Title: Systematic CO₂ monitoring using machine learning enabled WSN to develop the anti-hazard strategies for the future

Authors: N. Jeyakkannan; P. Manimegalai; G.K.D. Prasanna Venkatesan

Addresses: ECE, Karpagam Academy of Higher Education, Coimbatore, India ' ECE, Karpagam Academy of Higher Education, Coimbatore, India ' Faculty of Engineering, Karpagam Academy of Higher Education, Coimbatore, India

Abstract: The life cycle of an organism does not complete without carbon dioxide (CO₂). CO₂ is an essential peculiar flavour ingredient gas that drives the world. On the other hand, the impact of excessive CO₂ affects the atmosphere with rapid climate changes, greenhouse effect, rain with corrosive implications, and many more hazards. The excessive CO₂ influences the natural resource and depletes it to the hazard nature. So the atmosphere needs attention towards monitoring under various conditions. In this paper, the technology of wireless sensor network (WSN) is used for monitoring the CO₂ and other gases through which data is stored and fed to the machine learning methodology for future strategies. This paper gives an analysis of the feasibility and effectiveness of the various methods. During the investigation, the generalised regression neural network (GRNN) is identified to be a suitable algorithm for the learning and anti-hazard strategies for monitoring the CO₂. The results produced by the GRNN method are promising, which particulates up to 96% of accuracy compared to other algorithms.

Keywords: generalised regression neural network; GRNN; CO₂; artificial neural networks; ANNs; atmospheric gas management.

DOI: 10.1504/IJBET.2020.110347

International Journal of Biomedical Engineering and Technology, 2020 Vol.34 No.1, pp.31 - 44

Received: 18 Mar 2019
Accepted: 05 Jul 2019
Published online: 15 Oct 2020 *

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