Smart E-Waste Classification Kiosk: Edge Computing Solution for Sustainable Electronics Management

Abstract

Electronic waste management presents one of the most pressing environmental challenges of our digital era, with India generating an alarming 1.7 million tonnes of e-waste annually while lacking efficient source segregation mechanisms. Traditional waste management approaches fail to address the complexity and rapid growth of electronic components, creating significant environmental and economic losses. This research introduces an innovative Smart Kiosk solution powered by ESP32S3 microcontrollers, designed to revolutionize e-waste classification through intelligent edge computing. Our comprehensive system integrates advanced computer vision algorithms with real-time IoT connectivity, enabling autonomous identification and categorization of electronic components directly at waste generation points. The architecture leverages optimized deep learning models specifically tailored for resource-constrained embedded environments, ensuring robust performance without compromising processing efficiency. The system addresses regulatory compliance with India’s E-Waste Management Rules 2022, covering 106 electronic equipment categories through intelligent classification algorithms.Extensive real-world testing shows strong performance, achieving 94.2 percentage classification accuracy across eight e-waste categories, with an average latency of 7 ms per item and optimized power consumption.The Smart Kiosk can be further enhanced with blockchain traceability, automated inventory tracking, and real-time analytics to support responsible recycling and compliance. This cost-effective, scalable solution bridges critical gaps in existing waste management infrastructure while supporting India’s ambitious circular economy transition through democratized access to intelligent e-waste segregation technology.