Smart Waste Management System

Traditional municipal waste collection systems operate on fixed schedules, sending collection vehicles regardless of actual bin conditions, which leads to inefficient resource utilization, unnecessary fuel consumption, higher operational costs, and environmental pollution. To address these challenges, this project proposes an intelligent IoT-based Smart Waste Management System that transforms conventional schedule-driven waste collection into a demand-based and data-driven process using real-time monitoring and predictive analytics. In the proposed system, ultrasonic sensors installed in waste bins continuously measure fill levels and transmit telemetry data to a centralized backend developed using the FastAPI framework at regular 30-second intervals. The collected sensor data is processed using a statistical prediction pipeline that applies Interquartile Range (IQR)– based outlier removal to eliminate abnormal readings and improve accuracy. After filtering, an Exponential Moving Average (EMA) model predicts the future fill level of each bin and estimates the expected overflow time. Based on these predictions, an urgency score is calculated for each bin to support intelligent collection prioritization. To further enhance operational efficiency, the system incorporates a route optimization engine that applies Greedy, Priority-based, Hybrid, and 2-Opt optimization algorithms to generate the most efficient waste collection routes for field crews while minimizing travel distance and servicing high-risk bins first. Field operators access optimized navigation through an installable Progressive Web Application (PWA) with live waypoint tracking, whereas administrators monitor the complete bin network through a Next.js-based dashboard that provides zone management, real-time status visualization using WebSocket communication, push notifications through Firebase Cloud Messaging, and automated report generation in PDF and Excel formats. The system also implements a dual authentication mechanism, using SHA-256–secured API keys for IoT device communication and Firebase JWT authentication for authorized users. All operational data is stored in a PostgreSQL database with Alembic migration support, ensuring scalability and structured data management. Overall, the proposed solution shifts municipal waste collection from a static schedule-based approach to a predictive and intelligent monitoring framework that reduces unnecessary vehicle trips, prevents bin overflow, improves operational efficiency, and supports environmentally sustainable smart city waste management practices.