AI-Powered Smart Waste Bins: Improving Hostel Mess Cleanliness with IoT and Machine Learning

This research study introduces an advanced system for waste management in hostel mess environments, leveraging IoT sensors and machine learning. The system integrates ultrasonic sensors to monitor waste levels and detect user presence for automatic lid operation. A load cell sensor accurately measures waste weight, enabling precise monitoring of waste accumulation. Data from these sensors is processed in real-time by an Arduino micro controller using fuzzy logic to optimize waste collection scheduling based on fill levels. This data is then transmitted to a cloud server via Wi-Fi, where a machine learning model, specifically an LSTM network, analyzes historical and real-time data to predict waste accumulation trends and detect anomalies. Notifications are sent to designated personnel when bins require attention, ensuring timely maintenance and cleanliness. The system also generates daily reports to provide insights into waste generation patterns, supporting informed decision-making for waste reduction strategies. By integrating IoT and machine learning technologies, this innovative waste management system enhances operational efficiency, promotes sustainability through reduced waste accumulation, and improves overall cleanliness in hostel mess environments.