Design and Development of an Automated River Cleaning Robot
DOI:
https://doi.org/10.15662/IJEETR.2026.0802463Keywords:
IoT, River Cleaning Robot, Waste Detection, Automated Waste Collection, Environmental Monitoring, Embedded Systems, Aquatic Pollution ControlAbstract
The rapid increase in plastic and solid waste accumulation in aquatic environments has become a critical environmental challenge, threatening marine ecosystems, water quality, and public health. This paper presents the design and development of an IoT-enabled aquatic garbage monitoring and collection system aimed at improving the efficiency of waste management in water bodies such as rivers and lakes. The proposed system integrates a NodeMCU (ESP8266) microcontroller with ultrasonic and proximity sensors to detect, monitor, and manage floating debris. A conveyor-based mechanism is employed for automated waste collection, followed by segregation using sensor-based identification techniques.
Real-time data acquisition and remote monitoring are achieved through cloud integration using the Blynk platform, enabling users to track waste levels and system performance efficiently. Compared to conventional manual and semi-automated cleanup methods, the proposed system reduces human intervention, enhances operational accuracy, and supports data-driven decision-making. Furthermore, the system enables predictive analysis of waste accumulation patterns, contributing to sustainable environmental management. The proposed model is cost-effective, scalable, and adaptable for deployment in diverse aquatic environments, offering a practical solution to mitigate water pollution and promote ecological sustainability.
References
1. N. A. Z. Raimi and M. M. Kamal, “Development of Smart Flood Monitoring System Using Ultrasonic Sensor with Blynk,” IEEE Conference on Systems Engineering, 2017.
2. C. A. Siregar, R. Munadi, and M. Reza, “Automation and Control System on Water Level of Reservoir Based on Microcontroller and Blynk,” IEEE International Conference on Electrical Engineering and Informatics, 2020.
3. Afzal, M. A. Khan, and S. Ahmad, “River Mapping System Using Ultrasonic Sensors and Flow Measurement,” IEEE Access, vol. 8, pp. 112233–112241, 2020.
4. Aziz, “IoT-Based Coastal Alert System for Environmental Monitoring,” IEEE Sensors Journal, vol. 19, no. 12, pp. 4567–4575, 2019.
5. S. Kumar, P. Tiwari, and M. Zymbler, “Internet of Things is a Revolutionary Approach for Future Technology Enhancement: A Review,” Journal of Big Data, vol. 6, no. 1, 2019.
6. M. Folianto, Y. S. Low, and W. L. Yeow, “Smart Garbage Management System Using Wireless Sensor Network,” IEEE Conference on Consumer Electronics, 2015.
7. C.Nagarajan and M.Madheswaran - ‘Stability Analysis of Series Parallel Resonant Converter with Fuzzy Logic Controller Using State Space Techniques’- Taylor &Francis, Electric Power Components and Systems, Vol.39 (8), pp.780-793, May 2011. DOI: 10.1080/15325008.2010.541746
8. C.Nagarajan and M.Madheswaran - ‘Experimental verification and stability state space analysis of CLL-T Series Parallel Resonant Converter’ - Journal of Electrical Engineering, Vol.63 (6), pp.365-372, Dec.2012. DOI: 10.2478/v10187-012-0054-2
9. C.Nagarajan and M.Madheswaran - ‘Performance Analysis of LCL-T Resonant Converter with Fuzzy/PID Using State Space Analysis’- Springer, Electrical Engineering, Vol.93 (3), pp.167-178, September 2011. DOI 10.1007/s00202-011-0203-9
10. J. Manley, “Unmanned Surface Vehicles, 15 Years of Development,” IEEE OCEANS Conference, 2008.
11. L. Chen, Y. Hu, and M. Q. Meng, “Design of an Intelligent Water Surface Cleaning Robot,” International Journal of Advanced Robotic Systems, vol. 18, no. 2, 2021.
12. Y. Li, H. Chen, and X. Zhang, “Deep Learning-Based Marine Debris Detection Using Convolutional Neural Networks,” IEEE Access, vol. 8, pp. 98294–98305, 2020.
13. R. Karthik, S. Vignesh, and R. Pravin, “Design and Implementation of River Cleaning Robot,” International Journal of Engineering Research & Technology, vol. 8, no. 5, 2019.
14. P. Sharma and A. Gupta, “Solar-Powered Autonomous Garbage Collector for Water Bodies,” IEEE International Conference on Smart Systems, 2021.
15. L. Lebreton et al., “Evidence That the Great Pacific Garbage Patch is Rapidly Accumulating Plastic,” Scientific Reports, vol. 8, 2018.
16. The Ocean Cleanup, “Interception Technologies for River Plastic Waste,” Technical Report, 2020.
17. H. Liang, S. Li, and J. Xu, “Simulation of Marine Plastic Debris Distribution Based on Ocean Currents,” IEEE Access, vol. 8, 2020.
