IoT-Based Deep Learning Model for Sustainable Waste Management System

Abstract

India faces significant challenges in waste management due to rapid urbanization, leading to health hazards and environmental degradation. In this research, we provide an IoT-driven, deep learning-based solution for efficient waste collection, segregation, and disposal. Bins are installed with ultrasonic sensors and GPS at individual households and community areas. These bins continuously monitor the level of waste, and when the trash exceeds 80% of the bin’s capacity, the waste is scheduled for collection. Garbage trucks transport the collected waste, with optimal routes generated using the Traveling Salesman Problem (TSP) algorithm and open street map API. The route includes the locations of bins that are ready for collection. Waste is segregated using moisture sensors to classify the waste based on its moisture content and the next level of segregation is done by using ResNet50 to classify the dry waste into subcategories like metal, cardboard, plastic, glass, and textiles for precise disposal. After segregation, the garbage is disposed into recyclable, non-recyclable, and biodegradable categories. such as incinerating non-recyclable waste to generate electricity, transferring recyclable waste to recycling facilities, and composting organic waste. The proposed solution reduces landfill dependency, increases recycling rates, and generates energy from waste. This work has potential to revolutionize waste management practices in urban and rural environments.