Abstract:

Electronic waste (e-waste) has become a critical global issue making innovative approaches for e-waste collection and management more necessary. This paper proposes a novel framework that utilize cloud-IoT technology integrated with machine learning for e-waste inventory in Saudi Arabia. The model’s architecture encompasses critical components, such as stakeholders, cloud architecture, IoT devices, and machine learning predictive model. The framework addresses technical aspects such as scalability, real-time data processing, traceability and transparency, privacy and predictive analysis. The framework will be validated using a secondary e-waste dataset, a survey of stakeholders, and simulation studies. Research consisting of surveys will also be undertaken to understand the challenges, benefits, and feasibility of implementing distributed systems for e-waste inventory and collection in Saudi Arabia. The survey data will be analysed using statistical procedures. The framework is designed to promote sustainability in Saudi Arabia while streamlining the e-waste collection process.