IRO Journals
Journal of Electrical Engineering and Automation

IoT-Integrated Intelligent Platform for Biomedical Waste Classification and Hazardous Emission Alerts

Open Access
Published: 17 October, 2026
Pages: 286-299
  • Jaswanth R. , Jaswanth R.
    Student, Department of Biomedical Engineering, Jerusalem College of Engineering, Chennai, India
    Student, Department of Biomedical Engineering, Jerusalem College of Engineering, Chennai, India
  • Ramya K.L. , Ramya K.L.
    Student, Department of Biomedical Engineering, Jerusalem College of Engineering, Chennai, India
    Student, Department of Biomedical Engineering, Jerusalem College of Engineering, Chennai, India
  • Shruthi S. , Shruthi S.
    Student, Department of Biomedical Engineering, Jerusalem College of Engineering, Chennai, India
    Student, Department of Biomedical Engineering, Jerusalem College of Engineering, Chennai, India
  • Archana T. , Archana T.
    Student, Department of Biomedical Engineering, Jerusalem College of Engineering, Chennai, India
    Student, Department of Biomedical Engineering, Jerusalem College of Engineering, Chennai, India
  • Anusuya K. Anusuya K.
    Assistant Professor, Department of Biomedical Engineering, Jerusalem College of Engineering, Chennai, India
    Assistant Professor, Department of Biomedical Engineering, Jerusalem College of Engineering, Chennai, India
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How to Cite

R., Jaswanth, Ramya K.L., Shruthi S., Archana T., and Anusuya K. 2026. “IoT-Integrated Intelligent Platform for Biomedical Waste Classification and Hazardous Emission Alerts”. Journal of Electrical Engineering and Automation 8 (4): 286-99. https://doi.org/10.36548/jeea.2026.4.003.
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Keywords

IoT-Enabled Waste Management
Biowaste Segregation
Machine Learning
Image-based Waste Classification
Hazardous-Gas Detection
Real-Time Monitoring
Alert Generation

Abstract

Improper handling of biomedical and bio-waste can result in exposing health care professionals and the surrounding environment to dangerous infections and emission of dangerous gases. Manual sorting and monitoring of waste is also time-consuming and cannot give real-time data on the state of stored waste. This research study proposes an IoT-integrated smart biowaste management system which incorporates waste identification using camera, waste classification using machine learning, gas sensing, and alerts generation. An image of the waste that is placed in the collection device is taken by the camera, while image preprocessing and machine learning is performed for the identification of the category of the waste. Along with this process, the MQ-2 sensor measures the condition of the gas in the surroundings of the collected waste and makes a comparison with the predefined threshold. A microcontroller controls the processing of the image as well as the sensor and sends the processed information to the cloud platform. Upon detecting any abnormal gas condition, the system triggers a local buzzer as well as remote alerts.

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