IoT-Enabled Autonomous Waste Segregation System with Real-Time Monitoring for Smart Cities

Abstract

Urban waste management represents a critical challenge for modern smart cities, where traditional collection methods prove increasingly inadequate due to rapid population growth and environmental concerns. This research presents the development and implementation of an innovative IoT-enabled autonomous waste segregation system that integrates real-time monitoring capabilities for enhanced municipal waste management operations. The proposed system utilizes ESP32-based microcontroller architecture combined with multiple sensor technologies including ultrasonic distance sensors, load cells, and optical classification modules to automatically categorize waste into biodegradable, non-biodegradable, and hazardous categories. Through comprehensive experimental validation, the system demonstrates 92.4% classification accuracy while providing continuous waste level monitoring with alert generation capabilities. The implementation incorporates cloud-based data analytics and a centralized dashboard interface that enables municipal authorities to optimize collection routes and make data-driven operational decisions. Field testing across multiple deployment scenarios reveals significant improvements in collection efficiency, reducing unnecessary trips by 38% and operational costs by 24%. The system's modular design ensures scalability for city-wide deployment while maintaining low power consumption through optimized embedded programming techniques. Results indicate substantial potential for transforming conventional waste management practices through intelligent automation and real-time data insights, contributing to sustainable urban development goals.