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Wireless Sensor Network-based Detection of Poisonous Gases Using Principal Component Analysis

N. Dharini1,*, Jeevaa Katiravan2, S. M. Udhaya Sankar3

1 Easwari Engineering College, Chennai, 600089, India
2 Velammal Engineering College, Chennai, 600066, India
3 Velammal Institute of Technology, Chennai, 601204, India

* Corresponding Author: N. Dharini. Email: email

Computer Systems Science and Engineering 2023, 44(1), 249-264.


This work utilizes a statistical approach of Principal Component Analysis (PCA) towards the detection of Methane (CH4)-Carbon Monoxide (CO) Poisoning occurring in coal mines, forest fires, drainage systems etc. where the CH4 and CO emissions are very high in closed buildings or confined spaces during oxidation processes. Both methane and carbon monoxide are highly toxic, colorless and odorless gases. Both of the gases have their own toxic levels to be detected. But during their combined presence, the toxicity of the either one goes unidentified may be due to their low levels which may lead to an explosion. By using PCA, the correlation of CO and CH4 data is carried out and by identifying the areas of high correlation (along the principal component axis) the explosion suppression action can be triggered earlier thus avoiding adverse effects of massive explosions. Wireless Sensor Network is deployed and simulations are carried with heterogeneous sensors (Carbon Monoxide and Methane sensors) in NS-2 Mannasim framework. The rise in the value of CO even when CH4 is below the toxic level may become hazardous to the people around. Thus our proposed methodology will detect the combined presence of both the gases (CH4 and CO) and provide an early warning in order to avoid any human losses or toxic effects.


Cite This Article

N. Dharini, J. Katiravan and S. M. Udhaya Sankar, "Wireless sensor network-based detection of poisonous gases using principal component analysis," Computer Systems Science and Engineering, vol. 44, no.1, pp. 249–264, 2023.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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