Edge-AI Powered Intelligent Waste Bins for Autonomous Urban Waste Segregation and Recycling

Innovative solutions to sorting, recycling, and environmentally friendly handling of urban waste, because we don’t know how to solve ‎urban waste management. An intelligent waste bin system with edge AI is being described to sense, classify, and manage the waste on its ‎own, as per this research. The above systems effectively combine a lightweight Edge-AI model based on CNNs and Vision Transformers ‎‎(ViTs) to detect waste in real time. The system identifies the materials using the appearance, the thermal appearance, and the strengths of the ‎density scan of the material using the RGB cameras, the LiDAR sensors, and the infrared scanners. Reinforcement Learning (RL) is used to ‎create such a robotic sorting mechanism that would allow sorting of the waste intelligently to improve classification precision ‎with time. With the system, the piezoelectric energy harvesting unit is used, and the system runs as long as the battery lasts. It also ‎permits municipal Authorities to leverage within an IoT-enabled monitoring framework that indicates if the bin is present, the fill level, and ‎alerts for odor. Furthermore, the platform provides an incentive to the public to participate in the system through a reward system using a ‎QR code, where people earn points when they dispose of waste responsibly. The finding is evaluated, and it is found that the accuracy of ‎the waste classification, the less power dependency, and waste collection scheduling are improved. It is a smart bin; the edge processing is using ‎it in real time, applying adaptive learning and energy integration for it to be energy sustainable for supporting the eco-friendly urban waste ‎management‎.

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Publication Details

Journal
International Journal of Basic and Applied Sciences
Published
2025-10-31
DOI
https://doi.org/10.14419/crxt0n36
Primary Topic
Municipal Solid Waste Management
Type
article
Field-Weighted Citation Impact
0.00
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article

Edge-AI Powered Intelligent Waste Bins for Autonomous Urban Waste Segregation and Recycling

Nikhil Singh, Sakib A. Mondal, Moti Ranjan Tandi
International Journal of Basic and Applied Sciences
Municipal Solid Waste Management
article

Edge-AI Powered Intelligent Waste Bins for Autonomous Urban Waste Segregation and Recycling

Nikhil Singh, Sakib A. Mondal, Moti Ranjan Tandi
article en

Abstract

Innovative solutions to sorting, recycling, and environmentally friendly handling of urban waste, because we don’t know how to solve ‎urban waste management. An intelligent waste bin system with edge AI is being described to sense, classify, and manage the waste on its ‎own, as per this research. The above systems effectively combine a lightweight Edge-AI model based on CNNs and Vision Transformers ‎‎(ViTs) to detect waste in real time. The system identifies the materials using the appearance, the thermal appearance, and the strengths of the ‎density scan of the material using the RGB cameras, the LiDAR sensors, and the infrared scanners. Reinforcement Learning (RL) is used to ‎create such a robotic sorting mechanism that would allow sorting of the waste intelligently to improve classification precision ‎with time. With the system, the piezoelectric energy harvesting unit is used, and the system runs as long as the battery lasts. It also ‎permits municipal Authorities to leverage within an IoT-enabled monitoring framework that indicates if the bin is present, the fill level, and ‎alerts for odor. Furthermore, the platform provides an incentive to the public to participate in the system through a reward system using a ‎QR code, where people earn points when they dispose of waste responsibly. The finding is evaluated, and it is found that the accuracy of ‎the waste classification, the less power dependency, and waste collection scheduling are improved. It is a smart bin; the edge processing is using ‎it in real time, applying adaptive learning and energy integration for it to be energy sustainable for supporting the eco-friendly urban waste ‎management‎.

International Journal of Basic and Applied SciencesVol. 14(SI-1)
International Management Institute (IN), Kalinga University (IN)
Openalex Percentile: Top 61%
Municipal Solid Waste Management
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