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Smart city article details
| Title | Efficient Handling Of Waste Using Deep Learning And Iot |
|---|---|
| ID_Doc | 22318 |
| Authors | Bonala K.; Saggurthi P.; Kambala P.K.; Voruganti S.; Utukuru S.; Sugamya K. |
| Year | 2024 |
| Published | 2nd International Conference on Sustainable Computing and Smart Systems, ICSCSS 2024 - Proceedings |
| DOI | http://dx.doi.org/10.1109/ICSCSS60660.2024.10625621 |
| Abstract | The proposed waste management solution integrates Deep Learning (DL) and the Internet of Things (IoT) to usher in a new era of efficiency and sustainability. At its core are two smart bins equipped with IoT sensors and DL algorithms, autonomously categorizing incoming waste as recyclable or non- recyclable, thereby reducing the need for manual sorting. These bins collect real-time data on waste composition, levels, and other metrics, which AI algorithms swiftly process, streamlining waste handling and minimizing human intervention. Despite its innovative approach, the system faces several challenges. Sensor limitations and the diversity of waste types can impact the accuracy of waste categorization, necessitating continuous refinement of DL algorithms. Real-time data processing demands robust network infrastructure and significant computational resources, posing scalability issues. Integrating IoT sensors with existing waste management infrastructure can be complex and costly, and ensuring data security and privacy is paramount given the extensive data collection involved. The system's predictive maintenance capabilities require constant monitoring, which can strain operational resources. Beyond sorting, the IoT sensors inform optimal waste collection routes and schedules, leading to reduced operational costs and a lower environmental footprint. However, the reliability of these improvements depends on consistent sensor accuracy and network communication. Advanced data analytics drive continuous improvement, but identifying meaningful trends requires large datasets and sophisticated analysis tools. User engagement features promote responsible waste disposal, fostering a sense of community involvement, yet motivating consistent user participation remains a challenge. The solution seamlessly integrates with smart city initiatives, contributing to overall urban sustainability, but this integration requires extensive collaboration and alignment with municipal policies. Designed for scalability and flexibility, the system can adapt to changing waste disposal needs, though scaling introduces complexities related to infrastructure and data management. Detailed reports facilitate regulatory compliance and provide insights for policymakers, but maintaining high data accuracy and integrity is essential. In summary, our Deep Learning and IoT-driven waste management system offers efficiency, cost-effectiveness, and a strong commitment to environmental conservation. While facing challenges such as sensor accuracy, data security, and integration costs, it represents a promising path towards a cleaner and more sustainable future for waste management. © 2024 IEEE. |
| Author Keywords | Deep Learning (DL); Internet of Things (IoT); Recycling Waste categorization; Smart bins |
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