A REVIEW OF MACHINE LEARNING TECHNIQUES FOR IOT-BASED REAL-TIME E-WASTE MONITORING AND ITS ENVIRONMENTAL IMPACT

Abstract

The scope of this research includes the integration of Internet of Things (IoT)-based real time monitoring systems with machine learning techniques in order to improve the management system of e-waste disposal system. Real time data collected at e-waste facilities across the network are utilized by advancing machine learning models to be used in predicting the composition, quantity, and environmental conditions of the waste. They are Convolutional Neural Networks (CNNs) for waste sorting, Predictive Analytics for forecasting waste volumes, and Reinforcement Learning for dynamic routing, seeking to solve the problem of organizing waste, collecting waste, and anticipating the amount of waste as efficiently as possible. These technologies’ adoption enhances operations performance, lower costs and are supportive of sustainable e-waste management practices.

However, these methodologies have several challenges for their application. Even greater barriers are, high deployment costs, the heavy computational demands, machine learning models require and relying on huge accurate datasets for training. In addition to this, there is complexity in implementation of the model to real time adaptation to changing waste patterns and environmental conditions. However, with technological advances and optimization algorithms, IoT and machine learning based solutions can now be more scalable and more effective for the purpose of waste management. A breakthrough in e-waste management can be brought about by the integration of the IoT based monitoring with machine learning techniques and Trustworthy Artificial Intelligence (TAI). The findings in this research demonstrate the promise of these technologies in reducing environmental impact of electronic waste, improving the operational efficiencies, and optimizing resource allocation. Moving forward, research and technological breakthroughs, most of the systems will be accessible and effective for us, and the future of e-waste implementation in our lives will be sustainable.