18. Botta, W. de Donato, V. Persico, and A. Pescapé, “Integration of Cloud Computing and Internet of Things: A Survey,” Future Generation Computer Systems, vol. 56, pp. 684–700, 2016.
19. M. Aazam and E. N. Huh, “Cloud of Things: Integrating Internet of Things with Cloud Computing,” IEEE Cloud Computing, vol. 1, no. 3, pp. 36–46, 2014.
20. V.Venkatesan, B.E,“Smart energy meter system using IOT Muthayammal Engineering College Dr. S. Perumal, M.E., Ph. D., “Smart AIPowered ChatBot using the ESP8266 Wi-Fi module for real-time response” Muthayammal Engineering College
21. Mr. P. Anbarasan M.E.,, “home monitoring and management system using IOT” Muthayammal Engineering College
22. Dr. R.Senthilkumar,“Remote Access ATM Security system Using IOT”M.E., Ph. D., Muthayammal Engineering College.
23. Anand, L., Maurya, M., Seetha, J., Nagaraju, D., Ravuri, A., &Vidhya, R. G. (2023, July). An intelligent approach to segment the liver cancer using Machine Learning Method. In 2023 4th international conference on electronics and sustainable communication systems (ICESC) (pp. 1488-1493). IEEE.
24. Rajendran, S., Sundarapandi, A. M. S., Krishnamurthy, A., &Thanarajan, T. (2022). An intelligent face recognition technology for iot-based smart city application using condition-cnn with foraging learning pso model. International Journal of Pattern Recognition and Artificial Intelligence, 36(14), 2256018.
25. Murugeshwari, B., &Sujatha, R. (2014). Preservation of Privacy for Multiparty Computation System with Homomorphic Encryption. International Journal of Emerging Technology and Advanced Engineering, 4(3), 530-535.
26. Sugumar, R. (2025). Unified AI Framework for Predictive Data Engineering and Real Time Prescription and Billing Systems. International Journal of Advanced Engineering Science and Information Technology (IJAESIT), 8(5), 17261.
27. Samrat, B., Thomas, P. K., Kumar, S., Benila, A., Bhardwaj, R., &Vigenesh, M. (2024, December). Industrial informatics in optimizing software-defined vehicles for logistics. In 2024 IEEE 2nd International Conference on Innovations in High Speed Communication and Signal Processing (IHCSP) (pp. 1-9). IEEE.
28. Soundappan, S. J. (2024). AI-driven customer intelligence in enterprise lakehouse systems Sentiment Mining Governance-Aware Analytics and Real-Time Data Synchronization. International Journal of Advanced Engineering Science and Information Technology.
29. Rajasekar, M. (2024). AI-Powered Cyber-Secure Federated Learning on AWS for Next-Generation Digital Banking Analytics. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 7(3).
30. Deivendran, P., Babu, P. S., Malathi, G., Anbazhagan, K., & Kumar, R. S. (2023). Emotion Recognition for Challenged People Facial Appearance in Social using Neural Network. arXiv preprint arXiv:2305.06842.
31. Sugumar, R., &Murugeshwari, B. (2016). An Efficient MChord based Authentication for Vehicular Ad-Hoc Networks.
32. Pandey, V. K., Mishra, S., Rengarajan, A., Savita, &Roomi, M. M. (2024, March). Enhancing Weather Forecasting with Machine Learning Techniques. In International Conference on Renewable Power (pp. 147-156). Singapore: Springer Nature Singapore.
33. Mathew, A., & Alex, H. (2025). Federated Learning for Secure Genomic Research: Privacy-Preserving AI Solutions for Precision Medicine. Science and Technology: Developments and Applications Vol. 9, 36-43.
34. Selvi, G. V., Anbarasan, A. B., Murthy, B. A., &Prabavathy, S. (2023). An Application Oriented Integrated Unequal Clustering Algorithm for Wireless Sensor Network. In Underwater Vehicle Control and Communication Systems Based on Machine Learning Techniques (pp. 140-154). CRC Press.
35. Soundappan, S. J. (2025). Next Generation AI Enabled Holistic Cognitive Platform for Secure Cloud Network Intelligence Enterprise Systems and Digital Trust Optimization. International Journal of Computer Technology and Electronics Communication, 8(5), 11534-11542.
36. Rajasekar, M. (2024). Real-Time Predictive DevOps Intelligence for Risk-Aware Digital Business Processes in Cloud and SAP Ecosystems. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 7(4), 10713-10718.
37. Jagadeesh, S., & Sugumar, R. (2017). A comparative study on artificial bee colony with modified ABC algorithm. European Journal of Applied Sciences, 9(5), 243–248.
