Data-Driven Recycling Transformation for Enhancing Paper and Cardboard Bin Efficiency through IoT and Random Forest
S. Srinivasan, R. Swathy, V. Srividhya, S. Murugan, C. Srinivasan, M. Muthulekshmi
Abstract
This study uses data to enhance paper and cardboard recycling bin efficiency for sustainable waste management. It uses Internet of Things (IoT) and Random Forest algorithms to dynamically optimize bin use to improve recycling. It starts by installing IoT sensors on paper and cardboard recycling bins to track fill levels and use. A Random Forest system trained on past data predicts bin fill levels from this continuous data stream. The predictive algorithm adapts collection schedules and resource distribution based on temporal trends, weather, and community events. IoT and Random Forest increase fill-level forecasts and enable data-driven recycling bin management. This reduces wasted collections, fuel use, and carbon emissions, making recycling more sustainable and cost-effective. The study also analyzes the system's real-world urban application, demonstrating its scalability and flexibility to varied waste management circumstances. Our empirical study and case studies show that the technique improves paper and cardboard recycling efficiency, contributing to data-driven sustainability programs.