Efficient waste collection plays a crucial role in maintaining clean urban environments while reducing operational costs. Traditional garbage collection systems follow fixed schedules, which often lead to unnecessary trips to halfempty bins or delayed pickups of overflowing bins. This project proposes a Smart Waste Monitoring and Collection System that uses ultrasonic sensors to continuously measure the fill level of garbage bins and transmit the data to a cloud platform. The collected data is visualized on a webbased dashboard that enables administrators to monitor the status of each bin in real time and assign optimized routes to garbage collection vehicles. The system includes an intelligent route optimization module that prioritizes bins requiring urgent attention, reducing fuel consumption and travel time. A key enhancement in this project is the integration of a predictive overflow feature. By analyzing historical filllevel patterns, the system forecasts when a bin is likely to reach its capacity. This prediction enables proactive scheduling of collection before overflow occurs, which improves cleanliness and resource utilization. The proposed solution enhances overall waste management efficiency through datadriven decision making. The system is scalable, costeffective, and suitable for implementation in smart city initiatives.
K.Karthick et al. (Thu,) studied this question.