Summary
Many cities struggle with garbage bins overflowing because trucks collect waste on fixed schedules, wasting time and fuel. The authors created CleanCity-BinSense, a system with solar-powered sensors that measure how full bins are in real time and send this data over Wi-Fi to a central platform. Their system can adjust to different bin shapes and sizes without changing the software, making it flexible for many urban settings. They also use a simple route-planning method to help garbage trucks visit bins that are closest and most in need of emptying, saving resources. Tests show the system works quickly and accurately, making it suitable for crowded and resource-limited cities like Dhaka.
IoTultrasonic sensorwaste managementreal-time monitoringroute optimizationsolar-powered sensorcalibration parametersnearest-neighbor algorithmurban infrastructureresource-constrained environment
Abstract
CleanCity-BinSense addresses inefficiencies in urban waste management in developing cities, where fixed-schedule collection routes lead to overflowing bins and wasted fuel. This paper presents CleanCity-BinSense, a low-cost, end-to-end IoT-enabled smart waste management system for scalable real-time waste monitoring and demand-driven collection. The system integrates a solar-powered sensor node with an ultrasonic sensor for real-time bin fill-level monitoring. A key contribution is a configurable sensing model based on two calibration parameters, FULL_DISTANCE and EMPTY_DISTANCE, enabling deployment across bins of varying sizes and geometries without firmware modification. Fill percentage is computed using a geometry-configurable linear normalization algorithm, validated through hardware experiments with a mean absolute error (MAE) of 0.38 cm, within the manufacturer-specified sensor tolerance. Sensor readings are transmitted via Wi-Fi to a centralized web platform providing role-based dashboards for administrators, operators, and drivers, along with a public real-time bin-status map. The system also incorporates a lightweight nearest-neighbor route planning algorithm using SQL Server's spatial function to generate proximity-based collection routes with low computational overhead. Experimental evaluation shows an average end-to-end system latency of 5.3 seconds, dominated by the sensing interval rather than network overhead, while route generation for typical urban collection zones completes in under 100 ms. These results demonstrate the feasibility of a low-cost, configurable, infrastructure-light smart waste management system for heterogeneous urban waste networks in resource-constrained environments such as Dhaka, Bangladesh.