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Wireless sensor network-based machine learning framework for smart cities in intelligent waste management.

Karan Belsare¹, Manwinder Singh¹, Anudeep Gandam²

¹School of Electronics and Electrical Engineering, Lovely Professional University, Phagwara, Punjab, 144411, India.

|September 10, 2024

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This summary is machine-generated.

This study introduces an intelligent waste management system using machine learning and the Internet of Things (IoT) for efficient trash sorting and monitoring. The system enhances recycling safety and environmental sustainability through real-time data analysis and autonomous waste classification.

Area of Science:

Environmental Science and Engineering
Computer Science and Artificial Intelligence
Sustainable Development

Background:

Effective waste management and recycling are crucial for a sustainable economy and environmental safety.
Traditional waste management methods lack efficiency and safety, necessitating intelligent solutions.
The integration of smart devices and IoT offers a pathway to optimize waste collection and sorting processes.

Purpose of the Study:

To develop an autonomous, intelligent waste parameter monitoring system using IoT and Long Range (LoRa) technologies.
To create an efficient and smart waste management system for improved recycling safety and effectiveness.
To design a machine learning-based architecture for real-time waste tracking and classification.

Main Methods:

Keywords:

Internet of things Machine learning Smart waste management system Wireless sensor network

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Utilized the Internet of Things (IoT) and Long Range (LoRa) technologies for real-time data collection from waste bins.
Implemented a four-layer intelligent waste classification framework: input, feature, classification, and output.
Employed Resnet-101 for feature extraction and multi-kernel Support Vector Machine (SVM) and Adaboost ensemble classifiers for waste categorization using the Thrash Box dataset.

Main Results:

The proposed system successfully tracked waste parameters like volume, stench, air quality, weight, and smoke levels in real-time.
Achieved accurate classification of waste into categories such as household, medical, and electronic garbage.
Demonstrated superior performance in waste classification and prediction compared to existing state-of-the-art models in experimental trials.

Conclusions:

The developed machine learning-based architecture provides an efficient and intelligent solution for waste management.
The integration of IoT and advanced ML models enhances the safety and effectiveness of recycling processes.
This novel system contributes to environmental safety and sustainable economic practices through optimized waste handling.