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
相关论文
共 50 条
  • [1] Deep learning enabled intrusion detection system for Industrial IOT environment
    Nandanwar, Himanshu
    Katarya, Rahul
    EXPERT SYSTEMS WITH APPLICATIONS, 2024, 249
  • [2] Optimal Hybrid Deep Learning Enabled Attack Detection and Classification in IoT Environment
    Alruwaili, Fahad F.
    CMC-COMPUTERS MATERIALS & CONTINUA, 2023, 75 (01): : 99 - 115
  • [3] Integrative Use of IoT and Deep Learning for Agricultural Applications
    Garg, Disha
    Khan, Samiya
    Alam, Mansaf
    PROCEEDINGS OF ICETIT 2019: EMERGING TRENDS IN INFORMATION TECHNOLOGY, 2020, 605 : 521 - 531
  • [4] Deep Learning in IoT Intrusion Detection
    Tsimenidis, Stefanos
    Lagkas, Thomas
    Rantos, Konstantinos
    JOURNAL OF NETWORK AND SYSTEMS MANAGEMENT, 2022, 30 (01)
  • [5] DDoS Attack Detection in a Real Urban IoT Environment using Federated Deep Learning
    Ahmadi, Khatereh
    Javidan, Reza
    2023 IEEE INTERNATIONAL CONFERENCE ON CYBER SECURITY AND RESILIENCE, CSR, 2023, : 117 - 122
  • [6] A Review of Machine Learning and Deep Learning Techniques for Anomaly Detection in IoT Data
    Al-amri, Redhwan
    Murugesan, Raja Kumar
    Man, Mustafa
    Abdulateef, Alaa Fareed
    Al-Sharafi, Mohammed A.
    Alkahtani, Ammar Ahmed
    APPLIED SCIENCES-BASEL, 2021, 11 (12):
  • [7] Machine and Deep Learning Approaches for IoT Attack Classification
    Nascita, Alfredo
    Cerasuolo, Francesco
    Di Monda, Davide
    Garcia, Jonas Thern Aberia
    Montieri, Antonio
    Pescape, Antonio
    IEEE INFOCOM 2022 - IEEE CONFERENCE ON COMPUTER COMMUNICATIONS WORKSHOPS (INFOCOM WKSHPS), 2022,
  • [8] Intrusion Detection in IoT Using Deep Learning
    Banaamah, Alaa Mohammed
    Ahmad, Iftikhar
    SENSORS, 2022, 22 (21)
  • [9] A Multi-Layer Classification Approach for Intrusion Detection in IoT Networks Based on Deep Learning
    Qaddoura, Raneem
    Al-Zoubi, Ala' M.
    Faris, Hossam
    Almomani, Iman
    SENSORS, 2021, 21 (09)
  • [10] Fast Detection and Classification of Dangerous Urban Sounds Using Deep Learning
    Momynkulov, Zeinel
    Dosbayev, Zhandos
    Suliman, Azizah
    Abduraimova, Bayan
    Smailov, Nurzhigit
    Zhekambayeva, Maigul
    Zhamangarin, Dusmat
    CMC-COMPUTERS MATERIALS & CONTINUA, 2023, 75 (01): : 2191 - 2208