Smart Street Level Autonomous Dustbin for Door-to-Door Waste Collection

Abstract

The rapid expansion of urban populations has increased pressure on conventional waste-collection practices, which largely depend on manual operations and lack real-time monitoring capabilities. To address these limitations, this paper presents the Smart Street-Level Autonomous Dustbin (SSAD), a mobile robotic system designed for door-to-door waste collection and on-device segregation. The SSAD integrates an Internet of Things (IoT) framework with embedded sensing modules, GPS-assisted navigation, and lightweight machine-learning-based waste classification. Ultrasonic and infrared sensors support obstacle detection and bin identification, while a convolutional neural network deployed on an onboard processor categorizes waste into biodegradable and non-biodegradable types. The system transmits operational data—including bin status, location, and segregation logs—to a cloud-based dashboard for real-time monitoring. Experimental evaluation demonstrates an average classification accuracy of 85–90% and a 25% improvement in route efficiency compared to manual collection. The proposed system illustrates the potential of autonomous mobile platforms for enhancing waste-management efficiency and enabling scalable smart-city solutions.

Introduction

The paper presents the Smart Street-Level Autonomous Dustbin (SSAD), a mobile robotic system designed to modernize urban waste management within Smart City initiatives. Traditional waste-collection methods rely on static bins and manual pickup, leading to inefficiencies, delayed collection, poor route planning, and lack of source-level waste segregation. Although previous solutions have introduced IoT-based fill monitoring, GPS route optimization, robotic transport, and machine-learning-based sorting, most provide only partial automation and still depend heavily on human intervention.

To address these gaps, SSAD integrates autonomous navigation, real-time waste segregation, and cloud-based monitoring into a single mobile platform. The system operates as a cyber-physical system with three layers: (1) perception and action (sensors, actuators, motion control), (2) embedded processing and communication (Raspberry Pi, Arduino, Wi-Fi), and (3) cloud and application services (analytics, route optimization, dashboards). It uses GPS-based optimized routing, obstacle detection via ultrasonic sensors, IR-based bin detection, and a compact CNN model to classify waste into biodegradable and non-biodegradable categories at the point of collection.

Experimental results show 85% waste-classification accuracy, a 25% reduction in travel distance through route optimization, and a 98% data transmission success rate. Although initial deployment costs are higher than traditional systems, reduced labor, fuel savings, and improved recycling efficiency yield a projected return on investment within 3–5 years. Overall, SSAD demonstrates a scalable, end-to-end automated solution for smart urban waste management.

Conclusion

The Smart Street-Level Autonomous Dustbin demonstrates that waste collection can be significantly enhanced through a combination of IoT technologies, autonomous navigation, and lightweight machine-learning-based segregation. The system reduces dependency on manual labour, increases operational efficiency, and provides continuous cloud-based monitoring. Future improvements include integrating solar charging, adopting reinforcement learning for adaptive navigation, scaling deployment to larger municipal networks, and extending ML capabilities to identify more diverse waste categories. The SSAD framework offers a solid foundation for developing cleaner and more sustainable smart-city waste-management solutions.

References

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Copyright

Copyright © 2026 Ms. N. Kameshwari, Tambali Shivaprasad, Palam Sathiswar Reddy, Ajay Kumar Rai, Chidurala Navaneeth Kumar. 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.