A reforming municipal waste management model with the internet of things (IoT) for smart garbage tracking and optimization

Abstract

Municipal waste management is crucial for cities as it enhances the urban atmosphere, conserves assets, and safeguards the ecological balance. An adequate and effective waste management strategy leads to significant environmental issues. The absence of dustbins, littering, and improper usage of dustbins create unsanitary conditions in cities and harm the ecosystem. The theft or destruction of the dustbins is a significant issue. This research uses deep learning-based classifiers with the Internet of Things (IoT) and a cloud computing approach to accurately categorize trash at the start of garbage collection. The research categorizes recyclable garbage into six groups: plastics, glass, paper or cardboard, metallic items, textiles, and other recyclable materials to aid future waste disposal. Convolutional Neural Networks (CNN) are used for trash categorization. This study tries to provide a basic answer to this issue via IoT technologies. A function will be added to the user's website to inform them about the present condition of the closest smart waste bins. This will allow users to locate and use the nearest bin if the one in their area is full. This research intends to enhance the safety of smart waste bins by securing the sensors and implementing bins with a concrete body to prevent theft and damage.