Use of IoT with Deep Learning for Classification of Environment Sounds and Detection of Gases

被引:0
|
作者
Mishra, Priya [1 ]
Mishra, Naveen [1 ]
Choudhary, Dilip Kumar [1 ]
Pareek, Prakash [2 ]
Reis, Manuel J. C. S. [3 ]
机构
[1] Vellore Inst Technol, Sch Elect Engn, Dept Commun Engn, Vellore 632014, Tamil Nadu, India
[2] Vishnu Inst Technol, Elect & Commun Engn, Kovvada 534202, Andhra Pradesh, India
[3] Univ Tras os Montes & Alto Douro, Inst Elect & Informat Engn Aveiro IEETA, Engn Dept, P-5000801 Vila Real, Portugal
关键词
IoT; deep learning; CNN model; environmental sounds; gas detection; MQ6; sensor; MQ135; DHT11; IFTTT; INTERNET; THINGS;
D O I
10.3390/computers14020033
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
The need for safe and healthy air quality has become critical as urbanization and industrialization increase, leading to health risks and environmental concerns. Gas leaks, particularly of gases like carbon monoxide, methane, and liquefied petroleum gas (LPG), pose significant dangers due to their flammability and toxicity. LPG, widely used in residential and industrial settings, is especially hazardous because it is colorless, odorless, and highly flammable, making undetected leaks an explosion risk. To mitigate these dangers, modern gas detection systems employ sensors, microcontrollers, and real-time monitoring to quickly identify dangerous gas levels. This study introduces an IoT-based system designed for comprehensive environmental monitoring, with a focus on detecting LPG and butane leaks. Using sensors like the MQ6 for gas detection, MQ135 for air quality, and DHT11 for temperature and humidity, the system, managed by an Arduino Mega, collects data and sends these to the ThingSpeak platform for analysis and visualization. In cases of elevated gas levels, it triggers an alarm and notifies the user through IFTTT. Additionally, the system includes a microphone and a CNN model for analyzing audio data, enabling a thorough environmental assessment by identifying specific sounds related to ongoing activities, reaching an accuracy of 96%.
引用
收藏
页数:21
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