Smart Urban Waste Management System: An IoT-Enabled Framework for Real-Time Monitoring and Optimization

Abstract

Urban waste management is a critical issue due to rapid urbanization and inefficient traditional collection methods, leading to pollution, health risks, and poor resource utilization. Existing systems rely on manual monitoring and fixed schedules, often resulting in bin overflow and delayed waste collection. The purpose of this research is to develop a smart waste management system that enables real-time monitoring, automated waste classification, and improved decision-making for urban cleanliness. The proposed system integrates Internet of Things (IoT) with image processing and machine learning techniques. Smart bins equipped with sensors and cameras collect data, which is processed using segmentation and clustering algorithms to classify waste types and evaluate bin conditions. The system sends real-time updates to a centralized dashboard, helping optimize collection processes. Results show improved classification performance and reduced overflow risk. In conclusion, the system offers an efficient and scalable solution for smart cities, with future scope including advanced models and real-time cloud integration