Version 1
: Received: 3 July 2024 / Approved: 4 July 2024 / Online: 4 July 2024 (08:47:56 CEST)
How to cite:
Mishra, P.; Mishra, N.; Choudhary, D. K.; Pareek, P.; Cabral dos Santos Reis, M. J. Use of IoT with Deep Learning for Classification of Environmental Sound and Detection of Gases. Preprints2024, 2024070389. https://doi.org/10.20944/preprints202407.0389.v1
Mishra, P.; Mishra, N.; Choudhary, D. K.; Pareek, P.; Cabral dos Santos Reis, M. J. Use of IoT with Deep Learning for Classification of Environmental Sound and Detection of Gases. Preprints 2024, 2024070389. https://doi.org/10.20944/preprints202407.0389.v1
Mishra, P.; Mishra, N.; Choudhary, D. K.; Pareek, P.; Cabral dos Santos Reis, M. J. Use of IoT with Deep Learning for Classification of Environmental Sound and Detection of Gases. Preprints2024, 2024070389. https://doi.org/10.20944/preprints202407.0389.v1
APA Style
Mishra, P., Mishra, N., Choudhary, D. K., Pareek, P., & Cabral dos Santos Reis, M. J. (2024). Use of IoT with Deep Learning for Classification of Environmental Sound and Detection of Gases. Preprints. https://doi.org/10.20944/preprints202407.0389.v1
Chicago/Turabian Style
Mishra, P., Prakash Pareek and Manuel José Cabral dos Santos Reis. 2024 "Use of IoT with Deep Learning for Classification of Environmental Sound and Detection of Gases" Preprints. https://doi.org/10.20944/preprints202407.0389.v1
Abstract
This article presents an Internet of Things (IoT)-enabled system meticulously engineered for comprehensive environmental assessment, encompassing air quality, temperature, and humidity. Simultaneously, the devised system integrates cutting-edge MQ6 sensors, adept at detecting ambient gases, with a primary focus on monitoring Liquefied Petroleum Gas (LPG) and Butane gases concentrations in the atmosphere. This distinctive feature not only facilitates the identifi-cation of hazardous gases leakages, particularly common in household LPG usage, but also empowers preemptive measures. The environmental parameters, air quality, temperature, and humidity, are precisely gauged through the utilization of MQ135 and DHT11 sensors. The con-trolling and managing of the hardware component of the system (i.e., device) is assured by an Arduino UNO, that orchestrates the collection of sensor data, transmitting it seamlessly to both the ThingSpeak cloud and a Convolutional Neural Network (CNN) model. By interfacing with ThingSpeak, the device ensures the systematic storage and visualization of environmental data, streamlining monitoring and analysis processes. In the event of perilous gases detection, the system promptly triggers an alarm, concurrently dispatching a real-time notification to the device owner via the integrated If-This-Then-That (IFTTT) application programming interface (API). To further enrich environmental awareness, an embedded microphone captures audio recordings, subsequently processed by a CNN-based deep learning model. This model employs advanced techniques such as spectrogram generation, Convolution2D, and MaxPooling2D within a Se-quential Model architecture, to attain elevated levels of accuracy, furnishing insightful interpre-tations of ongoing activities within the environment.
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
