Integration of AI and Machine Learning in Smart Environment Monitoring Systems

Year : 2024 | Volume : 15 | Issue : 02 | Page : 13 19
    By

    Mohammed Mian A.,

  • Mohamed Yousuff Caffiyar,

  • Priyankaa R.,

  • Abirami S.,

  • Karthiga B.,

  • Nathiya M.,

  • Meena M.,

  1. Assistant Professor, Department of Electronics and Communication Engineering, C. Abdul Hakeem College of Engineering and Technology, Melvisharam, Ranipet District, Tamil Nadu, India
  2. Associate Professor, Department of Electronics and Communication Engineering, C. Abdul Hakeem College of Engineering and Technology, Melvisharam, Ranipet District, Tamil Nadu, India
  3. Student, Department of Electronics and Communication Engineering, C. Abdul Hakeem College of Engineering and Technology, Melvisharam, Ranipet District, Tamil Nadu, India
  4. Student, Department of Electronics and Communication Engineering, C. Abdul Hakeem College of Engineering and Technology, Melvisharam, Ranipet District, Tamil Nadu, India
  5. Student, Department of Electronics and Communication Engineering, C. Abdul Hakeem College of Engineering and Technology, Melvisharam, Ranipet District, Tamil Nadu, India
  6. Student, Department of Electronics and Communication Engineering, C. Abdul Hakeem College of Engineering and Technology, Melvisharam, Ranipet District, Tamil Nadu, India
  7. Student, Department of Electronics and Communication Engineering, C. Abdul Hakeem College of Engineering and Technology, Melvisharam, Ranipet District, Tamil Nadu, India

Abstract

The Internet of Things (IoT) plays an important role in our lives. Many real-time changes in logistics environment monitoring and location tracking can be measured using IoT. It uses a wireless sensor network to monitor important changes in the environment. In this article a comparative review study has been performed in which one side wireless sensor network is integrated with IoT only while on the other side wireless sensor network is integrated with IoT and LTE-M modules to collect the data of environmental pollution. In comparative study moving vehicles will detect the data of environmental pollution while they are on the road which will further used to develop smart vehicles releases only small amount of environment harmful material in atmosphere. In this approach two categories of vehicles have been assigned in which one will detect the data with the help of sensor with integrated internet of things while the other category will detect the data with sensors integrated with LTE-M. Here, a real-time logistics environment Android application for location-based humidity, temperature, and smoke monitoring was developed. These settings are detected and sent to the web server via WIFI. Information is accessible from anywhere. The developed Android application can access real-time data and display results. If the detected value exceeds the limit or any critical value, the buzzer will be used to send a warning signal to the user. After Comparison and obtaining data, it has been concluded that sensors integrated with LTE-M obtained data with full efficiency and accurately in speed while sensors integrated with only IoT is less efficient and accurate as compare to alternate one.

Keywords: Internet of things, Android, Wi-Fi, Humidity, temperature

[This article belongs to Journal of Alternate Energy Sources & Technologies ]

How to cite this article:
Mohammed Mian A., Mohamed Yousuff Caffiyar, Priyankaa R., Abirami S., Karthiga B., Nathiya M., Meena M.. Integration of AI and Machine Learning in Smart Environment Monitoring Systems. Journal of Alternate Energy Sources & Technologies. 2024; 15(02):13-19.
How to cite this URL:
Mohammed Mian A., Mohamed Yousuff Caffiyar, Priyankaa R., Abirami S., Karthiga B., Nathiya M., Meena M.. Integration of AI and Machine Learning in Smart Environment Monitoring Systems. Journal of Alternate Energy Sources & Technologies. 2024; 15(02):13-19. Available from: https://journals.stmjournals.com/joaest/article=2024/view=177806


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Regular Issue Subscription Review Article
Volume 15
Issue 02
Received 11/06/2024
Accepted 25/06/2024
Published 16/07/2024
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