38. Murugeshwari, B., Sarukesi, K., &Jayakumar, C. (2010, March). An efficient method for knowledge hiding through database extension. In 2010 International Conference on Recent Trends in Information, Telecommunication and Computing (pp. 342-344). IEEE.
39. Reddy, K. V. V. K., &Vimal, V. R. (2024, July). A novel approach on improved segmentation and classification of remote sensing images using AlexNet compared over linear discriminant analysis with improved accuracy. In 2024 Second International Conference on Advances in Information Technology (ICAIT) (Vol. 1, pp. 1-6). IEEE.
40. Gowthami, D., &Vigenesh, M. (2024). Distributed and Lightweight Intrusion Detection for IoT: A Lightweight Pyramidal U-Net With Tri-Level Dual Inception-Based Framework. In The Convergence of Self-Sustaining Systems With AI and IoT (pp. 154-173). IGI Global Scientific Publishing.
41. Anand, P. V., &Anand, L. (2023, December). An Enhanced Breast Cancer Diagnosis using RESNET50. In 2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES) (pp. 1-5). IEEE.
42. Mathew, A. (2022). Leveraging Big Data Analytics to Power AI and ML (Machine Learning) Automation. Educational Research (IJMCER), 4(5), 131-134.
43. Dhinakaran, D. (2022). Joe Prathap P. M, Selvaraj D, Arul Kumar D and Murugeshwari B," Mining Privacy-Preserving Association Rules based on Parallel Processing in Cloud Computing,". International Journal of Engineering Trends and Technology, 70(3), 284-294.
44. Poornima, G., &Anand, L. (2024, April). Effective Machine Learning Methods for the Detection of Pulmonary Carcinoma. In 2024 Ninth International Conference on Science Technology Engineering and Mathematics (ICONSTEM) (pp. 1-7). IEEE.
45. Rengarajan, A., Jayakumar, C., & Sugumar, R. (2012). Optimization Of Recent Attacks Using Internet Protocol. National Journal of System and Information Technology, 5(1), 8.
46. Mathew, A., &Romasco, L. (2024). Forensic Investigation of Artificial Intelligence Systems. Research Updates in Mathematics and Computer Science Vol. 4, 154-164.
47. Vekariya, V., Kumar, S., &Rengarajan, A. (2024). A distinctive and smart agricultural knowledge-based framework using ontology. In Sustainability in Digital Transformation Era: Driving Innovative & Growth (pp. 207-213). CRC Press.
48. Soundappan, S. J. (2020). Big data analytics in healthcare: Applications for pandemic forecasting. International Journal of Advanced Research in Computer Science & Technology, 3.
49. Sugumar, R. (2024). AI-Augmented Quality Engineering for Performance Optimization and Test Orchestration in Distributed Systems. International Journal of Science, Research and Technology, 7(5), 12835-12846.
50. Soundappan, S. J., & Sugumar, R. (2016). Optimal knowledge extraction technique based on hybridisation of improved artificial bee colony algorithm and cuckoo search algorithm. International Journal of Business Intelligence and Data Mining, 11(4), 338–356.
51. Mathew, A. (2025). Ahead of the breach: Predictive threat intelligence in aviation inspired by Scattered Spider attacks. Multidisciplinary International Journal of Research and Development (MIJRD), 4(6), 54–58.
52. Soundappan, S. J. (2021). DataOps: Orchestrating Reliable ML Data Pipelines. International Journal of Research and Applied Innovations, 4(4), 5533-5537.
53. Garg, V. K., Soundappan, S. J., &Kaur, E. M. (2020). Enhancement in intrusion detection system for WLAN using genetic algorithms. South Asian Research Journal of Engineering and Technology, 2(6), 62–64.
54. Anand, L., Tyagi, R., & Mehta, V. (2024, January). Food recognition using deep learning for recipe and restaurant recommendation. In Proceedings of Eighth International Conference on Information System Design and Intelligent Applications (pp. 269-279). Singapore: Springer Nature Singapore.
55. Kumar, A., &Anand, L. (2025). A Novel EEG-Based Deep Learning Framework for Enhancing Communication in Locked-In Syndrome Using P300 Speller and Attention Mechanisms. KSII Transactions on Internet and Information Systems (TIIS), 19(11), 3841-3855.
56. Soundappan, S. J. (2022). AI-Based Fault Detection and Isolation for Reliability in Modern Power Systems. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(4), 7106-7110.
57. Chandra, S., Rengarajan, A., Sahoo, G. S., & Sharma⁴, S. (2024, October). Identifying Neuronal Damage and Plasticity by Analyzing Changes in Diffusion Tensor. In Proceedings of the 5th International Conference on Data Science, Machine Learning and Applications; Volume 2: ICDSMLA 2023, 15–16 December, Hyderabad, India (Vol. 2, p. 433). Springer Nature.
