Automated Waste Management using a Customized Vision-based Transformer Model

This research paper embarks on an in-depth exploration of the critical role played by the Internet of Things (IoT) in advancing waste management systems in the context of smart city development. Drawing from a comprehensive review of twenty scholarly articles, our study begins by highlighting the fundamental significance of IoT as the driving force behind the evolution of smart cities. This paper then extends its focus to elaborate on the methodologies and practical steps involved in integrating IoT technology to automate the categorization of waste. Central to this advancement is the implementation of the innovative deep learning-based Vision Transformer (ViT) Model, which is adept at classifying various waste types typically encountered in urban settings before their disposal in landfills. The study underscores the transformative potential of IoT in promoting the development of urban spaces that are not only sustainable and comfortable but also highly efficient. Delving into the core elements and technological underpinnings of IoT within smart urban environments, the paper places a special emphasis on waste management applications. It encompasses a thorough analysis of advanced artificial intelligence algorithms and a critical evaluation of architectural frameworks necessary for optimizing waste management operations. Furthermore, the paper addresses the complex challenges involved in deploying IoT systems in waste management. It also explores existing practices and their applications across different sectors within smart cities. Through bibliometric analysis, the research reveals the evolving landscape of IoT in the context of smart cities, pinpointing influential authors, key publications, and leading nations in this field. This analysis highlights the rapid growth of IoT research and its integration with cutting- edge technologies. At the heart of this research lies the domain of waste management. The study focuses on how IoT can radically improve this vital aspect of smart city functioning. The goal is achieved by designing and implementing automated waste detection systems that harness deep learning algorithms, notably Convolutional Neural Networks (CNN) and Vision Transformers (ViT). These advanced methods are pivotal in automating the sorting of waste, enhancing recycling efforts, and ensuring the efficient disposal of waste materials. In conclusion, this research contributes significantly to the vision of creating environmentally responsible and highly efficient smart cities, showcasing the pivotal role of IoT and advanced machine learning technologies in revolutionizing urban waste management systems