Edge AI: A Survey of Next-Generation Waste Classification and Routing Architectures
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2026
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| author | Yash Saini Anmol Tyagi Priyanshu Kumar Sagar Priyanka |
| author_facet | Yash Saini Anmol Tyagi Priyanshu Kumar Sagar Priyanka |
| contents | The recent advances in edge computing and computer vision are completely transforming the way cities handle solid waste. In this review, close attention will be paid to the way smart city systems have changed to separate various types of waste and direct users to the appropriate bins in real-time. Our particular focus is the fact that the trend has shifted to less heavy and bulky smart bins and to the touchless mobile-edge systems. We go through the decisions of hardware, the move away towards large neural networks (as in VGG16) to thinner ones (such as MobileNetV2 and YOLOv8n) and the usage of location-finding algorithms such as Haversine formulas. The traditional smart-city systems are usually based on expensive embedded sensors, thus becoming difficult to implement and expand to larger cities. In the present-day, however, devices such as WebGL, Tensorflow.js, and an ordinary smartphone camera are utilized to perform the heavy lifting on the device. This decentralized design is faster, less expensive, more secret and more effortless to expand. Reviewing the studies of 2015 to 2026, we mention the constant problem of obtaining correct classification in poor lighting and solid communication systems are necessary. Finally, we outline the current state of edgedriven waste management, indicate the weaknesses of cloudheavy systems and consider the future of this technology, such as federated learning, blockchain rewards, and enhanced IoT synchronization. |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19425963 |
| institution | Zenodo |
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| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Edge AI: A Survey of Next-Generation Waste Classification and Routing Architectures Yash Saini Anmol Tyagi Priyanshu Kumar Sagar Priyanka Waste Segmentation Edge computing Tensorflow. js Nearest Bin Recommendation Smart City Architecture Transfer Learning MobileNetV2 MQTT. The recent advances in edge computing and computer vision are completely transforming the way cities handle solid waste. In this review, close attention will be paid to the way smart city systems have changed to separate various types of waste and direct users to the appropriate bins in real-time. Our particular focus is the fact that the trend has shifted to less heavy and bulky smart bins and to the touchless mobile-edge systems. We go through the decisions of hardware, the move away towards large neural networks (as in VGG16) to thinner ones (such as MobileNetV2 and YOLOv8n) and the usage of location-finding algorithms such as Haversine formulas. The traditional smart-city systems are usually based on expensive embedded sensors, thus becoming difficult to implement and expand to larger cities. In the present-day, however, devices such as WebGL, Tensorflow.js, and an ordinary smartphone camera are utilized to perform the heavy lifting on the device. This decentralized design is faster, less expensive, more secret and more effortless to expand. Reviewing the studies of 2015 to 2026, we mention the constant problem of obtaining correct classification in poor lighting and solid communication systems are necessary. Finally, we outline the current state of edgedriven waste management, indicate the weaknesses of cloudheavy systems and consider the future of this technology, such as federated learning, blockchain rewards, and enhanced IoT synchronization. |
| title | Edge AI: A Survey of Next-Generation Waste Classification and Routing Architectures |
| topic | Waste Segmentation Edge computing Tensorflow. js Nearest Bin Recommendation Smart City Architecture Transfer Learning MobileNetV2 MQTT. |
| url | https://doi.org/10.5281/zenodo.19425963 |