Subject: Other engineering and technologies
Year: 2026
Type: Article
Type: NonPeerReviewed
Title: A Deep Learning and Sensor-Based Internet of Things Framework for Intelligent Waste Management: A Comparative Analysis
Author: Mustafovski, Rexhep
Author: Petrovski, Aleksandar
Author: Radovanović, Marko
Author: Behlic, Aner
Author: Ilievski, Kristijan
Abstract: The escalating volume of municipal solid waste has intensified the need for intelligent waste management systems capable of improving operational efficiency, classification accuracy, and sustainability. In recent years, the integration of Internet of Things technologies, deep learning algorithms, and sensor-based monitoring has significantly transformed conventional waste collection and sorting practices. In this study, an intelligent waste management framework was proposed and comparatively evaluated against twelve contemporary smart waste management systems reported in the literature. The proposed architecture integrates a Raspberry Pi 3 embedded platform, You Only Look Once version 8 (YOLOv8) deep learning models for real-time waste classification, and ultrasonic bin-fill sensors for monitoring container capacity, enabling automated lid operation, and supporting optimized waste collection scheduling. A comprehensive comparative analysis was conducted across multiple performance dimensions, including classification accuracy, system responsiveness, scalability, deployment cost, and operational efficiency. Experimental evaluation demonstrates that the deep learning–driven framework achieved high real-time classification accuracy while maintaining low computational overhead on resource-constrained edge devices. In addition, the incorporation of bin-fill sensing and automated actuation enhanced system responsiveness and supported data-driven collection planning, thereby reducing unnecessary collection trips and operational costs. The findings highlight the significant potential of combining advanced deep learning algorithms with sensor-based Internet of Things infrastructures to develop sustainable, intelligent, and cost-effective waste management ecosystems. These insights provide a foundation for future research aimed at enhancing intelligent waste infrastructure and supporting environmentally sustainable urban development.
Publisher: Acadlore Publishing Services Limited
Relation: https://eprints.ugd.edu.mk/38179/
Identifier: oai:eprints.ugd.edu.mk:38179
Identifier: https://eprints.ugd.edu.mk/38179/1/MSc.%20Rexhep%20Mustafovski.pdfIdentifier: Mustafovski, Rexhep and Petrovski, Aleksandar and Radovanović, Marko and Behlic, Aner and Ilievski, Kristijan (2026) A Deep Learning and Sensor-Based Internet of Things Framework for Intelligent Waste Management: A Comparative Analysis. Acadlore Transactions on AI and Machine Learning, 5 (1): 050106. pp. 58-72. ISSN 2957-9570
Identifier: https://doi.org/10.56578/ataiml050106
