

Y1 - 2016/6/14

N2 - Crown fires are extremely dangerous, very difficult to fight and often have a rate of spread over 100 times more than a surface fire. Therefore, it is important to determine the type of forest fire in the early detection based on wireless sensor networks (WSNs) to adopt the proper strategy to fight the fire. It is shown that this could be done analyzing the noise power spectrum of forest fires: surface fires noise spectrum can be modeled as the red noise (gradual increase of trend line amplitude toward lower frequencies), while for crown fires noise spectrum trend line has an almost bell-shaped (Gaussian) type. The noise frequency range is relatively narrow for crown fires and ranged from 250 to 450 Hz. The intermediate type of fires (strong surface fire and incipient crown fire) has a transient noise spectrum from broadband red to narrowband Gaussian. The article presents the spectrums of 9 different forest fires. The different trend line of the forest fire noise power spectrum is the parameter that can be used to determine the type of forest fire in WSNs.

AB - Crown fires are extremely dangerous, very difficult to fight and often have a rate of spread over 100 times more than a surface fire. Therefore, it is important to determine the type of forest fire in the early detection based on wireless sensor networks (WSNs) to adopt the proper strategy to fight the fire. It is shown that this could be done analyzing the noise power spectrum of forest fires: surface fires noise spectrum can be modeled as the red noise (gradual increase of trend line amplitude toward lower frequencies), while for crown fires noise spectrum trend line has an almost bell-shaped (Gaussian) type. The noise frequency range is relatively narrow for crown fires and ranged from 250 to 450 Hz. The intermediate type of fires (strong surface fire and incipient crown fire) has a transient noise spectrum from broadband red to narrowband Gaussian. The article presents the spectrums of 9 different forest fires. The different trend line of the forest fire noise power spectrum is the parameter that can be used to determine the type of forest fire in WSNs.

KW - acoustic emission

KW - crown fire

KW - early detection of forest fire

KW - spectral analysis

KW - surface fire

KW - wireless sensor network

UR - http://www.scopus.com/inward/record.url?scp=84978153441&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84978153441&partnerID=8YFLogxK

U2 - 10.1109/SIBCON.2016.7491654

DO - 10.1109/SIBCON.2016.7491654

M3 - Conference contribution

AN - SCOPUS:84978153441

BT - 2016 International Siberian Conference on Control and Communications, SIBCON 2016 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

ER -