IoT-Based Application for Real-Time Monitoring and Segregation of Solid Waste in Urban and Municipal Environments

Abstract

Conventional waste management systems are unable to cope with fast-paced urbanization, resulting in over flowing garbage cans and mounting expenditure. With the inclusion of wireless modules and ultrasonic sensors, an IoT-based smart dustbin management system driven by Arduino can efficiently track the fill levels of bins. Authorities are notified when bins are full, allowing for timely collection and minimizing fuel usage. A servo motor prevents unauthorized dumping and facilitates waste separation. The YOLO algorithm sorts the waste in real time, routing it to correct compartment. The system sends information to a cloud dashboard, maximizing recycling processes and collection schedules for a smarter and more efficient waste management solution.

Introduction

Traditional waste management struggles with inefficiencies caused by rapid urbanization, leading to overflowing bins, environmental harm, and high operational costs. To address these issues, a Smart Waste Management System using Arduino is proposed. It uses ultrasonic sensors to monitor bin fill levels and sends real-time alerts via Wi-Fi or GSM. The system employs the YOLO computer vision algorithm to classify waste into biodegradable and non-biodegradable categories, with a servo motor managing controlled disposal. A cloud-based dashboard enables authorities to analyze data and optimize collection schedules, enhancing efficiency and sustainability, especially in smart city contexts.

The system integrates multiple components—Arduino Uno microcontroller, ultrasonic sensors, machine learning modules, servo motors, GSM communication, and LCD displays—to automate waste monitoring and management. Machine learning improves servo motor control and decision-making, while GSM modules send emergency alerts. The YOLO algorithm enables real-time waste classification for automated sorting.

Test results show that the system successfully improves waste segregation and disposal, provides timely alerts when bins are full, and supports cleaner, more efficient waste management with real-time updates via SMS and LED displays. This approach reduces overflow, operational costs, and environmental impact, making it ideal for urban and industrial smart waste management solutions.

Conclusion

With smart sorting and automation, the smart waste segregation system shown in the picture is a cutting-edge way of optimizing the efficiency of garbage disposal. To make automatic garbage detection, sorting, and disposal easy, it integrates Arduino with the required components like ultrasonic sensors, GSM modules, LCD displays, relays, motors, and buzzers. One of the most striking aspects of the system is that it can utilize machine learning, in the form of the YOLO (You Only Look Once) algorithm, which enables quick and precise object detection, to identify and categorize trash in real time. The amount of waste in the bin is always measured by the ultrasonic sensor, which provides feedback in the form of signals to show that it is almost full. For on-time collection, the GSM module sends an automatic message to waste management officials when it is full. Real-time status is also provided by an LED or LCD display, which enhances user awareness and alerts garbage collectors. The system is also equipped with an automatic DC motor system for enabling garbage transportation to enhance segregation efficiency further. Power supply and battery backup provide continuous and smooth operation. This system minimizes manual handling, prevents overflow, and optimizes the collection of trash via automated alarms and real-time observation. It is an excellent investment for use in smart cities as it helps ensure a cleaner, greener, and more sustainable world if adopted in modern waste management systems. Efficiency and sustainability will be the heart of future development in the smart waste segregation system. Accuracy of garbage classification can be improved by the incorporation of advanced AI models like Faster R-CNN. Cloud monitoring via IoT-based solutions will allow remote trash management and real-time evaluation. Segregation can be optimized by automation of the sorting process through conveyor belts or robotic arms. Solar panel integration can give a clean source of power to facilitate energy efficiency. A mobile app can also involve the community by providing trash can locations and promoting proper disposal habits. Waste management will be more automated, green, and efficient because of these developments.

References

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Copyright

Copyright © 2025 Dr. N. Malathy, Sangeetha L, Suchendra Reddy V , Reena Sree G, Dhanush M. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.