IoT-enabled smart waste monitoring and predictive route optimization system
| dc.contributor.author | Itungo Agaba | |
| dc.contributor.author | Alvin Rubagumya | |
| dc.contributor.author | Mark Calvin Obba | |
| dc.date.accessioned | 2026-09-02T08:51:56Z | |
| dc.date.available | 2026-09-02T08:51:56Z | |
| dc.date.issued | 2026-06-01 | |
| dc.description | Undergraduate | |
| dc.description.abstract | Rapid urbanization in Uganda has led to a significant increase in solid waste generation, while existing collection systems remain largely manual, inefficient and reactive. Municipal waste collection typically relies on fixed schedules without real-time visibility into bin fill levels, resulting in overflow, increased operational costs and environmental risks. This project presents an IoT-enabled Smart Waste Monitoring and Predictive Route Optimization System designed to address these challenges. The system integrates smart bin sensors, real-time telemetry, predictive analytics and route optimization algorithms to enable a data- driven waste collection process. The proposed solution utilizes ultrasonic sensors and Arduino UNO R4 microcontrollers to monitor bin fill levels, combined with machine learning models to forecast waste accumulation patterns. A routing optimization engine based on Vehicle Routing Problem (VRP) techniques is implemented to generate efficient collection routes. Results from simulation and system testing demonstrated that the proposed system improved operational efficiency through real-time telemetry monitoring and predictive routing. The forecasting models were able to identify high-priority bins before overflow occurred, while the route optimization engine reduced unnecessary collection trips and overall travel distance. System testing further confirmed reliable telemetry transmission, responsive dashboard visualization and effective integration between IoT devices, backend services and machine learning components. The system in general demonstrates the potential to reduce operational costs, prevent bin overflow and improve urban sanitation. The project contributes a scalable and cost-effective solution tailored for Ugandan municipalities, aligning with Sustainable Development Goals (SDG 11 and SDG 12) on sustainable cities and responsible resource management. | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12311/3606 | |
| dc.language.iso | en | |
| dc.publisher | Uganda Christian University | |
| dc.title | IoT-enabled smart waste monitoring and predictive route optimization system | |
| dc.type | Dissertation |