IoT-Enabled Waste Tracking and Recycling Optimization : Enhancing Sustainable Waste Management - Eugine Teh, Yin Jie; Chee Soon, Chong; Rozaimi, Ghazali; Hazriq Izzuan, Jaafar; Muhamad Fadli, Ghani; Howe Cheng, Teng; Nur Farhanah, Zulkipli; Siaw Hong, Liew (2025)
The increasing inefficiencies in conventional waste management systems, including suboptimal recycling rates and environmental degradation, necessitate innovative solutions. This paper discusses the development of an Internet of Things (IoT)-enabled waste tracking and recycling optimization system designed to address these challenges and contribute to sustainable waste management practices. The primary focus is on automating the waste classification process and enhancing recycling efficiency through real-time monitoring and data-driven analysis. The methodology integrates IoT technology and machine learning to tackle waste classification and collection inefficiencies. A Convolutional Neural Network (CNN) trained on a dataset of aluminium cans and plastic bottles is deployed for waste identification. Real-time monitoring is enabled by IoT sensors and machine vision algorithms, facilitating precise detection of waste levels and material types. Advanced data preprocessing, such as augmentation and normalization, ensures robust model training, while optimized algorithms guide waste sorting based on classification results. Findings demonstrate that the system achieves over 90% accuracy in classifying recyclable materials. Real-time data logging enables analysis of waste composition, container utilization, and operational patterns, enhancing efficiency and reducing overflow incidents. Data visualization highlights the system’s potential for providing actionable insights to improve recycling practices. In conclusion, this project validates the feasibility of integrating IoT and machine learning to optimize waste management. The system reduces environmental impact and promotes sustainability, offering a scalable framework for addressing global waste challenges.