Developed as part of the 2024-2025 senior year graduation project at Tekirdağ Namık Kemal University, Department of Computer Engineering, and supported by the TÜBİTAK 2209-A Research Support Program.
EcoRoute is an IoT and AI-powered system developed to make the urban waste collection process more efficient and environmentally friendly. The project includes:
- Measuring waste bin fill levels with Raspberry Pi-based sensor hardware.
- Real-time monitoring and management via mobile (Flutter) and web (Blazor) applications.
- AI-assisted waste fill level prediction and route optimization to save time and fuel.
- Visualization through Google Maps API.
This project was shaped based on the insights of the following academic works:
- Sahoo et al. (2004), route optimization in waste collection.
- Gürcan & Açıksöz (2023), smart city applications and data visualization.
- Medvedev et al. (2015), HTTP-based communication in IoT devices.
- Sosunova & Porras (2022), systematic review of smart waste systems.
- Cormen et al. (2009), fundamentals of algorithms and dynamic programming.
| Area | Technology |
|---|---|
| Web Application | ASP.NET Blazor |
| Mobile Application | Flutter (iOS & Android) |
| Hardware | Raspberry Pi Zero WH, LDR, Laser Module |
| Data Communication | HTTP protocol |
| Map & Visualization | Google Maps API |
| Database | SQL Server |
| Authentication | JWT Token |
| Architecture | Microservices + Docker |
| Artificial Intelligence | LSTM |
- Raspberry Pi devices transmit fill level data to the central server via HTTP.
- The server is managed through a Blazor-based web UI.
- Mobile users (field workers) are provided with optimized routes.
- Routes are optimized based on traffic, bin fill status, or minimum distance.
- Predicts bin fill levels using historical data.
- Supports proactive route planning before bin overflows.
- Each Raspberry Pi device is connected to 4 LDR sensors and a laser module.
- Fill level logic:
- All sensors active → 0% full
- Bottom 1 sensor blocked → 25% full
- Bottom 2 sensors blocked → 50% full
- etc.
- GPIO pins are used for real-time sensor readings.
- Environmental sustainability
- Efficient waste collection routes
- Visualized data for monitoring and decision-making
- Seamless mobile and web integration
- Academic publication potential
- Gürcan, C., & Açıksöz, S. (2023). Smart Waste Management and Case Studies. Kent Akademisi.
- Sahoo, S. P. et al. (2004). Routing Optimization for Waste Management.
- Medvedev, A. et al. (2015). Waste Management as an IoT-enabled Service in Smart Cities.
- Cormen, T. H. et al. (2009). Introduction to Algorithms.
- Sosunova, I., & Porras, J. (2022). IoT-enabled Smart Waste Management Systems.
- Dreyfus, S. E. Dynamic Programming Principles and Applications.
Sertaç YILDIRIM
Tekirdağ Namık Kemal University - Computer Engineering
📬 sertac1911u@gmail.com
This study is supported by TÜBİTAK 2209-A, a national research funding program for university students in Turkey in the year 2024.