Investigation of a deep learning-based waste recovery framework for sustainability and a clean environment using IoT

Abstract

The growing concern over environmental sustainability has prompted the development of various technologies for waste material recovery and management. One promising approach involves leveraging Internet of Things (IoT) platforms combined with deep learning (DL) models to enhance the efficiency and effectiveness of waste recovery systems. Due to manual processes and limited automation, waste recovery methods face challenges such as inadequate waste sorting, high energy consumption, and low recovery rates. These methods often struggle to scale effectively, leading to inefficiencies in waste management and sustainability efforts. The proposed framework, Waste Material Recovery using Deep Learning (WMR-DL), aims to address these issues by integrating IoT sensors for real-time data collection and deep learning algorithms for automated waste identification and classification. This system improves sorting accuracy, reduces human intervention, and enhances the recovery of valuable materials from waste. The IoT platform allows for continuous monitoring, while deep learning models analyze data to predict and optimize the waste recovery process. The proposed method can be applied in various waste management sectors, such as recycling plants, e-waste recovery, and municipal waste systems. The system supports intelligent decision-making using IoT-enabled devices and DL models, optimizing real-time waste sorting and material recovery processes. Preliminary findings show that the WMR-DL framework improves recovery efficiency by up to 30%, with reduced operational costs and better resource management. This approach promotes sustainability and significantly reduces the environmental impact of waste disposal systems, contributing to a cleaner and greener environment.