Automated E-Waste Collection and Analysis Using Smart Technologies

The escalating volume of electronic waste (e-waste) presents ongoing difficulties for contemporary metropolitan areas, as conventional collection methods frequently lack real-time data on waste type and highly optimized logistics. This research introduces an Automated E-Waste Collection and Analysis System (ECAS), a smart platform enabling city dwellers to contribute actively to formal disposal channels through on-demand submission requests and photographic documentation of their e-waste. The platform combines location-based mapping with advanced Artificial Intelligence (AI) tools to establish a dynamic, comprehensive operational framework. Our methodology incorporates Convolutional Neural Networks (CNNs) for accurate preliminary e-waste classification from user images, and the A* Search Algorithm for real-time dynamic route optimization of municipal collection fleets. The system operates on contemporary technological infrastructure featuring Node.js server architecture paired with Supabase database management (PostgreSQL foundation), guaranteeing expandability and instantaneous information updating. Initial evaluation of the system, involving a simulated deployment across a municipal zone, demonstrated a significant enhancement in operational efficiency, specifically achieving a [Insert Specific Reduction % Here, e.g., 22%] reduction in collection route mileage and decreasing the overall request-to-dispatch time. This confirms that an AI-powered, citizen-sourced platform substantially enhances municipal effectiveness, provides essential data for resource recovery, and establishes a clear logistical framework connecting residents with centralized e-waste processing.